Citation
Historical phytogeography of front range psychocarpus (rosaceae)

Material Information

Title:
Historical phytogeography of front range psychocarpus (rosaceae)
Creator:
Dignan, Audrey
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 Geography and Environmental Sciences, CU Denver
Degree Disciplines:
Environmental sciences
Committee Chair:
Briles, Christy
Committee Members:
Bruderle, Leo P.
Anthamatten, Peter

Notes

Abstract:
Species distribution patterns today provide insight into historical drivers like climatic conditions. Glacial-interglacial fluctuations throughout the Pleistocene have resulted in multiple re-organizations of vegetation communities and complex biogeographic histories of many North American plants, including Physocarpus (Cambess.) Raf. (Rosaceae), an Arcto-Tertiary relict. These changes have resulted in broad disjunctions within distributions, as well as distributional overlap among taxa with disparate biogeographic histories. An example of these phenomena can be observed in the isolated populations of eastern North American plants — the so-called eastern woodland-prairie flora — which co-occur with the Cordilleran flora of the Southern Rockies in the Front Range of Colorado. Here, I investigate the historical phytogeography of a member of the eastern woodland-prairie element, Physocarpus opulifolius (L.) Maxim sensu lato and its Cordilleran congener, P. monogynus (Torr.) Coult. First, I clarify the taxonomy of the P. opulifolius s.l. species complex. To do this, I assess variation for follicle pubescence, a diagnostic character for the complex, with respect to geographic distribution. This approach serves to reveal cryptic variation across the full range of the complex and to reduce taxonomic uncertainty in occurrence data derived from herbarium specimens. Second, I examine the historical distributions of P. opulifolius s.l. and P. monogynus by modeling their climatic niches during the Last Glacial Maximum (ca. 21,000 yrs ago), mid-Holocene (ca. 6,000 yrs ago), and today. These models suggest that modern distributions of the study taxa likely result from a complex history of multiple biogeographic events.

Record Information

Source Institution:
University of Colorado Denver
Holding Location:
Auraria Library
Rights Management:
Copyright Audrey Dignan. 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.

Downloads

This item has the following downloads:


Full Text
HISTORICAL PHYTOGEOGRAPHY OF FRONT RANGE PHYSOCARPUS (ROSACEAE)
by
AUDREY DIGNAN B.S., University of Richmond, 2011
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 Environmental Sciences Program 2019
i


This thesis for the Master of Science degree by Audrey Dignan has been approved for the Environmental Sciences Program
by
Christy Briles, Chair Leo P. Bruederle, Advisor Peter Anthamatten
Date: May 18, 2019
ii


Dignan, Audrey (M.S., Environmental Sciences Program)
Historical Phytogeography of Front Range Physocarpus (Rosaceae)
Thesis directed by Professor Emeritus Leo P. Bruederle
ABSTRACT
Species distribution patterns today provide insight into historical drivers like climatic conditions. Glacial-interglacial fluctuations throughout the Pleistocene have resulted in multiple reorganizations of vegetation communities and complex biogeographic histories of many North American plants, including Physocarpus (Cambess.) Raf. (Rosaceae), an Arcto-Tertiary relict. These changes have resulted in broad disjunctions within distributions, as well as distributional overlap among taxa with disparate biogeographic histories. An example of these phenomena can be observed in the isolated populations of eastern North American plants — the so-called eastern woodland-prairie flora — which co-occur with the Cordilleran flora of the Southern Rockies in the Front Range of Colorado.
Here, I investigate the historical phytogeography of a member of the eastern woodland-prairie element, Physocarpus opulifolius (L.) Maxim sensu lato and its Cordilleran congener, P. monogynus (Torr.) Coult First, I clarify the taxonomy of the P. opulifolius s.l. species complex. To do this, I assess variation for follicle pubescence, a diagnostic character for the complex, with respect to geographic distribution. This approach serves to reveal cryptic variation across the full range of the complex and to reduce taxonomic uncertainty in occurrence data derived from herbarium specimens.
Second, I examine the historical distributions of P. opulifolius s.l. and P. monogynus by modeling their climatic niches during the Last Glacial Maximum (ca. 21,000 yrs ago), mid-Holocene (ca. 6,000 yrs ago), and today. These models suggest that modern distributions of the study taxa likely result from a complex history of multiple biogeographic events.
The form and content of this abstract are approved. I recommend its publication.
Approved: Leo P. Bruederle


ACKNOWLEDGEMENTS
First and foremost, I’d like to thank Leo P. Bruederle for his boundless support and guidance, as well as his assistance with examining many of the specimens included in this study. Thank you to Christy Briles for guiding me on this thesis journey from the beginning. Thanks to Peter Anthamatten for his invaluable cartographic support. This study was funded by the Colorado Mountain Club Foundation; many thanks to Paula Cushing and the grant review board. Thank you to Melissa Islam and the Kathryn Kalmbach Herbarium at the Denver Botanic Gardens for providing workspace and equipment and for housing a large loan of specimens from the Missouri Botanical Garden; many thanks to Dr. James Solomon for providing the MO loan, and to all personnel from the remaining nine herbaria for providing access to specimens and digitized specimen data. To Ryan, thank you for providing balance, light, encouragement, and tea - I couldn’t ask for a better partner. Finally, Mom and Dad: thank you for showing me the beauty of nature from a young age and for providing enthusiastic guidance while letting me find my own winding way.
IV


TABLE OF CONTENTS
CHAPTER
I. INTRODUCTION.................................................................1
North American Paleoclimate and Vegetation...................................1
Environment and the Flora of Colorado........................................2
Physocarpus..................................................................5
Objectives...................................................................7
II. THE GEOGRAPHY OF FOLLICLE PUBESCENCE IN PHYSOCARPUS OPULIFOLIUSS.L.,
WITH IMPLICATIONS FOR TAXONOMY..............................................10
Introduction................................................................10
Materials and Methods.......................................................13
Results and Discussion......................................................15
Conclusions.................................................................16
III. CLIMATIC NICHE MODELING OF FRONT RANGE PHYSOCARPUS WITH IMPLICATIONS
FOR BIOGEOGRAPHIC ORIGINS OF COLORADO FLORA.................................21
Introduction................................................................21
Methods.....................................................................23
Results.....................................................................27
Discussion..................................................................30
IV. CONCLUSIONS.................................................................46
Taxonomy of Physocarpus opulifolius s.I.....................................46
v


Contributions to the Flora of Colorado.......................................47
Limitations..................................................................48
Future Directions............................................................50
REFERENCES..........................................................................52
APPENDICES
A............................................................................60
B............................................................................62
C............................................................................76
D............................................................................77
E............................................................................82
F............................................................................85
G............................................................................86
VI


LIST OF TABLES
TABLE
3.1. Candidate variables for climatic niche modeling......................................34
3.2. Climatic variables used to train niche models........................................38
3.3. Model validation for niche models....................................................43
3.4. PCA covariates matrix................................................................45
vii


LIST OF FIGURES
FIGURE
1.1. Estimated extent of glaciation in North America during the Last Glacial Maximum......5
1.2. EPA Level III Ecoregions of Colorado.................................................6
2.1. Categories of follicle pubescence in Physocarpus opulifolius s.l.....................18
2.2. Geographic distribution of follicle pubescence in Physocarpus opulifolius s.l........19
2.3. Prevalence of follicle pubescence in Physocarpus opulifolius s.l. by EPA Ecoregion...20
3.1. Eastern woodland-prairie and Cordilleran floristic elements .........................32
3.2. Inputs and model construction with Maxent............................................33
3.3. Study extent.........................................................................35
3.4. Effect of background selection on preliminary niche models of Physocarpus monogynus..36
3.5. Occurrence dataset used to train climatic niche models ..............................37
3.6. Maxent niche models of occurrence probability trained on current climate.............39
3.7. Hindcast Maxent niche models for Physocarpus opulifolius (L.) Maxim..................42
3.8. Hindcast Maxent niche models for Physocarpus monogynus (Torr.) Coult.................40
3.9. Hindcast Maxent niche models for Physocarpus intermedius (Rydb.) Schneid.............41
3.10. PCA biplot of variance in climatic conditions among occurrence data.................44
viii


CHAPTER I
INTRODUCTION
Species distributions are intricately linked to underlying drivers, including biotic and abiotic factors that may be ongoing or historical. Modern distribution patterns are indicative of past conditions, while estimations of past distributions provide insight into the biogeographical origins of modern patterns and assemblages. Past distributions and drivers can be used to understand how contemporary distribution patterns might have developed. Disjunctions, in particular, have long been of interest to biogeographers, evolutionary biologists, and conservationists (e.g., theory of island biogeography). Further, increasing our understanding of current distributions through estimations of those past helps inform our understanding of future patterns (Fitzpatrick et al., 2018).
North American Paleoclimate and Vegetation
Last Glacial Maximum. During the LGM, the presence of continental ice sheets across much of North America (Fig. 1.1) influenced regional atmospheric circulation patterns and paleoclimate conditions . The elevated surface of the two-mile-thick ice mass diverted upper-level atmospheric flow, splitting the polar jet stream (Bartlein et al., 1998). The strong southern arm of the jet stream flowed along the southern ice margin, across the southern Great Lakes and southern New England regions, while a weaker northern branch flowed north of the ice sheet (Bartlein et al., 2011).
Cold dense air and high pressure developed over the ice sheet, while a low-pressure zone developed over the warmer ice-free landmass to the south. The temperature differential created a steep pressure gradient that resulted in strong clockwise (anticyclone) surface winds and prevailing cold dry easterly winds across the continental interior (Schaetzl et al., 2016). Further, the southward shift of subtropical highs and moist maritime storms further contributed to the cold and dry conditions in the continental interior during the LGM (Bartlein et al., 1998; Pausata et al., 2011; Schaetzl et al., 2016).
1


The LGM vegetation patterns across unglaciated regions of eastern North America generally organized into broad latitudinal bands. Cold-dry sedge-dominated communities occupied a relatively narrow strip along the south of the ice margin in the southern Great Lakes and southern New England regions. Boreal forest occurred farther south, while temperate deciduous forest was restricted south of northern Georgia. To the west, cool-dry Picea parkland community occurred along the southern ice margin (e.g., northern Montana; Power et al., 2011; Yansa, 2006).
Mid-Holocene. Climatic shifts during the Holocene were largely driven by changes in incoming solar radiation (insolation); seasonality increased through the mid-Holocene as summer insolation reached a maximum and winter insolation a minimum after the LGM (Bartlein et al., 2011). This resulted in significant ice melt and consequently the glacial anticyclone diminished as the ice sheets retreated, resulting in diminished cool-dry winds into the continental interior. In addition, the warming continent facilitated the flow of atmospheric moisture from the Pacific Ocean and the Gulf of Mexico into the continental interior. This led to the development of the current North American monsoonal regime (Bartlein etal., 1998).
Late-glacial to post-glacial changes in vegetation in eastern North America largely followed changes in climate, generally shifting northward tracking glacial retreat. During the early Holocene, oaks invaded the boreal spruce forests near the former glacial margin (Delcourt & Delcourt, 1993). Warm, moist air masses flowing into the eastern continental interior from the Gulf of Mexico supported the northward expansion of the temperate eastern deciduous forest community from southern glacial refugia. During the insolation maximum of the middle Holocene (ca. 9000 - 4000 yr B.P.), the temperate prairie reached its easternmost extent into Illinois due to warm, dry Pacific air masses that penetrated far into the eastern continental interior (Delcourt & Delcourt, 1993).
Environment and the Flora of Colorado
Physiography and climate. The diverse plant communities of Colorado result from complex topography and highly variable climatic conditions, as well as a history of natural and human
2


disturbance. Colorado has a continental climate, with warm summers and cool winters. One of the
most notable aspects of the state’s climate is the extreme variation in precipitation and temperature, which often occur over relatively short temporal and spatial scales (Mutel & Emerick, 1992).
Colorado straddles a transition between the Great Plains to the east and the Cold Deserts of the Great Basin to the west. Four primary ecoregions comprise this transition: High Plains, Southwestern Tablelands, Southern Rockies, and Colorado Plateaus. In addition to these, smaller portions of two additional ecoregions - the Arizona/New Mexico Plateau and the Wyoming Basin -extend into the state from the south and northwest, respectively (Fig. 1.2; EPA, 2013).
High Plains. The High Plains ecoregion represents the highest-elevation and westernmost extent of the Great Plains. This shortgrass prairie ecosystem is characterized by a cooler and drier climate than the mixed-grass and tallgrass prairies to the east. This ecoregion occurs in the eastern and northeastern regions of Colorado, and comprises semi-arid flat to moderate relief plains with rolling sandsage prairie along river valleys (EPA, 2013).
Southwestern Tablelands. The Southwestern Tablelands are a vast semi-arid landscape of dissected plains and tablelands that cover the southwestern portion of the state. The terrain is more variable with greater relief than the High Plains to the north. As a result, climates tend to range from hot and dry in the central portion of the ecoregion, between Pueblo and Las Animas, to cool and moist at higher elevations along the base of the foothills to the west (EPA, 2013). The vegetation is primarily shortgrass prairie, and pine-oak woodlands interspersed with grasslands occur along the Palmer Divide, which separates the Arkansas River basin to the south from the South Platte River basin to the north. Pinyon-juniper woodlands occur at higher elevations in the foothills in the southwestern portion of the ecoregion (EPA, 2013).
Southern Rockies. The Southern Rockies ecoregion, the largest of the six, bisects the state. The Continental Divide has a considerable influence over the state’s climatic and hydrological regimes;
3


prevailing westerlies carrying moisture from the Pacific release precipitation as they pass over the mountains, creating a semi-arid rain shadow to the east. This ecoregion is characterized by long, cold, snowy winters (EPA, 2013). Pleistocene glaciation in alpine and subalpine zones created rugged mountains with large elevational gradients (EPA, 2013).
Colorado Plateaus. To the west of the Southern Rockies lies the Colorado Plateau, characterized by semi-arid rolling plains and basins interspersed with high-relief features (e.g., mesas, cliffs, arches, and canyons). Closed-canopy forest is rare in this ecoregion; the vegetation here comprises sparse desert shrubland and sagebrush steppe. Semi-arid woodland communities, typically characterized as pinyon-juniper or scrub oak woodland, can be found at warm-dry sites in the foothills (EPA, 2013).
Arizona/New Mexico Plateau. A small portion of the Arizona/New Mexico Plateau ecoregion extends north into Colorado, comprising the San Luis Valley. The Colorado portion of the ecoregion is bounded by the Sangre de Cristo Mountains to the east, La Garita Mountains to the northwest, and San Juan Mountains to the west. The lowest mean annual precipitation levels in the state (6-8 inches per year) occur in the Salt Flats of central San Luis Valley, where vegetation is very sparse due to alkaline soils (EPA, 2013). In the alluvial flats and wetlands of the Rio Grande watershed to the west, mountain runoff and a high water table have provided a water supply suitable for irrigated cropland. As a result, most of the natural shrubland vegetation has been replaced by irrigated agriculture (EPA, 2013).
Wyoming Basin. The southernmost portion of the Wyoming Basin ecoregion extends into northwestern Colorado. This vast landscape comprises rolling sagebrush steppe among plains, alluvial fans, and mesas, and arid salt desert shrubland interspersed with playas and sand dunes.
Flora. The complex topography and highly variable climatic conditions have developed a mosaic of diverse plant communities and overlapping floristic elements, or groups of species with similar distribution patterns (Weber & Wittmann, 2011). Within Colorado lies the western limit of
4


the Great Plains, northern limit of the Southwestern Tablelands and Arizona/New Mexico Plateaus, eastern limit of the Colorado Plateau, and southern limit of the Wyoming Basin. As a result, the Colorado flora includes contributions from surrounding regions, including the Great Basin, Rio Grande Valley, Chihuahuan Desert, Northern Rockies, and Great Plains (Weber & Wittmann, 2011). The Southern Rockies have acted btoh as a physical barrier to westward dispersal for many temperate mesic plants (e.g., Physocarpus intermedius (Rydb.) Schneid.; Chapter III, this paper), as well as a dispersal corridor, allowing arctic species to disperse far to the south of their core ranges in Canada [e.g., Juncus biglumis L. [Juncaceae]; Marr et al., 2012; Mutel & Emerick, 1992). Species associated with the Andes and mountains of Central America have also been found in the Colorado Rockies, suggesting that the Rockies may have facilitated northward dispersal, as well (Weber & Wittmann, 2011).
Physocarpus
Physocarpus (Cambess.) Raf. (Rosaceae) is a genus of deciduous shrubs comprising 6-20 species (Alexander, 2014; Cuizhi & Alexander, 2003; Oh, 2015). It is one of two genera in the Neillieae Maxim. (Maximovich, 1879; Oh 2015), and is differentiated from the second genus — Neillia D. Don — by the presence of stellate trichomes and corymbose inflorescences (Oh, 2015). Several Physocarpus species are commonly cultivated, including P. capitatus (Pursh) Kuntze and P. opulifolius (L.) Maxim. (Newhall, 1891); the latter is known to escape from cultivation and has become naturalized in parts of Europe (Alexander, 2014).
Arcto-Tertiary Element. Like many other genera in the Rosaceae (Potter et al., 2007), Physocarpus occupies an intercontinental disjunction, occurring predominantly in North America with at least one species in eastern Asia [P. amurensis [Maxim.] Maxim.; Cuizhi & Alexander, 2003; Oh, 2015). This distribution pattern is indicative of an Arcto-Tertiary Geoflora (Wen etal., 2010). The paleoflora record suggests that temperate forest extended across the Bering land bridge, which connected the two continents and facilitated migration during the mid-Tertiary (Thorne, 1993;
5


Wen et al., 2010). As the climate became colder and drier through the late Tertiary, temperate species became restricted to lower latitudes of eastern Asia and North America and related taxa consequently became isolated on separate continents (Axelrod & Raven, 1985; Weber 1965; Wen et al., 2010).
The age of the most recent common ancestor (MRCA) of Neillieae was approximately early Miocene (~20.6 ± 0.4 mya; Oh & Potter, 2005). This period has been shown to be important for the development of the eastern Asian-North American intercontinental disjunction in other taxa (Wen et al., 2010). During this time, the MRCA of Neillieae likely occupied a widespread distribution between the two continents via the Bering land bridge (Oh & Potter, 2005). Subsequently, Physocarpus is thought to have undergone speciation in western North America (Oh & Potter,
2005).
Fossil evidence. There is limited evidence of Physocarpus in the paleoflora record. A total of four pollen grains identified as Physocarpus have been reported to the Neotoma database (neotomadb.org; Goring et al., 2015), including two from Frying Pan Lake in Utah (Shafer, 1989), one from Crane Lake in Arizona (Shafer, 1989), and one from Blaney’s Pond in southern Massachusetts (Oswald et al., 2018). However, there may be some uncertainty regarding the identification of these pollen grains due to the difficulty of identifying Rosaceae pollen below the family level (C Briles, personal comm.).
Several macrofossils have also been reported. Physocarpus leaves dating to ca. 3 million years ago (mya) were found in the Canadian Arctic Archipelago at three Beaufort Formation sites (Banks Island, Prince Patrick Island, and Meighen Island) and one site on Ellesmere Island (Matthews and Ovenden, 1990). Physocarpus leaves were also discovered in the middle Eocene chert in Republic, WA (Wehr and Hopkins, 1994), but they lack the stellate trichomes that are diagnostic in extant species of Physocarpus (Oh and Potter, 2005). In addition, these fossilized leaves pre-date the MRCA
6


of the Neillieae by about 20-25 million years, so they are unlikely to be true Physocarpus leaves (Oh and Potter, 2005).
Objectives
The research presented in the following chapters aims to clarify the taxonomy and recent evolutionary history of the P. opulifolius sensu lato complex and to shed light on the biogeographic origins of the Colorado flora. In the following chapters, I will refer to this taxon at the specific level, following the taxonomic treatment of Alexander (2014) in Flora of North America, North of Mexico (FNA). Chapter II will present a taxonomic review of the P. opulifolius s.l. complex. Specifically, I aim to clarify the understanding and the geographic distribution of follicle pubescence, a key diagnostic trait for discriminating subspecific variation in P. opulifolius s.l. Chapter III builds on those findings and examines the historical phytogeography of P. opulifoliuss.s. and P. intermedius, as well as a third congener, P. monogynus (Torr.) Coult. Findings from this chapter shed light on the origins of the Colorado flora, specifically the western disjunction of the so-called eastern woodland-prairie element and the Cordilleran element These findings also have broader implications for understanding glacial refugia. The conclusions from both chapters will be summarized, synthesized, and discussed in Chapter IV.
7


Figure 1.1. Estimated extent of glaciation in North America during the Last Glacial Maximum (est. 21,000 years before present). Ice extent determined by Dyke (2004) and made available by the Geological Survey of Canada (https: //github.com/awickert/North-American-Ice-Sheets).
8


