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Long term urban wildlife research potential through course-based undergraduate research experiences

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Title:
Long term urban wildlife research potential through course-based undergraduate research experiences
Creator:
St. Onge, Sarah
Place of Publication:
Denver, CO
Publisher:
University of Colorado Denver
Publication Date:
Language:
English

Thesis/Dissertation Information

Degree:
Master's ( Master of science)
Degree Grantor:
University of Colorado Denver
Degree Divisions:
Department of Integrative Biology, CU Denver
Degree Disciplines:
Biology
Committee Chair:
Hartley, Laurel
Committee Members:
Greene, Michael
Magle, Seth

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University of Colorado Denver
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Auraria Library
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Copyright Sarah St. Onge. Permission granted to University of Colorado Denver to digitize and display this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.

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LONG TERM URBAN WILDLIFE RESEARCH POTENTIAL THROUGH COURSE-BASED
UNDERGRADUATE RESEARCH EXPERIENCES by
SARAH ST. ONGE
B.S., Northern Arizona University, 2007
A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Master of Science Biology Program
2018


This thesis for the Master of Science degree by Sarah St. Onge has been approved for the Biology Program by
Laurel Hartley, Chair Michael Greene Seth Magle
Date: December 15, 2018
ii


St. Onge, Sarah (M.S., Biology Program)
Long Term Urban Wildlife Research Potential Through Course-based Undergraduate Research Experiences
Thesis directed by Associate Professor Laurel Hartley
ABSTRACT
Urbanization is increasing rapidly worldwide, leading to highly fragmented habitats and habitat loss, which have been shown to be the leading cause of local wildlife species endangerment. Urban wildlife monitoring has the potential to support land and wildlife management decisions, wildlife and habitat conservation, zoonotic disease monitoring, help with human-wildlife conflicts, and educate the community about urban ecological issues. The goals of this thesis were to implement a long term urban wildlife monitoring program by establishing the University of Colorado Denver as a partner in the nationwide Urban Wildlife Information Network (UWIN), analyze preliminary urban wildlife data, and to create and assess a Course-based Undergraduate Research Experience (CURE) for a General Biology laboratory course using the long-term monitoring of urban wildlife as a context.
The first chapter presents the urban wildlife monitoring protocol that was established in the Denver metro area and initial data. The protocol used 40 motion-activated cameras along an urban to rural urbanization gradient set up for one month in each season in 2017, resulting in the detection of 15 medium to large mammalian species, four domestic and 11 wild. Preliminary single-season occupancy models that account for imperfect detection were created for wild mammalian species that were commonly detected. Models for coyotes showed higher
probability of use in naturals areas further to the west, and for mule deer in more rural natural


areas. The occupancy models were not predictive for red fox, raccoon, and eastern cottontail
rabbit site use probabilities.
The second chapter presents the development, implementation, and assessment of the CURE curriculum using the urban wildlife project as a context. Incorporating relevant, local, and authentic research into introductory undergraduate courses has been shown to have positive impacts on student interest, engagement, and retention. Curriculum creation included developing learning objectives and identifying core experiences for scientific practices that are consistent with current national directives and that comply with important aspects of CUREs. Assessment of this CURE was accomplished using published surveys related to essential CURE elements, a conceptual inventory related to experimental design, and student work products. Results indicate that this CURE curriculum aligned with important CURE aspects, student perceptions of authentic research experiences were overall positive, and students showed skill gains of using scientific processes.
The form and content of this abstract are approved. I recommend its publication.
Approved: Laurel Hartley
IV


ACKNOWLEDGEMENTS
For her guidance in scientific inquiry, pedagogy, and curriculum development, a great thanks to my advisor, Dr. Laurel Hartley. For their help and support in the planning and implementation of my ever-evolving project, from urban ecology to curriculum development and implementation, thank you to my committee members, Dr. Michael Greene and Dr. Seth Magle. Thank you to the UWIN staff for help during the implementation of a long-term wildlife monitoring program and with data management and analysis. An enormous thanks to Dr. Tod Duncan for his assistance and support in creating and implementing this curriculum into a laboratory course. Thank you to the general biology 2 lab students who participated in the new curriculum and the assessment efforts. Much gratitude to all of my graduate and undergraduate colleagues who have helped me emotionally and professionally, especially Scott Yanco, Paul Le, Andrew McDevitt, Ryan Parker, Tyler Michels, Katie Kilpatrick, Marianne Davenport, and Thomas Kennedy. Last but not least, thanks to my family and friends for their support, especially to my partner Marcus Szwankowski.
All wildlife research methods were approved by the University of Colorado Institutional Animal Care and Use Committee (IACUC Protocol #114516(12)1B)
All human subject research methods were approved by the University of Colorado Denver Colorado Multiple Institutional Review Board (COMIRB #17-2148)
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TABLE OF CONTENTS
CHAPTER
I. PRELIMINARY RESULTS OF URBAN WILDLIFE MONITORING IN DENVER, CO: DETECTED
SPECIES, SITE CHARACTERISTICS, AND SITE USE.....................................1
Introduction...............................................................1
Methods....................................................................6
Results...................................................................15
Discussion................................................................27
II. DEVELOPMENT AND ASSESSMENT OF AN URBAN WILDLIFE ECOLOGY COURSE-BASED UNDERGRADUATE RESEARCH EXPERIENCE IN AN INTRODUCTORY BIOLOGY LABORATORY 32
Introduction..............................................................32
Methods...................................................................38
Results and Discussion....................................................46
Summary and Conclusions...................................................60
REFERENCES.........................................................................64
APPENDIX
A. Chapter 1 Additional Tables and Figures...............................73
B. Chapter 2 Additional Tables and Figures...............................74
C. UWIN CURE Curriculum..................................................82
VI


CHAPTER I
PRELIMINARY RESULTS OF URBAN WILDLIFE MONITORING IN DENVER, CO: DETECTED SPECIES, SITE CHARACTERISTICS, AND SITE USE Introduction
Natural areas are increasingly being converted to urban areas around the globe. According to the United Nations, global urbanization has increased from 30% in 1930 to 55% in 2018 and is projected to be 68% in 2050 (United Nations, 2018). In the United States, it is projected that more than 80% of the population will live in urban areas by 2050. This escalation in urbanization leads to an increase in wildlife habitat loss and fragmentation, which in turn has been shown to be the leading cause of local species endangerment and extinction worldwide (Mcdonald, Kareiva, & Forman, 2008; McKinney, 2008). Many developing areas have been managed to support local habitat and wildlife with parks, open spaces, and wildlife refuges in order to mitigate some of these consequences (Armstrong, Fitzgerald, & Meaney, 2010; Marzluff & Rodewald, 2008; Ruliffson, Haight, Gobster, & Homans, 2003). These areas can provide important ecosystem services in urban areas, such as microclimate regulation, pollution reduction, noise reduction, water regulation, carbon sequestration, human health benefits, and provide plant and animal habitat (Chiesura, 2004; Elmqvist et al., 2015). However, when wildlife inhabit urban open spaces, there can be wildlife-human conflicts in addition to wildlife desensitization to people, alteration of normal animal behaviors, an increase of invasive nonnative species, and an increase of zoonotic disease spread (Adams, 2005; Gottdenker, Streicker, Faust, & Carroll, 2014; Magle, Simoni, Lehrer, & Brown, 2014; Messmer, 2009). Also, even though open space can provide many human benefits, there is evidence that recreation can
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affect native wildlife species distributions and densities negatively (Reed & Merenlender, 2008).
Therefore, as human population and urbanization expands, it will be important to monitor and investigate the positive and negative effects of urbanization on wildlife and urban habitats. Establishment and Mission of the Urban Wildlife Information Network:
The Urban Wildlife Information Network (UWIN) is a newly formed and growing national network of cities that believe in the importance of urban wildlife monitoring and have the goal of city to city collaboration and long-term investigation of our nations urban ecosystems (Magle et al., 2018). The mission of the UWIN is for cities to investigate their specific urban wildlife ecology, and to compare data across cities with goals to investigate wildlife population trends, promote wildlife conservation, increase public education and engagement, decrease human-wildlife conflict, and influence city planning and policy making (Magle et al., 2018). As of summer 2018, 16 cities in the U.S. and one in Canada are either currently participating or planning on participating in the network, which is actively recruiting more partners. The UWIN monitoring protocol requires that camera-trap monitoring transects be set up over an urbanization gradient in each participating city, and that data are collected over one month in each season (see detailed methods below). Camera traps have become a preferred method for studying spatial and temporal dynamics of medium to large wildlife populations because they are a relatively low cost, easy to use, non-invasive, remote sampling technique that allow for continual observation and reviewable, permanent data records (A. F. O'Connell, Nichols, & Karanth, 2011). The UWIN camera trap methods limit the detection of animals to medium to large mammalian species. However, UWIN has suggested additional techniques to include
2


monitoring for small mammals, birds, amphibians, reptiles, insects, and vegetation (Magle et
al., 2018).
As with any long-term monitoring network, the data generated can be used to look for patterns within and across sites and can be used to answer both current questions and questions yet to be conceived (Gitzen, Millspaugh, Cooper, & Licht, 2012; Knapp et al., 2012). Published research from UWIN
To date, published data from the UWIN site in Chicago, IL have investigated invasive species impacts, wildlife behavior, important habitat characteristics, and socioeconomic impacts on urban wildlife. Vernon et al. (2014) investigated the effects of an invasive species on urban wildlife mammal species distributions and found that invasive buckthorn (Rhamnus cathartica) impacted habitat use and presence negatively for white-tailed deer (Odocoileus virginianus), and positively for coyotes (Canis latrans) and opossums (Didelphis virginiana). Using patch occupancy models, Magle et al. (2014) investigated habitat characteristics, human activity, and species presence effects on coyote and white-tailed deer distributions and found that, because of scarce quality habitat, the two species were co-occurring, contrary to hypothesized behavior. In another study, Magle et al. (2016) researched the effects of landscape characteristics and socioeconomic factors on coyotes, raccoons (Procyon lotor), and opossums using occupancy models and found that the socioeconomic factors were as important as the ecological factors in determining their distributions. Potential discussed explanations for this are that income, education level, property stewardship, etc. may lead to fine-scale environmental differences (e.g., habitat quality, food and water availability, etc. ) that wildlife utilize. Fidino et al. (2016) studied habitat characteristics that influence opossum
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occupancy and found that water source availability was important and that human provided
water sources are likely allowing opossums to occupy areas previously thought uninhabitable. These studies show how monitoring patches of open space over an urbanization gradient can provide insight into urban wildlife ecology, including habitat preferences and human impacts on wildlife behavior, but is also informative for management and conservation. Data from multiple municipalities in the Denver metro area will be available from this study and could be useful in many ways, including monitoring species population and interaction dynamics, examining effects of management strategies, effects from neighboring areas, responses to environmental change, just to name a few (Gitzen et al., 2012). In addition, consistent long-term monitoring data is fundamental to conservation and management because it can provide context for ecosystem dynamics over time, and could provide the basis for new management practices, or for changing existing ones (Lovett et al., 2007).
Wildlife Research in the city of Denver. CO.
In the past, many urban wildlife studies have focused on a single species of mammal or bird over a relatively short time period (Magle, Hunt, Vernon, & Crooks, 2012). This holds true to published research for Denver, CO as well. For example, there are a few studies on coyotes in the Denver area, studying coyote management strategies, home ranges, sightings, disease prevalence, and human-coyote conflicts due to spatial and temporal patterns related to land cover type and housing density (Breck, Poessel, & Bonnell, 2017; Magle, Poessel, Crooks, & Breck, 2014; Malmlov, Breck, Fry, & Duncan, 2014; Poessel et al., 2013; Poessel, Breck, & Gese, 2016). Another example would be of the black-tailed prairie dog (Cynomys ludovicianus) in urban Denver, with research into genetic diversity and distribution patterns based on
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landscape, habitat and connectivity characteristics, and public sentiment (Magle & Crooks,
2009; Magle, Ruell, Antolin, & Crooks, 2010; Magle, Theobald, & Crooks, 2009; Morse, Powell,
& Sutton, 2012). Otherwise, research into historical mammalian species throughout Denver is limited and hard to find. One study by Jones et al. (2003) researched bird and mammal presence and habitat use of six areas along the river corridor of the South Platte River, which runs south-north through urban Denver, during 1998-1999. They detected 64 bird species and 15 mammalian species, including small mammals such as rodents (Order Rodentia), and medium to large mammals, including cottontail rabbit (Sylvilagus floridanus), fox squirrel (Sciurus niger), raccoon (Procyon lotor), red fox (Vulpes Vulpes), coyote, deer (Odocoileus Spp.), and domestic cat (Felis catus) and domestic dog (Cam's familiaris). Cited within the article were unpublished results from other studies from the early 1980's and 90's, which included only birds and small mammals.
Mammals of Colorado (Armstrong et al., 2010), is an extensive book reference on Colorado, with some general urban wildlife information (Armstrong et al., 2010). The book gives a historical account of Colorado ecosystems and mammals, explaining how Euro-American settlement led to agricultural development and urbanization, which altered waterways for irrigation, changed land use for crop cultivation, added built infrastructures, and established non-native landscaping. Along with this came an increase of deliberate non-native introductions, artificial feedings (e.g. trash cans or bird feeders), and fire suppression (Armstrong et al., 2010). Human expansion in Colorado ultimately led to the extirpation of the gray wolf (Canis lupus), grizzly bear (Ursus arctos), bison (Bison bison), and black-footed ferrets (Mustela nigripes), and allowed for introduction, widespread distribution, and increased
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abundance of urban adaptors and habitat generalists, such as the Norway rat (Rattus norvegicus), house mouse (Mus musculus), fox squirrel, striped skunk (Mephitis mephitis), raccoon, and red fox (Armstrong et al., 2010).
Overall Goals
Denver is an ideal location to start a long term urban wildlife monitoring program. The Denver metro area is an urbanized area with projected continual growth. There is limited data on Denver urban wildlife. Denver sits on a unique ecotone that spans the shortgrass prairie and foothills habitat. The goals of this project are to establish Denver as a study location with the UWIN and to examine the preliminary data. Specifically, we are interested in the following questions:
1) What mammalian species are present in parks and open spaces along the general urbanization gradient provided by Colfax Avenue in Denver, CO?
2) Of species abundantly detected, are any generalized site level characteristics (urbanization, habitat management type, longitude, etc.) good estimators for site use by that species?
Methods
Long Term Monitoring Methods:
The study design guidelines for this project have been standardized by the Urban Wildlife Information Network (UWIN) in order to contribute to their nationwide goal of being able to readily compare urban wildlife composition data between different cities. Therefore, the site selection, data collection, data processing, and data management procedures of this
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study discussed below meet the requirements that were supplied by the UWIN (Fidino et al.,
2016; Magle et al., 2016, Magle et al., 2018).
Site Selection and Study Design:
The Denver metropolitan area in Colorado was selected as a long term urban wildlife monitoring city due to Denver being a large metro area, the 26th largest in the U.S., with an estimated population of 2.8 million people (U.S. Census Bureau, 2010). In addition, Denver has consistently been one of the fastest growing cities in the country, growing 16.7% from 2000 to 2010, with an estimated increase to 3.3 million people by 2020, making Denver an appropriate candidate for urban wildlife monitoring (MDEDC, 2018; U.S. Census Bureau, 2010). The Denver area has an average elevation of 1610 m (5280 ft), an average annual temperature of 10.3°C (50.5°F), and an average annual precipitation of 39.5cm (15.5 in) (U.S. Climate Data). In addition, the Denver metro area includes and is surrounded by different native ecosystem types, with grassland prairie to the north, east, and south, and a strip of montane shrubland leading into montane forest to the west, with riparian/wetlands along waterways (Armstrong et al., 2010). Denver is unique because, unlike many other cities in the UWIN, it sits on an ecotone at the urban wildland interface.
Colfax Avenue was selected as the center line for the transect because it extends east and west through the center of Denver, CO, and supplies two 20 km urban to rural gradients that captures the range of urbanization present in the Denver metro area. Each transect was segmented into 5km sections, in which five parks or green spaces in each section were selected for camera placement. The 40 sites are within 2 km of the transect line, and at least 1 km away
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from each other. This ensures an even and well-spaced distribution of sampling effort along the
transect, and the >1 km spacing helps to promote site independence (O'Connell et al., 2006).
Site approvals or site use permits for all 40 locations were obtained from Aurora Parks, Recreation, and Open Space, Denver Parks and Recreation, Lakewood Parks, Forestry, and Open Space, Jeffco Open Space, Pleasant View Metropolitan District, and Prospect Recreation and Park District. Figure 1 shows a map of all the site locations. Along with obtaining permission from land managers, an IACUC protocol was submitted and approved by the University of Colorado Institutional Animal Care and Use Committee (Protocol # 114516(12)1B).

Inortheast-
(Golden
Glendale;
Figure 1. Site Location map of the Colfax Avenue transect in Denver, CO. Green/yellow and blue/orange lines represent the two 20km urban to rural gradients split into 5km sections. Pins represent locations and land managers: Orange=Jefferson County, Green= Pleasant View Metro District, Purple= Prospect Recreation and Parks District, Blue= Lakewood, Red= Denver Parks, Yellow= Aurora Parks
Camera Deployment:
One motion-activated camera equipped with a passive infrared trigger and infrared flash (Bushnell E2 Trophy Cam, Model 119836, Bushnell, Overland, KS) was placed at each approved site for four weeks during each season in 2017 (winter: January, spring: April, summer: July, fall: October). Cameras were strapped to trees or posts that allow for secure attachment and were aimed toward a carnivore attractant tablet located approximately 3-5 meters from the camera
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(Figure 2) and were removed after four weeks. The attractant tablets are commercially available fatty acid discs (FAS tablets, USDA Pocatello Supply Depot, Pocatello, ID) and were enclosed in a zip-tied mesh screen pouch that was secured to a tree, log, or stake. The tablet is a mild, malodorous, short range attractant and does not cause population-level responses or wildlife problems for surrounding homeowners. Trees were not harmed by these study methods. Cameras also had an informational sticker that provides basic project information and an email address for contact.
Figure 2: Example of a UWIN camera-trap set up. Camera is secured to a tree with a nylon strap and cable lock and is aimed toward a scent attractant tablet in a mesh pouch approximately 3-5 meters away.
Data Collection. Processing, and Management:
After at least 4 weeks in the field, all pictures from each SD card were downloaded and backed up to an external hard drive in files dedicated to each location. The pictures were then uploaded to a database created by Colorado Parks and Wildlife (Ivan & Newkirk, 2016; Newkirk, 2016) that was adapted by UWIN. Through the database, pictures were "tagged" with species, number of individuals, and comments by two different individuals. Less difficult mismatched
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photo tags (such as 5 humans vs 6 humans) were decided upon and verified, more difficult photos were verified by more than one individual. Data collected from each image include date, time, species present, and number of individuals. The relational Microsoft Access database has built in occupancy (detection/non-detection) queries that were exported for analysis. The database can also filter the pictures by time, location, species, activity, or comments.
Estimating Site Characteristics
Each site was broadly classified by habitat management type, e.g. how the habitat at each site is managed, via site visit observations. Sites that are highly managed, with manicured, artificial landscaping were given the classification of "artificial" habitat management type, examples being city and sports parks. Sites that appear left alone with evidence of natural vegetation and little to no maintenance (e.g., no mowing, regular planting of vegetation) were given the classification of "natural" habitat management type, examples being open spaces, wildlife reserves, and unmanicured parks. In addition, sites that had both manicured landscaping and wild natural areas were given the classification of "mixed" habitat management type. While this is a broad classification scheme, it can give an idea of the types of habitat available for wildlife at each site, and is similar to other studies' general site level categories (Gallo, Fidino, Lehrer, & Magle, 2017; Gehrt, Anchor, & White, 2009; Haverland & Veech, 2017; Luck & Wu, 2002; Markovchick-Nicholls et al., 2008; Ordenana et al., 2010;
Poessel et al., 2016). Area of each park was measured using satellite imagery via ArcGIS 10.2.1 (ESRI Inc., 2014) by creating polygons around each park that were defined by landscape boundaries (roads, large rivers, etc.) while attempting to omit structures, parking lots, and bodies of water, after which the polygon area (in acres) was calculated. Another habitat
10


characteristic of average percent tree canopy cover within a 1km buffer was measured via ArcGIS 10.2.1 (ESRI Inc., 2014) using the 2011 National Land Cover Database (NLCD) percent tree canopy cover GIS layer, which has a 30m pixel resolution (Homer, C.G. et al., 2015).
Characteristics that could be associated with urbanization were also estimated for each site by examining GIS layers of impervious surface, human population, and light pollution radiance. Average percent impervious surface within a 1km buffer of each camera location was measured using ArcGIS 10.2.1 (ESRI Inc., 2014) using the 2011 NLCD percent imperviousness GIS layer, which has a 30m pixel resolution (Homer, C.G. et al., 2015). We estimated human population within a 1km buffer around each camera site using the census block data within the 2010 SILVIS lab U.S. housing density shapefile, summing the census population data located within the buffer using RStudio 1.0.153 (RStudio Team, 2016; SILVIS Lab, 2010). A proxy for light pollution was estimated using the online open source light pollution mapping software and 2017 Visible Infrared Imaging Radiometer Suite (VIIRS) by recording the light radiance level at each camera location (Falchi et al., 2016). To reduce the dimensionality of these 3 urbanization parameters, we used principle component analysis (PCA), and used the first principal component (PCA1) which accounted for 78% of the variation in the data, see appendix Al for PCA loadings and variance percentages (similar to Gallo et al., 2017). The PCA1 score for each site was used as an urbanization covariate in our models, with positive values indicating higher site estimates of percent imperviousness, human population, and light pollution, and thus an indication of being "more urban". In addition, we calculated the percent of land use categories within a 5 km buffer of our transect line, to characterize the land use close to our study sites
11


using the 30 m pixel raster data of the 2011 NLCD land use data (see Appendix A2, Homer, C.G.
etal., 2015).
Wildlife Site Use and Detection Probability Estimation
To describe preliminary site use and detection probabilities of wildlife, we used singleseason single-species occupancy models from the occu() function from the unmarked package in RStudio (Fiske & Chandler, 2015; Mackenzie et al., 2002; RStudio Team, 2016). Occupancy models use binomial presence/absence data to estimate detection probabilities, which accounts for imperfect detection of a species, along with estimating occupancy probability estimates (both with or without variance explanatory covariates) (Mackenzie et al., 2006). Occupancy models are used often in species distribution and metapopulation dynamics research, but have also be used in invasive species, disease dynamic, and climate change studies (Bailey & Adams, 2005; Kendall, Hines, Nichols, & Campbell, 2013; Mackenzie et al., 2006). These single-season occupancy models have numerous assumptions, including site closure (species stay at the site during the survey), site independence (an individual is not detected at multiple sites), and that occupancy and detection probabilities are the same across sites (unless explained by site or survey characteristics) (Bailey & Adams, 2005; Mackenzie et al., 2006). Violating these assumptions can change the interpretation of the probabilities and potentially lead to biased estimates of detection and occupancy (Bailey & Adams, 2005). Potential biases in our estimates are discussed below.
Our photo data were queried for daily detection and non-detection data for each species for the 112 days surveyed in 2017. Due to having 40 sites and one year of data, the models shown here are meant to show general site use trends for this preliminary data in
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Denver, CO. It is important to note that as most of the species have home ranges larger than
the distances between sites, the assumption of site independence is not necessarily being met. This results in interpreting site "occupancy" as "use" and makes the estimate of detection probability harder to interpret (Bailey & Adams, 2005; Mackenzie et al., 2006). However, we do still want to show preliminary results of site use trends while accounting for imperfect detection. In addition, O'Connell et al. (2006) discusses that models with a detection probability below 0.15 do not produce valid site occupancy estimates due to not being able to distinguish between true species absence and undetectability. However, since we violate the site independence assumption of the model (leading to potentially uninterpretable detection probability estimates), along with violating the assumption of site closure (by having our singleseason occasions defined as the 112 days of 2017 leading to potentially biased occupancy/use estimates), and having the goal of describing preliminary data (i.e. not creating predictive models), the results from the models having less than 0.15 detection probability will be displayed and discussed.
Site use and detection were estimated for wild species that were detected in at least 25% of the sites, which included coyote, mule deer (Odocoileus hemionus), red fox, raccoon, and eastern cottontail rabbit. Fox squirrels were seen in 39 out of 40 sites, and due to their ubiquitous detection across our sites, it is nearly impossible to assess spatial distribution due to site level characteristics, therefore we did not run occupancy models for this species (similar to raccoons in Lesmeister, Nielsen, Schauber, & Hellgren, 2015). Hypothetical models for site use were created for each species based on previous research and our a priori thinking about urbanization effects. For coyotes, we hypothesized that urbanization would have a negative
13


effect on site use due to previous studies showing low occupancy near human made structures
and development (Gehrt et al., 2009; Lesmeister et al., 2015). We also hypothesized that the habitat management variable would be important, with areas that are more "natural" having higher use (Gehrt et al., 2009). We included our habitat management type variable (which generalizes the habitat in each park categorically as natural, artificial, or mixed) and a longitude variable (that generalizes the potential habitat over the ecotone, with foothill scrub to the west, and prairie grassland to the east). The longitude variable was calculated in RStudio by using the longitude of the far west site as a starting point, then the distance (km) east to every other site. It is important to note that for coyotes, we cannot assume that our sites are independent of one another, as home ranges of coyotes in urban areas can be highly variable and up to over 400 km2 (Gehrt et al., 2009; Poessel et al., 2016). For red fox, we hypothesized that urbanization would have a positive effect on site use, from previous studies showing increased activity near human development which is thought to be from seeking refuge from agricultural practices and coyotes (Black, Preckler-Quisquater, Batter, Anderson, & Sacks, 2018; Gosselink, Deelen, Warner, & Joselyn, 2003; Lesmeister et al., 2015). In addition, we hypothesized that habitat characteristics would show increased use in areas more similar to natural grassland areas (Black et al., 2018; Gosselink et al., 2003; A. O'Connell et al., 2006), with increased use in natural habitat management type and decreasing with longitude. As with coyotes, the home range of urban red foxes is variable and larger than our site spacing, up to over 35 km2 with daily movements of up to 10 km (Armstrong et al., 2010; Gosselink et al., 2003). As raccoons are known urban habitat exploiters with a highly diverse omnivorous diet, we hypothesized that we would see similar results of urbanization showing higher site use probability estimates, with
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parks that have mixed habitat type, and that are closer to residential areas (Armstrong et al.,
2010; Gross, Elvinger, Hungerford, & Gehrt, 2012; Lesmeister et al., 2015). Therefore, we used the urbanization, habitat management type, and longitude covariates in our models. Mule deer are the most abundant cervid in Colorado, with the widest range occurring in all ecosystems, from riparian grassland to alpine tundra (Armstrong et al., 2010). They also have a broad herbaceous diet, but have a small rumen and gut, therefore need small amounts of high quality food (Armstrong et al., 2010). In addition, they are likely to avoid areas with high human and dog use (Miller, Knight, & Miller, 2001). Therefore, we predicted that urbanization would have a negative effect on mule deer use, natural habitat having a positive effect, and longitude potentially having a positive to negative effect as they are found in all ecosystem types. Eastern cottontail rabbits have been documented in the northeastern riparian areas of Colorado through to the northern foothills, including the Denver area (Armstrong et al., 2010). They are commonly found in habitats containing brush and grass, and are commonly found in urban areas, especially when shrub cover and herbaceous food is present (Baker, Emerson, & Brown, 2015). We therefore hypothesized that eastern cottontail rabbits use would be influenced positively due to mixed and natural habitats, urbanization, and when getting closer to natural grassland areas towards the east.
Results
Wildlife Inventory Summary:
Cameras were set up seasonally for at least 4 weeks starting winter 2017, resulting in 39,389 photos. Domestic mammalian species detected included: domestic cats, domestic dogs, horses' (Equus caballus), and humans (Homo sapiens). Wild mammalian species detected
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included: coyotes, eastern cottontail rabbits, elk (Cervus elaphus), fox squirrels, mountain lions
(Puma concolor), mule deer, black-tailed prairie dogs, raccoons, rats (Rattus spp.), red fox, and striped skunks (see Table 1 for summary totals). Total count data are not useful for summarizing wildlife density as we cannot differentiate multiple detections of the same individual animals, however, it does show that the method is successful at capturing these wildlife species.
Table 1. 2017 Camera and Picture Summaries.
Season Dates Out Total Pictures (39,389) Species Identified (Total # of Pictures)
Winter 12/29/16 - 1/29/17 10,055 Domestic Wild Mammalian
Cat(169) Dog(4242) Horse (31) Human (15,436) Coyote (296) Prairie Dog (143) E. Cottontail Rabbit Raccoon (914) (1789) RodentSpp. (22) Elk(107) Red Fox(110) Fox Squirrel (7274) Striped Skunk (30) Mt. Lion (3) Mule Deer (570)
Spring 4/1/17 - 4/30/17 14,088
Summer 6/29/17 - 7/28/17 8,687
Fall 9/30/17-10/29/17 6,559
Figure 3 shows the total frequency of each species that was detected at the 40 sites across Denver, CO during the seasonal 2017 samples. A majority of the sites detected use by humans, fox squirrels, dogs, and raccoons, and around half the sites detected use by coyotes, eastern cottontail rabbits, and domestic cats. There was low detection of elk, mountain lions, prairie dogs, horses, rodents, and striped skunks, therefore these species were not included in the site use and detection probability models.
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1/
Species Identified
Figure 3. Detected species frequency at all UWIN camera sites. Shows the total number of sites that each species was detected during the four sampling seasons of 2017.
Site characteristics that could be broadly associated with urbanization, wildlife use, and wildlife detection were estimated for each site. Figure 4 shows the general trend of the urbanization gradients with the human population, percent impervious surface, and light pollution estimates. Also shown is the PCA1 score of the principal component analysis of these three parameters, that is used as an urbanization covariate in our site use and detection probability models. In addition, habitat characteristic covariates of tree canopy cover, park area, and habitat management type were estimated and are also shown in Figure 4. General observations from this data show that the variables we thought would be a decent proxy for urbanization (imperviousness, human population, and light pollution) did show a general increase towards the urban center. Further, PCA analysis showed high correlation between the three variables, with the first principal component accounting for 78% of the variance and displaying a general trend of increasing toward the urban center. However, it is important to note that the trend is not perfectly linear, showing there can be pockets of more urban areas
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not within the urban center, and using distance from the urban center as a proxy for an urbanization gradient may not be appropriate for many cities, especially larger cities. Nevertheless, looking at the urbanization proxy variable graphs for these Denver sites, there does seem to be decent variation between the ends of both the transects, leading to higher confidence that we obtained two generally urban to rural gradients. The percent tree canopy cover around each site appears more variable, which was expected due to the natural variation of the native ecotone across the Denver metro area, generally with foothills and forested mountainous areas when heading west, and prairie grasslands and agriculture when heading east, with naturally forested riparian areas throughout (see figure 5 for general land use in and around Denver). In addition, using the 2011 NLCD land use data we calculated the percent of land use categories within a 5 km buffer of our transect line to characterize our study area, with the top land use types being developed (62.4%), developed-open space (11.7%), grassland (8.7%), and shrub/scrub (6.6%) (see Appendix A2).
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Percent Impervious Surface (1km buffer around site)
80 60 40 20 0
West Urban Center East
• Site
• •• •
VW-.
•• • • • •• • •
r
Human population ^ (1km buffer around site)
CD
E
2 40000
o 30000
■§ 20000 Q.
£ 10000 ro o
3
West Urban Center East

T
Light Pollution Estimate
(NLCD percent imperviousness, 2011)
(SILVIS census block data, 2010)
(VIIRS, March 2017)
PCA Urbanization Index (1km buffer)
(from human population, impervious surface, and light pollution data)
Tree Canopy Cover Estimate (1km buffer around site)
(NLCD percent tree canopy cover, 2011)
Park Area (acres)
(Measured using ESRI ArcMap 10.2.1)
Park Habitat Management Type
(Classification via site visit observations)
Figure 4. Estimated camera site park characteristics. Characteristics measured or calculated for each site and shown spatially from west to east, data sets used for each measurement are mentioned below each graph.
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National Land Cover Database 2011 (U.S.)
Open Water Perennial Ice/Snow Developed, Open Space Developed, Low Intensity Developed, Medium Intensity Developed, High Intensity
Barren Land Deciduous Forest Evergreen Forest Mixed Forest Shrub/Scrub Grassland/Herbaceous
Pasture Hay
Cultivated Crops
Woody Wetlands
Emergent Herbaceous Wetlands
Denver UWIN Sites
Figure 5. Regional land cover classifications and UWIN camera locations in Denver, CO. Map created using Data Basin online mapping software, and the 2011 NLCD dataset to show how Denver is an ecotone, with forest on the west end and grassland/agriculture east end of the transect, also shows the general land use around the general Denver metro area.
To show how species are using each location across the urbanization gradients, figure 6 shows the raw data (which does not account for imperfect detection) of total percent daily occurrence of 12 species over all four seasons (out of 112 days during 2017) at each location and shows each location spatially as they relate to each other in a west to east fashion. Very general observations from this data suggest that only one site had mountain lion and elk detection, mule deer were detected near the rural edges, foxes were more often using areas in
20


the east transect and coyote were using areas more often in the west transect, and there were
low detections of striped skunk.
Mt. Lion
Elk
Mule Deer
Cat
r
West
Striped Skunk
Red Fox
100 - 100 - 100 -
80 - 80 - 80 -
60 - 60 - 60 -
40 - 40 - 40 -
20 - 20 - 20 - • •
0 - «■ ■»•*# 0 - • 0 -
~T
Urban Center
East
E. Cottontail Rabbit
r
West
Urban Center Raccoon
East
l
West
Urban Center Coyote
East
Squirrel
o
C
OJ
r
West
Dog
Humans
100 - • •• 100 - • 100 - «♦ •
80 - • * . . * 80 - • • 80 -
60 - • • * 60 - • 60 - • .
40 - . *. • - •• • • • 40 - • • • •• • 7v 40 - • • * #
20 - # • # • « v.. “• 20 - * • * : • \ 20 - • » S • • • • •
0 - • 0 - • • • • • s 0 -
Urban Center
East
r
West
Urban Center
East
r
West
Urban Center
East
Figure 6. 2017 percent species occurrence across all sites. Shows the total percent for each species daily occurrence at each site during the four seasons collected during 2017 (out of 112 days). Because the two urban to rural transects run from the urban center out east and west, each point represents each location spatially from west to east with the urban center at the midpoint.
21


Occupancy Modelling Results
Results from the coyote occupancy models produced a detection probability estimate of 0.065 (SE=0.005). Table 3 shows the models tested, our original prediction of the model, and how the models performed in relation to each other. The top models for coyote site use included the longitude variable (AIC weight=0.63) with a negative correlation heading east (see figure 7A) and the habitat management variable (AIC weight= 0.37) with a positive correlation towards natural areas (see figure 7B). Surprisingly, the urbanization model performed very poorly when predicting site use, with the constant (.) model performing the worst.
Table 3. Coyote occupancy models (detection estimate p(.) =0.065, SE= 0.005)
Model Prediction AIC A AIC AIC weight cum. AIC weight
*4J(l°ng)p(.) - 1278.985 0.00 0.63 0.63
4J(mgmt)p(.) + 1280.041 1.06 0.37 1.00
MJ(urb)p(.) - 1390.837 111.85 0.00 1.00
H)p(-) NA 1413.849 134.86 0.00 1.00
mgmt= habitat management type (artificial, mixed, natural) long= longitude proxy= distance from the farthest west site heading east in km urb= PCA1 urbanization proxy (.)= constant model, 4>= occupancy/use, p= detection. AIC= Akaike Information Criterion, A AIC= change in AIC compared to the top model, AIC weight= the
conditional probability of the model, cum. AIC weight= cumulative model weights
22


0 10 20 30 40
artificial mixed natural
Distance East (km) (long model)
Habitat Management Type (mgmt model)
Figure 7. Coyote model estimates with 95% confidence intervals in gray. Shows the top two models showing decreased probability of use heading east (A), and increased probability of use in mixed and naturally managed habitats (B).
The red fox models had a detection probability estimate of 0.058 (SE= 0.005) and did not perform well with the constant (.) model being the top model with an AIC weight of 0.39 (see Table 4). The second model (AIC weight= 0.32) was the urbanization model, and the third was the longitude model (AIC weight= 0.22), both predicting a very slight increase in use. The habitat management model showed no real difference in predicting use (AIC weight= 0.07) (see probability estimates graphed in Figure 8).
Table 4. Red fox occupancy models (detection estimate p(.) =0.058, SE= 0.005)
models Prediction AIC A AIC AIC weight cum. AIC weight
H)p(-) NA 689.46 0 0.39 0.39
ip(urb)p(.) + 689.86 0.41 0.32 0.71
*4J(l°ng)p(.) + 690.57 1.12 0.22 0.94
4J(mgmt)p(.) + 693.05 3.6 0.07 1.00
mgmt= habitat management type (artificial, mixed, natural) long= longitude proxy= distance from the farthest west site heading east in km urb= PCA1 urbanization proxy (.)= constant model, 4>= occupancy/use, p= detection. AIC= Akaike Information Criterion, A AIC= change in AIC compared to the top model, AIC weight= the
conditional probability of the model, cum. AIC weight= cumulative model weights
23


-4 -2 0 2 4
PCA1 (positive= more urban)
(urb model)
c
<>
a)
a.
artificial mixed natural
Habitat Management Type (mgmt model)
Figure 8. Red fox model estimates with 95% confidence interval in gray. Occupancy model estimates showing slight increased probability of red fox use when (A) urbanization increases (AICw=0.32), and (B) heading east (AICw=0.22), and (C) no difference in use by habitat management type (AICw=0.07). The constant (.) model performed best (AICw=0.39).
Results from the raccoon occupancy models produced a detection probability estimate of 0.106 (SE=0.005). Table 5 shows similar results as the red fox models, with the top model for site use being the constant (.) model (AIC weight= 0.53). The rest of the models performed poorly as covariates to predict raccoon use with the urbanization model (AIC weight= 0.2), the longitude model (AIC weight=0.19), and the habitat management model (AIC weight= 0.08), all showing high probability of use consistently along each variable (see figure 9).
24


Table 5. Raccoon occupancy models (detection estimate p(.)=0.106, SE= 0.005)
models Prediction AIC A AIC AIC weight cum. AIC weight
H)p(-) NA 2461.44 0.00 0.53 0.53
ip(urb)p(.) + 2463.41 1.97 0.20 0.73
*4J(l°ng)p(.) - 2463.44 2.00 0.19 0.92
4J(mgmt)p(.) 0 2465.25 3.80 0.08 1.00
mgmt= habitat management type (artificial, mixed, natural) long= longitude proxy= distance from the farthest west site heading east in km urb= PCA1 urbanization proxy (.)= constant model, 4>= occupancy/use, p= detection AIC= Akaike Information Criterion, A AIC= change in AIC compared to the top model, AIC weight= the
conditional probability of the model, cum. AIC weight= cumulative model weights
-4 -2 0 2 4
PCA1 (positive= more urban)
(urb model)
0 10 20 30 40
Distance East (km)
(long model)
artificial mixed natural
Habitat Management Type (mgmt model)
Figure 9. Raccoon occupancy model estimates with 95% confidence interval in gray. Occupancy model estimates showing no changes in probability of raccoon use from (A) urbanization (AICw=0.2), (B) longitude (AICw=0.22), and (C) habitat management type (AICw=0.07). The constant model performed the best (AICw=0.53).
Results from the mule deer occupancy models produced a detection probability estimate of 0.073 (SE=0.008). Table 6 shows that the top two models are similar for estimating
25


site use, with habitat management type with an AIC weight of 0.58, and urbanization with an
AIC weight of 0.42, with figure 10 showing the model estimates.
Table 6. Mule deer occupancy models (detection estimate p(.)=0.073, SE= 0.008)
models prediction AIC A AIC AIC weight cum. AIC weight
4J(mgmt)p(.) + 609.61 0.00 0.58 0.58
MJ(urb)p(.) - 610.28 0.67 0.42 1.00
H)p(-) NA 621.78 12.17 0.00 1.00
*4J(l°ng)p(.) + or - 623.35 13.74 0.00 1.00
mgmt= habitat management type (artificial, mixed, natural) long= longitude proxy= distance from the farthest west site heading east in km urb= PCA1 urbanization proxy (.)= constant model, 4>= occupancy/use, p= detection AIC= Akaike Information Criterion, A AIC= change in AIC compared to the top model, AIC weight= the conditional probability of the model, cum. AIC weight= cumulative model weights
PCA1 (positive= more urban) (urb model)
Habitat Management Type (mgmt model)
Figure 10. Mule deer occupancy model estimates with 95% confidence interval in gray. Occupancy model estimates showing (A) decreased probability of mule deer use when urbanization increases (AICw=0.58), and (B) increased probability of use in natural habitat (AICw= 0.42).
Results from the eastern cottontail occupancy models produced a detection probability estimate of 0.208 (SE=0.008). Table 7 shows similar results as the raccoon and red fox models, with the top model for site use being the constant (.) model (AIC weight= 0.51). The rest of the models performed poorly as covariates to predict eastern cottontail use with the urbanization model (AIC weight= 0.22) with a slight decrease probability of site use (figure 11A), and the
26


longitude model (AIC weight=0.19), and the habitat management model (AIC weight= 0.09),
both showing mid-level probability of use along each variable (figure 11B and C).
Table 7. Eastern cottontail rabbit occupancy models detection estimate p(.)=0.208, SE= 0.008
models Prediction AIC A AIC AIC weight cum. AIC weight
H)p(-) NA 2449.35 0.00 0.51 0.51
MJ(urb)p(.) + 2451.03 1.68 0.22 0.73
*4J(l°ng)p(.) + 2451.35 2.00 0.19 0.91
ip(mgmt)p(.) + 2452.86 3.51 0.09 1.00
mgmt= habitat management type (artificial, mixed, natural) long= longitude proxy= distance from the farthest west site heading east in km
urb= PCA1 urbanization proxy (.)= constant model, 4>= occupancy/use, p= detection AIC= Akaike Information Criterion, A AIC= change in AIC compared to the top model, AIC weight=
the conditional probability of the model, cum. AIC weight= cumulative model weights
-4 -2 0 2 4
PCA1 (positive= more urban)
(urb model)
0 10 20 30 40
Distance East (km)
(long model)
Figure 11. Eastern cottontail rabbit occupancy model estimates with 95% confidence interval in gray. Occupancy model estimates showing a slight decrease in probability of eastern cottontail site use from (A) urbanization (AICw=0.2), and no changes in probability from (B) longitude (AICw=0.19), and (C) habitat management type (AICw=0.08). The constant (.) model performed the best (AICw=0.53).
Discussion
Our objectives were to establish a long term urban wildlife monitoring program in Denver, CO that would contribute to the international Urban Wildlife Information Network and
27


to present the preliminary data that were collected over four seasons in 2017. In the 40 sites
we established, we detected 11 wild mammalian species. In addition, we ran single-season occupancy models to account for imperfect detection, and to see if any general site level characteristics would show trends in site use probabilities the five species abundantly detected.
Results from the coyote site use models are consistent with previous studies with regards to having higher probability of use in areas with natural habitat (Gehrt et al., 2009). In addition, the top model suggests that coyotes have a higher probability of using areas that are further west along the transect. While we used longitude as a proxy of general potential natural habitat (e.g. shrub/forest towards the west, and prairie grassland towards the east), there could be other variables, or combination of variables, that vary along the longitudinal gradient that could be responsible for the estimates, and should be looked into further, such as actual vegetation/habitat types and structure, connectivity of the urban matrix, prey availability, etc. Unexpectedly, the urbanization model was not a good predictor for use, as has been seen in other urban areas (Gehrt et al., 2009; Lesmeister et al., 2015). None of our generalized urbanization and habitat covariate models performed well for eastern cottontail rabbit, raccoon, or red fox. For these species the constant (.) model had the lowest AIC. However, competing covariate model estimates showed a slight increase in probability of use from urbanization and longitude for red fox, which was consistent with our hypotheses, and a slight decrease in probability of use for eastern cottontail rabbit in more urban areas and no changes due to longitude or habitat management type, which was unexpected. Raccoon models showed consistently high probability of use regardless of any of the covariates. Mule deer models had
28


top models that were consistent with our hypotheses, showing a decreased probability of use
in urban areas, and higher probability in naturally managed habitats.
We acknowledge that these are rudimentary models, with generalized proxies for urbanization and habitat characteristics, and in reality, there are multiple different habitat, climatic, community, and urbanization factors, and combinations of factors, that are affecting site use. However, the models do show general site use probabilities for each species, allowing for the development of more detailed hypotheses, characterization of sites, and random sampling along the variability of covariates hypothesized. As the Denver UWIN dataset becomes more robust, we would like to see how well these model estimates perform, add in seasonal and yearly covariates, and consider polynomial models in addition to the linear models to look at potential intermediate effects. In addition, there are multiple factors not addressed in this study that could be warranted for future research. One example would be to characterize sites more specifically, such as measuring more detailed habitat characteristics, like vegetation inventories with classifying native vs non-native species along with habitat structure and density. This may help discern more exact habitat use patterns for each species. For example, in previous studies eastern cottontail rabbits used urban areas with more shrub cover areas (Baker et al., 2015). Perhaps in Denver, our urban parks do not supply this habitat type and structure in comparison to other cities.
In addition, it would be interesting to study species interaction effects, as studies have shown that occupancy/use can be affected by competition, avoidance, prey availability, human activity, and/or harassment by domestic species (Armstrong et al., 2010; Lesmeister et al.,
2015; Miller et al., 2001). Looking at our preliminary data, it is interesting that, generally,
29


coyotes are seen more in natural areas in the west, and red fox are seen more in the urban areas in the east. It would be interesting to explore if the east and west transects are different in terms of habitat, prey availability, proximity to anthropogenic feature, and/or avoidance of coyotes by red fox that has been suggested (Black et al., 2018; Gosselink et al., 2003; Lesmeister et al., 2015). Also, it may be interesting to try to find out if there are any management strategies of specific species, as it is currently unknown if any population reduction or conservation efforts are underway along the transects. Another factor that should be acknowledged is connectivity of the urban matrix along with the availability and quality of habitat patches, which has also been shown to influence use (Baguette & Van Dyck, 2007; Beninde, Veith, & Hochkirch, 2015; Gese, Morey, & Gehrt, 2012; Rudd, Vala, & Schaefer, 2002).
Continued monitoring of these species will be important to contribute to scientific knowledge of urban wildlife spatial and temporal dynamics. Understanding how wildlife are using these areas could be very beneficial for conservation efforts with city planning and development, especially considering how fast Denver is growing. In addition, monitoring can be useful in managing wildlife communities. Species activity patterns, interaction effects, and disease dynamics information can be useful when managing wildlife communities. Monitoring wildlife in urban areas that have the potential to spread disease could be very important, especially since increased disease prevalence and transmission are seen in urban areas (Gottdenker et al., 2014). Many of the species we detected in urban Denver can be affected by disease, which may be important in spatial or temporal changes in detection and occurrence of the species, along with being a health concern for transmittable pathogens to other species, including humans, pets, and livestock (Armstrong, Fitzgerald, & Meaney, 2010; Catalano et al.,
30


2008; Malmlov, Breck, Fry, & Duncan, 2014). One example is coyote susceptibility to and being
hosts of zoonotic bacterial, viral, and parasitic diseases such as Toxoplasmosis gondi bacteria, rabies virus, and Echinococcus multiocularis tapeworm to name a few examples (Armstrong et al., 2010; Catalano et al., 2008; Malmlov et al., 2014). In addition, coyotes could spread diseases that could be transmitted to other species, including domestic dogs, such as distemper and parvo viruses, and many parasites (Kapil & Yeary, 2011; Malmlov et al., 2014). Coyotes are not the only urban species that can harbor and spread zoonotic disease, examples include raccoons with rabies virus and raccoon roundworm (Baylisascaris procyonis) or eastern cottontail rabbits and rodents with tularemia (Kazacos, 2010).
Overall, we have established a long-term monitoring protocol that is successful at detecting many species in the Denver metro area. As we have discussed, the data presented here were collected over a short time span, which is insufficient when trying to make inferences about ecological processes, but it does provide baseline observations about Denver's urban wildlife. Our preliminary data have shown some potential spatial and site use trends that can be used to create more specific hypotheses and complimentary methods for testing them. In addition, this data will contribute to the international Urban Wildlife Information Network (UWIN) monitoring program that can be used to compare urban wildlife patterns among and across cities.
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CHAPTER II
DEVELOPMENT AND ASSESSMENT OF AN URBAN WILDLIFE ECOLOGY COURSE-BASED UNDERGRADUATE RESEARCH EXPERIENCE IN AN INTRODUCTORY BIOLOGY LABORATORY
Introduction
There has been a national push to increase authentic research experiences for undergraduate students due to the positive outcomes realized, such as increased understanding of the scientific process, increased retention in STEM programs, and the achievement of many nationally recommended competencies for biology students (AAAS, 2011; NRC, 2003; PCAST, 2012). By having an engaging and authentic research experience, students can form or solidify an interest in biological research, and even if they don't, they still are better equipped to evaluate scientific claims in everyday life by better understanding the scientific process (AAAS, 2011). National directives have suggested that undergraduate biology students should master a set of core competencies and learning outcomes (summarized in Table 1 below) by the time they graduate and that authentic research experiences could help students achieve those desired outcomes. In response to these directives, there has been an influx of Course-based Undergraduate Research Experience (CURE) curriculums (a few examples: Campbell et al., 2007; Caruso, Sandoz, & Kelsey, 2009; Jordan et al., 2014; Olimpo, Fisher, & Dechenne-Peters, 2016; Thompson, Neill, Wiederhoeft, & Cotner, 2016). The NSF funded Course-based Undergraduate Research Experience Network (CUREnet) defines a CURE as a research experience in an undergraduate course that focuses on the following important aspects: uses scientific practices, results in discovery of new knowledge, investigates relevant work, and involves collaboration and iteration (summarized in Table 2, Auchincloss et al., 2014).
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CUREs in the literature have been widely variable in terms of how they are offered to students
(elective vs. manditory), when they are implemented in the students career (first year level to senior level), how much time is committed (a few weeks to multi-semester), and the level of scientific practices used (very few to many), how many students are involved (one small class to many large enrollment classes), to name a few. In addition, research related to the effectiveness of specific CUREs and what aspects of the CURE experience are most important have been ongoing and widely variable in terms of what instruments are employed (published vs not-published, validated vs not-validated, graded assessments vs student reported metrics), how much data are used (one small class over a partial semester to multiple large-enrollment classes over multiple semesters), and how the data are analyzed. Even with the wide variability in CURE curriculums and evaluation methods, the overall outcomes of student participation in CUREs are generally positive. For example, CUREs can be particularly influential in the introductory courses where many studies have shown positive student outcomes such as developing long term understanding of core concepts, increasing interest and motivation, addressing negative perceptions of science, forming an identity as a scientist, helping to form scientific career goals, and acquiring skills like problem solving and critical thinking (AAAS, 2011; Auchincloss et al., 2014; M. T. Jones, Barlow, Villarejo, Amy, & Barlow, 2010; Mordacq, Drane, Swarat, & Lo, 2017; NRC, 2009; PCAST, 2012). In addition, these positive outcomes are disproportionately seen with underrepresented groups (Bangera & Brownell, 2014; Chemers, Zurbriggen, Syed, Goza, & Bearman, 2011; Estrada, Hernandez, & Schultz, 2018; M. T. Jones et al., 2010). Furthermore, CUREs offer an authentic research experience in an undergraduate course, where many barriers of traditional undergraduate research experiences can be
33


overcome; such as limited availability of research lab positions, potentially biased acceptance of
students, financial and/or personal barriers, and perceived barriers of interacting with a faculty member or lack of knowledge about cultural norms associated with research (Bangera & Brownell, 2014).
Table 1: Core competencies for biology education from the American Association for the Advancement of Science (A) and the Association of American Colleges and Universities (B). Many of the competencies and outcomes have shared qualities and reiterate the importance of students acquiring these skills during their undergraduate experience.
A. Core Competencies for Biology (Adapted from AAAS, 2011 Table 2.1)
AAAS Core Competency Why Important? Demonstration
Apply the scientific process Biology is evidence-based knowledge through hypothesis generation, observation, and experimentation Practice scientific process to understand systems
Use quantitative reasoning Biology relies on data analysis and interpretation Apply quantitative analysis to interpret data
Use and Interpret modeling and simulation Biology focuses on understanding complex systems and predicting outcomes Use modeling and simulation to describe systems
Apply interdisciplinary knowledge Biology incorporates all the fields of sciences Apply concepts from other sciences to interpret systems
Communicate and collaborate Biology is a collaborative Collaborate on projects and communicate concepts to others
Understand science and society relationship Biology is conducted in a societal context Identify social, historical, and ethical contexts in biological practice
B. AAC&U Essential Learning Outcomes (Adapted from AAC&U, 2007)
Essential Learning Outcomes Specific Outcome/Skill
Knowledge of cultures and the physical/natural world Study science, math, social science, history, arts, etc.
Intellectual and practical Skills Inquiry, analysis, critical thinking, written and oral communication, quantitative literacy, teamwork, and problem solving
Personal and social responsibility Civic knowledge and engagement, intercultural knowledge, ethical reasoning
Integrative and applied learning Application of knowledge to new and different problems
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Table 2. Important CURE aspects and explanations (summarized from Auchincloss et al., 2014).
CURE Aspect Explanation / Importance
Use of scientific practices Student authetically practices: scientific literacy, asking questions, proposing multiple hypotheses, designing experiments, making observations, collecting and analyzing data, constructing and performing models/simulations, interpretating data, realizing common variability and failure, and communicating findings
Discovery of new knowledge Both student and instructor will not know the results beforehand, can promote informed reasoning and exploration
Investigates relevant work Gives authenticity to the experience and a feeling of connection and contribution to the scientific community
Involves collaboration Iteration Important and authentic networking with mentors and peers Repetition, conformation, and extension is how research occurs, can build confidence and project ownership
Due to national recommendations to incorporate authentic research experiences, and the positive student outcomes seen from research experiences, we developed an urban wildlife monitoring CURE for a first-year undergraduate biology laboratory. The context for this CURE curriculum is the Urban Wildlife Information Network's (UWIN) international urban wildlife monitoring project. UWIN is a growing network of cities that believe in the importance of urban wildlife monitoring and have the goal of multicity collaboration and long-term investigation of our nation's urban ecosystems (Magle et al., 2018), see website at
http://www.lpzoo.org/conservation-science/proiects/urban-wildlife-information-network-uwjn). The mission of the UWIN is for cities to investigate their specific urban wildlife ecology, and to compare data across cities to investigate widespread patterns in urban wildlife. The ultimate goals are to contribute to the scientific knowledge base about urban ecology, promote wildlife conservation, increase public education and engagement, decrease human-wildlife conflict, and influence city planning and policy making (Magle et al., 2018). The UWIN monitoring protocol requires that motion activated camera-trap transects be set up over an
35


urbanization gradient in each participating city, and that data are collected daily over one month in each season. As with any long-term monitoring network, the data generated can be used to look for patterns within and across sites and can be used to answer current questions and questions yet to be conceived (Gitzen et al., 2012; Knapp et al., 2012). The study of urban ecosystems has been increasing since the 1990's but is still relatively new in terms of ecological research (Adams, 2005). With the rapid expansion of urban areas, the effects of urbanization can be lagging and evolving (Ramalho & Hobbs, 2011). Therefore, it will be important to monitor urbanization effects and study urban ecosystems in expanding cities over time.
Potential research projects that student teams could collaboratively explore would be based on an urbanization related variable (like human population density or noise pollution) and how that might impact a wildlife variable (like mammalian species richness or site use by a specific species). Because this project requires long term seasonal wildlife monitoring, results in a continually growing data set to explore, contributes to an authentic international project, and is relevant research being conducted where students are living, we determined that this project was a good candidate to be created into a CURE.
The goals of this paper are to describe the development and implementation assessment of this CURE, specifically with respect to fidelity to the core elements of a CURE (sensu Auchincloss et al. 2014), student affect related to persistence in STEM, and student competencies related to conducting research. We approached the following research questions using both published, validated surveys and concept inventories, as well as student work samples:
36