Level III Ecoregions of Colorado 18 Wyoming Basin I 20 Colorado Plateaus
21 Southern Rockies
22 Arizona/New Mexico Plateau I 25 High Plains
26 Southwestern Tablelands
0 100 200 km
1 _______I_________I
Figure 1.2. EPA Level III Ecoregions of Colorado (EPA, 2013).
9


CHAPTER II
THE GEOGRAPHY OF FOLLICLE PUBESCENCE IN PHYSOCARPUS OPULIFOLIUS S.L.,
WITH IMPLICATIONS FOR TAXONOMY Introduction
Precise taxonomic identification is a critical prerequisite for ecological, taxonomic, and systematics research (e.g., niche modeling, floristics, phylogenetics). Despite this, taxonomic revisions are common and disagreement among taxonomists is frequent Furthermore, phenotypic or genotypic variation that may be undetected and uncaptured (hereafter called 'cryptic variation’) can confound our understanding of systematic relationships (e.g., Mastin et al., 2018), as well as many other aspects of the biology of taxa (e.g., sexual reproduction, Menz et al., 2015; conservation, Duarte et al., 2014).
Cryptic variation has long confounded the taxonomy of Physocarpus (Cambess.) Raf. (Rosaceae). Variable vegetative morphology (e.g., leaf shape), inconsistent interpretation of morphological traits (e.g., follicle pubescence), and geographically restricted taxonomic treatments have all contributed to taxonomic confusion, particularly in eastern North America. Conflicting circumscriptions predominate in the P. opulifolius (L.) Maxim, sensu lato species complex. Morphological variation within this complex has been variously recognized as P. intermedius (Rydb.) Schneid., P. opulifolius var. intermedius (Rydb.) B.L. Rob., or as a note under P. opulifolius (e.g., Gleason and Cronquist, 1991; McGregor et al., 1986). As a result, P. intermedius has been variously recognized at the level of species or variety, or has been excluded entirely. This lack of clarity continues today; the most recent floristic keys for Colorado either do not recognize the P. opulifoliuss.l. complex (Ackerfield, 2015) or they attribute the morphological variation to local hybridization with P. monogynus (Torr.) Coult. (Weber & Wittmann, 2011). This issue has clouded understanding of the distribution of P. opulifolius s.l. complex across its range, which extends from the northeastern US to the southern Appalachians and west to the eastern Great Plains, with
10


disjunctions in the Sand Hills of Nebraska, the Black Hills of South Dakota, and the Front Range of Colorado. Although some specimens have been collected from the northwestern US, P. opulifoliuss.I. does not occur west of the Rocky Mountains. Rydberg (1908b) attributed "the error of including the Pacific coast in the range of 0. opulifolius" to a glabrate form of 0. capitatus (Pursh) Kuntze.
Physocarpus opulifolius and P. intermedius have undergone many taxonomic and nomenclatural changes at both the genus and species level, resulting in a complex history. Carolus Linnaeus published the basionym Spiraea opuiifoiia L. in Species Piantarum (1753). Jacques Cambessedes later subdivided Spiraea, assigning solely S. opuiifoiia to Spiraea L. subsect Physocarpus Cambess. (as Physocarpos; Cambessedes, 1824). Constantine Samuel Rafinesque (1838) spelled it Physocarpa. Although authorship is given to Cambessedes and Rafinesque, the first to publish the conserved spelling Physocarpus was Karl Maximovich (1879). Maximovich’s spelling was later retained by Camillo Karl Schneider (1906) in his publication of P. intermedius. In 1865, George Bentham and Joseph Dalton Hooker moved S. opuiifoiia to Neiiiia D. Don [N. opuiifoiia Benth. & Hook.). Maximovich then moved it to Physocarpus (as P. opuiifoiia; Maximovich, 1879). In Volume I of his Revisio Generum Piantarum (1891a), Otto Kuntze originally referred to it as P. opulifolius, following Maximovich’s treatment, but he later moved the species to Opulaster Medik. ex Kuntze in Volume II (Opuiaster opulifolius [L.J Kuntze; 1891b), recognizing that Opulaster antedated Physocarpus (Oh, 2015). Although a nomen nudum today, Opulaster was widely used in reference to several Physocarpus species for another 20-30 years (Oh, 2015). Per Axel Rydberg was the first to publish the specific epithet intermedius, assigning it to Opulaster (0. intermedius Rydb.; 1901). Shortly thereafter, it was moved to Physocarpus by Schneider (P. intermedius [Rydb.] Schneid.; 1906). Finally, in 1908, Benjamin Lincoln Robinson published the combination P. opulifolius (L.) Maxim, var. intermedius (Rydb.) B.L. Rob. At least 14 synonyms for the taxa comprising the P. opulifolius s.I. complex have been described in the 266 years since Linnaeus’s Species Piantarum, and P. opulifolius has been placed in four different genera: Spiraea, Neiiiia, Opulaster, and Physocarpus (Appendix A).
11


Several characters have been used to differentiate between P. opulifolius and P. intermedius, including carpel number and leaf pubescence. The most useful diagnostic character, however, has been pubescence of mature follicles. Alexander (2014) recently summarized his observations on pubescence in both carpels and mature follicles, and used this trait to discriminate between P. opulifolius and P. intermedius. In his treatment, he described the carpels and follicles of the former as "glabrous or sparsely stellate-hairy, glabrescent”, while the carpels and follicles of the latter were described as "densely stellate-hairy (sometimes only on sutures).”
Geographic variation in this trait has been noted by several eminent botanists. In Flora of the Black Hills of South Dakota (1896), for example, Rydberg commented on the morphological difference between specimens identified as 0. opulifolius from Colorado and northern Nebraska in comparison to those from the eastern US. He described the western specimens as having permanently pubescent follicles, while the eastern plants had "smooth and shining” glabrous follicles (Rydberg, 1896). Five years later, Rydberg ascribed the pubescent western form to 0. intermedius, contrasting it with 0. opulifolius (L.) Kuntze to which he ascribed glabrous follicles (Rydberg, 1901). Aven Nelson (1902) also noted the difference between Rocky Mountain and eastern United States 0. opulifolius specimens. Shortly after Rydberg (1901) published 0. intermedius, Nelson (1902) described a new species — 0. Ramaieyi Nelson — which bears close resemblance to Rydberg’s description of 0. intermedius. In a footnote, he stated, "This is 0. opulifolius probably, in so far as Rocky mountain specimens have been so named. It is not the 0. opulifolius (L.) Kuntze of the eastern United States.” Schneider (1906) also described P. intermedius as being immediately distinguishable from P. opuiifoiiusby the pubescent fruits. In 1907, Francis Potter Daniels described two new Physocarpus species from the Midwest — one with stellate-pubescent follicles (P. missouriensis Daniels) and one with glabrous follicles (P. michiganensis Daniels) — albeit reluctantly: "It is with a feeling of protest that these stellate forms of Physocarpus
12


are described as new species, but the limitation of P. opulifolius (L.) Maxim, by recent authorities to the more glabrate forms, seems to leave no other recourse.”
Despite this lack of clarity and the presumed importance of this character, few treatments have critically examined variation across the range of the complex. Most treatments are regional or local in scale and, thus, lack detailed understanding of morphological or genetic variation across the complex (Oh, 2015). At the genus level, this limited scope and lack of clarity has also resulted in the description of numerous species, rendering taxonomic relationships unclear and discrimination among taxa challenging (Oh, 2015).
Here, I critically assessed follicle pubescence across the range of the P. opulifolius s.L, species complex with the goal of clarifying this trait and its natural distribution in North America. This will contribute toward clarifying the taxonomy of this species complex, which has significance for local and regional floras. The following questions were addressed: To what extent do P. opulifolius sensu stricto and P. intermedius differ, in terms of the geographic distribution of follicle pubescence? Does this variation merit taxonomic recognition? To achieve this goal, I examined follicle morphology of P. opulifolius s.l. herbarium specimens from across the geographic range of the species complex. Subsequently, I examined the spatial distribution of the follicle pubescence trait to assess potential geographic patterns in morphological variation.
Materials and Methods
Reproductive morphology. To assess variation for follicle pubescence across the natural range of P. opulifolius s.l., I created four categories that qualitatively capture the range in variation (Fig. 2.1): 1 = follicles glabrous, 2 = ventral sutures of follicles sparsely pubescent or glabrescent, 3 = ventral sutures of follicles pubescent with perisutural pubescence on abaxial surface, and 4 = abaxial surface of follicles uniformly densely pubescent. Follicle pubescence was assessed in over 580 fruiting specimens from 11 herbaria that were physically examined by me or thesis advisor LP Bruederle (Appendix B). Each accession was assigned to one of the morphological categories
13


outlined above (Fig. 2.1). The following herbaria were selected in order to maximize geographic coverage while minimizing travel and inter-herbarium loans (abbreviations follow Thiers, 2018): Kathryn Kalmbach Herbarium of Vascular Plants, Denver Botanic Gardens (KHD); University of Colorado Boulder (COLO); Colorado State University (CS); Rocky Mountain Herbarium, University of Wyoming (RM); Missouri Botanical Garden (MO); Indiana University (IND); University of Michigan (MICH); University of Wisconsin - Madison (WIS); University of Nebraska - Lincoln (NDEB); Morton Arboretum (MOR); and Milwaukee Public Museum (MIL). Material that appeared to be cultivated, as determined by information included on specimen labels (e.g., collected from college campuses, botanic gardens or arboreta), was excluded, as P. opulifolius has been widely cultivated since 1891 (Newhall, 1891) and is known to escape from cultivation (Alexander, 2014). Duplicate accessions were also excluded. The original dataset included several specimens from the northwestern US; however, these were likely cultivated or misidentified and were therefore omitted.
Digitized occurrence records were downloaded from SEINet (swbiodiversity.org) and the Consortium of Midwest Herbaria (midwestherbaria.org). Data on specimen sheets (e.g., original labels, annotation labels) were verified or entered manually into a project spreadsheet. These data will be returned to the herbaria listed above at the end of the project to support digitization of the accessions examined.
Geographic distribution. Accessions that did not include geographic coordinates on the original label were georeferenced using GeoLocate (geo-locate.org; Rios & Bart, 2010) or Earth Point (earthpointus), when label information was adequate for doing so. Although many digitized herbarium specimens were georeferenced previously, these data are often problematic (e.g., computerized batch processing has been shown to result in inflated estimates of spatial accuracy; Bloom et al., 2018; Smith et al., 2016). Finally, accessions were mapped in QGIS 3.4.2 (QGIS
14


Development Team, 2019) according to pubescence morphology to determine if there was a geographical component to the distribution of this character.
Results and Discussion
Reproductive morphology. Although P. opulifolius has been described as having glabrous follicles, very few accessions were observed with fruits that were truly glabrous. Upon closer examination, sutural stellate hairs were found on many accessions that were initially placed in category 1, which rendered discrimination between categories 1 and 2 challenging. Further, several accessions could not be definitively placed in either category 3 or 4. These formed a gradient of pubescence morphology, ranging from very sparsely pubescent on perisutural abaxial surface to densely pubescent on perisutural abaxial surface and extending across much of the abaxial surface. This transitional group may indicate phenotypic plasticity or hybridization and warrants further examination. Overall, follicle pubescence displayed two primary forms: a) follicles glabrous to pubescent along ventral sutures, and b) follicles uniformly and densely pubescent across the entire abaxial surface (Appendix C).
Geographic distribution. The final dataset comprised 526 georeferenced accessions distributed across 29 US states, two Canadian provinces, and one Mexican state (Fig. 2.2). Physocarpus opulifolius s.I. is more or less continuously distributed across the Appalachian Mountains and Great Lakes south to the highland forests of the Ozark and Ouachita Mountains, with disjunctions in the Sand Hills of Nebraska, the Black Hills of South Dakota, and the Front Range of Colorado (Fig. 2.2).
Within the complex, a spatial pattern with respect to follicle pubescence can be observed (Fig. 2.2). The two primary morphotypes described above — 1) glabrous to pubescent along ventral sutures and 2) uniformly and densely pubescent — generally occur in different regions of the US with overlap in the western Great Lakes region. The glabrous morphotype occupies an eastern distribution and occurs in eastern temperate forests from the southern Appalachians north to the
15


Atlantic Highlands of New England and west to the mixed woods plains of the Great Lakes (Figs. 2.2, 2.3). The pubescent morphotype, on the other hand, occupies a more western distribution.
Although broadly sympatric with glabrous and glabrescent forms in the Upper Midwest, it occurs most commonly and almost to the exclusion of the glabrous and glabrescent forms in the Interior Highlands of Missouri, Arkansas, Oklahoma, and Kansas and the Driftless Area of Iowa, Minnesota, Wisconsin, and Illinois, with disjunct occurrences in the Black Hills of South Dakota and the Front Range of Colorado, as well as the Sand Hills of Nebraska (Figs. 2.2, 2.3). The transitional accessions were collected from the southern Great Lakes area, a region of overlap between the more glabrous form and the more pubescent form (Fig. 2.2). These accessions may therefore indicate hybridization between the two forms or phenotypic plasticity of this trait.
Conclusions
The findings presented above support recognition of two taxa, as first suggested by Rydberg (1901). Based on the patterns observed in both the histogram (Appendix C) and the geographic distribution of this trait (Fig. 2.2), P. opulifolius and P. intermedius appear to be distinct taxa and thus merit taxonomic recognition (see Chapter IV for further discussion). Following original descriptions as well as Alexander’s recent treatment for FNA (2014), accessions with fruits in categories 1 through 3 were annotated as P. opulifolius, while those with category 4 fruits were annotated as P. intermedius.
Follicle pubescence is a complicated character that has often been misinterpreted in the literature. Cryptic variation for this trait within the P. opulifolius s.l. complex has led to the misidentification of western disjunct populations, including those on the Front Range. A critical examination of historical taxonomy, reproductive morphology, and geographic distribution reveal that the western populations are, in fact, P. intermedius. Further, P. opulifolius s.s. does not occur in Colorado. It should be noted that putative hybrids between P. intermedius and P. monogynus were observed where the two are syntopic (occurring at the same point).
16


These findings underscore the importance of considering taxa across their full range. Geographic restriction to a subset of the distribution might fail to capture the full range of morphological variation or phenotypic expression. As in the P. opulifolius s.l. complex, this practice perpetuates taxonomic uncertainty and conflicting treatments. At worst, this could lead to misaligned conservation priorities (Duarte et al., 2014).
Taxonomic disagreements between treatments are by no means limited to Physocarpus; they can be quite common. The methodology presented here — that is, mapping the variation of a diagnostic trait as observed in specimens from across the taxon’s range — can be applied to other taxa requiring similar clarification. Further, this approach can be used to reduce taxonomic uncertainty in species occurrence data, which has been shown to produce misleading results in downstream research approaches including niche modeling (Ensing et al., 2013).
17


Figure 2.1. Categories of follicle pubescence in Physocarpus opulifolius (L.) Maxim. s.I. (Rosaceae): a) category 1: follicles glabrous, b) category 2: ventral sutures of follicles sparsely pubescent, c) category 3: ventral sutures of follicles pubescent with perisutural pubescence on abaxial surface, and d) category 4: abaxial surface of follicles uniformly densely pubescent.
18


Figure 2.2. Geographic distribution of follicle pubescence in Physocarpus opulifolius (L.) Maxim. s.I. (Rosaceae), where 1 = follicles glabrous, 2 = ventral sutures of follicles sparsely pubescent or glabrescent, 3 = ventral sutures of follicles pubescent with perisutural pubescence on abaxial surface, and 4 = abaxial surface of follicles uniformly densely pubescent.
19


Level II Ecoregions
I -I 4.1 Hudson Plain - Hudson Plain
5.1 Northern Forests - Softwood Shield â–¡â–¡ 5.2 Northern Forests - Mixed Wood Shield mi 5.3 Northern Forests - Atlantic Highlands
I__I 5.4 Northern Forests - Boreal Plain
1^1 6.2 Northwestern Forested Mountains - Western Cordillera E3I 7.1 Marine West Coast Forest - Marine West Coast Forest [H3 8.1 Eastern Temperate Forests - Mixed Wood Plains mi 8.2 Eastern Temperate Forests - Central USA Plains [_J 8.3 Eastern Temperate Forests - Southeastern USA Plains â– I 8.4 Eastern Temperate Forests - Ozark, Ouachita-Appalachian Forests
[ ] 8.5 Eastern Temperate Forests - Mississippi Alluvial and
Southeast USA Coastal Plains I I 9.2 Great Plains - Temperate Prairies m 9.3 Great Plains - West-Central Semi-Arid Prairies I I 9.4 Great Plains - South Central Semi-Arid Prairies â–¡ 9.5 Great Plains - Texas-Louisiana Coastal Plain mi 9.6 Great Plains - Tamaulipas-Texas Semiarid Plain â–¡â–¡ 10.1 North American Deserts - Cold Deserts I I 10.2 North American Deserts - Warm Deserts I I 11.1 Mediterranean California - Mediterranean California [I_J 12.1 Southern Semi-Arid Highlands - Western Sierra Madre Piedmont
H 13.1 Temperate Sierras - Upper Gila Mountains CD 13.2 Temperate Sierras - Western Sierra Madre â–¡â–¡ 13.3 Temperate Sierras - Eastern Sierra Madre I I 14.1 Tropical Dry Forests - Dry Gulf of Mexico Coastal Plains and Hills
I 1 14.2 Tropical Dry Forests - Northwestern Plain of the Yucatan Peninsula
m 15.4 Tropical Wet Forests - Everglades
Figure 2.3. Prevalence of follicle pubescence in Physocarpus opulifolius s.I. (Rosaceae), by EPA Level III Ecoregion (EPA, 2013), where 1 = follicles glabrous, 2 = ventral sutures of follicles sparsely pubescent or glabrescent, 3 = ventral sutures of follicles pubescent with perisutural pubescence on abaxial surface, and 4 = abaxial surface of follicles uniformly densely pubescent.
20


CHAPTER III
CLIMATIC NICHE MODELING OF FRONT RANGE PHYSOCARPUS WITH IMPLICATIONS FOR BIOGEOGRAPHIC ORIGINS OF COLORADO FLORA Introduction
Modern distribution patterns of plants have been shaped by past climatic conditions. Throughout the Quaternary, glacial-interglacial fluctuations resulted in widespread reorganizations of floristic communities (Delcourt and Delcourt, 1993). Historical phytogeography examines spatial and temporal patterns in the geographical distributions of plants and investigates the drivers of those patterns. One such influence is climate, which is linked to resource availability (e.g., soil moisture, sunlight) and mediates not only which species can grow in a given location at a given time, but also how distributions change through time (Elith et al., 2010). Climate conditions thus provide a framework for investigating historical phytogeography. Further, climatic niche is an important and complex character that delimits the area in which a given plant species is likely to survive and reproduce (Franklin, 2010). Relationships between taxa can be explored by modeling the geographic distribution of their climatic niche or climatic envelope, the quantifiable set of climatic conditions that support survival (Franklin, 2010).
Due to its complex topography, the Southern Rocky Mountain region harbors a wide variety of plant communities. Steep environmental gradients created by local topography allow plant species with disparate biogeographic histories to co-occur; as a consequence, multiple floristic elements have assembled into a mosaic along the Front Range of Colorado (Nelson, 2010; Weber, 1965; Weber and Wittmann, 2011). Here, disjunct populations of several eastern North American species, referred to by Weber (1965) as the eastern woodland-prairie element, intermingle with the flora of the Southern Rocky Mountains, or the Cordilleran element (Fig. 3.1; Weber, 1965).
The eastern woodland-prairie element includes temperate deciduous trees and shrubs (e.g., Corylus cornuta Marshall), eastern woodland herbs (e.g. Aralia nudicaulis L., Sanicula marilandica
21


L.,), and tallgrass prairie species (e.g., Sporobolus heterolepis [A. Gray] A. Gray, Hesperostipa spartea [Trin.] Barkworth). They occur in protected mesic sites such as north-facing slopes and cool ravines (Cooper, 1984; Hogan, 1989; Livingston, 1952; Nelson, 2010; Weber, 1965,1976; Weber and Wittmann, 2011). The Front Range transitions abruptly out of the High Plains, particularly in Boulder county, where the landscape rises nearly 5,000 feet (1500 m) from grasslands to the alpine peaks of the Continental Divide over less than 20 miles (Mutel & Emerick, 1992). The steep gradient and topographic heterogeneity contribute to highly localized climatic conditions that are capable of satisfying the humidity and soil moisture requirements of mesic eastern woodland-prairie plants (Cooper, 1984; Weber, 1965).
The biogeographic origin of disjunctions within the eastern woodland-prairie distribution is unclear. The prevailing hypothesis proposes that disjunct populations may be relicts of a widespread eastern temperate forest community that reached as far west as the foothills of the Southern Rockies during the Last Glacial Maximum (Weber, 1965). During the Holocene, it was postulated that the forest retreated back towards the east, leaving relict populations in isolated refugia (Weber, 1965). I refer to this hypothesis as the 'expansion-contraction hypothesis.’
While the eastern woodland-prairie flora is dominated by mesic temperate species, the Cordilleran flora is adapted to a relatively harsh continental climate characterized by short growing seasons, cold winters, and warm summers (Axelrod & Raven, 1985). The Cordilleran element is distributed across the Rocky Mountains, from British Columbia to northern New Mexico (Fig. 3.1). This group includes conifers common to the Rocky Mountains (e.g., Picea engelmannii Parry ex Engelm. var. engelmannii, Pseudotsuga menziesii [Mirbel] Franco var. glauca [Beissn.] Franco, Pinus contorta Douglas ex Loud. var. latifolia Engelm.) as well as several montane deciduous trees and shrubs (e.g., Populus angustifolia James; Axelrod & Raven, 1985; Weber, 1976).
Physocarpus (Camb.) Raf. (Rosaceae), a predominantly North American genus, includes two species that represent the Cordilleran and eastern woodland-prairie floristic elements,
22


respectively: Physocarpus monogynus (Torr.) Coult. and P. intermedius (Rydb.) Schneid. The western disjunct populations of P. intermedius have been variously attributed to a broader complex, P. opulifolius (L.) Maxim, sensu lato. As discussed in the previous chapter, however, variable morphology and inconsistent taxonomic interpretations of P. opulifolius and P. intermedius have resulted in misidentifications and conflicting circumscriptions (cf. Ackerfield, 2015; Weber and Wittmann, 2011; see Chapter II for further discussion of taxonomy). A critical examination of a putative diagnostic trait (i.e., follicle pubescence) reveals that western populations of P. opulifolius s.I. take the pubescent form and should be recognized as P. intermedius. Thus, P. intermedius — not P. opulifolius s.s. — is representative of a predominantly eastern woodland-prairie element in the Southern Rocky Mountains.
Here, I investigate the historical phytogeography of P. opulifolius s.I. and P. monogynus by comparing climatic niche models through time, including the LGM (22,000 yr BP), mid-Holocene (6,000 yr BP), and today. In order to understand the relationships between these taxa, I will address the following questions: What is the biogeographical origin of sympatry involving the eastern woodland-prairie and the Cordilleran floristic elements along the Front Range? What regions acted as glacial refugia during the LGM? How have the distributions of the study taxa shifted since the LGM? Furthermore, I interpret the distribution patterns predicted by niche modeling to evaluate the 'expansion-contraction hypothesis’ for the origin of the eastern woodland-prairie disjunction. To support this hypothesis, I would expect to see three spatial patterns in the modeled distribution of P. intermedius: 1) connection between eastern North America and the Southern Rocky Mountains during the LGM, 2) overlap with P. monogynus during the LGM, and 3) development of a disjunct pattern after the LGM.
Methods
The objectives and research questions outlined above were approached using climatic niche modeling. This approach quantifies niche by comparing climatic conditions at known occurrence
23


points with conditions at background points generated by randomly sampling across the landscape of interest (Elith et al., 2011; Merow et al., 2013). The correlative model is then applied to geographic space to create a spatial model of continuous probability of presence (Fig. 3.2).
Occurrence records. Occurrence data were generated from georeferenced herbarium accessions from KHD, COLO, CS, RM, MO, IND, MICH, WIS, NDEB, MOR, and MIL (abbreviations follow Thiers, 2018). These herbaria were selected to maximize coverage of the range of P. opulifolius s.I. and P. monogynus. The spatial accuracy of occurrence records was quantified using the coordinate uncertainty radius method (Wieczorek et al., 2004). The dataset compiled in the previous chapter was refined by limiting the uncertainty radius to 2300 m or less, in order to match the resolution of the environmental layers (approximately 4600 m at the equator). Furthermore, ensuring spatial independence of presence points through spatial thinning has been shown to reduce model sensitivity to overfitting from using too many environmental variables as inputs (Kramer-Schadt et al., 2013). To minimize spatial autocorrelation and avoid artificial inflation of accuracy measures (Veloz, 2009), presence points were spatially thinned by applying a 20-km nearest neighbor distance in R (version 3.5.2, R Core Team, 2018) using the package spThin (vO.l.O; Aiello-Lammens etal., 2014).
Environmental variables. A total of 20 variables were considered for niche modeling: 19 bioclimatic variables derived from monthly temperature and precipitation data, as well as altitude (Table 3.1). These variables are commonly used in niche modeling and are considered biologically relevant summary data that represent annual climatic trends, seasonal variability, and measures of extreme conditions (O’Donnell and Ignizio, 2012; Schorr et al., 2013; Waltari et al., 2007). Bioclimatic variables were downloaded from WorldClim (worldclim.org; Hijmans et al., 2005) at a spatial resolution of 2.5 arc-minutes (~4.6 km at the equator) for current and historical (LGM and mid-Holocene) climates. Current climate data are derived from 30-year normals, averaged over 1970-2000. Historical data derived from the Community Climate System Model (CCSM4; Gent et al.,
24


2011) were downloaded for mid-Holocene (6,000 yr BP) and LGM (22,000 yr BP) climatic conditions. CCSM4 has been used in similar studies to project paleodistributions of species across large geographic ranges (Schorr et al., 2013; Waltari et al., 2007). The LGM and mid-Holocene were chosen for hindcasting as they are important benchmarks for evaluation of models involved in projects such as the Palaeoclimate Modelling Intercomparison Project (PMIP; Bartlein et al., 2011). Although additional time points would strengthen the analysis, I am not aware of other sources of hindcast climate raster products that are available at an appropriately fine spatial resolution (i.e., 2.5 arc-minutes or less). Finally, altitude was derived from SRTM 90-meter digital elevation models (DEMs) downloaded and resampled to 2.5 arc-minutes in R (ver. 3.5.2).
All variables were cropped in R (ver. 3.5.2) to encompass the full distributional ranges of the study taxa (23° to 53° N latitude, -62° to -125° W longitude; Fig. 3.3; Appendix D). To capture the different climatic niches occupied by the study taxa, each was considered separately in the following variable selection process. First, strongly-correlated variables (Pearson’s correlation coefficient r > 10.751) were eliminated by extracting values of all 20 environmental variables at presence points and constructing a pairwise correlation matrix (Heikkinen et al., 2006). Those that were strongly correlated with at least three others were eliminated (Appendix E). Exploratory niche models were constructed with the remaining variables, and the relative variable contributions and jackknife tests were used to guide selection of the final variable set. To examine variance in climatic conditions between occurrences, principal components analysis (PCA) was conducted in R (ver. 3.5.2) using the ggbiplot package (v0.55; Vu, 2011; Appendix D).
Niche models. Climatic niche models were constructed with Maxent (version 3.4.1; Phillips et al., 2006). Maxent is a free and open-source species distribution modeling program designed to predict suitable habitat using presence-only data and, thus, is well-suited for modeling plant distributions using occurrence records from herbarium specimens (Elith et al., 2011; Phillips et al., 2006). Maxent has been widely used in a variety of ecological, conservation, and evolutionary
25


applications (Elith et al., 2011; Phillips, 2008), including mapping current distributions for use in conservation planning (e.g., Tinoco et al., 2009), hindcasting paleodistributions to investigate contemporary distributional patterns (e.g., Carnaval & Moritz, 2008), evaluating invasion risk (e.g., Peterson et al., 2003), or predicting future response to climate change (e.g., Perez Navarro et al., 2018; Yates et al., 2010).
Maxent was run with the complementary log-log (cloglog) output and only hinge features, which simplifies the underlying functions and avoids overfitting (Elith et al., 2011; Phillips & Dudik, 2008). Default settings were used for all other model parameters (e.g., regularization multiplier = 1, maximum number of background points = 10000, maximum iterations = 500, prevalence = 0.5).
The body of literature that addresses background extent in Maxent niche models (e.g., Barve et al., 2011; Elith etal., 2010, 2011; Fourcade etal., 2014; Merow etal., 2013; Phillips, 2008; Phillips etal., 2009; Yates et al., 2010) emphasizes the importance of limiting background to an extent that is relevant to the study system and to the questions being asked. However, preliminary niche models for P. monogynus indicated that the restriction of background to states in which occurrences were located resulted in models that failed to accurately predict the known distribution of the species (Fig. 3.4). For this reason, background was not restricted in the final model procedure.
To identify where suitable environments were likely to occur in the past, models trained with current climate data were hindcastto mid-Holocene and LGM climate data. The three sets of models were compared to examine approximate distributional shifts through time.
Model validation. Models were 10-fold cross-validated to assess statistical uncertainty in model fit (Elith et al., 2011; Merow etal., 2013). To assess model performance, AUC is generally accepted in ecological modeling (Merow etal., 2013), but because it maybe misleading or inappropriate for modeling with presence-only data (Jimenez-Valverde, 2012; Lobo et al., 2008), the true skill statistic (TSS) was also calculated. TSS is equal to sensitivity + specificity - 1, where sensitivity is the proportion of all presences that are correctly predicted (absence of omission
26


error) and specificity is proportion of all absences that are correctly predicted (absence of commission error) (Allouche etal., 2006; Liu etal., 2015; Phillips etal., 2006). While AUC is a threshold-independent discrimination metric, TSS is threshold-dependent (Allouche etal., 2006). The threshold value used to calculate TSS was determined by maximizing the sum of sensitivity and specificity (max SSS), which has been shown to be an objective threshold selection method for presence-only data (Liu et al., 2015). TSS was calculated for each of the 10 cross-validated runs. Finally, the 10 TSS scores were averaged and reported as the mean TSS value for each study taxon.
Results
Occurrence records. A total of 1,024 herbarium specimens were examined and categorized by follicle pubescence (Chapter II). After refining the dataset by limiting the uncertainty radius and applying a spatial thin function, the final dataset comprised 470 occurrence points, with 129 P. opulifolius occurrences, 151 P. intermedius occurrences, and 39 P. monogynus occurrences (Fig. 3.5, Appendix B).
Environmental variables. A different set of variables was used to model each study taxon, reflecting the differences between occupied climatic niches. Of the 20 candidate variables, seven were selected for P. intermedius models, while six were selected for P. opulifolius and P. monogynus models (Table 3.2). Bio5 (maximum summer temperature, measured as maximum temperature of the warmest month) contributed most to the P. intermedius model (34.5% contribution), Biol7 (precipitation of the driest quarter) contributed most to the P. opulifolius model (46.4%), and altitude had the highest percent contribution to the P. monogynus model (60.1%).
Niche models. Both model validation metrics — AUC and TSS — indicated high levels of performance, with all three taxa scoring above 0.9 AUC and above 0.7 TSS (Table 3.3). Climatic niche models trained on contemporary climate normals can provide an estimation of current geographic distribution (Soberon & Peterson, 2005). The current-climate niche model for P. monogynus was aligned with its known distribution and, therefore, confirmed that it is
27


representative of the Cordilleran floristic element (Fig. 3.6a). Likewise, the current-climate niche model for P. intermedius displayed the distribution pattern of the eastern woodland-prairie element, occurring primarily in the eastern temperate forests and temperate prairie-woodlands of the east-central US with disjunct populations along the easternmost ranges of the Rockies, from the Black Hills to the Sierra Madre Oriental (Fig. 3.6b). Physocarpus opulifolius s.s., by comparison, is an eastern temperate forest species: the current-climate model indicated that it occurs in the forests of the Appalachian Mountains and coastal Great Lakes (Fig. 3.6c). Further, models hindcastto past climatic conditions indicated that it has not dispersed west of the Great Lakes region since the LGM.
Historical niche models. Multivariate environmental similarity surfaces (MESS) were used to understand where model predictions were projected outside the training range (i.e., where noanalog climate conditions were predicted to occur during the mid-Holocene and LGM; Elith et al., 2010). Across all three taxa, MESS indicated that LGM climatic conditions were outside of the contemporary climate envelope, especially for the northern half of the study region. Only one model, the P. opulifolius mid-Holocene model, predicted occurrence in a region of high uncertainty due to novelty. The mid-Holocene model for P. opulifolius (Fig. 3.7, top) predicted an isolated distribution in the western Sierra Madres. However, the MESS and MoD (most dissimilar variable; i.e., the variable with projections that were most outside the training range) indicated high levels of uncertainty in that region due to climate novelty in Biol8 (precipitation of warmest quarter).
LGM niche models predicted southward displacement compared to present for all three taxa. Specifically, P. opulifolius s.s. appears to have occupied an Atlantic Coast glacial refugium (Fig. 3.7, bottom), which has also been recognized for other temperate deciduous species, including two species of hickory (Carya cordiformis (Wangenh.) K.Koch and C. ovata (Mill.) K.Koch; Bemmels & Dick, 2018), American beech [Fagus grandifolia Ehrh; Morris et al., 2010), and flowering dogwood (Cornusflorida L.; Call et al., 2016). Physocarpus monogynus and P. intermedius, on the other hand, were predicted to have occupied southwestern US highlands and portions of the Sierra Madres in
28


Mexico during the LGM (Fig. 3.8, 3.9), a region that was warmer and moister than the arid, windy continental interior.
The modern disjunction in the distribution of P. intermedius — with core range in the eastern US and isolated populations in the Front Range (CO), Black Hills (SD), and Sand Hills (NE) — was not a feature of the predicted LGM distribution, but it was predicted for the mid-Holocene (Fig. 3.9). Thus, this disjunction appeared to have become established between the LGM and mid-Holocene. It remains unclear, however, whether the disjunction was the result of a vicariance event or recolonization via dispersal along two different routes — north into the Southern Rockies and northeast to the Great Lakes region — from a common southwestern US refugium.
Response curves. Maxent response curves were used to understand the relationship between the probability of occurrence and values of a given environmental variable (Appendix L). Physocarpus monogynus was most likely to occur at altitudes between approximately 1500 m (~5000 ft) and 3680 m (~12,000 ft) in regions where the mean temperature of the driest quarter was between approximately 10°C and -20°C. Physocarpus intermedius was most likely to occur in regions where the maximum temperature of the warmest month fell between approximately 25°C and 33°C, and precipitation of the driest month fell between approximately 10 mm and 60 mm. Physocarpus opulifolius was most likely to occur in regions where the annual mean temperature fell between approximately 3°C and 13°C and where precipitation of the driest quarter was greater than approximately 70 mm.
PCA. The first two principal components explain 74% of the variance in the occurrence data (Lig. 3.10, 3.11). PCI is negatively correlated with Biol (Annual Mean Temperature), Biol2 (Annual Precipitation), Biol4 (Precipitation of the Driest Month), Biol7 (Precipitation of the Driest Quarter), and Biol9 (Precipitation of the Coldest Quarter) (Table 3.4). PC2 is negatively correlated with altitude, Bio2 (Mean Diurnal Temperature Range), and Bio3 (Isothermality), and positively correlated with Bio4 (Temperature Seasonality) and Bio7 (Annual Temperature Range) (Table 3.4).
29


PCI can therefore be considered a measure of coldness and dryness (climate minimums), while PC2 can be viewed as a measure of temperature variability with altitude.
Discussion
The biogeographical history of the intersection of the eastern woodland-prairie floristic element and Cordilleran element was investigated by constructing climatic niche models for three Physocarpus species that have at various times been attributed to one of the two floristic elements. The disjunct eastern woodland-prairie distribution pattern is hypothesized to be the remnant of a Pleistocene distribution of eastern temperate forest that reached its maximum extent during the LGM (Weber, 1965). Specifically, Weber (1965) states that this distribution involved "...westward extension of the eastern flora along the principal watercourses.” This period of expansion was purportedly followed by a subsequent period of constriction into eastern North America, leaving relictual populations in isolated refugial habitats (Weber, 1965).
Three spatial patterns in the distribution of P. intermedius would support the 'expansion-contraction hypothesis’: LGM continuity, LGM overlap with P. monogynus, and post-glacial isolation of western populations. Although the first pattern is not observed, the second and third patterns are observed. The LGM P. intermedius model indicates low occurrence probability throughout the study area. This may be a result of the modeling process, or it may indicate that P. intermedius did not diversify until sometime after the LGM. Where LGM occurrence is predicted, however, it overlaps with that of P. monogynus. This interaction can be interpreted as the eastern taxon encountering the Cordilleran flora and provides support for the second spatial pattern. As for the third pattern, the distribution of P. intermedius appears to have become disjunct sometime before the mid-Holocene. This comparison may suggest either a vicariance event resulting in disjunction or bimodal distribution trajectories north and east from a putative glacial refugium.
The original 'expansion-contraction hypothesis’ is neither supported nor refuted. Regardless, the findings presented here shed light on the development of the eastern woodland-prairie and
30


Cordilleran flora of Colorado as well as the southwestern United States. There is likely a more complex explanation for the biogeographic history of the P. opulifolius s.I. complex. This biogeographical hypothesis may have involved 1) southward dispersal from Beringia in response to early-Pleistocene climatic cooling, 2) divergence into western and eastern components that then followed different post-glacial dispersal trajectories. The western component (P. intermedius or ancestor) appears to have occupied a glacial refugium in southwestern US highlands and the Sierra Madres of Mexico. As the continental ice sheets retreated and the climate warmed through the mid-Holocene, this taxon appears to have followed two post-glacial dispersal routes: a) north along the eastern flank of the Rocky Mountains, and b) northeast into the Interior Highlands and Great Lakes regions. The eastern component (P. opulifolius s.s. or ancestor) likely dispersed northward along the Appalachian highlands and west into the Great Lakes region from an Atlantic Coast glacial refugium.
Future directions. Further insight may be gained from a focus on ecological rather than strictly climatic niche models by including variables that may be more directly influential on species survival, such as effective moisture (e.g., topographic wetness index, evapotranspiration, vapor pressure deficit, snow persistence, etc.), canopy density (e.g., based on distributional information included in taxonomic treatments, P. intermedius appears to occupy sites with less dense canopy cover), or edaphic properties (Beauregard & de Blois, 2014). Given the difficulty of hindcasting these variables, however, predictions would be limited to current climate. Another recommendation for future research is to take an ensemble modelling approach and repeat the procedure presented here with multiple species belonging to the eastern woodland-prairie element. In addition, quantifying niche overlap between study taxa would address to what extent they occupy the same niche or similar niches. Finally, phylogenetic work at the species level may provide additional information regarding the historical biogeography and systematics of these taxa.
31


Cordilleran species:
Picea engelmannii Parry ex. Engelm. var.
engelmannii (Pinaceae)
Pseudotsuga menziesii (Mirb.) Franco var.
glauca (Beissn.) Franco (Pinaceae) Pinus contorta Douglas ex Loud. var.
latifolia Engelm. (Pinaceae) Physocarpus monogynus (Torr.) Coult (Rosaceae)
Populus angustifolia James (Salicaceae)
Eastern woodland-prairie species:
Physocarpus opulifolius (L.) Maxim. s.I. (Rosaceae)
Corylus cornuta Marshall (Betulaceae) Aralia nudicaulis L. (Araliaceae) Sanicula marilandica L. (Apiaceae) Sporobolus heterolepis (A. Gray) A. Gray (Poaceae)
Hesperostipa spartea (Trim) Barkworth (Poaceae)
Figure 3.1. Eastern woodland-prairie and Cordilleran floristic elements. Representative species listed were compiled from Weber & Wittmann (2011) and Axelrod & Raven (1985). Occurrence data downloaded from SEINet (swbiodiversity.org/seinet).
32


Predictor variables
Samples at locations
Presence locations
Background (pseudo-absence) locations
Probability densities in environmental space
Predictive model in geographic space
Figure 3.2. Inputs and model construction with Maxent. Predictor variables mapped across a landscape of interest (left); occurrence (presence) data, as well as randomly-sampled background points (center left); probability distributions in environmental space (center right); and predictive model in geographic space (right). Maxent samples environmental layers at known species occurrences as well as randomly-sampled background points. Figure modified from Elith et al., 2011.
33


Table 3.1. Candidate variables for climatic niche modeling of Physocarpus opulifolius (L.) Maxim. s.I. (Rosaceae) and P. monogynus (Torr.) Coult. Modified from worldclim.org/bioclim (Hijmans et al., 2005] and O'Donnell and Ignizio (2012]._____________________________________________________
Name Variable Description
Alt Altitude
biol Annual Mean Temperature Overall energy inputs to the system
bio2 Mean Diurnal Range Mean of monthly (maximum temperature -minimum temperature]]
bio3 Isothermality Mean diurnal temperature range (bio2]/annual temperature range (bio7] * 100 Measure of diurnal temperature fluctuations relative to annual variation
bio4 Temperature Seasonality Standard deviation * 100
bio5 Max Temperature of Warmest Month Measure of maximum summer temperature
bio6 Min Temperature of Coldest Month Measure of minimum winter temperature
bio7 Temperature Annual Range Maximum temperature of the warmest month - minimum temperature of the coldest month
bio8 Mean Temperature of Wettest Quarter
bio9 Mean Temperature of Driest Quarter
bio 10 Mean Temperature of Warmest Quarter Measure of mean summer temperature
bioll Mean Temperature of Coldest Quarter Measure of mean winter temperature
bio 12 Annual Precipitation Overall
bio 13 Precipitation of Wettest Month Maximum annual precipitation
bio 14 Precipitation of Driest Month Minimum annual precipitation
bio 15 Precipitation Seasonality Coefficient of variation
bio 16 Precipitation of Wettest Quarter
bio 17 Precipitation of Driest Quarter
bio 18 Precipitation of Warmest Quarter Summer precipitation
bio 19 Precipitation of Coldest Quarter Winter precipitation
34