1. To what extent are the important CURE design features (sensu Auchincloss et al. 2014)
present in the developed CURE curriculum?
2. How has this CURE influenced student perceptions related to scientific identity and persistence in the sciences?
3. Do students who participate in this CURE achieve targeted scientific process skills and have a better understanding of experimental design?
These research objectives were selected due to recommendations from Corwin et al.'s (2015) conceptual model of CURE activities and potential outcomes. For CURE evaluation, the authors recommend selecting the CURE outcomes of interest, to consider the time-frame that the expected outcome would be realized, and to explore published examples of evaluation for specific outcomes. For this study, we were interested in the Corwin et al.'s (2015) models' early phase evaluation of short to mid-term student outcomes. The outcomes of interest to us were the level of students' sense of having project ownership, students confidence level with scientific skills, a sense of belonging to a scientific community, and the assessment of actual scientific process skills (Corwin, Graham, & Dolan, 2015). In addition, we were interested in measuring the implementation of important CURE design features (i.e. collaboration, discovery, relevance, and iteration) that are important to attain the above CURE outcomes. Using the backward design approach of CURE evaluation as described by Shortlidge et al. (2016), we selected specific assessments and instruments to gather data related to our outcomes of interest. The findings from this study are important in understanding to what extent the developed urban wildlife CURE incorporates important aspects described in published literature and what impacts are seen on students' scientific skills and perceptions.
37


Methods
CURE Development
AAAS (2001) recommends that developed curricula include interactive, inquiry driven, collaborative, and relevant activities in order to create a more student-centered experience with increased gains in student knowledge and skills (Handelsman et al., 2004; Reynolds & Kearns, 2017). We developed the urban wildlife CURE curriculum considering the above and by taking a backward design approach. Backward design has three major steps; first to identify desired learning objectives, second to determine acceptable evidence that those objectives are being met, and third to plan learning experiences (Wiggins et al. 2001). Backward design has been recommended for CURE development, with the addition of a fourth step of iteration and revision (Cooper, Soneral, & Brownell, 2017).
CURE Learning Objectives
Learning objectives for this CURE were created based on the national recommendations for biology education core competencies (Table 1A), the AAC&U essential learning outcomes (Table IB), the important CURE aspects (Table 2), and from recommendations of the biology department instructors at our university who provided anecdotyl evidence of deficient student outcomes, particularly with the respect to generating testable and specific research questions, reading and using primary literature, and writing critically and coherently about conducted research. Four general learning goals, each with multiple specific objectives, were selected and are described in Table 4 below. It is important to note that these learning objectives are generalized, and could be used as a starting point for any CURE development.
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Table 4. Learning objectives selected for the CURE; general objectives are numbered, specific objectives are lettered, and important aspects are highlighted.
1) Apply scientific process through inquiry and analysis
AAAS core competency= Apply the process of science AAC&U essential learning outcome= Inquiry and analysis
CURE aspect= use of scientific practices, discovery, relevance, collaboration, iteration
a. Identify and discuss relevance of the topic
b. Search and locate relevant primary literature
c. Identify, interpret, and explain sections of primary literature
d. Create, evaluate, and improve specific, relevant, & testable research questions
e. Create and carry out relevant data collection protocols
f. Analyze and interpret data to form conclusions
2) Use quantitative reasoning
AAAS core competency= Ability to use quantitative reasoning
AAC&U essential learning outcome= Quantitative Literacy
CURE aspect= use of scientific practices, discovery, collaboration, iteration
a. Manage and organize large data sets
b. Recognize and discuss variability in nature and data collection
c. Evaluate strengths and weaknesses in data & data collection methods
d. Create and interpret appropriate data visualizations (e.g. graphs, tables)
3) Collaborate and communicate
AAAS core competency= Ability to collaborate and communicate
AAC&U essential learning outcome= Oral and written communication, teamwork
CURE aspect= use of scientific practices, collaboration
a. Use appropriate conventions of organization, content, formatting, and style in writing
b. Correctly cite high-quality, relevant sources to support arguments and statements
c. Orally communicate scientific understanding, findings, and conclusions
d. Use collaborative technology for data collection and analysis
e. Work efficiently and professionally in teams
4) Develop identity as student scientists
AAAS core competency= Ability to understand the relationship between science and society AAC&U essential learning outcome= Civic engagement, ethical reasoning
a. See science as a social endeavor of value to society
b. Take ownership of and find meaning in their work
c. Perceive themselves as efficacious
d. Begin to realize their potential as a scientist
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CURE Implementation Context and Participation
The University of Colorado Denver is a highly diverse urban public research university, with an approximate enrollment of 10,500 students, comprising 42% students of color, and 25% underrepresented minorities (URM) (African-American, Hispanic, American Indian, and Native Pacific Islander). The Biology Major is the largest in the university and comprises 17% of all undergraduates enrolled in the College of Liberal Arts and Sciences, of which 27% are URM. From 2010-2016, 39% of all enrolled students received a D, F, or withdrew (DFW) from general biology 1, including 44% of the new freshman, and 54% of new URM freshman.
The first iteration of this CURE was implemented in the General Biology 2 Laboratory course at the University of Colorado Denver during the spring 2018 semester. The spring 2018 semester included 16 regular sections, and 2 honors sections that were taught by 10 different Graduate Teaching Assistants (GTAs). Each section has a maximum of 24 students, resulting in approximately 400 students participating in the CURE. The majority of our work focused on the Spring 2018 students. However, for research question 3, we also examined term papers from a sample of students from the same course in spring 2017, the semester before the CURE was implemented.
CURE Curriculum Development
Once the learning objectives and research context were decided, the student assessments and activities were created and aligned with research logistics in order to comply with the UWIN data collection schedules and protocols. Due to other objectives of the lab course, the UWIN CURE was designed to span six 2 % hour weekly lab periods of the General Biology 2 lab course; the second of the freshman level general biology series that is required for
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biology majors. Key assessments for evidence of student achievement were created and included pre and post lab assignments for each week, an oral presentation, and a culminating research paper. Activities were then designed to be engaging, collaborative, introduce students to real research practices, and to address specific learning objectives. Table 5 shows the basic curriculum outline with the associated learning objectives. In addition to student materials, teaching materials were also created to support a wide range of Graduate Teaching Assistants (GTAs). To alleviate some issues surrounding the difficulty of consistent implementation across multiple sections taught by GTAs with a wide range of experience in teaching and research, a GTA version of the materials was constructed to explain the logistics, reasoning, and intended outcomes of the activities and assessments. In addition, Google presentations with speaker notes, guided worksheets, and online materials were created and weekly GTA training meetings were conducted (see the GTA curricular manual and materials in Appendix C). Furthermore, after each week of the CURE, a voluntary GTA google form survey was available to solicit feedback on what went well, what didn't go well, timing of activities, and any comments. This allowed TAs to give feedback directly after teaching, when their ideas were fresh. It also served as a useful reference for curricular revisions.
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Table 5. Basic curriculum outline with associated learning objectives from Table 4 (specific learning objective in parentheses). Curriculum was designed to have peer and instructor feedback throughout, activities were created to be engaging and collaborative with peers (3e for all), and to introduce students to research with goals of developing scientific identity (4a-d for all).
Lab 1- Introduction to UWIN and Urban Ecology
Introduce and explore urban wildlife photos (la)
Brainstorm urbanization effects on wildlife (la)
How to read primary literature activity (lc)
Lab 2- Formalizing your Research Question
Discuss methods and assumptions (2b, 2c)
Assign sites to visit and go over data collection methods (le)
Create, evaluate, and revise research questions with peer interview (Id) Search and locate primary literature, while creating a bibliography (lb,lc, 3d) Students visit sites and collect site level data between Lab 2 and Lab 3 (le) Lab 3- Collaborative Data Collection, Tagging Practice, and Writing Collaborative GIS data collection and data entry (le, 3d)
Practice tagging photos/species identification (le)
Picture verification activity/ variation discussion (2b, 2c)
Outline Title, Introduction, Methods, and References (la, lb, lc, Id, 3a, 3b)
Lab 4- Photo Tagging and Figure Practice
Tag photos with species ID, #, details, comments (le)
Data and figure creation activity (2d, If)
Lab 5- Calculate Photo Data, Figure Creation, and Writing
Calculate wildlife data from the photo database (le, 2a)
Create figures for specific research question (If, 2a, 2d)
Outline Results and Discussion (If, 2b, 2c, 2d, 3a, 3b)
Lab 6- Mini Presentations
Mini-presentation of specific topic, results, conclusion (la, If, 2d, 3c, 3d) Finals Week- Term paper due (la, lb, lc, Id, le, If, 2b, 2c, 2d, 3a, 3b)
CURE Assessment
Students were offered 2% extra credit to complete both voluntary, in-class, online pre and post CURE surveys, of which 374 students participated. To increase confidence in the reliability of the student results, we removed surveys from analysis for respondents that took less than 8 minutes on the pre survey, less than 10 minutes on the post survey, and more than 60 minutes for either. These times were a priori chosen based off what we thought was an
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appropriate time for just reading the entire survey, and that approximately 30 minutes was
given in-class for students to complete it. This resulted in a sample size of 251 completed and matched pre and post survey responses. The demographic breakdown of participants and culled participants were similar, so it is assumed that no particular demographic group was singled out during the culling process (see Appendix Bl). In addition, from students who consented to participate in this research, writing samples were sampled from the fall 2017 semester before the CURE and from the spring 2018 semester after the CURE. The term paper in fall 2017 was focused on a two lab period inquiry lab about tree species richness, and the term paper in spring 2018 was focused on the six lab period urban wildlife CURE. The fall 2017 semester consisted of eight sections of lab that were taught by four GTAs, resulting in a total number of consenting participants of 151 students. Due to timing constraints, Fall 2017 students were offered 2% extra credit for completing the only a post survey and consenting to participate in this research. Due to uneven student participation and differences in GTAs between the two semesters, it was assumed that a truly random sampling procedure of ~50 papers from each semester would not be representative of the student population. Therefore, we sampled one course each from three TAs that taught the course both semesters and scored all the papers within those sections. This resulted in 54 papers from fall 2017 and 60 papers from spring 2018. All research methods were approved by the University of Colorado Denver Colorado Multiple Institutional Review Board (COMIRB #17-2148). The assessment instruments described below were given online and in-class with the components consolidated into one Qualtrics survey. Portions of the instruments were either given before and after the CURE, or just after the CURE, as appropriate for the instrument. As this CURE only spanned six lab
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periods, students were asked to consider the questions in terms of the UWIN CURE. In addition
to the surveys, writing samples from class assignments were sampled to address research question #3.
Research Question #1: CURE Aspect Implementation
To address our first research question assessing the extent of CURE aspect implementation, an available and validated post-course survey called the Laboratory Course Assessment Survey (LCAS) was used (Auchincloss et al., 2014; Corwin, Runyon, Robinson, & Dolan, 2015). The LCAS consists of Likert-scale responses to statements to see to what extent the course incorporated the important CURE aspects of collaboration (6 items), discovery & relevance (5 items), and iteration (6 items).
Research Question #2: Measures of Impact on Students' Perceptions Important for STEM Retention
To address our second research question about student perceptions of participation in research practices and affectual outcomes of the CURE, a published post course survey called the Persistence in the Sciences (PITS) was used (Auchincloss et al., 2014; Hanauer, Graham, & Hatfull, 2016). The PITS survey measures psychological components that correlate with a student's intention to continue in the sciences and consists of Likert scale responses that target: student project ownership (emotion (10 items) and content (6 items)), self-efficacy (6 items), science identity (5 items), scientific community values (4 items), networking (5 items), and intent (5 items).
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Research Question #3: Measures of Impact on Experimental Design Concepts and Scientific
Skills
To determine if students had shifts in their thinking about experimental design, a prepost 14 multiple choice question concept inventory called the Biological Experimental Design Concept Inventory (BEDCI) was given (Auchincloss et al., 2014; Deane, Nomme, Jeffery, Pollock, & Birol, 2014). In addition to the BEDCI, specific scientific skills were also assessed through analysis of student writing samples. Student research papers were evaluated using a checklist type rubric that was created by loose adaptation of a universal rubric (Timmermana, Strickland, Johnson, & Paynec, 2011) that was customized to assess our specific scientific process learning objectives. Due to anecdotal evidence from prior instructors that students in lab were not meeting expected scientific literacy skills prior to the CURE development, such as critically thinking about data and scientific writing, we decided to compare writing samples from before and after the CURE along with assessing how students performed after the curriculum. Because of the differences in student enrollment and GTA's teaching between semesters, a random sample of 50 papers from each semester would not have been representative of the student population. Therefore, we decided to sample all the papers from one section each of three GTAs that taught both in the fall 2017 semester and the spring 2018 semester. This resulted in 54 papers from fall 2017, and 60 papers from spring 2018. After the sampled papers were deidentified, two coders independently coded a subsample of papers using the first iteration of the rubric. The rubric was refined through discussion and editing to increase inter-rater reliability to 90% and to assess applicable learning objectives with ease (complete rubric and scoring can be seen in Appendix B6). For example, the learning objective "Students will be able
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to identify and discuss relevance of the research topic" would be assessed by analyzing the rubric responses to "The rationale and importance of the study is clearly stated- reasons for doing the research are given (yes/no)" and "Includes an argument as to how doing the research will contribute to the greater picture (yes/no)". Our goal in using a highly partitioned checklist type rubric was to allow for analysis of very specific aspects of scientific practices that are assessable through writing samples.
Results and Discussion
Research Question #1: CURE Aspects Successfully Implemented
To evaluate if the developed CURE was successful at implementing important CURE aspects, the Laboratory Course Assessment Survey (LCAS) was given to students after the Spring 2018 semester. As shown in Figure 1, the combined responses in each category of the survey were positive (see specific items with responses in Appendix B2).
Collaboration
Discovery & Relevance
Iteration
Percent
Figure 1: Combined student responses to the Laboratory Course Assessment Survey (LCAS) Categories n=251. Shows positive implementation of CURE aspects: combined responses to six items with a "weekly" collaboration response (71.7%), and combined agreement for five Discovery & Relevance items (91.2%) and six Iteration items (80.8%). Specific survey items and responses shown in appendix table B2.
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The six collaboration items asked students how often (i.e., never, 1-2 times, monthly,
weekly, NA) they were encouraged to collaborate in different ways. Students most often selected "weekly" for all six collaboration items, with the highest item being "I was encouraged to discuss my investigation with peers/instructor" at 86.1%. Even though in a weekly interactive and collaboratively designed lab course we would expect for students to perceive that they have collaborated with others every week, there are items in the survey that were not designed to be weekly activities, such as "help other students collect or analyze data" and "provide constructive criticism and challenge others interpretations", which had the lowest weekly response rates at 64.1% and 50.2% respectively. The five discovery and relevance items asked to what degree students agreed or disagreed with various research expectations that were asked of them. This category had the highest combined positive response at 91.2%, with the response rate of somewhat agree, agree, and strongly agree at 16.9%, 43.3%, and 29.4% respectively. Four of the five items scored a ~90% agreement or higher, the highest showing that 97.6% of the students agreed that they were expected to formulate their own research question to guide an investigation. The lowest combined agreement was still very positive at 77.3% for the item asking students if they were expected to generate novel results that would be of broader scientific interest. The six iteration items asked students to what degree they agreed with having time to revise or repeat certain aspects of their investigation and showed a combined agreement of 80.8%. The highest agreement item was about sharing and comparing data with others at 93.6%, with the lowest at 69.4% agreeing that there was time for changing the methods if needed. Overall, we are pleased that our developed CURE has hit targeted goals
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of implementing the important CURE aspects of collaboration, discovery, relevance, and iteration.
Research Question #2: Positive Impacts on Students' Affectual Outcomes Important for STEM Retention
To assess how the urban wildlife CURE implementation influenced students' affectual outcomes related to our learning objectives of applying scientific processes and developing scientific identity that also have been shown to be related to STEM retention (Hanauer,
Graham, & Hatfull, 2016), the Persistence in the Sciences (PITS) survey was given at the end of the spring 2018 semester. The survey consists of seven different categories that assess how students felt about their research project and its execution, how they connect with science in general, and their level of intention to stay in a scientific field. As shown in Figure 2, results show positive responses when compared to neutral or negative responses. There was positive affinity for community values (90%), and combined category of "agreement" (i.e. strongly agree and agree) for the six ownership-content items (55.6%), ten ownership-emotion items (47.1%), six self efficacy items (87.8%), four science identity items (56.7%), five networking items (48.1%), and five intent items (61.1%) (for specific items and responses see Appendix B3).
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Community Values
J-------------------------------------------U
50 0 50 100
Ownership- Content Ownership- Emotion Self Efficacy Science Identity Networking Intent
50 0 50 100
Not like me at all Not like me A little like me Somewhat like me Like me
Very much like me â– 
Strongly Disagree I Disagree Neither Agree
Strongly Agree I
Percent
Figure 2: Combined student responses to the PITS survey categories post CURE SP18, n=251. Shows positive responses for potential STEM retention: positive identity for Community Values (90%), and combined agreement for the six Ownership-Content items (55.6%), ten Ownership-Emotion items (47.1%), six Self Efficacy items (87.8%), four Science Identity items (56.7%), five Networking items (48.1%), and five Intent items (61.1%).
The community values category had the highest positive results, with 90% of students on average feeling an affinity to scientific community values of research importance and excitement. This is encouraging that students are seeing science as a value to society and shows that the CUREs' associated science identity learning objective is being met. However, these results cannot be assumed to be solely due to the curriculum, as the survey does not distinguish when and how these feelings were acquired. The ownership-content category had ten items that assessed how well students felt ownership and connection to their research project. This category had a wide variation among the items ranging from 37.1% to 70.9% agreement. The low agreement items were related to having personal reasons for the research (37.1%) and feeling excitement about the project (47.8%). These findings are not overly surprising because the research context needed to be highly structured for the high enrollment lab. While students
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were able to create and execute their own project, it was limited to the urban wildlife monitoring topic and methods, and many students seemed more interested in health-related fields (from anecdotal conversations). The higher agreement items included finding the research interesting (70.9%), having to overcome challenges (66.4%), and feeling responsible for the research outcomes (70.6%). This shows that the even though students may have not have been overly excited about the project, they still had ownership of their research, which was a goal within our scientific identity learning objectives. In addition to the lower excitement result, the entire category for ownership-emotion had the lowest combined score of 47% for agreement to feeling certain emotions during the project. The lowest agreement emotions were astonished (30.3%), joyful (43.1%), and delighted (45.8%), whereas the highest agreement emotions were happy (60.4%), surprised (52.2 %), and amazed (49.8%). While the emotional ownership category had the lowest agreement rates of all the categories, students didn't overly disagree with them either (average disagreement rate was 13.5%). The self efficacy category had six items that asked students about their confidence in performing scientific skills and had a high agreement average of 87.8%. Approximately 80% or higher agreement rate for each item was seen, with the highest score being that students were confident in being able to create a testable research question (92.8%). As these items are scored on self-perceived basis, we will compare a few applicable items with students writing samples to see how well students thought they did, to how they actually did, in their written work. The science identity items asked students to what level they agreed with thinking of themselves as scientists. The highest agreement items were feeling like they belong in the field of science (69.7%) and deriving great personal satisfaction from working on a team doing important research (65%). Again, it is hard
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to know if these positive results are a direct impact from the CURE, but it is encouraging that a
majority of the students feel like they belong and can be satisfied with scientific work. With these results, however, it was surprising to see that the lowest agreement score items were having a strong sense of belonging to the community of scientists (46.6%) and thinking of themselves as scientists (43.9%). This may be attributed to not having a societal/community component to the CURE. If students were to present their work to members of community (schools, land managers, etc.), they may not only increase their sense of scientific identity, but also increase levels of project ownership as well. The networking category had various items related to students discussing their research with others, either personally or professionally. This category showed high variability in agreement responses, with the lowest items being discussing their research with other professors (23.5%) and students not at their university (38.3%). Even though at first glance the average agreement rate for the category was only 48.1%, what was encouraging was the higher response rate items, with students saying they discussed their project with their friends (70.9%), and other students not in their class (57.8%). The last PITS category had five items asking about students' intentions about various levels of future scientific endeavors, from taking similar classes to becoming a research scientist. The highest agreement rates indicate a high level of intent to graduate with a science degree (82.9%) and to continue with a science related graduate program (71.7%).
Overall, the results of the PITS survey were positive and suggest that all of our developing scientific identity learning objectives for the course are being met (Table 4, #4a-d). In addition, we compared our first implementation data to available published data that compared PITS responses of traditional lab courses to the established SEA-PHAGES CURE
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(Hanauer et al., 2017). As shown in Table 6, our overall averaged results fall in-between the
two. This is encouraging that our results are overall better than traditional courses, and it is not overly surprising that the results from our first implementation and iteration of the UWIN CURE would have results not as positive as the SEA-PHAGES CURE, as it has been implemented since 2008, has instructor training programs, and has over 100 institutions participating (Hanauer et al., 2017).
Table 6. PITS category results compared to traditional labs and an established CURE. Likert scale data was converted to a 1 to 6 scale for Community Values (responses from not like me at all to very much like me), and 1 to 5 for the others (responses from strongly disagree to strongly agree) and calculated for mean and (standard deviation). Published data from Hanauer et al., 2017 Table S5.
PITS category Traditional Labs (Hanauer 2017 data) n=1263 Post UWIN CURE (Spring 2018 data) n=251 SEA-PHAGES CURE (Hanauer 2017 data) n=1587
Ownership-Content 3.40 (0.02) 3.50 (0.91) 3.96 (0.03)
Ownership-Emotion 3.32 (0.04) 3.38 (0.86) 3.82 (0.03)
Self Efficacy 3.99 (0.07) 4.10 (0.64) 4.12 (0.03)
Science Identity 3.47 (0.03) 3.56 (0.89) 3.90 (0.04)
Networking 3.03 (0.03) 3.10 (1.17) 3.74(0.05)
Community Values 4.76 (0.03) 4.85 (0.99) 5.13 (0.05)
It is important to note that this is not entirely the best comparison for our data, as the Hanauer (2017) data has a much higher sample size and has been taken from multiple institutions. Since our data has a smaller sample size there is a much higher variation (standard deviation) around the mean. In addition, for our purposes of describing these student affectual outcomes, it may be better to compare frequencies of specific items and categories instead of means and standard deviation for Likert scale data (Sullivan & Artino, 2013). This would better compare the response distribution of the data sets, and would be interesting to see the direct comparisons of each item, especially due to the variation seen within some of our categories.
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Albeit, it is encouraging to see our average results higher than published data on traditional lab
courses.
Research Question #3: Increased Scientific Skills with Minimal Change in Experimental Design Conceptual Thinking
In order to assess how the CURE impacted students' scientific skills, two measures were assessed: the Biological Experimental Design Concept Inventory (BEDCI) and scoring students writing samples of a research paper. The BEDCI has 14 multiple choice questions about experimental design within 8 different categories related to controls, hypotheses, limiting biological variation, accuracy affecting conclusions, controlling extraneous factors, independent and random sampling, and the purpose of doing experiments with the goal of identifying areas of students' experimental design conceptual deficiencies and to assess learning gains after a course (Deane et al., 2014). Our results showed no overall difference in pre to post scores with a pre score average and standard deviation of 6.77 (2.3) and the post score at 6.67 (2.3) out of the 14 available points, with normalized gains of -0.014, and effect size of -0.046 (see pre to post overall score distributions in Appendix B4). However, when examining matched pre to post changes of each student, there were 42% of students with improved scores, 17% with no change, and 41% with decreased scores (see Figure 3).
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o
-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5
Matched PRE to POST point difference (total points= 14)
Figure 3. Matched pre to post BEDCI scores for spring 2018 students (n=251). Shows 42% of students with improved scores, 17% with no change, and 41% with decreased scores.
Of the decreased scores, 7.2% of students had a 4 to 7 point decrease that likely affected the post average negatively. When scores drop several points from pre to post testing, it is generally thought that there was poor effort during the post test, assuming there was no other extraneous explanation, such as cognitive fatigue or injury, or just random answering (National Evaluation and Technical Assistance Center, 2006). The same could be true concerning high increases in score as well if students exerted low effort on the pre-test and high effort on the post-test. Because of this, there should be some caution when interpreting the BEDCI results. To see how students scored on individual questions from pre to post, each question was grouped according to the designed category (see Figure 4). Overall, most questions did not show any change from pre to post, with one question about biological variation having a significant positive change, and two questions, one about accuracy and one about independent sampling, showing a significant negative change.
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80
u
01
L_
1—
o
u
4—*
c
01
u
i_
01
Q.
70
60
50
40
30
20
10
0
SP18 PRE
â–  SP18 POST
Controls Hypotheses Biological Accuracy Extraneous Independent Random Purpose of
Variation Factors Sampling Sampling Experiments
BEDCI question and concept category
Figure 4. BEDCI individual questions pre vs post scores, n=251. Individual questions are shown along with their related concept. Two sample t-test showed significant differences (p<0.05) shown in graph with directional difference (plus or minus sign).
The BEDCI is argued to be cross disciplinary, since the fundamentals of experimental design concepts could be applied in any field (Deane et al., 2014), however, the questions asked in the concept inventory are mostly based on manipulative experiments. The UWIN project has more of a focus on observational ecological methods and data, and some of the pointed misconceptions targeted in the BEDCI were not explicitly discussed in our CURE. For the first iteration of our CURE assessment, the BEDCI may have not been the optimal instrument to look for pre to post gains in students thinking about experimental design, however, it does show where students are deficient in conceptual thinking (such as the extraneous factors and independent sampling categories shown in Figure 4), which can be explored for future iterations of the curriculum.
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In addition to the BEDCI, we scored student writing samples to see if students were
competent with targeted scientific skills. As discussed in the methods, a checklist type rubric was created to score each section of students' end of term research papers. The checklist comprised of multiple items for each section of the paper that are thought to be important for writing mechanics, scientific literacy, and critically displaying and thinking about data. Overall, out of 60 term papers scored from the spring 2018 semester, 45 out of 54 (89%) of the scored items were accomplished by 50% or more of the students (see the full rubric and scoring in Appendix B5). Some rubric items are directly related to our learning objectives and some are consistent with items from the self-reported surveys. Table 7 shows the learning objective being assessed, the students self-reported agreement with their ability to perform the item, and the associated writing rubric items that were scored in order to assess student science process skills along with comparing it to their self-reported assessment. In addition, we were interested in the change from the previous semesters writing samples due to anecdotal evidence that students were deficient in these skills. It is important to note that the research paper from the previous semester (fall 2017) was based off of a two-lab period, field ecology, inquiry based project where students had specific instructions for what was expected of them, but had a short amount of time to complete their inquiry and write about it. Even with the high variation between the two student projects that the research papers were based on, we did want to see the changes after the CURE. Therefore, along with assessing the post CURE spring 2018 papers, we also show the scores of the fall 2017 papers, and the percent change between the two. For all the learning objectives that we could assess via the research papers, the post
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CURE papers achieved the related skills at average levels of 62% or more, with all items having a
positive percent change compared to the previous semester (see Table 7 and 8).
Table 7. Urban wildlife CURE learning objectives with self-reported and writing sample assessment metrics. See specific self-reported metric items in Appendix B2 and B3.
Learning Objective Self-Reported Metric-Question= Agreement Result (%) n=251 Writing Sample Rubric Item (% of students) FA 17 n=54 SP 18 n=60 % change
Id. Create specific, relevant, testable research questions LCAS- Discovery/Relevance #3 - 97% PITS-Self Efficacy #2 = 92.8% Research question is testable 76% 93% 22.2
Research question is specific 40% 63% 58.3
Research question is relevant 35% 85% 146.1
Average (SD) 50.3% (15.5) 80.3% (22.4) 75.5 (63.7)
le. Create and carry out relevant data collection protocols PITS-Self Efficacy #3 = 89% Specific description of how the dependent variables were collected 45% 70% 54.0
Specific description of how the independent variables were collected 58% 60% 3.1
Average (SD) 51.8% (9.2) 65% (7) 28.6 (36)
If. Analyze and interpret data to form conclusions LCAS- Discovery/Relevance #4 - 93.6% PITS-Self Efficacy #4 = 86.8% All data from the results are meaningfully discussed 28% 70% 152.0
Conclusions are clearly and logically drawn from the data 33% 62% 85.0
Connections between research question, data, and conclusions are logical, consistent, and persuasive 22% 53% 140.0
Average (SD) 27.7% (5.5) 61.7% (8.5) 125.7 (35.7)
The self-reported metrics for confidently creating research questions, carrying out data collection, and analyzing and interpreting data had higher rates than the closest items scored on the writing sample. It is important to note that the writing rubric sample items are more specific than the self-reported metrics and students may have completed the task they are confident in, but they may not have done it well. While self-reported skills were not entirely in
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line with the term paper scores, we were very pleased that (on average) 60% or more of the
samples showed the targeted skills, and all writing items had a positive change compared to the semester before. These findings were also consistent with additional learning objectives assessed through the writing samples (Table 8). While all of the scientific skills related to learning objectives were shown to have 60-80% achievement rates from the writing samples, we are very encouraged that many targeted skills from the CURE had a very high positive change compared to the previous semester. For example, students discussing variability in data and discussing study design limitations on the ability to draw confident conclusions were up by 260% and 85% respectively.
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Table 8. Learning objectives assessed by writing samples.
Learning Objective Writing Sample Rubric Item (% of students) FA 17 n=54 SP 18 n=60 % change
la. Discuss the relevance of the research topic The importance of the study is clearly stated 44% 80% 83.3
Includes an argument as to how the research will contribute to the "greater picture" 20% 62% 208.3
Average (SD) 32% (17) 71% (13) 145.8 (88.4)
2b. Discuss variability in data Discussed variability in the data 19% 67% 260.0
2c. Evaluate strengths and weaknesses in data & data collection methods Important limitations of the study design and data are correctly discussed and linked to the ability to draw confident conclusions 33% 62% 85.0
2d. Create and interpret appropriate data visualizations Includes a table or figure that addresses the research question 63% 90% 42.9
Does NOT Include a table or figure that are NOT necessary or do not address the research question 20% 50% 49.0
Data/analysis appear error free 69% 83% 21.6
Data presented is easy to draw conclusions from 22% 60% 170.0
Average (SD) 44% (26) 71% (18.8) 70.8 (67.1)
3a. Use appropriate conventions of organization, content, formatting, and style in writing Overall, writing is concise and focused 59% 65% 9.7
Appropriate organization and formatting used 81% 92% 12.5
Average (SD) 70% (15.5) 79% (19) 11.1 (2)
3b. Correctly cite high-quality, relevant sources Formatting of citations is consistent and correct 54% 87% 61.4
Number of relevant primary literature citations (Average (SD)) 0.51 (0.9) 1.87 (1.8) 266.0
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Summary and Conclusions
Considerations about developing, implementing, and assessing a CURE
When first considering developing a CURE, it is important to think about potential barriers to success, such as department and faculty buy-in, time investment (for development and face-to-face time with students), logistics for the project and how it will fit into predetermined class times, financial constraints for research supplies, comfort level with dealing with uncertainty in the classroom (and frustrations with uncertainty from students), and scaling-up issues, including varying research and teaching experience of instructors (Shortlidge, Bangera, & Brownell, 2015; Spell, Guinan, Miller, & Beck, 2014). In addition, Kloser et al. (2011) gives six guidelines for implementing research into coursework that include: (1) minimal technical expertise needed for data collection, (2) student mistakes will not compromise research quality by establishing double checks, (3) a diverse set of variables that present many choices for students to investigate, (4) a central database into which students can upload and access data, (5) assessment measures that are representative of real-world science (written and oral communication), and (6) involvement of instructors with expertise in the study system. The urban wildlife CURE meets these guidelines by (1) having low-technology camera set up, site level observations, and basic computer skills needed for data collection, (2) incorporating multiple picture tagging verification as a data double check, (3) having many investigable variables such as single-species, multi-species, habitat area, habitat type, building density, etc., (4) having access to a centralized database continuously updated data, (5) having multiple assessment measures through term papers, presentations, and homework, and (6) having a research PI in charge of the overall research and supplying instructors with ample materials that
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can result in expert-like involvement. In addition, to try to alleviate issues with varying GTA experience with research and teaching, a teaching manual and supporting materials were created to highlight evidence-based reasoning for each activity, attempting to increase GTA buy-in, standardize the level of content and pedagogical knowledge, and achieve equity across all sections of the course. Moving forward, we are intending on expanding the scope of this curriculum to become a collaborative program that involves multiple institutions that are already participating (and those interested in participating) in the Urban Wildlife Information Network (UWIN). This collaboration could have the potential to create a central support system for participating instructors that could include training sessions, lesson development and iteration, shared curriculums, technical support, and scientific expertise, all of which has been shown to decrease CURE implementation barriers and increase success (such as GCAT (Campbell et al., 2007), and SEA-PHAGES (Jordan et al., 2014; Lopatto et al., 2014). In addition, CURE assessment strategies could be more robust and move toward assessing what aspects of the CURE are more effective at achieving learning and affectual goals, and short to long-term outcomes, by collaboratively creating appropriate situated assessments for our specific urban ecology context (Corwin, Runyon, et al., 2015; Shortlidge & Brownell, 2016).
Successful Development and Implementation of an Urban Wildlife CURE
This urban wildlife CURE was developed using backwards design with emphasis on incorporating published national directives for biology education and important CURE aspects. Our CURE integrates a local, relevant, and authentic urban wildlife monitoring project into the classroom, where students learn authentic scientific process skills by participating in real research. Shown through the Laboratory Course Assessment Survey our CURE has successfully
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incorporated aspects of collaboration, discovery, relevance, and iteration. There were also positive affectual gains seen through the Persistence in the Sciences Survey, showing achievement of our science identity learning objectives and high likelihood of student retention in a scientific field. In addition, there were increases in scientific process skills seen through student writing samples. Overall, this CURE has contributed data to a national research collaborative program (the Urban Wildlife Information Network), has introduced students to authentic research processes, and has accomplished many, if not all, of our learning objectives. Limitations and Ideas for Revision
In most research projects, scientists will first find an interesting objective, and form specific methods to address that objective. Because this project is formed around a long-term wildlife monitoring context and the logistical process of obtaining permissions to set up cameras, the methods of camera locations areas are set. While students do not get to research and decide on appropriate methods for their interests, we feel that students still get the benefit of discussing the pros and cons of the study design. In addition, having students create different methods would not be feasible because of the time allotted for the CURE, and the logistical issues with a high-enrollment laboratory course. This CURE is logistically complex, with setting up and collecting cameras seasonally across the city, dealing with SD cards with tens of thousands of photos, and a Microsoft Access photo database, however those who are already participating in UWIN have already dealt with these complexities with the support of the network. As for the curriculum itself, it could easily be expanded to a full semester project, having extended emphasis in some areas, such as reading primary literature and bibliography creation, and adding in additional modules related to other aspects of urban ecology or the
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scientific process. Some ideas that did not make it in this first version of the CURE included having multiple iterative peer review assignments of their writing drafts and figure creations, a deeper discussion/ activity about the iterative process of their project (the future directions section of their paper), and a societal outreach component, where students would present their research somewhere in the community.
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experiences in introductory biology laboratories and barriers to their implementation. CBE Life Sciences Education, 13(1), 102-110. https://doi.org/10.1187/cbe.13-08-0169
Sullivan, G. M., & Artino, A. R. (2013). Analyzing and Interpreting Data From Likert-Type Scales. Journal of Graduate Medical Education, 5(4), 541-542. https://doi.org/10.4300/JGME-5-4-18
Thompson, S., Neill, C., Wiederhoeft, E., & Cotner, S. (2016). A Model for a Course-Based
Undergraduate Research Experience (CURE) in a Field Setting. Journal of Microbiology & Biology Education, 17(3), 469-471.
Timmermana, B. E. C., Strickland, D. C., Johnson, R. L., & Paynec, J. R. (2011). Development of a "universal" rubric for assessing undergraduates' scientific reasoning skills using scientific writing. Assessment and Evaluation in Higher Education, 36(5), 509-547. https://doi.org/10.1080/02602930903540991
U.S. Census Bureau. (2010). Census Urban Classification. Retrieved from https://www.census.gov/geo/reference/ua/urban-rural-2010.html
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72


APPENDIX A. Chapter 1 Additional Tables and Figures
Table Al. Principle Component (PC) Analysis Loading Results and Variance.
PCI PC2 PC3
Human Population (1km buffer) -0.520 0.853 -0.032
Light Radiance -0.601 -0.393 -0.695
Percent Imperviousness (1km buffer) -0.606 -0.343 0.718
Percent Variance Explained 78% 17% 5%
Table A2. Area of each land use type within a 5 km buffer around the UWIN transect in Denver, CO. Calculated using 2011 NLCD.
Land Use Type Area (km2) Total % (out of 480 km2)
Developed, Low Intensity 164.0 34.2
Developed, Medium Intensity 88.7 18.5
Developed, Open Space 56.3 11.7
Developed, High Intensity 46.5 9.7
Grassland/Herbaceous 41.8 8.7
Shrub/Scrub 31.8 6.6
Cultivated Crops 23.2 4.8
Evergreen Forest 13.2 2.8
Woody Wetlands 7.4 1.5
Open Water 4.5 0.9
Deciduous Forest 1.3 0.3
Barren Land 0.7 0.1
Pasture/Hay 0.6 0.1
Emergent Herbaceous Wetlands 0.4 0.1
Mixed Forest 0.0 0.0
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APPENDIX B. Chapter 2 Additional Tables and Figures
Bl. Demographics of Spring 2018 Participants (A) and Culled Participants (B)
A Characteristic n= Percent= D ° Characteristic n= Percent=
Gender Gender
Female 171 68.1% Female 83 67.4%
Male 78 31.1% Male 38 30.9%
Other 2 0.8% Other 2 1.6%
English First Language? English First Language?
Yes 177 70.5% Yes 76 61.8%
No 74 29.4% No 47 38.2%
Race Race
White 145 57.8% White 68 55.3%
Asian 55 21.9% Asiatt 28 22.7%
Black 24 9.6% Black 12 9.8%
Middle Eastern 5 1.9% Middle Eastern 0 0.0%
American Iudiau/Alaskan 4 sO o'- vq American Indian/Alaskan 5 4%
NA 17 6.7% NA 10 8.1%
LatinX? LatinX?
No 186 74.1% No 97 78.9%
Yes 59 23.5% Yes 25 20.3%
NA 6 2.4% NA i 0.8%
Parents College Graduates? Parents College Graduates?
No 116 46.2% No 55 44.7%
1 Parent 64 25.5% 1 Parent 25 20.3%
2 Parents 61 24.3% 2 Parents 35 28.5%
NA 10 3.9% NA 8 6.5%
Demogr aphic char acteristics of pre and post survey Culled (% out of the total 374 students)
participants of the spring 2018 semester, given by student PRE < 8 minutes 41 10.9%
response. Full particaption (n= :374) was culled for any PRE > 60 minutes 4 1.0%
responses where students took less than 8 minutes (pre) or less POST < 10 minutes 88 23.5%
than 10 min (post) and more that 60 minutes to complete. POST > 60 minutes 15 4.0%
estimated for the expected completion times.
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B2. Laboratory Course Assessment Survey (LCAS) items and responses.
A. LCAS Collaboration Results (n=251)
Survey Response: n (%)
Question Never 1-2 times Monthly Weekly NA
In this course I was encouraged to ...
1) discuss elements of my investigation with classmates or instructors 3 (1.2%) 17 (6.8%) 14 (5.6%) 216 (86.1%) 1 (0.4%)
2) reflect on what I was learning 1 (0.4%) 25 (10.0%) 22 (8.8%) 199 (79.3%) 4 (1.6%)
3) contribute my ideas and suggestions during class discussions 1 (0.4%) 29 (11.6%) 30 (12.0%) 185 (73.7%) 6 (2.4%)
4) help other students collect or analyze data 7 (2.8%) 30 (12.0%) 45 (17.9%) 161 (64.1%) 8 (3.2%)
5) provide constructive criticism to classmates and challenge each other's 10 50 58 126 7
interpretations (4.0%) (19.9%) (23.1%) (50.2%) (2.8%)
6) shar e problems I encountered during my investigation and seek on how to addr ess them input 7 (2.8%) 36 (14.3%) 33 (13.1%) 170 (67.7%) 5 (2.0%)
Summary
Average 4.8 31.2 32.0 176.2 5.2
(1.9%) (12.4%) (12.7%) (70.2%) (2.1%)
+/- Standar d Deviation 3.7 (1.5%) 11.2 (4.4%) 15.1 (6.0%) 31.5 (12.6%) 2.5 (1.0%)
B. LCAS Discovery and Relevance Results (n=251)
Survey Response: n (%)
Question Strongly Disagree Somewhat Somewhat Disagree Disagree Agree Agree Strongly Agree NA
In this course I was expected to ... 1) generate novel results that are unknown to the instructor and that could be of interest to the broader scientific community or others outside of class 8 (3.2%) 17 (6.8%) 23 (9.2%) 62 (24.7%) 89 (35.5%) 43 (17.1%) 9 (3.6%)
2) conduct an investigation to find something previously unknown to myself, other students, and the instructor 3 (1.2%) 8 (3.2%) 11 (4.4%) 49 (19.5%) 110 (43.8%) 66 (26.3%) 4 (1.6%)
3) formulate my own research questions or hypothesis to guide an investigation 1 (0.4%) 1 (0.4%) 2 (0.8%) 22 (8.8%) 109 (43.4%) 114 (45.4%) 2 (0.8%)
4) develop new arguments based on data 2 (0.8%) 2 (0.8%) 10 (4.0%) 40 (15.9%) 119 (47.4%) 76 (30.3%) 2 (0.8%)
5) explain how my work has resulted in new 3 3 14 39 116 70 6
scientific knowledge Summary’ (1.2%) (1.2%) (5.6%) (15.5%) (46.2%) (27.9%) (2.4%)
Average 3.4 6.2 12.0 42.4 108.6 73.8 4.6
(1.4%) (2.5%) (4.8%) (16.9%) (43.3%) (29.4%) (1.8%)
+/- Standard Deviation 2.7 (1.1%) 6.6 (2.6%) 7.6 (3.0%) 14.7 (5.8%) 11.7 (4.7%) 25.7 (12.2%) 3.0 (1.2%)
C: LCAS Iteration Results (n=251)
Survey Response: n (%)
Question Strongly . Disagree Disagree Somewhat Somewhat Disagree Agree Agree Strongly Agree NA
In this course I had time to ...
1) revise or repeat work to account for errors or 4 12 30 52 98 49 6
fix problems (1.6%) (4.8%) (12.0%) (20.7%) (39.0%) (19.5%) (2.4%)
2) change the methods of the investigation if it 7 19 44 43 89 42 7
was not unfolding as predicted (2.8%) (7.6%) (17.5%) (17.2%) (35.5%) (16.7%) (2.8%)
3) share and compare data with other students 1 (0.4%) 3 (1.2%) 12 (4.8%) 48 (19.1%) 113 (45.0%) 74 (29.5%) 0 (0.0%)
4) collect and analyze additional data to address 3 14 30 44 98 55 7
new questions or further test hypotheses that arose (1.2%) (5.6%) (12.0%) (17.5%) (39.0%) (21.9%) (2.8%)
5) revise or repeat analyses based on feedback 4 (1.6%) 16 (6.4%) 34 (13.5%) 46 (18.3%) 92 (36.7%) 54 (21.5%) 5 (2.0%)
6) revise dr afts of papers or presentations about 5 18 27 52 82 59 8
my investigation based on feedback Summary (2.0%) (7.2%) (10.8%) (20.7%) (32.7%) (23.5%) (3.2%)
Average 4.0 13.7 29.5 47.5 95.3 55.5 5.5
(1.6%) (5.4%) (11.8%) (18.9%) (38.0%) (22.1%) (2.2%)
+/- Standard Deviation 2.0 (0.8%) 5.8 (2.3%) 10.4 (4.2%) 3.9 (1.5%) 10.5 (4.2%) 10.8 (4.3%) 2.9 (1.1%)
Student survey responses for the Laboratory Course Assessment Survey (LCAS) after the Spring 2018 CURE curriculum implementation, n
is the number of students and % is the percent of students.
75


B3. The Persistence in the Sciences (PITS) survey items and student responses. Each section includes the categories Ownership-Content (A), Ownership- Emotion (B), Self-Efficacy (C), Science Identity (D),
Networking (E), Intent (F), and Community Values (G). A. PITS Ownership-Content Results (u=251)
Survey Response: n (%)
Question Strongly Disagree Disagree Neither Agree Strongly Agree
1) My research will help to solve a problem in the 10 30 93 101 17
world (4.0%) (12.0%) (37.0%) (40.2%) (6.8%)
2) My findings were important to the scientific 9 28 71 121 22
community (3.6%) (11.2%) (28.3%) (48.2%) (8.8%)
3) I faced challenges that I managed to overcome 2 20 60 153 16
(0.8%) (8.0%) (23.9%) (61.0%) (6.4%)
4) I was responsible for the outcomes of my research 1 20 53 146 31
(0.4%) (8.0%) (21.1%) (58.2%) (12.4%)
5) The findings of this research gave me a sense of 4 34 74 114 25
personal achievement (1.6%) (13.5%) (29.5%) (45.4%) (10.0%)
6) I had a personal reason for choosing my project 16 59 83 73 20
(6.4%) (23.5%) (33.1%) (29.1%) (8.0%)
7) The research I worked on was important to me 12 36 89 97 17
(4.8%) (14.3%) (35.5%) (38.6%) (6.8%)
8) In conducting my research, I actively sought 3 25 77 125 21
advice/assistance (1.2%) (10.0%) (30.7%) (50%) (8.4%)
9) My research project was interesting 6 21 46 157 21
(2.4%) (8.4%) (18.3) (62.5%) (8.4%)
10) My research project was exciting 8 34 89 104 16
Summary (3.2%) (13.5%) (35.5%) (41.4%) (6.4%)
Average 7.1 30.7 73.5 119.1 20.6
(2.8%) (12.2%) (29.3%) (47.5%) 8.2%)
+/- Standard Deviation 4.8 11.6 16.0 27.0 4.7
(1.9%) (4.6%) (6.4%) (10.7%) (1.9%)
Student survey responses for tlie PITS ownership-content items after the Spring 2018 CURE curriculum implementation, n is the number of students and % is the percent of students.
B. PITS Ownership-Emotion Results (n=251)
Survey Response: n (%)
Indicate the extent that each word decribes your experience. Strongly Disagree Disagree Neither Agree Strongly Agree
1) Delighted 5 26 105 98 17
(2.0%) (10.4%) (41.8%) (39%) (6.8%)
2) Happy 5 19 73 133 21
(2.0%) (7.6%) (29%) (52%) (8.4%)
3) Joyful 5 24 114 94 14
(2.0%) (9.6%) (45.4%) (37.5%) (5.6%)
4) Amazed 7 27 92 105 20
(2.8%) (10.8%) (36.7%) (41.8%) (8.0%)
5) Surprised 5 29 86 112 19
(2.0%) (11.6%) (34.3%) (44.6%) (7.6%)
6) Astonished 12 39 124 64 12
(4.8%) (15.5%) (49.4%) (25.5%) (4.8%)
Summary Average 6.5 27.3 99 101 17.2
(2.6%) (10.9%) (39.4%) (40.2%) (6.8%)
+/- Standard Deviation 2.8 6.7 18.9 22.8 3.5
(1.2%) (2.7%) (7.5%) (9.1%) (1.4%)
Student survey responses for the PITS ownership-emotion items after the Spring 2018 CURE curriculum implementation, n is the number
of students and % is the percent of students.
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B3 continued.
C. PITS Self Efficacy Results (n=251)
Survey Response: n (%)
Question Strongly Disagree Disagree Neither Agree Strongly Agree
I am confident that I can...
1) use technical science skills (use of tools and 0 5 20 169 57
techniques) (0.0%) (2.0%) (8.0%) (67.3%) (22.7%)
2) generate a research question to answer 0 (0.0%) 1 (0.4%) 17 (6.8%) 161 (64.1%) 72 (28.7%)
3) figure out what data/observations to collect and 0 2 24 165 60
how to collect them (0.0%) (0.8%) (9.6%) (65.7%) (23.9%)
4) create explanations for the results 0 (0.0%) 3 (1.2%) 30 (12%) 157 (62.5%) 61 (24.3%)
5) use scientific literature to guide my research 0 (0.0%) 3 (1.2%) 28 (11.2%) 154 (61.4%) 66 (26.3%)
6) develop theories (integr ate and coordinate results from multiple studies) 1 (0.4%) 10 (4.0%) 40 (15.9%) 153 (61.0%) 47 (18.7%)
Summary
Average 0.2 (0.07%) 4 (1.6%) 26.5 (10.6%) 159.8 (63.7%) 60.5 (24.1%)
+/- Standard Deviation 0.4 (0.16%) 3.2 (1.3%) 8.2 (3.3%) 6.3 (2.5%) 8.5 (3.4%)
Student survey responses for the PITS self effacacy items after the Spring 2018 CURE curriculum implementation, n is the number of students and % is the percent of students.
D. PITS Science Identity Results (n=251)
Survey Response: •> (%)
Question Strongly Disagree Disagree Neither Agree Strongly Agree
1)1 have a strong sense of belonging to the 2 29 103 100 17
community of scientists (0.8%) (11.6%) (41.0%) (39.8%) (6.8%)
2) I derive great personal satisfaction fr om working on a team doing important resear ch 1 (0.4%) 13 (5.2%) 74 (29.5%) 139 (55.4%) 24 (9.6%)
3) I have come to think of myself as a 'scientist' 9 (3.6%) 32 (12.7%) 100 (39.8%) 83 (33.1%) 27 (10.8%)
4) I feel like I belong in the field of science 5 (2.0%) 18 (7.2%) 53 (21.1%) 123 (49.0%) 52 (20.7%)
5) The daily work of a scientist is appealing to me 5 (2.0%) 32 (12.7%) 68 (27.1%) 113 (45.0%) 33 (13.1%)
Summary
Average 4.4 24.8 79.6 111.6 30.6
(1.8%) (9.9%) (31.7%) (44.5%) (12.2%)
+/- Standard Deviation 3.1 (1.2%) 8.8 (3.5%) 21.4 (8.5) 21.4 (8.5%) 13.3 (5.3%)
Student siuvey responses for the PITS science identity items after the Spring 2018 CURE curriculum implementation. n is the number of
students and % is the percent of students.
77


B3 Continued.
E. PITS Networking Results (n=251)
Survey Response: n (%)
Question Strongly Disagree Disagree Neither Agree Strongly Agree
I have discussed my research in this course...
1) with my par ents (or guardian) 24 (9.6%) 64 (25.5%) 37 (14.7%) 98 (39%) 28(11.2%)
2) with my friends 12 (4.8%) 36 (14.3%) 25 (10%) 143 (57%) 35 (13.9%)
3) with students not in my class, but in my institution 13 (5.2%) 54 (21.5%) 39 (15.5%) 128 (51%) 17 (6.8%)
4) with students who are not at my institution 24 (9.6%) 90 (35.9%) 41 (16.3%) 80 (31.9%) 16 (6.4%)
5) with professors other than my course instructor Summary 38(15.1%) 116 (46.2%) 38(15.1%) 52 (20.7%) 7 (2.8%)
Average 22.2 (8.8%) 72 (28.7%) 36 (14.3%) 100.2 (39.9%) 20.6 (8.2%)
+/- Standar d Deviation 10.5 (4.2%) 31.4(12.5%) 6.3 (2.5) 36.5 (14.6%) 11 (4.4%)
Student survey responses for the PITS networking items after the Spring 2018 CURE curriculum implementation, n is the number of
students and % is the percent of students.
F. PITS Intent Results (n=251)
Survey Response: n (%)
Question Strongly Disagree Disagree Neither Agree Strongly Agree
1) Following this course, I intend to enroll in 2.0 18 56 132 43
similar lab research courses (0.8%) (7.2%) (22.3%) (52.6%) (17.1%)
2) I intend to complete a science related 1.0 16 26 119 89
undergraduate degr ee (0.4%) (6.4%) (10.4%) (47.4%) (35.5%)
3) In the future, I intend to enroll in a science 4.0 22 45 101 79
related graduate progr am (1.6%) (8.8%) (17.9%) (40.2%) (31.5%)
4) My future career will involve collecting. 12 38 80 87 34
analyzing, and reporting scientific data (4.8%) (15.1%) (31.9%) (34.7%) (13.5%)
5) In the future, I would like to be a research 26 63 79 62 21
scientist (10.4%) (25%) (31.5%) (24.7%) (8.4%)
Summary’
Average 9.0 31.4 57.2 100.2 53.2
(3.6%) (12.5%) (22.8%) (39.9%) (21.2%)
+/- Standard Deviation 10.4 19.7 23 27.4 29.4
(4.2%) (7.8%) (9.2%) (10.9%) (11.7%)
Student survey responses for tlie PITS intent items after the Spring 2018 CURE curriculum implementation, n is the number of students and % is the percent of students.
G. PITS Community Values Results (n=251)
Survey Response: n (%)
Question Not like me at all Not like me A little like me Somewhat like me Like me Very much like me
I am a person who...
1) thinks discussing new theories and ideas between 1.0 ii 23 58 115 43
scientists is important (0.4%) (4.4%) (9.2%) (23.1%) (45.8%) (17.1%)
2) thinks it is valuable to conduct research that builds the 0.0 5.0 17 51 114 64
world's scientific knowledge (0.0%) (2.0%) (6.8%) (20.3%) (45.4%) (25.5%)
3) thinks that scientific research can solve many of today's 0.0 5.0 12 41 109 84
world challenges (0.0%) (2.0%) (4.8%) (16.3%) (43.4%) (33.5%)
4) feels discovering something new in the sciences is 0.0 7.0 19 37 110 78
thrilling (0.0%) (2.8%) (7.6%) (14.7%) (43.8%) (31.1%)
Summary
Average 0.25 7.0 17.8 46.8 112 67.3
(0.1%) (2.8%) (7.1%) (18.6%) (44.6%) (26.8%)
+/- Standard Deviation 0.5 2.8 4.6 9.5 2.9 18.2
(0.2%) (1.1%) (1.8%) (3.8%) (1.2%) (7.3%)
Student survey responses for the PITS community values items after the Spring 2018 CURE curriculum implementation, n is the number
of students and % is the percent of students.
78


Frequency of Students
B4. Distribution of BEDCI scores before and after the CURE during the spring 2018 semester (n=251). No difference in pre to post scores on average (standard deviation), pre= 6.77 (2.3), post= 6.67 (2.3). Normalized gains= -0.014, effect size= -0.046.
50
45
40
35
30
25
20
15
10
5
0
I. II
ll
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Score
â–  SP18 pre scores BSP18 post scores
79


B5. Writing samples from before and after the CURE scored for targeted scientific literacy skills. Each item listed below was given a yes/no/# score, with the "yes" frequencies are shown via percent for each semester, along with percent change from fall 2017 (FA 17) to spring 2018 (SP 18). Green indicates a percent change of 50% or more, and blue shows 50% of students or greater (or less for the "yes" answer leing negative-these items are in bold).
Paper Section Specific characteristic scored FA 17 n=54 SP 18 n=60 Percent change
Title Is specific to the research question 47% 87% 83.3
Is specific to research location 33% 32% -3.2
Is informative 18% 37% 101.7
Abstract Includes a compelling reason why this proposed work is important (from intro) 16% 47% 185.2
Includes a clear description of the main question/objective (from intro) 45% 70% 54.0
Includes a clear summary of the methods/ what was done (from methods) 22% 50% 129.2
Includes a clear synopsis of main findings (from results) 25% 57% 122.6
Includes a clear summary of the conclusions drawn from the results (from discussion) 35% 52% 49.6
Intro: Relevant Background Includes background information that is sufficient to help the reader understand the specific research topic 45% 73% 61.3
Includes background information that is NOT relevant to the topic (if very off topic) 16% 3% -79.6
Intro: Rationale/ Importance The rationale/ importance of the study is clearly stated- reasons for doing the research are given 44% 80% 83.3
Includes an argument as to how doing the research will contribute to the greater picture 20% 62% 208.3
Intro: Research Question Research question/ objective is testable 76% 93% 22.2
Research question/ objective is specific 40% 63% 58.3
Research question/ objective has scientific merit that is consistent with the rationale given 35% 85% 146.1
Methods The methods are described in enough detail that the reader can replicate the approach. 38% 58% 52.8
The methods relate to just the specific research question (not everything done in class) 85% 57% -33.7
Necessary details about sample sizes, study area, and time frame given 55% 83% 52.8
Necessarv and sufficient descriotion of how the deoendent variables (specific to the research question) were measured/collected is included 45% 70% 54.0
Necessarv and sufficient description of how the independent variables (specific to the research question) were measured/collected is included 58% 60% 3.1
Correct description of data analysis techniques given 55% 78% 43.6
Data analysis techniques are appropriate for the research question 76% 82% 6.9
All data presented are consistent with the research question 76% 80% 4.8
Results- Text All data are correctly summarized in writing 38% 50% 31.0
All tables/figures are correctly referred to by number in the text 40% 60% 50.0
No interpretations given 85% 88% 3.4
80


B5 continued.
Paper Section Specific characteristic scored FA 17 n=54 SP 18 n=60 Percent change
Results-Figures and Tables Includes an appropriate figure and/or table in intro or methods that shows relevant information (i.e., study organism/system, distribution maps, historical data, etc.)- in any section 65% 60% -7.4
Includes figure(s) and/or table(s) that addresses the research question 63% 90% 42.9
Data is presented in the correct figure type (scatter plot/bar graph/etc.) 65% 72% 10.6
Includes figures/tables/data that are NOT necessary or do not address the specific research question 80% 50% -37.2
Data / analysis presented appears error free 69% 83% 21.6
Data presented is easy to draw conclusions from 22% 60% 170.0
Formatting is professional (doesn't mean free of mistakes) (e.g., not hand drawn, appropriate axis scales, no blank areas, cropped nicely, multiple figures are similar in formatting, etc.) 37% 78% 111.5
Has a label, title, and caption. 31% 82% 159.4
Title and caption are informative enough to stand outside of the text 4% 23% 530.0
Has appropriate figure/table components: axes labels, legends, color matches, error bars, etc. 59% 78% 32.2
Discussion: Interpret- ation All data from the results section are meaningfully discussed 28% 70% 152.0
Conclusions are clearly and logically drawn from the data provided in the results section 33% 62% 85.0
No new data (that is not in the results section) is presented 91% 97% 6.5
Connections between research question, data, and conclusions are logical, consistent, and persuasive 22% 53% 140.0
Discussion: Limitations Important limitations of the study design and data are correctly discussed and are linked to the ability to draw confident conclusions are discussed 33% 75% 125.0
Discussed variability in the data 19% 67% 260.0
Discussion: Assumption s Important assumptions that are made throughout the study are correctly addressed 31% 53% 69.4
The ability to draw confident conclusions are discussed due to assumptions 9% 38% 314.0
Discussion: Future Directions Future directions include how to improve upon the study design to better study the specific research question 52% 58% 12.5
Future directions are plausible 56% 62% 11.0
Includes examples beyond "more samples" 20% 38% 88.2
References and Citations All information is cited in text and the full citation is also present in References section 48% 77% 59.2
Formatting of citations is consistent and correct throughout the document (i.e., CSE format) 54% 87% 61.4
Citation format is correct 35% 72% 103.7
How many primary literature citations? (Average (SD)) 0.51 (0.91) 1.87 (1.77) 266.0
Writing Mechanics Overall, writing is concise and focused 59% 65% 9.7
Appropriate organization and formatting used 81% 92% 12.5
Rate the level spelling or grammatical issues (low score) 57% 50% -12.9
81


APPENDIX C. UWIN CURE Curriculum
UWIN CURE curriculum GTA manual and materials. Original version was created as a living document in Google drives with links to websites, worksheets, literature, etc. Full website links were added and any other documents were added within the associated lab week in the manual. Worksheet spacing given for students was removed to save space. Google presentations with notes were added to the end of the manual. This is the first iteration of the curriculum that was used for the CURE assessment.
Starts on the next page.
82


UWINTALab Manual
Contents
Introduction to CURES (TA Manual only)..............................................86
Google Folder Instructions (TA Manual only).........................................90
UWIN Student Lab Manual- Front Page..................................................92
UWIN Overview........................................................................93
Basic Outline........................................................................94
UWIN Week 1: Introduction to UWIN and Urban Ecology.................................95
Goals/Objectives.................................................................95
Action Items for Students........................................................95
UWIN 1.1- Introduction to UWIN and Urban Ecology- (15-30 min)...................96
UWIN 1.2- Database Activity (20-40 min).........................................100
Database Activity Instructions..................................................102
Database Activity Worksheet.....................................................109
UWIN 1.3- Introduction to Research Types and Variables (30 min).................Ill
UWIN 1.4- Primary Literature Activity (remainder of class period)...............112
How to read a scientific paper worksheet........................................114
UWIN Week 2: Methods and Formalizing Research Questions.............................116
Goals/Objectives................................................................116
Action Items....................................................................116
UWIN 2.1- Methods in Ecological Research (5-10 min).............................118
UWIN 2.2- Specific UWIN Methods (5 min).........................................121
UWIN 2.3- Site Level Data Collection Planning (15-20 min).......................123
Site Level Data Collection Field Sheet..........................................125
UWIN 2.4- Creation and Revision of a Research Question (45-60 min)..............128
Worksheet- Research Question Reflection. Peer Interview, and Revision...........130
UWIN 2.5- Camera Trap Assumptions (30 minutes)..................................133
UWIN 2.6- Finding Relevant Sources (45 min-rest of lab period)..................134
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Bibliography Instructions.............................................................136
UWIN Week 3: Collaborative Data Collection. Tagging Practice, and Writing................138
Goals/Objectives......................................................................138
Action Items for Students.............................................................138
UWIN 3.1- Geographic Information Systems (GIS) (50-65 min)............................140
GIS Data Collection Instructions......................................................143
GIS Site Level Measurements Worksheet.................................................149
UWIN 3.2- Data Entry (10-15 min)......................................................152
UWIN 3.3- Practice Tagging Photos (30-40 min).........................................153
Tagging Practice Instructions.........................................................154
Tagging Shortcuts.....................................................................160
UWIN 3.4- Outlining Title. Intro. Methods, and References Sections (45 min- rest of lab period)..........................................................................161
Instructions for Authors (Writing Guidelines).........................................162
UWIN Term Paper Outline Guide.........................................................168
UWIN Week 4: Photo Tagging and Figures Practice............................................173
Goals/Objectives......................................................................173
Action Items..........................................................................173
UWIN 4.1- Tagging New Photos (1.5 hours)..............................................174
Tagging Instructions..................................................................175
UWIN 4.2- Introduction to Tables and Figures (1 hour- rest of lab)....................177
Figures Activity Instructions.........................................................179
UWIN Week 5: Analyze Photo Data and Create Figures.........................................186
Goals/Objectives......................................................................186
Action Items for Students.............................................................186
UWIN 5.1- Analyze photo data and data entry (45 min)..................................187
Database Data Analysis Instructions...................................................189
Photo Data Collection Worksheet.......................................................193
UWIN 5.2- Analyze the data specific to your research question (rest of lab- along with 5.3, 5.4).............................................................................194
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UWIN 5.3- Outline Results and Discussion Sections (rest of lab).................195
UWIN 5.4- Preparation for the mini-presentation (rest of lab)...................195
Presentation Instructions........................................................197
UWIN Week 6: Last Day/ Presentations.................................................199
Goals/Objectives.................................................................199
Action Items.....................................................................199
UWIN 6.1- Presentations..........................................................199
UWIN 6.2- Voluntary Survey.......................................................199
UWIN 6.3-Open Lab................................................................199
UWIN Google Presentations and Notes..................................................200
UWIN Lab 1.......................................................................200
UWIN Lab 2.......................................................................255
UWIN Lab 3.......................................................................286
UWIN Lab 4.......................................................................297
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Introduction to CURES (TA Manual only)
By Sarah St. Onge What is a CURE?
The NSF funded Course-based Undergraduate Research Experience Network (CUREnet) defines a CURE as a research experience in an undergraduate course that uses scientific practices, results in discovery of new knowledge, investigates relevant work, and involves collaboration and iteration (Auchincloss-Corwin et al. 2014). Use of scientific practices is a fairly broad term that includes many aspects of the scientific process, such as scientific literacy, asking questions, proposing multiple hypotheses, designing experiments, making observations, collecting and analyzing data, constructing and performing models/simulations, interpreting data, realizing common variability and failure, and communicating findings. CUREnet realizes that incorporating all these scientific practices in one CURE is unrealistic, but encourages trying to incorporate as many as possible. Discovery of new knowledge means that the both the student and the instructor will not know what the results will be beforehand, promoting informed reasoning and exploration. Investigation of relevant work is important for the authenticity of the research experience and can give students a feeling of connection and contribution to the broader scientific community around them. Collaboration is important for networking with peers and mentors, and represents how the true scientific community works. Iteration is also important because also represents how science works, many times experiments need to be repeated, confirmed, or built upon, and builds student confidence and project ownership.
Why are CUREs useful for promoting learning, skills development and diversity?
For many years, there has been a national push to replace traditional "cookbook" laboratory courses with research based experiences (AAAS 2011; PCAST 2012; NRC 2009). "Cookbook" labs are known to be confirmatory and often are intended illustrate a well-known concept.
They focus more on how well students can follow directions, and can gloss over the conceptual and procedural aspects of the experiment (Brownell et al. 2012). CUREs, however, can increase understanding of the scientific process and increase retention in STEM programs. By having an engaging research experience, students can form or solidify an interest in biological research, and even if they don't, they still are better equipped to evaluate scientific claims in everyday life by better understanding the scientific process (AAAS 2011). CUREs can be particularly influential in the introductory courses, where many studies have shown positive student outcomes, such as developing long term understanding of core concepts, increased interest and motivation, addressing negative perceptions of science, forming an identity as a scientist, help to form scientific career goals, and acquiring skills like problem solving and critical thinking (AAAS 2011; PCAST 2012; NRC 2009; Jones et al. 2010; Auchincloss-Corwin et al. 2014; Mordacq et at. 2017). In addition, these positive outcomes are especially seen with underrepresented
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minorities (Jones et al. 2010). Undergraduate research experiences are often gateways to graduate and professional schools, but underrepresented students get fewer opportunities to do mentored research as undergraduates. The reasons for this are varied and still being explored. Bangera et al. (2014) argue that underrepresented students have less awareness about research opportunities and the importance of undergraduate research for career advancement. There are also perceived barriers to interacting with faculty, financial barriers, personal barriers, and a lack of knowledge about the cultural norms associated with doing science. CUREs have been proposed as introductory research experience that will help overcome these barriers.
National Directives for Undergraduate Biology Education:
In 2011, the American Association for the Advancement of Science (AAAS) published the initiative "Vision and Change in Undergraduate Biology Education: A Call to Action" with funding from National Science Foundation (NSF), the Howard Hughes Medical Institution (HHMI), and the National Institutes of Health (NIH). In the directives, there are six core competencies that are proposed for all biology graduates to achieve that relate to the practice and skills in the biology field (Figure 1).
Figure 1: Core Competencies for Biology Education (Adapted from AAAS, 2011 table 2.1)
AAAS Core Competency Why Important? Demonstration
Apply the scientific process Biology is evidence based knowledge through hypothesis generation, observation, and experimentation Practice scientific process to understand systems
Use quantitative reasoning Biology relies on data analysis and interpretation Apply quantitative analysis to interpret data
Use and Interpret modeling and simulation Biology focuses on understanding complex systems and predicting outcomes Use modeling and simulation to describe systems
Apply interdisciplinary knowledge Biology incorporates all the fields of sciences Apply concepts from other sciences to interpret systems
Communicate and Biology is a collaborative Collaborate on projects
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Collaborate and communicate concepts to others
Understand science and society relationship Biology is conducted in a societal context Identify social, historical, and ethical contexts in biological practice
In 2007, the Association of American Colleges and Universities (AAC&U) recommended goals for undergraduate learning outcomes with input from educators, policymakers, and business leaders from across the country (Figure 2). Many of these learning outcomes have shared qualities with the AAAS core competencies, and reiterate the importance of students acquiring these skills during their undergraduate experience.
Figure 2: AAC&U Essential Learning Outcomes (AAC&U 2007)
AAC&U Essential Learning Outcomes Specific Outcome/Skill
Knowledge of cultures and the physical/natural world Study science, math, social science, history, arts, etc.
Intellectual and Practical Skills Inquiry, analysis, critical thinking, written and oral communication, quantitative literacy, teamwork, and problem solving
Personal and Social Responsibility Civic knowledge and engagement, intercultural knowledge, ethical reasoning
Integrative and Applied Learning Application of knowledge to new and different problems
The UWIN CURE curriculum for General Biology 2 Laboratory will incorporate as many of these aspects as possible, were used to create the learning objectives and activities, will be centered around the context of urban wildlife monitoring, and will contribute data to the UWIN.
Cited:
(AAAS) American Association for the Advancement of Science, 2011. Vision and change in undergraduate biology education: a call to action, Available at: http://oreos.dbs.umt.edu/workshop/sharedfiles/Final VandC Draft Decl.pdf.
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(AAC&U). 2007. Executive Summary: College Learning for the New Global Century. Association of American Colleges and Universities, 1-20. Website: https://www.aacu.org/leap/essential-learning-outcomes
Auchincloss-Corwin, L., Laursen, S., Branchaw, J., Eagan, K., Graham, M., Hanauer, D., Lawrie,
G., McLinn, C., Pelaez, N., Rowland, S., Towns, M., Trautmann, N., Varma-Nelson, P., Weston, T., Dolan, E. 2014. Assessment of course-based undergraduate research experiences: A meeting report. CBE Life Sciences Education, 13(l):29-40
Bangera, G., & Brownell, S. E. 2014. Course-based undergraduate research experiences can make scientific research more inclusive. CBE Life Sciences Education, 13(4), 602-606. https://doi.org/10.1187/cbe.14-06-0099
Brownell, S., Kloser, M., Fukami T., Shavelson, R. 2012 Undergraduate Biology Lab Courses: Comparing the Impact of Traditionally Based "Cookbook" and Authentic Research-Based Courses on Student Lab Experiences. Journal of College Science Teaching. 41(4):36-45.
Jones, M.T. et al., 2010. Importance of Undergraduate Research for Minority Persistence and Achievement in Biology. The Journal of Higher Education, 81(1):82-115.
Mordacq, J., Drane, D., Swarat, S., Lo, S. 2017. Development of course-based undergraduate research experiences using a design-based approach. Journal of College Science Teaching. 46(4):64
(NRC) National Research Council, 2009. A New Biology for the 21st Century, Available at: http://books.google.com/books?hl=en&lr=&id=QSQJxn09pJUC&pgis=l.
(PCAST) President's Council of Advisors on Science and Technology, 2012. Engage To Excel: Producing One Million Additional College Graduates With Degrees in Science, Technology, Engineering, and Mathematics. Report to the President.
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Google Folder Instructions (TA Manual only)
Having a Google folder that you will share with students gives a central area where you can share documents and spreadsheets with students, and where students can add documents during lab activities so you can check for participation and completion. This also gives students some exposure and practice using a free, widely used sharing platform.
1. First, you should create a Google folder for your lab section (if not already done).
a. Name it something appropriate for your section number and semester/year.
2. Within this folder, create a UWIN folder.
a. Within the UWIN folder, you will add weekly folders as appropriate
i. Contents given below
3. Share a "can edit" link to the students via an announcement within Canvas, or you can add the URL within the UWIN overview Canvas Module.
a. NOTE: students will need to sign into their Google account to access all the functions of each program (docs, sheets, mymaps, slides, etc.). If they are viewing the files as a "guest", some actions are unavailable!
In the "Lab Materials" TA folder, there is a "Copy and Share with students" folder that contains different folders that you will COPY and then move to YOUR SPECIFIC shared folder with students throughout the labs. Some folders are basically empty placeholders for students to navigate to and add items, others have subfolders and many documents within it (such as "Site Information"). There is no current way to copy folders in Google Drive, BUT there is a CHROME APP called "Copy Folders" (you do need to be in a Chrome browser to use it). Watch a 2:40 minute tutorial here https://youtu.be/bmqYR h lJc. It works as of 3/17/2018. Maps do not copy over when using the "Copy Folders" app, so you will have to manually copy the site map from site information folder, and the UWIN 3 folder (bolded below).
"Share w/ students stuff" folder contents:
1. UWIN folder (Main folder)
a. UWIN 1-Occupancy Sheets folder
i. Example sheet with occupancy query results (for a different species)
ii. Otherwise, empty placeholder for student pairs to navigate to and add to
b. UWIN 2-Bibliography folder
i. Example bibliography entry using the paper we read in UWIN 1
ii. Otherwise, empty placeholder for student pairs to navigate to and add to
c. Site Information folder (to be used in UWIN 2 for student site visit planning, and students could reference anytime)
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i. Spreadsheet with all the sites= site number, park name, park ID, and GPS coordinates for the camera and parking.
ii. Map of all the sites and suggested parking locations
iii. Folders
1. Sites 1-20 (west transect)- each camera location has a document
2. Sites 21-40 (east transect)- each camera location has a document
d. UWIN 3-Maps folder
i. Example of a google map, with all the layers/measurements students will make
ii. Otherwise, empty placeholder for student pairs to navigate to and add to
e. UWIN 5-Data
i. This is where the verified database and site level data spreadsheet will be for students to access for data analysis
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UWIN Student Lab Manual-
Front Page
urban wildlife information network
Welcome to the Urban Wildlife Information Network (UWIN) portion of lab! This student manual starts on the next page with the "Table
iHr LINCOLN PARK ZOO-
of Contents", where each item listed can be quickly navigated to by clicking on the item of interest, then clicking on the link that pops up.
Each week of lab will start with a front page that contains the "Goals/Objectives" for that lab period followed by the "Action Items", which will be a list of all the things you will need to do to prepare and complete the lab for that week. After the front page for each lab, there are subsections that will contain content and activity information that you should read prior to coming to that lab period, and you should reference throughout the lab as needed. In addition to the text in this manual, there will be many links throughout that may direct you to videos, step-by-step instructions, websites, worksheets, and more, so be aware that you may need to read, reference, and/or print items that are associated with links in this manual.
This manual is provided to you in electronic form that is free for you to use so you don't have to purchase a separate lab manual, however, please print this manual to use during lab. You can print 350 pages (700 sides) free at the library, or the campus computing labs such as the one found in North Classroom. Please speak to one of the lab assistants in the library or computing lab for assistance. More information on how to print and where to print can be found on page 9 of the UC Denver "Survival guide". You will still need to access the electronic version to access the many links used throughout.
Please start by reading the "UWIN Overview" and the "Basic Outline" to give you an overall sense of the project as a whole. Let your TA know if you have any questions about the project or this student manual.
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UWIN Overview
The Urban Wildlife Information Network (UWIN) is a newly formed and growing national network of cities that believe in the importance of urban wildlife monitoring and have the goal of city to city collaboration and long-term investigation of our nation's urban ecosystems.
The mission of UWIN is for cities to investigate their specific urban wildlife ecology, and to compare data across cities to investigate widespread urban wildlife patterns and differences with goals to investigate wildlife population trends, promote wildlife conservation, increase public education and engagement, decrease human-wildlife conflict, and influence city planning and policy making. As of January 2018, ten cities are participating in UWIN, including us in Denver, CO, with potential for more cities to join.
The UWIN monitoring protocol requires that cities set up camera-trap monitoring transects in the same way using a transect that goes through the center of the city and expands out to the rural edge. Along this transect, potential wildlife habitats, such as parks and open spaces are identified and motion-activated passive infrared cameras are placed in these parks for one month, four times a year (seasonally). These cameras use motion and heat to trigger photos to be taken, and we use these photos to see what kinds of wildlife are using these areas, how often they are using them, and when they are using them. In Denver, we place 40 cameras along 40 km of Colfax Avenue that runs east from Golden, CO through downtown Denver to Aurora (more detailed methods will be given later!).
The Department of Integrative Biology at the University of Colorado-Denver has teamed up with UWIN to accomplish two main goals: 1) provide an authentic research experience for undergraduate students that leads to discovery of previously unknown knowledge, and 2) create a long-term urban wildlife monitoring dataset in Denver, CO that can contribute to the mission of UWIN. Students at CU Denver (i.e., you) are in charge of the data collection for Denver.
Over the next 6 lab periods, we will go through a typical research process where you will conduct background research on the topic, create a research question that interests you, participate in data collection procedures, analyze data to address your question, think about and discuss what the data means by presenting your project in the form of a results minipresentation and a scientific term paper. Because scientific research is rarely done alone, you will also be expected to work collaboratively with your peers and your TA in a professional manner.
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Basic Outline
Lab 1- Introduction to UWIN and Urban Ecology
1. Introduce and explore the project
2. Brainstorm urbanization effects on wildlife
3. Primary literature activity
Lab 2- Formalizing your Research Question
1. Discuss methods and assumptions
2. Assign sites to visit and go over data collection sheet
3. Create, evaluate, and revise research questions with peer interview
4. Search and locate primary literature, and create a bibliography while learning more about urban ecology
Students visit sites between Lab 2 and Lab 3
Lab 3- Collaborative Data Collection, Tagging Practice, and Writing
1. Collaborative GIS data collection and data entry
2. Practice tagging photos/species identification
3. Outline Title, Introduction, Methods, and Literature Cited
Lab 4- Photo Tagging and Figure Practice
1. Tag photos with species ID, #, details, comments
2. Data and figures activity
Lab 5- Calculate Photo Data, Figure Creation, and Writing
1. Picture verification activity/variation discussion
2. Calculate species totals, species richness, and daily presence of each species
3. Create figures
4. Outline Results and Discussion
Lab 6- Peer Review and Blast Presentations
1. Blast mini-presentation of results
2. Voluntary Survey
3. Open lab with TA
Finals Week- not meeting 1. Term paper due
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Full Text

PAGE 1

LONG TERM U RBAN WILDLIFE RESEAR CH POTENTIAL THROUGH COURSE BASE D UNDERGRADUATE RESEAR CH EXPERIENCES by SARAH ST. ONGE B.S. , Northern Arizona University, 2007 A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Master of Science Biology Program 2018

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ii This the sis for the Master of Science degree by Sarah St. Onge has been approved for the Biology Program by Laurel Hartley, Chair Michae l Greene Seth Magle Date: December 15, 2018

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iii St. Onge , Sarah (M.S. , Biology Program ) Long Term Urban Wildlife Research Potential Through Course based Undergraduate Research Experiences Thesis dir ected by Associate Professor Laurel Hartley ABSTRACT Urbanization i s increasing rapidly worldwide, leading to highly fragmented habitats and habitat loss , which have been shown to be the leading cause of local wildlife species endangerment. Urban wildlife monitoring has the potential to support land and wildlife managemen t decisions, wildlife and habitat conservation, zoonotic disease monitoring , help with human wildlife conflicts , and educate the community about urban ecological issues . The goals of this thesis were to implement a long term urban wildlife monitoring progr am by establish ing the University of Colorado Denver as a partner in the nationwide Urban Wildlife Information Network (UWIN) , analyze preliminary urban wildlife data, and to create and assess a Course based Undergradua te Research Experience (CURE) for a G eneral Biology laboratory course using the long term monitoring of urban wildlife as a context. The first chapter presents the urban wi ldlife monitoring protocol that was establ ished in the Denver metro area and initial data. The protocol used 40 motion a cti vated cameras along an urban to rural urbanization gradient set up for one month in each season in 2017 , resulting in the detection of 15 medium to large mammalian species , four domestic and 11 wild. Preliminary single season occupancy models that accou nt for imperfect detection were created for wild mammal ian species that were commonly detected. Models for coyotes showed higher probability of use in na turals areas further to the w est , and for mule deer in more rural natural

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iv areas . The occupancy models w ere not predictive for red fox, raccoon, and eastern cottontail rabbit s ite use probabilities . The seco nd chapter presents the development , implementation, and assessment of the CURE curriculum using the u rban wildlife project as a context . Incorporating r elevant, local, and authentic research into introductory undergraduate courses has been shown to have positive impacts on student interest, engagement, and reten tion. Curriculum creation included developing learning objectives and identifying core experien ces for scientific practices that are consistent with current national directives and that comply with important aspects of CUREs. Assessment of this CURE was accomplished using published surveys related to essential CURE elements, a conceptual inventory r elated to experimental design, and student work products. Results indicate that this CURE curriculum aligned with important CURE aspects , student perception s of authentic re search experiences were overall positive , and student s showed skill gains of using scientific processes. The form and content of this abstract are approved. I recommend its publication. Approved: Laurel Hartley

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v ACKNOWLEDGEMENTS For her gu idance in scientific inquiry, pedagogy, and curriculum development, a great thanks to my advi sor, Dr. Laurel Hartley. For their help and support in the planning and implementation of my ever evolving project, from urban ecology to curriculum development and implementation, thank you to my committee members, Dr. Michael Greene and Dr. Seth Magle. T hank you to the UWIN staff for help during the implementation of a long term wildlife monitoring program and with data management and analysis. An enormous thanks to Dr. Tod Duncan for his assistance and support in creating and implementing this curriculum into a lab oratory course . Thank you to the general biology 2 lab students who participated in the new curriculum and the assessment efforts. Much gratitude to all of my graduate and undergraduate colleagues who have helped me emotionally and professionall y, especially Scott Yanco, Paul Le, Andrew McDe vi t t, Ryan Parker, Tyler Michels, Kat ie Kilpatrick, Marianne Davenport , and Thomas Kennedy. Last but not least, thanks to my family and friends for their support, especially to my partner Marcus Szwankowski. All wildlife research methods were approved by the University of Colorado Institutional Animal Care and Use Committee (IACUC Protocol #114516(12)1B) All human subject research methods were approved by the University of Colorado Denver Colorado Multiple Ins titutional Review Board (COMIRB #17 2148)

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vi TABLE OF CONTENTS CHAPTER I. PRELIMINARY RESULTS OF URBAN WILDLIFE MO NITORING IN DENVER, CO: DETECTED SPECIES, SITE CHARAC TERISTICS, AND SIT E USE ................................ ................................ ....... 1 Introduction ................................ ................................ ................................ .................... 1 Methods ................................ ................................ ................................ .......................... 6 Results ................................ ................................ ................................ ........................... 15 Discussion ................................ ................................ ................................ ...................... 27 II. DEVELOPMENT AND ASSESSMENT OF AN URBAN WILDLIFE ECOLOGY COURSE BASED UNDERGRADUATE RESEARCH EXPERIENCE IN AN INTRODUCTORY BIOLOGY LABOR ATORY 32 Introduction ................................ ................................ ................................ .................. 32 Methods ................................ ................................ ................................ ........................ 38 Results and Discussion ................................ ................................ ................................ .. 46 Summary and Conclusions ................................ ................................ ............................ 60 REFERENCES ................................ ................................ ................................ ................................ .. 64 APPENDIX A. Chapter 1 Additiona l Tables and Figures ................................ ................................ .. 73 B. Chapter 2 Additional Tables and Figures ................................ ................................ .. 74 C. UWIN CURE Curriculum ................................ ................................ ............................ 82

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1 CHAPTER I PRELIMINARY RESULTS OF URBAN WILDLIFE MO NITORING IN DENVER, CO: DETECTED SPECIES, SITE CHARAC TERISTICS, AND SITE USE Introduction Natural areas are increasingly being converted to urban areas around the globe. According to the United Nations , glo bal urbanization has increased from 30% in 1930 to 55 % in 2018 and is projected to be 68 % in 205 0 ( United Nations, 2018) . In the United States, it is projected that more than 80% of the population wil l live in urban areas by 2050. This escalation in urbanization leads to an incre ase in wildlife habitat loss and fragmentation, which in turn has been shown to be the leading cause of local species endangerment and extinction worldwide (Mcdonald, Kareiva, & Forman, 200 8; McKinney, 2008) . Many developing areas have been managed to support local habitat and wildlife with parks, open spaces, and wildlife refuges in order to mitigate some of these consequences (Armstrong, Fitzgerald, & Meaney, 2010; Marzluff & Rodewald, 2008; Ruliffson, Haight, Gobster, & Homans, 2003) . These areas can provide important ecosystem services in urban areas, such as microclimate regulation, pollution reduction, noise reduction, water regulation, carbon sequestration, human health benefits, and provide plant and animal habitat (Chiesura, 2004; Elmqvist et al., 2015) . However, when wildlife inhabit urban open spaces, there can be wildlife huma n conflicts in addition to wildlife desensitization to people, alteration of normal animal behaviors, an increase of invasive non native species, and an increase of zoonotic disease spread (Adams, 2005; Gottdenker, Streicker, Faust, & Carroll, 2014; Magle, Simoni, Lehrer, & Brown, 2014; Messmer, 2009) . Also, even though open space can provide many human benefits, there is evidence that recreation can

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2 affect native wildlife species distributions and densities negatively (Ree d & Merenlender, 2008) . Therefore, as human population and urbanization expands, it will be important to monitor and investigate the positive and negative effects of urbanization on wildlife and urban habitats. Establishment and Mission of the Urban Wildl ife Information Network: The Urban Wildlife Information Network (UWIN) is a newly formed and growing national network of cities that believe in the importance of urban wildlife monitoring and have the goal of city to city collaboration and long term inves tigation of our nations urban ecosystems (Magle et al., 2018) . The mission of th e UWIN is for cities to investigate their specific urban wildlife ecology, and to compare data across cities with g oals to investigate wil dl ife population trends, promote wildlife conser vation, increase public ed ucation and engagement, decrease human wildl ife co nflict, and influenc e c ity planning and policy makin g (Magle et al., 2018) . As of summer 2018, 16 cities in the U.S. and one in Canada are either currently participating or planning on participating in the network , which is actively recruiting more partners. The UWIN monitoring protocol requires that camera trap monitoring trans ects be set up over an urbanization gradient in each participating city, and that data are collected over one month in each season (see detailed methods below). Camera traps have become a preferred method for studying spatial and temporal dynamics of mediu m to large wildlife populations because they are a relatively low cost, easy to use, non invasive, remote sampling technique that allow for continual observation and reviewable, permanent data records Karanth, 2011) . The UWIN camera trap methods limit the detection of animals to medium to large mammalian species. However, UWIN has suggested additional techniques to include

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3 monitoring for small mammals, birds, amphibians, r eptiles, insects, and vegetation (Magle et al., 2018 ) . As with any long term monitoring network, the data generated can be used to look for patterns within and across sites and can be used to answer both current questions and questions yet to be conceived (Gitzen, Millspaugh, Cooper, & Licht, 2012; Knapp et al., 2012) . Published research from UWIN To date , published data from the UWIN site in Chicago, IL have investigated invasive species impacts, wildlife behavior, important habitat characteristics, and socioeconomic impacts on urban wildlife. Vernon et al. ( 2014) investigated the effects of an invasive species on urban wildlife mamm al species distributions and found that invasive buckthorn ( Rhamnus cathartica ) impacted habitat use and presence negatively for white tailed deer ( Odocoileus virginianus ), and positively for coyotes ( Canis latrans ) and opossums ( Didelphis virginiana ). Usi ng patch occupancy models, Magle et al. ( 2014) investigated habitat ch aracteristics, human activity, and species presence effects on coyote and white tailed deer distributions and found that, because of scarce quality habitat, the two species were co occurring, cont rary to hypothesized behavior. In another study, Magle et al . ( 2016) researched the effects of landscape characteristics and socioeconomic factors on coyotes, raccoons ( Procyon lotor ), and opossums using occupancy models and found that the socioeconomic factors were as important as the ecological factors in determining their distribution s. Potential discussed explanations for this are that income, education level, property stewardship, etc. may lead to fine scale environmental differences (e.g., habitat quality, food and water availability, etc. ) that wildlife utilize . F idino et al. (2016) studied habitat characteristics that influence opossum

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4 occupancy and found that water source availability was important and that human provided water sources are likely allowing opossums to occupy ar eas previously thought uninhabit able. These studies show how monitoring patches of open space over an urbanization gradient can provide insight into urban wildlife ecology, including habitat p references and human impacts on wildlife behavior, but is also i nformative for management and conservation. Data from multiple municipalities in the Denver metro area will be available from this study and could be useful in many ways, including monitoring species population and interaction dynamics, examining effects o f management strategies, effects from neighboring areas, responses to env ironmental change, just to name a few (Gitzen et al., 2012) . In addition, consistent long term monitoring data is fundamental to conservation and management because it can provide context for ecosystem dynamics over time, and could provide the basis for new management practices, or for changing existing ones (Lovett et al., 2007) . Wildlife Research in the city of Denver, CO. In the past, many urban wildlife studies have focused on a single species of mammal or bird over a re latively short time period (Magle, Hunt, Vernon, & Cr ooks, 2012) . This holds true to published research for Denver, CO as well. For example, there are a few studies on coyotes in the Denver area, studying coyote management strategies, home ranges, sightings, disease prevalence, and human coyote conflicts du e to spatial and temporal patterns related to land cover type and housing density (Breck, Poessel, & Bonnell, 2017; Magle, Poessel, Crooks, & Breck, 2014; Malmlov, Breck, Fry, & Duncan, 2014; Poessel et al., 2013; Poessel, Breck, & Gese, 2016) . Another example would be of the black tailed prairie dog ( Cynomys ludovicianus ) in urban Denver , with research into gene tic diversity and distribution patterns based on

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5 landscape, habitat and connectivity characteristics , and public sentiment (Magle & Crooks, 2009; Magle, Ruell, Antolin, & Crooks, 2010; Magle, Theobald, & Crooks, 2009; Morse, Powell, & Sutton, 2012) . Otherwise, research into historical mammalian species throughout Denver is limited and hard to find. One study by Jone s et al. ( 2003) researched bird and mammal presence and habitat use of six areas along the river corridor of the South Pla tte River, which runs south north through urban Denver, during 1998 1999. They detected 64 bird species and 15 mammalian species, including s mall mammals such as rodents (Order Rodentia ) , and medium to large mammals, including cottontail rabbit ( Sylvilagus floridanus ) , fox squirrel ( Sciurus niger ) , raccoon ( Procyon lotor ) , red fox ( Vulpes Vulpes ) , coyote, deer ( Odocoileus Spp. ) , and domestic cat ( Felis catus ) and domestic dog ( Canis familiaris ) . Cited within the article were unpublished results from other s tudies from , which included only birds and small mammals . Mammals of Colorado (Armstrong et al., 2010), is a n extensive book reference on Colorado, with some general urban wildlife information (Armstrong et al., 2010) . The book gives a historical account of Colorado ecosyste ms and mammals, explaining ho w Euro A merican settlement led to agricultural development and urbanization, which altered waterways for irrigation, changed land use for crop cultivation, added built infrastructures, and established non native landscaping. Al ong with this came an increase of deliberate non native introductions, artificial feedings (e.g. trash cans or bird feeders), and fire suppression (Armstrong et al., 2010) . Human expansion in Colorado ultimately led to the extirpation of the gray wolf ( Canis lupus ) , grizzly bear ( Ursus arctos ) , bison ( Bison bison ) , and black footed ferrets ( Mustela nigripes ) , and allowed for introduction, widespread distribution, and increased

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6 abundance of urban adaptors and habitat generalist s , such as the Norway rat ( Rattus norvegicus ) , house mouse ( Mus musculus ) , fo x squirrel, striped skunk ( Mephitis mephitis ) , raccoon, and red fox (Armstrong et al., 2010) . Overall Goals Denver is an ideal location to start a long term urban wildlife monitoring program. The Denver metro area is an urbanized area with projected continual growth. There is limited data on Denver u rban wildlife. Denver sits on a unique ecotone that spans the shortgrass prairie and foothills habitat. The goals of this project are to establish Denver as a study location with the UWIN and to examine the preliminary data. Specifically, we are interested in the following questions: 1) What mammalian species are present in parks and open spaces along the general urbanization gradient provided by Colfax Avenue in Denver, CO? 2) Of species abundantly detected, are any generalized site level characteristics (urbani zation, habitat management type, longitude, etc.) good estimators for site use by that species? Methods Long Term Monitoring Methods: The study design guidelines for this project have been standardized by the Urban Wildlife Information Network (UWIN) in or der to contribute to their nationwide goal of being able to readily compare urban wildlife composition data between different cities. Therefore, the site selection, data collection, data processing, and data management procedures of this

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7 study discussed be low meet the requirements that were supplied by the UWIN (Fidino et al., 2016; Magle et al., 2016 , Magle et al., 2018 ) . Site Selection and Study Design : The Denver metro politan area in Colorado was selected as a long term urban w ildlife monitoring city due to Denver being a large metro area, the 26 th largest in the U.S. , with an estimated po pulation of 2.8 million people (U.S. Census Bureau, 2010) . In addition, Denver has consistently been one of the fastest growing cities in the country, growing 16.7% from 2000 to 201 0, with an estimated increase to 3.3 million people by 2020 , making Denver an appropriate candidate for urban wildlife monitoring (MDEDC, 2018; U.S. Census Bureau, 2010) . The Denver area has an average elevation of 1610 m (5280 ft) , an average annual temperature of 10 .3 o C (50.5 o F) , and an average annual precipitatio n of 3 9. 5cm (15.5 in) (U.S. Climate Data ) . In addition, the Denver metro area includes and is surrounded by different native ecosystem types, with grassland prairie to the north, east, and south, and a strip of montane shrubland leading into montane forest to the west, with riparian/wetlands along waterways (Armstrong et al., 2010) . Denver is unique because, unlike many other cities in the UWIN, it sits on an ecotone at the urban wildland interface. Colfax Avenue was sele cted as the center line fo r the transect because it extends east and west through the center of Denver, CO, and supplies two 20 km urban to rural gradients that captures the range of urbanization present in the Denver metro area . Each transect was segmented into 5km sections, in wh ich five parks or green spaces in each section were selected for camera placement. The 40 sites are within 2 km of the transect line, and at least 1 km away

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8 from e ach other. This ensures an even and well spaced distribution of sampling effort along the tra nsect, and the >1 km spacing helps to promote site independence ( . Site approvals or site use permits for all 40 locations were obtained from Aurora Parks, Recreation, and Open Space, Denver Parks and Recrea tion, Lakewood Parks, Forestry, and Open Space, Jeffco Open Space, Pleasant View Metropolitan District, and Prospect Recreation and Park District. Figure 1 shows a map of all th e site locations. Along with obtaining permission from land managers, an IACUC protocol was submitted and approved by the University of Colorado Institutional Animal Care and Use Committee (Protocol # 114516(12)1B). Camera Deployment: One motion activated camera equipped with a passive infrared trigger and infrared flash (Bushnell E2 Trophy Cam, Model 119836, Bushnell, Overland, KS) was placed at each approved site for four wee ks during each season in 2017 (winter: January, spring: April, summer: July, fall: October). Cameras were strapped to trees or posts that allow for secure att achment and were aimed toward a carnivore attractant tablet located approximately 3 5 meters from the camera Figure 1. Site Location map of the Colfax Avenue transect in Denver, CO. Green/yellow and blue/orange lines represent the two 20km urban to rural gradients split in to 5km sections. Pins represent locations and land managers: Orange=Jefferson County, Green= Pleasant View Metro District, Purple= Prospect Recreation and Parks District, Blue= Lakewood, Red= Denver Parks, Yellow= Aurora Parks

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9 (Figure 2) and were removed after four weeks. The attractant tablets are commercially available fatty acid discs (FAS tablets, USDA Pocatello Suppl y Depot, Pocatello, ID) and were enclosed in a zip tied mesh screen pouch that was secured to a tree, log, or stake. The tablet is a mild, malodorous, short range attractant and does not cause population level responses or wildlife problems for surround ing homeowners. Trees were not h armed by these study methods. Cameras also had an informational sticker that provides basic project information and an email address for contact. Data Collection, Processing, and Management: After at least 4 weeks in the fi eld, all pi ctures from each SD card were downloaded and backed up to an external hard drive in files dedicated to e ach location. The pictures were then uploaded to a database created by Colorado Parks and Wildlife (Ivan & Newkirk, 2016; Newkirk, 2016) that was adapte d by UW IN. number of individuals, and comments by two different individuals. Less difficult mismatched Figure 2: Example of a UWIN camera trap set up. Camera is secured to a tree with a nylon strap and cable lock and is aimed toward a scent attractant tablet in a mesh pouch approximately 3 5 meters away.

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10 photo tags (such as 5 humans vs 6 humans) were decided upon and verified, more difficult photos were verified by more than one individual. Dat a collected from each image include date, time, species present , and number of individuals. The relational Microsoft Access database has built in occupancy (detectio n/non detection) queries that were exported for an alysis. The database can also filter the pictures by time, location, species, activity, or comments. Estimating Site Characteristics Each site was broadly classified b y habitat management type, e.g. how the habitat at each site is managed , via site visit observations . Sites that are highly managed, with manicured, examples being city and sports parks. Sites that appear left alone with evidence of natural vegetatio n and little to no maintenance (e.g., no mowing, regular planting of vegetation) were wildlife reserves, and unmanicured parks . In addition, sites that had both mani cured landscaping and management type. While this is a broad classification scheme, it can give an idea of the types of habitat available for wildlife at each site, and is similar to other general site level categories (Gallo, Fidino, Lehrer, & Magle, 2017; Gehrt, Anchor, & White, 2009; Haverland & Veech, 2017; Luck & Wu, 2002; Markovchick Nicholls et al., 2008; Ordeñana et al., 2010; Poessel et al., 2016) . Area of each park was measured using satellite image ry via ArcGIS 10.2.1 (ESRI Inc., 2014) by creating polygons around each park that were defined by landscape boundaries (roads, large rivers, etc.) while attempting to omit structures, parking lots, and bodies of water , after which the polygon area (in acres) was calcu lated . Another habitat

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11 characteristic of average percent tree canopy cover within a 1km buffer was measured via ArcGIS 10.2.1 (ESRI Inc., 2014) using the 2011 National Land Cover Database (NLCD) percent tree canopy cover GIS layer, which has a 30m pixel resolution (Homer, C.G. et al., 2015) . Characteristics that could be associa ted with u rbanization were also estimated for each site by examining GIS layer s of imper vious surface , human population, and light pollution radiance. Average percent impervious surface within a 1km buffer of each camera location was measured using ArcGIS 10.2.1 (E SRI Inc., 2014) using the 2011 NLCD percent imperviousness GIS layer, which has a 30m pixel resolution (Homer, C.G. et al., 2015) . We e stimated human population within a 1km buffer arou nd each camera site using the census block data within the 2010 SILVIS l ab U.S. housing density shapefile, summing the census population data located within the buffer using RStudio 1.0.153 (RStudio Team, 2016; SILVIS Lab, 2010) . A proxy for light pollution was estimated using the online open source light pollution mapping software and 2017 Visible Infrared Imaging Radiometer Suite (VIIRS) by recording the light radiance level at each camera location (Falchi et al., 2016) . To reduce the dimensionality of these 3 urbanization parameters, we used principle component analys is (PCA), and used the first principal component (PCA1) which accounted for 78% of the variation in the data , see appendix A1 for PCA loadings and variance percentages ( similar to Gallo et al., 2017) . The PCA1 score for each site wa s used as an urbanization covariate in our models, with positive values indicating higher site estimates of percent imperviousness, human population, and light pollution , and thus an in of land use categories within a 5 km buffer of our transect line, to characterize the land use close to our study sites

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12 using the 30 m pixel raster data of the 2011 NLCD land use data (see Appendix A 2, Homer, C.G. et al., 2015) . Wildlife Site Use and Detection Probability Estimation To describe preliminary site use and detection probabilities of wildlife , we used single season single species occupancy models from the occu() function from the unmarked package in R Studio (Fiske & Chandler, 2015; Mackenzie et al., 2002; RStudio Team, 2016) . Occupancy models use binomial presence/absence data to estimate detect ion probabilities , which account s for imperf ect detection of a species, along with estimating occupancy probability estimates (both with or without variance explanatory covariates) (MacKenzie et al., 2006) . Oc cupancy models are used often in species distribution and metapopulation dynamics research, but have also be used in invasive species, disease dynamic , and climate change studies (Bailey & Adams, 2005; Kendall, Hines, Nichols, & Campbell, 2013; MacKenzie et al., 2006) . T hese single season occupancy models have numerous assumptions , including site closure (species stay at the s ite during the survey), site independence (an individual is not detected at multiple sites), and that occupancy and detection probabilities are the same across sites (unless explained by site or survey characteristics) (Bailey & Adams, 2005; MacKenzie et al., 2006) . Violating these assumptions can change the interpretation of the probabilities and potentially lead to biased estimates of detection and occupancy (Bailey & Adams, 2005) . Potential b iases in our estimates are discussed below. Our photo data were queried for daily detection and non detection data for ea ch species for the 112 days surveyed in 2017. Due to having 40 sites and one year of data, the models shown here are meant to show general site use trends for this preliminary data in

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13 Denver, CO. It is important to note that as most of the species have hom e ranges larger than the distances between sites, the assumption of site independence is not necessarily being met. This results in interpreting site probability harder to interpret (Bailey & Adams, 2005; Mac Kenzie et al., 2006) . However, we do still want to show preliminary results of site use trends while accounting for imperfect below 0.15 do not produce vali d site occupancy estimates due to not being able to distinguish between true species absence and undetectability. However, since we violate the site independence assumption of the model (leading to potentially uninterpretable detection probability estimate s), along with violating the assumption of site closure (by having our single season occasions defined as the 112 days of 2017 leading to potentially biased occupancy/use estimates ), and having the goal of describing preliminary data (i.e. not creating pre dictive models), the results from the models having less than 0.15 detection probability will be displayed and discussed. Site use and detection were estimated for wild species that were detected i n at least 25 % of the sites, which included coyote, mule d eer ( Odocoileus hemionus ) , red fox, raccoon, and eastern cottontail rabbit . Fox squirrels were seen in 39 out of 40 sites, and due to the ir ubiquitous detect ion across our sites, it is nearly impossible to assess spatial distribution due to site level char acteristics, therefore we did not run occupancy models for this species ( similar t o raccoons in Lesmeister, Nielsen, Schauber, & Hellgren, 2015) . Hypo thetical models for site use were created for each species based on pr evious research and our a priori thinking about urbanization effects. For coyotes , we hypothesized that urbanization would have a negative

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14 effect on site use due to previous studies show ing low occupancy near human made structures and development (Gehrt et al., 2009; Lesmeister et al., 2015) . We also hypothesized that the habitat management variable would be important, with higher use (Gehrt et al., 2009) . We included our habita t management type variable (which generalizes the habitat in each park categorically as natural, artificial, or mixed ) and a longitude variable (that generalizes the p otential habitat over the ecotone , with foothill scrub to the west, and prairie grassland to the east). The longitude variable was calculated in RStudio by using the longitude of the far west site as a starting point, then the distance (km) east to every o t her site . It is important to note that for coyotes, we cannot assume that our sites are independent of one another, as home ranges of coyotes in urban areas can be highly variable and up to over 400 km 2 (Gehrt et al., 2009; Poessel et al., 2016) . For red fox , we hypothesized that urbanization would have a positive effect on site use, from previous studies showing increased activity near human development which is th ought to be from seeking refuge from agricultural practices and coyotes (Black, Preckler Quisquater, Batter, Anderson, & Sacks, 2018; Gosselink, Deelen , Warner, & Joselyn, 2003; Lesmeister et al., 2015) . In addition, we hypothesized that habitat characteristics would show increased use in areas more similar to natural grassland areas , with increased use in natural habit at management type and decreasing with longitude. As with coyotes, the home range of urban red foxes is variable and larger than our site spacing, up to over 35 km 2 with daily movements of up to 10 km (Armstrong et al., 2010; Gosselink et al., 2003) . As raccoons are known urban habitat exploiters with a highly diverse omnivorous diet , we hypothesized that we would see similar results of urbanization showing higher site use probability estimates, with

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15 park s that have mixed habitat type, and tha t are closer to residential areas (Armstrong et al., 2010; Gross, Elvinger, Hungerford, & Gehrt, 2012; Lesmeister et al., 20 15) . Therefore, we used the urbanization, habitat management type, and longitude covariates in our models. Mule deer are the most abundant cervid in Colorado, with the widest range occurring in all ecosystems, from riparian grassland to alpine tundra (Armstrong et al., 2010) . They also have a broad h erbaceous diet, but have a small rumen and gut, therefore need small amounts of high quality food (Armstrong et al., 2010) . In addition, they are likely to avoid areas with high human and dog use (Miller, Knight, & Miller, 2001) . Therefore, we predicted that urbanization would have a negative effect on mule deer use , natural h abitat having a positive effect, and longitude potentially having a positive to negative effect as they are found in all ecosystem types. Eastern c ottontail r abbit s have been documented in the northeastern riparian areas of Colorado through to the northern foothills, including the Denver area (Armstron g et al., 2010) . They are commonly found in habitats containing brush and grass, and are commonly found in urban areas, especially when shrub cover and herbaceous food is present ( Baker, Emerson, & Brown, 2015) . We therefore hypothesized that eastern cottontail rabbits use would be influenced positively due to mixed and natural habitats, urbanization, and when getting closer to natural grassland areas towards the east. Results Wil dlife Inventory Summary: Cameras were set up seasonally for at least 4 weeks starting winter 201 7, resulting in 39,389 photos. Domestic mammalian species de tected included: domestic cats, domestic dogs , ( Equus caballus ), and humans ( H omo sapiens ). Wild mammalian species detected

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16 included: coyotes, eastern cottontail r abbits, elk ( Cervus elaphus ), fox squirrels , mountain lions ( Puma concolor ), mule deer , black tailed prairie dog s, raccoons , rats ( Rattus spp .), red fox, and striped skunks (see Table 1 for summary totals). T otal count data are not useful for summarizing wildlife density as we cannot differentiate multiple detections of the same individual animals, however, it does show that the method is successful at cap turing these wildlife species. Table 1. 2017 Camera and Picture Summaries . Season Dates Out Total Pictures (39,389) Species Identified (Total # of Pictures) Winter 12/29/16 1/29/17 10,055 Domestic Wild Mammalian Cat (169) Dog (4242) Horse (31) Human (15,43 6) Coyote (296) E. Cottontail Rabbit (1789) Elk (107) Fox Squirrel (7274) Mt. Lion (3) Mule Deer (570) Prairie Dog (143) Raccoon (914) Rodent Spp. (22) Red Fox (110) Striped Skunk (30) Spring 4/1/17 4/30/17 14,088 Summer 6/29/17 7/28/17 8,687 Fall 9/30/17 10/29/17 6,559 Figure 3 shows the total frequency of each species that was detected at the 40 sites acr oss Denver, CO during the seasonal 2017 samples . A majority of the sites detected use by humans, fox squirrels, dogs, and raccoons, and around half the sites detected use by coyotes, eastern cottontail rabbits, and domestic cats. There was low detection of elk, mountain lions, pra irie dogs, horses, rodents, and striped skunks, therefore these species were not included in the site use and detection probability models.