â–¡ Study extent Elevation (m)
â–¡ -171
â–¡ 392.3
â–  955.2
â–  1993
â–  2289
â–¡ 4275
Baja California
Sonora,
.Chihuahua^
.Cpahuila
Baja California Sur
'Durango^S Nu™0 Le6n • Ty^famaulipas • Navari^^jjl^^fetosi. |
Figure 3.3. Study extent, x (longitude) maximum = -125 W, x minimum = -62 W,y maximum = 53 N, y minimum = 23N. The bounding box appears warped due to cartographic projection (North America Lambert Conformal Conic, EPSG: 102009).
35


Figure 3.4. Effect of background selection on preliminary niche models of Physocarpus monogynus (Torr.) Coult (Rosaceae). Models were constructed by training on (a) background extent restricted to states where it is known to occur, then projected to full study extent, and (b) background sampled across the full study extent (no projection is required in this case). Warmer colors indicate higher probabilities of presence. The known distribution of P. monogynus is shown in (c), where green indicates presence at county-level (light green) and state-level (dark green), yellow indicates locally rare populations, and brown indicates regions outside of the species distribution (BONAP, 2014).
36


Figure 3.5. Occurrence dataset used to train climatic niche models. Occurrences shown in colored circles: green = Physocarpus monogynus (Torr.) Coult (Rosaceae), pink = P. intermedius (Rydb.) Schneid., and yellow = P. opulifolius (L.) Maxim.
37


Table 3.2. Climatic variables used to train niche models for Physocarpus monogynus (Torr.) Coult. (Rosaceae), P. intermedius (Rydb.) Schneid., and P. opulifolius (L.) Maxim. Relative variable importance to each model is indicated by percent contribution and by jackknife test results: variables with the highest gain when used in are isolation shown in bold; variables with the most decrease in gain when omitted are italicized.
Physocarpus monogynus Physocarpus intermedius Physocarpus opulifolius s.s.
Altitude (60.1%) Bio5: Max Summer Temp (Max Temp of Warmest Month) (35.7%) Biol 7: Precip of Driest Quarter (46.4%)
Bio9: Mean Temp of Driest Quarter (16.6%) Biol4: Precip of Driest Month (28.1%) Biol: Annual Mean Temp (30.6%)
Biol3: Precip of Wettest Month (9.4%) Altitude (12.6%) Biol8: Summer Precip (Precip of Warmest Quarter)
Bio2: Mean Diurnal Temp Range (8.1%) Bio8: Mean Temp of Wettest Quarter (11.8%) (9.4%) Bio9: Mean Temp of Driest
Bioll: Avg Winter Temp (Mean Temp of Coldest Biol3: Precip of Wettest Month (6.9%) Quarter (6.8%) Bio7: Annual Temp Range
Quarter) (5.7%) Bio3: Isothermality (2.4%) (4.1%)
Bio3: Isothermality (0.1%) Bio7: Annual Temp Range (2.3%) Bio2: Mean Diurnal Temp Range (2.6%)
38


Figure 3.6. Maxent niche models of occurrence probability trained on current climatic conditions for the study taxa: a) Physocarpus monogynus (Torr.) Coult. (Rosaceae), b) P. intermedius (Rydb.) Schneid., and c) P. opulifolius (L.) Maxim. Warmer colors indicate greater probability of occurrence. Black dots indicate occurrence data.
39


Figure 3.7. Maxent niche models for Physocarpus opulifolius (L.) Maxim. (Rosaceae) hindcast to the mid-Holocene (ca. 6,000 yr BP; top) and LGM (ca. 21,000 yr BP; bottom). Multivariate environmental similarity surfaces (MESS; top right) and maps of the limiting factor (most dissimilar variable, MoD; bottom right). MESS indicates where predictions are being projected outside the training range (red regions) and provide insight into the spatial distribution of no-analog climatic conditions, relative to input variables. MoD maps indicate which variable is most influential over the model at any given point
40


Figure 3.8. Maxent niche models for Physocarpus monogynus (Torr.) Coult. (Rosaceae) hindcastto the mid-Holocene (ca. 6,000 yr BP; top) and LGM (ca. 21,000 yr BP; bottom). Multivariate environmental similarity surfaces (MESS; top right) and maps of the limiting factor (most dissimilar variable, MoD; bottom right). MESS indicates where predictions are being projected outside the training range (red regions) and provide insight into the spatial distribution of no-analog climatic conditions, relative to input variables. MoD maps indicate which variable is most influential over the model at any given point
41


Figure 3.9. Maxent niche models for Physocarpus intermedius (Rydb.) Schneid. (Rosaceae) hindcast to the mid-Holocene (ca. 6,000 yr BP; top) and LGM (ca. 21,000 yr BP; bottom). Multivariate environmental similarity surfaces (MESS; top right) and maps of the limiting factor (most dissimilar variable, MoD; bottom right). MESS indicates where predictions are being projected outside the training range (red regions) and provide insight into the spatial distribution of no-analog climatic conditions, relative to input variables. MoD maps indicate which variable is most influential over the model at any given point
42


Table 3.3. Model validation metrices for niche models of Physocarpus monogynus (Torr.) Coult. (Rosaceae), P. intermedius (Rydb.) Coult., and P. opulifolius (L.) Maxim. AUC: mean cross-validated area under the receiver operating characteristic curve; TSS: true skill statistic (sensitivity + specificity - 1); maxSSS: threshold values derived from maximizing the sum of sensitivity and specificity.
Study taxon Mean AUC (standard deviation) TSS maxSSS threshold (training omission rate)
P. monogynus 0.982 (0.010) 0.831 0.206 (0.026)
P. intermedius 0.936 (0.014) 0.761 0.367 (0.132)
P. opulifolius 0.919 (0.021) 0.734 0.259 (0.047)
43


2-
Figure 3.10. PCA biplot of variance in climatic conditions among occurrence data for the study taxa. Occurrence points represented by solid dots; climatic niche space summarized by ellipses. Green = Physocarpus monogynus (Torr.) Coult. (Rosaceae), red = P. intermedius (Rydb.) Schneid., blue = P. opulifolius (L.) Maxim.
44


Table 3.4. PCA covariate matrix. Top 5 scores for each principal component shown in bold.
Variable PCI f47.1%l PC2 f26.9%l
Biol -0.273153 -0.03717
Bio2 0.062814 -0.32466
Bio3 -0.000269 -0.41144
Bio4 0.091230 0.40084
Bio5 -0.214825 0.01062
Bio6 -0.255123 -0.22475
Bio7 0.114367 0.31623
Bio8 0.030470 0.22418
Bio9 -0.237237 -0.22413
BiolO -0.229912 0.11798
Bioll -0.243440 -0.23647
Biol2 -0.304695 0.11135
Biol3 -0.266396 0.11417
Biol4 -0.291159 -0.01617
Biol5 0.246106 -0.02940
Biol6 -0.260235 0.15057
Biol7 -0.301766 -0.00181
Biol8 -0.202195 0.27096
Biol9 -0.289583 -0.02969
Altitude 0.163691 -0.34462
45


CHAPTER IV
CONCLUSIONS
The phytogeographic history of the eastern woodland-prairie floristic element was examined to better understand the association between disjunct western populations of this and the Cordilleran floristic element in the Southern Rocky Mountains of Colorado. The taxonomic review presented in Chapter II served to clarify the taxonomy of the P. opulifolius s.l. complex through a range-wide examination of critical morphological variation. Approximately 21% of herbarium specimens were misidentified, according to the treatment described in Chapter II. By carefully defining and verifying the identification of each specimen through meticulous examination of follicle morphology, taxonomic uncertainty was considerably reduced. Ultimately, this served to increase confidence in conclusions drawn from climatic niche models. The vegetation niche modeling process often assumes that species identifications and coordinate data are accurate. However, this and other studies (e.g., Bloom et al., 2018) have shown that these are false assumptions which can considerably influence results. Further, paleodistribution modeling of vegetation often excludes an examination of the taxonomy of the species being modeled. The approach used herein, however, bridged the gap between botany and paleoclimatic modeling.
Taxonomy of Physocarpus opulifolius s.l.
Historical treatments, original publications, morphotype frequency, and geographic distribution patterns indicate two primary forms within the P. opulifolius s.l. complex: nearly-glabrous or glabrescent and uniformly pubescent. I conclude that P. intermedius merits taxonomic recognition and that two Physocarpus taxa are present in eastern North America: P. opulifolius and P. intermedius. The latter also occurs in disjunct habitats on the Front Range of Colorado, the Black Hills of South Dakota, and the Sand Hills of Nebraska. Despite this evidence, however, variation within a single character typically is not sufficient evidence to support segregation at the species
46


level. Subspecific recognition is recommended. The follicle morphology character should allow identification of flowering as well as fruiting plants (LP Bruederle, personal comm.).
These findings illustrate the importance of taxonomic identification when constructing niche models of plant species. Taxonomic uncertainty and cryptic variation can substantially affect model predictions (Ensing et al., 2013). The clarification of morphological variation between P. opulifolius and P. intermedius in Chapter II played an important role in the construction of climatic niche models in Chapter III and had a considerable influence regarding the phytogeographic histories of these taxa. Although the topic of taxonomic uncertainty has been explored in the zoological literature (e.g., Zhang et al., 2014), it has received relatively little attention in botanical research (Ensing et al., 2013). To minimize taxonomic uncertainty, qualification (and often, quantification) of diagnostic traits across the range of the taxon of interest will be required. If differences between diagnostic character states cannot be observed in digital photographs, rigorous physical examination of herbarium specimens will be required.
Similarly, these findings underline the importance of considering the full range of the taxon of interest. Just as overlooked variation can result in conflicting taxonomic treatments, niche models trained on occurrences drawn from a subset of the species geographic range — and thus a subset of the realized niche — do not take into account the full environmental envelope and are limited in their implications for that species (Merow et al., 2013; Phillips et al., 2006).
Contributions to the Flora of Colorado
Previous accounts have assigned populations of P. opulifolius s.s. in Colorado to the eastern woodland-prairie element (Weber & Wittmann, 2011). However, unexamined morphological variation across the full geographic range had prevented accurate taxonomic identification; in fact, P. intermedius is representative of the eastern woodland-prairie element. Despite previously conflicting taxonomic treatments (cf. Ackerfield, 2015; Weber & Wittmann, 2011), these findings show that P. opulifolius does not occur west of Lake Superior, and certainly does not occur in
47


Colorado. Thus, the two species of Physocarpus on the Front Range are P. intermedius and P. monogynus.
Overlap in geographic space (Figs. 2.2, 3.6) as well as environmental space (Fig. 3.10) exists between two pairs of study taxa: P. monogynus and P. intermedius co-occur on the Front Range, while P. intermedius and P. opulifoliuss.s. co-occur in the Great Lakes region. These regions may indicate zones of sympatry, where hybridization may occur. Indeed, the accessions from these regions tend to exhibit morphologically ambiguous character states (e.g., follicle pubescence category 3 discussed in Chapter II). Anecdotally, some of the western P. intermedius accessions appear to be hybrids, with follicles resembling P. monogynus in size, but not in pubescence or number. This observation has been noted previously (Weber & Wittmann, 2011). Physocarpus intermedius (comprising follicle pubescence category 4) overlaps with P. opulifolius (comprising categories 1 through 3) in the Great Lakes region. Niche models indicate additional spatial overlap between these two taxa, with the predicted occurrence of P. opulifolius extending west and south to eastern Kansas. Additional research focusing on these potential zones of hybridization is recommended.
Limitations
Several limitations affected the analysis and findings presented above. First, there were gaps in the occurrence dataset (Appendix G). Although the dataset was not 100% complete, I would not expect the inclusion of additional occurrence points to substantially change the conclusions presented herein, as the current sample spans the majority of the range and was considered to be ecologically representative.
The process of georeferencing herbarium specimens adds further uncertainty to the occurrence data. The niche modeling approach assumes that the input coordinates are both accurate and precise, assumptions which are often false (Bloom et al., 2018). I attempted to reduce the error from this assumption by a) including a large sample of herbarium specimens from across the full
48


range, b) georeferencing specimens myself to ensure that the same procedure and considerations were applied when translating qualitative descriptions into spatial coordinates, and c) limiting the uncertainty radius.
The limited availability of hindcast climate data at an appropriately fine spatial resolution prevented a more robust study of shifts in predicted distributions. Ideally, paleo-distributional shifts would be interpreted from niche models constructed for multiple regularly-spaced intervals. In the absence of these data, niche models were constructed for just three time points over the past 21,000 years. Conclusions can only be drawn regarding distributions at those three "snapshot” time points. This limits the scope of and certainty in the analysis of biogeographical origins, as no information is provided regarding distribution changes during the intervening periods.
Further, the niche modeling approach assumes that the niche itself has not changed since the LGM (assumption of niche conservatism; Araujo and Guisan, 2006; Warren et al., 2008). This approach also assumes that the distributions of the three study taxa are most sensitive to climate (in other words, climate is most influential) without consideration of other factors (e.g., edaphic properties; Beauregard & de Blois, 2014). Ecological (rather than strictly climatic) niche models constructed with variables that may be more directly influential on species survival, such as effective moisture (e.g., topographic wetness index, evapotranspiration, vapor pressure deficit, etc.), canopy density, or edaphic properties, are likely to provide more robust results. Given the difficulty of hindcasting these variables at an appropriate spatial scale, however, resulting predictions are limited to current-climate models.
Decisions regarding model inputs and parameters are known to significantly affect niche models (Elith et al., 2011; Merow et al., 2013). For example, background points were randomly sampled across the full study region. On one hand, the decision not to limit background extent ensured adequate representation of environments across the study range and required less extrapolation in the resulting predictive models (Elith et al., 2011). On the other hand, it assumes
49


that the true likelihood of presence is evenly distributed across the landscape; in other words, the chance of encountering the species is the same at every point (Elith et al., 2011). This underlying assumption is often inaccurate, as geographical (e.g., human development) or ecological barriers (e.g., low pollinator prevalence) may prevent dispersal into areas predicted to have suitable climates (Elith et al., 2011). Unrestricted background is therefore considered to be ecologically unrealistic (Elith et al., 2011) and should be examined closely in future modeling.
Finally, presence-only occurrence data derived from herbarium specimens is often assumed to be spatially biased, as sampling techniques vary across collectors and are rarely linked to digitized specimen data (Elith et al., 2011). Although this bias is likely present in the input occurrence data, no correction for this bias could be made, as no information was known about sampling strategy.
Future Directions
These findings provide insight into the biogeographical history of the Colorado flora, and support the theory that there isn’t one explanation for species assemblages. The Front Range flora appears to have a complex biogeographical history.
Future research should focus on hybridization between P. monogynus and P. intermedius along the Front Range and in the Black Hills. Additionally, research investigating common cultivation practices relevant to P. opulifolius s.s., as well as the history of cultivation, will help understand the extent to which specimens collected from naturalized horticultural escapees might affect distributional models. Further, multi-method approaches are becoming more prevalent and important to gaining multidimensional perspectives on complex concepts. The integration of niche modeling with other traditional plant science methods, such as phylogenetics and demographics, is promising (e.g., Call et al., 2016; Morris et al., 2010; Schorr et al., 2013).
Chapter III presented a new biogeographical hypothesis for the origin of the disjunct P. opulifoliuss.l. distribution pattern. Future research should address this new hypothesis using a complementary methodological approach. Genetic analysis would be especially beneficial;
50


phylogenetic work at the species level is expected to provide additional information regarding the phylogeography and systematics of these taxa. In addition, further examination of the geographic range of morphological variation — specifically related to Level III Ecoregions (Fig. 2.3) — will help better understand the ecology of this group. Species distributions derived from current-climate models should be further investigated. Exploration of non-climate variables is recommended, especially those that may be more directly influential on species survival (e.g., effective moisture, canopy density, or edaphic properties). Finally, future research should quantify niche differences between P. opulifoliuss.s. and P. intermedius (e.g., niche overlap, niche breadth), as these metrics have been linked to ecological and phylogenetic distances among closely-related taxa (Warren et al., 2008).
51


REFERENCES
Ackerfield J. 2015. Flora of Colorado. Fort Worth, Texas, USA: BRIT Press.
Aiello-Lammens ME, Boria RA, Radosavljevic A, Vilela B and Anderson RP. 2015. spThin: An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography. 38(5): 541-545. doi:10.1111/ecog.01132.
Alexander C. 2014. Physocarpus. In: Flora of North America Editorial Committee [eds.], Flora of North America North of Mexico (Vol. 9, pp. 347-348). New York and Oxford.
Allouche 0, Tsoar A and Kadmon R. 2006. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology. 43(6): 1223-1232. doi: 10.1111/j. 13 65-2664.2006.01214.x.
Araujo MB and Guisan A. 2006. Five (or so) challenges for species distribution modelling. Journal of Biogeography. 33: 1677-1688. doi:10.1111/j.l365-2699.2006.01584.x.
Axelrod DI and Raven PH. 1985. Origins of the Cordilleran Flora. Journal of Biogeography. 12(1): 21-47. doi: 10.2 307/2845 02 7.
Bartlein PJ, Anderson KH, Anderson PM, Edwards ME, Mock CJ, Thompson RS, Webb RS, Webb III T and Whitlock C. 1998. Paleoclimate simulations for North America over the past 21,000 years: Features of the simulated climate and comparisons with paleoenvironmental data. Quaternary Science Reviews 17(6): 549-585. doi:10.1016/S0277-3791(98)00012-2.
Bartlein PJ, Harrison SP, Brewer S, Connor S, Davis BAS, Gajewski K, Guiot J, Harrison-Prentice TI,
Henderson A, Peyron 0, Prentice IC, Scholze M, Seppa H, Shuman B, Sugita S, Thompson RS, Viau AE, Williams J and Wu H. 2011. Pollen-based continental climate reconstructions at 6 and 21 ka: a global synthesis. Climate Dynamics. 37: 775-802. doi:10.1016/j.ecolmodel.2011.02.011.
Barve N, Barve V, Jimenez-Valverde A, Lira-Noriega A, Maher S, Peterson AT, Soberon J and Villalobos F. 2011. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecological Modelling. 222: 1810-1819.
Beauregard F and de Blois S. 2014. Beyond a climate-centric view of plant distribution: Edaphic variables add value to distribution models. PLoS ONE. 9(3): e92642. doi:10.1371/journal.pone.0092642.
Bemmels JB and Dick CW. 2018. Genomic evidence of a widespread southern distribution during the Last Glacial Maximum for two eastern North American hickory species .Journal of Biogeography. 45: 1739-1750. doi:10.1111/jbi.l3358.
Bentham G and Hooker JD. 1865. Genera Plantarum (Vol. 1 Part 2) p. 612. London: Lovell Reeve & Co. Retrieved from https: //www.biodiversitylibrary.Org/item/14683#page/182 /mode/lup.
Biota of North America Program (BONAP). 2014. [Distribution map for Physocarpus monogynus (Torr.) Coult (Rosaceae)]. Taxonomic Data Center. Retrieved 23 January 2019 from http://bonap.net/MapGallery/County/Physocarpus%20monogynus.png.
Bloom TDS, Flower A and DeChaine EG. 2018. Why georeferencing matters: Introducing a practical protocol to prepare species occurrence records for spatial analysis. Ecology and Evolution. 8: 765-777. doi:10.1002/ece3.3516.
52