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17 Site characteristics that could be broadly associated with urbanization, wildlife use, and wildlife detection were estimated for each site. Figure 4 shows the general trend of the urbanization gradients with the human population, percent impervious surface, and light pollution estimates. Also shown is the PCA1 score of the principal component analysis of these three p arameters, that is used as an urbanization covariate in our site use and detection probability models. In addition, habitat characteristic covariates of tree canopy cover, park area, and habitat management type were estimated and are also shown in Figure 4 . General observations from this data show that the variables we thought would be a decent proxy for urbanization (imperviousness, human population, and light pollution) did show a general increase towards the urban center. Further, PCA analysis showed hig h correlation between the three variables, with the first principal component accounting for 78% of the variance and displaying a general trend of increasing toward the urban center. However, it is important to note that the trend is not perfectly linear, showing there can be pockets of more urban areas F igure 3. Detected species f requency at all UWIN camera sites. Shows the total number of sites that each species was detected during the four sampling seasons of 2017. 1 1 1 2 3 7 10 14 21 21 23 32 38 39 40 0 5 10 15 20 25 30 35 40 Number of sites (out of 40) Species Identified

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18 not within the urban center, and using distance from the urban center as a proxy for an urbanization gradient may not be appropriate for many cities, especially larger cities. N evertheless, looking at the ur banization proxy variable graphs for these Denver sites, there does seem to be decent variation between the ends of both the transects, leading to higher confidence that we obtained two generally urban to rural gradients. The percent tree canopy cover arou nd each site appears more variable, which was expected due to the natu ral variation of the native ecotone across the Denver metro area, generally with foothills and forested mountainous areas when heading west, and prairie grasslands and agriculture when h eading east, with naturally forested riparian areas throughout (see figure 5 for general land use in and around Denver ). In addition, using the 2011 NLCD land use data we calculated the percent of land use categories within a 5 km buffer of our transect li ne to characterize our study area , with the top land use types being developed (62.4%), developed open space (11.7%), grassland (8.7%), and shrub/scrub (6.6%) (see Appendix A 2).

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19 F igure 4 . Estimated camera site park characteristics. Characteristics measured or calculated for each site and shown spatially from west to east, data sets used f or each measurement are mentioned below each graph.

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20 To show how species are using each location across the urbanization gradi ents, figure 6 shows the raw data (which does not account for imperfect detection) of total percent daily occur rence of 12 species over all four seas ons (out of 112 days during 2017 ) at each location and shows each location spatially as they relate to each other in a west to east fashion. Very general observati ons from this data suggest that only one site had mountain lion and elk detection , mule deer were detected near the rural edges, foxes were more often using areas in F igure 5 . Regional land cover classifications and UWIN camera locations in Denver, CO. Map created using Data Basin online mapping soft ware, and the 2011 NLCD dataset to show how Denver is an ecotone, with forest on the west end and grassland/agriculture east end of the transect, also shows the general land use around the general Denver metro area.

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21 the east transect and coyote were using areas more often in the west transect, and there were low detections of striped skunk. F igure 6 . 2017 percent species occurrence across all sites. Shows the total percent for each species da ily occurrence at each site during the four seasons collected during 2017 (out of 112 days) . Because the two urban to rural transects run from the urban center out east and west, each point represents each location spatially from west to east with the urba n center at the midpoint.

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22 Occupancy Modelling Results Results from the coyote occupancy models produced a detection probability estimate of 0.065 (SE=0.005). Table 3 shows the model s test ed, our original prediction of the model, and how the models performed in relation to each other. T he top models for coyote site use included the longitude variable (AIC weight=0.63) with a negative correlation heading east (see figure 7A ) and the habitat management variable (AIC weight= 0.37) with a positive correlation towards natural areas (see figure 7B ). Surprisingly, the urbanization model performed very poorly when pr edicting site use, with the constant (.) model performing the wors t . Table 3. Coyote occupancy m odels ( detection estimate p(.) =0.065, SE= 0.005) Model Prediction AIC AIC weight c um. AIC weight 1278.985 0.00 0.63 0.63 + 1280.041 1.06 0.37 1.00 1390.837 111.85 0.00 1.00 NA 1413.849 134.86 0.00 1.00 mgmt= habitat management ty pe (artificial, mixed, natural) lo ng= longitude proxy= distance from the farthest west site head ing east in km urb= PCA1 urbanization proxy (.)= constant AIC= the conditional probability of the model, cum. AIC weight= cumulative model weights

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23 The red fox models had a detection probability estimate of 0.058 (SE= 0.005) and did not perform well with the constant (.) model being the top model with an AIC weig ht of 0.39 (see T able 4) . The second model (AIC weight= 0.32) was the urbanization model, and the third was the longitude model (AIC weight= 0.22), both predicting a very slight increase in use . The habitat management model showed no real difference in pre dicting use (AIC weight= 0.07) (see probability estimates graphed in Figure 8). Table 4. Red f ox occupancy models ( detection estimate p(.) =0.058 , SE= 0.005) models Prediction AIC AIC weight cu m . AIC weight NA 689.46 0 0.39 0.39 + 689.86 0.41 0.32 0.71 + 690.57 1.12 0.22 0.94 + 693.05 3.6 0. 07 1.00 mgmt= habitat management ty pe (artificial, mixed, natural) long= longitude proxy= distance from the farthest west site hea d ing east in km urb= PCA1 urbanization proxy (.)= constant AIC= conditional probability of the model, cum. AIC weight= cu mulative model weights Figure 7. Coyote model estimates with 95% confidence intervals in gray. Shows the top two models showing decreased probability of use heading east (A) , and increased probability of use in mixed and naturally managed habit ats (B) . A B

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24 Results from the raccoon occupancy models produced a detecti on probability estimate of 0.106 (SE=0.005). Table 5 shows similar results as the red fox models, with t he top model for site use being the constant (.) model (AIC w eight= 0.53). The rest of the models performed poorly as covariates to predict raccoon use with the urbanization model (AIC weight= 0.2), the longitude model (AIC weight=0.19 ) , and the habitat management model ( AIC weight= 0.08 ) , all showing high probabili ty of use consistently along each variable (see figure 9 ). Figure 8. Red fox model estimates with 95% confidence interval in gray. Occupancy model estimates showing slight increased probability of red fox use when (A) urbanization increases (AICw=0.32), and (B) heading east (AICw=0.22), and (C) no difference in use by habitat management type (AICw=0.07) . The constant (.) model performed best (AICw=0.39). B C

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25 Table 5. Raccoon occupancy models (detection estimate p(.)=0.106, SE= 0.005) models Prediction AIC AIC weight cum. AIC weight NA 2461.44 0.00 0.53 0.53 + 2463.41 1.97 0.20 0.73 2463.44 2.00 0.19 0.92 0 2465.25 3.80 0.08 1.00 mgmt= habitat management ty pe (artificial, mixed, natural) long= longitude proxy= distance from the farthest west site head ing east in km urb= PCA1 urbanization prox y (.)= constant model, AIC= conditional probability of the model, cum. AIC weight= cumulative model weights Results from t he mule deer occupancy models produced a detecti on probability estimate of 0.073 (SE=0.008 ). Table 6 shows that t he top two model s are similar for estimating Figure 9. Raccoon occupancy model estimates with 95% confidence interval in gray. Occupancy model estimates showing no changes in probability of raccoon use from (A) urbanization (AICw=0.2), (B) longitude (AICw=0.22), and (C) habitat management type (AICw=0.07) . The constant model performed the best (AICw=0.53). A B C

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26 site use , with habitat management type with an AIC weight of 0.58, and urbanization with an AIC we ight of 0.42, with figure 10 showing the model estimates. Table 6. Mule d eer occupancy models (detection estimate p(.)=0.073, SE= 0.008) models p rediction AIC AIC weight cum. AIC weight + 609.61 0.00 0.58 0.58 610.28 0.67 0.42 1.00 NA 621.78 12.17 0.00 1.00 + or 623.35 13.74 0.00 1.00 mgmt= habitat management ty pe (artificial, mixed, natural) long= l ongitude proxy= distance from the farthest west site head ing east in km urb= PCA1 urbanization proxy (.)= constant model, AIC= weight= the co nditional probability of the model, cum. AIC weight= cumulative model weights Results from the eastern cottontail occupancy models produced a detecti on probability estimate of 0.208 (SE=0.008 ). Table 7 shows similar results as the raccoon and red fo x models, with t he top model for site use being the constant (.) model (AIC weight= 0.51 ). The rest of the models performed poorly as covariates to predict eastern cottontail use with the urbanization model (AIC weight= 0.2 2 ) with a slight decrease pr obabi lity of site use (figure 11 A) , and the Figure 10. Mule deer occupancy model estimates with 95% co nfidence interval in gray. Occupancy model estimates showing (A) decreased probability of mule deer use when urbanization increases (AICw=0.58), and (B) increased probability of use in natural habitat (AICw= 0.42). A B

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27 longitude model (AIC weight=0.19 ) , and the habitat management model ( AIC weight= 0.09 ) , both showing mid level probability of use along each variable (figur e 11 B and C ). Table 7. Eastern cottontail rabbit occupancy m o dels detection estimate p(.)=0.208, SE= 0.008 models Prediction AIC AIC weight cum. AIC weight NA 2449.35 0.00 0.51 0.51 + 2451.03 1.68 0.22 0.73 + 2451.35 2.00 0.19 0.91 + 2452.86 3.51 0.09 1.00 mgmt= habitat management ty pe (artificial, mixed, natural) long= longitude proxy= distance from the farthest west site head ing east in km urb= PCA1 urbanization proxy (.)= constant model, AIC= Akaike Information Criterion the conditional probability of the model, cum. AIC weight= cumulative model weights Discussion Our objectives were to establish a long term urban wildlife monitoring program in Denver, CO that would contribute to the international Urban Wildlife Information Network and Figure 11. Eastern cottontail r abbit occupancy model estimates with 95% confidence interval in gray. Occupancy model estimates showing a slight decrease in probability of eastern cottontail site use from (A) urbanization (AICw=0.2), and no changes in probability from (B) longitude (AICw =0.19), and (C) habitat management type (AICw=0.08) . The constant (.) model performed the best (AICw=0.53). A B

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28 to prese nt the preliminary data that were collected over four seasons in 2017. In the 40 sites we established, we detecte d 11 wild mammalian species. In addition, we ran sing le season occupancy models to account for imperfect detection, and to see if any general site level characteristics would show tren ds in site use probabilities the five species abundantly detected . R esults from the coyote site use models are con sistent wit h previous studies with regards to having higher probability of use in areas with natural habitat (Gehrt et al., 2009). In addition, the top model suggests that coyotes have a higher probability of using areas that are further west along the transect. Whil e we used longitude as a proxy of general potential natural habitat (e.g. shrub/forest towards the west, and prairie grassland towards the east), there could be other variables, or combination of variables, that vary along the longitudinal gradient that co uld be responsible for the estimates, and should be looked into further, such as actual vegetation/habitat types and structure, connectivity of the urban matrix, prey availability, etc. Unexpectedly, the urbanization model was not a good predictor for use, as has been seen in other urban areas (Gehrt et al., 2009; Lesmeister et al., 2015) . None of our generalized urbanization and habitat covariate models performed well for eastern cottontail rabbit, raccoon, or red fox . For these species the constant (.) mod el had the lowest AIC. However , competing covariate model estimates showed a slight increase in probability of use from urbanization and longitude for red fox, which was consistent with our hypotheses, and a slight decrease in probability of use for easter n cottontail rabbit in more urban areas and no changes due to longitude or habitat management type, which was unexpected. Raccoon models showed consistently high probability of use regardless of any of the covariates. Mule deer models had

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29 top models that w ere consistent with our hypotheses, showing a decreased probability of use in urban areas, and higher probability in naturally managed habitats. We acknowledge that these are rudimentary models, with generalized proxies for urbanization and habitat charac teristics, and in reality, there are multiple different habitat, clim atic , community, and urbanization factors, and combinations of factors, that are affecting site use. However, the models do show general site use probabilities for each species, allowing for the development of more detailed hypotheses, characterization of sites, and random sampling along the variability of covariates hypothesized. As the Denver UWIN dataset becomes more robust, we would like to see how well these model estimates perform , a dd in seasonal and yearly covariates, and consider polynomial models in addition to the linear models to look at potential intermediate effects . In addition, there are multiple factors not addressed in this study that could be warranted for future research . One example would be to characterize sites more specifically, such as measuring more detailed habitat characteristics, like vegetation inventories with classifying native vs non native species along with habitat structure and density. This may help disce rn more exact habitat use patterns for each species . F or example , in previous studies eastern cottontail rabbits used urban areas with more shrub cover areas (Baker et al., 2015) . Perhaps in Denv er, our urban park s do not supply this habitat type and structure in comparison to other cities. In addition, it would be interesti ng to study species interaction effects, as studies have shown that occupancy/use can be affected by competition, avoidance, prey availabilit y, human activity, and/or harassment by domestic species (Armstrong et al., 2010; Lesmeister et al., 2015; Miller et al., 2001) . Looking at our preliminary data, it is intere sting that, generally,

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30 coyotes are seen more in natural areas in the west, and red f ox are seen more in the urban areas in the east. It would be inter esting to explore if the east and west transects are different in terms of habitat, prey availability , pro ximity to anthropogenic feature, and /or avoidance of coyotes by red fox that has been suggested (Black et al., 2018; Gosselink et al., 2003; Lesmeister et al., 2015) . Also, it may be in teresting to try to find out if there are any manag ement strategies of specific species , as it is currently unknown if any population reduction or c onservation efforts are underway along the transect s . Another factor that should be acknowledged is connectivity of the urban matrix along with the availabil ity and quality of habitat patches , which has also been shown to influence use (Baguette & Van Dyck, 2007; Beninde, Veith, & Hochkirch, 2015; Gese, Morey, & Gehrt, 2012; Rudd, Va la, & Schaefer, 2002) . Continued monitoring of the se species will be important to contribute to scientific knowledge of urban wildlife spatial and temporal dynamics. Understanding how wildlife are using these areas could be very beneficial for conservati on efforts with city planning and development, especially considering how fast Denver is growing. In addition, monitoring can be useful in managing wildlife communities. S pecies activity patterns , interaction effects, and disease dynamics information can b e useful when managing wildlife communities. Monitoring wildlife in urban areas that have the potential to spread disease could be very important , especially since increased disease prevalence and transmission are seen in urban areas (Gottdenker et al., 2014) . M any of the species we detected in urban Denver can be affected by disease, which may be important in spatial or temporal changes in detection and occurrence of the species, along with bein g a health concern for transmittable pathogens to other species, including humans, pets, and livestock ( Armstrong, Fitzgerald, & Meaney, 2010; Catalano et al.,

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31 2008; Malmlov, Breck, Fry, & Duncan, 2014) . One example is coyote susceptibility to and being hosts of zoonotic bacterial, viral , and parasitic diseases such as Toxoplasmosis gondi bacteria, rabies virus, and Echinococcus multiocularis tapeworm to name a few examples (Armstrong et al., 2010; Catalano et al., 2008; Malmlov et al., 2014) . In addition, coyotes could spread diseases that could be transmitted to other species , including domestic dogs , su ch as dis temper and parvo viruses, and many parasites (Kapil & Yeary, 2011; Malmlov et al., 2014) . Coyotes are not the only urban species that can harbor and spread zoonotic disease, examples include raccoons with rabies virus and raccoon roundworm ( Baylisascaris procyonis ) or eastern cotton tail rabbits and rodents with tularemia (Kazacos, 2010) . Overall, we have established a long term monitoring protocol that is successful at detecting many species in the Denver metro area. As we have discu ssed, the data presented here were collected over a short time span, which is insufficient when trying to make inferences about ecological processes, but it does provide wildlife. Our p reliminary data have shown some potential spatial and site use trends that can be used to c reate more specific hypotheses and complimentary methods for testing them. In addition, this data will contribute to the international Urban Wildlife Information Network (UWIN) monitoring program that can be used to compare urban wildlife patterns among an d across cities.

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32 CHAPTER II DEVELOPMENT AND ASSE SSMENT OF AN URBAN W ILDLIFE ECOLOGY COUR SE BASED UNDERGRADUATE RESEAR CH EXPERIENCE IN AN INTRODUCTORY BIOLOGY LABORATORY Introduction T here has been a national push to increase authentic research experienc es for undergraduate students due to the positive outcomes realized , such as increased understanding of the scientific process, increased retention in STEM programs, and the achievement of many nationally recommended competencies for biology students (AAAS, 2011; NRC, 2003; PCAST, 2012) . By having an engaging and authentic research experience, students they still are better equipped to evaluate scientific claims in everyday life by better understanding the scientific process (AAAS, 2011) . N ational directives have suggested that undergradu ate biology students should master a set of core competencies and learning outcomes (summarized in Table 1 below ) b y the time they g raduate and that authentic re search experiences could help students achieve those desired outcomes . In response t o these directives, there has been an influx of Course based Undergraduate Research Experience (CURE) curriculums ( a few examp les: Campbell et al., 2007; Caruso, Sandoz, & Kelsey, 2009; Jordan et al., 2014; Olimpo, Fisher, & Dechenne Peters, 2016; Thompson, Neill, Wiederhoeft, & Cotner, 2016) . T he NSF funded Course based Undergraduate Research Experience Network (CUREnet) defines a CURE as a research experience in an undergraduate course that focuses on the following important aspects: use s scientific practices, results in discovery of new knowledge, investigates relevant work, and involves coll aboration and iteration (summarized in Table 2 , Auchincloss et al., 2014) .

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33 CUREs in the literature have been widely variable in terms of how they are offered to students (elective vs. manditory), when they are implemented i n the students career (first year level to senior level), how much time is committed ( a few weeks to multi semester), and the level of scientific practices used (very few to many ) , how many students are involved (one small class to many large enrollment classes), to name a few. In addition, res earch related to the effectiveness of specific CURE s and what aspects of the CURE experience are most important have been ongoing and widely variable in te rms of what instruments are employed (published vs not published, validated vs not validated, graded assessments vs student reported metrics) , how much data are used (one small class over a partial semester to multiple large enrollment classes over multiple semesters) , and how th e data are analyzed. Even with the wide variability in CURE curriculums and e valuation methods, the overall outcomes of student participation in CUREs are generally positive. For example, CUREs can be particularly influential in the in troductory courses where many studies have shown positive student outcomes such as developing long term understanding of core concepts, increasing interest and motivation, addressing negative perceptions of science, forming an identity as a scientist, helping to form scientific career goals, and acquiring skills like problem solving and critical thinki ng (AAAS, 2011; Auchincloss et al., 2014; M. T. Jones, Barlow, Villarejo, Amy, & Barlow, 2010; Mordacq, Drane, Swarat, & Lo, 2017; NRC, 2009; PCAST, 2012) . In addition, these positive outco mes are disproportionately s een with underrepresented groups (Bangera & Brownell, 2014; Chemers, Zurbriggen, Syed, Goza, & Bearman, 2011; Estrada, Hernandez, & Schultz, 2018; M. T. Jones et al., 2010) . Furthermore, CUREs offer an authentic resea rch experience in an undergraduate course, where many barriers of traditional undergraduate research experiences can be

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34 overcome; such as limited availability of research lab positions, potentially biased acceptance of students, financial and/or personal b arriers, and perceived barriers of interacting wi th a faculty member or lack of k nowledge about cultural norms associated with research (Bangera & Brownell, 2014) . Table 1: Core c ompetencies for biology e ducation from the American Association for the Advancement of Science (A) and the Association of American Colleges and Universities (B). Many of the competencies and outcomes have shared qualities and reiterate the importance of students acquiring these skills during their undergraduate experience. A. Core Competencies for Bio l ogy (Adapted from AAAS, 2011 T able 2.1) AAAS Core Competency Why Important? Demonstration Apply the scientific process Biology is evidence based knowledge through hypothesis generation, observation, and experimentation Practice scientific process to und erstand systems Use quantitative reasoning Biology relies on data analysis and interpretation Apply quantitative analysis to interpret data Use and Interpret modeling and simulation Biology focuses on understanding complex systems and predicting outcom es Use modeling and simulation to describe systems Apply interdisciplinary knowledge Biology incorporates all the fields of sciences Apply concepts from other sciences to interpret systems Communicate and collaborate Biology is a collaborative Collabora te on projects and communicate concepts to others Understand science and society relationship Biology is conducted in a societal context Identify social, historical, and ethical contexts in biological practice B. AAC&U Essential Learning Outcomes (Adapte d from AAC&U, 2007) Essential Learning Outcomes Specific Outcome/Skill Knowledge of cultures and the physical/natural world Study science, math, social science, history, arts, etc. Intellectual and practical Skills Inquiry, analysis, critical thinking, written and oral communication, quantitative literacy, teamwork, and problem solving Personal and social responsibility Civic knowledge and engagement, intercultural knowledge, ethical reasoning Integrative and applied learning Application of knowledge t o new and different problems

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35 Table 2. Important CURE aspects and e xplanations (summarized from Auchincloss et al., 2014) . CURE Aspect Explanation / Importance Use of scientific practices Student authetically practices: s cientific literacy, asking questions, proposing multiple hypotheses, designing experiments, making observations, collecting and analyzing data, constructing and performing models/sim ulations, interpretating data, realizing common variability and failure, and communicating findings Discovery of new knowledge Both student and instructor will not know the results beforehand, can promote informed reasoning and exploration Investigates r elevant work Gives authenticity to the experience and a feeling of connection and contribution to the scientific community Involves collaboration Important and authentic networking with mentors and peers Iteration Repetition, conformation, and extension is how research occurs, can build confidence and project ownership Due to national recommendations to incorporate authentic research experiences, and the positive student outcomes seen from research experiences, we developed an urban wildlif e monitoring CURE for a first year undergraduate biology lab oratory . The context for this CURE monitoring project. UWIN is a growing network of cities that believe in the import ance of urban wildlife monitoring and have the goal of multicity collaboration and long term investigation of (Magle et al., 2018) , see website at http://www. lpzoo.org/conservation science/projects/urban wildlife information network uwin ). The mission of the UWIN is for cities to investigate their specific urban wildlife ecology, and to compare data across cities to investigate widespread patterns in urban wil dlif e. The ultimate goals are to contribute to the scientific knowledge base about urban ecology, promote wildlife conservation, increase public education and engagement, decrease human wildlife conflict, and influence city planning and policy maki ng (Magle et al., 2018) . The UWIN monitoring protocol requires that motion activated camera trap transects be set up over an

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36 urbanization gra dient in each participating city, and that data are collected daily over one month in each season. As with any long term monitoring network, the data generated can be used to look for patterns within and across sites and can be used to answer current quest ions and questions yet to be conceived (Gitzen et al., 2012; Knapp et al., 2012) . The st udy of urban ecosystems has been relatively new in terms of ecological research (Adams, 2005) . With the rapid expansion of urban areas, the effects of urbanization can be lagging and evolving (Ramalho & Hobbs, 2011) . Therefore, it will be important to monitor urbanization effects and study urban ecosystems in expanding cities over t ime. Potential research projects that student teams could collaboratively explore would be based on an urbanization related variable (li ke human population density or noise pollution) and how that might impact a wildlife variable (like mammalian species richness or site use by a specific species). Because this project requires long term seasonal wildlife monitoring, results in a continuall y growing data set to explore, contributes to an authentic international project, and is relevant research being conducted where students are living, we determined that this project was a good candidate to be created into a CURE. The goals of this paper ar e to describe the development and implementation assessment of this CURE, specifically with respect to fidelity to the core elements of a CURE (sensu Auchincloss et al. 2014), student affect related to persistence in STEM, and student competencies related to conducting research. We approached the following research questions using both published, validated surveys and concept inventories, as well as student work samples:

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37 1. To what extent are the important CURE design features (sensu Auchincloss et al. 2014) present in the developed CURE curriculum? 2. How has this CURE influenced student perceptions related to scientific identity and persistence in the sciences? 3. Do students who participate in this CURE achieve targeted scientific process skills and have a bette r understanding of experimental design? These research o bjectives were selected (2015) conceptual model of CURE activities and potential outcomes. For CURE evaluation, t he authors recommend selecting the CURE out comes of interest, to consider the time frame that the expected outcome would be realized, and to explore published examples of evaluation for specific outcomes . For this study, we were interested in the Corwin et al early phase evaluatio n of short to mid term student outcomes . The outcomes of interest to us were the level of sense of having project ownership, students confidence level with scientific skills, a sense of belonging to a scientific community, and the assessment of a ctual scientific process skills (Corwin, Graham, & Dolan, 2015) . In additio n, we were interested in measuring the implementation of important CURE design features (i.e. collaboration, discovery, relevance, and iteration) that are important to attain the above CURE outcomes. Using the backward design approach of CURE evaluation as described by Shortlidge et al. (2016) , we selected specific assessments and instruments to gather data related to our outcomes of interest . The findings from this study are important in understanding to what extent the developed urban wildlife CURE incorp orates important aspects described in published literature and what impacts are seen o n students scientific skills and perceptions.

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38 Methods CURE Development AAAS (2001) recommends that developed curricula include i nteractive, inquiry driven, collaborativ e, and relevant activities i n order to create a more student centered experience with increased gains in student knowledge and skills (Handelsman et al., 2004; Reynolds & Kearns, 20 17) . We developed the urban wildlife CURE curriculum considering the above and by taking a backward design approach. Backward design has three major steps; first to identify desired learning objectives, second to determine acceptable evidence that those o bjectives are being met, and third to plan learning experiences (Wiggins et al. 2001). Backward design has been recommended for CURE development, with the addition of a fourth step of iteration and revision (Cooper, Soneral, & Brownell, 2017) . CURE Learning Objectives Learning objectives for this CURE were created based on the national recommend ations for biology education core competencies (Table 1A), the AAC&U essential learning outcomes (Table 1B), the important CURE aspects (Table 2 ), and from recommendations of the biology department instructors at our university who provided anecdotyl evide nce of deficient student outcomes , particularly with the respect to generating testable and specific research questions, reading and using primary literature, and writing critically and coherently about conducted research . Four general learning goals , each with multiple specific objectives, were selected and are described in Table 4 below. It is important to note that these learning objectives are generalized, and could be used as a starting point for any CURE development.

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39 Table 4. Learning objectives sel ected for the CURE; general objectives are numbered, specific objectives are lettered, and important aspects are highlighted. 1) Apply scientific process through inquiry and analysis AAAS core competency= Apply the process of science AAC&U essential lea rning outcome= Inquiry and analysis CURE aspect= use of scientific practices, discovery, relevance, collaboration, iteration a. Identify and discuss relevance of the topic b. Search and locate relevant primary literature c. Identify, interpret, and ex plain sections of primary literature d. Create, evaluate, and improve specific, relevant, & testable research questions e. Create and carry out relevant data collection protocols f. Analyze and interpret data to form conclusions 2) Use quantitative re asoning AAAS core competency= Ability to use quantitative reasoning AAC&U essential learning outcome= Quantitative Literacy CURE aspect= use of scientific practices, discovery, collaboration, iteration a. Manage and organize large data sets b. Recogni ze and discuss variability in nature and data collection c. Evaluate strengths and weaknesses in data & data collection methods d. Create and interpret appropriate data visualizations (e.g. graphs, tables) 3) Collaborate and communicate AAAS core compe tency= Ability to collaborate and communicate AAC&U essential learning outcome= Oral and written communication, teamwork CURE aspect= use of scientific practices, collaboration a. Use appropriate conventions of organization, content, formatting, and sty le in writing b. Correctly cite high quality, relevant sources to support arguments and statements c. Orally communicate scientific understanding, findings, and conclusions d. Use collaborative technology for data collection and analysis e. Work effic iently and professionally in teams 4) Develop identity as student scientists AAAS core competency= Ability to understand the relationship between science and society AAC&U essential learning outcome= Civic engagement, ethical reasoning a. See science as a social endeavor of value to society b. Take ownership of and find meaning in their work c. Perceive themselves as efficacious d. Begin to realize their potential as a scientist

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40 CURE Implementation Context and Participation The University of Co lorado Denver is a highly diverse urban public research university, with an approximate enrollment o f 10,500 students, comprising 42% students of color, and 25% underrepresented minorities (URM) (African American, Hispanic, American Indian, and Native Paci fic Islander). The Biology Major is the largest in the university and comprises 17% of all undergraduates enrolled in the College of Liberal Arts and Sciences, of which 27% are URM . From 2010 2016, 39% of all enrolled students received a D, F, or withdrew (DFW) from general biology 1, including 44% of the new freshman, and 54% of new URM freshman. The first iteration of this CURE was implemented in the General Biology 2 Laboratory course at the University of Colorado Denver during the spring 2018 semester . The spring 2018 semester included 16 regular sections, and 2 honors sections that were taught by 10 different G raduate Teaching Assistants (G TAs ) . Each section has a maximum of 24 students, resulting in approximately 400 students participating in the CUR E. The majority of our work focused on the Spring 2018 students. However, for research question 3, we also examined term papers from a sample of students from the same course in spring 2017, the semester before the CURE was implemented. CURE Curriculum Dev elopment Once the learning objectives and research context were decided, the student assessments and activities were created and aligned with research logistics in order to comply with the UWIN data collection schedules and protocols. Due to other objectiv es of the lab course, the UWIN CURE was designed to span six 2 ½ hour weekly lab periods of the General Biology 2 lab course; the second of the freshman level general biology series that is required for

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41 biology majors. Key assessments for evidence of stude nt achievement were created and included pre and post lab assignments for each week, an oral presentation, and a culminating research paper. Activities were then designed to be engaging, collaborative, introduce students to real research practices, and to address specific learning objectives. Table 5 shows the basic curriculum outline with the associated learning objectives. In addition to student materials, teaching materials were also created to support a wide range of Graduate Teaching Assistants (GTAs). To alleviate some issues surrounding the difficulty of consistent implementation across multiple sections taught by GTAs with a wide range of experience in teaching and research, a GTA version of the materials was constructed to explain the logistics, rea soning, and intended outcomes of the activities and assessments. In addition, Google presentations with speaker notes, guided worksheets, and online materials were created and weekly GTA training meetings were conducted (see the GTA curricular manual and m aterials in App endix C ). Furthermore, after each week of the CURE, a voluntary GTA google form survey was available to solicit allowed TAs to give feedback direct ly after teaching, when their ideas were fresh. It also served as a useful refer ence for curricular revisions.

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42 Table 5. Basic curriculum outline with associated learning objectives from Table 4 (specific learning objective in parentheses) . Curriculum was designed to have peer and instructor feedback throughout, activities were created to be engaging and collaborative with peers (3e for all), and to introduce students to research with goals of developing scientific identity (4a d for all). Lab 1 In troduction to UWIN and Urban Ecology Introduce and explore urban wildlife photos (1a) Brainstorm urbanization effects on wildlife (1a) How to read primary literature activity (1c) Lab 2 Formalizing your Research Question Discuss methods and assumptio ns (2b, 2c) Assign sites to visit and go over data collection methods (1e) Create, evaluate, and revise research questions with peer interview (1d) Search and locate primary literature, while creating a bibliography (1b,1c, 3d) Students visit sites and collect site level data between Lab 2 and Lab 3 (1e) Lab 3 Collaborative Data Collection, Tagging Practice, and Writing Collaborative GIS data collection and data entry (1e, 3d) Practice tagging photos/species identification (1e) Picture verificatio n activity/ variation discussion (2b, 2c) Outline Title, Introduction, Methods, and References (1a, 1b, 1c, 1d, 3a, 3b) Lab 4 Photo Tagging and Figure Practice Tag photos with species ID, #, details, comments (1e) Data and figure creation activity (2d , 1f) Lab 5 Calculate Photo Data, Figure Creation, and Writing Calculate wildlife data from the photo database (1e, 2a) Create figures for specific research question (1f, 2a, 2d) Outline Results and Discussion (1f, 2b, 2c, 2d, 3a, 3b) Lab 6 Mini Pr esentations Mini presentation of specific topic, results, conclusion (1a, 1f, 2d, 3c, 3d) Finals Week Term paper due (1a, 1b, 1c, 1d, 1e, 1f, 2b, 2c, 2d, 3a, 3b) CURE Assessment Students were offered 2% extra credit to complete both voluntary, in clas s, online pre and post CURE surveys, of which 374 students participated. To increase confidence in the reliability of the student results, we removed surveys from analysis for respondents that took less than 8 minutes on the pre survey, less than 10 minute s on the post survey, and more than 60 minutes for either. These times were a priori chosen based off what we thought was an

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43 appropriate time for just reading the entire survey, and that approximately 30 minutes was given in class for students to complete it. This resulted in a sample size of 251 completed and matched pre and post survey responses. The demographic breakdown of participants and culled participants were similar, so it is assumed that no particular demographic group was singled out during the culling process (see Appendix B1 ). In addition, from students who consented to participate in this research, writing samples were sampled from the fall 2017 semester before the CURE and from the spring 2018 semester after the CURE. The term paper in fall 2 017 was focused on a two lab period inquiry lab about tree species richness, and the term paper in spring 2018 was focused on the six lab period urban wildlife CURE. The fall 2017 semester consisted of eight sections of lab that were taught by four GTAs, r esulting in a total number of consenting participants of 151 students. Due to timing constraints, Fall 2017 students were offered 2% extra credit for completing the only a post survey and consenting to participate in this research. Due to uneven student pa rticipation and differences in GTAs between the two semesters, it was assumed that a truly random sampling procedure of ~50 papers from each semester would not be representative of the student population. Therefore, we sampled one course each from three TA s that taught the course both semesters and scored all the papers within those sections. This resulted in 54 papers from fall 2017 and 60 papers from spring 2018. All research methods were approved by the University of Colorado Denver Colorado Multiple Ins titutional Review Board (COMIRB #17 2148). The assessment instruments described below were given online and in class with the components consolidated into one Qualtrics survey. Portions of the instruments were either given before and after the CURE, or jus t after the CURE, as appropriate for the instrument. As this CURE only spanned six lab

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44 periods, students were asked to consider the questions in terms of the UWIN CURE. In addition to the surveys, writing samples from class assignments were sampled to addr ess research question #3 . Research Question #1: CURE Aspect Implementation To address our first research question a ssessing the extent of CURE aspect implementation , an available and validated post course survey called the Laboratory Course Assessment Surv ey (LCAS) was used (Auchincloss et al., 2014; Corwin, Runyon, Robinson, & Dolan, 2015) . The LCAS consists of Likert scale responses to statements to see to what extent the course incorporated the important CURE aspects of collaboration (6 items) , discovery & relevance (5 items) , and iteration (6 items) . Research Question #2: tant for STEM Retention T o address our second research question about student perceptions of participatio n in research practices and affectual outcomes of the CURE , a p ublished p ost course survey called the Persistence in the Sciences (PITS) was used (Auchincloss et al., 2014; Hanauer, Graham, & Hatfull, 2016) . The PITS survey mea s ures psychological components that correlate with a and consists of Likert scale responses that target: student project ownership (emotion (10 items) and c ontent (6 items)), s elf e fficacy (6 items), science i dentity ( 5 items), scientific community values (4 items), networking (5 items), and i ntent (5 items) .

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45 Research Question #3 : Measures of Impact on Experimental Design Concepts and Scientific Skills To determine if students had shifts in their thinking abo ut experimental design , a pre post 14 multiple choice question concept inventory called the Biological Experimental Design Concept Inventory (BEDCI) was given (Auchincloss et al., 2014; Deane, Nomme, Jeffery, Pollock, & Birol, 2014) . In addition to the BEDCI , specific scientific skills were also assessed through analysis of student writing samples . Student research papers were eva luated using a checklist type rubric that was created by loose adaptation of a universal rubric (Timmermana, Strickland, Johnson, & Paynec, 2011) that was customized to assess our specific scientific process learning objectives . Due to anecdotal evidence from prior in structors that students in lab were not meeting expected scientific literacy skills prior to the CURE development , such as critical ly thinking about data and scientific wr iting , we decided to compare writing samples from before and after the CURE along wit h assessing how students performed after the curriculum . Because of between semesters , a random sample of 50 papers from each semester would not have be en representative of the student population. Th erefore, we decided to sample all the papers from one section each of three GTAs that taught both in the fall 2017 semester and the spring 2018 semester. This resulted in 54 papers from fall 2017, and 60 papers from spring 2018. After the sampled papers we re deidentified, t wo coders independently coded a subsample of papers using the first iteration of the rubric. The rubric was refined through discussion and editing to increase inter r a ter reliability to 90% and to assess applicable learning objectives wit h ease (complete rubric and scoring can be seen in Appendix B 6 ) .

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46 mportance of the study is clearly stated reasons for Our goal in using a highly partitio ned checklist type rubr ic was to allow for analysis of very specific aspects of scientific practices that are assessable through writing samples . Results and Discussion Research Question #1: CURE Aspects Successfully Implemented To evaluate if the developed CURE was successful a t implementing important CURE aspects, the Laboratory Course Assessment Survey (LCAS) was given to students after the Spring 2018 semester. As shown in Figure 1, the combined responses in each category of the survey were positive (see specific items with r esponses in Appendix B2 ). Figure 1: Combined student responses to the Laboratory Course Assessment Survey (LCAS) Categories n=251. Shows positive implementation of CURE as pects: combined responses to six Discovery & Relevance items (91.2%) and six Iteration items (80.8%). Specific survey items and responses shown in appendix table B2.

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47 The six collaboration items asked students how often ( i.e., never, 1 2 times, monthly, weekly, NA) they were encouraged to collaborate in different ways . Students most often selected weekly collaboration item s , w and collaboratively designed lab course we would expect for students to perceive that they have collaborated with others every week, there are items in the survey that were not designed re sponse rates at 64.1% and 50.2% respectively. The five discovery and relevance items asked to what degree students agreed or disagreed with various research expectations that were asked of them. This category had the highest combined positive response at 9 1.2%, with the response rate of somewhat agree, agree, and strongly agree at 16.9%, 43.3%, and 29.4% respectively. Four of the five items scored a ~90% agreement or higher, the highest showing that 97.6% of the students agreed that they were expected to fo rmulate their own research question to guide an investigation. The lowest combined agreement was still very positive at 77.3% for the item asking students if they were expected to generate novel results that would be of broader scientific interest. The six iteration items asked students to what degree they agreed with having time to revise or repeat certain aspects of their investigation and showed a combined agreement of 80.8%. The highest agreement item was about sharing and comparing data with others at 93.6%, with the lowest at 69.4% agreeing that there was time for changing the methods if needed. Overall, we are pleased that our developed CURE has hit targeted goals

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48 of implementing the important CURE aspects of collaboration, discovery, relevance, and i teration. Research Question #2: Retention To assess how the urban wildlife CURE implementation influenced outcomes related to our learning objectives of applying scient ific processes and developing scientific identity that also have been shown to be related to STEM retention ( Hanauer, Graham, & Hatfull, 2016) , the Persistence in the Sciences (PITS) survey was given at the end of the spring 2018 semester . The survey consi sts of seven different categories that assess how students felt about their research project and its execution, how they connect with science in general, and their level of intent ion to stay in a scientific field. As shown in Figure 2, results show positiv e responses when compared to neutral or negative responses. There was positive affinity for community values (90%), and combined agreement and agree) for the six ownership content items (55.6%), ten ownership emotion item s (47.1%), six self efficacy items (87.8%), four science identity items (56.7%), five networking items (48.1%), and five intent items (61.1%) (for specific items and responses see Appendix B3 ).

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49 The community values category had the highest positive resu lts, with 90% of students on average feeling an affinity to scientific community values of research importance and excitement. This is encouraging that students are seeing science as a value to society and shows learning objective is being met. However, these results cannot be assumed to be solely due to the curriculum, as the survey does not distinguish when and how these feelings were acquired. The ownership content category had ten items that assessed how well students felt ownership and connection to their research project. This category had a wide variation among the items ranging from 37.1% to 70.9% agreement. The low agreement items were related to having personal reasons for the research (37.1%) and feeling excitement about the project (47.8%). These findings are not overly surprising because the research context needed to be highly structured for the high enrollment lab. While students Figure 2: Combined student responses to the PITS survey categories post CURE SP18, n=251. Shows positive responses for potential STEM retention: positive identity for Community Values (90%), and combined agreement for the six Ownership Content items (55.6 %), ten Ownership Emotion items (47.1%), six Self Efficacy items (87.8%), four Science Identity items (56.7%), five Networking items (48.1%), and five Intent items (61.1%).

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50 were able to create and execute their own project, it was limited to the urban wildlife monitoring topic and methods, and many students seemed more interested in health related fields (from anecdotal conversations). The higher agreement items included finding the research interesting (70.9%), having to overcome challenges (66. 4%), and feeling responsible for the research outcomes (70.6%). This shows that the even though students may have not have been overly excited about the project, they still had ownership of their research, which was a goal within our scientific identity le arning objectives. In addition to the lower excitement result, the entire category for ownership emotion had the lowest combined score of 47% for agreement to feeling certain emotions during the project. The lowest agreement emotions were astonished (30.3% ), joyful (43.1%), and delighted (45.8%), whereas the highest agreement emotions were happy (60.4%), surprised (52.2 %), and amazed (49.8%). While the emotional disagree with them either (average disagreement rate was 13.5 %) . The self efficacy category had six items that asked students about their confidence in performing scientific skills and had a high agreement average of 87.8%. Approximately 80% or higher agr eement rate for each item was seen, with the highest score being that students were confident in being able to create a testable research question (92.8%). As these items are scored on self perceived basis, we will compare a few applicable items with stude nts writing samples to see how well students thought they did, to how they actually did, in their written work. The science identity items asked students to what level they agreed with thinking of themselves as scientists. The highest agreement items were feeling like they belong in the field of science (69.7%) and deriving great personal satisfaction from working on a team doing important research (65%). Again, it is hard

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51 to know if these positive results are a direct impact from the CURE, but it is encour aging that a majority of the students feel like they belong and can be satisfied with scientific work. With these results, however, it was surprising to see that the lowest agreement score item s were having a strong sense of belonging to the community of s cientists (46.6%) and thinking of themselves as scientists (43.9%). This may be attributed to not having a societal/community component to the CURE. If students were to present their work to members of community (schools, land managers, etc.), they may not only increase their sense of scientific identity, but also increase levels of project ownership as well. The networking category had various items related to students discussing their research with others, either personally or professionally. This categor y showed high variability in agreement responses, with the lowest items being discussing their research with other professors (23.5%) and students not at their university (38.3%). Even though at first glance the average agreement rate for the category was only 48.1%, what was encouraging was the higher response rate items, with students saying they discussed their project with their friends (70.9%), and other students not in their class (57.8%). The last PITS category had five items asking about ntentions about various levels of future scientific endeavors, from taking similar classes to becoming a research scientist. The highest agreement rates indicate a high level of intent to graduate with a science degree (82.9%) and to continue with a scienc e related graduate program (71.7%). Overall, the results of the PITS s urvey were positive and suggest that all of our developing scientific identity learning objectives for the course are being met (Table 4, #4a d) . In addition, we compared our first imple mentation data to available published data that compared PITS responses of traditional lab courses to the established SEA PHAGES CURE

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52 (Hanauer et al., 2017) . As shown in T able 6, our overall averaged results fall in between the two. This is encouraging that our results are overall bett er than traditional courses, and it is not overly surprising that the results from our first implementation and iteration of the UWIN CURE would have results not as positive as the SEA PHAGES CURE, as it has been implemented since 2008, has instructor trai ning programs, and has over 100 institutions participating ( Hanauer et al., 2017). Table 6. PITS category results compared to traditional labs and an established CURE. Likert scale data was converted to a 1 to 6 scale for Community Values (responses from n ot like me at all to very much like me), and 1 to 5 for the others (responses from strongly disagree to strongly agree) and calculated for mean and (standard deviation). Published data from Hanauer et al., 2017 Table S5. PITS category Traditional Labs (H anauer 2017 data) n=1263 Post UWIN CURE (Spring 2018 data) n=251 SEA PHAGES CURE (Hanauer 2017 data) n=1587 Ownership Content 3.40 (0.02) 3.50 (0.91) 3.96 (0.03) Ownership Emotion 3.32 (0.04) 3.38 (0.86) 3.82 (0.03) Self Efficacy 3.99 (0.07) 4.10 (0.64) 4.12 (0.03) Science Identity 3.47 (0.03) 3.56 (0.89) 3.90 (0.04) Networking 3.03 (0.03) 3.10 (1.17) 3.74 (0.05) Community Values 4.76 (0.03) 4.85 (0.99) 5.13 (0.05) It is important to note that this is not entirely the best comparison for our data, a s the Hanauer (2017) data has a much higher sample size and has been taken from multiple institutions. Since our data has a sma ller sample size there is a much higher variation (standard deviation) around the mean. In addition, for our purposes of describi ng these student affectual outcomes, it may be better to compare frequencies of specific items and categories instead of means and standard deviation for Likert scale data (Sullivan & Artino, 2013) . This would better compare the response distribution of the data sets, and would be interesting to see the direct comparisons of each item , especially due to the variat ion seen within some of our categories .

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53 Albeit, it is encouraging to see our average results higher than published data on traditional lab cours es. Research Question #3: Increased Scientific Skills with Minimal Change in Experimental Design Conceptual Thinking In order to assess how the CURE impacted scientific skills, two measures were assessed: the Biological Experimental Design Concep t Inventory (BEDCI) and scoring students writing samples of a research paper. The BEDCI has 14 multiple choice questions about experimental design within 8 different categories related to controls, hypotheses, limiting biological variation, accuracy affect ing conclusions, controlling extraneous factors, independent and random sampling, and the purpose of doing experim ents with the goal of identifying areas of experimental design conceptual deficiencies and to assess learning gains after a course (Dean e et al., 2014) . Our results showed no overall difference in pre to post scores with a pre score average and standard deviation of 6.77 (2.3) and the post score at 6.67 (2.3) out of the 14 available points , with normalized gains of 0.014, and effect size of 0.046 (see pre to post overall score distributions in Appendix B4 ). However, when examining matched pre to post ch anges of each student, there were 42% of students with improved scores, 17% with no change, and 41% with decreased scores (see Figure 3) .

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54 Of the decreased scores, 7.2% of students had a 4 to 7 point decrease that likely affected the post average negatively. When scores drop several points from pre to post testing, it is generally thought that there was poor effort during the post test, a ssuming there was no other extraneous explanation, such as cognitive fatigue or injury, or just random answering (National Evaluation and Technical Assistanc e Center, 2006) . The same could be true concerning high increases in score as well if students exerted low effort on the pre test and high effort on the post test. Because of this, there should be some caution when interpreting the BEDCI results. To see h ow students scored on individual questions from pre to post, each question was grouped according to the designed category (see Figure 4). Overall, most questions did not show any change from pre to post, with one question about biological variation having a significant positive change, and two questions, one about accuracy and one about independent sampling, showing a significant negative change. Figure 3 . Matched pre to post BEDCI scores for spring 2018 students (n=251 ). Sh ows 42% of students with improved scores, 17% with no change, and 41% with decreased scores.

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55 The BEDCI is argued to be cross disciplinary, since the fundamentals of experimental design concepts could b e applied in any field ( Deane et al., 2014) , however, the questions asked in the concept inventory are mostly based on manipulative experiments. The UWIN project has more of a focus on observational ecological methods and data, and some of the pointed misc onceptions targeted in the BEDCI were not explicitly discussed in our CURE. For the first iteration of our CURE assessment, the BEDCI may have not been the optimal instrument to look for pre to post gains in students thinking about experimental design, how ever, it does show where students are deficient in conceptual thinking (such as the extraneous factors and independent sampling categories shown in Figure 4) , which can be explored for future iterations of the curriculum . F igure 4 . BEDCI individual question s pre vs post scores, n=251. Individual questions are shown along with their related concept . Two sample t test showed sign ificant differences (p<0.05) shown in graph with directional difference (plus or minus sign).