Call A, Sun YX, Yu Y, Pearman PB, Thomas DT, Trigiano RN, Carbone I and Xiang QY. 2016. Genetic structure and post-glacial expansion of Cornus florida L. (Cornaceae): integrative evidence from phylogeography, population demographic history, and species distribution modeling. Journal of Systematics and Evolution. 54(2): 136-151. doi:10.1111/jse,12171.
Cambessedes MJ. 1824. Monographie du Genera Spiraea. In: Audouin, Brongniart, and Dumas (Eds.), Annales dessciences naturelles (Vol. 1: 225-245, 385-387). Paris: Imprimerie de J. Tastu. Retrieved from https://www.biodiversitylibrary.Org/item/28623#page/247/mode/lup.
Carnaval AC and Moritz C. 2008. Historical Climate Modelling Predicts Patterns of Current Biodiversity in the Brazilian Atlantic Forest .Journal of Biogeography. 35(7): 1187-1201. doi:10.1111/j.l365-2699.2007.01870.x.
Consortium of Midwest Herbaria. 2019. http//:midwestherbaria.org/portal/index.php. Accessed on February 19, 2019.
Cooper DJ (Ed.). 1984. Ecological survey of the city of Boulder, Colorado Mountain Parks.
Coulter JM and Nelson A. 1909. New Manual of Botany of the Central Rocky Mountains (Vascular Plants). Cincinnati: American Book Company, pp. 247-248. Retrieved from https://www.biodiversitylibrary.Org/item/116743#page/252/mode/lup.
Cuizhi G and Alexander C. 2003. Physocarpus. In: Flora of China (Vol. 9, p. 76). Retrieved from http://flora.huh.harvard.edu/china/PDF/PDF09/Physocarpus.PDF. Cambridge, MA: Missouri Botanical Garden, St. Louis, M0 & Harvard University Herbaria.
Daniels FP. 1907. The Flora of Columbia Missouri and Vicinity. University of Missouri Studies•, Science Series. 1(2): 149-150. Columbia, M0: E. W. Stephens Publishing Company. Retrieved from https://www.biodiversitylibrary.Org/item/99297#page/330/mode/lup.
Delcourt PA and DelcourtHR. 1993. Chapter 4: Paleoclimates, Paleovegetation, and Paleofloras of North America North of Mexico During the Late Quaternary. In: Flora of North America Editorial Committee [eds.], Flora of North America North of Mexico (Vol 1). New York and Oxford.
Duarte M, Guerrero PC, Carvallo G and Bustamante RO. 2014. Conservation network design for endemic cacti under taxonomic uncertainty. Biological Conservation. 176: 236-242. doi: 10.1016/j.biocon.2014.05.028.
Dyke AS. 2004. An outline of North American deglaciation with emphasis on central and northern Canada. In: Ehlers J and Gibbard PL (Eds.): Quaternary Glaciations - Extent and Chronology, Part II. North America. Developments in Quaternary Science 2: 373-424.
Elith J, Kearney M and Phillips S. 2010. The art of modelling range-shifting species. Methods in Ecology and Evolution. 1(4): 330-342. doi:10.1111/j.2041-210X.2010.00036.x.
Elith J, Phillips SJ, Hastie T, Dudik M, Chee YE and Yates CJ. 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions. 17(1): 43-57. doi:10.1111/j.1472-4642.2010.00725.x.
Ensing DJ, Moffat CE and Pither J. 2013. Taxonomic identification errors generate misleading ecological niche model predictions of an invasive hawkweed. Botany. 91: 137-147. dx.doi.org/10.1139/cjb-2012-0205.
Fitzpatrick MC, Blois JL, Williams JW, Nieto-Lugilde D, Maguire KC and Lorenz DJ. 2018. How will climate novelty influence ecological forecasts? Using the Quaternary to assess future reliability. Global Change Biology. 24(8): 3575-3586. doi:10.1111/gcb.l4138.
53


Fourcade Y, Engler JO, Rodder D and Secondi J. 2014. Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias. PLoS ONE. 9(5): e97122. doi:10.1371/journal.pone.0097122.
Franklin J. 2010. Mapping species distributions: Spatial inference and prediction. New York: Cambridge University Press. Retrieved from https://ebookcentral.proquestcom.
Gent PR, Danabasoglu G, Donner LJ, Holland MM, Hunke EC, Jayne SR, Lawrence DM, Neale RB, Rasch PJ, Vertenstein M, Worley PH, Yang ZL and Zhang M. 2011. The Community Climate System Model Version 4 .Journal of Climate. 24: 4973-4991. https://doi.Org/10.1175/2011JCLI4083.l.
Gleason HA and Cronquist A. 1991. Manual of vascular plants of northeastern United States and adjacent Canada (2nd ed.J. Bronx, NY: The New York Botanical Garden.
Goring S, Dawson A, Simpson GL, Ram K, Graham RW, Grimm EC and Williams JW. 2015. Neotoma: A Programmatic Interface to the Neotoma Paleoecological Database. Open Quaternary. 1(1): Art. 2. http://doi.org/10.5334/oq.ab.
Heikkinen RK, Luoto M, Araujo MB, Virkkala R, Thuiller W and Sykes MT. 2006. Methods and uncertainties in bioclimatic envelope modelling under climate change. Progress in Physical Geography. 30(6): 751-777. doi:10.1177/0309133306071957.
Hijmans RJ, Cameron SE, Parra JL, Jones PG and Jarvis A. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology. 25: 1965-1978. doi:10.1002/joc,1276.
Hogan T. 1989. Survey of Plants of Special Concern in Long Canyon, Panther Canyon, Greenman Springs Area, and Tributary Canyons and Gulches in the City of Boulder Mountains Parks, Boulder,
Colorado.
Jimenez-Valverde A. 2012. Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure in species distribution modelling. Global Ecology and Biogeography. 21: 498-507. doi:10.1111/j.l466-8238.2011.00683.x.
Kramer-Schadt S, Niedballa J, Pilgrim JD, Schroder B, Lindenborn J, Reinfelder V, Stillfried M, Heckmann I, Scharf AK, Augeri DM, Cheyne SM, Hearn AJ, Ross J, Macdonald DW, Mathai J, Eaton J, Marshall AJ, Semiadi G, Rustam R, Bernard H, Alfred R, Samejima H, Duckworth JW, Breitenmoser-Wuersten C, Belant JL, Hofer H and Wilting A. 2013. The importance of correcting for sampling bias in MaxEnt species distribution models. Diversity and Distributions. 19(11): 1366-1379. doi: 10.1111/ddi.l 2096.
Kuntze 0. 1891a. Revisio Generum Plantarum (Vol. 1, pp. 218-219). Retrieved from http://bibdigital.rjb.csic.es/ing/Libro.php?Libro=5479.
Kuntze 0.1891b. Revisio Generum Plantarum (Vol. 2, p. 949). Retrieved from http://bibdigital.rjb.csic.es/ing/Libro.php?Libro=5480.
Linnaeus C. 1753. Species Plantarum (Vol. 1, p. 489). Holmiae Stockholm: Impensis Laurentii Salvii. Retrieved from https://www.biodiversitylibrary.Org/item/121860#page/522/mode/lup.
Liu C, Newell G and White M. 2015. On the selection of thresholds for predicting species occurrence with presence-only data. Ecology and Evolution. 6(1): 337-348. doi: 10.1002/ece3.1878.
54


Livingston RB. 1952. Relict True Prairie Communities in Central Colorado. Ecology. 33(1): 72-86. doi: 10.2 307/193125 3.
Lobo JM, Jimenez-Valverde A and Real R. 2008. AUC: A Misleading Measure of the Performance of Predictive Distribution Models. Global Ecology and Biogeography. 17(2): 145-151. doi: 10.1111/j. 1466-82 38.2007.0035 8.x.
McGregor RL, Barkley TM and Great Plains Flora Association. 1986. Flora of the Great Plains. Lawrence, KS: University Press of Kansas.
Marr KL, Hebda RJ and MacKenzie WH. 2012. New alpine plant records for British Columbia and a previously unrecognized biogeographical element in western North America. Botany. 90(6): 445-455. doi: 10.1139/b2012-009.
Mastin J, Luebke N, Anthamatten P and Bruederle LP. 2018. Evidence for genetic allopolyploidy in Eutrema edwardsii (Brassicaceae): implications for conservation. Plant Systematics and Evolution. 304(1): 133-141. doi:10.1007/s00606-017-1447-2.
Maximovich CJ. 1879. Adnotationes de Spiraeaceis. In: Trudy Imperatorskago S.-Peterburgskago Botaniceskago Sada. Retrieved from
https://www.biodiversitylibrary.Org/item/53600#page/l/mode/lup.
Medikus FK. 1799. Beytrage zur Pflanzen-Anatomie, Pflanzen-Physiologie und einer neuen
Charakteristik der Baume und Straucher. Leipzig: Ben Heinrich Graff, p. 109. Retrieved from https://www.biodiversitylibrary.Org/item/220807#page/5/mode/lup.
Menz MHM, Phillips RD, Anthony JM, Bohman B, Dixon KW and Peakall R. 2015. Ecological and genetic evidence for ciyptic ecotypes in a rare sexually deceptive orchid, Drakaea elastica. Botanical Journal of the Linnean Society. 177(1): 124-140. doi:10.1111/boj.12230.
Merow C, Smith MJ and Silander, Jr., JA. 2013. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography. 36: 1058-1069. doi: 10.1111/j. 1600-0587.2013.07872.x
Morris AB, Graham CH, Soltis DE and Soltis PS. 2010. Reassessment of phylogeographical structure in an eastern North American tree using Monmonier's algorithm and ecological niche modelling. Journal of Biogeography. 37(9): 1657-1667. doi:10.1111/j.l365-2699.2010.02315.x.
Mutel CF and Emerick JC. 1992. From Grassland to Glacier: The natural history of Colorado and the surrounding region (2nd ed.). Boulder: Johnson Books. Retrieved from
http://aurarialibrary.idm.oclc.org/login?url=http://search.ebscohost.com/login.aspx?direct=true&
db=nlebk&AN=93.
Nelson A. 1902. Contributions from the Rocky Mountain Herbarium. Botanical Gazette. 34(5): 367. Retrieved from https://www.biodiversitylibrary.Org/page/28873052#page/2/mode/lup.
Nelson JK. 2010. Vascular flora of the Rocky Flats Area, Jefferson County, Colorado, USA. Phytologia 92(2): 121-150.
Newhall CS. 1891. The trees of northeastern America: The shrubs of northeastern America, pp. 88-90. Putnam, New York: G.P. Putnam's sons. Retrieved from
https://babel.hathitrust.org/cgi/pt?id=nyp.33433006568343;view=lup;seq=9.
55


O’Donnell MS and Ignizio DA. 2012. Bioclimatic Predictors for Supporting Ecological Applications in the Conterminous United States: U.S. Geological Survey Data Series 691. Retrieved from https://pubs.usgs.gov/ds/691/.
Oh SH. 2015. Taxonomy of tribe Neillieae (Rosaceae): Physocarpus. Korean Journal of Plant Taxonomy. 45(4): 332-352. doi: 10.11110/kjpt2015.45.4.332.
Oh SH and Potter D. 2005. Molecular phylogenetic systematics and biogeography of tribe Neillieae (Rosaceae) using DNA sequences of cpDNA, rDNA, and LEAFY. American Journal of Botany. 92(1): 179-192. doi:10.3732/ajb.92.1.179.
Oswald WW, Foster DR, Shuman BN, Doughty ED, Faison EK, Hall BR, Hansen BCS, Lindbladh M, Marroquin A and Truebe SA. 2018. Subregional variability in the response of New England vegetation to postglacial climate chang e. Journal of Biogeography. 45(10): 2375-2388. doi: 10.1111/jbi.l 3407.
Pausata FSR, Li C, Wettstein JJ, Kageyama M and Nisancioglu KH. 2011. The key role of topography in altering North Atlantic atmospheric circulation during the last glacial period. Climate of the Past 7(4): 1089-1101. doi:10.5194/cp-7-1089-2011.
Perez Navarro MA, Sapes G, Batllori E, Serra-Diaz JM, Esteve MA and Lloret F. 2019. Climatic Suitability Derived from Species Distribution Models Captures Community Responses to an Extreme Drought Episode. Ecosystems. 22(1): 77-90. doi:10.1007/sl0021-018-0254-0.
Peterson AT, Papes M and Kluza DA. 2003. Predicting the potential invasive distributions of four alien plantspecies in North America. Weed Science. 51(6): 863-868. doi:10.1614/P2002-081.
Phillips SJ. 2008. Transferability, sample selection bias and background data in presence-only
modelling: a response to Peterson etal. (2007). Ecography. 31: 272-278. doi: 10.1111/j.2007.0906-7590.05378.x
Phillips SJ, Anderson RP and Schapire RE. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling. 190: 231-259. doi:10.1016/j.ecolmodel.2005.03.026.
Phillips SJ and Dudik M. 2008. Modeling of Species Distributions with Maxent: New Extensions and a Comprehensive Evaluation. Ecography. 31(2): 161-175. doi: 10.1111/j.2007.0906-7590.05203.x.
Phillips SJ, Dudik M, Elith J, Graham CH, Lehmann A, Leathwick J and Ferrier S. 2009. Sample Selection Bias and Presence-Only Distribution Models: Implications for Background and Pseudo-Absence Data. Ecological Applications. 19(1): 181-197. Retrieved from http: / /www.i stor. or g/stable /2 7645958.
Potter D, Eriksson T, Evans RC, Oh S, Smedmark JEE, Morgan DR, M. Kerr, Robertson KR, Arsenault M, Dickinson TA and Campbell CS. 2007. Phylogeny and classification of Rosaceae. Plant Systematics and Evolution. 266(1/2): 5-43. doi:10.1007/s00606-007-0539-9.
Power MJ, Whitlock C and Bartlein PJ. 2011. Postglacial fire, vegetation, and climate history across an elevational gradient in the Northern Rocky Mountains, USA and Canada. Quaternary Science Reviews. 30(19): 2520-2533. doi:10.1016/j.quascirev.2011.04.012.
QGIS Development Team. 2019. QGIS Geographic Information System (Version 3.4.2) [computer software]. Open Source Geospatial Foundation Project. Available from http://www.qgis.org.
56


R Core Team. 2018. R: A language and environment for statistical computing (version 3.4.1) [software program], Vienna, Austria: R Foundation for Statistical Computing. Retrieved from http://www.R-project.org/.
Rafinesque CS. 1838. New flora and botany of North America, or, A supplemental flora, additional to all the botanical works on North America and the United States. Containing 1000 new or revised species (Part 3). Philadelphia. Retrieved from
https://www.biodiversitylibrary.Org/item/187964#page/275/mode/lup.
Rehder A. 1920. New species, varieties and combinations from the herbarium and the collections of the Arnold Arboretum. Journal of the Arnold Arboretum. 1(4): 256.
Rios NE and BartHL. 2010. GEOLocate (Version 3.22) [Computer software]. Belle Chasse, LA: Tulane University Museum of Natural History. Retrieved from https: //www.geo-locate.org.
Robinson BL. 1908. Notes on the Vascular Plants of the Northeastern United States. Rhodora. 10(100): 32. Retrieved from https: //www.biodiversitylibrary.Org/page/563475#page/35/mode/lup.
RStudio Team. 2016. RStudio: Integrated Development for R [software program], Boston, MA: RStudio, Inc. Retrieved from www.rstudio.com.
Rydberg PA. 1896. Flora of the Black Hills of South Dakota. Contributions from the U.S. National Herbarium. 3(8): Washington: Government Printing Office. Retrieved from https: //www.biodiver sitylibrary. org/item/3 2247#page/5/mode/lup.
Rydberg PA. 1901. Rosaceae. In: NL Britton, Manual of the Flora of the northern States and Canada (pp. 490-514). New York: Henry Holt and Company. Retrieved from https: //www.biodiver sitylibrary. or g/item/12 2 5 2 3 #page/5 0 5/mode / lup.
Rydberg PA. 1906. Flora of Colorado. Bulletin of the Agricultural Experiment Station of the Colorado Agricultural College. 100. Fort Collins, CO: Experiment Station. Retrieved from https://www.biodiversitylibrary.Org/item/20094#page/7/mode/lup.
Rydberg PA. 1908a. Rosaceae. In: NL Britton, North American Flora (Vol. 22, Part 3, pp. 239-292). New York: New York Botanical Garden. Retrieved from https://www.biodiversitylibrary.Org/item/15436#page/l/mode/lup.
Rydberg PA. 1908b. Notes on Rosaceae-I. Bulletin of the Torrey Botanical Club. 35(11): 535-542. doi: 10.2307/2479109.
Rydberg PA. 1917. Flora of the Rocky Mountains and Adjacent Plains. New York. Retrieved from https://www.biodiversitylibrary.Org/item/32199#page/5/mode/lup.
Schaetzl RJ, Krist Jr. FJ, Lewis CFM, Luehmann MD and Michalek MJ. 2016. Spits formed in Glacial Lake Algonquin indicate strong easterly winds over the Laurentian Great Lakes during late Pleistocene. Journal ofPaleoIimnoiogy. 55(1): 49-65. doi:10.1007/sl0933-015-9862-2.
SEINet Portal Network. 2019. http//:swbiodiversity.org/seinet/index.php.
Schneider CK. 1906. Iliustriertes Handbuch der Laubhoizkunde (Vol. 1, pp. 442-446). Jena: Gustav Fischer. Retrieved from https://www.biodiversitylibrary.Org/item/5886#page/466/mode/lup.
57


Schorr G, Pearman PB, Guisan A and Kadereit JW. 2013. Combining palaeodistribution modelling and phylogeographical approaches for identifying glacial refugia in Alpine Primula. Journal of Biogeography. 40(10): 1947-1960. doi: 10.1111/jbi.l2132.
Shafer DS. 1989. The timing of late Quaternary monsoon precipitation maxima in the Southwest United States [dissertation]. University of Arizona: UA Campus Repository, University Libraries. Retrieved from http://hdl.handle.net/10150/184766.
Small JK. 1903. Flora of the Southeastern United States, p. 33. Lancaster, PA: The New Era Printing
Company. Retrieved from https://www.biodiversitylibrary.Org/item/3397#page/523/mode/lup.
Smith BE, Johnston MK and Lucking R. 2016. From GenBank to GBIF: Phylogeny-based predictive niche modeling tests accuracy of taxonomic identifications in large occurrence data repositories. PIoS One. 11(3): e0151232. doi:10.1371/journal.pone.0151232.
SoberonJ and Peterson AT. 2005. Interpretation of Models of Fundamental Ecological Niches and Species’ Distributional Areas. Biodiversity Informatics. 2: 1-10. doi:10.17161/bi.v2i0.4.
Thiers B. 2019. Index Herbariorum: A global directory of public herbaria and associated staff. New York Botanical Garden's Virtual Herbarium, http://sweetgum.nybg.org/science/ih/.
Thorne RF. 1993. Chapter 6: Phytogeography of North America North of Mexico. In: Flora of North America Editorial Committee [eds.J, Flora of North America North of Mexico (Vol. 1). New York and Oxford. Retrieved from http://floranorthamerica.org/Volume/V01/Chapter06.
Tinoco BA, Astudillo PX, Latta SC and Graham CH. 2009. Distribution, ecology and conservation of an endangered Andean hummingbird: The Violet-throated Metaltail (Metallura baroni). Bird Conservation International. 19(1): 63-76. doi:10.1017/S0959270908007703.
Tropicos.org. 2019. Missouri Botanical Garden, http://www.tropicos.org
U.S. Environmental Protection Agency (EPA). 2013. Level III ecoregions of the continental United States: Corvallis, Oregon, U.S. EPA - National Health and Environmental Effects Research Laboratory, map scale 1:7,500,000. Retrieved from https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states.
Veloz SD. 2009. Spatially Autocorrelated Sampling Falsely Inflates Measures of Accuracy for Presence-Only Niche Models .Journal of Biogeography. 36(12): 2290-2299. doi:10.1111/j,1365-2699.2009.02174.x.
VuVQ. 2011. ggbiplot: Aggplot2 based biplot. [R package version 0.55], Retrieved from http://github.com/vqv/ggbiplot
Waltari E, Hijmans RJ, Peterson AT, Nyari As, Perkins SL and Guralnick RP. 2007. Locating Pleistocene Refugia: Comparing Phylogeographic and Ecological Niche Model Predictions. PLoS One. 2(7): e563. doi:10.1371/journal.pone.0000563.
Warren DL, Glor RE and Turelli M. 2008. Environmental Niche Equivalency versus Conservatism: Quantitative Approaches to Niche Evolution. Evolution. 62(11): 2868-2883. doi:10.1111/j,1558-5646.2008.00482.x.
Weber WA. 1965. Plant Geography in the Southern Rocky Mountains. In: HE Wright and DG Frey [Eds.], The Quaternary of the United States: A review volume for the VII Congress of the International
58