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56 In addition to the BEDCI, we scor ed student writing samples to see if students were competent with targeted scientific skills. As discussed in the methods, a checklist type rubric comprised of multip le items for each section of the paper tha t are thought to be important for writing mechanics, scientific literacy, and critically displaying and thinking about data. Overall, out of 60 term papers scored from the spring 2018 semester, 45 out of 54 (89%) o f the scored items were accomplished by 50% or more of the students (see the full rubric and scoring in Appendix B5 ). Some rubric items are directly related to our learning ob jectives and some are consistent with items from the self reported surveys. Table 7 shows the learning objective being assessed, the students self reported agreement with their ability to perform the item, and the associated writing rubric items that were scored in order to assess student science process skills along with comparing it to their self reported assessment. In addition, we were interested in the change from the previous semesters writing samples due to anecdotal evidence that students were deficient in these skills. It is important to note that the research paper from the pr evious semester (fall 2017) was based off of a two lab period , field ecology, inquiry based project where students had specific instructions for what was expected of them, but had a short amount of time to complete their inquiry and write about it. Even wi th the high var iation between the two student projects that the r esearch papers were based on , we did want to see the changes after the CURE. Therefore, along with assessing the post CURE spring 2018 papers, we also show the scores of the fall 2017 papers, and the percent change between the two. For all the learning objectives that we could assess via the research papers, the post

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57 CURE papers achieved the related skills at average levels of 62% or more, with all items having a positive percent change compar ed to the previous semester (see T able 7 and 8). Table 7. Urban wildlife CURE l earning objectives with self reported and writing sample assessment metrics . See specific self reported metric items in Appendix B2 and B3. Learning Objective Self Reported Met ric Question= Agreement Result (%) n=251 Writing Sample Rubric Item (% of students) FA 17 n=54 SP 18 n=60 % change 1d. Create specific, relevant, testable research questions LCAS Discovery/Relevance #3 = 97% PITS Self Efficacy #2 = 92.8% Research question is testable 76% 93% 22.2 Research question is specific 40% 63% 58.3 Research question is relevant 35% 85% 146.1 Average (SD) 50.3% (15.5) 80.3% (22.4) 75.5 (63.7) 1e. Create and carry out relevant data collection pr otocols PITS Self Efficacy #3 = 89% Specific description of how the dependent variables were collected 45% 70% 54.0 Specific description of how the independent variables were collected 58% 60% 3.1 Average (SD) 51.8% (9.2) 65% (7) 28.6 (36) 1f. Ana lyze and interpret data to form conclusions LCAS Discovery/Relevance #4 = 93.6% PITS Self Efficacy #4 = 86.8% All data from the results are meaningfully discussed 28% 70% 152.0 Conclusions are clearly and logically drawn from the data 33% 62% 85.0 Connections between research question, data, and conclusions are logical, consistent, and persuasive 22% 53% 140.0 Average (SD) 27.7% (5.5) 61.7% (8.5) 125.7 (35.7) T he self reported metrics for confidently creating research questions, carryi ng out data collection, and analyzing and interpreting data had higher rates than the closest items scored on the writing sample. It is important to note that the writing rubric sample items are more specific than the self reported metrics and students may have completed the task they are confident in, but they may not have done it well. While self reported skills were not entirely in

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58 line with the term paper scores, we were very pleased that (on average) 60% or more of the samples showed the targeted skill s, and all writing items had a positive change compared to the semester before. These findings were also consistent with additional learning objectives assessed through the writing samples (Table 8). While all of the scientific skills related to learning o bjectives were shown to have 60 80% achievement rates from the writing samples, we are very encouraged that many targeted skills from the CURE had a very high positive change compared to the previous semester. For example, students discussing variability i n data and discussing study design limitations on the ability to draw confident conclusions were up by 260% and 85% respectively .

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59 Table 8. Learning objectives assessed by writing samples. Learning Objective Writing Sample Rubric Item (% of students) F A 17 n=54 SP 18 n=60 % change 1a. Discuss the relevance of the research topic The importance of the study is clearly stated 44% 80% 83.3 Includes an argument as to how the research will 20% 62% 208.3 Average (SD) 3 2% (17) 71% (13) 145.8 (88.4) 2b. Discuss variability in data Discussed variability in the data 19% 67% 260.0 2c. Evaluate strengths and weaknesses in data & data collection methods Important limitations of the study design and data are correctly discuss ed and linked to the ability to draw confident conclusions 33% 62% 85.0 2d. Create and interpret appropriate data visualizations Includes a table or figure that addresses the research question 63% 90% 42.9 Does NOT Include a table or figure that are NOT necessary or do not address the research question 20% 50% 49.0 Data/analysis appear error free 69% 83% 21.6 Data presented is easy to draw conclusions from 22% 60% 170.0 Average (SD) 44% (26) 71% (18.8) 70.8 (67.1) 3a. Use appropriate co nventions of organization, content, formatting, and style in writing Overall, writing is concise and focused 59% 65% 9.7 Appropriate organization and formatting used 81% 92% 12.5 Average (SD) 70% (15.5) 79% (19) 11.1 (2) 3b. Correctly cite high qualit y, relevant sources Formatting of citations is consistent and correct 54% 87% 61.4 Number of relevant primary literature citations (Average (SD)) 0.51 (0.9) 1.87 (1.8) 266.0

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60 Summary and Conclusions Considerations about developing , implementing, a nd assessing a CURE When first considering developing a CURE, it is important to think about potential barriers to success, such as department and faculty buy in, time investment (for development and face to face time with students), logistics for the proj ect and how it will fit into pre determined class times , financial constraints for research supplies, comfort level with dealing with uncertainty in the classroom (and frustrations with uncertainty from students), and scaling up issues, including varying r esearch and teaching experience of instructors (Shortlidge, Bangera, & Brownell, 2015; Spell, Guinan, Miller, & Beck, 2014) . In addition, Kloser et al. (2011) gives six guidelines for implementing research into coursework that include: (1 ) minimal technical expertise nee ded for data collection, (2) student mistakes will not compromise research quality by establishing double checks, (3) a diverse set of variables that present many choices for students to investiga te, (4) a central database into which students can upload and access data , (5) assessment measures that are repre sentative of real world science (written and oral communication), and (6) involve ment of instructors with exper tise in the study system . The urban wildlife CURE meets the se guidelines by (1) having low technology camera set up, site level observations, and basic computer skills needed for data collection, (2) incorporating multiple picture tagging verification as a data double check, (3) having many investigable variables such as single species, multi species, habitat area, habitat type, building density, etc., (4) having access to a cent ralized database continuously updated data, (5) having multiple assessment measures through term papers, presentations, and homework, and (6) having a research PI in charge of the overall research and supplying instructors with ample materials that

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61 can result in expert like involvement. In addition, to try to alleviate issues with varying GTA experience with research and teaching, a teaching manual and supporting materials were created to highlight evidence based reasoning for each activity, attempting to increase GTA buy in, standardize the level of content and pedagogical knowledge, and achieve equity across all sections of the course . Movi ng forward, we are intending on expanding the scope of this curriculum to become a collaborative prog ram that involves multiple institutions that are already participating (and those interested in participating) in the Urban Wildlife Information Network (U WIN) . This collaboration could have the potential to create a central support system for participating instructors that could include training sessions, lesson development and iteration, shared curriculums, technical support, and scientific expertise, all of which has been shown to decrease CURE implementation barriers and increase success (such as GCAT (Campbell et al., 2007) , and SEA PHAGES (Jordan et al., 2014; Lopatto et al., 2014) . In addition, CURE a ssessment strategies could be mor e robust and move toward assessing what aspects of the CURE are more effective at achieving learning and affectual goals , and short to long term outcomes , by collaboratively creating appropriate situated assessments for our specific urban ecology context (Corwin, Runyon, et al., 2015; Shortlidge & Brownell, 2016) . Successful Development and Implementation of an Urban Wildlife CURE This urban wildlife CURE was d eveloped using backwards design with emphasis on incorporating published national directives for biology education and important CURE aspects. Our CURE integrates a local, relevant, and authentic urban wildlife monitoring project into the classroom, where students learn authentic scientific process skills by participating in real research . Shown through the Laboratory Course Assessment S urvey our CURE has successfully

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62 incorporated aspects of collaboration, discovery, relevance, and iteration . There were als o positive affectual gains seen through the Persistence in the Sciences Survey, showing achievement of our science identity learning objectives and high likelihood of student retention in a scientific field. In addition, there were increases in scientific process skills seen through student writing samples. Overall, this CURE has contributed data to a national research collaborative program (the Urban Wildlife Information Network), has introduced students to authentic research processes, and has accomplishe d many, if not all, of our learning objectives. Limitations and Ideas for Revision In most research projects , scientists will first find an interesting objective, and fo rm specific methods to address that objective. Because this project is formed around a long term wildlife monitoring context and the logistical process of obtaining permissions to set up cameras, the methods of camera location s areas are set. While students do not get to research and decide on appropriate methods for their interests, we feel that students still get the benefit of discussing the pros and cons of the study design. In addition, having students create different methods would not be feasible because of the time allotted for the CURE, and the logistical issues with a high enrollmen t laboratory course. This CURE is logistically complex, with setting up and collecting cameras seasonally across the city, dealing with SD cards with tens of thousands of photos, and a Microsoft Access photo database, however those who are already particip ating in UWIN have already dealt with these complexities with the support of the network. As for the curriculum itself, it could easily be expanded to a full semester project, having extended emphasis in some areas, such as reading primary literature and b ibliography creation, and adding in ad ditional modules related to other aspects of urban ecology or the

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63 scientific process . Some ideas that did not make it in this first version of the CURE included having multiple iterative peer review assignments of thei r writing drafts and figure creations , a deeper discussion / activity about the ite rative process of their project (the future directions section of their paper ) , and a societal outreach component, where students would present the i r research somewhere in th e community.

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64 REFERENCES AAAS. (2011). Vision and change in undergraduate biology education: a call to action . https://doi.org/10.1002/(SICI)1098 2736(200004)37:4<295::AID TEA2>3.0.CO;2 2 Adams, L. W. (2005). Urban wildlife ecology and conservation: A brief history of the discipline. Urban Ecosystems , 8 (2 SPEC. ISS.), 139 156. https://doi.org/10.1007/s11252 005 4377 7 Armstrong, D., Fitzgerald, J., & Meaney, C. (2010). Mammals of Colorado (Second). Uni versity Press of Colorado. E. L. (2014). Assessment of course based undergraduate research experiences: Ameeting report. CBE Life Sciences Education , 13 (1) , 29 40. https://doi.org/10.1187/cbe.14 01 0004 Baguette, M., & Van Dyck, H. (2007). Landscape connectivity and animal behavior: Functional grain as a key determinant for dispersal. Landscape Ecology , 22 (8), 1117 1129. https://doi.org/10.1007/s10980 007 91 08 4 Bailey, L., & Adams, M. (2005). Occupancy Models to Study Wildlife. USGS Fact Sheet , (September), 6. Retrieved from http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Occupancy+Models+to+ Study+Wildlife#4 Baker, M., Emerson, S., & Brown, J. (2015). Foraging and habitat use of eastern cottontails (Sylvilagus floridanus) in an urban landscape. Urban Ecosystems , 18 (3), 977 987. https://doi.org/10.1007/s11252 015 0463 7 Bangera, G., & Brownell, S. E. (2014). Course based undergraduate research ex periences can make scientific research more inclusive. CBE Life Sciences Education , 13 (4), 602 606. https://doi.org/10.1187/cbe.14 06 0099 Beninde, J., Veith, M., & Hochkirch, A. (2015). Biodiversity in cities needs space: A meta analysis of factors determ ining intra urban biodiversity variation. Ecology Letters , 18 (6), 581 592. https://doi.org/10.1111/ele.12427 Black, K. M., Preckler Quisquater, S., Batter, T. J., Anderson, S., & Sacks, B. N. (2018). Occupancy, habitat, and abundance of the Sacramento Vall ey red fox. The Journal of Wildlife Management . https://doi.org/10.1002/jwmg.21556 Breck, S. W., Poessel, S. A., & Bonnell, M. A. (2017). Evaluating lethal and nonlethal management options for urban coyotes. Human Wildlife Interactions , 11 (2), 133 145.

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67 Hanauer, D. I., Graham, M. J., & Hatfull, G. F. (2016). A measure of college student persistence in the sciences (PITS). CBE Life Sciences Education , 15 (4), 1 10. http s://doi.org/10.1187/cbe.15 09 0185 Handelsman, J., Ebert (2004). Scientific Teaching. Science , 304 , 521 522. Haverland, M. B., & Veech, J. A. (2017). Examining the occurrence of mammal spe cies in natural areas within a rapidly urbanizing region of Texas, USA. Landscape and Urban Planning , 157 , 221 230. https://doi.org/10.1016/j.landurbplan.2016.06.001 ). Completion of the 2011 National Land Cover Database for the conterminous United States Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing , 81 (5), 345 354. Retrieved from https://www.mrlc.gov/nlcd11_dat a.php%0D Ivan, J. S., & Newkirk, E. S. (2016). Cpw Photo Warehouse: A custom database to facilitate archiving, identifying, summarizing and managing photo data collected from camera traps. Methods in Ecology and Evolution , 7 (4), 499 504. https://doi.org/10 .1111/2041 210X.12503 Jones, C. A., Beane, R. D., & Dickerson, E. A. (2003). Habitat Use by Birds and Mammals Along the Urban South Platte River in Denver, Colorado. Occasional Papers: Museum of Texas Tech University , 221 , 1 16. Jones, M. T., Barlow, A. E. L., Villarejo, M., Amy, ^j E, & Barlow, E. L. (2010). Importance of Undergraduate Research for Minority Persistence and Achievement in Biology. The Journal of Higher Education , 81 (1), 82 115. Retrieved from http://www.jstor.org/stable/27750767 Jordan, T. (2014). A broadly implementable research course for first year undergraduate students. MBio , In Press (1), 1 8. https://doi.org/10.1128/mBio.01051 13.Editor Kapil, S., & Yeary, T. J. (2011). Canine Distemper Spillover in Domestic Dogs from Urban Wildlife. Veterinary Clinics of North America Small Animal Practice , 41 (6), 1069 1086. https://doi.org/10.1016/j.cvsm.2011.08.005 Kazacos, K. (2010). The zoonotic threat of ra bbits and other wild animals. Veterinary Medicine , (July), 296. Retrieved from http://link.galegroup.com/apps/doc/A236556670/ITOF?u=auraria_main&sid=ITOF&xid=23 2f98b5

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68 Kendall, W. L., Hines, J. E., Nichols, J. D., & Campbell, E. H. (2013). Relaxing the cl osure assumption in occupancy model http://www.jstor.org/stable/23436264 Relaxing the closure assumption in occupancy. Ecology , 94 (3), 610 617. Knapp, A. K., Smith, M. D M. (2012). Past, Present, and Future Roles of Long Term Experiments in the LTER Network. BioScience , 62 (4), 377 389. https://doi.org/10.1525/bio.2012.62.4.9 Lesmeister, D. B., Nielsen, C. K., Schauber, E. M., & Hellgren, E. C. (2015). Spatial and temporal structure of a mesocarnivore guild in midwestern north America. Wildlife Monographs , 191 (1), 1 61. https://doi.org/10.1002/wmon.1015 Lopatto, D., Hauser, C., Jones, C. J., Paet (2014). A central support system can facilitate implementation and sustainability of a classroom based undergraduate research experience (CURE) in genomics. CBE Life Sciences Education , 13 (4), 711 723. https://doi.org/10.1187/cbe.13 10 0200 (2007). Who needs environmental monitoring? Frontiers in Ecology and the Environment , 5 (5), 253 260. https: //doi.org/10.1890/1540 9295(2007)5[253:WNEM]2.0.CO;2 Luck, M., & Wu, J. (2002). A gradient analysis of urban landscape pattern: A case study from the Phoenix metropolitan region, Arizona, USA. Landscape Ecology , 17 (4), 327 339. https://doi.org/10.1023/A:10 20512723753 Mackenzie, D. I., Nichols, J. D., Lachman, G. B., Droege, S., Andrew, J., Langtimm, C. A., & Langtimm, C. A. (2002). Estimating Site Occupancy Rates When Detection Probabilities Are Less Than One. Ecology , 83 (8), 2248 2255. MacKenzie, D. I., Ni chols, J. D., Royle, J. A., Pollack, K. H., Bailey, L. L., & Hines, J. E. (2006). Occupancy Estimation and Modeling. Elsevier , (1), 1 324. https://doi.org/10.1017/CBO9781107415324.004 Magle, S. B., & Crooks, K. R. (2009). Investigating the distribution of prairie dogs in an urban landscape. Animal Conservation , 12 (3), 192 203. https://doi.org/10.1111/j.1469 1795.2009.00237.x Advancing Urban Wildlife Research t hrough a Multi City Collaboration. Frontiers in Ecology and the Environment (IN REVIEW) . Magle, S. B., Hunt, V. M., Vernon, M., & Crooks, K. R. (2012). Urban wildlife research: Past, present, and future. Biological Conservation , 155 , 23 32. https://doi.org /10.1016/j.biocon.2012.06.018

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69 Magle, S. B., Lehrer, E. W., & Fidino, M. (2016). Urban mesopredator distribution: Examining the relative effects of landscape and socioeconomic factors. Animal Conservation , 19 (2), 163 175. https://doi.org/10.1111/acv.12231 M agle, S. B., Poessel, S. A., Crooks, K. R., & Breck, S. W. (2014). More dogs less bite: The relationship between human coyote conflict and prairie dog colonies in an urban landscape. Landscape and Urban Planning , 127 , 146 153. https://doi.org/10.1016/j.lan durbplan.2014.04.013 Magle, S. B., Ruell, E. W., Antolin, M. F., & Crooks, K. R. (2010). Population genetic structure of black tailed prairie dogs, a highly interactive species, in fragmented urban habitat. Journal of Mammalogy , 91 (2), 326 335. https://doi .org/10.1644/09 MAMM A 019.1 Magle, S. B., Simoni, L., Lehrer, E. W., & Brown, J. (2014). Urban predator prey association: coyote and deer distributions in the Chicago metropolitan area. Urban Ecosystems , 875 891. https://doi.org/10.1007/s11252 014 0389 5 Magle, S. B., Theobald, D. M., & Crooks, K. R. (2009). A comparison of metrics predicting landscape connectivity for a highly interactive species along an urban gradient in Colorado, USA. Landscape Ecology , 24 (2), 267 280. https://doi.org/10.1007/s10980 00 8 9304 x Malmlov, A., Breck, S., Fry, T., & Duncan, C. (2014). Serologic Survey for Cross Species Pathogens in Urban Coyotes (Canis latrans), Colorado, USA. Journal of Wildlife Diseases , 50 (4), 946 950. https://doi.org/10.7589/2014 03 065 Markovchick Nicho lls, L., Regan, H. M., Deutschman, D. H., Widyanata, A., Martin, B., Noreke, L., & Ann Hunt, T. (2008). Relationships between human disturbance and wildlife land use in urban habitat fragments. Conservation Biology , 22 (1), 99 109. https://doi.org/10.1111/j .1523 1739.2007.00846.x Marzluff, J. M., & Rodewald, A. (2008). Conserving biodiversity in urbanizing areas: Cities and the Environment , 1 (2), 1 28. https://doi.org/papers3://publication/uuid/12FB569F B1A8 41 B2 858E 4A5DA2050C82 Mcdonald, R. I., Kareiva, P., & Forman, R. T. T. (2008). The implications of current and future urbanization for global protected areas and biodiversity conservation. Biological Conservation , 141 (6), 1695 1703. https://doi.org/10.1016/ j.biocon.2008.04.025 McKinney, M. L. (2008). Effects of urbanization on species richness: A review of plants and animals. Urban Ecosystems , 11 (2), 161 176. https://doi.org/10.1007/s11252 007 0045 4 MDEDC. (2018). Denver Metro Growth Overview. Retrieved fro m source: U.S. Census Bureau, Population Estimates Program.%0A

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70 Messmer, T. A. (2009). Human wildlife conflicts: emerging challenges and opportunities. Human Wildlife Conflicts , 3 (1), 10 17. Retrieved from http://digitalcommons.unl.edu/hwi%5Cnhttp://digita lcommons.unl.edu/hwi/24 Miller, S. G., Knight, R. L., & Miller, C. K. (2001). Wildlife Responses to Pedestrians and Dogs. Wildlife Society Bulletin , 29 (1), 124 132. https://doi.org/10.2307/3783988 Mordacq, J. C., Drane, D. L., Swarat, S. L., & Lo, S. M. (2 017). Development of course based undergraduate research experiences using a design based approach. Journal of College Science Teaching , 46 (4), 64 64. Morse, L. K., Powell, R. L., & Sutton, P. C. (2012). Scampering in the city: Examining attitudes toward b lack tailed prairie dogs in Denver, Colorado. Applied Geography , 35 (1 2), 414 421. https://doi.org/10.1016/j.apgeog.2012.09.005 National Evaluation and Technical Assistance Center. (2006). A brief guide to selecting and using pre post assessments, 1 13. Re trieved from https://neglected delinquent.ed.gov/sites/default/files/docs/guide_prepost.pdf Newkirk, E. S. (2016). Colorado Parks and Wildlife Photo Warehouse Software. Retrieved December 12, 2016, from http://cpw.state.co.us/learn/Pages/ResearchMammalsSof tware.aspx NRC. (2003). Bio2010: Transforming Undergraduate Education for Future Research Biologists . National Research Council of The National Academies Press. https://doi.org/10.17226/10497 NRC. (2009). A New Biology for the 21st Century . National Resear ch Council of The National Academies Press. https://doi.org/10.17226/12764 Camera Traps in Animal Ecology, Methods and Analyses (1st ed.). Springer. https://doi.org/10.1007/978 4 431 99495 4_6 Site Occupancy and Detection Probability Parameters for Meso And Large Mammals in a Coastal Ecosystem. The Journal of Wildlife Management , 70 (6), 1625 1 633. https://doi.org/10.2193/0022 541X(2006)70 Olimpo, J. T., Fisher, G. R., & Dechenne Peters, S. E. (2016). Development and evaluation of the tigriopus course knowledge, attitudes, and motivation in a majors introductory biology course. CBE Life Sciences Education , 15 (4), 1 15. https://doi.org/10.1187/cbe.15 11 0228

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71 Vuren, D. H. (2010). Ef fects of urbanization on carnivore species distribution and richness. Source: Journal of Mammalogy , 91 (6), 1322 1331. Retrieved from http://www.jstor.org/stable/40961860 PCAST. (2012). Engage To Excel: Producing One Million Additional College Graduates Wit h Degrees in Science, Technology, Enineering, and Mathematics. Report to the President . Poessel, S. A., Breck, S. W., & Gese, E. M. (2016). Spatial ecology of coyotes in the Denver metropol itan area: Influence of the urban matrix. Journal of Mammalogy , 97 (5), 1414 1427. https://doi.org/10.1093/jmammal/gyw090 Poessel, S. A., Breck, S. W., Teel, T. L., Shwiff, S., Crooks, K. R., & Angeloni, L. (2013). Patterns of Human Coyote Conflicts in the Denver Metropolitan Area Management and Conservation Patterns of Human Coyote Conflicts in the Denver Metropolitan Area. The Journal of Wildlife Management , 77 (2), 297 305. https://doi.org/10.1002/jwmg.454 Ramalho, C. E., & Hobbs, R. J. (2011). Time for a change: dynamic urban ecology. Trends in Ecology & Evolution , 27 , 179 188. https://doi.org/10.1016/j.tree.2011.10.008 Reed, S. E., & Merenlender, A. M. (2008). Quiet, Nonconsumptive Recreation Reduces Protected Area Effectiveness. Conservation Letters , 1 , 146 154. https://doi.org/10.1111/j.1755 263X.2008.00019.x Reynolds, H. L., & Kearns, K. D. (2017). A Planning Tool for Incorporating Backward Design, Active Learning, and Authentic Assessment in the College Classroom. College Teaching , 65 (1), 17 27. RStudi o Team. (2016). RStudio: Integrated Development for R. Boston, MA: RStudio, Inc. Retrieved from http://www.rstudio.com/ Rudd, H., Vala, J., & Schaefer, V. (2002). Importance of backyard habitat in a comprehensive biodiversity conservation strategy: a conne ctivity analysis of urban green spaces. Restoration Ecology , 10 (2), 368 375. Ruliffson, J. A., Haight, R. G., Gobster, P. H., & Homans, F. R. (2003). Metropolitan natural area protection to maximize public access and species representation. Environmental S cience and Policy , 6 (3), 291 299. https://doi.org/10.1016/S1462 9011(03)00038 8 Shortlidge, E. E., Bangera, G., & Brownell, S. E. (2015). Faculty Perspectives on Developing and Teaching Course Based Undergraduate Research Experiences. BioScience , 66 (1), 54 62. https://doi.org/10.1093/biosci/biv167

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72 Shortlidge, E. E., & Brownell, S. E. (2016). How to Assess Your CURE: A Practical Guide for Instructors of Course Based Undergraduate Research Experiences. Journal of Microbiology & Biology Education , 17 (3), 399 408. SILVIS Lab. (2010). U.S. Housing Density (Census Block Level) Shapefile. Retrieved from silvis.forest.wisc.edu/data/housing/block_change%0D Spell, R. M., Guinan, J. A., Miller, K. R., & Beck, C. W. (2014). Redefining authentic research experiences in introductory biology laboratories and barriers to their implementation. CBE Life Sciences Education , 13 (1), 102 110. https://doi.org/10.1187/cbe.13 08 0169 Sullivan, G. M., & Artino, A. R. (2013). Analyzing and Interpreting Data From Likert Type Scales. Jo urnal of Graduate Medical Education , 5 (4), 541 542. https://doi.org/10.4300/JGME 5 4 18 Thompson, S., Neill, C., Wiederhoeft, E., & Cotner, S. (2016). A Model for a Course Based Undergraduate Research Experience (CURE) in a Field Setting. Journal of Microb iology & Biology Education , 17 (3), 469 471. Timmermana, B. E. C., Strickland, D. C., Johnson, R. L., & Paynec, J. R. (2011). Development of a writing. Assessment and Evaluation in Higher Education , 36 (5), 509 547. https://doi.org/10.1080/02602930903540991 U.S. Census Bureau. (2010). Census Urban Classification. Retrieved from https://www.census.gov/geo/reference/ua/urban rural 2010.html U.S. Climate Data. (n.d.). D enver, CO Climate Data (1981 2010). Retrieved from https://www.usclimatedata.com/climate/denver/colorado/united states/usco0105 United Nations. (2018). . Retrieved from https://population.un.org/wup/Publicati ons/Files/WUP2018 KeyFacts.pdf Vernon, M. E., Magle, S. B., Lehrer, E. W., & Bramble, J. E. (2014). Invasive European Buckthorn ( Rhamnus cathartica L.) Association with Mammalian Species Distribution in Natural Areas of the Chicagoland Region, USA. Natura l Areas Journal , 34 (2), 134 143. https://doi.org/10.3375/043.034.0203

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73 APPENDIX A. Chapter 1 Additional Tables and Figures Table A2 . Area of each land use type within a 5 km buffer around the UWIN transect in Denver, CO. Calculated using 2011 NLCD. Land Use Type Area (km 2 ) Total % (out of 480 km 2 ) Developed, Low Intensity 164.0 34.2 Developed, Medium Intensity 88.7 18.5 Developed , Open Space 56.3 11.7 Developed, High Intensity 46.5 9.7 Grassland/Herbaceous 41.8 8.7 Shrub/Scrub 31.8 6.6 Cultivated Crops 23.2 4.8 Evergreen Forest 13.2 2.8 Woody Wetlands 7.4 1.5 Open Water 4.5 0.9 Deciduous Forest 1.3 0.3 Barren Land 0.7 0.1 Pasture/Hay 0.6 0.1 Emergent Herbaceous Wetlands 0.4 0.1 Mixed Forest 0.0 0.0 Table A1. Principle Component (PC) Analysis Loading Results and Variance. PC1 PC2 PC3 Human Population (1km buffer) 0.520 0.8 53 0.032 Light Radiance 0.601 0.393 0.695 Percent Imperviousness (1km buffer) 0.606 0.343 0.718 Percent Variance Explained 78% 17% 5%

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74 APPENDIX B . Chapter 2 Additional Tables and Figures B1. Demographics of Spring 2018 Participants (A) and Culled Participants (B)

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75 B2. Laboratory Course Assessment Survey (LCAS) items and responses.

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76 B3. The Persistence in the Sciences (PITS) su rvey items and student responses. Each section includes the categories Ownership Content (A), Ownership Emotion (B), Self Efficacy (C), Science Identity (D), Networking (E), Intent (F), and Community Values (G).

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77 B3 continued .

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78 B3 C ontinued.

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79 B4. Distribution of BEDCI scores before and after the CURE during the spring 2018 semester (n=251). No difference in pre to post scores on average (standard deviation), pre= 6.77 (2.3), post= 6.67 (2.3). Normalized gains= 0.014, effect size= 0.046. 0 5 10 15 20 25 30 35 40 45 50 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Frequency of Students Score SP18 pre scores SP18 post scores

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80 B5. Writing samples from before and after the CURE scored for targeted scientific literacy skills. Each semester, along with perce nt change from fall 2017 (FA 17) to spring 2018 (SP 18). Green indicates a being negative these items are in bold). Paper Section Specific characteris tic scored FA 17 n=54 SP 18 n=60 Percent change Title Is specific to the research question 47% 87% 83.3 Is specific to research location 33% 32% 3.2 Is informative 18% 37% 101.7 Abstract Includes a compelling reason why this proposed work is imp ortant (from intro) 16% 47% 185.2 Includes a clear description of the main question/objective (from intro) 45% 70% 54.0 Includes a clear summary of the methods/ what was done (from methods) 22% 50% 129.2 Includes a clear synopsis of main findings (from results) 25% 57% 122.6 Includes a clear summary of the conclusions drawn from the results (from discussion) 35% 52% 49.6 Intro: Relevant Background Includes background information that is sufficient to help the reader understand the specific rese arch topic 45% 73% 61.3 Includes background information that is NOT relevant to the topic (if very off topic) 16% 3% 79.6 Intro: Rationale/ Importance The rationale/ importance of the study is clearly stated reasons for doing the research are given 44 % 80% 83.3 Includes an argument as to how doing the research will contribute to the greater picture 20% 62% 208.3 Intro: Research Question Research question/ objective is testable 76% 93% 22.2 Research question/ objective is specific 40% 63% 58.3 Research question/ objective has scientific merit that is consistent with the rationale given 35% 85% 146.1 Methods The methods are described in enough detail that the reader can replicate the approach. 38% 58% 52.8 The methods relate to just the speci fic research question (not everything done in class) 85% 57% 33.7 Necessary details about sample sizes, study area, and time frame given 55% 83% 52.8 Necessary and sufficient description of how the dependent variables (specific to the research quest ion) were measured/collected is included 45% 70% 54.0 Necessary and sufficient description of how the independent variables (specific to the research question) were measured/collected is included 58% 60% 3.1 Correct description of data analysis techn iques given 55% 78% 43.6 Data analysis techniques are appropriate for the research question 76% 82% 6.9 All data presented are consistent with the research question 76% 80% 4.8 Results Text All data are correctly summarized in writing 38% 50% 31.0 All tables/figures are correctly referred to by number in the text 40% 60% 50.0 No interpretations given 85% 88% 3.4

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81 B5 continued. Paper Section Specific characteristic scored FA 17 n=54 SP 18 n=60 Percent change Results Figures and Tables In cludes an appropriate figure and/or table in intro or methods that shows relevant information (i.e., study organism/system, distribution maps, historical data, etc.) in any section 65% 60% 7.4 Includes figure(s) and/or table(s) that addresses the rese arch question 63% 90% 42.9 Data is presented in the correct figure type (scatter plot/bar graph/etc.) 65% 72% 10.6 Includes figures/tables/data that are NOT necessary or do not address the specific research question 80% 50% 37.2 Data / analysis p resented appears error free 69% 83% 21.6 Data presented is easy to draw conclusions from 22% 60% 170.0 Formatting is professional (doesn't mean free of mistakes) (e.g., not hand drawn, appropriate axis scales, no blank areas, cropped nicely, multiple figures are similar in formatting, etc.) 37% 78% 111.5 Has a label, title, and caption. 31% 82% 159.4 Title and caption are informative enough to stand outside of the text 4% 23% 530.0 Has appropriate figure/table components: axes labels, legend s, color matches, error bars, etc. 59% 78% 32.2 Discussion: Interpret ation All data from the results section are meaningfully discussed 28% 70% 152.0 Conclusions are clearly and logically drawn from the data provided in the results section 33% 62% 85. 0 No new data (that is not in the results section) is presented 91% 97% 6.5 Connections between research question, data, and conclusions are logical, consistent, and persuasive 22% 53% 140.0 Discussion: Limitations Important limitations of the study design and data are correctly discussed and are linked to the ability to draw confident conclusions are discussed 33% 75% 125.0 Discussed variability in the data 19% 67% 260.0 Discussion: Assumption s Important assumptions that are made throughout the study are correctly addressed 31% 53% 69.4 The ability to draw confident conclusions are discussed due to assumptions 9% 38% 314.0 Discussion: Future Directions Future directions include how to improve upon the study design to better study the specific research question 52% 58% 12.5 Future directions are plausible 56% 62% 11.0 Includes examples beyond "more samples" 20% 38% 88.2 References and Citations All information is cited in text and the full citation is also present in References section 48% 77% 59.2 Formatting of citations is consistent and correct throughout the document (i.e., CSE format) 54% 87% 61.4 Citation format is correct 35% 72% 103.7 How many primary literature citations? (Average (SD)) 0.51 (0.91) 1.87 (1.77) 266.0 Writing Mechanics Overall, writing is concise and focused 59% 65% 9.7 Appropriate organization and formatting used 81% 92% 12.5 Rate the level spelling or grammatical issues (low score) 57% 50% 12.9

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82 APPENDIX C. UWIN CURE Curriculum UWIN CURE curriculum GTA manual and materials. Original version was created as a living document in Google drives with links to websites, worksheets, literature, etc. Full website links were added and any other documents were adde d within the associated lab week in the manual. Worksheet spacing given for students was removed to save space. Google presentations with notes were added to the end of the manual. This is the first iteration of the curriculum that was used for the CURE as sessment. Starts on the next p age .

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83 UWIN TA Lab Manual Contents Introduction to CURES (TA Manual only) ................................ ................................ ...................... 86 Google Folder Instructions (TA Manual only) ................................ ................................ ............... 90 UWIN Student Lab Manual Front Page ................................ ................................ ........................ 92 UWIN Overview ................................ ................................ ................................ ............................ 93 Basic Outline ................................ ................................ ................................ ................................ . 94 UWIN Week 1: Introduction to UWIN and Urban Ecology ................................ ........................... 95 Goals/Objectives ................................ ................................ ................................ .................... 95 Action Items for Students ................................ ................................ ................................ ...... 95 UWIN 1.1 Introduction to UWIN and Urban Ecology (15 30 min) ................................ ...... 96 UWIN 1.2 Database Activity (20 40 min) ................................ ................................ ........... 100 Database Activity Instructions ................................ ................................ ............................. 102 Database Activity Worksheet ................................ ................................ .............................. 109 UWIN 1.3 Introduction to Research Types and Variables (30 min) ................................ ... 111 UWIN 1.4 Primary Literature Activity (remainder of class period) ................................ .... 112 How to read a scientific paper worksheet ................................ ................................ ........... 114 UWIN Week 2: Methods and Formalizing Research Quest ions ................................ ................. 116 Goals/Objectives ................................ ................................ ................................ .................. 116 Action Items ................................ ................................ ................................ ......................... 116 UWIN 2.1 Methods in Ecological Research (5 10 min) ................................ ...................... 118 UWIN 2.2 Specific UWIN Methods (5 min) ................................ ................................ ........ 121 UWIN 2.3 Site Level Data Collection Planning (15 20 min) ................................ ................ 123 Site Level Data Collection Field Sheet ................................ ................................ ................. 125 UWIN 2.4 Creation and Revision of a Research Question (45 60 min) .............................. 128 Worksheet Research Question Reflection, Peer Intervi ew, and Revision ......................... 130 UWIN 2.5 Camera Trap Assu mptions (30 minutes) ................................ ........................... 133 UWIN 2.6 Finding Rele vant Sources (45 min rest of lab period) ................................ ....... 134

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84 Bi bliography Instructions ................................ ................................ ................................ ..... 136 UWIN Week 3: Collab orative Data Collection, Tagging Practice, and Writing ........................... 138 Goals/Objectives ................................ ................................ ................................ .................. 138 Action Items f or Students ................................ ................................ ................................ .... 138 UWIN 3.1 Geographic Informatio n Systems (GIS) (50 65 min) ................................ .......... 140 GIS Data Collec tion Instructions ................................ ................................ .......................... 143 GIS Site Level Measuremen ts Worksheet ................................ ................................ ........... 149 UWIN 3.2 Data Entry (10 15 mi n) ................................ ................................ ...................... 152 UWIN 3.3 Practice Tagging Photos (30 40 min) ................................ ................................ . 153 Tagging Practice Instructions ................................ ................................ ............................... 154 Tagging Shortcuts ................................ ................................ ................................ ................ 160 UWIN 3.4 Outlining Title, Intro, Methods, and References Sections (45 min rest of lab period) ................................ ................................ ................................ ................................ . 161 Instructions for Autho rs (Writing Guidelines) ................................ ................................ ..... 162 UWIN Term Paper Out line Guide ................................ ................................ ........................ 168 UWIN Week 4: Photo Tagging and F igures Practice ................................ ................................ ... 173 Goals/Objectives ................................ ................................ ................................ .................. 173 Action Items ................................ ................................ ................................ ......................... 173 UWIN 4.1 Tagging New Photos (1.5 hours) ................................ ................................ ........ 174 Tagging Instructions ................................ ................................ ................................ ............. 175 UWIN 4.2 Introduction to Tables and Figures (1 hour rest of lab) ................................ ... 177 Figures Activity Instructions ................................ ................................ ................................ 179 UWIN Week 5: Analyze Photo Data and Create Figures ................................ ............................. 186 Goals/Objectives ................................ ................................ ................................ .................. 186 Action Items for Students ................................ ................................ ................................ .... 186 UWIN 5.1 Analyze photo data and data entry (45 min) ................................ ..................... 187 Database Data Analysis Instructions ................................ ................................ ................... 189 Photo Data Collection Worksheet ................................ ................................ ....................... 193 UWIN 5.2 Analyze the data specific to your research question (rest of lab along with 5.3, 5.4) ................................ ................................ ................................ ................................ ....... 194

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85 UWIN 5.3 Outline Results and Discussion Sections (rest of lab) ................................ ........ 195 UWIN 5.4 Preparation for the mini presentation (rest of lab) ................................ .......... 195 Presentation Instructions ................................ ................................ ................................ .... 197 UWIN Week 6: Last Day/ Presentations ................................ ................................ ..................... 199 Goals/Objectives ................................ ................................ ................................ .................. 199 Action Items ................................ ................................ ................................ ......................... 199 UWIN 6.1 Presentations ................................ ................................ ................................ ..... 199 UWIN 6.2 Voluntary Survey ................................ ................................ ................................ 199 UWIN 6.3 Open Lab ................................ ................................ ................................ ............ 199 UWIN Google Presentations and Notes ................................ ................................ ...................... 200 UWIN Lab 1 ................................ ................................ ................................ .......................... 200 UWIN Lab 2 ................................ ................................ ................................ .......................... 255 UWIN Lab 3 ................................ ................................ ................................ .......................... 286 UWIN Lab 4 ................................ ................................ ................................ .......................... 297

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86 Introduction to CURES (TA Manual only) By Sarah St. Onge What is a CURE? The NSF funded Course based Undergraduate Research Experience Network (CUREnet) defines a CURE as a research experience in an undergraduate course that uses scie ntific practices, results in discovery of new knowledge, investigates relevant work, and involves collaboration and iteration (Auchincloss Corwin et al. 2014). Use of scientific practices is a fairly broad term that includes many aspects of the scientific process, such as scientific literacy, asking questions, proposing multiple hypotheses, designing experiments, making observations, collecting and analyzing data, constructing and performing models/simulations, interpreting data, realizing common variabilit y and failure, and communicating findings. CUREnet realizes that incorporating all these scientific practices in one CURE is unrealistic, but encourages trying to incorporate as many as possible. Discovery of new knowledge means that the both the student and the instructor will not know what the results will be beforehand, promoting informed reasoning and exploration. Investigation of relevant work is important for the authenticity of the research experience and can give students a feeling of connection an d contribution to the broader scientific community around them. Collaboration is important for networking with peers and mentors, and represents how the true scientific community works. Iteration is also important because also represents how science works , many times experiments need to be repeated, confirmed, or built upon, and builds student confidence and project ownership. Why are CUREs useful for promoting learning, skills development and diversity? For many years, there has been a national push to r labs are known to be confirmatory and often are intended illustrate a well known concept. They focus more on how well students c an follow directions, and can gloss over the conceptual and procedural aspects of the experiment (Brownell et al. 2012). CUREs, however, can increase understanding of the scientific process and increase retention in STEM programs. By having an engaging re search experience, students can form or solidify an interest in biological research, life by better understanding the scientific process (AAAS 2011). CUREs can be particularly influential in the introductory courses, where many studies have shown positive student outcomes, such as developing long term understanding of core concepts, increased interest and motivation, addressing negative perceptions of science, f orming an identity as a scientist, help to form scientific career goals, and acquiring skills like problem solving and critical thinking (AAAS 2011; PCAST 2012; NRC 2009; Jones et al. 2010; Auchincloss Corwin et al. 2014; Mordacq et at. 2017). In addition, these positive outcomes are especially seen with underrepresented

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87 minorities (Jones et al. 2010). Undergraduate research experiences are often gateways to graduate and professional schools, but underrepresented students get fewer opportunities to do mento red research as undergraduates. The reasons for this are varied and still being explored. Bangera et al. (2014) argue that underrepresented students have less awareness about research opportunities and the importance of undergraduate research for career ad vancement. There are also perceived barriers to interacting with faculty, financial barriers, personal barriers, and a lack of knowledge about the cultural norms associated with doing science. CUREs have been proposed as introductory research experience th at will help overcome these barriers. National Directives for Undergraduate Biology Education: In 2011, the American Association for the Advancement of Science (AAAS) published the funding from National Science Foundation (NSF), the Howard Hughes Medical Institution (HHMI), and the National Institutes of Health (NIH). In the directives, there are six core competencies that are proposed for all biology graduates to a chieve that relate to the practice and skills in the biology field (Figure 1). Figure 1: Core Competencies for Biology Education (Adapted from AAAS, 2011 table 2.1) AAAS Core Competency Why Important? Demonstration Apply the scientific process Biology is evidence based knowledge through hypothesis generation, observation, and experimentation Practice scientific process to understand systems Use quantitative reasoning Biology relies on data analysis and interpretation Apply quantitative analysis to interp ret data Use and Interpret modeling and simulation Biology focuses on understanding complex systems and predicting outcomes Use modeling and simulation to describe systems Apply interdisciplinary knowledge Biology incorporates all the fields of sciences Apply concepts from other sciences to interpret systems Communicate and Biology is a collaborative Collaborate on projects

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88 Collaborate and communicate concepts to others Understand science and society relationship Biology is conducted in a societal conte xt Identify social, historical, and ethical contexts in biological practice In 2007, the Association of American Colleges and Universities (AAC&U) recommended goals for undergraduate learning outcomes with input from educators, policymakers, and business leaders from across the country (Figure 2). Many of these learning outcomes have shared qualities with the AAAS core competencies, and reiterate the importance of students acquiring these skills during their undergraduate experience. Figure 2: AAC&U Esse ntial Learning Outcomes (AAC&U 2007) AAC&U Essential Learning Outcomes Specific Outcome/Skill Knowledge of cultures and the physical/natural world Study science, math, social science, history, arts, etc. Intellectual and Practical Skills Inquiry, analysi s, critical thinking, written and oral communication, quantitative literacy, teamwork, and problem solving Personal and Social Responsibility Civic knowledge and engagement, intercultural knowledge, ethical reasoning Integrative and Applied Learning Appl ication of knowledge to new and different problems The UWIN CURE curriculum for General Biology 2 Laboratory will incorporate as many of these aspects as possible, were used to create the learning objectives and activities, will be centered around the co ntext of urban wildlife monitoring, and will contribute data to the UWIN. Cited: (AAAS) American Association for the Advancement of Science, 2011. Vision and change in undergraduate biology education: a call to action, Available at: http://oreos.dbs.umt.edu/workshop/sharedfiles/Final_VandC_Draft_Dec1.pdf .

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89 (AAC&U). 2007. Executive Summary: College Learning for the New Global Century. Association of American Colleges and Universities , 1 20. Website: https://www.aacu.org/leap/essential learning outcomes Auchincloss Corwin, L., Laursen, S., Branchaw, J., Eagan, K., Graham, M., Hanauer, D., Lawrie, G., McLinn, C., Pelaez, N., Rowland, S., Towns, M., Trautmann, N., Varma Nelson, P., Weston, T., Dola n, E. 2014. Assessment of course based undergraduate research experiences: A meeting report. CBE Life Sciences Education , 13(1):29 40 Bangera, G., & Brownell, S. E. 2014. Course based undergraduate research experiences can make scientific research more in clusive. CBE Life Sciences Education , 13 (4), 602 606. https://doi.org/10.1187/cbe.14 06 0099 Brownell, S., Kloser, M., Fukami T., Shavelson, R. 2012 Undergraduate Biology Lab Courses: Comparing the Impact of Traditionally Based "Cookbook" and Authentic Re search Based Courses on Student Lab Experiences. Journal of College Science Teaching . 41(4):36 45. Jones, M.T. et al., 2010. Importance of Undergraduate Research for Minority Persistence and Achievement in Biology. The Journal of Higher Education , 81(1):8 2 115. Mordacq, J., Drane, D., Swarat, S., Lo, S. 2017. Development of course based undergraduate research experiences using a design based approach. Journal of College Science Teaching . 46(4):64 (NRC) National Research Council, 2009. A New Biology for th e 21st Century , Available at: http://books.google.com/books?hl=en&lr=&id=QSQJxnO9pJUC&pgis=1 . Excel: Producing One Million Additional College Graduates With Degrees in Science, Technology, Engineering, and Mathematics. Report to the President .

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90 Google Folder Instructions (TA Manual only) Having a Google folder that you will share with students gi ves a central area where you can share documents and spreadsheets with students, and where students can add documents during lab activities so you can check for participation and completion. This also gives students some exposure and practice using a free, widely used sharing platform. 1. First, you should create a Google folder for your lab section (if not already done). a. Name it something appropriate for your section number and semester/year. 2. Within this folder, create a UWIN folder. a. Within the UWIN folde r, you will add weekly folders as appropriate i. Contents given below 3. add the URL within the UWIN overview Canvas Module. a. NOTE: students will need to sign into their Google account to access all the functions of each program (docs, sheets, mymaps, slides, etc.). If they are ntains different folders that you will COPY and then move to YOUR SPECIFIC shared folder with students throughout the labs. Some folders are basically empty placeholders for students to navigate to and add items, others have subfolders and many documents w minute tutorial here https://youtu.be/bmqYR_h_1J c . It works as of 3/17/2018. Maps do not from site information folder, and the UWIN 3 folder (bolded below). 1. UWIN folder (Main folder) a. UWIN 1 Occupancy Sheets folder i. Example sheet with occupancy query results (for a different species) ii. Otherwise, empty placeholder for student pairs to navigate to and add to b. UWIN 2 Bibliography folder i. Example bibliography entry using the p aper we read in UWIN 1 ii. Otherwise, empty placeholder for student pairs to navigate to and add to c. Site Information folder (to be used in UWIN 2 for student site visit planning, and students could reference anytime)

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91 i. Spreadsheet with all the sites= site number , park name, park ID, and GPS coordinates for the camera and parking. ii. Map of all the sites and suggested parking locations iii. Folders 1. Sites 1 20 (west transect) each camera location has a document 2. Sites 21 40 (east transect) each camera location has a docum ent d. UWIN 3 Maps folder i. Example of a google map , with all the layers/measurements students will make ii. Otherwise, empty placeholder for student pairs to navigate to and add to e. UWIN 5 Data i. This is where the verified database and site level data spreadsheet w ill be for students to access for data analysis

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92 UWIN Student Lab Manual Front Page Welcome to the Urban Wildlife Information Network (UWIN) portion of lab! This student d can be quickly navigated to by clicking on the item of interest, then clicking on the link that pops up. a list of all the things you will need to do to prepare and complete the lab for that week. After the front page for each lab, there are subsections that will contain content and activity information that you should read prior to coming to that lab period , and you should reference throughout the lab as needed. In addition to the text in this manual, there will be many links throughout that may direct you to videos, step by step instructions, websites, worksheets, and more, so be aware that you may need to read, reference, and/or print items that are associated with links in this manual. purchase a separate lab manual, however, please print this manual to use during lab. You can print 350 pages (700 sides) free at the library, or the campus computing labs such as the one found in North Classroom. Please speak to one of the lab assistants in the library or computing lab for assistance. More information on how to print and where to print can be found on page 9 the many links used throughout. you an overall sense of the project as a whole. Let your TA know if you have any questions about the project or this student manual.

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93 UWIN Overview The Urban Wildlife Information Network (UWIN) is a newly formed and growing national network of cities th at believe in the importance of urban wildlife monitoring and have the goal of city to city collaboration and long term investigation of our nation's urban ecosystems. The mission of UWIN is for cities to investigate their specific urban wildlife ecology, and to compare data across cities to investigate widespread urban wildlife patterns and differences with goals to investigate wildlife population trends, promote wildlife conservation, increase public education and engagement, decrease human wildlife confl ict, and influence city planning and policy making. As of January 2018, ten cities are participating in UWIN, including us in Denver, CO, with potential for more cities to join. The UWIN monitoring protocol requires that cities set up camera trap monitorin g transects in the same way using a transect that goes through the center of the city and expands out to the rural edge. Along this transect, potential wildlife habitats, such as parks and open spaces are identified and motion activated passive infrared ca meras are placed in these parks for one month, four times a year (seasonally). These cameras use motion and heat to trigger photos to be taken, and we use these photos to see what kinds of wildlife are using these areas, how often they are using them, and when they are using them. In Denver, we place 40 cameras along 40 km of Colfax Avenue that runs east from Golden, CO through downtown Denver to Aurora (more detailed methods will be given later!). The Department of Integrative Biology at the University of Colorado Denver has teamed up with UWIN to accomplish two main goals: 1) provide an authentic research experience for undergraduate students that leads to discovery of previously unknown knowledge, and 2) create a long term urban wildlife monitoring datase t in Denver, CO that can contribute to the mission of UWIN. Students at CU Denver (i.e., you) are in charge of the data collection for Denver. Over the next 6 lab periods, we will go through a typical research process where you will conduct background rese arch on the topic, create a research question that interests you, participate in data collection procedures, analyze data to address your question, think about and discuss what the data means by presenting your project in the form of a results mini present ation and a scientific term paper. Because scientific research is rarely done alone, you will also be expected to work collaboratively with your peers and your TA in a professional manner.