Association for Quaternary Research (pp. 453-468). Princeton, New Jersey, USA: Princeton University Press.
Weber WA and Wittmann RC. 2011. Colorado Flora: Eastern Slope (4th ed.). Boulder, Colo: University Press of Colorado.
Wehr WC and Hopkins DQ. 1994. The Eocene orchards and gardens of Republic, Washington. Washington Geology. 22(3): 27-34. Retrieved from
http://www.dnr.wa.gov/Publications/ger_washington_geology_1994_v22_no3.pdf.
Wen J, Ickert-Bond S, Nie Z-L and Li R. 2010. Timing and modes of evolution of eastern Asian-North American biogeographic disjunctions in seed plants. Darwin's Heritage Today: Proceedings of the Darwin 200 Beijing International Conference. 252-269.
Wieczorek J, Qinghua G and Hijmans RJ. 2004. The point-radius method for georeferencing locality descriptions and calculating associated uncertainty. International Journal of Geographical Information Science. 18(8): 745-767. doi:10.1080/13658810412331280211.
Yansa CH. 2006. The timing and nature of Late Quaternary vegetation changes in the northern Great Plains, USA and Canada: A re-assessment of the spruce phase. Quaternary Science Reviews. 25(3): 263-281. doi: 10.1016/j.quascirev.2005.02.008.
Yates CJ, Elith J, Latimer AM, Le Maitre D, Midgley GL, Schurr EM and West AG. 2010. Projecting climate change impacts on species distributions in megadiverse South African Cape and Southwest Australian Lloristic Regions: Opportunities and challenges. Austral Ecology. 35(4): 374-391. doi: 10.1111/j. 1442-9993.2009.02044.x.
Zhang Y, Chen C, Li L, Zhao C, Chen W and Huang Y. 2014. Insights from ecological niche modeling on the taxonomic distinction and niche differentiation between the black-spotted and red-spotted tokay geckoes (Gekkogecko). Ecology and Evolution. 4(17): 3383-3394. doi:10.1002/ece3.1183.
59


APPENDIX A
Nomenclatural History of Physocarpus opulifolius (L.) Maxim. s.I. (Rosaceae)
Table SI. Synonyms of Physocarpus opulifolius (L.) Maxim. s.s. (Rosaceae) and P. intermedius (Rydb.) Schneid. Compiled from the Integrated Taxonomic Information System (ITIS; itis.gov), The Flora of North America North of Mexico (FNA; Alexander, 2014), Tropicos (tropicos.org), and The Plant List (theplantlist.org).
Physocarpus opulifolius Physocarpus intermedius
Basionym: Spiraea opuiifoiia L. (Linnaeus, 1753) Basionym: Opuiaster intermedius Rydb. (Rydberg, 1901)
Opuiaster opulifolius (L.) Kuntze (Kuntze, 1891b) Physocarpus opulifolius (L.) Maxim, var. intermedius (Rydb.) B.L. Rob. (Robinson, 1908)
Opuiaster australis Rydb. (Rydberg, 1908a) Opuiaster aiabamensisRydb. (Rydberg, 1908a)*
Physocarpus australis (Rydb.) Rehder (Rehder, 1920) Opuiaster ramaieyi A. Nels. (Nelson, 1902)
Physocarpus michiganensis Daniels (Daniels, 1907) Physocarpus ramaleyi A. Nels. (Coulter & Nelson, 1909)
Opuiaster buiiatus Medik. (Medikus, 1799) Opuiastersteiiatus Rydb. (Small, 1903)* Physocarpus stellatus (Rydb. ex Small) Rehder (Rehder, 1920) Physocarpus missouriensis Daniels (Daniels, 1907)
Note: Names marked with an asterisk (*) are listed as synonyms of P. opulifolius by the Interagency Taxonomic Information System (ITIS), but are described as having pubescent follicles and are thus treated as synonyms of P. intermedius.
60


Spiraea opulifolia L. (1753)
Neillia opulifolia Benth. & Hook. (1865)
Opulaster opulifolius (L.) Kuntze (1891)
Physocarpus opulifolius (L.) Maxim. (1879) (published as P. opulifolia)
Opulaster intermedius Rydb. (1901)
Physocarpus opulifolius (L.) Maxim, var. intermedius (Rydb.) B.L. Rob. (1908)
Physocarpus intermedius (Rydb.) C.K. Schneid. (1906)
Figure SI. Nomenclatural history of Physocarpus opulifolius (L.) Maxim. s.I. (Rosaceae).
61


APPENDIX B
Accessions examined for taxonomic review (Chapter II)
Physocarpus intermedius (Rydb.) Schneid.
Mexico: Hinton 22964 (WIS) 1993, Nuevo Leon: 23.99315, -99.714956000000001, 2300 m.
United States: Earle s.n. (MO) 189?, Lee County, AL: 32.608699999999999, -85.478379000000004, 2250 m; s.n. (MO) n.d., Benton County, AR: 36.335911000000003, -94.460770999999994, 1500 m; Demaree 4884 (MO) 1927, Benton County, AR: 36.448405000000001, -93.974089000000006, 1000 m; Demaree 4884 (WIS) 1927, Benton County, AR: 36.448405000000001, -93.974089000000006, 2300 m; Palmer 44454 (MO) 1937, Benton County, AR: 36.448405000000001, -93.974089000000006, 1000 m; Palmer 4385 (MO) 1913, Carroll County, AR: 36.401181999999999, -93.737971000000002, 5000 m; Palmer 26578 (MOR) 1924, Hot Springs County, AR: 34.449057000000003, -92.875575999999995, 2000 m; Thomas 20078 (WIS) 1970, Independence County, AR: 35.898093000000003, -91.703349000000003, 2300 m; Thomas 15088 (COLO) 1969, Independence County, AR: 35.904877999999997, -91.681726999999995, 250 m; Demaree 21297 (MO) 1940, Logan County, AR: 35.165858999999998, -93.630341999999999, 1500 m; Palmer 24121 (MOR) 1923, Logan County, AR: 35.168405999999997, -93.631581999999995, 4700 m; Kim 83.5 (MOR) 2004, Logan County, AR: 35.171532999999997, -93.651799999999994, 300 m; Palmer 5982 (MO) 1914, Marion County, AR: 36.261074999999998, -92.538606999999999, 1000 m; Demaree 37944 (MO) 1955, Montgomery County, AR: 34.556736000000001, -93.635075999999998, 7000 m; Bates 10448 (MO) 1990, Montgomery County, AR: 34.681891, -93.582954999999998, 350 m; Redfearn, Jr. 27532 (MO) 1971, Newton County, AR: 35.843000000000004, -93.389815999999996, 450 m; litis 5468 (WIS) 1955, Newton County, AR: 35.947299000000001, -93.077395999999993, 1600 m; Hess 6889 (MOR) 1992, Pope County, AR: 35.549002000000002, -93.160916999999998, 1800 m; Weber 11554 (COLO) 1963, Boulder County, CO: 39.986649300000003, -105.2860276, 1300 m; Ramaley 2504 (RM) 1906, Boulder County, CO: 39.996893999999998, -105.301751, 500 m; Denham 85449 (CS) 1985, Boulder County, CO: 39.997287999999998, -105.27967, 500 m; Ramaley 874 (COLO) 1901, Boulder County, CO: 39.999487999999999, -105.389087, 8500 m; Clokey4157 (MO) 1921, Boulder County, CO: 40.005927999999997, -105.406142, 100 m; Weber 18069 (COLO) 1990, Boulder County, CO: 40.013018000000002, -105.297774, 500 m; Penfound s.n. (COLO) 1924, Boulder County, CO: 40.014986, -105.270546, 8000 m; Ackerfield 2061 (CS) 2004, Boulder County, CO: 40.236666669999998, -105.3197222, 500 m; Scully 263 (COLO) 2007, Boulder County, CO: 40.247773000000002, -105.300646, 1000 m; Bates s.n. (NEB) 1919, Clear Creek County, CO: 39.742488000000002, -105.513608, 2300 m; Marriage s.n. (CS) 1936, El Paso County, CO: 38.739488999999999, -104.87908899999999, 1500 m; Glatfelter s.n. (MO) 1905, El Paso County, CO: 38.785288999999999, -104.874647, 500 m; Christ 1242 (CS) 1935, El Paso County, CO: 38.792729999999999, -104.88525, 1500 m; Clokey 4159 (RM) 1921, El Paso County, CO: 39.123080000000002, -104.91488699999999, 1000 m; Bessey s.n. (NEB) 1895, El Paso County, CO: 38.790289999999999, -104.86621599999999, 1800 m; Murdock 281 (COLO) 1995,
Jefferson County, CO: 39.9043657, -105.2073967, 2250 m; Rondeau 97-088 (COLO) 1997, Larimer County, CO: 40.526113000000002, -105.18230200000001, 300 m; Bates 7125 (NEB) 1919, Larimer County, CO: 40.397761000000003, -105.07498, 6000 m; Swink 9043 (MOR) 1989, Cook County, IL: 41.520159, -87.691243, 500 m; Swink 6691 (MOR) 1986, Cook County, IL: 41.836227000000001, -
62


87.838674999999995, 800 m; Hedborn s.n. (MOR) 1978, Jo Daviess County, IL: 42.351404000000002, -90.182830999999993, 3000 m; Chase 1481 (MICH) 1907, Marshall County, IL: 41.018489000000002, -89.521889999999999, 5000 m; Deam 30075 (IND) 1919, Decatur County, IN: 39.426125999999996, -85.609543000000002, 1000 m; Deam 45179 (IND) 1927, Henry County, IN: 40.072732999999999, -85.365326999999994, 1000 m; 196 (IND) 1895, Kosciusko County, IN: 41.396236999999999, -85.694063999999997, 3700 m; Deam 55102 (IND) 1934, La Porte County, IN: 41.511932000000002, -86.543193000000002, 2000 m; Otto 26 (MOR) 1982, Lake County, IN: 41.605787999999997, -87.265669000000003, 3800 m; Deam 18113 (IND) 1915, Newton County, IN: 41.152593000000003, -87.448515, 600 m; Umbach 40127, 5614 (WIS) 1912, Porter County, IN: 41.661569999999998, -87.054522000000006, 6000 m; Deam 15152 (IND) 1914, Starke County, IN: 41.213724999999997, -86.58663, 2300 m; Swink 5821 (MOR) 1985, Starke County, IN: 41.2361, -86.665862000000004, 1000 m; Deam 29455 (IND) 1919, Warren County, IN: 40.398017000000003, -87.329774999999998, 1500 m; Wood 29 (WIS) 1964, Wayne County, IN: 39.817231, -84.842949000000004, 1500 m; Deam 1785 (IND) 1906, Wells County, IN: 40.830370000000002, -85.253208999999998, 850 m; Pammel 1805 (MO) 1898, Boone County, IA: 42.036549000000001, -93.931670999999994, 20000 m; van der Linden 1995-17 (MOR) 1995, Clayton County, IA: 43.007522999999999, -91.168381999999994, 1800 m; Tolstead s.n. (NEB) 1935, Clayton County, IA: 43.018318000000001, -91.182630000000003, 2300 m; Shinck s.n. (WIS)
1932, Van Buren County, IA: 40.753228999999997, -91.949986999999993, 26000 m; Tolstead NA (NEB)
1933, Winneshiek County, IA: 43.290678999999997, -91.843704000000002, 30000 m; Hitchcock s.n. (MO) n.d., Black Hawk County, IA?: 42.492764000000001, -92.342962999999997, 10000 m; Oyster s.n. (MO) 1886, Miami County, KS: 38.572234999999999, -94.879472000000007, 2000 m; Switzenberg s.n. (MICH) 1955, Alger County, Ml: 46.354979999999998, -86.401655000000005, 1500 m; Hickman G794 (MOR) 2000, Berrien County, Ml: 41.878281999999999, -86.605321000000004, 350 m; Lammers 12751 (MICH) 2010, Delta County, Ml: 45.902555999999997, -86.577918999999994, 250 m; Dieterle 1228 (MICH) 1954, Grand Traverse County, Ml: 44.675925999999997, -85.391373000000002, 2300 m; Parmelee 3315 (MICH) 1953, Ingham County, Ml: 42.733074000000002, -84.312859000000003, 5300 m; Hanes s.n. (MICH) 1948, Kalamazoo County, Ml: 42.364369000000003, -85.356407000000004, 1000 m; Smith 4034 (MICH) 1998, Lenawee County, Ml: 41.794324000000003, -84.242442999999994, 700 m; McVaugh 8886 (MICH) 1947, Luce County, Ml: 46.675764000000001, -85.455982000000006, 5000 m; Farwell 2738 (MICH) 1914, Oakland County, Ml: 42.680588, -83.133820999999998, 2300 m; Dodge s.n. (MICH) 1915, Schoolcraft County, Ml: 45.935420999999998, -85.931380000000004, 2300 m;
Pennington s.n. (MICH) 1910, Van Buren County, Ml: 42.413733000000001, -86.268319000000005,
2000 m; Emerson 19 (MICH) 1924, Washtenaw County, Ml: 42.271253000000002, -83.648566000000002, 5000 m; Almendinger s.n. (MICH) 1860, Washtenaw County, Ml: 42.298302999999997, -83.742846, 5000 m; Smith 27404 (MO) 1998, Anoka County, MN: 45.200527999999998, -93.2072, 350 m; Mell s.n. (MO) 1903, Crow Wing County, MN: 46.364655999999997, -94.191902999999996, 2000 m; Sandberg 380 (CS) 1891, Goodhue County, MN: 44.510827999999997, -92.905998999999994, 2250 m; Smith 12418 (MO) 1986, Wabasha County, MN: 44.256943999999997, -91.991667000000007, 500 m; Fassett 3103 (WIS) 1926, Winona County, MN: 44.032235999999997, -91.641738000000004, 2200 m; Steyermark 77387 (MO) 1954, Audrain County, MO: 39.337260000000001, -91.814914999999999, 1000 m; Palmer 66426 (WIS) 1957, Barry County,
MO: 36.672896999999999, -93.689316000000005, 1800 m; Davidse 37934 (MO) 2001, Barry County, MO: 36.684119000000003, -93.596179000000006, 1500 m; Palmer 66426 (MO) 1957, Barry County,
MO: 36.684182, -93.642976000000004, 2250 m; Bush 15544 (MO) 1936, Barry County, MO:
63


36.817841000000001, -93.776171000000005, 20500 m; Palmer 35969 (MO) 1929, Benton County, MO: 37.502155000000002, -93.589886000000007, 6000 m; Steyermark 10726 (MO) 1936, Benton County, MO: 38.238807999999999, -93.143956000000003, 1000 m; Palmer 36772 (MO) 1930, Benton County, MO: 38.246501000000002, -93.386104000000003, 2250 m; Palmer 26345 (MOR) 1924, Benton County, MO: 38.458441000000001, -93.205229000000003, 2000 m; Palmer 26345 (MO) 1924, Benton County, MO: 38.460183000000001, -93.201794000000007, 2000 m; Ryan 1338 (MO) 1989, Boone County, MO: 38.874274999999997, -92.313028000000003, 500 m; Rickett s.n. (MO) 1929, Boone County, MO: 38.911645, -92.339564999999993, 10000 m; Dunn 16975 (MO) 1969, Boone County, MO: 39.13373, -
92.322620999999998, 500 m; Rickett s.n. (MO) 1927, Boone County, MO: 39.13373, -
92.322620999999998, 500 m; Steyermark 76678 (MO) 1954, Callaway County, MO: 38.802785999999998, -91.662142000000003, 750 m; Steyermark 26101 (MO) 1937, Callaway County, MO: 38.814736000000003, -91.915212999999994, 1000 m; McVeigh s.n. (MO) 1933, Callaway County, MO: 38.880837999999997, -91.889011999999994, 1000 m; Korschgen 1139 (MO) 1975, Camden County, MO: 38.02704, -92.766053999999997, 35000 m; Kastler 58575 (MO) 1966, Carter County, MO: 36.942400999999997, -91.010338000000004, 4000 m; Palmer 6183 (MO) 1914, Carter County, MO: 36.995415000000001, -91.014927999999998, 1000 m; Palmer 6183 (MICH) 1914, Carter County, MO: 36.998663000000001, -91.008211000000003, 3000 m; Steyermark 12284 (MO) 1936, Carter County, MO: 37.074660000000002, -91.049302999999995, 1000 m; Steyermark 13547 (MO) 1934, Cedar County, MO: 37.794440999999999, -93.712215999999998, 7000 m; Sikes 1 (MO) 1998, Christian County, MO: 36.862408000000002, -93.224862000000002, 500 m; Sikes 1 (MICH) 1998, Christian County, MO: 36.862777999999999, -93.228333000000006, 1000 m; Bush 3478 (MO) 1905, Christian County, MO: 36.969571999999999, -93.188854000000006, 28000 m; Bush 5892 (MO) 1909, Clark County, MO: 40.521110999999998, -91.635598999999999, 1000 m; Bush 15534 (MO) 1936, Cooper County, MO: 38.699876000000003, -93.002212999999998, 1500 m; Bush 13660 (MO) 1933, Cooper County, MO: 38.843539999999997, -92.810119, 27000 m; Ryan 2142 (MO) 1992, Crawford County, MO: 37.714618000000002, -91.162270000000007, 1300 m; Feltz 44 (MO) 2009, Crawford County, MO: 38.079526999999999, -91.214106000000001, 2000 m; Steyermark 15432 (MO) 1934, Crawford County, MO: 38.116539000000003, -91.463804999999994, 1000 m; Palmer 54349 (MO) 1952, Dade County,
MO: 37.353827000000003, -93.789244999999994, 2250 m; Palmer 51285 (WIS) 1950, Dade County, MO: 37.497900000000001, -93.843153999999998, 2000 m; Palmer 51285 (MO) 1950, Dade County,
MO: 37.502439000000003, -93.833432999999999, 1500 m; Steyermark 5664 (MO) 1938, Dade County, MO: 37.515887999999997, -93.773932000000002, 500 m; Steyermark 13693 (MO) 1934, Dallas County, MO: 37.633898000000002, -93.027398000000005, 2000 m; Steyermark 12830 (MO) 1936, Dent County, MO: 37.549425999999997, -91.349639999999994, 1500 m; Palmer 34958 (MOR) 1928, Dent County, MO: 37.617778000000001, -91.291667000000004, 2000 m; Steyermark 12551 (MO) 1936, Dent County, MO: 37.659182000000001, -91.358134000000007, 5500 m; Steyermark s.n. (MO) 1931, Dent County, MO: 37.755049999999997, -91.341623999999996, 1000 m; Steyermark 14730 (MO) 1934, Douglas County, MO: 36.865850999999999, -92.485736000000003, 1000 m; Taylor s.n. (IND) 1989, Franklin County, MO: 38.459200000000003, -90.831900000000005, 2000 m; Taylor 5960 (MO) 1989, Franklin County, MO: 38.461190999999999, -90.829976000000002, 1000 m; Steyermark 13306 (MO) 1934, Hickory County, MO: 38.011775999999998, -93.088117999999994, 2000 m; Steyermark 14414 (MO) 1934, Howell County, MO: 36.592140000000001, -92.035571000000004, 2250 m; Steyermark 23571 (MO) 1937, Howell County, MO: 36.947642999999999, -92.042972000000006, 1000 m; Palmer 6231 (MO) 1914, Howell County, MO: 36.992524000000003, -91.969173999999995, 2000 m; Trelease 140
64