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94 Basic Outline Lab 1 Introduction to UWIN and Urban Ecology 1. Introduce and explore the project 2. Brainstorm urbanization effects on wildlife 3. Primary literature activity Lab 2 Formalizing your Research Question 1. Discuss methods and assumptions 2. Assign sites to visit and go over data collection sheet 3. Create, evaluate, a nd revise research questions with peer interview 4. Search and locate primary literature, and create a bibliography while learning more about urban ecology Students visit sites between Lab 2 and Lab 3 Lab 3 Collaborative Data Collection, Tagging Practice, and Writing 1. Collaborative GIS data collection and data entry 2. Practice tagging photos/species identification 3. Outline Title, Introduction, Methods, and Literature Cited Lab 4 Photo Tagging and Figure Practice 1. Tag photos with species ID, #, details, comment s 2. Data and figures activity Lab 5 Calculate Photo Data, Figure Creation, and Writing 1. Picture verification activity/variation discussion 2. Calculate species totals, species richness, and daily presence of each species 3. Create figures 4. Outline Results and Disc ussion Lab 6 Peer Review and Blast Presentations 1. Blast mini presentation of results 2. Voluntary Survey 3. Open lab with TA Finals Week not meeting 1. Term paper due

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95 UWIN Week 1: Introduction to UWIN and Urban Ecology Goals/Objectives 1. Introduce and explore the UWIN project a. You will learn about the experimental system and become familiar with the pr oject and its relevance to the study of urban ecology. i. Intro to Urban Ecology, Urbanization effects, and UWIN specifics ( 15 25 min ) ii. Muddiest Point (3 5 min) iii. Database Activity (20 40 min) 2. Brainstorm urbanization effects on wildlife a. You will start to determine which ecological variables you would like to study as part of the UWIN research project. i. Discuss independent/dependent variables (20 30 min) 3. Primary literature act ivity a. You will learn strategies to read primary literature, which is an important part in the process of conducting research. i. Reading activity (60 min Rest of period) Action Items for Students 1. Watch the video in the UWIN Overview module in Canvas (https://youtu.be/0qzbAGdCKrM) 2. Read the relevant sections of the lab manual (this document) and the UWIN Overview page in Canvas before lab starts. 3. PRINT , read, sign, and dat e the UWIN Waiver that is located in Canvas (and below). Turn in the UWIN Waiver to your TA during lab during THIS week. 4. Prior to lab, PRINT this article and the associated worksheet , bring both to class. (article= Markovchick Nicols et. Al. 2008. Relationships between human disturbance and wildlife use in urban ha bitat fragments . Conservation Biology . 22:1. 99 109.) 5. Complete the UWIN Week 1 Pre lab quiz in Canvas before lab starts. 6. Make sure that you keep excellent notes throughout the lab period; you will need them for your post lab assignments. 7. Complete the UWIN Week 1 Post lab quiz in Canvas within 3 days of the end of this

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96 MATERIALS: 1) Google Slides Presentation (TA will use this to introduce the project and lead the lesson). 2) Share your sections google folder with students (will use throughout all U WIN labs) TAs URL within a Canvas Module (see Google Folder Instructions above). 3) Thumb drives in key cupboards in each lab (keep locked and key hidden) (should be numb ered 1 12, 1 per pair of students for UWIN 1.2). For today, students will use the Exploration Database. 4) Database Instructions to use in UWIN 1.2 1 per pair of students (LAB COPY for all sections to use (should already be printed) is online as well). 5) D at abase activity worksheet for students (1 per student, this is the ONLY thing you will have to print). 6) Notecards (1 per student) and blank paper. These will be in the lab as they are on the prep sheet. 7) Students should print the reading article AND worksheet LAB COPIES available in the lab (should already be printed). If students want to mark a copy of the article, they need to bring their own. 8) There should be 2 lab laptops with power cords (and ethernet cords) on each main table . UWIN 1.1 Introduction to UWIN and Urban Ecology (15 30 min) 1. Collect the waiver students should have printed it out and signed (not typed) before class (points for completion). 2. og into the lab laptop they will need to access the database. This is because it takes a long time for the a. Make sure that students log out of laptops at the end of the lab period!! 3. TA should use the Google Slides Presentation to Intro duce the topic and specific project details (slide 1 17). a. Use the leading questions (found in the presenter notes per slide) in the google presentation to have engaging discussions with students. Tips to increase

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97 equitable engagement Ask more than one st udent to contribute (e.g., one per table group). Give students time to think before you ask for answers. 4. Muddiest Point (slide 18). a. TA should hand out note cards. Ask students to write down at least 1 question or confusing thing about the project or what i s expected of them on one side. Collect the note cards after activity 1.3 or at the end of the lab. What is Ecology and Urban Ecology? Ecology is a broad field of science that studies the distribution, abundance, and behavior of organisms and their interac tions with each other and the environment. As you can imagine, there are many different interweaving factors that can contribute to ecology as shown in Figure 1, making studying ecological processes very complex. Ecological studies can encompass varying ty pes of research questions at very different scales, from an individual organism to the biosphere and from the smallest microbes to the largest mammals, all while contemplating the biotic and abiotic interactions in between.

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98 Urban ecology is the ecolog y of all organisms ( including humans! ) in urban environments. Urban ecological studies are becoming more prevalent due to the recent rapid conversion of natural areas to urban cities as human population size increases. The 2014 United Nations Urbanization Report, Figure 2 below, shows the current and predicted number of people living in urban areas as human population grows. As more people living in urban areas increase and city growth leads to wildlife habitat loss and fragmented natural areas it will be important to understand how wildlife adapt to urbanization and how we can more effectively design, plan, and manage urban areas to benefit all species that live in cities. Why are urban ecosystems important to you? Urban ecosystems can provide many impor tant ecosystem services that benefit all organisms, including humans. Ecosystem services can be classified into the following four groups: provisioning, supporting, regulating, and cultural services, and are shown in the Figure 3 below. Provisioning servic es are products of the ecosystem, such as water, habitat, plants, animals, medicine, and raw materials. Supporting services are the functions provided by the ecosystems that support all other services such as soil formation, primary production, biodiversit y, and nutrient cycling. Regulating services are the results of natural processes such as flood regulation, pollution regulation, disease and pest regulation, and carbon sequestration. Cultural rom our interaction with the natural environment, such as improved physical and mental health, ecotourism, and cultural heritage

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99 values which can increase monetary value of the area. As you can see, having functional ecosystems, especially in urban areas, provides many benefits for all species living in the area. Why study wildlife? (Besides the fact that wildlife are awesome!) Wildlife and wildlife biodiversity help to maintain functioning ecosystems. For example, coyotes and foxes in a system can pr event overpopulation of rabbits. As a recent example, in the Front Range of Colorado, abundant rabbit populations were associated with an increase in human

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100 and wildlife cases of tularemia, an infectious disease (if interested, read more at http://csu cvmbs .colostate.edu/vdl/Pages/watch for plague tularemia in colorado.aspx). In general, biodiversity of native wildlife can help control populations of interacting species populations, lower disease potential, and contribute to functioning ecosystems. What are some effects of urbanization? As natural areas are converted to urban areas, the obvious main effect is the change of land use of the area, leading to habitat fragmentation. As buildings and roads are constructed natural areas become smaller in area, can b e physically changed or highly maintained, and/or become cut off from each other. As the natural ecosystems become smaller, the benefits of all the ecosystem services as discussed above can be affected. For example, air, water, soil, noise, and light pollu tion could increase, and microclimate changes, such as increased heat due to concrete absorption covering areas that previously regulated these processes naturally. In addition, increased human population can lead to increased recreational pressure and wil dlife human conflicts, especially with increased interactions between wildlife and domestic animals. Also, these changes could potentially affect normal wildlife behaviors. Furthermore, urbanization pressure on habitat fragments can lead to local species e ndangerment and invasive species occupation through intentional or unintentional introduction. Brief Summary Points Ecology is a broad field that studies life, interactions, and the environment at many different scales. Urban ecology is a subfield of ecolo gy that studies all organisms, including humans, in urban areas. Urban ecosystems can provide many beneficial ecosystem services. Urbanization can have negative effects (but not always!) on those ecosystem services. Wildlife can be an important part of fun ctioning ecosystems, making them a good system to study. Urbanization is rapidly occurring across the globe, making identifying positive and negative urbanization effects important for urban planning, restoration, and management. UWIN 1.2 Database Activit y (20 40 min) 1. TA should pass out the Database Instructions (1/pair) and tell students that these instructions must stay in lab for other sections. a. Is available online too link is in student manual 2. TA should pass out the Database Worksheet (1/student) th at you printed for students before lab. This is theirs to fill out and keep. Tell them that this worksheet will be VERY important for the POSTLAB.

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101 3. TA should Assign/Pass out thumb drives (1/pair). Tell the students to hand them back to you before the next a ctivity starts. These MUST be collected because they will used in all sections. a. Thumb drives will be hanging in a locked cabinet. b. Recommend assigning thumb drive numbers to student pairs, so you can track it down if it goes missing! Tables may be labelled 1 12 for consistency. 4. Thumb Drives need to be in E: drive for the pictures to show should be the default drive. a. If not in E:, need to open the Disk Management b. Need to find the Patriot USB and right click on it c. Select Change Drive Letter and Paths d. Change t o E This activity will introduce you to the photo management database that is used by UWIN and to calculate metrics from the photo data to answer ecological research questions that interest you. Your TA will assign you and your partner a USB thumb drive that will have a small subset of pictures that you can explore through the database. The database was created by Colorado Parks and Wildlife and was made usin g the program Microsoft Access. Microsoft Access will only work on PCs (not Macs), so please use the lab laptops. Need to have Microsoft Access 2016 on a PC OLD Access versions will not work with the database and they will actually damage the database. T he lab laptops are updated with 2016 should use 1 LAB laptop per student. There is a worksheet that accompanies this activity that will walk through the different strategies for filtering pictures and creating queries (queries can pull specific informati on from the photo data). Please follow the instructions , and associated worksheet , and make sure you answer all the questions as you will be asked these questions in the post lab. This is practice, but later you will be tagging photos that will be part of the UWIN long term database.

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102 Database Activity Instructions The purpose of this activity is to introduce you to the database you will be using for picture data analysis, to familiarize you with some mammalian wildlife species seen in Denver, and to allow you to explore some data ana lysis procedures that are available to you. This is going to be important when you have collectively created a database from all 40 of the cameras that have been placed at various locations across the Denver metro area. You will need to be able to determin e the kinds of animals detected by each camera and be able to calculate species richness from that set of data. Doing so requires your accurate identification of animal species from the camera pictures, and being able to filter the datasets by date, time, and location. This exercise allows you to learn these skills and practice them on a small dataset with only a few hundred photos versus the tens of thousands that will be in the database that you and your classmates create. Instructions for the database: 1. Work in pairs, it is recommended to have one computer showing these instructions (or use a printed copy), and a LAB LAPTOP to use for the database exploration 2. of pictu res that the database is linked to. 3. Insert the USB thumb drive assigned to you into a PC Lab computer a. You must use a PC computer that has Microsoft Access, Mac computers will not work 4. Go to your File Explorer by searching for it, or clicking on the symb ol 5. In the left menu, find where the USB thumb drive is located and make sure it is an (E:) drive a. If the drive is NOT (E:), please have your TA help you

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103 6. Open the Exploration Database Folder, and Click on the Exploration Database (.accdb) a. You may ne

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104 7. a. pictures to open the module i. If you accidentally cl ose out of this and see a blank page with a list of items on the left, and it will pop up again. 8. Explore PhotoViewer tab, notice: (picture below) a. The thumbnails you can scroll through on the left

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105 b. The total number of pic tures in the bottom left (or can be found by clicking on c. The 1st picture that is selected is large in the middle d. The selected picture shows the name of the picture in the upper left= showing the location and se ason/year i. DECO = Denver, CO. E03 = 3rd section of the East transect, GEN1 = Generals Park, JA17 = taken in the January 2017 session, and 00004 = the 4th picture taken at that location e. The picture shows the date and time on the bottom i. e bottom right shows the same date and time f. The image data also shows what species have been tagged in this photo g. The filters in the upper right i. You can filter by location, species, and date and time 1. Filters stay when you switch between the three, unless y ou click 9. Click on the picture in the top left, then you can use your down arrow on the keyboard to scroll through the pictures a. Cover the lower right image data, and see if you can find and identify the species in each photo 10. Explore the f ilters in this tab to answer the Photo Viewer questions in the worksheet a. Complete Q1 Q11 on the worksheet

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106 Occupancy Analysis 11. a. Go to your class shared Google folder i. Make sure you are log ged into your Google account b. Open the UWIN folder c. Open the UWIN 1 Occupancy Sheets d. e. f. RENAME your sheet (Species_SemesterYear_your name_ partner name) i. EC Rabbit_SP18_Sasha_Meda 12. Now, go back to the Database 13. Close out of the Photo Viewer tab (the lower x in the upper right corner of the tab, NOT the entire database. a. If you do close out of the database, just open it again b. When you close the tab, the Switchboard tab will open again c. 14.

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107 15. 16. De 17. day, and the buffer ti me to 0 a. This will give us a 0 if the species was not observed that day and a 1 if it was observed that day 18. 19. Select the upper left box of the results preview, this will select all of the cells

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108 20. Copy the cell s a. Right click and select copy, OR hit Ctrl and c 21. Paste the cells into your google sheet a. Select the upper left cell in google sheets, b. Right click and select paste, OR hit Ctrl and v 22. Calculate the Percent of days of the month that an eastern cottontail rabb it picture was taken a. After the Occasion 28 column, insert 1 empty columns b. i. In this columns first cell, enter =AVERAGE(the 28 cells) *100 ii. Select all the the numeric values for the 28 occasions 1. Hint =AVERAGE(B2:AC2)*100 23. U se the percentage value to answer the Occupancy question on the worksheet

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109 Database Activity Worksheet Use this worksheet in conjunction with the Database Activity Instructions in the UWIN student manual in order to explore the data management databa se that will be used during this project. Photo Viewer Questions Filter pictures by location Filter pictu res by date Q2. How many pictures were taken between 1/1/17 and 1/30/17? _________________ Filter pictures by a specific species Q3. How many pictures contain Coyotes?_____________________ Q4. Click on the top thumbnail picture on the left and scroll thr ough the pictures that contain Coyotes. What characteristics do Coyotes have that would help you identify other Coyotes in other pictures?________________________________________________ Q5. How many pictures contain Red Foxes?____________________ Q6. Sc roll through the Red Fox pictures, what are some characteristics that the Red Foxes have that would help you identify other Red Foxes? ________________________________________________ Q7. How many pictures contain Racoons? ___________ Q8. Scroll through pictures containing Raccoons. What characteristics do Raccoons have that would allow you to identify Raccoons in other pictures?? ________________________________________________ Q9. How many pictures contain Eastern Cottontail Rabbits? ___________ Q10. Scroll through pictures of Eastern Cottontail Rabbits, What characteristics do Eastern Cottontail Rabbits have that would help you identify Eastern Cottontail Rabbits in other pictures?? ________________________________________________

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110 Calculate Mammal Species richness is a basic metric that shows how many species are in a specified location. down tab. How many different wild mammalian sp ecies were observed? ______________ (hint do not include non mammal species or domestic species) Occupancy Questions Calculate species daily occurrence of Eastern Cottontail Rabbits for all pictures in this dataset. Daily occurrence of a species show s how often a certain species was captured by camera on a daily basis over a specified time period (in this case 28 days). cupancy query? _____________

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111 UWIN 1.3 Introduction to Research Types and Variables (30 min) 1. TA should use the ppt to introduce different types of research and discuss that we are doing observational/correlational research exploring what wildlife are in the Denver Metro Area and different variables that are associated with urbanization that could potentially influence wildlife species presence and use. Please reference the data cted and were given. 2. TAs should ask groups to have a discussion about WHAT aspects of urbanization might impact wildlife, and specific examples of HOW/WHY those different aspects of urbanization might affect wildlife, and HOW might you measure them? These could be project. a. (Slides 21 as TAs can use. 3. we could explore with the photo data. Ask students to think about what kind of information they can extract from the database that they just explored. a. students to be more specific. For example, all of the following are measurable: Species richness, single species/multi species, daily use 4. TAs should ask students to write down the inde pendent and dependent variables they are interested in on the back of their notecard then turn it in. a. After lab, read these notecards to get a sense of what students are interested in and be ready to share at the TA meeting. Student interest could affect the data collection worksheet/ what students do for data collection. What kind of research are we doing? Start by watching the following YouTube video on research study types. Be sure to at watch at least the first four minutes, but we encourage you to go further. https://youtu.be/lsbK6g10a c In the video, there were 3 different types of studies talked about: descriptive, correlational, and experimental. Each type can make important contributions to science, an d they build upon each other. As an introduction to research studies, we will be focusing on the descriptive and correlational realms of research, but it is important to understand the limitations of these types of studies. As shown in the video, descripti ve studies can identify and describe the system that is being studied, but cannot be used to make predictions or determine what caused the process being described. Correlational studies can determine if a relationship exists between two

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112 variables, but cann ot determine if one variable actually causes the other to change. This is an especially important consideration in ecology studies, where there are often many interactions and variables that could be involved in a process. Even though we cannot make conclu sions of causality with these studies, they can provide insight into what urban ecologists should research more closely. Brainstorm about Urbanization During lab, your TA will lead a brainstorming discussion about WHY urbanization might impact wildlife, and specific examples of HOW different aspects of urbanization might affect wildlife. During lab, think about these different aspects of urbanization, which ones interest you, and what you might want to research for this project. After we compile the ideas from all lab sections, a data collection sheet will be compiled for the variables that could be feasibly collected. UWIN 1.4 Primary Literature Activity (remainder of class period) 1. TA should have students pull out the article and worksheet They should have printed and brought their own copy IF they want to mark a copy. a. Have LAB COPIES available that they can use, but not mark up they would have to take good notes needed for POSTLAB 2. Research shows that students engage more when they see value in the a ctivity they are doing. For this reason, make sure to reinforce that reading primary literature is an important skill in any field of study, and this activity will help in reading and extracting information from other primary articles, identifying importan t aspects of each section will help with writing your term paper, and will give you a foundation for reading that will be required in upper division classes. They WILL have to read primary literature in future classes. 3. TA should use the google slides prese ntation to walk through the activity, pause during the presentation for each section of the paper and have the students fill out the worksheet. Walk around and monitor what students are doing. Take mental or actual notes about what students are thinking. B e ready to share your notes at the TA meeting. a. The presentation slides correspond to questions in the worksheet. 4. shorten the activity. The best part to shorten is the par t about the Discussion section of a primary literature paper. 5. Before you dismiss the students, make this announcement

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113 a. Students, make sure you have turned in the thumb drive, given me your notecard, and logged out of your computer. Professional points will be taken b. Remind them to do the post lab within 72 hours of the end of lab. In order to be an informed scientist, researcher, doctor, and/or member of society, knowledge of current research in the field is essential. In order to o btain background information of the field requires reading about the research being conducted. Reading or hearing about the subject matter on social media, newspapers, and websites can be a great first start to gain knowledge in a field, but sometimes they can be unreliable sources. They can twist information to promote a cause, sometimes intentionally but it can also happen unintentionally. The best way to know for sure is to go directly to the source. Look for and read the sources that are given in articl es, and be very skeptical if no source is given! As a student, you have access to many resources through the Auraria Library website, which you can also access via our Canvas shell. When you log in with your un iversity credentials, you can search for and have access to many full length textbooks and primary literature articles that can be useful in any of your classes. Please use this awesome, FREE, resource while you have it as a student! We will practice searc hing and locating resources in the next lab. In this activity, you will work through a strategy for reading this primary literature article with a partner and with the clas s. The goal is to give you a strategy to tackle any primary research, and to practice it with peers. This is an important skill you will need for the rest of your time at CU Denver and in any field of work you go into. There is a worksheet that accompanies the activity that you should pay close attention to. Make sure you fill it out and take good notes as some of the questions on the post lab wi ll be about this activity.

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114 How to read a scientific paper worksheet 1. Start with the title. Using only this title, summarize the topic of the paper What is this paper about? What suggestions do you have to improve this title? 2. SKIM the Introduction. W rite down at least 5 keywords within the introduction that would help clarify the title. Also write what you think each of these keywords mean. 3. Now, read the title again. Summarize what this paper is about. In a few minutes, you will be asked to compare your summary with another pair. 4. Re read the Introduction, more in depth this time. Any other terms needed to be looked up? DO NOT read past words you do not know look them up! Also highlight the important aspects of the Intro listed below and summariz e them in your own words. a. Terms and definitions b. Background information, summarize in bulleted list. The authors reviewed past work that is relevant to their work presented in this paper. What are the main points they made in their review of past work? Wha t do the authors write about? what has been done in the past? c. State 2 importance statements What is the problem the field is trying to solve or address? d. State 2 purpose and rationale statements Why do this specific study? e. Research questions or objec tives What did the authors do? 5. Methods Skim the methods on the next few pages and identify the KEY techniques. Circle key techniques used to address the study objectives, take notes as you read. Use this space to list out the techniques and DRAW out a methods concept map or cartoon.

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115 6. Now read the Results. Summarize and list the results in bullet points (note figures if mentioned) 7. Summarize each table and figure that are embedded throughout the paper: state the main conclusion or objective of the table/f igure, and state what type of data the table/figure is addressing (methods, summary data, main results, etc.). a. Figure 1 b. Table 1 c. Figure 2 d. Table 2 e. Figure 3 8. Read the Discussion. Address the following questions as you read it. a. Summarize the main interpre tations of the results that the authors give. Is their summary consistent with your interpretations of the data? b. Authors should comment about assumptions and errors that could limit the reliability of their findings. List at least 4 assumptions and errors the authors give. c. Authors often include guidance about what future work needs to be done in the field and why. What future work do they suggest? d. Authors sometimes include recommendations for how their work might inform policy or management decisions. W hat recommendations did these authors give, if any? 9. Read the Abstract. State the items that should be in the abstract that summarize the entire paper. Circle or underline these items, and label them. a. Looking over your summaries of the each section of the paper, do you think the authors do a good job of summarizing the paper? b. Does it match what the authors said in the paper?

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116 UWIN Week 2: Methods and Formalizing Research Questions Goals/Objectives 1. Discuss methods in ecological research a. You will learn about different types of research done in ecology, and what type of research you will do. i. Briefly go over methods in ecology (5 10 min) 2. Go over the UWIN methods camera trap protocol and data collection a. You will learn about the UWIN specific camera trap p rotocols used by all involved cities i. Go over the UWIN methods (5 min) b. You will plan your site visit and data collection protocol you will do before next lab. i. Students to pick a site to visit and find the site info in Google Folder (10 min) ii. Students to go o ver the data collection worksheet (5 10 min) c. You will also discuss assumptions of the camera trap data collection method. i. Assumption Scenarios Activity (30 min) 3. Finalize your research question a. You will create, evaluate, and revise a research question that will be the focus of your research and your term paper. i. Research Question and Peer Interview Activity (45 60 min) 4. Search for relevant resources a. You will learn about searching and locating sources that are relevant for your research question that will be u seful for writing your term paper. i. Searching, locating, downloading, and citing primary literature, while creating a bibliography (45 min rest of lab period) Action Items 1. Complete the UWIN 2 Pre quiz prior to lab. 2. Prior to lab, PRINT the Research Question Worksheet and the Bibliography Worksheet , and bring both to lab. 3. Make sure that you keep excellent notes throughout the lab period and complete all activities; you will need them for your post lab assignments.

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117 4. PRIOR to next lab, meet your group and visit the camera locations you are responsible for. Fill out the d ata collection sheet while making observations about the sites and bring the completed sheet to class next lab. 5. Complete the UWIN 2 Post quiz within 72 hours after this lab. Materials: 1. Google Presentation to facilitate the lab activities find it in the l ab materials folder here . 2. FOLDER; MAKE A COPY. 3. folder shared with your class. 4. Have the sites that your students will visit ready (6 per section, ~1 per 4 students). Each section will have differe nt sites given in this google sheet . a. Devise a plan for having students pick a location. b. Have your classes google folder open, so you can s there is a map of all the sites and parking locations, and a document for each site. (They have to be signed in to Google to see all the features) c. Be somewhat familiar with the sites your section has, there will be n otes in each sites document of anything abnormal (park hours, hiking involved, fences, alternate parking, etc.) 5. data collection sheet 6. Make sure the Scenario laminated sheets are in lab; if you cannot find them, ask Enrique where they are. 7. Be ready to show students the Auraria Library website, and the CSE citation website, and how to copy pdf links. 8. Have a camera and stinky tablet out for students to look at. Enrique will put one in a container with a hole in the top that can stay in the flow hood. Pass it around for people to get a whiff of. Show them how to smell something scientifically (ask how if you are unsure). Give a warning that it is very smelly and if you are concerned you may react adversely to not sniff it. 9. 2 lab laptops should still be on the tables, with power cord and ethernet cord.

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118 UWIN 2.1 Methods in Ecological Research (5 10 min) 1. In lab, briefly 6) 2. The goal of this information is to point out to students that conventional experiments with well replicated treatments and controls are hard to do ecology, especially using camera traps instead ecologists collect data across a landscape or a span of time to infer an ecological process. Also to show that there are many different ways to study ecology, and to point out some dif ficulties in ecological studies. You could probably compare the ecological study setups you are familiar with, with some of the physiology type labs (e.g. digestion) that we used to do, or the kinds of experiments the students do in GB1 they should be fa miliar with the labs on fermentation and osmosis etc, where they have very discrete variables and very easily controlled situations. How many types of ecological studies are there? Well, many! Remember that ecological studies could be looking at different scales, from an individual organism to the entire globe. However, in general there are 3 major types of ecological studies: 1) Descriptive, 2) Functional, and 3) Evolutionary. Descriptive ecology looks at overall natural history through observations, and a organisms and environments are present? What are their distributions? What are their camera trap data. As the dataset grows from year to year and we identify potential trends, we Difficulties in ecology studies? First of all, conventional experiments with replicated treatments and controls are hard to do in ecology. Can you think of a few reasons why? One important reason is t hat many ecological systems can be very complex! There are too many variables, known and not known. It can be very difficult to identify and measure all the potential variables that might be important in an ecological process, especially in urban ecology studies where human influences become important! Think about economics, politics, public health, sociology, and many more human related influences that could impact urban areas. So, urban ecologists need to consider the ecology IN the city and ecology OF t he city (examples shown in Figure 4 below). In addition, it can be very difficult to control for all those variables,

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119 like what is conducted in a traditional treatment vs. control experiment. Also, ecological patterns can take a long time to emerge and can be hard to replicate. Another difficulty in ecological studies that we have already talked about is that correlation does NOT imply causation. Correlational results might give us insight on a potential pattern, but there might be more than one explan ation that fits a particular situation just as well or even better! Because of this, ecologists try to avoid overly simplistic and one dimensional studies. This is what makes the UWIN project you are participating in great, because everyone can work togeth er to make many different observations of how urbanization might impact wildlife, and to think about further research that could be conducted. What kinds of methods do ecologists use? Natural experiments when manipulations happened naturally or out of the control of the observers, such as studying a disease outbreak. Monitoring use of technology and observations such as remote sensing and field instrumentations. Controlled experiments having control plots and treatment plots in an experimen tal design that is as controllable as possible.

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120 Modeling using computer modelling and simulations to create population or ecosystem models that represent the system studied as closely as possible. We will be employing a combination of natural experimenta tion (urbanization has happened) and monitoring (using camera traps).

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121 UWIN 2.2 Specific UWIN Methods (5 min) 1. In lab, briefly go over the UWIN methods, using the google presentation (slides 7 9) 2. Show students the camera when you talk about it 3. Pass around the FAS tablet, if students want to smell it. There should be one or two containers in the fume hood with small holes punched in the top. Do not allow students to open the tablet or to handle the tablet. The Urban Wildlife Information Network (UWIN) has the goal of comparing urban wildlife data across different cities nationwide, therefore, all cities set up camera traps in a similar way. First, a transect is selected that generally spans an urban to rural gradient. In Denver, Colfax Avenue was selected b ecause it runs straight through downtown Denver, out west to Golden and out east to Aurora. The transect was partitioned into 5 km sections, in which five parks or green spaces in each section was selected for camera placement (see Figure 5). The 40 sites are within 2 km of the transect line, and at least 1 km away from each other. This ensures an even and well spaced distribution of sampling effort along the transect, and the >1 km spacing helps to promote site independence. Once sites were identified, per mits and approvals from the land managers was obtained. We use motion activated cameras (Spartan GoCam) equipped with a passive infrared (IR) trigger and IR flash that are set to take pictures up to every 30 seconds. One camera is placed at each appr oved site for four weeks during each season (winter: ~January; spring: ~April; summer: ~July; fall: ~October). Cameras are strapped to trees or posts that allow for secure attachment, and will be aimed toward a carnivore attractant tablet located approxima tely 3 5

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122 meters from the camera, as shown in Figure 6. The Fatty Acid Scent (FAS) tablets are commercially available from the US Department of Agriculture and will be enclosed in a mesh screen pouch that will be secured to a tree, log, or stake. The FAS t ablet is a mild, malodorous (stinky), short range attractant and will not cause population level responses or wildlife problems for surrounding homeowners. Pictures taken by the cameras will be tagged (by you!) using a Microsoft Access database create d by the Colorado Parks and Wildlife called CPWPhotoWarehouse (more information here: http://cpw.state.co.us/learn/Pages/ResearchMammalsSoftware.aspx ). This is the database you explored in Lab 1. Picture metadata that is stored in the database could include: the species in the photos, how many of each species were seen, the date and time the photo was taken, the location of the photo, and any details that were tagged.

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123 UWIN 2. 3 Site Level Data Collection Planning (15 20 min) 1. near the east. Each lab section will have different site numbers in this google sheet , which also contains the si te information (site name, GPS coordinates). This sheet is also found in Lab Materials Folder, under UWIN 2. This way all SECTIONS will work together to collect data on all of the sites (splits up the work). (slides 10 12 of the Google presentation) a. Devise a way to have student groups (4 students) pick one of the locations that works, groups can mix up depending on the locations they want to go to. b. Make sure you (and all of the students) know where they are going i. Have students write down where they are goin g on the board or in a google sheet and keep track of that information. c. They can visit all together, or in pairs but NEVER go alone! Ideally they all go at the same time to reduce disruption at the site. i. Try to make sure students have collected contact i nfo and have an agreed upon plan d. Make sure you have signed waivers (should have from UWIN 1 lab); keep yourself (you can scan and email them to yourself using the dept copier), or give them to Duncan. e. Go over health and safety of field work (slide 10). 2. Pas s out the data collection sheet, and have students read through all the variables they will be collecting and think of HOW they are going to measure them. Should make clarifications now, before going into the field. 3. Point out to students that the data col lection worksheet will encompass all the information we will be collecting, so to keep that in mind when we get to creating research questions next. During lab your TA will instruct you on how to form groups and what is expected of you. You will need to p lan a time to visit one of the camera site locations BEFORE the next lab period. Your TA will give you access to the information for all the locations, therefore you will have to locate the information for your sites, and plan driving directions and times to meet up with your groupmates. Make sure you take with you the data collection sheet , which we will go over in lab. Data will be collected ac ross all the GB2 lab sections and will be pooled together so everyone can contribute to the dataset. Because your data collection will be used by other lab mates and be included in the long term data set for everyone, including UWIN, please try to collect data accurately to the best of your abilities and work together with your group. The point of this exercise is to collect site level data in the field, and for you to make attentive observations about areas affected by urbanization and what these observat ions might mean for

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124 wildlife. Think about the measurements you are recording holistically, to give you an overall sense of the parks characteristics.

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125 Site Level Data Collection Field Sheet This sheet is to be used to record site s location. Once you have located the camera, record the following information in the table provided. All data must be accurately recorded as it is used by all students in every section of lab. Please be aware of your surroundings, and always go to sites w ith your partner! If you feel unsafe at any time, leave immediately. If you think the camera is missing, take a picture of where you think it should be based on the information you received, and any signs of there having been a camera present. Collect data based on where the camera should have been and we will investigate. Email your TA immediately if you think the camera is missing. Team member names__________________________________________________________ Location name________________________________ Date__________________________________ Time________________________________ GPS coordinates: LAT:_________________________LONG: ___________________________ Take a picture of the camera set up location Take a picture of you and your partners at the ca mera set up location ______Artificial (highly landscaped, fully managed + maintained) ______Mixed (Is partially maintained + partially natural) ______Natural (no maintenance, naturally occurring plan ts) What best describes the land use around the site? ______ Urban/City (lots of buildings, heavily populated) ______ Suburban (residential, neither rural or urban) ______ Rural (farm, countryside, not heavily populated) bitat structure? ______Very heterogeneous (many different levels of structure: grass/forbs, shrubs, young trees, Adult trees, dead trees, many different types of the above, etc.) ______Slightly heterogenous (a few different levels of structure) ______Homo geneous (only 1 or 2 different levels of structure: grass and adult pine trees, etc.)

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126 How far is the nearest water source from the camera location? (10 meters is about 20 steps) _____ 0 10 meters (0 33ft) _____10 100 meters (33 330ft) _____100 meters+ ___ __ No water seen If water is seen, what term best describes the water source? _____ puddle _____ small stream/ drainage run off area _____ river _____ pond or lake _____ no water seen How far is the nearest trash can from the camera location? _____ 0 10 meters (0 33ft) _____10 100 meters (33 330ft) _____ No trash cans seen What best describes the amount of litter/trash that occurs within a 10 meter radius of the camera location? (10 meters is about 20 steps) _____ no litter seen _____ small amount of li tter (1 10 pieces scattered around) _____ high amount of litter (10+ pieces of litter) Using the free decibel meter app (Decibel X for IOS and Android) take 4 measurements close to the camera location and average them, what best describes the noise level at the park? Recorded decibels ______, ______, ______, _______ Average:___________ _____ low noise levels (0 40 decibels) _____ mid noise levels (40 90 decibels) _____ high noise levels (90+ decibels) What best describes the roads that occur within 100 meters of the camera location? _____ no roads within 100 meters _____ dirt roads _____ paved neighborhood/street roads _____ 2 4 lane paved roads with street lights _____ highways (no street lights)

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127 Spend 10 minutes close to the camera and record t he type of animals you see (including humans and dogs) and tally how many you see. Try to remain still and quiet, do not feed or entice animals, do not pet dogs you do not know. Add animals to the table as you see them. Animal Tally marks over 10 min. Tot al Human Dog Bird Using the table you filled out above, what best describes the level of human use of the park? ______ low (0 10 humans/10min) ______ medium (10 40 humans/10 min) Total # of humans______ ____ ______ heavy (50+ humans) Using the table you filled out above, what best describes the level of dog use of the park? ______ low (0 10 dogs/ 10 min) ______ medium (10 20 dogs/ 10 min) Total # of dogs__________ ______ heavy (20+ dogs/ 10 min) Using the table you filled out above, how many different types of WILD animals did you see? # of different WILD species seen______________ Using the table you filled out above, how many different DOMESTIC animals (including humans) did you see over 10 minutes? # of different DOMESTIC species seen_____________

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128 UWIN 2.4 Creation and Revision of a Research Question (45 60 min) 1. Students should get out the Research Question Worksheet (they should have printed and brought to lab). 2. Use the g oogle presentation to walk through the activity and keep timing, the slides show what students should be doing. There are 3 parts, as discussed below in the text. (slides 12 15) 3. The goal of this activity is for student pairs to create a concise, informativ e, and testable research question that can be addressed using photo data and site characteristic data. While students can work together on ideas, all assessments MUST be done individually in their own words, unless otherwise directed. The interview portion is suppose to help questions become more informative and to make sure students understand what they are proposing to research. 4. derivations of that as they wish and as you guide them. The data collection sheet we will go over in class encompasses most of the variables we brainstormed in Lab 1, however due to resources, time, feasibility, or safety concerns not all of the variables we discussed can be collected. However, we will take into account ideas for site level measurements that could be done in the future. Now that you know what variables will be collected about the sites, we will spend time in class formulating and revising a specific and testable research question that you will explore and discuss in your term paper. Your TA will lead you and the class through a question creation, peer interview, and revision activity by which at the end you should have finalized your resear ch question for this project and your term paper. We strongly encourage working in pairs for this project. You will find your partner a resource and you will reduce your workload by working together with another. If you choose to work alone, we respect tha t choice. That being said, however, we create activities and determine appropriate workloads based on pairs of students. In working alone, you acknowledge that you will have to modify our instructions so that you can participate as expected, and that you w ill likely be burdened with a greater workload than you would otherwise be if you worked with a partner. Follow through the numbered items in the list below along with the associated worksheet to refine the question you will try to address this semester in the UWIN project. Your TA will stop you at some point for a class discussion and then give you some more time to work again. In total this sho uld take about one hour to complete. There are 3 parts to this activity: Part 1 . Think individually about what you might want to research. Then work with a partner to work on creating a question together.

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129 Part 2 . You and your partner will interview anoth er team about their research question, then you and your partner will be interviewed by a different pair. Each interview should take at least 15 minutes and should be an in depth conversation. This will help everyone think deeply and critically about their research goals. Make sure to take notes on the answers the other pair gives you, as you will need to discuss it in your post lab . Part 3 . With your partner you should now use the feedback from the interview to refine your question into a final version. Y ou should keep this safe as it will need to be referenced extensively during the data collection and analysis stages of UWIN, so that you can be sure you are addressing the question you sought out to.

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130 Worksheet Research Question Reflection, Peer Intervi ew, and Revision Part 1: Create a Research Question To start with, you should spend a quiet 5 minutes alone between the end of the last lab and now and answer Q1 3 below. Use that time to refine the general question that you probably came up with by the end of the last lab period. Your refined question will likely now be limited by what you learned earlier today. You must integrate those limitations into your question and consider if you can answer it within the confines o f the system we are using. 1. Based on the brainstorming activity from the previous lab and the data collection worksheet you have been given , jot down some of the habitat variables that are measurable, interesting to you, and that you think could affect wild life in Denver. 2. Write some specific ideas of Think about what kinds of data we can get from the photos/ database (think about the database activity from Lab 1, and the assumptions of camera trap data we went over). Narrow down what you will actually measure. Think about things like what species or groups of species you are interested in, whether you care about total number of photos taken, % of sampling days that a species was seen, or how many species were seen, etc. Be as specific as possible. 3. Try to write a specific question with a dep endent variable and an independent variable that can be addressed with the data. STOP AT THIS POINT, Your TA will go over the data collection sheet in detail as a group. 4. Based on the variables we will be collecting, discuss with your partner if your questi on is reasonably answerable. You and your partner can choose to work together on a single question throughout the project, but understand your finished products MUST be done individually. a. Together, decide on a question as a pair and write a specific and c oncise question. b. State your dependent and independent variables, and explain how will you measure them. Part 2: Guided Peer Interview The questions below are to help guide your questioning of another group with the goal of requiring the other group to defe nd their research question and predictions. Your goal is to be critical but constructive. You can be critical of the questions and predictions of others, but should do this only with the intention of helping them improve their work. You can of course ask o ther questions than those provided here, but we feel these will guide your discussions, and be a productive way in which to revise research questions. You can take notes on the response

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131 of the other group in the spaces between questions/prompts and should record your own notes when you are being interviewed. You will be asked to discuss some of these questions in your post lab. lay person with no science backgro und could understand? Who cares? Why is this interesting or important? What is the specific question you are asking? What are dependent variables in your question? What are the independent habitat variables in your question? What exactly will you be measu ring? Why are you using those variables? Describe how the habitat variables you are measuring might reflect urbanization, or might be affected by urbanization. Are the measurements or variables flawed in any ways? Could you make a better or more accurate m easurement in some way? What is a potential outcome of your study? Explain to us what a data set would look like to support this outcome. What is your biggest concern about the study you are doing? How could you mitigate that concern? Part 3: AFTER the p eer interview, Revise your question 1. Think about the questions you were asked about your research question, could you refine your question to be more clear? Can you address your question with the data you question should include time frames, locations, and specific dependent and independent variables explained in terms of how you would measure or estimate them. a. Revise and finalize your research question These Examples Might Help You: Initial question : How does a plague epizootic in prairie dogs change plant communities? Revised Question : What is the peak summer biomass of grasses and forbs on a prairie dog colony 1, 2, and 3 years after a plague outbreak in Boulder County, C olorado? Initial question : Has reintroduction of wolves decreased elk? Revised Question : Over the last ten years since wolves were reintroduced to Yellowstone National Park, how has the yearly population size of elk changed?

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133 UWIN 2.5 Camera Trap Assum ptions (30 minutes) 1. There are 5 scenarios that attempt to show different assumptions we make with this camera trap set up. This activity is suppose to get students to think about different assumptions, and how they might affect the photo data that they ana lyze. It is important in any research design to know the limitations of the data, and how those limitations might affect their confidence in the data, and the conclusions that can be made. (slides 16 22) a. Instead of just listing assumptions off in a lecture , this is an active way that students can think about them, and try to articulate them. 2. Logistics example: a. There are enough scenario sheets for every pair to have one. (5 scenarios total) b. Pass out scenarios so each table has 2 copies of 3 of the scenarios i. You can pick which ones c. Have student pairs all discuss the same scenario, then share what they think with another pair d. Go over the scenario as a class (google presentation has each scenario with notes) e. Then repeat for 3+ scenarios, or until you get to abo ut 30 minutes Using trail cameras, i.e., camera traps, is a great way to obtain data on wildlife that is non obtrusive for sometimes cryptic, hard to observe species. Cameras can take pictures for extended periods of time, 24 hours a day which would be ve ry difficult for human to do! Although many ecological and wildlife studies use camera traps in their study design, some assumptions are made about the data that gets recorded. Your table will be given a set of ons that we make. When discussing the scenario, try to articulate the assumption that is being discussed about the camera trap data. Knowing the limitations of the data you are collecting is important when thinking about the conclusions you can make!

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134 UW IN 2.6 Finding Relevant Sources (45 min rest of lab period) The main goal of this assignment is to show students how to search and find primary literature. Many students have NO idea that they have access to this resource, therefore showing them how to us e the library will help them in future classes. A secondary goal is for students to practice searching, locating, and downloading primary literature, that could be helpful in writing their term paper. A third goal is to go over citations basics, and what we expect in this class. 1. Prepare and go through a demonstration of using the Auraria Library a. Recommend showing the NEW sidebar link in Canvas, and getting to the website by searching for the Auraria library. b. Show the front search engine, and different dat abases (google scholar, web of science) 2. Prepare and briefly show students the CSE name year citation website https://writing.wisc.edu/Handbook/DocCSE_NameYear.html 3. Prepare and show students the example bibliography (last slide) 4. Then turn students loose for the rest of the lab period, having them use the Bibliography Worksheet (they should have printed and brought to lab) to guide them. a. Tell students you will check their references for participation dely available information. However, as we mentioned in Lab 1, not all information available is based on facts and has questionable reliability. As a member of society, you should be able to think critically about information presented to you with an appro things to think about when you read Facebook posts or watch the news! Most primary literature is peer reviewed by oth they are 100% correct in how they did things, or in their conclusions. This is why replication of results is SO IMPORTANT in scientific studies, no matter what field you are interested in! For the rest of the lab period, you and your partner will work together to search and locate some primary literature sources that are relevant to your research question. This will help you learn more about the study system you are interested in within urban ecolo gy, should give you some citable sources that you can use in your term paper, and give you practice using some free resources that are available to you as a student! There are many ways to search and locate literature, books, articles, etc. that you might be interested in. Some literature is available through open access journals that do not require a subscription, but some literature is not. However, the University of Colorado Denver tries to acquire access to as many journals and books as possible, which are available to you through

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135 the Auraria Library Website when you sign in with your university credentials. Your TA will demonstrate some different ways you can use the Auraria Library website. Learning how to search literature is a skill that you will ne ed in future courses. You will search for articles that are relevant for your research question. Use the worksheet to help you locate sources, and create a shared bibliography. This bibliography will be a central location where you and your partner can share sources that you find useful for your term paper. As a part of the bibliography, you will have to create citations for the sources you fin d. Every class, and every journal, requires specific citation requirements that may change or look different. For this project, you will need to cite your sources in accordance with CSE name year formatting style. Please familiarize yourself with the citat ion requirements for this class by reviewing the specific CSE name year rules and examples here . ( https://writing.wisc.edu/Handbook/DocCSE_NameYear.html)

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136 Bibliography Instructions I nstructions: 1. You and your partner will create a shared google sheet as a team bibliography a. Go to your class google folder and go to UWIN > UWIN 2 Bibliographies b. Create a new sheet, and rename it Bib_name_partners name i. ex) Bib_Meda_Jack c. Make column heading s that include: name, citation, pdf link, summary (see the example that is in the folder) i. Name = your name, to show what you contributed ii. Citation = the citation of the source in CSE name year format that will be used for this class, use the CSE name year guidelines iii. Pdf link = when you open a paper from the library website, copy the online URL, and paste it into the google sheet cell. 1. This will give you, and others, easy access to the pa per if needed 2. Note: If you want to download a copy you can read offline, click computer. iv. Summary = After you read the abstract for the paper, write 1 2 summary statements or bullet points of wh y the article might be useful for understanding urban ecology or specific wildlife ecology background, your variables, methods, study system, comparable results, etc.. 2. Read your research question(s), write down at least 5 potential keywords or key phrases that you could use to look up related articles. You might be interested in learning more about the main topic, your specific variables chosen, camera trap methods, similar studies done, etc. 3. Try looking up some articles at the Auraria Library website usi ng the Auraria Library front page, Google Scholar database, and/or the Web of Science database. 4. specific? What specifically would you like to know more about going forward with your research question? 5. Skim through the titles, and open a few that appear to be about your topic. Read the abstract. Does the paper seem like you might be able to cite it when thinking about

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137 setting up your term paper in terms of: background information , importance, methods, similar studies, etc.? a. This is different from the reading literature activity, but sometimes you need to b. If it appears useful, add it to your team bibliography and f ill in the columns as stated above. Make sure to state a summary and potential use. For example, you could: i. Summarize the result of the paper ii. Note how it could be useful in terms of your research c. If you find a particularly relevant paper, you can look at w hat papers that paper cited. Also, you will usually find a place on the web page that says something 6. Once you have at least 2 papers EACH, start to read and decode the papers you chose. Start to make notes (IN YOUR OWN WORDS) about things that you learn be sure to also write what paper you are taking notes about, so it will be easy to cite that information later. You can also add notes to your bibliography as a reference. 7. it will be asked for in your Post Lab.

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138 UWIN Week 3: Collaborative Data Collection, Tagging Practice, and Writing Goals/Objectives 1. Discuss methods of Geographic Information Systems (GIS) a. You will be introduced to GIS technology and opportunities. (~5 min) b. You will practice some basic GIS measurement techniques to collect more information about the camera sites. (45 60 min) 2. Enter field and GIS collected data (10 15 min) a. You will enter the data you collected to contribute to collaboratively collected data. 3. Practice tagging photos (30 40 min) a. s within a tagging module to get you ready for tagging the pictures for the current session next week. 4. Start outlining (45 min rest of lab period) a. The rest of the lab period will be dedicated to outlining sections of your term paper. Action Items for Stud ents 1. Bring your completed field data collection sheet to lab. 2. Read the UWIN 3 part of this manual before coming to lab. 3. Complete the UWIN 3 Pre Lab Quiz prior to lab. 4. Take excellent notes throughout the activities in lab, they will help you with the Post l ab. 5. Complete the UWIN 3 Outline Post Lab Assignment in Canvas within 72 hours after this lab.

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139 Materials: 1. Google Presentation to facilitate the lab activities find it in the Lab Materials > UWIN 3 folder 2. Copy the Example Map in the UWIN 3 fold SHARE THE ACTUAL FOLDER/MAP; MAKE A COPY. 3. Be fam iliar with the GIS techniques watch a screencast here if needed (https://youtu.be/IBBVPQ0Rc3A) 4. Have your GIS site allocations ready for your section (24 sites= 2/student pair) a. Sites allocations are here , need to split them up 2/student pair 5. Lab copies of the GIS Instructions in lab (1/pair of students) 6. Thumb drives ready (1/pair of students) 7. Lab computers should be on the desks, with power cord and ethernet cord 8. Lab copies of the Tagging Activity Instructions in lab (1/pair of students) 9. Be familiar with th e Tagging Module watch a screencast here if needed (https://youtu.be/8x7BvT4hMJ0) 10. Print out Writing Outline Worksheet (1/student) they can keep

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140 UWIN 3.1 Geographic Information Systems (GIS) (50 65 min) 1. Students can use their own computers for this if they want, BUT they will need to use the lab computers later 2. Pass out the LAB COPY GIS Instructions (1/pair) and have students get out the data collection worksheet they filled out in the fi eld. 3. Briefly introduce some basic GIS information that student should have read, using the text below and the google presentation. (~5 min) 4. Emphasize to students to use the GIS Instructions. a. They will create their own maps in the class shared folder. Coul d show students the example map that you shared with them in the UWIN 3 Maps folder. Their map should look similar to the example map, but with 2 camera locations. b. They will be exploring a light pollution map, and recording the radiance level at 2 camera locations. i. Devise a way to split up the sites so each PAIR of student has 2 sites each section has been aliquoted 24 sites in this google sheet 5. When students are finished, they can start UWIN 3.2 entering the data in the google forms available via Canvas . Geographic Information Systems (GIS) are designed to capture, store, manipulate, analyze, manage, and present spatial or geographic data. Using maps to the first applications was done in Paris using city maps with color gradients to represent the number of people who died from a cholera disease outbreak. With technological advances, we now have access to a plethora of information based on geography. We can pick from a multitude of differen are probably aware of is the satellite vs. map views in google maps. In more robust spatial analyses, researchers can pay satellite companies or special planes or drones to create specific maps of interest and/or can collect s pecific information (such as heat, elevation, or color scales). Once a map is selected, data can be (see figure 7). These layers can help us visualize data spatially, and allows us to analyz e spatial data. Many datasets and layers are created and shared openly, many by government agencies interested in specific data. For example, denvergov.org has many available datasets such as crimes, parks, traffic accidents, zoning boundaries, etc. Nation al datasets are also available, three of which we will be given to you to explore.

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141 GIS skills are becoming more sought after in many fields; such as landscape planning and management, natural resource management, transportation planning, navigation (th ink driverless cars!), environmental impact analysis, disaster management, tourism, business, energy, utilities, infrastructure, public health, economics, and much, much more! There are many different types of GIS software out there. A popular subscriptio n software by ERSI is called ArcGIS, and is used in our geography department and by many professionals. However, there are also many open source (free) software programs that have varying uses that you could explore, such as QGIS, Google (Maps, MyMaps, and Earth), Data Basin, and many more! If you are interested in learning more about GIS, it is highly recommended to check out GIS classes offered in the geography department. There is also a GIS certificate that is offered at CU Denver check it out here . (https://clas.ucdenver.edu/ges/programs/certificates/gis certificate) In lab today, we will collect more data about our sites using the open source GIS platform Google MyMa and how to do some basic measurements, such as measuring distances and area. This will expose you to some basic GIS techniques by taking measurements around our sites. In lab, you will use the GIS Instructio ns to guide you and help you fill out the GIS data collection sheet. You will also be given spatially analyzed data that was calculated using ArcGIS, which took national dataset (NLCD 2011, SILVIS 2010) to calculate the average % impervious surface, aver age % tree canopy cover, and estimated human population within a 1 km buffer around each site. This was done by importing all the site locations using GPS coordinates, then importing the impervious surface, tree canopy, and population estimate layers on to p of the map. These layers are comprised of 30 meter by 30 meter pixels that correspond to the spatial data, the picture below shows the impervious surface layer for denver blue pixels are low impervious surface and red pixels are high you can also see t he black dots are our sites.

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142 Then we can add 1km buffers around all our sites, and the GIS program will give us an average of the pixel values within that buffer. See picture below that shows the 1 km buffers. Your TA will give you access to this da ta, if you are interested in these variables.

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143 GIS Data Collection Instructions Make sure you are signed into your Google account. Your TA will assign you up to four sites. You and your partner will be responsible for collecting GIS data for those sites. Make sure you have the GIS data collection worksheet (the last pages of the field data collection worksheet) to write down the measurements you make. 1. Create a Google MyMap with a partner: a. Go to the Google folder that has been shared with the class b. Go to Maps folder c. d. e. 2. Rename your map: a.

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144 b. i. Example: Sites 5 9_Noor_Step h ii. Example MAP is in the shared google folder in UWIN 2, and shown in pictures on page 5 3. Explore the map features: a. Hover over the buttons near the top of the page to see what they do b. Notice the search bar, where you can search for any location i. Perhaps sear ch for Denver the map will zoom into Denver c. Look at the bottom right corner, test the zoom in and out buttons d. Now explore the map contents box located in the upper left i. The top will be the name of your map ii. Next down are options to add a layer, share, and preview iii. After that is the area for your layers that you will create iv. And last is the available base maps 1. 2. Explore the different base maps available a. Map and Satellite Base Maps are used the most 4. Create Layers: a. Clic b. c. i. Notice a break between the 2 layers, and the blue bar to the left of the layer this shows that that layer is currently active d. Rename the layer e. 5. a. b. Put the GPS coordinates for your first site in the search bar i. Copy both the lat/long together from the site information spreadsheet, and paste into the search bar ii. You can zoom in, switch to satellite view, etc however you want to look at the map c. i. Notice ho

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145 ii. Notice the icons in your new pin information box, hover over them to see what they do d. Rename the pin i. In the pins information box, click on the 2nd icon which is the edit pencil 1. If the info box went away, click on th e pin and it will reappear ii. Rename to the site number and name 1. 6. Distance to Water measurement layer: a. b. c. Zoom in or out to t ry to locate the nearest water source should be in blue d. Once it is found, zoom to a good view that has the water and the pin in view e. i. ii. iii. Notice your mouse cursor is now a cross, instead of a little hand iv. Click on the site pin v. Draw a line to the nearest water, and click 2 times to finalize the line vi. Rename the line to site number and name vii. Click on the style icon to change the color and width, if you want viii. Record the line distance in your worksheet 1. You may need to convert the units to meters, using an online converter Tip: If you want to just check measurements without drawing a line, you could use the measure tool (the ruler under the search bar). Ti p: layer name in the map contents box. You may want to turn layers off when making new layers. Tip: To make your cursor turn back to the little hand, Click on th search menu (the little hand). 7. Park Area layer: a. Zoom in/out to your first site so that you can see the entire area of the park b.

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146 c. Select the Draw a line icon again d. This time you will draw an outline of your park, clicking at areas where you need to change directions i. Use roads as boundaries ii. Use large natural features as boundaries (larger rivers) iii. Try to exclude large bodies of water, buildings, parking lots, tennis courts (usually enclosed) iv. Do the best you can! (some parks are much more defined than others) e. f. Rename the polygon to the site number and name, style if you like g. Record the area given in the polygon in formation box in your worksheet i. You may need to convert to square kilometers, using an online converter 8. potential habitats for wildlife use.) a. Zoom in and out to se e areas around your park, and toggle between map and satellite views b. Use the ruler tool, and click close to the pin i. Scan around and choose the closest nearest potential habitat 1. Could be green areas in map view, but not all potential habitats are shaded gre en 2. Check satellite view, too; is there a golf course or private park that is not shaded green in map view? Use your best judgement. c. Once you and your partner have decided on a location, click within the Distance to Nearest Habitat layer to make it active d. Draw a line from the edge of the park the camera is in to the nearest habitat you chose i. Rename the line (site # and name) and style as you like e. Record the line distance in your worksheet, you may need to convert to meters 9. Count how many habitats are with a 1 km buffer of the site. Another metric that could area around the site. a. Center the map on your site, zoom out so you can see 1 km (0.62mi) around the camera b. Toggle between satellite and map basemaps to find other nearby parks/green spaces/etc. c. Click on the measurement tool (the little ruler under the search bar)

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147 i. Now your cursor is a little plus symbol d. Click on your camera site e. Now drag your cursor away from the came ra location until you get out to 1 km (0.62mi) f. Do your best to keep the cursor at this distance as you slowly make a circle around the site, counting each potential habitat until you make a full circle. g. Record the # of potential habitats within 1km of eac h site 10. Repeat steps 5 9 for all of your sites. Make sure you complete all 4 layers for your sites (like in the example below, showing 2 base maps of the same map), your TA will check the maps for completion. CONTINUE TO THE NEXT PAGE The measurements you just made were made by GIS data layers that YOU created based on Google satellite imagery. There are also MANY open source (free) datasets and maps where you can locate and record data at specific points. We will now explore one and record light pollut ion radiance levels (unit = 10 9 W/cm 2 * sr = generally meaning Watts/Area) at the

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148 camera locations using an available map with a 2017 VIIRS (Visible Infrared Imaging Radiometer Suite) GIS layer. 1. Have the site GPS information of the camera locations ready . a. Open the Site Information Folder in the class google folder. b. Open the Site Information Google Sheet. 2. Go to the website that has the light pollution data map https://www.lightpollutionmap.info 3. 4. In the search bar, search for Denver, CO a. Notice the different colors across the Denver area i. right sidebar 5. Go back to your Google Sheet, and... a. Copy the lat/long GPS coordinates together for 1 of your locations b. Paste it into the search bar of the map Select the point in the drop down menu that the map recognizes 6. Zoom in close to the point using the + button in the upper left corner 7. Click on the map as close to the pin as possible. A box will appear with some data, location on your GIS data collection worksheet

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149 8. Repeat for the other locations. Bring this sheet to lab. We will finish the GIS site level data collection in lab. GIS Site Level Measurements Worksheet Using the GIS Instructions in the UWIN 3 section of the lab manual, create your own Google MyMap of the sites that were assigned to your team and explore an open source data map. Using the GPS coordinates of your sites, create site each site. Complete the following: Center the map on your site, toggle between satellite view and map view, looking for water sources nearby. Using the directions in the manual, create a Distance to Water layer and draw a line from the camera to the nearest water source that could be available to wildlife (e.g. not an indoor pool, etc.). Click on the line and record the distance in meters. Site #_________ Location Code_______________ Distance to water _____________ ______ Site #_________ Location Code_______________ Distance to water____________________ Site #_________ Location Code_______________ Distance to water____________________ Site #_________ Location Code_______________ Distance to water__________________ __ Center the map on your site, zoom the map so that you can see the boundaries of your park. Using the directions, create a Park Area layer and draw a polygon on the boundaries of the park. Try to avoid buildings, roads, lakes, and parking lots with you r measurement. Google maps will give different area in different units, use an online unit converter to convert your measurement to square kilometers (km 2 ). Site #_________ Location Code_______________ Area (in km 2 ) ___________________ Site #_________ L ocation Code_______________ Area (in km 2 ) ___________________ Site #_________ Location Code_______________ Area (in km 2 ) ___________________

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150 Site #_________ Location Code_______________ Area (in km 2 ) ___________________ Center the map on your site, t oggle between satellite view and map view, looking for the next closest potential habitat for wildlife (another park, cemetery, golf course, recreation/stream corridor, etc.). This is a proxy for habitat connectivity how connected a park is to other habit at fragments. Using the directions, create a Distance to Nearest Habitat layer and draw a line from the camera to the nearest potential habitat, record the distance in meters. Site #_________ Location Code_______________ Distance to next habitat ________ ________ Site #_________ Location Code_______________ Distance to next habitat ________________ Site #_________ Location Code_______________ Distance to next habitat ________________ Site #_________ Location Code_______________ Distance to next habitat ________________ Another proxy for habitat connectivity could be how many potential habitats are within a radius around the sites. Use the measurement tool (the little ruler) to count how many potential habitats are within 1 km (0.62 mi) of the site. Site #_________ Location Code__________ # habitats/ km buffer ___________________ Site #_________ Location Code__________ # habitats/ km buffer ___________________ Site #_________ Location Code__________ # habitats/ km buffer ___________________ Site #_________ Location Code__________ # habitats/ km buffer ___________________ CONTINUE TO THE NEXT PAGE Now we will record light pollution radiance levels at the camera locations using an available map with a 2017 VIIRS (Visible Infrared Imaging Radio meter Suite) GIS layer. USE THE GIS INSTRUCTIONS to record light radiance levels (unit = 10 9 W/cm 2 * sr = generally meaning Watts/Area) at your assigned locations. (Higher levels= more light)

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151 Site #_________ Location Code_______________ Radiance Level ___________________ Site #_________ Location Code_______________ Radiance Level ___________________ Site #_________ Location Code_______________ Radiance Level ___________________ Site #_________ Location Code_______________ Radiance Level __________ _________ In addition to these variables , you will have access to 3 GIS spatially calculated datasets that were calculated within a 1 km buffer around each site: 1. Average percent impervious surface 2. Average percent tree canopy cover 3. Estimated human popu lation Now that you have measured site characteristics both in the field and using GIS technology, please enter your data as directed by your TA.

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152 UWIN 3.2 Data Entry (10 15 min) 1. There are 2 google forms for data entry, one for the FIELD data and o ne for the GIS data. 2. Have student groups enter the FIELD data together as ONE entry with everyones name on it if they did not go as a group of four, they can enter separate entries. 3. Have student PAIRS enter the GIS data together as TWO separate entries E MPHASIZE that they will have 2 form entries, one per location! a. The site level data will be compiled and available for students to use in UWIN 5. Once you are done recording your GIS measurements, get out your field data collection sheet as well. Enter all the data you collected into the google form or as directed by your TA. This data will be compiled so everyone can have access to the full dataset.

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153 UWIN 3.3 Practice Tagging Photos (30 40 min) 1. Students will use the LAB computer and thumbdrive for this a ctivity a. Students will navigate to E: \ UWIN \ Tagging Practice \ (YOUR SECTION) \ Database for Tagging Practice (this is given step by step in the instructions) 2. Emphasize to USE the Tagging instructions and point out the SHORTCUTS. a. This is not a race, but to becom e familiar with the tagging process and shortcuts available Students will be tagging a large amount of photos next week in UWIN 4. 3. After about 20 minutes Ask students how many pictures they got through a. Write this down and report in the TA reflection sur vey 4. Have students move on to the verification portion of this activity (#10 in the instructions) a. b. Ask students about the value of multiple people tagging the same photo 5. Screencast for TA to watch if needed (http s://youtu.be/8x7BvT4hMJ0) The tagging module is a smaller version of the PhotoWarehouse database that just allows for tagging of the photos with species ident ification, number of individuals, and any details. During this practice time, you should take time to familiarize yourself with the species in the photos to make identification easier the more you do it. Also, try to figure out the species shortcuts and pi cture advancing techniques to make the identification process as efficient as possible. Your TA will assign a USB thumb drive to your team that will contain a tagging module and the associated pictures. You will be given a time limit to identify photos, t his is not a race to tag more photos than another group, but to become familiar with the species and the tagging process, so we can get started right away next week when you will have a larger amount of photos to tag. Use the tagging instructions to guide you through the tagging proces s. photo IDs by having multiple people tag each photo. The database has a nice feature that will pull out all the mismatched photos, and show each ID and how th ey do not match. You will see how this works during this activity. Next week, we will be tagging photos for your research and for UWIN, so we need to be as accurate and efficient as possible.

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154 Tagging Practice Instructions The purpose of this activity is to introduce you to the tagging module where pictures are This practice will familiarize you with the tagging module and species identification using keyboard sho rtcuts. The goal is not to see how fast you can tag the photos, it is to become efficient and accurate at tagging. Because next week you will have a larger set of photos to tag and we want to work out any confusions or issues before then. After about 20 mi have multiple people tag the same photos to try to limit any photo ID errors. This is done through the database itself. When all the pictures have been tagged, or when prompted, mo ve on to #10 of these instructions. Instructions for the database: Work in pairs, it is recommended to have one computer showing these instructions (or use a printed copy), and a Lab laptop to use for the tagging practice. Unfortunately, for technical rea sons you cannot use your own laptop for this activity. Under no circumstances should the provided USB drives be inserted into your personal computers or removed from lab. folder and will include a small subset of pictures that the database is linked to. 1. Insert the USB thumb drive assigned to you into a PC Lab computer a. You must use a PC computer that has Microsoft Access, Mac computers will not work b. Under no circumstances should the USB drive be inserted into your own computer 2. Go to your File Explorer by searching for it, or clicking on the symbol 3. In the left menu, find where the USB thumb drive is located and make sure it is an (E:) drive a. If the drive is NOT (E:), please call your TA over before proceeding 4. a. Open the UWIN folder, b.

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155 c. i. Right click on the folder ii. iii. Within the folder, ri iv. Rename the folder to your semester and section (i.e. Spring 2018 003) 5. a. Example see the below picture, the newly copied folder is 6. 7.

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156 a. This time, select the box to open the tagging module. i. You will have to enter your first and last name in the box, and hit the ii. continue. iii. Update the initials, if you want (not necessary) 1. 2.

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157 8. Notice: a. The record number in the bottom left, this shows how many photos are in the module and which photo you are on b. You can see the photo name at the top of the photo shows the location and season /year the photo was taken c. d. e. the picture above), however it is the same information on the Shortcuts cheat sheet that will be available to you. Note any tips/tricks on the cheat sheet. 9. Tagging: a. You should scan the ph oto for all species look closely at the edges, in camouflaged areas, and in heavily shaded areas b. You can only use the shortcut key for the first species, if two species are seen, you have to type it out in the second species row c. If you click inside the sp ecies box, the shortcut key will not work you will have to type out the species as it appears on the species list, or select it from the dropdown menu

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158 d. If you think you have identified a new species not on the current list, check with other students and yo ur TA before you add it to the species list e. arrow key. f. HINT g. ALSO, if you find a species that was hard to find, you can add a box around it. This way if there are discrepancies in the tagging, it is easy to find what you saw when you did your tagging. i. Click you let off of the click). If you want to delete the box, just click inside it. ii. You can add multiple boxes, if needed h. Start tagging the photos with a partner, after about 20 minutes your TA will (orange box around a fox squirrel) MOVE ON TO THE NEXT PAGE WHEN YOUR TA PROMPTS YOU.

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159 10. Check your tagging: a. Now, you can check your tags against another person who has already tagged these just come to a consensus of what the most reasonable species tag would be. b. i. cident, just reopen it c. d. i. The database will open a module that looks just like the tagging module, but it shows all the tags for that photo tags in red indicate tags added by t match up. They need to be resolved. 1. See how many you need to resolve in the bottom left Records box ii. e. Take note of wher e you missed something or accidentally put in the wrong species. Again, this is practice for next week, when you will have to tag a larger amount of photos.

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160 Tagging Shortcuts Shortcut Key Detail Shortcut Common Name Genus species q bird g bird (canada goose) Branta canadensis y u (unhealthy) coyote Canis latrans c domestic cat Felis catus d domestic dog Canis familiaris e elk Cervus elaphus f u (unhealthy) fox (red) Vulpes vulpes o horse Equus caballus h human Homo sapiens m mountain lion Puma concolor l mule deer Odocoileus hemionus n NONE p prairie dog (black tailed) Cynomys ludovicianus b rabbit (eastern cottontail) Sylvilagus floridanus r u (unhealthy) raccoon Procyon lotor t rat spp. Rattus spp. u skunk (striped) Mephiti s mephitis s squirrel (fox) Sciurus niger Only differentiate geese from other birds (g=canada goose, q= all other birds)

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161 ONLY count up to 6 individuals , for example can review) You can use the z key to repeat ALL data from the last record Right arrow mov es to the next picture, Left arrow moves to the previous picture You can add comments, but they are not filterable Only tag easily identifiable species do not zoom in to count humans way in the background UWIN 3.4 Outlining Title, Intro, Methods, and Re ferences Sections (45 min rest of lab period) 1. Pass out the Outline Guide Worksheet (theirs to keep). a. Point out to students that this resource is general enough to be used for future scientific papers they will write in future classes. b. Student pairs can wo rk together on ideas, BUT they must write answers for the worksheet IN THEIR OWN WORDS. c. Remind students of the potential sources they could use from the bibliography they created last week. 2. Tell students that you will provide feedback on these sections of the worksheet in the postlab assignment, so to be as detailed as possible. Students will then use this outline as a guide to write their term paper. Using the rest of the lab period, use the writing guidelines and the guided worksheet to work with your partner to start to outline the above sections of your paper (you will outline the other sections later). It is recommended to outline the importa nt points that are required first with your partner, then at home should use the outline to help you write a draft of these sections of your term paper.

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162 Instructions for Authors (Writing Guidelines) The work of scientists culminates in the disseminatio n of findings to the scientific community. The process of assembling research findings into a coherent story that is interesting to others is an important part of the work of all scientists. As with many things in life, there are rules to follow in present ing the findings made in your work to others. Such rules are designed to make it as easy as possible for your work to be evaluated by journals and websites that might be interested in publishing your work. Also, the rules make it easier for your peers to q uickly internalize and understand your work. Your further development as a scientist depends upon your ability to master the form, style, and art of scientific writing. What follows is a format for the scientific paper for this course. This handout is orga nized into general instructions, then specific instructions for each section. These formatting and section guidelines are similar to the instructions sent to authors by scientific journals which are called l to journal but all serve the goals of creating some uniformity in the journal, and increasing communication. Since this assignment mimics real journal requirements, you will have seen this formatting and structure in much of the primary literature articl es you have read this semester. General Instructions 1. Your article should contain the following sections: (1) Title Page/Abstract, (2) Introduction, (3) Methods, (4) Results, (5) Discussion, and (6) Literature Cited. Each serves a specific purpose which is detailed below. Take care to place the right text in the right section; format matters. 2. Use word processing software to write your paper. Use an easily read font such as Times New Roman, of a suitable size (12 points is standard). Double space the lines an d maintain at least 1" margins along all edges. 3. Be clear, concise, and insightful with your prose. 4. Avoid all anthropomorphisms, awkward phrasings, and grammatical illegalities. 5. Do not use quotes. 6. Use Council of Science Editors (CSE) name year format for c itations. Cite in text and in 7. Proofread and spell check your work. 8. Any technical terms you use to communicate your question, hypothesis, methods, results, etc, need to be briefly defined on first use with abbreviations in parentheses following the full description, and can subsequently be used as abbreviations. 9.

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163 Specific Instructions for the Title P age (Title, Names, and Abstract). 1. The title page contains your title, your name, and your abstract on one page; the name of your TA, the course, or the course code are not necessary. 2. The title should relate to your investigation. The title should be clear, concise, and appropriate. Ideally it summarizes the main finding without including detail. 3. Following the title, list your name (bolded), followed by additional contributors (for example: Fall 2011 GB2 Lab Students, not bolded) 4. Abstract a. Write this section last b. The Abstract contains a short summary of every section in your report. Cover the main points only without dwelling on details of your methods or results. In reality, the Abstract will be the only part of your paper that will be read by the majority o f those who get past the title; therefore, tailor the prose for maximum speed, simplicity, and impact. c. Summarize each section of the paper 1. Introduction 1 2 sentences that includes the rationale for the study and the main question/objective 2. Methods 1 sent ence that briefly describe what was done 3. Results 1 3 sentences that describe specifically what was found 4. Discussion 1 2 sentences that gives a conclusion what do your results mean? d. Length = 250 300 words e. Write clearly, use good college level English, proofread, spell check, and explain Specific Instructions for the Introduction (length = ~1 1.5 pages). The Introduction should be like a funnel: it starts broad and gets more s pecific. The introduction should provide relevant background, comment on relevance of the work, and suggest specific goals. Your introduction should synthesize the following: 1. kground 2. The reasons for doing the research; explain why this study is important and how it will contribute to the scientific community.

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164 3. A statement of the research question(s) or objective(s); what information will you obtain doing this study? The question should be specific and testable and consistent with the background information given. Use CSE name year fo rmatting for your citations; most of your citations will be in the Introduction. At least 2 primary literature citations are required. Specific Instructions for the Methods (length ~ 1 2 pages) The Methods section should provide sufficient information to allow someone to repeat your work. A clear description of the experimental design, sampling procedures, and data analysis is you. Synthesize the various approache s into your own specific section. The Methods section should: 1. Describe the materials and procedures used during your investigation, in your OWN words! You are encouraged to use sub headings to subdivide this section if necessary 2. Introduce the study area (Denver), give the reader an idea of the basic ecology of the area where your sites are located. 3. Briefly describe the site selection protocol, and provide any detailed fi gures including maps showing the general location of the sites as well as any photos that you took that will help describe the methods used. 4. Describe how you collected/measured the data you will use in your analysis, not all the data collected. Make sure t o explicitly state how your dependent and independent variables were measured. 5. Describe how you analyzed your data a. For this project, we will not be using statistical tests instead you will think critically about the data and trends you might see. In later classes you will learn how to use statistical tests appropriately and how to interpret statistical outputs. For this subsection, describe what type of research you are doing, and how you will display/organize your data in order to address your question. S hould be in enough detail that someone could repeat it if they had the raw data. 6. Not be written in the first or second person. See your TA for help with this. Specific Instructions for the Results (length of text ~1 page, not including Tables and Figures) .

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165 This section contains all of the results of the research and other measurements you made presented objectively WITHOUT interpretation, usually the shortest section. The Results section should: 1. Present the results from each of the sets of data you said yo u would collect in the Methods Section, in the same order as in the Methods. 2. Not include interpretations of your results. Simply state the results in a logical, reasoned manner 3. Graphically present data as is appropriate and relevant to the research questio n/objectives. For every data set there exists an optimum format for presentation. This format may be a combination of tables and figures (e.g. scatterplots, bar graphs, histogram, etc.) that are: a. well formatted and easy to read; b. Illustrative of the data wi th a minimum of redundancy; and c. enable the reader to quickly internalize the results. Poorly conceived graphs will obscure the data and leave readers unconvinced. It is up to you to decide how best to present your data we will not tell you how to do thi s. Based on discussions in lab and prior lab experiences you should be making sound decisions on how to present different data sets. 4. Have the required data shown a. Show the distribution of your independent variable b. Show the data that addresses your specific research question 5. Not show the same data in multiple tables/figures. 6. Have all tables and figures numbered sequentially (i.e., Table 1, Table 2, Figure 1, etc.), and all must be referred to by number in the text. All tables and figures must have legends or captions that: a. Use complete sentences or sentence fragments b. Include clear units c. Standalone from the results or methods text; a figure should be understood from itself plus its caption 7. Computer generated tables and figures only; nothing hand drawn and photo graphed. 8. Clearly explain every Table and Figure in the Results using text very similar if not identical to text in each Table or Figure legend. 9. Clearly indicate whenever possible the variation in any average you present (graphically on each figure or numer ically on each table). Specifically explain replication as needed

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166 to explain any measures of sample variation you present. This may or may not be appropriate to your investigation students come up with different investigations. Specific Instructions for the Discussion (length ~ 1 page). The Discussion should explain the significance of the results and explain the new knowledge obtained from the research. The discussion is the opportunity for speculation, generalization, and interpretation. You are encour aged to use sub headings to subdivide this section if The Discussion should: 1. Re introduce the general topic of the report. Work especially hard at getting a good o pening sentence. Why is the general topic of the report of any interest to you and to the reader? Your job is to motivate interest in the reader, but keep it brief (1 2 sentences). 2. Articulate your interpretation of your results. Support your conclusions wi th evidence (refer to your data/figures/tables/ other citable literature). Re connect to the themes of your paper that you laid out in your Introduction. What do your findings reveal about the goal of your investigation? 3. Interpret all of your results in th e same order as in the Results section. Explain to the reader what the Tables and Figures show. Avoid recitation of previous detail, but cite Tables and Figures to support your statements and conclusions. Never over extend yourself beyond your database. Ab stain from speculations that your data do not specifically support. Be conservative in your assessments, but do not make excuses. 4. Discuss any assumptions, limitations, or potential issues that could have influenced your are in your findings. It is important to not make conclusions that the data does not represent. 5. Include a discussion of what the future directions/ next observations/ experiments/ refinements could or should be. Explain how to improve upon the study desig n to better study your specific research question(s) and why your suggestions will provide better data to address your question. 6. Not introduce any new results; all results should be in the results section. Specific Instructions for the Literature Cited Se ction. The Literature Cited section should contain the reference to works you cite somewhere in your paper. Citations should be in CSE name year format for all types of reference. See htt p://writing.wisc.edu/Handbook/DocCSE_NameYear.html . At least 2 primary literature cites.

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167 General writing and grammar tips: 1. Blank documents are scary. Start with a general structure on your page just put down the section headings and go from there. Use your Outline to help you structure your novel. Use a standard format for headings etc. Word and Google Docs have heading styles which create uniformity for the reader 2. You cannot be your own lexicographer: never create words (e.g. "obtaination," "mobilate"). Use a dictionary. 3. 4. https://www.grammarly.com/blog/affect vs effect/ 5. possessive form of it 6. 7. Be careful with pronouns. A pronoun replaces a noun when repeated use of the noun sounds strange. Avoid introducing ambiguou crystal clear to what the pronoun refers. 8. Write organisms names correctly using the binomial convention: i.e., the first letter of the genus name is capitalized and the species name should all be lowercase. Italiciz e the entire name. 9. Colons are used to introduce elements or list items, or to clarify an idea: (they can also a. Vulcans have three ears: a left ear, a right ear, and a final frontier. b. You h ave two choices: stand and fight, or run. 10. Semi colons are used to join two independent clauses that clearly relate to each other when two independent sentences seem awkward: a. Wednesday was hot; Thursday was cooler. b. The rough and smooth endoplasmic reticulum s (ER) are structurally and functionally contiguous; the Golgi is only functionally contiguous with the smooth ER. 11. processing software. Carbon dioxide abbreviated to CO2 sho ws you are being lazy and is not the same as CO 2 . 12. This is a good site to check general college level grammar rules: https://www.grammarly.com/blog/category/handbook/

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168 UWIN Term Paper Outline Guide When writing a scientific paper, it is helpful to outline your ideas before you start writing. Outlining can help you organize the sections, help you relate different ideas and parts of your writing to each other, and ensures that you incorporate a ll the important parts of each section. Use this guide along with the writing guidelines to help you start writing. Throughout the semester, we will work on outlining specific sections, which may not always occur in the same order as they are presented in this worksheet. This outline follows the order of the term paper. You can work with others when discussing ideas, BUT everything MUST be in your own words . you can use this when reviewing your own , or others, outlines/written work. Title: Write after your introduction you might consider revision, as your work progresses. 1. Write down your research question/specific research objectives. 2. Write down where your study is taking place. 3. Using the writin g guidelines, write out how you will present the authors under the title. 4. Incorporate the above information into a concise and informative title that reflects your study. Abstract: Write LAST . Everything in the Abstract must be in the main text somewher e. Write down some bullet points or notes for the following questions that you should pull out of other areas of this guide or your written paper. 1. What is the main rationale/importance statement you give for your study? In other words, what did you say to 2. What is/are the main objective(s) of your study? 3. In bullet point form, summarize the methods what was done for your specific study? 4. In bullet points, list out your results.

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169 5. In bullet points, what did you c onclude in your discussion section? Introduction: Write this section first . Remember, a good introduction includes relevant background information, importance of the overall study system/topic, your specific research question/research objectives, and a ra tionale for why you are doing your specific research. This will be the section that has most of your citations, so as you fill these questions out and have summaries from citable sources, write a citation note next to your bullet point , so you can easily a dd citations when you write your paper. Note: this does not necessarily need to be the order you write about these points. 1. In bullet point form (aim for about 2 4 points), write why this research topic (i.e., studying urban wildlife) is important. 2. State your specific and testable research question(s). Double check that you included your independent and dependent variables. Only include what you will do in your analysis, not all of the things that we measured for the class. 3. In bullet point form, give the background information that would be needed to understand your research question. 4. In bullet point form, give reasons why your research question is important. Methods: Write after you have written the introduction. The methods section should give a cle ar overview of what was done to complete your research. A good portion of the methods was already selected, so you will have to summarize these portions in your own words . Do not copy and paste from any sources, including your lab manual and do not use quo tations in your work. You can include subsections, as suggested in the writing guidelines. If you have summaries from citable sources, write a citation note next to your bullet point , so you can easily add citations when you write your paper. 1. In bullet po int form, describe some relevant information/basic ecology of the study area (i.e., Denver, CO)? 2. In bullet point form, describe how were the camera sites picked?

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170 3. In bullet point form, describe the methodologies used in the study (i.e., cameras/set up) so t hat the design of the study can be completely understood and replicated. 4. State your independent variable(s) and your dependent variable(s) for your research question. 5. In bullet point form, state how the independent variable for your research question was/w ill be measured. 6. In bullet point form, state how the dependent variable for your research question was/will be measured. 7. Describe the figures or tables you need to create to summarize/illustrate your results. For each graph, state what the axes will be. Fo r each table, state what the column and row headers will be. If you want, you can sketch the figures and tables out (without data). 8. State how the data you collect will directly provide necessary evidence to address your research question. One way would be to look at your figures/tables. These should be set up in a way that helpfully summarizes/visualizes the data. Results: Write this section after you create your visualizations of the data (tables or figures). Only present data that is relevant to your re search question/objectives. Only present specific data once, do not make multiple figures/tables that show the same exact data, and do not show gratuitous data (data that does not contribute to the conclusions you make) present the representations of data that visually helps the reader see the data the most clearly. 1. Copy and paste each results figure and/or table you created in your electronic version of this worksheet (or take a screenshot and insert a picture), make sure each one has a label (Table 1, Fi gure 1, etc.), an informative caption explaining the figure/table, that all headings, axes, colors, etc. are correctly labelled, and it is professional looking. Use figures and tables that you see in primary literature as models for how yours should look. 2. In bullet point form, list and summarize the relevant numerical results of your data state the figure/table that the result is shown (if applicable). Discussion:

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171 Write this section after the results section. The discussion should explain the significance of the results you presented, along with any speculations, potential errors, potential contributions, and potential future directions. There is potential to compare your results to similar previous studies as well. 1. the topic to readers briefly. Then, restate your objectives to remind the reader of what you are doing and why. 2. For each of the numerical results (figures/tables) you have decided to report make a list of them and briefly state what each means, and why it might be important. Try to list them in the order you will present them in the results section. When you write your dis cussion fully, you will restate the the main point of the figures and table and interpret them each both in light of your research question. 3. List any assumptions, limitations, and/or potential issues that could have influenced your results. For each assum ption, limitation, or potential issue you list, explain how/why it is problematic or how/why it impacts your study and validity of your conclusions. 4. Write down how confident you are in the results. In what ways could you increase your confidence level in future studies? How would you set up the cameras (generally) in order to better address your specific question? 5. What is the overall conclusion that you can make based on the study you conducted? 6. What is the significance of your findings in terms of the your study contribute to the field of urban ecology?) Literature Cited, or References, Section Add to this section electronically as you are writing . Use CSE name year citation formatting. This is an important section for reade rs to see what you have referenced, in order to do further reading on the subject. References must be BOTH in the text (with in line citation formatting) and in the References section (full citations in alphabetical order). Anytime you write about someone else's work, it must be cited. Please review the plagiarism resources in Canvas.

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172 Section Areas for Feedback (for the outline and/or the term paper) Title Is the title Specific? Concise? Able to tell what th e research is about? Is the formatting for authors correct? Abstract Are all major parts of the paper summarized? (Intro, Methods, Results, Discussion) Is everything in the Abstract also in the paper? Make sure there is no new information presented here. Introduction: Background Put a star next to the information you found valuable. Put a triangle next to any information you thought was not necessary. Intro: Importance Is this rationale of importance compelling? Why or why not? Intro: Research Question Is the question Specific? Testable? Is the rationale for the question compelling? Why or why not? Methods Is the site description adequate? If not, how you would you improve it? Did the methods provide the data needed to address the question? Why or why not? Could you replicate the data collection for the specific question given the information provided here? If, not, what is missing? Results Is the data summarized? Are all figures/tabl es referred to? Do all figures/tables have the necessary components? If not, what is missing? Can you state all the figures and tables main objective? Do you need any clarification on any of the figures/tables? Is only relevant data presented? Point out da Does any data seem missing? Discussion Is the topic reintroduced? Are the results interpreted? What does the data show? Are only the important findings/patterns/trends focused on? Put a star next to the conclusions you foun d valuable Put a triangle next to the conclusions you thought were not necessary What questions do you have about the conclusions? Are assumptions, inconsistent results, issues, and limitations addressed? Is the significance of the findings addressed in te the study contribute to the field?) Are future directions of how to further study the question addressed? References Are citations compliant with CSE name year formatting? Are all citations both in the text and in t he References section? Is there any evidence of plagiarism?

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173 UWIN Week 4: Photo Tagging and Figures Practice Goals/Objectives 1. Collaborative data management (1.5 hours) a. This will contribute to having all the pictures tagged in order for everyone to be able to analyze the data. 2. Figure Activity (1 hour) a. You will practice making different types of figures using a similar dataset that you could use to create figures that will addr ess your own research question. Action Items 1. Read UWIN week 4 materials. 2. Prior to lab, print the figures activity worksheet and bring to lab. 3. P articipate in all lab activities, make sure to complete all activities and take excellent notes. 4. Complete the UWIN 4 post lab within 72 hours after this lab. Materials: 1. Google Presentation to facilitate the lab activities 2. Lab computers, with power cord a nd ethernet cord (2/table) 3. Thumb drives with tagging modules (1/pair of students) 4. Lab Copies of the Tagging Instructions and Tagging Shortcuts 5. Lab Copies of the Figures Activity Worksheet 6. May want to let students know to bring their computers to class, and to make sure to download Office 365 (microsoft 2016 products) for free from the university by Visit a. Can get here through universit y onedrive app as well.

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174 UWIN 4.1 Tagging New Photos (1.5 hours) 1. a. Pass out thumb drives, Tagging Instructions, and the Tagging Shortcuts b. Each thumb drive has different photos on th em c. Make sure you check you have all drives before you start lab and all drives as stable at that point. Check as students leave. 2. Let students know that classes after them will tag the same photos, for many verifications of Identifications please be as accurate and efficient as possible! a. Use the instructions/shortcuts! b. Spend more time on this and less time on the next part if need be. 3. Photo ranges will be assigned to each sectio n with some overlap, so there is a greater chance of tagging all the photos. a. Once we know the total number of photos in each module, we will assign each section a subset. For example: i. 001= pictures 0 300 ii. 002= pictures 100 400 iii. 003= pictures 200 500, etc. b. I f students finish early, have them continue to tag more photos. 4. IMPORTANT!! a. It is VERY important that students CLOSE OUT of the database BEFORE removing the thumb drive! b. Durin g lab, you will be given a USB thumb drive again, this time with the photos from this of students will tag a different subset of the photos. Use the tagging directions and species shortcuts that will be very similar to what you used during last lab. Since everyone is looking at different photos, if you c ome across a particularly interesting photo, feel free to shout and have everyone see. Your TA may create a space for screenshots of awesome pictures to share.

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175 Tagging Instructions Your TA will assign you a certain range of photos that you will work on t agging for a large portion of lab. Tagging the photos is an important contribution to the nationwide project, along with obtaining mammalian wildlife data for your own research. Please use the tagging shortcuts to help you be efficient at tagging, however also try to be as accurate as possible. You can double check the species identification guide, or look up what different species look like online. Instructions for the database: Work in pairs, it is recommended to have one computer showing these instruct ions (or use a printed copy), and a Lab laptop to use for the database exploration. Unfortunately, for technical reasons you cannot use your own laptop for this activity. Under no circumstances should the provided USB drives be inserted into your personal computers or removed from lab. The database you will use for this activity will be on a USB thumb drive. 1. Open the UWIN > Tagging Modules folder 2. Open the folder for the season/year that your TA assigned to you 3. Open the Access Database within that folder 4. Ent er a student name for the Observer (you will have to enter a name again, because this is a different database module) 5. Tag the photos within the range your TA assigned to you a. Each lab period will tag a different range of photos to ensure full coverage 6. IMPOR TANT : When you are finished, exit out of the database before ejecting the thumbdrive! Tagging: 1. You should scan the photo for all species look closely at the edges, in camouflaged areas, and in heavily shaded areas a. You can double click on the photo to zoom in, just close out of the photo when done 2. You can only use the shortcut key for the first species, if two species are seen, you have to type it out in the second species row 3. If you think you have identified a new species not on the current list, check wit h other students and your TA before you add it to the species list

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176 4. ALSO, if you find a species that was hard to find, you can add a box around it. This way if there are discrepancies in the tagging, it is easy to find what you saw when you did your tagging . a. off of the click). If you want to delete the box, just click inside it. b. You can add multiple boxes, if needed

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177 UWIN 4.2 Introduction to Tables and Figures (1 hour rest of lab) 1. Students should have printed their own Figures Worksheet. a. The goal of this activity is to have students practice creating different types of graphs in a very structured manner; they will have to think about how best to display their data using the se skills. b. The instructions are step by step for what is expected of them for the post lab and for the term paper so advise students to follow the directions. i. They will be expected to show (1) distribution of the independent variable across the sites, and (2) a correlation of their independent and dependent variable or data that addresses their research question. c. things in Excel if you are struggling with how to do something, l ook it up! d. Make sure you are familiar with how to use Excel to make data. If you have a preferred way to present data and are willing to teach it to your students, that is fine. Please read the results section of the writing guidelines , paying particular attention to the tables and figures instructions. When creating tables or figures, you will want to make sure to have all the components so that it can be understood without any other explanation. Make sure to have the following: 1. Label with Table 1., Figure 1, etc. a. Tables and figures are numbered consecutively but independently. So, you do Figure 1, Figure 2, Figure 3, Table 1, Figure 4, Table 2, not Figure 1, Figure 2, Figure 3, Table 4, Figure 5, Table 6 etc. 2. Create a title for your table/figure a. The title must communicate meaning b. 3. Write an informative caption in sentence form, should include any informat ion needed to understand the table/figure. You should also include units. 4. Make sure everything is labelled (columns, axes, colors, etc.) 5. Visually clear: the reader should be able to make some conclusions on the data by just looking at your table/figure Du ring lab we will practice making some figures that will most likely be what you will do with the new data next week. The product of this activity will be required for in your post lab, so be sure to finish before you leave lab, or be sure you understand wh at is needed before you leave lab. We want you to work alongside a partner to figure out making different types of figures in Excel and you will be using the same data, however you must make your own graphs; no sharing tables or figures. This means that yo ur figures will show the same data, but you yourself

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178 will need to create it and make it your with your own style. Download a Microsoft Excel version of a google sheet with a dataset from last year , this will look similar to the dataset you will get next week but this one only has a few of the variables you will measure/collect. Some data will be given as categorical data and some will be given as continuous data . Categorical data uses words or rankings to put data into categories, this could be because there is no numerical val ue to the data (such as happy/sad, male/female, or category (such as low/medium/high). You will sometimes hear people refer to this kind of data as nominal data. C ontinuous data is numerical data where each individual data point can be placed on a continuous scale, such as distance, weight, area, volume, time, etc. Due to constraints for setting up cameras, you were not able to select locations that would st your specific question. Therefore, you should look at the distribution of your specific variable across all the sites, whether it is categorical or continuous. For this activity you will look at percent impervious surface presented in different ways. Th is data was measured using GIS techniques and an impervious surface layer resulting in an average percent of impervious surface around each site. The measurements in this dataset are taken within a 1 km buffer around each site. Use the figure activity instructions worksheet that will lead you through how to make a few figures, and get you thinking about the different ways that this data could be looked at. You will have to manage a large dataset and choose how to show your data that is specific to your question. However, for your term paper, you will need to show the distribution of the specific variable(s) you are testing across all the sites yo u are studying and the data that addresses your specific question.

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179 Figures Activity Instructions This activity will give you an opportunity to practice making some figures like the ones you will need to make in your term paper. This will also get you thinking about the data you might want to use to address your research question, and the best way(s) to present that data. You will need to make sure you have Microsoft Office 365 (Microsoft Office 2016 products), you can download software for free from th e university by visiting https://www.office.com/ and logging in with university credentials. These instructions and photos are for PC users with 2016 Excel , Mac users can use a lab laptop or be comfortable using YouT ube to find videos for how to do these functions on a Mac. First , download an Excel version of a dataset from last year , this will look si milar to the dataset you will get next week but this one only has a few of the variables that you and class measured/collected. Distribution of an independent variable across all sites locations that would test your specific question as ideally as you might have liked. Therefore, you should look at the distribution of your specific variable across all the sites, regardless of whether the variable yields categorical or continuous data. Fo r this activity you will look at percent impervious surface (IS) presented in different graphical formats. This data was collected using GIS techniques and an IS layer resulting in an average percent of IS within a 1 km buffer (i.e. area) around each camer a location. Scatter Plot this is to relate % calculated IS to the geographical location of sites. Since the transect we used runs directly East to West, a axis and then plotting the % calculated IS on the Y axis. In Excel, make a scatter plot of site longitude vs % calculated IS. 1. Select all values in the longitude column 2. Then hold down t a. Now both columns should be highlighted 3.

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180 Now, you need to make sure you have a title, caption, and have the axes labelled appropria tely a wise student would get their work checked by their TA. You may need to look up how to add various chart items (like title, axes, etc.) within Excel, work together with your partner to figure it out. YouTube and Google are great ways to easily fin d specific functions. Finished example: Hint : To move the y axis label to the left side of the graph, you need to change the label position to What is the range of % IS values? Min___________ to Max___________ Do the sites seem to be evenl y distributed? (Are there equal number of sites from low to high?)

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181 Histogram Another way to look at the distribution of IS across all sites is to do a histogram values of IS. "Binning" values means taking continuous measurement data and categor izing each data point into a specific range, or "bin". For example, the values 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 could be binned into 1 3, 4 6, and 7 10 bins. Some literature has classified urbanization based on IS: low urbanization is set at <30% IS, medi um urbanization is set at 30 50% IS, and high urbanization at >50% IS. To create a histogram of the binned % IS, you may need to install the data analysis tool: 1. Click "File" and then "Option" 2. Click "Add Ins" and then "Go" beside the "Excel Add ins" drop down list 3. Check "Analysis ToolPak" and click "OK" To make a histogram : 1. Create bin values using a separate area of the sheet. Start a column with bin values, using the larger value you wa nt for a bin, in this case 30, 50, 100 (% IS). 2. Click on the Data tab, click on data analysis, and find histogram. 3. Enter the cell values asked for (picture below). a. Input range will be the values in the % IS column b. Bin range will be the four cells you made (bins, 30, 50, 100) c. d. Select a random cell for the output to go e.

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182 4. Update your graph This is where you make it informative, with your own style a. Histograms should have touching bars set gap width to 0 b. Crea te a text box for figure number, informative title, and caption c. axis (see hint below) d. Delete frequency legend e. Label your axes f. Format to your liking ave to update After:

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183 Hint you can change the bin value labels and in the bins/frequency table, and it will change in the graph Looking at your histogr am, are the sites evenly distributed among the low/med/high IS bins? Could this affect the confidence you have in any conclusions that could be drawn from the relationship between the plotted variables?? If so, how so?

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184 Correlation Graphs OK, now that bins and compare it to April 2017 Species Richness values. To bin IS in your exce l file: Bar Graph 1. Add a column to the right of your IS column. Name your new column, binned % IS. a. Highlight the column where you want a new one b. 2. In the first cell, you can cre ate a logic statement for these bins, type =IF(click on the cell also shown in the equation bar in picture below. 3. Then click on the cell , and hover over the box in the lower right corner. Grab the box and pull down the column to fill in the rest of the cells bar graph that shows the average species richness with standard deviation error bars for the low, medium, and hi gh % IS categories. 1. Filter the table by the Binned % IS column a. Click on the down arrow b. Select Sort from A to Z 2. To the right of the data table create another table a. Column headers % IS, Average Species Richness, and Standard Deviation b. Fill in low, medium, hi gh under the % IS column 3. a. ex) =AVERAGE (select the SR cells next to the low values in the table) b. ex)=STDEV(select the SR cells next to t he low values in the table)

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185 4. Repeat in the appropriate cells for Medium and High % IS 5. Select the % IS and Average SR columns 6. appropriate 7. Adjust the y 8. Adding error bars a. Click on the bars inside the graph you will notice they look selected b. c. d. For the positive values, select the cells where you calculated STDEV e. For the negative values i. If your SD values are larger than the average numbers, select the average numbers ii. If your SD values are smaller than the average number, select the SD values again 9. Add chart elements that are mis sing, and format and stylize to look professional Scatter Plot Now look at the correlation of species richness and % IS, but this time this with % IS as continuous data. Create another scatter plot, this time of % IS (on the x axis) vs. species richness (on the y axis).

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186 Now think about your question , How will you show the distribution of your independent variable(s)? What type of graph or table would be a good way to address your specific question? What data will you need? Sketch out how your graphs or tables might look. Label your axes, include a caption, include units. This will prepare you for making these graphs next week! UWIN Week 5: Analyze Photo Data and Create Figures Goals/Objectives 1. Analyze photo data and data entry (45 min) a. You will be ass igned a few sites and/or a few species to calculate species totals, species richness, and daily presence using the photo database. b. Enter your calculations into a google form or spreadsheet as directed by your TA. 2. Create figures/ think about your data (rest of lab) a. Use the practice and brainstorming you did in lab 4 to start to create your figures or tables. 3. Outline your results and discussion sections (rest of lab) a. Start to outline your results and discussion sections of your term paper with your partner. 4. G o over the presentation instructions for next week. (rest of lab) Action Items for Students 1. Read the relevant sections of this manual. 2. Complete the UWIN 5 Pre quiz prior to lab. 3. Participate in lab activities and use time wisely, this will prepare you fo r your presentation and term paper. 4. Plan your presentation, and submit google slides in the Post lab Assignment. 5. Complete the UWIN 5 Post lab Outline Assignment in Canvas Materials/Prep: 1. a. Can put t he copy of the SP18 data google sheet and a copy of the database here 2. move it to your class shared google folder (located in Lab Materials > UWIN 5) 3. 5 4.

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187 UWIN 5.1 Analyze photo data and data entry (45 min) 1. Be familiar with the database instructions , worksheet , and data entry plan 2. Go over the Database Instructions with students a. The tagged photos were uploaded into the database and photos were verified by GB2 staff over the weekend, students now can have access to the verified photo data b. Point out that the database will not be linked to phot os, but students will have access to all the photo metadata. c. Student pairs will use the photo database to: (students practiced this back in UWIN 1) i. Record the # of photos taken at 2 3 locations 1. Assign each student pair 2 3 locations 2. Students to use the pho to data collection worksheet ii. Calculate species richness (the number of species at each park) 1. For all mammals, and all WILD mammals (not the # of photos, the number of species!) 2. Students to use the photo data collection worksheet iii. Use the Occupancy Query to calculate the % daily use of 1 species for all locations 1. Assign each student pair 1 2 species 2. Students to use the database instructions to complete d. Students will need to enter the data MANUALLY in the google sheet (#3 below) e. There are citations for the 3 GIS derived datasets on the last page (if needed) 3. Materials > UWIN 5) a. There are sheet tabs that show the original field and GIS data that students collected i. Hidden at the momen t to see them go to View > Hidden sheets b. You could point out the variability of the data collected at a site, and some known errors that occurred i. Some people did not enter the correct site location for their data, mismatches on field data measurements, so me errors in GIS (not converting to correct units, measuring differently, etc.) c. Ask students for ideas on how to mitigate some of these mismatches or errors in the future

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188 i. Like: more structured training on how to measure certain variables, double checking data entries, etc. d. 1 data entry for each location. i. Point out that the field data columns start with (F), the GIS data columns start with (G) and the photo data that students will enter today starts with (P) ii. Sh making it easy to keep on track for data entry 1. Give students a tip to scroll down to the location they will work on, having the location row appear at the top making it easier to keep on track for da ta entry. 4. IMPORTANT: The Occupancy Query gives the data in alphabetical order for the PARK ID (not the Park #). The FULL DATASET sheet is in the correct order to start. This is very important when students paste their occupancy data into their tab. The pho tos that you tagged last week were also tagged by other lab periods. After the last lab session, all the tagging modules were uploaded into one main database. The database flags the nd/or other staff to come to a consensus of what species was present (or not present!). Now we can use the database associated with the photos that we tagged to extract data that we are interested in. Your TA will give you access to the main database via google drive. When you click on a Microsoft Access database file in google, it will download a copy to the PC computer you are for some reason you close out of it, you can always download another copy. Because storing a large amount of photos takes a lot of memory space and is difficult to move around, you will only be able to see the data associated with the pictures, and not the pictures themselves. Use the database instructions and worksheet to calculate some of the depe ndent variables we are interested in and that can be calculated from photos. As with other data we have collected for this project, we will split up the work so we can work together to create a dataset that covers all 40 sites and all species that were cap tured on camera. You and your partner will calculate total pictures for each species and wild mammalian species richness for 3 or 4 sites, and the percent daily use of all the parks for 1 or 2 species. These calculations should be somewhat familiar, as yo u calculated these during UWIN 1. Once you and your partner have finished your calculations, your TA will instruct you on how to enter the data.

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189 Database Data Analysis Instructions The pictures that you and your colleagues tagged last week were uploaded into one main database. Your TA will give you access to the database online. Since it is difficult to share all the photos, the database you have access to will only have the metadata (location, date, time, species, number of individuals, details, etc), ho wever, you will not be able to see the actual photos. You will work together as a class to compile usable data from the photos. With a partner, first you will record the number of pictures taken for a location, and the number of pictures tagged for each s pecies at that location. You will repeat this for 3 locations. Then, you will run an occupancy query to calculate the percent daily use of one species, at all locations, using the database analysis tools. Open the main Database with all the photo metadata 1. Using a lab laptop, navigate to the google folder shared with your class. 2. Record the number of pictures taken for each species for your locations 1. 2. Filter by your location a. Re cord the total # of pictures for your location 3. a. This will show all the species that were tagged for that location 4. Fill in the Species Table in the photo data collection worksheet 5. Repeat for a ll your assigned locations Calculate the # of total mammalian species, and # of wild mammalian species (species richness) 1. Using your photo data collection sheet, count the number of different species seen in each location. a. This is the number of species, not the number of photos taken. 2. Record the number of all mammalian species (domestic and non include birds)

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190 3. Record the number of wild mammalian species (non domestic only) Enter the data from the worksheet into the class data google shee t 1. Navigate to the google class folder, and open the DATA SP18 google spreadsheet 2. Scroll down to your location, putting the location as the row at the top of the sheet 3. Scroll to the right, find the first empty column (Column V= Total # of pics) 4. Use your dat a collection table to enter all the columns (V through AP) for # of pictures and species richness calculations. Calculate the percent daily use of one species for all the locations 1. First, open the sheet tab at the bottom of the SP18 google spreadsheet tha t corresponds with your species (example= % Use Coyote). This is where you will paste your occupancy query results. 2. corner) 3. The switchboard should appear again 4. Open th 5. 6. Enter the species you are assigned 7. a. We are interested in daily use over 4 weeks, so our occasions will be days chan ge the number of occasions to 28 b. 8. 9. DO NOT EXPORT, since we want to have all the data in one spot= copy and paste into your new sheet you made. a. IMPORTANT: We ONLY want the 1st column through the Occasion 28 column. columns. An occasion is a day when the cameras were out collecting data. i. Click on the header in the first column (Location) to highlight it

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191 ii. Scroll over to the Occasion 28 column iii. Hold down the shift key, and click on the Occasion 28 header 1. This will highlight all the columns between location and Occasion 28 iv. Copy the selection b. Go back to the SP18 class data google sheet, and open the tab at the bottom for your species c. Paste the data in the first cell (A1) 10. Your species % Use google sheet tab has the % daily use equation already set up for you. Scroll over to Column AD and check that it is filled in with values. Click on one of the cells to see the equat ion in the formula bar. 11. The FULL DATASET tab in the google sheet is also set up to automatically reference all the species % Use tabs. Go to the FULL DATASET tab, and scroll over and find your to make sure your v alues transferred correctly. 12. Once everyone has completed entering the photo data into the google sheet you are now ready to start analyzing the data that is appropriate for your research. a. e i. Or make a copy of the sheet and save it to your own Google Drive, if you plan on using Google b. This is now your personal copy of the full dataset c. Use the skills you learned last week to create the figures needed to show the data that will address your res earch question. Useful Information: 1. over the past few weeks. Columns that start with (F) are field collected data, and columns that start with (G) are GIS collected data , and columns that start with (P) are data from photos. 2. Citations for the 3 given datasets:

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192 a. National Land Cover Database (NLCD). [Internet] 2011. Percent Developed Imperviousness. Multi Resolution Land Characteristics Consortium. [Cited 1/20/2018]. Availab le from https://www.mrlc.gov/nlcd11_data.php i. In line cite= (NLCD 2011) b. National Land Cover Database (NLCD). [Internet] 2011. USFS Tree Canopy Analytical. Multi Resolution Land Characteristics Consortium. [Cited 1/20/2018]. Available from https://www.mrlc. gov/nlcd11_data.php i. In line cite= (NLCD 2001) c. SILVIS Lab. [Internet] 2010. U.S. Housing Density Census Block. University of Wisconsin Madison Forest and Wildlife Ecology Laboratory. [cited 1/20/2018]. Available from: http://silvis.forest.wisc.edu/maps/bl k_pla/2010/download i. In line cite= (SILVIS 2010)

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193 Photo Data Collection Worksheet Park # AND ID code (ex 1 WSP) TOTAL # of pictures # bird pics # goose pics # coyote pics # cat pics # dog pics # ec rabbit pics # elk pics # empty pics Continued from above... Park # AND ID code # fox pics # horse pics # human pics # mt. lion pics # mule deer pics # prairie dog pics # raccoon pics # rat pics # skunk pics # squirrel pics Park # & ID # of different mammal species tagged (species richness of all mammals) # of different wild mammal species tagged (species richness of wild mammals)

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194 UWIN 5.2 Analyze the data specific to your research question (rest of lab along with 5.3, 5 .4) Go over UWIN 5.2, 5.3, 5.4 at this point the students will have the rest of the lab to work Once everyone has completed entering data, students should now download their own personal copy of the SP18 data google sheet as an Excel file, or make a copy if using google. Students can use the skills they learned/practiced last week and apply them to create the 2 figures (distribution graph and a correlation graph) that is appropriate for their research question. You may point out how to select 2 columns t hat are not next to each other (using control click) Advise students to be careful and precise when using large datasets. Once all the data has been entered, your TA will point you to the spreadsheets that contain all the data that we have collected durin g this project. Throughout the project, especially during the UWIN 4 lab, you should have been thinking about what data you will need in order to address your research question, and how you might display your data. You will need to download a copy of the google sheet(s) as a Microsoft Excel file(s). From here, work on separate computers but alongside your partner. You will need to find the data you need for your specific research question or objective. REMEMBER, you will want to create at least 2 figures: 1) a graph that shows the distribution of the independent variable across all the sites (think back to the first 2 figures you made last week) and 2) a graph that shows the dependent and the independent variables that addresses your question (like the las t 2 figures from last week). Please reference the figures activity instructions for basic scatter plot, bar graph, and histogram creation help you will need to use the examples and apply them to your specific variables/ question. You will need to create YOUR OWN graphs/tables/etc. for your term paper, but you and your partner will need to present and discuss these figures and results next week. You may want to work together to create the basic figure, then add YOUR OWN title, caption, style/formatting, etc. at home, maybe having you present your figure, and your partner present their figure more on that in the Presentation Instructions in Canva s and in UWIN 5.4 below.