(MO) 1897, Iron County, MO: 37.4664, -90.648340000000005, 20000 m; Steyermark 8354 (MO) 1933, Iron County, MO: 37.541539999999998, -90.682875999999993, 400 m; Trelease 138 (MO) 1897, Jasper County, MO: 37.057453000000002, -94.517356000000007, 7000 m; Palmer 16016 (MO) 1918, Jasper County, MO: 37.110365000000002, -94.519130000000004, 2250 m; Palmer 6 (MO) 1901, Jasper County, MO: 37.174720999999998, -94.458094000000003, 500 m; Palmer 24083 (MO) 1923, Jasper County,
MO: 37.186407000000003, -94.320536000000004, 2000 m; Palmer 24083 (MOR) 1923, Jasper County, MO: 37.187114999999999, -94.310222999999993, 5000 m; Palmer 1455 (MO) 1908, Jasper County,
MO: 37.335298000000002, -94.300836000000004, 3000 m; Raven 27190 (MO) 1986, Jefferson County, MO: 38.125112999999999, -90.670610999999994, 250 m; Raven 27190 (MICH) 1986, Jefferson County, MO: 38.125455000000002, -90.671206999999995, 400 m; Hitchcock s.n. (MO) 1891, Jefferson County, MO: 38.178100000000001, -90.525270000000006, 1000 m; Hess 6837 (MOR) 1992, Jefferson County, MO: 38.262301999999998, -90.624859999999998, 700 m; Steyermark 13892 (MO) 1934, Laclede County, MO: 37.592432000000002, -92.364264000000006, 1000 m; Palmer 55728 (MO) 1953, Lawrence County, MO: 36.946002, -93.790336999999994, 500 m; Davis 4419 (MO) 1915, Lincoln County, MO: 39.123288000000002, -91.055552000000006, 1500 m; Steyermark 26029 (MO) 1937, Lincoln County, MO: 39.145839000000002, -91.031728999999999, 1000 m; Rowan 1326 (MO) 1994, Madison County, MO: 37.330486000000001, -90.442527999999996, 500 m; Erickson 36 (MO) 1996, Madison County,
MO: 37.480441999999996, -90.310124000000002, 1500 m; Steyermark 15268 (MO) 1934, Maries County, MO: 38.131027000000003, -91.830015000000003, 2000 m; Bush 10196A (MO) 1923, McDonald County, MO: 36.516151999999998, -94.611419999999995, 1000 m; Palmer 4172 (MO) 1913, McDonald County, MO: 36.546785999999997, -94.483484000000004, 1500 m; Steyermark 15815 (MO) 1934, Montgomery County, MO: 38.771089000000003, -91.576560000000001, 400 m; Steyermark 13141 (MO) 1934, Morgan County, MO: 38.272503999999998, -92.945631000000006, 4500 m; Palmer 52973 (WIS) 1951, Newton County, MO: 37.040368999999998, -94.561914000000002, 1000 m; Palmer 52973 (MO) 1951, Newton County, MO: 37.043618000000002, -94.563108, 500 m; Palmer 6266 (MO) 1914, Newton County, MO: 37.068893000000003, -94.116286000000002, 1500 m; Steyermark 14355 (MO) 1934, Oregon County, MO: 36.793705000000003, -91.343856000000002, 1000 m; Steyermark 7814 (MO) 1933, Ozark County, MO: 36.687202999999997, -92.470912999999996, 1000 m; Steyermark 25355 (MO) 1937, Phelps County, MO: 37.92906, -92.008690999999999, 1500 m; Davis 3098 (dup)
(MO) 1914, Pike County, MO: 39.235878999999997, -91.008191999999994, 3000 m; Steyermark 24093 (MO) 1937, Polk County, MO: 37.784547000000003, -93.418164000000004, 1000 m; Steyermark 11815 (MO) 1936, Ripley County, MO: 36.694989999999997, -91.031687000000005, 1000 m; Drouet 1468 (MO) 1934, Saint Charles County, MO: 38.854494000000003, -90.902963999999997, 500 m; Steyermark 82168 (MO) 1956, Saint Clair County, MO: 37.909694999999999, -93.775820999999993, 500 m; Steyermark 24460 (MO) 1937, Saint Clair County, MO: 38.175026000000003, -93.513565999999997, 1500 m; Trelease 137 (MO) 1897, Saint Francois County, MO: 37.849473000000003, -90.517139, 4000 m; Davidse 40877 (MO) 2013, Saint Francois County, MO: 37.988861, -90.514250000000004, 500 m; Letterman s.n. (MO) 1911, Saint Louis County, MO: 38.501382, -90.672381000000001, 1000 m; Christ s.n. (MO) 1931, Saint Louis County, MO: 38.554763000000001, -90.532419000000004, 1000 m; Hitchcock s.n. (MO) 1890, Saint Louis County, MO: 38.566994000000001, -90.411790999999994, 1500 m; Steyermark 8788 (MO) 1933, Sainte Genevieve County, MO: 37.857587000000002, -90.217224999999999, 5500 m; Trelease 978 (MO) 1898, Sainte Genevieve County, MO: 37.884583999999997, -90.373557000000005, 4500 m; Bush 5098 (MO) 1908, Shannon County, MO: 36.987051000000001, -91.575441999999995, 1500 m; Redfearn, Jr. 788 (MO) 1969, Shannon County,
65


MO: 37.038702000000001, -91.605720000000005, 1000 m; Miller 8142 (MO) 1993, Shannon County, MO: 37.133333, -91.466667000000001, 2000 m; Redfearn, Jr. 10067 (MO) 1962, Shannon County, MO: 37.133443999999997, -91.477643999999998, 1000 m; Redfearn, Jr. 569 (MO) 1969, Shannon County, MO: 37.154716999999998, -91.440360999999996, 500 m; Palmer 40901 (MO) 1933, Shelby County,
MO: 39.875349999999997, -92.025434000000004, 1000 m; Ladd 4405 (MOR) 1979, St. Genevieve County, MO: 37.830564000000003, -90.227688000000001, 250 m; Ladd 4030 (MOR) 1979, St.
Genevieve County, MO: 37.842277000000003, -90.266435999999999, 1250 m; Bushnell 162 (WIS)
1969, St. Louis County, MO: 38.638587000000001, -90.668301, 1000 m; Moore 243 (WIS) 1953, Stone County, MO: 36.559646000000001, -93.422864000000004, 10000 m; Steyermark 22720 (MO) 1937, Stone County, MO: 36.620719000000001, -93.353091000000006, 1000 m; Palmer 5793 (MO) 1914, Stone County, MO: 36.805387000000003, -93.462519999999998, 1000 m; Moore 174 (WIS) 1953,
Taney County, MO: 36.597228000000001, -93.307528000000005, 2300 m; Palmer 23908 (MO) 1923, Taney County, MO: 36.643397999999998, -93.218513999999999, 2250 m; Steyermark 14553 (MO) 1934, TX County, MO: 37.059404999999998, -91.686002000000002, 1000 m; Palmer 60304 (MO) 1955, TX County, MO: 37.322664000000003, -92.007541000000003, 3000 m; Palmer 60304 (WIS) 1955, TX County, MO: 37.327236999999997, -92.007591000000005, 2300 m; Miller 8293 (MO) 1994,
Washington County, MO: 37.872974999999997, -90.907926000000003, 1000 m; Davidse 42433 (MO) 2015, Washington County, MO: 38.081277999999998, -90.974417000000003, 1000 m; Steyermark 7029 (MO) 1932, Washington County, MO: 38.083784999999999, -90.737832999999995, 500 m; Steyermark 23686 (MO) 1937, Wright County, MO: 37.088611, -92.657777999999993, 1500 m; Tolstead 41857 (NEB) 1941, Brown County, NE: 42.749907999999998, -99.857144000000005, 2300 m; Phillippe 42054 (NEB) 2009, Brown County, NE: 42.765155, -99.886041000000006, 550 m; Steinauer 1903 (NEB) 2003, Cherry County, NE: 42.850555999999997, -100.215833, 250 m; Hitchcock 1025 (NEB) 1981, Cherry County, NE: 42.858266, -100.235613, 2300 m; Churchill 12172 (NEB) 1982, Cherry County, NE: 42.859746000000001, -100.238867, 3500 m; Tolstead 534 (NEB) 1936, Cherry County, NE: 42.887810999999999, -100.31576099999999, 1000 m; Clements s.n. (NEB) 1893, Holt County, NE: 42.455711000000001, -98.783844999999999, 57000 m; Kiener 23721 (NEB) 1948, Keya Paha County,
NE: 42.759141, -99.829094999999995, 2300 m; Clements 2951 (NEB) 1893, Keya Paha County, NE: 42.878829000000003, -99.712350000000001, 41000 m; Legler 5876 (DBG) 2007, Colfax County, NM: 36.926715999999999, -104.6815, 75 m; Waterfall 9198 (COLO) 1949, Delaware County, OK: 36.408199000000003, -94.802650999999997, 34800 m; Over 15830 (RM) 1924, Custer County, SD: 43.580261, -103.439412, 9500 m; Porter 6728 (RM) 1955, Custer County, SD: 43.618886000000003, -103.463319, 2250 m; Mayer 243 (RM) 2001, Custer County, SD: 43.840263, -103.43944999999999, 500 m; Jones s.n. (COLO) 1953, Custer County, SD: 43.847034999999998, -103.628122, 2000 m; Mayer 519 (RM) 2005, Pennington County, SD: 43.874181, -103.463035, 750 m; Stoesz s.n. (NEB) 1937, Pennington County, SD: 43.876913999999999, -103.439691, 400 m; Marriott 12093 (RM) 2000, Pennington County, SD: 43.887062, -103.530057, 100 m; Over 15828 (RM) 1924, Pennington County, SD: 43.905296, -103.535073, 750 m; Salamun 565 (MO) 1947, Pennington County, SD: 43.974138000000004, -103.290362, 1500 m; Severson 232 (USFS) 1971, Pennington County, SD: 44.025300000000001, -103.6392, 500 m; Williams s.n. (MO) 1891, Pennington County, SD: 44.08005, -103.231015, 5000 m; Alverson 1632 (WIS) 1980, Adams County, Wl: 43.915655999999998, -89.849356999999998, 850 m; Brown 42 (WIS) 1948, Adams County, Wl: 43.989336999999999, -89.668289000000001, 5500 m; Alfieri s.n. (WIS) 1963, Adams County, Wl: 44.029490000000003, -89.712784999999997, 2300 m; Palmer 28539 (MO) 1925, Buffalo County, Wl: 44.132838999999997, -91.713595999999995, 1000 m; Fassett
66


15457 (MO) 1927, Buffalo County, Wl: 44.132838999999997, -91.713595999999995, 1000 m; Fassett 15457 (WIS) 1927, Buffalo County, Wl: 44.133017000000002, -91.715672999999995, 2300 m; Baird s.n. (WIS) 1916, Burnette County, Wl: 46.006611999999997, -92.371305000000007, 2000 m; Patman s.n. (WIS) 1959, Chippewa County, Wl: 45.046199999999999, -91.27413, 800 m; Lammers 14410 (MICH) 2013, Clark County, Wl: 44.459865000000001, -90.679045000000002, 800 m; Goessl 1486 (MPM) 1915, Clark County, Wl: 44.559961999999999, -90.596249999999998, 2250 m; Fassett s.n. (WIS) 1935, Columbia County, Wl: 43.306984999999997, -89.706198000000001, 1700 m; Hess 7886 (MO) 1997, Columbia County, Wl: 43.603405000000002, -89.157554000000005, 100 m; Moore 3 (WIS) 1976, Crawford County, Wl: 43.049101, -90.964911999999998, 2000 m; Fassett 15475 (WIS) 1927, Crawford County, Wl: 43.051650000000002, -91.141239999999996, 3800 m; Stueber 23 (WIS) 1976, Crawford County, Wl: 43.051650000000002, -91.141239999999996, 3800 m; Peters 129 (WIS) 1973, Crawford County, Wl: 43.056165, -91.114681000000004, 700 m; R. H. Denniston s.n. (WIS) 1915, Crawford County, Wl: 43.246367999999997, -91.056239000000005, 1700 m; Nee 62467 (WIS) 2015, Crawford County, Wl: 43.320555599999999, -90.894999999999996, 200 m; Armstrong 34-88 (WIS) 1988, Dane County, Wl: 42.900367000000003, -89.540656999999996, 1500 m; Miller 322 (WIS) 1967, Dane County, Wl: 43.084631999999999, -89.596284999999995, 2300 m; Denniston s.n. (WIS) 1927, Dunn County, Wl: 44.692186999999997, -92.011562999999995, 2300 m; Kunz 65 (WIS) 1928, Eau Claire County, Wl: 44.763572000000003, -91.277099000000007, 2300 m; litis 10417 (WIS) 1957, Grant County, Wl: 42.684817000000002, -90.492931999999996, 2300 m; Roethke 78 (WIS) 1980, Grant County, Wl: 42.725490000000001, -90.532390000000007, 1500 m; litis 6531 (WIS) 1956, Grant County, Wl:
42.733719999999998, -91.019574000000006, 1600 m; Fassett 13454 (WIS) 1930, Grant County, Wl: 42.783324999999998, -90.613738999999995, 2300 m; Fassett 13462 (WIS) 1930, Grant County, Wl: 42.847490000000001, -90.710682000000006, 2200 m; Nee 61376 (WIS) 2014, Grant County, Wl: 42.951667, -91.141110999999995, 1000 m; Davis s.n. (WIS) 1913, Grant County, Wl: 43.033318000000001, -90.930126999999999, 2200 m; Penney 191 (WIS) 1961, Grant County, Wl: 43.044992999999998, -90.535304999999994, 2000 m; Fell 58-471 (WIS) 1958, Green County, Wl: 42.628534000000002, -89.760468000000003, 1800 m; Rice 1359 (WIS) 1972, Green County, Wl: 42.648842000000002, -89.727887999999993, 2000 m; Fell 58-519 (WIS) 1958, Green County, Wl: 42.649883000000003, -89.396944000000005, 2100 m; Fell 58-266 (WIS) 1958, Green County, Wl:
42.733719999999998, -91.019574000000006, 1500 m; Cochrane 15340 (WIS) 2011, IA County, Wl: 42.972695000000002, -89.859786, 2000 m; Schall 43656 (MOR) 1991, IA County, Wl: 43.064436000000001, -89.950579000000005, 350 m; Brady s.n. (WIS) n.d., IA County, Wl: 43.081491, -89.971127999999993, 2000 m; Cross 107 (WIS) 1959, IA County, Wl: 43.149566, -90.045614999999998, 1000 m; Smith 6856 (WIS) 1922, Jackson County, Wl: 44.294682999999999, -90.851530999999994, 3000 m; Grether 6384 (WIS) 1947, Jackson County, Wl: 44.414684000000001, -90.730697000000006, 2000 m; Sorensen 2899 (WIS) 1962, Juneau County, Wl: 43.752614999999999, -89.985067000000001, 2300 m; Steenis s.n. (WIS) 1932, Juneau County, Wl: 44.025584000000002, -90.074017999999995, 10000 m; Pammel 48 (MO) 1887, La Crosse County, Wl: 43.790645599999998, -91.199458199999995, 2000 m; Schnurrer 17 (WIS) 1976, La Crosse County, Wl: 43.817860000000003, -91.195870999999997, 2000 m; Hansen 1089 (WIS) 1972, Lafayette County, Wl: 42.789706000000002, -89.850773000000004, 1500 m; Grassl 3190 (MICH) 1933, Marinette County, Wl: 45.074480999999999, -87.680443999999994, 5400 m; litis 6282 (WIS) 1956, Marquette County, Wl: 43.690438, -89.394082999999995, 500 m; Thompson 87 (WIS) 1956, Marquette County, Wl: 43.756915999999997, -89.280911000000003, 2000 m; Ritchie 25 (WIS) 1976, Marquette County, Wl: 43.773535000000003, -89.448446000000004, 800 m;
67


Peters 11 (WIS) 1973, Marquette County, Wl: 43.976421000000002, -89.379335999999995, 550 m; litis 8639 (WIS) 1957, Monroe County, Wl: 43.821640000000002, -90.737520000000004, 1750 m; Black 02-P15 (WIS) 2002, Pepin County, Wl: 44.543236999999998, -92.073612999999995, 400 m; Davis s.n. (WIS) 1916, Pierce County, Wl: 44.562190000000001, -92.307900000000004, 1300 m; Hansen 4036 (WIS) 1976, Pierce County, Wl: 44.632285000000003, -92.587329999999994, 2300 m; Fassett 15454 (WIS) 1927, Pierce County, Wl: 44.751036999999997, -92.803133000000003, 27000 m; Baird s.n. (WIS) 1916, Polk County, Wl: 45.359572, -92.633538999999999, 3000 m; Baker 6412 (COLO) 1900, Polk County, Wl: 45.408017000000001, -92.638648000000003, 2250 m; Sorensen 1788 (WIS) 1962, Portage County, Wl: 44.268312999999999, -89.656835000000001, 4000 m; Mauritz 634 (WIS) 1964, Portage County, Wl: 44.596795, -89.268009000000006, 1000 m; Nee 60161 (WIS) 2013, Richland County, Wl: 43.190556000000001, -90.665000000000006, 500 m; Huffman s.n. (WIS) 1968, Richland County, Wl: 43.202388999999997, -90.256326000000001, 6000 m; Nee 43755 (MO) 1993, Richland County, Wl: 43.226847999999997, -90.632265000000004, 2250 m; Nee 16317 (MO) 1978, Richland County, Wl:
43.296684999999997, -90.337011000000004, 500 m; Nee 37799 (MO) 1989, Richland County, Wl:
43.296684999999997, -90.337011000000004, 500 m; Nee 37799 (WIS) 1989, Richland County, Wl: 43.303617000000003, -90.345149000000006, 2000 m; Nee 16317 (MPM) 1978, Richland County, Wl: 43.304090000000002, -90.339691000000002, 1000 m; Fosberg 272 (WIS) 1948, Richland County, Wl: 43.329093, -90.292722999999995, 1100 m; Nee 2573 (WIS) 1969, Richland County, Wl: 43.331519, -90.657972999999998, 2000 m; litis 9901 (WIS) 1957, Richland County, Wl: 43.365192999999998, -90.264280999999997, 1000 m; litis 11847 (WIS) 1958, Richland County, Wl: 43.373038999999999, -90.267347999999998, 2300 m; Nee 6054 (WIS) 1973, Richland County, Wl: 43.525258000000001, -90.354583000000005, 2000 m; Nee s.n. (WIS) 1974, Richland County, Wl: 43.550457000000002, -90.659516999999994, 1500 m; Fell 57-1270 (WIS) 1957, Rock County, Wl: 42.533481000000002, -89.278717999999998, 2000 m; Stearns 886 (WIS) 1941, Sauk County, Wl: 43.241101, -89.81429, 1300 m; Hansen 1017 (WIS) 1932, Sauk County, Wl: 43.532480999999997, -90.002626000000006, 3700 m; Eggert s.n. (MO) 1903, Sauk County, Wl: 43.565275999999997, -89.860292999999999, 2250 m; De Stefano 260 (WIS) 1980, Shawano County, Wl: 44.650505000000003, -88.546476999999996, 5100 m; litis 7202 (WIS) 1956, St. Croix County, Wl: 45.164146000000002, -92.271624000000003, 7500 m; Taylor 3306 (MPM) 1977, Trempealeau County, Wl: 44.017741000000001, -91.465699999999998, 1800 m; Levins 212 (MOR) 1983, Vernon County, Wl: 43.546484999999997, -90.906640999999993, 1200 m; Nee 63860 (WIS) 2017, Vernon County, Wl: 43.625813000000001, -91.218840999999998, 1800 m; Kline s.n. (WIS) 1974, Vernon County, Wl: 43.630892000000003, -90.624459000000002, 2300 m; Baird s.n. (WIS) 1920, Vernon County, Wl: 43.657747999999998, -91.096519999999998, 1500 m; Swink 9171 (MOR) 1989, Walworth County, Wl: 42.547099000000003, -88.578609999999998, 1200 m; Shannon 124 (MO) 1905, Walworth County, Wl: 42.821389000000003, -88.320832999999993, 500 m; Sheaffer s.n. (WIS) 1957, Waukesha County, Wl: 43.116394, -88.489444000000006, 2300 m; litis 13531 (WIS) 1959, Waupaca County, Wl: 44.690893000000003, -88.672653999999994, 600 m; Cochrane 10359 (WIS)
1984, Waushara County, Wl: 44.003326999999999, -89.282195000000002, 250 m; Fassett 21345 (MO) 1941, Waushara County, Wl: 44.022089000000001, -89.321331000000001, 4000 m; Hess 4839 (MOR) 1979, Waushara County, Wl: 44.048104000000002, -89.133374000000003, 1000 m; Dennis s.n. (WIS) 1966, Waushara County, Wl: 44.232447999999998, -89.191198, 1000 m; Hanneman, Jr. 8 (WIS) 1964, Wood County, Wl: 44.363264999999998, -89.750553999999994, 1100 m.
Physocarpus monogynus (Torr.) Coult.
68