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195 UWIN 5.3 Outline Results and Discussion Sections (rest of lab) Once students have finished creating graphs, advise students to think critically about how they displayed their data, and what it means. This is a good opportunity to look at and check students figures, as they are making them. Data is the data shown appropriately? Is the data specific to the research question? Formatting are the axes units appropriate/ make sense for the unit/ labelled? Is the formatting professiona l looking? Is there a figure number, title, caption? etc. Students should pull out their Outline Guide, and start to fill in the results and discussion portions. This will be beneficial when putting together the presentation slides/discussion, and help wh en writing these sections in the term paper. As you and your partner are looking at the data and creating figures, start to think critically about what you see. How is your independent variable distributed across all the sites? Do you see any trends when comparing your dependent and independent variables? Use the outlining guide and the writing guidelines to outline your results and discussion section. Discuss this with your partner, or others in the class this will help you write the rest of your term pa per and will be the basis of your mini presentation next week. UWIN 5.4 Preparation for the mini presentation (rest of lab) Go over below information, and point out the presentation instructions/ grading rubric in Canvas. The rest of the lab period you can use to start preparing for the presentation next week. You and your partner should read the instructions for the presentation together and plan a time to work on it and practice presenting it out loud. You will need to create a google slides presenta tion that you share with your partner, and eventually with your TA. The goal of this mini presentation is to communicate your specific research to your peers. As every pair of students was able to choose what question they were interested in, there will be many different aspects of urban wildlife studied in the class. This will be an opportunity to share what you learned and studied with your peers, and hear about what others were interested in.

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196 That said, this will be a MINI presentation! You and your partner will be given ONLY ~4 minutes to showcase your research question, why you chose to study it, what you found, and what it means/ conclusions you have drawn. This exercise should also help when writing your results and discussion section of your term paper. You and your partner will need to plan the presentation and who will say/do what, so please be sure to create a deadline plan that works for both you and your partner. Remember 4 minutes at the most, keep it concise and informative (and FUN!).

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197 Presentation Instructions You and your partner, or yourself alone if you worked alone, will give a 3 5 minute presentation of your research question, data, and conclusions based on the data. Remember, research often leads to more questions than it provides answers for, so try not to be too frustrated if you do not find beautifully crystal clear answers to questions you have asked. Many of us involved in research have spent many years uncovering negative results, trying to unravel confusing and ambiguous dat a, and have often been left scratching our heads. But, with every experiment we learn something and for you as a novice scientist these early experiments will provide some of the most profound and useful lessons for when you have to think about other r esearch or become involved in some form of research. Presentation format: problems during presentations due to different platforms in which Powerpoint slides were co mpiled. You should have three four slides and plan for 1 2 min per slide. This will require rehearsal. Your presentation should be no more than 5 minutes and no shorter than 3 minutes. Again, this will require rehearsal. Purpose of the presentation: There are several reasons for you to do these presentations: 1. You get to share your ideas and what you found/learned in your project; 2. You get to learn about what others did, and what they learned in their project; 3. You get to practice making and delivering short presentations (most of you will never do an hour long seminar like presentation, but will do short intense presentations for your careers); and 4. You get to show you can work with another (if you did) in a collaborative tool (Google slides) to create a capti vating presentation. The slides: As mentioned, you only need three four slides. If for some reason you think you need more, talk with your TA about using more slides. The slides should be presented in the following order and aid in your communication of t he following: Slide 1: Your research question, any hypotheses and predictions you formed based on the question, and why you chose to study that question; Slides 2 3: What you found e.g. show and explain the two different types of graphs made; and

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198 Slide 4: The meaning of what you found e.g. your conclusions and interpretations. Top 10 presentation tips from the TAs: 1. a. You want to maximize contrast when you select colors. b. Many templates in softw add anything. Use them with extreme caution. 2. Do not put everything you are going to say on your slide! a. A keyword to remind you, and point the audience in the right direction is OK b. Remember, your au dience can read faster than you can speak. If you put a lot of text on the slide, your audience has no need for you. BAD! 3. Use your slide real estate to make figures and images large enough! a. nicate well b. Make graphs large and use as much real estate as possible 4. aide memoire a. Glance at it as you need it, but keep your attention on the audience at all times. 5. Be consistent! a. Use the same font and slide design for each slide; only use different fonts for clearly different needs. 6. Use style! a. Avoid obnoxious (comic sans for example) or brightly colored fonts (fluorescent) 7. Use large text and sentence fragments if you use text! 8. Use hig h quality graphics or photos that are relevant! a. 9. Practice makes perfect! a. this slide mean, oh, yeah, errr, yeah, like this graph, well, we know it needs a title 10. Be confident! Your presentation is onl y a few minutes know your content and deliver it with confidence!

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199 UWIN Week 6: Last Day/ Presentations Goals/Objectives 1. Mini Presentations (1 1.5 hr) a. You will present your research question and results to your peers. 2. Voluntary Survey (30 min) a. Conside r completing the voluntary survey to help with the lab redesign and get extra credit. 3. Open workshop (rest of lab) a. You could work on your term paper and/or complete the last post lab during this time, while your TA is available for questions. Action Items 1. H ave planned and practiced your 4 minute presentation prior to lab. 2. Participate in and actively listen to the mini presentations. 3. Complete the post lab within 72 hours after this lab. 4. Turn in your Term Paper via Canvas Monday of finals week. UWIN 6.1 Pres entations During class, you and your partner will present your mini presentation that showcases what you researched, what you found, and your conclusions as instructed in the assignment instructions. UWIN 6.2 Voluntary Survey You will be given time during lab to complete the voluntary post UWIN survey for extra credit. UWIN 6.3 Open Lab The rest of the lab period will be an open lab time with your TA where you are encouraged to ask questions about your term paper. You could also use this time to complete the post lab.

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200 UWIN Google Presentations and Notes UWIN Lab 1 Slide 1 Tell the students to take notes during the presentation. this will hopefully help with long log in times, and will be ready to go later in lab when we will need to use them.

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201 Slide 2 Introduce and explore the UWIN project You will learn about the experimental system and become familiar with the project and its relevance to the study of urban ecology. + Photo Database exploration Brainstorm urbanization effects on wildlife You will start to determine which ecological variabl es you would like to study as part of the UWIN research project. Primary literature activity You will learn strategies to read primary literature, which is an important part in the process of doing research.

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202 Slide 3 Le ad a discussion about what urbanization means you could have students think about the following questions What does it mean to be urban? How would compare urban to suburban or rural? What structures get built during urbanization? What structures get lost during urbanization? highlight the students inputs ideas: conversion of natural or rural areas into cities, the expansion of cities with population growth, increased concrete lowered green spaces, etc.

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203 Slide 4

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204 Sli de 5 (satellite picture of Denver, and a coyote with mange in Aurora, CO) encourage a discussion at each table, then as a group put ideas on the board Some leading questions: How could urbanization affect wildlife habita t? Think about Denver 200 years ago how has urbanization has changed the landscape? Think of a space that YOU KNOW of that went from undeveloped to developed think about the animals that were there, what is there now, how has urbanization affected the a rea? Some examples Habitat Loss permanent changes to the land (concrete) Habitat Fragmentation hard to move/ disperse Altered land use (industry, residential, agriculture, roads) Local species endangerment Increased recrea tion pressure (human disturbance) Emerging disease risks Wildlife human conflicts Increase of invasive species and/or generalist species Pollution effects

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205 Slide 6 Even though urbanization has decreased and fragmented m any ecosystems, Urban ecosystems can still provide many ecosystem services, as shown here there is a great diagram in the student manual that students could reference

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206 Slide 7 Have students take a few minutes to dis cuss with their group Why study urban wildlife? Why might wildlife diversity be important? ****MAKE SURE to have students take notes Some EXAMPLES: Wildlife/ diversity can help maintain functioning ecosystems trophic interactions and natural selection co ntrolling populations of interacting species increased stability with resistance to disturbances and disease Wildlife/ diversity can benefit humans increased productivity and economic growth lower risks of disease spread Understand how wildlife live in cit ies, help future city planning Monitoring species and outcomes positive and negative (high # of rats= disease issue)

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207 Slide 8 However, Human population worldwide is growing fast, and with that growth it is estimat ed that more and more people will live in urban areas, leading to an increase of urbanization worldwide. Therefore, it will be important to Denver Metro Area has ~ 3 million people, estimated to increase 300,000 people by 2020...

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208 Slide 9 TA to briefly go through the next few slides more in depth methods will be done next week this is supposed to be a quick overview (UWIN) i s a newly formed and growing national network of cities that believe in the importance of urban mammalian wildlife monitoring and have the goal of city to city collaboration and long term investigation of our nation's urban ecosystems.

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209 Slide 10 After starting a long term biodiversity study in 2010, the Urban Wildlife Institute (UWI) based out of the Lincoln Park Zoo in Chicago, Illinois wanted to expand the project nationwide, therefore the UWIN was created. As of 20 17, 8 cities across the US are participating Including us in Denver. ASK Students: Take a few minutes to discuss with your group WHY should we care about monitoring urban wildlife? What do you think some of the goals of the UWIN are? (was in the video and some in the reading) Some stated goals of UWIN: Better understand urban ecosystems Investigate urbanization effects and important habitat characteristics Mitigate human wildlife conflict Invest igate urban wildlife disease dynamics Preserve biodiversity and influence conservation Understand urban wildlife adaptation and behavior Educate the public and promote connections with nature Compare and contrast urban wildlife data across the nation

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210 S lide 11 These methods are standardized across the cities, to have similar data collection methods across the Network. A transect was selected that attempts to capture a general urban to rural gradient. Colfax Rd was selec ted as it runs through downtown Denver east and west, giving 2 represented by the pins, and the colors are different land managers where permissions were obtained.

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211 Slide 12 Camera Traps are used for this project there are many other methods that could be used to monitor wildlife. This is a passive, continuous method for wildlife monitoring. Cameras are strapped to trees or poles and are pointed towards a carnivore attractant tablet. Cameras are placed seasonally for 4 weeks at a time.

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212 Slide 13 For this project, you will work together with other lab sections in order to collect data about t he sites during a visit to the camera location, This will depend on what abiotic habitat variables you think might be important or affected by urbanization. We will brainstorm some of these later in lab today. This data can be explored along with the phot o data

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213 Slide 14 Once the cameras are collected and the pictures are uploaded to the Microsoft Access Afterwards, you will be able to use this database to filter and organize the photo data you will explore this database today during lab.

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214 Slide 15 TA to ask students: Now that you have read about the project and we have our introduction discussion, what ben Most of below is in the UWIN overview page of the student manual: Contribute to a national long term monitoring dataset AND Learn the Scientific Process through Authentic Research Getting closer to do ing things that scientist do. GETTING BETTER AT: Scientific literacy Formulate a Research Question Carry out data collection Discuss variation, strengths/weaknesses, assumptions of methods Analyze data Present data in writing and in a presentation Collabo rate with peers

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215 Slide 16 Here are some of the wildlife pictures that have been captured in Denver. Just for fun, can you name the species? Coyote, eastern cottontail rabbit, red fox, fox squirrel, mule deer, raccoon, striped skunk, mountain lion (cougar)

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216 Slide 17 How about now? point out that sometimes it can be harder to identify raccoon, eastern cottontail rabbit, cat, fox squirrel, coyote

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217 Slide 18 Muddiest point Provide a notecard/paper on which students can write one (or more) question about the Write down one thing that you are confused about or have a question about. (Collect later, after they write on the other side) compile for all TAs to discuss for improvements

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218 Slide 19 The purpose of this activity is to introduce you to the database you will be using for picture data analysis, to familiarize you with some mammalian wildlife species seen in Denver, and to allow you to explore some data analysis procedures that are available to you. This is going to be important when you have collectively creat ed a database from all 40 of the cameras that have been placed at various locations across the Denver metro area. You will need to be able to determine the kinds of animals detected by each camera and be able to calculate species richness from that set of data. Doing so requires your accurate identification of animal species from the camera pictures, and being able to filter the datasets by date, time, and location. This exercise allows you to learn these skills and practice them on a small dataset with onl y a few hundred photos versus the tens of thousands that will be in the database that you and your classmates create.

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219 Slide 20 Have this up during the activity lets students know exactly what they should be doing right now. Pass out the LAB COPY database activity instructions (1/pair) and the database worksheet that you printed for them to keep (1/student) Have students use the Lab computer because it is a PC with updated Access 2016 on it, needed to use the data base Assign thumb drives to pairs of students, so if one goes missing you can track it down NEED to make sure they are all collected for the next lab Students will need to access the class Google folder that you should have shared with them, if this is the first time using the google folder: point students to where they can find it, tell them that they must SIGN IN to their Google account to get all the feature, that this folder will be used throughout the project because it will be an easy place to share information with the class, it is a place to check for me (TA) to check for your completion of lab activities, and will get practice using a collaborative platform Emphasize that questions on the worksheet will be asked for during the post lab

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220 Slid e 21 Go over types of research, each builds upon the other, emphasize that we will be in the observational/correlational realm. Correlational DOES NOT = Causation need experimental/explanatory methods for that. Some vide https://youtu.be/lsbK6g10a c https://youtu.be/FlBFdEgrTBM

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221 Slide 22 abiotic habitat factor , . Have a discussion with your group about WHAT aspects of urbanization might impact wildlife, and specific examples of HOW/WHY those different aspects of urbanization might affect wildlife, and HOW might you measure them? These could be potential abiotic habitat STUDENTS put ideas on the board?? Have students give different factors/ independent variables and explain WHY/HOW it might affect wildlife could give an example if needed. such as roads (maybe types around the site, highways vs street vs dirt), cars could kill animals, cars could deter animals from crossing, or roads could be a source of food (e.g., roadkill), dirt roads= use as a trail, highway= lower connectivity/accessibility of an area Examples: Habitat management, habitat structure, urban/sub/rural, distance to water, distance to trash can, litter amount, noise levels, road type, habitat connectivity, habitat area, impervious surface, human population

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222 Slide 23 estimate with the camera data and write them down lead students to be more specific After a few minutes, have students give some ideas Examples: photo data can give us: Species, Date, Time, Number, Comments.= Photo database can give us: Species Richness, single/multiple species specific totals, daily use of a species

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223 Slide 24 On the back of your notecard Turn into TA afterward Have students write down the variables they are interested in exploring this is to get a clue of what students are interested in, Note any that are not already on the data collection sheet to see if we can add them Let Dr. Duncan know. (TA can give back to students next week, if they want them back)

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224 Slide 25 For the rest of the lab period, work together as a pair and we will work through a strategy on how to read a scientific paper together. TA to ask students to get out the article and the worksheet they should have printed and brought to class. Make sure to fill in the worksheet as we go through, as it will be important for your post lab. The goal for t his activity is to introduce you to a strategy for reading scientific papers, as this is an important skill for staying up to date in research in whatever field you are interested in. This skill is increasingly important in post graduation careers that req uire you to keep up with current trends, especially in research based careers, medicine, technology, and many more! Because of this, you will be exposed to primary literature in many upper division classes, so this activity will hopefully give you a good s tep by step strategy that will give you a foundation for tackling literature in the future! Modified from: Bakshi A, Patrick LE, and Wischusen EW. 2016. The American Biology Teacher 78(6):448 455, and

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225 Slide 26 Start out generally with thinking about science communication to see the variation in communication media Ask for thoughts from class on how science might be communicated from scienti sts to the public and between scientists Journals, Presentations, Reports, Posters, Books, Blogs, News, Social Media Ask how do these media differ? Which are reliable and why? Next slide starts to focus on journal articles, primary sources

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226 Slide 27 TA to go over the basics of Journal Articles: There are thousands of journals, such as Science, Nature, Journal of Ecology Different impacts in their fields, some are very high impact and well read Others are less impact ful overall, but may be highly influential within their discipline the field to review and comment on papers before they get published. It is meant to ensure that data is accurate and reproducible, and that conclusions are supported by the data. Despite peer review some articles contain data that have poor statistical support, or which contain data that cannot be replicated Not all that is published in peer reviewed articles is true You should adopt the persona of a very skeptical reader and look for holes in the reasoning or analysis of data Many articles follow the basic struct ure described here but there are variations depending on the type of article and the journal it is published in. Title: Should summarize the main conclusion and give a general idea of the study Abstract: Summarizes the objectives, methods, results, and mai n conclusions but does not get into depth Introduction: Presents the state of the field, relevant information needed to interpret the relevance of the findings, and states the specific objectives of the research Materials and methods : Documents the way the research was conducted including any statistical analysis

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227 Results: Presents data that is supported by a written explanation not every single piece of data that is collected is presented, however increasingly journals require extensive supplemental data be posted online so that full data sets can be evaluated by readers Discussion: Interprets the data and reports conclusions; discusses any potential errors or assumptions made, makes inferences and extrapolates to other systems if relevant References: Docu ments the material that was cited in support of the authors claims about the topic at hand, methods, analyses, or conclusions. This activity will work through each section. Note that your term paper should be in this same basic format, and with all the co mponents we will talk about today!

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228 Slide 28 TA to convey the information below in your own words Scientists at different stages of educational development should aim for mastery of different levels of skill with readin g scientific literature We can think of four levels of skill with reading scientific literature: decoding, understanding, evaluating and critiquing, and extending (GOAL FOR STUDENT) By the end of semester you should be competent at decoding a piece of pri mary research literature (what is the meaning and overall idea of the paper?), and understanding what was done, the results, the conclusions, and the significance. The hardest part of doing that is decoding the complex language used, and the techniques app lied. In upper division classes, you will be pushed to extend your skills such that you can evaluate the data and critique the approaches used to collect the data, you will even find yourself disagreeing with conclusions Professional scientists are also able to take the findings of the work and extend it to come up with new experiments, or to apply the research techniques or analytical methods to their particular field to create novel experiments to test new questions that arise from the research in the literature. Reaching this level takes not only some years of graduate study, but many years of reading the literature and discussing it with colleagues. Many professors will still find pieces of literature with methods or analytical techniques that they ar e unfamiliar with, and may need to do additional learning to figure out the work being discussed

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229 Slide 29 Have students work together as a pair and fill out Q1 3. Take a read of the title, it should tell you the most i mportant stuff about the paper. A good title summarizes the main conclusion and reveals the general area of the research. Ask yourself if you can figure out the general area of the paper from the title. You should be able to summarize it without using word s from the title and expand on it to explain what is going on. look over the INTRO finding keywords. Write them down in your notes. Go and look them up and figure out their meaning. You will even have to look at sets of words to decode their meaning when put together. All of the information you need is out there on the web these days. You can use a dictionary, and even Wikipedia is OK for a general idea about meaning. Try again at the general area of the paper. You must force yourself to summarize the tit le into a paragraph or so to expand upon words that need explaining. Consider the meanings of words and their relationships with each other After awhile, ask students to shout out some keywords they pulled out Some examples: Habitat fragmentation, Urban, Urbanization, San Diego, Conservation, Positively or Negatively associated, etc. Slide 30

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230 Have student pairs compare their summary with another pair at a different tables, and have them note similarities and differences. Have a few students give their summary. Ask the class, do we now have a good idea of what the paper is about? Moving onto the more in depth analysis that will lead to understanding how the paper reaches the conclusions it does This is a lot more work tha n what we did with the title you are well prepared to tackle the INTRO Dr. Duncan example of a summary) This paper looks at how humans use of land could i mpact wildlifes use of land. It likely looks into whether human land use negatively impacts wildlife use of lands, but there could be positive correlations between wildlife use and human use. The land it is talking about is in cities or towns. It sounds li ke the normal ecosystem in which the wildlife lives has been broken up into smaller pieces. Presumably this may disrupt the wildlife in some way. Putting it all together it seems as though the paper will be about how ecosystems have been disrupted and brok en into smaller pieces, and how human use of those pieces of ecosystem impact wildlife use of those same areas.

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231 Slide 31 TA: Go over what to do/look for in an intro have student work together, Pause here have everyon e get through Q4 A E, then go through the following slides as a group Look up any terminology that your are unfamiliar with this will help with understanding what the author is trying to get across Try to connect what you are reading to things you already know about the topic. This will help you make more sense of what you are reading. List out some of the background knowledge given. Ask yourself why the author conducted this study? What is interesting about it? Why is it so important that someone would sp end so much time working on the subject matter? What is the author trying to find out? What is the reason behind the study? Are they trying to help urban planners do a better job of maintaining biodiversity? Are they trying to find a better treatment for a disease? Are they trying to make diagnosis of a genetic disorder better than it is already? Papers should have well stated objectives or research questions. What exactly is the objectives of this study? Use different colors, or circle and note these in the article give time for students to work on this in pairs

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232 Slide 32 TA: Ask students to offer what they wrote down for 4b, see what different people got for this question. What are the main points the authors made i n their review of past work? What do the authors write about? what has been done in the past? Examples Habitat loss= permanent change= lower biodiversity U.S. urbanization endangers species Wildlife adapt, move, or die Human disturbance in habitat frag ments can negatively affect most wildlife by avoidance, but some may benefit

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233 Slide 33 TA: Ask students to offer what they wrote down for 4c, see what different people got for this question. SHOWN in next slide: The main importance statements are focused on to better understand major irreversible threats to biodiversity posed by urbanization Biodiversity threats Irreversible threats permanent changes to the environment Urbanization endangers species more than any ot her human activity

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234 Slide 34 For these slides, try to point out specific language to look out for The motivations appear elsewhere, but the main importance statements are fo cused on to better understand major irreversible threats to biodiversity posed by urbanization Biodiversity threats Irreversible threats permanent changes to the environment Urbanization endangers species more than any other human activity

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235 Slide 35 TA: Ask students to offer what they wrote down for 4d, see what different people got for this question. Why is the author doing this specific study? Point out that purpose is a little ambiguous ngs why do this specific study? Why is somewhat related to motivation/importance It is crucial to understand the effects of human activity on wildlife to have conservation success in urban areas (shown in paper in next slide)

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236 Slide 36 For these slides, try to point out specific language to look out for The authors in this study investigated how impactful human behavior was on a variety of animal species in different habitat fragments They did this to better understand how likely it is that conservation plans will be successful Reword of highlighted section: It is crucial to understand the effects of human activity on wildlife to determine conservation success in urban areas

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237 Slide 37 What did the authors do? What was the SPECIFIC objectives of the study? Shown in the next slide Investigate human activities and environmental factors on animal occurrence and abundance in habitat fragments in urban San Diego

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238 Slid e 38 For these slides, try to point out specific language to look out for What did the authors do? What was the SPECIFIC objectives of the study? Top example is at the bottom of the first in tro column and is more broad, the second example is at the end of the intro and is more specific. Investigate human activities and environmental factors on animal occurrence and abundance in habitat fragments in urban San Diego

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239 Slide 39 TA to summarize the important components of an intro: All Research introduction sections should contain all these elements we just went over Background, Importance, Rationale, and Objectives Keep this in mind when writing your term pa per. why, you are in a good place to develop your understanding of the paper.

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240 Slide 40 At this point in your education you just want a general idea of how they did their study. You you just want a framework in your mind about how they did their work. For example, did the y use transects, did they use camera traps, were they doing direct observations, were they using animal tracks and so on. If something looks like it is important as a technique that is it keeps coming up or is a subheading in the methods, go look it up and get a general idea of what it is about. Make a chart of techniques to help you keep track of what was measured using what technique. List the technique or method in one column and then what it was used to measure t be intimidated by technical stuff. You can look things up online and get a general idea of what is going on. Wikipedia helps a ton with that kind of level of inquiry. Might show an example? Have students explain their diagram to another pair.

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241 Slide 41 As you read, start making bullet points/short notes of the results note if a visual is mentioned RECOMMEND grouping results by species STATED RESULTS: OBSERVATIONAL more generalists than specialists (fig 2) Coyo tes in 60% of areas (most) Mule Deer in 50% of areas (2nd most) Raccoons in 25% of areas (3rd most) CORRELATIONAL Oppossum + with intensity of land use Cougar with road intensity, bike use, utilities presence Gray Fox with road intensity Wood R at + utilities presence, horse presence Raccoons plant bulk and permanence Coyotes + plant bulk and permanence Bobcat + water availability Roadrunner + litter amount Jack rabbit and mule deer= NO correlations

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242 Slide 42 TA to pause the presentation here to let students work through the figures State the main conclusion or objective of the table/figure, AND state what type of data the table/figure is addressing (methods, summary data, main results, etc.). We will discu ss a few of these together. In order to do this, work together to tackle one figure at a time. Read the legend for the figure. Do you have a listed result for this table/figure? (Find where this figure is discussed in the results section read that.) Look at graphs and tables from an analytical perspective. If there is a graph with several lines, look at one line at a time. You must break the complex down into its component parts this is the essence of being analytical. Write down any questions you might have.. Figures info for TA: Figure 1 A map that shows the study locations, the climate type, MSCP lands, and Urban areas also shows an example of a transect split into sections (METHODS FIGURE) Table 1 A table showing the 12 preserves sampled an d some of their characteristics (METHODS TABLE) Table 2 The main results table, shows the positive and negative correlation coefficients for different species and different independent variables (MAIN RESULTS) Will walk through

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243 Figure 2 relative abunda nce of species across all areas (SUMMARY RESULTS Figure) Will walk through Table 3 Environmental variables and how they ranked them (METHODS TABLE) Figure 3 Trend in species observations over time (used 2004 data METHODS, and used in discussion sect ion)

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244 Slide 43 Ask students: What type of data is this graph showing? IS this showing the results for the main objectives? NO this is summary results for some of the da ta, NOT addressing the main objective, BUT is interesting observational data from the study Ask students: What is the main objective or conclusion of this figure? spe cies, shows that coyotes, mule deer, and raccoons were the 3 most seen species in the preserves. Some formatting/quality items to point out: Scale changes half way through (not the best practice when showing data visually but scale can be hard to show s ometimes when showing 1% to 60%) showing it this way could confuse readers, such as when looking at bobcats vs raccoons (looks close in %, Axes are labelled clearly The caption does a pretty good job explaining the graph (stands alone), little unclear but is defined in the paper Sideways bars are a little unconventional authors probably did this so readers can easily read the species name Slide 44

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245 Ask Students: W hat type of data is this graph showing? (main results) Is this showing results for the main objectives? (YES) Ask Students: What is this table showing us? Each of the 10 species has results for 2 different metrics shows the correlation of different h uman activity and land use influences on the indirect species observations (animal tracks/signs) on this slide) (TIME SAVER skip this part and move on and discuss the next slide) Put the screen UP Call on pairs of students to come to the board Have them circle these in their own paper for future r eference Do these correlations match with your bulleted list of results? From this table alone, what conclusions could you draw? Discuss that results tables and figures should address the main objective or research question, and give enough information for the reader to draw conclusions without reading the results section.

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246 Slide 45 Just a place to show the green (+) /red ( ) areas that students should have circled OR if skipped that part: correlations in red. Have students circle these in their own paper for future reference. Do these correlations match with your bulleted list of results? From this table alone, what conclus ions could you draw? Discuss that results tables and figures should address the main objective or research question, and give enough information for the reader to draw conclusions without reading the results section.

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247 Slide 46 (DO THIS Time Saver) Now that you have an idea of decoding sections, we will have you try to decode this section at home use the guided worksheet you will need to do this for part of the postlab. TA= briefly go over the info below, and what eac h question of the worksheet is asking for (next 4 slides), and have them complete it at home. Continue with the activity on Slide 51 the Abstract. it all. Remem ber, results are just that: results; Discussion is where interpretation is done, conclusions are reached, and inferences made. This section should be read critically. To start it will be hard for you to make criticisms of the conclusions. See if you agre e with the authors interpretations of the results based on the data themselves. This will often help you start to pull together the motivation of the author, the purpose of their work, their findings and what they think it all means. Try to summarize the answers to the discussion questions in the worksheet in your own words.

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248 Slide 47 Summarize the main interpretations of the results that the authors give For TA: What are the authors main conclusions? (some) weird that no correlations with plant cover (except opossum) If data is accurate, species must be using all of the fragment, and have nowhere else to go hard to make conclusions on rarely seen species Mule deer and coyotes seen the most= must be adaptable to urban a reas and not affected by human activity most species seen are likely urban adapted

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249 Slide 48 For TA: Assumptions / potential errors Sample size too small Land use and human activity variables not appropriate for these species Inconsistent data collection between different people Perhaps, no errors

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250 Slide 49 Authors often include guidance about what future work needs to b e done in the field and why. What future work do they suggest? For TA: landscape variables may reveal different patterns with species with large home ranges (not just local variables) conflicting results indicate that area of fragment should be considere d considering intermediate disturbance hypothesis, level of disturbance where the maximum species richness is found is unknown long term population dynamics of each species is warranted local extinction colonizations unknown Slide 50

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251 Authors sometimes include recommendations for how their work might inform policy or management decisions. What recommendations did these authors give, if any? Do they make sense in terms of the data presented? For TA: negative asso ciation with roads= recommendation for restoration of corridors to increase movements across the landscape multi species approaches should take into account land use and human activity types recommend focusing restoration on large reserves and avoiding fur ther fragmentation Even without reading the discussion now, we still have a good understanding of the paper,

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252 Slide 51 TA to point out the important pa rts of an abstract Pause to have students complete Q9 of the worksheet You should identify these parts of every abstract you read. (NOTE you will want these items in your term paper as well) An abstract pulls out the most important aspects of each sect ion of the paper as a quick summary everything in an abstract should be in the paper somewhere! Direct students to go their abstracts, Ask them to mark them up looking for 1 6: Importance, Purpose/rationale, Objective, Methodology, Main findings, Main co nclusions

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253 Slide 52 The abstract has a ton of information in ~250 words! It is supposed to be a good representation of what will be read in the paper and convey the most important aspects of each section. This abstra ct has all the components: introduction, methods, results, and discussion. of the paper first, then see how the authors summarized it. Ask Students : What do you thin k? DOES the abstract match what you summarized while reading the paper? Does the abstract summarize the paper well? so not sure that this conclusion is appropriate with the data they s Titles and Abstracts are usually what most people will read first to decide whether or not to continue reading the paper. So it is important to be very specific when creating a title and when constructing your abstract you want to make sure that yo u are conveying the message you want/can convey! As you can see, papers can be hard to read, but if you take it piece by piece it becomes manageable and if you take summary notes or highlight the important things it makes it easy to summarize what is going on and what the authors are talking about, and to think critically about how confident they think their data is, and how confident YOU are in the data and conclusions they present.

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254 Slide 53 TA to summarize this re ading primary literature activity there are other strategies as well, as Take the paper and deconstruct it piece by piece un derstand. Try to form your own conclusions before reading the discussion section. Read the abstract last, does it truly match and summarize the contents of the paper? Hopefully you have learned a strategy for tackling a primary literature article. This should help with reading articles in the future, and with writing your term paper

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255 UWIN Lab 2 Slide 1

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256 Slide 2

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257 Slide 3 In reading materi al Descriptive ecology looks at overall natural history through observations, and answers the Functional ecology focuses on the role s or functions that species play in their environment, and Evolutionary ecology considers the evolutionary history of the relationships of species and the ir Ask students before the circles animation what types of studies will we be focussing on? Again, as an introduction t can ask of camera trap data. As the dataset grows from year to year, we may be able to start KEEP this in mind when you are thinking about what you want t o research

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258 Slide 4 There are many general types of methods in ecological studies Natural experiments when manipulations happened naturally or out of the control of the observers, such as studying a disease outbreak. M onitoring use of technology and observations such as remote sensing and field instrumentations. Controlled experiments having control plots and treatment plots in an experimental design that is as controllable as possible. Modelling using computer model ling and simulations to create population or ecosystem models that represent the system studied as closely as possible. We will be employing a combination of natural experimentation (urbanization has happened) and monitoring (using camera traps).

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259 Slide 5 There are challenges in ALL research, no matter what field of study you go into. Ask students to discuss some potential challenges in ecological studies Have students discuss for a few minutes, then ask for some an swers (This is all in the reading material) Answers on next slide Ecological processes = COMPLEX! Many variables = difficult to control for Hard to replicate results = constant changes Ecological patterns = takes a long time to emerge Correlation DOES NO T = Causation Correlational results might give us insight on a potential pattern, but there might be more than one explanation that fits a particular situation just as well or even better

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260 Slide 6 Ecological proces ses = COMPLEX! Many variables = difficult to control for Hard to replicate results = constant changes Ecological patterns = takes a long time to emerge Correlation DOES NOT = Causation Correlational results might give us insight on a potential pattern, but there might be more than one explanation that fits a particular situation just as well or even better KEEP these difficulties in mind when thinking about your research and any ecological research you read

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261 Slide 7 (More in depth methods discussion compared to UWIN1) UWIN has a standard sampling protocol so every city has a similar sampling design A general urban center to rural edge is identified as a transect here we have 2, and east transect and a west transe ct Each transect is divided into 5km sections, in which 5 potential wildlife habitats are chosen (parks, open space, etc.) Sites are to be within 2km of the transect, and at least 1km from each other (as best as possible) ATTEMPTING to have even sampli ng effort and to try to have sites not too close together. Then permissions were obtained by the land managers shown here in colors yellow= aurora parks, red= denver parks, blue= lakewood parks, orange= JeffCo parks, Purple= Prospect District, and Green = Pleasant View (is in the manual)

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262 Slide 8 Have a camera out to show, and a stinky tab to smell Passive Infrared (heat) and Motion activated cameras set to take pictures every 30 seconds. Because of permissions ne eded to place cameras, locations have already been picked. All 40 cameras have already been placed by the TAs, and will be taken down by the TAs after 4 weeks. Cameras are placed for 4 weeks at a time, seasonally Pointed toward a fatty acid soaked tablet a carnivore attractant used by the USDA.

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263 Slide 9 Uploaded to the Access PhotoWarehouse Database Tagged by you (we will practice tagging next week) Then can filt er and calculate photo data (like we practiced last week) for analysis

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264 Slide 10 Have this slide up during the activity, then go through each scenario, having different groups contribute. Pass out the scenarios to s tudents. TA to tell students: Have one person read aloud their scenario, discuss the situation and prompted questions, try to write down and articulate the assumption that is made about camera traps and the data that is collected from them. Please TAKE NOT ES so we can discuss these as a group, and you will have them when you start thinking about your data. Logistics example: There are enough scenario sheets for every pair to have one. (5 scenarios total) Pass out scenarios so each table has 2 copies of 3 o f the scenarios You can pick which ones Have student pairs all discuss the same scenario, then share what they think with another pair Go over the scenario as a class (google presentation has each scenario with notes) Then repeat for 3+ scenarios, or until you get to about 30 minutes (From the student manual:) Using trail cameras, i.e., camera traps, is a great way to obtain data on wildlife that is non obtrusive for sometimes cryptic, hard to observe species. Cameras can take pictures for extended periods of time, 24 hours a day which would be very difficult for human led observations! Although many ecological and wildlife studies use camera traps in their study design, some assumptions are made about the data that gets recorded. Your table will be given

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265 scenario, try to articulate the assumption that is being discussed about the camera trap data. Knowing the limitations of the data you are collecting is important w hen thinking about the conclusions you can make!

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266 Slide 11 Scenario Prompt on the card: Aviva wants to count the number of different individual coyotes that are seen at each of the 40 on a camera image. Do you think Aviva or Billie is right? Why? If you use total counts of a species, what is the assumption are you making? ASSUMPTION = total counts are different individuals I ssue: We cannot accurately distinguish individuals, so if there are 30 coyote pictures, it could be 30 different coyotes, 1 coyote, or anywhere in between.

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267 Slide 12 Scenario Prompt on the card: Park X is a lot larger in size than Park Y. We put one camera at each park. In April, the camera from Park Y captured images of coyotes, but the camera from Park X did not. Can you say that there were no coyotes at Park X? Why or why not? What assumption are you making about co yote photo data with 1 camera in each park? ASSUMPTION : Cameras are detecting all coyotes in the park, regardless of the area sampled around each camera Potential Issue of imperfect detection due to sampling area differences

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268 Slide 13 Scenario Prompt on the card: A camera was placed in Cheesman park for the months of January and October. In October, the camera was angled downward slightly more than it was in January. How might this affect the field of view? How might When we report camera trap data of a park from season to season, what assumption are we making? Is this assumption also true when thinking about photo data from park to park in the same season? ASSUMPTION: th e field of view/ ability to capture animals is the same for every camera trap. Potential Issue of imperfect detection due to camera placement issues field of view of the camera.

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269 Slide 14 Scenario Prompt on the card: Morse Park has a camera in the southeast corner of the park pointing towards the south, and Welchester Tree Park has a camera near the center of the park pointing towards the east. Amara is using the photo data from each camera to compare the species richn ess of wild mammals between the two sites. What assumption is Amara making about the photo data when making her comparison? ASSUMPTION : The probability of capturing animals within the park is the same, no matter where the camera is placed within the park Potential Issue of imperfect detection due to where the camera is placed, and where it is pointing.

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270 Slide 15 Scenario Prompt on the card: The UWIN protocol requires that cameras be pointed towards a carnivore attract ant called a Fatty Acid Scent (FAS) tablet that is secured to something, like another tree or stake. When thinking about the photo data, can you think of 2 assumptions that are being made about the FAS tablet? ASSUMPTIONS: 1) the tablet is working, and is attracting carnivores in the area to the camera + the camera captured the animal 2) The tablet was there for the entire camera set up

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271 Slide 16 Ask Students: What is the overall assumption that we are making about phot o data, regardless of the exact reason? Example: That the photo data we have is representative of animal/human use of the parks we sampled.

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272 Slide 17 TAs: There are six sites for each lab section. You should work with the students in groups of four to make sure that each site is visited by ONE group using the spreadsheet provided here and the information in the folder you shared with the students. For our lab sections, we will be exploring different abiotic habitat var iables that are related to urbanization (independent variables) We pooled all the ideas from lab sections from last week, and created a data collection sheet that encompasses as many as we thought were feasible to collect given safety, time, and monetary constraints which will we go over after planning the site visit Your group is responsible for visiting 1 site and collecting this data, every site will have data collected by pooling all lab sections collected data you can use data from ALL sites Have 6 sites available per section (one per table) have groups sign up for their site (MUST HAVE SIGNED WAIVERS! should have from UWIN1) Go over safety concerns

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273 Slide 18 TAs: There are six sites for each lab section. Y ou should work with the students in groups of four to make sure that each site is visited by ONE group using the spreadsheet provided here and the information in the folder you shared with the students. For our lab sections, we will be exploring different abiotic habitat variables that are related to urbanization (independent variables) We pooled all the ideas from lab sections from last week, and created a data collection sheet that encompasses as many as we thought were feasible to collect given safety, time, and monetary constraints which will we go over after planning the site visit Your group is responsible for visiting 1 site and collecting this data, every site will have data collected by pooling all lab sections collected data Have 6 sites avai lable per section (one per table) have groups sign up for their site (MUST HAVE SIGNED WAIVERS! should have from UWIN1) Go over safety concerns

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274 Slide 19 Before we move on to creating a research question for this pr that shows what data might look like when thinking about dependent and independent variables. Here is some made up data that is looking at how buildling density around the camera sites correlate with the average percen t of dinosaur use of parks in Denver. This shows the data categorically the data points were placed in LOW, MED, HIGH categories and averages and standard deviations were calculated and presented in a bar graph. Ask Students: From this graph alone, what might we conclude from the data? (That dinosaurs use parks more often with mid level building density around them. Ask students: Can we say that % building density is the cause for dinosaurs using those parks? (NO correlation does not imply causation)

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275 Slide 20 Here is the same data, but shown on a continuous scale (not binned) and shown in a scatter plot. Data shown this way does give more information about the data points (you can see the distribution across the x axis, and the % dino use at each point) There are pros and cons to each type of graph. Some of the variables we are measuring are categorical, some are continuous, and some could be shown either way. As you start thinking about the variables you are in terested in and creating a research question, as you can probably tell from the data collection sheet, some variable measurements will be more consistent, and more reliable, than others. Try to pick variables that are interesting, relevant, and less error prone. How you might present your data visually

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276 Slide 21 PASS OUT the data collection worksheet (field and GIS) Have students review eac h item: Point out to students that the data collection worksheet will encompass all the information we will be collecting, so to keep that in mind when we get to creating research questions later in lab. TAKE A PIC of the camera set up, surrounding area, anything you might want to share or have for reference TAKE A PIC of you and your partners at the site

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277 Slide 22 Students should get out the research/interview worksheet that they printed and brought to class, these a Use the next 3 slides to help with timing and showing what students should be doing

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278 Slide 23 Guide students through the activity Give students timing warnings/prompts

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279 Slide 24 15 minutes then have partners switch with a different pair of students give timing cues 10 minutes in (5 minutes left) then switch and the same.

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280 Slide 25 Take about 10 minu tes to discuss the interview and to revise your question with your partner. This will be the foundation of your research and your term paper.

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281 Slide 26 Ask Students: We learned a strategy for reading primary literature, but how will we find peer reviewed primary literature to read, for FREE?? Through the library of course! SHOW students how to search through the library over the projector Show the Auraria library link in our canvas shell sidebar links Show the Auraria library website, by searching for it. Show the front page search bar and databases available point out google scholar and web of science are popular Tell Students something like: The goal of this activity is for you to use university resources that you have access to, and to search and locate primary literature that will be useful learning more about the topic, your specific variables chosen, camera trap methods, similar studies done, etc. This will help you when writing your term paper. This will also b e an important resource to utilize throughout their college classes.

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282 Slide 27 TA to talk about citations. HyperLink works. Every class, and every journal, requires specific citation requirements that may change or lo ok different. For this project, you will need to cite your sources in accordance with CSE name year formatting style. You can find this by searching CSE name year, or find it in the many links in the student manual.

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283 Slide 28 If you write about a source, it needs to be cited within the text and in the references section. This is a primary literature example . The in text cites could be part of the sentence, Poessel et the author is cited within the se ntence with the year in parentheses, or if you don't want to put the author in the sentence, cite the source at the end of your statement, all in parentheses (Poessel et al. 2016). Then in your references section, the citation gives the information neede d to go and read that source if they want. It will shows the author's Last Name First and Middle initial comma next authors period year Title Journal Volume Issue Pages

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284 Slide 29 Here is a website exa mple.

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285 Slide 30 Go over this bibliography example (it is also in the example sheet in the shared google folder UWIN > UWIN 2 Bibliographies if they want to navigate there and follow along). Citation is in CSE name y ear format (easy to copy and paste into a References Section if students decide to article is useful or interesting) This example is the paper we read last week. Tel l students: Get out the bibliography worksheet that you should have printed and brought to lab. This activity will give you practice using the Auraria Library to search, locate, download, and cite primary literature. By working together to find interesting and useful papers can help with learning more about our research topic and methods, and will help with citing primary literature when writing your term paper. USE the worksheet to guide you through this activity. Pdf Link Hyperlink on the slide is in a t extbox on top of a picture it will work if you click on it, then will have you sign in with credentials, then the pdf will open.

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286 UWIN Lab 3 Slide 1

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287 Slide 2 TA to Introduce GIS to students Wikipedia definition: A geographic information system ( GIS ) is a system designed to capture, store, manipulate, analyze, manage, and present spatial or geographic data . The idea to create maps to depict geographic data has been around for a l ong time, estimates around 10,000 15,000 years.

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288 Slide 3 The first recorded use of modern geographical principles of depicting AND analyzing data geographically was made by Dr. John Snow in 1854 when he was trying to tr ack a Cholera outbreak in London were REPLACED with orange dots so you could see the wells) and the cases of cholera (little black dots). Wells is a layer, and Cholera cases another layer both overlayed on a map. Because of this map, Dr. Snow saw that the cases clustered around a well centrally located within the outbreak (larger orange dot), and this well was found to be the source.

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289 Slide 4 Mo dern uses of GIS use computers and ever evolving GIS software programs some you need a subscription to, some are open source. GIS programs use data that can be overlayed onto a map this data is called a Layer. Layers show an attribute of somesort, and ca n be analyzed. This image shows a few types of layers RASTERS that show pixels of color associated with data like land use classifications or elevation, and VECTORS that show data as points, lines, or polygons here shown as land parcels, streets, and c ustomers. These are only a few examples. GIS is all about layers= associating attributes to spatial data.

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290 Slide 5 A few layers have already been overlaid onto our camera sites in Denver, and using ArcGIS the layers were used to estimate % Impervious Surface, % Tree Canopy Cover, and Estimated human population by creating a 1 km buffer around each site and having the program average the colored pixels for us. This image is from the reading, and shows the impervious s urface layer the different colored pixels are 30m x 30m in resolution that go from high impervious (red) to less impervious (blue). The yellow circles show the 1 km buffer around each site, MEANING that there is a 1 km RADIUS around each site drawn. These are more advanced GIS techniques that would require a lot more training for you to complete, so they were calculated for you. If you are interested in learning more there are GIS courses in the geography department including a GIS certificate program, b ut there are also MANY online courses or resources you could explore.

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291 Slide 6 Now, with the aid of computers, satellites, GIS software programs, and the ability to share/collaborate via the internet, GIS applications are used in any field that benefits from depicting and analyzing data geographically. Such as landscape planning and management, natural resource management, transportation planning, navigation (think driverless cars!), environmental impact analysis, dis aster management, tourism, business, energy, utilities, infrastructure, public health, economics, and much, much more!

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292 Slide 7 Now, you will learn and practice some basic GIS skills in 2 different ways: 1) Create la yers on your own Google Map to measure and record distance to water, park area, distance to nearest habitat, and number of habitats within a 1 km buffer. Please USE the step by step instructions to help you create your map and layers. Record your measureme nts in your Data collection sheet. was confusing in earlier sessions DONT unclick the ruler, you make a 0.62mi radius with the ruler (measurement tool) then as you go around in a circle around the camera site y ou count the number of potential habitats. 2) Use an open source online GIS map of light pollution, and record the light radiance level at each of your sites. Again use the instructions and record the data in your data collection sheet. TA could show th e example map in the UWIN 3 Maps folder online. Point out the different layers they will make.

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293 Slide 8 Have this slide up during the activity HOPEFULLY 3.1 and 3.2 together will take 60 80 minutes Go over the go als and logistics of the activity. Have students work together, but try to not have one of the pair doing everything. Devise a way to have student PAIRS each have 2 different sites Pass out the LAB COPY of GIS Instructions Students will enter ALL the dat a now in class Point out there are 2 different forms the field data form they will need to do 1 time, the GIS form will need to be submitted 2 DIFFERENT times one for each location (this will give each location to have a separate row in the google shee t) BASICALLY 1 form for every location.

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294 Slide 9 Tagging Practice Have assigned thumb drives and LAB COPIES of the instructions ready to go. This is a different database than used in UWIN 1 Students will navigate to E: \ UWIN \ Tagging Practice \ (YOUR SECTION) \ Database for Tagging Practice (this is given step by step in the instructions) Please tell students that this is not a race, but to become familiar with identifying species, using the shortcuts, and getting used to the tagging module. Next week, we will be tagging a larger amount of photos so hopefully this practice will get us ready for tagging accurately and efficiently! e, Ask students How many photos did people get through? Move on to the next slide.

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295 Slide 10 Do you think that photo tags should be verified by diffe rent people? (yes) Ask students What are some reasons why? Examples: Species ID error (not experts), Accidentally entering the species wrong, Sometimes species are hard to find/ID, Maybe going through photos too quickly and missed something, etc. OK, l out the ones that mismatch. Move on to #10 in the Tagging Instructions to verify your tags compared to someone else who has already tagged these photos. As you w alk around, ask students how many mismatches they had. ASK if they are surprised by the mismatches/ point out that by verifying the the tags we can reduce errors in the photo data. Give about 10 minutes or so for this, THEN ask student to QUIT out of the database, eject their thumb drives, and return them to the cabinet (or however you want to manage the thumb drives but you MUST make sure they are all accounted for!!) Move on to the next activity.

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296 Slide 11 TA to pass out the Outline Worksheet for students to keep. It is a good practice to outline your ideas before you start writing your term paper. This is a way to make sure you include all the required elements of a research paper, and to put all your ideas down in a logical manner. For the rest of lab, you can start working on outlining sections of your term paper. At this point you should have a solid idea of your SPECIFIC research, and some background information from the searching and locating literature last we ek. You can work together to discuss different ideas, BUT YOUR ANSWERS MUST BE IN YOUR OWN WORDS!! The outline guide has ALL the sections of the term paper here, BUT for this week we will focus on the TITLE, INTRO, METHODS, and REFERENCES sections Keep t his OUTLINE GUIDE (on paper and electronically) to have all your ideas and thoughts so you can continually add to it and reference it as needed. It is HIGHLY recommended to spend time on this NOW, as the more you get done here the better feedback you can get from me (the TA) and the easier it will be to transfer your outline to a written format.

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297 UWIN Lab 4 Slide 1 Slide 2 Slide 3

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298 All cameras were co llected over the weekend 2 cameras were stolen and 1 was beat up. Over 15,000 pictures were uploaded into the database, and tagging modules were created and put on the thumbdrives. Each thumb drive has a different set of pictures, so if you see something interesting share with others. There are 1200 1300 pictures on the thumbdrives, but we will work on tagging a subset of them, pictures # (range of pictures), this way we should get all the photos tagged by multiple students throughout the week. At the en d of the week, all the tagging modules will be uploaded into the database, and pictures will be verified. Work in pairs on the lab laptop to tag pictures (# #) use the instructions and the species shortcuts When done CLOSE OUT OF THE MODULE BEFORE EJECT ING THE DRIVE!

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299 Slide 4 Tables and figures help to visually show data that readers can look at to see results of, or make conclusions about, that data. We will go over some of the formatting basics of each.

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300 Slide 5 Table number, title, and caption are above the table. Make sure all columns and rows are properly labelled. paper.

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301 Slide 6 Figures help visually show data, it is important to make sure that the axis make sense for what you are looking at and that they are labelled correctly. Notice there is a figure number, a title, and an informative caption below the figure. There or to be understood without prior knowledge.

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302 Slide 7 Tables are a great way to display data and you could make tables for your t erm paper if you would like, but for this activity we are going to practice making graphs in a few different ways. We will walk through an example of each figure you will make. Note that there are MANY different types of figures, we will practice making s catter plots, histograms, and bar graphs You will need to create these figures independently, BUT you can work together to figure out how to make them, and discuss what they are showing and what they mean.

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303 Slide 8 Fi rst, you will look at the distribution of an independent variable across all the sites. The instructions for how to do this are for 2016 Excel on a PC. If you have a MAC, you could use a lab laptop or look up how to do certain functions online. This is a it is plotting the % impervious surface and the longitude of the site locations on a scatter plot . Each point is a site location, shown from east to west along the transect (the middle being the center of Denver). The instructions will have you think about what the distribution looks like, and what it means. (there are few low values, and few high values, but a lot of values in the middle) Notice the graph has axis labels, a text box for figure nu mber, title, and caption.

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304 Slide 9 The second type of figure you will create to look at the distribution of a variable across the sampled locations, is a histogram. This histogram example looks at the frequency, or how many, of the sites fall into a certain category, or bin in this case a range of values for % IS. When making histograms, you have to decide what the bin ranges are going to be, so it is extremely important to have a reasonable explanation of why the bin ranges you choose are relevant or important to your research. For this example the % impervious surface ranges selected have been used in previous primary literature to correspond with urbanization intensity, low urbanization = 30% IS or less, medium ur banization = 30 50%, and high level of urbanization is 50% or greater IS. For your specific independent variable, you will have to decide how to show the distribution across all the sites. Notice the formatting.

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305 Slide 10 The first one is a bar graph . This will show the data categorically , where the categories, or bins, are the low/med/high values of the % impervious surface, to the species richness of wil d mammals seen in the photos. Notice that this graph is showing an average value, and the standard deviation as error bars, showing the variance around the average. Notice that the species richness axis label show what the error bars mean in parentheses ( +/ SD). The caption explains how the dependent and independent variables are measured.

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306 Slide 11 The last one is another scatter plot looking at the % IS as continuous data this time. Instead of binning the IS values in low/med/high, you could plot the data along a continuous scale. There are pros and cons to showing data on a categorical or continuous scale (if you are able to choose one way or the other) for example, when you put data into bins, you lose some of th e context of that data are the bin ranges appropriate? How many data points are in each bin? And for continuous data, you may be showing more context, but the data may be difficult to see / distinguish points. Research scientists use many different appro aches to display their data, as you will see as you start reading more primary literature. You will have to decide how you will want to display your data, which will be dependent on your specific question

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307 Slide 12 You will need to create these figures independently, BUT you can work together to figure out how to make them, and discuss what they are showing and what they mean. You will be asked for one of these graphs in your post lab, make sure to have all the componen ts of a professional graph.