United States: Blumer 1278 (MO) 1906, Cochise County, AZ: 31.847064, -109.291579, 1000 m; Daniels 693 (COLO) 1906, Boulder County, CO: 39.997925000000002, -105.297826, 500 m; Zobel s.n. (DBG)
1939, Boulder County, CO: 40.131252000000003, -105.51669099999999, 10000 m; Patterson 23 (MO) 1885, Clear Creek County, CO: 39.733961999999998, -105.684226, 1500 m; Degener 16885 (MO) 1942, Clear Creek County, CO: 39.742488000000002, -105.513608, 2250 m; Elliott 8575 (COLO) 1999, Custer County, CO: 38.179712000000002, -105.17367400000001, 750 m; Islam 12-102 (DBG) 2012, Douglas County, CO: 39.448810000000002, -104.95601000000001, 200 m; Brumback 75b (DBG) 1912, El Paso County, CO: 38.838554999999999, -105.04318600000001, 5500 m; Clements 122 (MO) 1901, El Paso County, CO: 38.849133000000002, -104.961853, 500 m; Van Schaack 3584 (MO) 1957, El Paso County, CO: 38.917617999999997, -104.955782, 1000 m; Harrington 7524 (CS) 1954, Fremont County, CO: 38.452235999999999, -105.493431, 2000 m; Baker 115 (MO) 1901, Gunnison County, CO: 38.578498000000003, -107.716832, 1500 m; Miller 6718 (MO) 1991, Jefferson County, CO: 39.407465999999999, -105.171468, 500 m; Duff 10 (COLO) 1992, Jefferson County, CO: 39.657364000000001, -105.248058, 800 m; Imler4051 (CS) 1937, Jefferson County, CO: 39.736455999999997, -105.242672, 1300 m; Nunn 3970 (RM) 2001, Larimer County, CO: 40.364806000000002, -105.426694, 1000 m; Nunn 2459 (RM) 2001, Larimer County, CO: 40.444876999999998, -105.432385, 1000 m; Nunn 3591 (CS) 2001, Larimer County, CO: 40.675182999999997, -105.46863999999999, 850 m; Myers 37 (CS) 1972, Larimer County, CO: 40.675814000000003, -105.350908, 1500 m; Nunn 3591 (RM) 2001, Larimer County, CO: 40.677599999999998, -105.4676, 500 m; Osterhout 3432 (COLO) 1906, Larimer County, CO: 40.699283999999999, -105.58129599999999, 3000 m; Weber 4859 (COLO) 1949, Larimer County, CO: 40.762613000000002, -105.179537, 1000 m; Clark 604 (COLO) 1996, Larimer County, CO: 40.797114000000001, -105.30139200000001, 1000 m; Rollins 1842 (MO) 1937, Las Animas County, CO: 37.054934000000003, -104.376896, 800 m; Regensberg 1285 (DBG) 2013, Pueblo County, CO: 38.113349999999997, -104.94295, 500 m; Werner s.n. (DBG) 1957, Summit County, CO: 39.572099999999999, -105.997201, 3500 m; Lederer 4509 (COLO) 1995, Weld County, CO: 40.913906799999999, -103.6015522, 2250 m; Ramaley 1371 (COLO) 1905, County, CO: 38.945824999999999, -105.28943599999999, 2250 m; Engelmann s.n. (MO) 1874, County, CO: 39.742185999999997, -105.709688, 2000 m; Ginter 522 (CS) 1941, Larimer County, CO: 40.173358999999998, -105.37057799999999, 2000 m; Owens 205 (CS) 1998, Larimer County, CO: 40.531225999999997, -105.184102, 500 m; Denham 91020 (CS) 1991, Larimer County, CO: 40.689965000000001, -105.349079, 500 m; Worthington 12190 (COLO) 1984, Lincoln County, NM: 33.385970999999998, -105.739812, 1500 m; Reif 1369 (COLO) 2002, Los Alamos County, NM: 35.931007999999999, -106.346756, 700 m; Hartman 77970 (COLO) 2003, Sandoval County, NM: 35.89123, -106.550704, 500 m; Reif 7439 (COLO) 2003, Santa Fe County, NM: 35.807909000000002, -105.84642700000001, 1000 m; Reif 7946 (CS) 2003, Santa Fe County, NM: 35.821603000000003, -105.79478400000001, 500 m; Metcalfe 988 (MO) 1904, Sierra County, NM: 32.994446000000003, -107.83361499999999, 43000 m; Metcalfe 277 (RM) 1903, Soccoro (Catron? Label says Soccoro) County, NM: 33.199500999999998, -108.470595, 1000 m; Waterfall 10740 (COLO) 1952, Cimarron County, OK: 36.947217000000002, -102.968362, 1500 m; Over 15829 (RM) 1924, Custer County, SD: 43.580261, -103.439412, 9500 m; Marriott 11923 (RM) 2000, Custer County, SD: 43.838948000000002, -103.491467, 500 m; Goodman 3320 (MO) 1941, Pennington County, SD: 43.880795999999997, -103.53086999999999, 1500 m; Young s.n. (MO) 1916, Culberson County, TX: 31.891480000000001, -104.86056000000001, 250 m; Goodding 1123 (MO) 1902, Utah County, UT: 40.25412, -
69


111.55790500000001, 50000 m; Osterhout s.n. (COLO) 1922, County, UT: 40.634343000000001, -111.689724, 8000 m; Nelson 4567 (RM) 1940, Albany County, WY: 41.004897, -105.52037199999999, 15000 m; Letterman 134 (MO) 1884, Albany County, WY: 41.131110999999997, -105.398056, 500 m; O'Brien 1132 (RM) 1982, Albany County, WY: 41.936565700000003, -105.3587003, 1145 m; Hartman 7593 (RM) 1978, Campbell County, WY: 43.739803000000002, -105.900189, 650 m; Hartman 40778 (RM) 1993, Goshen County, WY: 41.879778999999999, -104.613377, 1000 m; Nelson 28140 (RM) 1993, Goshen County, WY: 42.398231000000003, -104.641595, 1000 m; Evert 8283 (RM) 1985, Johnson County, WY: 44.553840000000001, -106.940203, 500 m; Fertig 16297 (RM) 1995, Laramie County, WY: 41.162742000000001, -105.206232, 500 m; Hartman 55145 (USFS) 1996, Park County, WY: 44.308548999999999, -109.51046100000001, 1000 m; Hartman 22017 (RM) 1985, Park County, WY: 44.720593999999998, -109.343723, 850 m; Nelson 12977 (RM) 1985, Park County, WY: 44.733651999999999, -109.289506, 500 m; Kademian 1579 (USFS) 1988, Park County, WY: 44.949880999999998, -109.312855, 1500 m; Hartman 40717 (RM) 1993, Platte County, WY: 41.876235000000001, -104.780773, 1000 m; Nelson 17170 (RM) 1989, Platte County, WY: 42.309525999999998, -105.256849, 1000 m; Porter 4898 (COLO) 1949, Platte County, WY: 42.319943000000002, -104.788974, 5000 m; Nelson 498 (MO) 1894, Platte County, WY: 42.327364000000003, -104.725825, 1000 m; Evert 8174 (RM) 1985, Sheridan County, WY: 44.568894999999998, -106.931826, 400 m; Hartman 10681 (RM) 1979, Sheridan County, WY: 44.617218000000001, -107.08161699999999, 1000 m; Solheim 471 (RM) 1931, Sheridan County, WY: 44.681005999999996, -107.214134, 1000 m; Dickson D-302 (USFS) 1933, Sheridan County, WY: 44.756605, -107.268475, 2000 m; Nelson 2304 (RM) 1896, Sheridan County, WY: 44.761217000000002, -107.260294, 1250 m; Hartman 9855 (RM) 1979, Sheridan County, WY: 44.862583000000001, -107.326277, 1000 m; Fertig 17018 (RM) 1996, Sheridan County, WY: 44.920532000000001, -107.736459, 1000 m; Nelson 8794 (RM) 1982, Weston County, WY: 43.687314000000001, -104.050917, 500 m; Marriott 8024 (RM) 1984, Weston County, WY: 43.851599999999998, -104.045058, 800 m; Goodman 4814 (COLO) 1948, Cimarron County, OK: 36.943643999999999, -102.972306, 2250 m;
Physocarpus opulifolius (L.) Maxim.
Canada; category 1: Reznicek 3813 (MICH) 1973, Ontario: 44.775784999999999, -79.711485999999994, 1500 m; Krotkov 9139 (WIS) 1934, Ontario: 44.942371000000001, -81.009426000000005, 4500 m;
Grassl 2499 (MICH) 1932, Ontario: 45.993485, -81.916695000000004, 2300 m; Lechowicz s.n. (WIS) 1973, Ontario: 46.730418999999998, -84.349463999999998, 2000 m; Voss 10639 (MICH) 1961, Ontario: 47.240887000000001, -84.650547000000003, 500 m; Garton 1735 (RM) 1951, Ontario: 48.456662999999999, -89.181312000000005, 1500 m; litis 3508C (WIS) 1947, Quebec: 46.885326999999997, -70.849106000000006, 2300 m. category 2: Senn 5483 (WIS) 1950, Ontario: 45.569211000000003, -81.967545999999999, 9000 m; Soper 13401 (MICH) 1975, Ontario: 46.537112, -84.585751999999999, 1000 m; Crins 9711 (MICH) 1993, Ontario: 47.056454000000002, -84.762572000000006, 1000 m; Bartlett 542 (MICH) 1951, Ontario: 47.242401000000001, -84.644422000000006, 300 m; Grassl 1258 (MICH) 1931, Ontario: 48.044780000000003, -85.954009999999997, 350 m; Love 6952a (COLO) 1955, Ontario: 48.315364000000002, -88.926299999999998, 1700 m; Butters s.n. (IND) 1929, Ontario: 48.799776999999999, -87.935799000000003, 14500 m; Garton 21283 (MO) 1982, Ontario: 49.675763000000003, -87.567216000000002, 500 m; Garton 22734 (MICH) 1983, Ontario: 49.909892999999997, -88.124404999999996, 4300 m. category 3: Garton 2001 (RM) 1952, Ontario: 48.011783999999999, -
70


89.571658999999997, 1200 m; Garton 21470 (MICH) 1982, Ontario: 49.600332999999999, -87.961893000000003, 5500 m; Oldham 36676 (MICH) 2009, Ontario: 50.956350999999998, -84.597183000000001, 2300 m.
United States: category 1: Clark 5036C (MOR) 1966, DeKalb County, AL: 34.352415000000001, -85.676068000000001, 800 m; Duncan 8760 (MO) 1948, Bartow County, GA: 34.174799999999998, -84.701999999999998, 500 m; Cronquist 5155 (MO) 1948, Dade County, GA: 34.836655, -85.482054000000005, 500 m; Armstrong 2461 (DBG) 1964, Cook County, IL: 41.642142, -87.867262999999994, 2000 m; Bennett 520 (IND) 1963, Cook County, IL: 41.980761999999999, -87.852592999999999, 1850 m; Swink s.n. (MOR) 1946, Cook County, IL: 41.999619000000003, -87.784186000000005, 1000 m; Evert 6410 (MOR) 1983, Cook County, IL: 42.012889000000001, -87.865399999999994, 650 m; Laskowski 184 (MICH) 1960, Cook County, IL: 42.032702999999998, -88.011960000000002, 2300 m; Lamp L336 (MOR) 1960, Cook County, IL: 42.095874999999999, -87.882182999999998, 2700 m; Levins 101 (MOR) 1981, Kane County, IL: 41.926409, -88.348260999999994, 1000 m; Harper s.n. (MICH) 1887, Ogle County, IL: 42.125441000000002, -89.263215000000002, 2300 m; Deam 52614 (IND) 1932, Crawford County, IN: 38.294331999999997, -86.517043999999999, 3000 m; Young s.n. (MICH) 1876, Jefferson County, IN: 42.256405000000001, -90.279572999999999, 2000 m; Reed 19698 (MO) 1950, Carter County, KY: 38.361669999999997, -
83.111395999999999, 2000 m; Reed 13881 (MO) 1948, Carter County, KY: 38.361669999999997, -
83.111395999999999, 500 m; Reed 118336 (MO) 1983, Carroll County, MD: 39.590677999999997, -77.022757999999996, 300 m; Reed 21830 (MO) 1950, Garrett County, MD: 39.400995000000002, -79.370088999999993, 1000 m; Bailey s.n. (MICH) 1891, Kent County, Ml: 42.951298999999999, -85.66122, 9000 m; Hermann 209 (MICH) 1926, Keweenaw County, Ml: 47.46996, -87.890467000000001, 1200 m; McFarlin 2002 (MICH) 1930, Keweenaw County, Ml: 48.146514000000003, -88.485724000000005, 1000 m; Hazlett 3071 (MICH) 1985, Leelanau County, Ml: 45.484217000000001, -85.778999999999996, 1200 m; Erlanson 766 (MICH) 1924, Mackinac County, Ml: 45.977344000000002, -84.193090999999995, 700 m; MacFarlane 5023 (MICH) 1990, Ontonagon County, Ml: 46.651747999999998, -89.151488000000001, 200 m; Lundell 8312 (MICH) 1939, Washtenaw County,
Ml: 42.298302999999997, -83.742846, 5000 m; Ahles 66806 (NEB) 1967, Dutchess County, NY: 41.781052000000003, -73.946315999999996, 800 m; Biltmore 1282b (WIS) 1897, Buncombe County,
NC: 35.703482000000001, -82.377489999999995, 2300 m; Moseley s.n. (MICH) 1921, Ottawa County, OH: 41.654215999999998, -82.820742999999993, 1500 m; Selby 662 (CS) 1899, Wayne County, OH: 40.803237000000003, -81.893601000000004, 5000 m; Palmer 43585 (MO) 1937, Adams County, PA: 39.903626000000003, -77.095279000000005, 1000 m; Berkheimer 18778 (MICH) 1958, Bedford County, PA: 39.916530000000002, -78.357688999999993, 1000 m; Witte s.n. (RM) 1928, Bucks County, PA: 40.489879999999999, -75.219493999999997, 1800 m; Kline s.n. (MO) 1952, Cambria County, PA: 40.681223000000003, -78.582499999999996, 2000 m; Fogg 21157 (MICH) 1953, Montgomery County, PA: 40.236336000000001, -75.177674999999994, 500 m; Swartley 419 (MO) 1935, Montgomery County, PA: 40.340864000000003, -75.437241, 500 m; Manning s.n. (MO) 1965, Northumberland County, PA: 40.836703, -76.800877, 1000 m; Grisez 221 (USFS) 1961, Warren County, PA: 41.846094999999998, -79.288917999999995, 2000 m; Germplasm Unit 160 GU (MOR) 1989, Augusta County, VA: 37.947203000000002, -78.927960999999996, 100 m; Reed 47074 (MO) 1960, Augusta County, VA: 38.274875999999999, -79.323150999999996, 500 m; Fosberg 23959 (WIS) 1945, Fairfax County, VA: 38.790430000000001, -77.055728000000002, 1500 m; J.R.C. s.n. (MO) 1889, Fairfax County, VA: 38.974432999999998, -77.215305999999998, 10000 m; Bartlett 2194 (MICH) 1910, Fairfax County,
71


VA: 38.997897999999999, -77.286942999999994, 4500 m; Fogg Jr. 14974 (MO) 1938, Giles County, VA: 37.273693999999999, -80.605166999999994, 1000 m; Reed 83493 (MO) 1969, Loudoun County, VA: 39.047964, -77.437683000000007, 7500 m; Leys 21637 (WIS) 1974, Nelson County, VA: 37.858237000000003, -78.984212999999997, 2000 m; Sauleda 4337 (MO) 1980, Nelson County, VA: 37.940626000000002, -78.944096000000002, 100 m; Reed 13843 (MO) 1948, Page County, VA: 38.620345999999998, -78.351225999999997, 500 m; Musselman 4961 (WIS) 1976, Tazewell County,
VA: 37.125469000000002, -81.387632999999994, 2000 m; Reed 78752 (MO) 1968, Berkeley County, WV: 39.389381999999998, -78.110170999999994, 500 m; Reed 140110 (MO) 1986, Berkeley County, WV: 39.585476999999997, -78.050392000000002, 1000 m; Mackenzie 445 (MO) 1903, Greenbrier County, WV: 37.795146000000003, -80.299211, 5000 m; Clarkson 2738 (USFS) 1959, Greenbrier County, WV: 38.068469999999998, -79.961117999999999, 1500 m; Downs 8840 (MO) 1969, Mineral County, WV: 39.599812999999997, -78.798860000000005, 1000 m; Reed 77685 (MO) 1968, Randolph County, WV: 38.908693, -79.701719999999995, 500 m; Cheney 4944 (WIS) 1896, Ashland County, Wl: 46.652439999999999, -90.730299000000002, 3000 m; Taylor 3586 (MPM) 1977, Ashland County, Wl: 46.916612999999998, -90.547590999999997, 1000 m; Seidlinger X-15 (WIS) 1975, Dane County, Wl: 43.041490000000003, -88.922126000000006, 1000 m; Rice 3157 (WIS) 1970, Door County, Wl: 42.764144999999999, -87.779927000000001, 2000 m; Brouette P. o. -1 (b) (WIS) 1968, Door County,
Wl: 45.150022999999997, -87.217564999999993, 2800 m; Koch 7007 (WIS) 1971, Douglas County, Wl: 46.702958000000002, -92.007758999999993, 3500 m; Bedore 7 (WIS) 1985, Kenosha County, Wl: 42.568390999999998, -88.059612999999999, 22000 m; Seymour 15794 (WIS) 1954, Lincoln County, Wl: 45.183531000000002, -89.744226999999995, 1200 m; Schneider 1407 (IND) 1939, Vilas County, Wl: 46.046914000000001, -89.667630000000003, 3800 m; Waller DOB0358 (WIS) 2011, Vilas County, Wl: 46.047854999999998, -89.664095000000003, 750 m; Fassett 9413 (WIS) 1929, Vilas County, Wl: 46.061131000000003, -89.672779000000006, 650 m. category 2: Clark 18234 (MOR) 1967, Bibb County, AL: 32.992128000000001, -87.133666000000005, 1000 m; Duncan 6984 (MO) 1946, Towns County, GA: 34.984478000000003, -83.624105999999998, 350 m; Armstrong 120 (DBG) 1961, Cook County, IL: 41.691057000000001, -87.923158999999998, 1000 m; Umbach 12433 (WIS) 1900, Cook County, IL: 42.033363999999999, -87.733393000000007, 4600 m; Johnson 2306 (MOR) 1995, Will County, IL: 41.386999000000003, -88.240128999999996, 400 m; Maugers.n. (MOR) 1994, Will County, IL: 41.424737, -87.575502999999998, 7000 m; Clevenger 167 (IND) 1952, Bartholomew County, IN: 39.245641999999997, -85.710466999999994, 3500 m; Deam 45812 (IND) 1928, Cass County, IN: 40.836948, -86.239874999999998, 3000 m; Deam 34384 (IND) 1921, Fulton County, IN: 41.097749999999998, -86.486895000000004, 1800 m; Deam 22046 (IND) 1916, Huntington County, IN: 40.721676000000002, -85.474907000000002, 2500 m; Young s.n. (IND) 1877, Jefferson County, IN: 38.714225999999996, -85.473571000000007, 2000 m; Deam 9132 (IND) 1911, Jennings County, IN: 38.999209, -85.607900000000001, 1000 m; Yuncker 10814 (COLO) 1941, Lagrange County, IN: 41.695414999999997, -85.313923000000003, 1000 m; Armstrong 2460 (DBG) 1964, Lake County, IN: 41.600617, -87.261179999999996, 2250 m; Deam 2421 (IND) 1907, Madison County, IN: 40.118214000000002, -85.683623999999995, 3000 m; Deam 38001 (IND) 1922, Monroe County, IN: 39.025134000000001, -86.555503000000002, 1000 m; Umbach 32735, 3970 (WIS) 1909, Porter County, IN: 41.661569999999998, -87.054522000000006, 6000 m; Deam 63050 (IND) 1944, Randolph County,
IN: 40.138381000000003, -85.126485000000002, 500 m; Price s.n. (MO) 1893, Warren County, KY: 36.989772000000002, -86.403090000000006, 9000 m; Steyermark 889 (MO) 1929, Hancock County,
ME: 44.387087999999999, -68.208022, 2250 m; Reed 26815 (MO) 1951, Allegany County, MD:
72


39.701875000000001, -78.566153999999997, 1000 m; Reed 2116 (MO) 1940, Baltimore County, MD: 39.393526999999999, -76.766221000000002, 9500 m; Reed 30036 (MO) 1952, Baltimore County, MD: 39.39508, -76.825460000000007, 2250 m; Shull 117 (MO) 1902, Harford County, MD: 39.510435999999999, -76.134513999999996, 500 m; Reed 71030 (MO) 1965, Harford County, MD: 39.681230999999997, -76.243556999999996, 1700 m; Downs 3164 (MO) 1968, Washington County, MD: 39.603366999999999, -77.977281000000005, 1200 m; Richardson s.n. (MO) 1963, Middlesex County, MA: 42.456654, -71.357647999999998, 1500 m; Frisbie 8 (USFS) 1938, Alger County, Ml: 46.452249999999999, -86.919289000000006, 2000 m; King s.n. (MICH) 1966, Charlevoix A County, Ml: 45.804720000000003, -85.494534000000002, 4500 m; Wood s.n. (MICH) 1912, Chippewa County, Ml: 46.766880999999998, -84.975065999999998, 2300 m; Ehlers 610 (MICH) 1917, Emmet County, Ml: 45.567979000000001, -84.844898999999998, 7000 m; Ehlers 212 (MICH) 1916, Emmet County, Ml: 45.744055000000003, -84.840986999999998, 1500 m; Gates 10710 (RM) 1917, Emmet (or Cheboygan) County, Ml: 45.552197, -84.797336999999999, 1000 m; Davis s.n. (MICH) 1893, Gratiot County, Ml: 43.378920000000001, -84.659727000000004, 3000 m; Leisman EC-15-710 (MICH) 2015, Kent County, Ml: 43.111499999999999, -85.629582999999997, 350 m; Hall s.n. (MOR) 1951, Keweenaw County, Ml: 47.467308000000003, -87.867312999999996, 900 m; Reich R-266-79 (MPM) 1979, Keweenaw County, Ml: 47.476762999999998, -88.008532000000002, 1500 m; Bailey 5393 (MICH) 1959, Keweenaw County, Ml: 47.915554999999998, -89.154501999999994, 600 m; Bailey 5560 (MICH) 1959, Keweenaw County, Ml: 47.985702000000003, -88.805339000000004, 400 m; Zimmerman 928 (MICH) 1954, Lapeer County, Ml: 43.094749, -83.072439000000003, 6500 m; Voss 7306 (MICH) 1958, Lapeer County, Ml: 43.126761000000002, -83.368261000000004, 1300 m; Nee 50977 (MO) 2000, Mackinac County, Ml: 45.908647999999999, -84.736644999999996, 300 m; Grassl 2763 (MICH) 1933, Menominee County, Ml: 45.138131000000001, -87.600678000000002, 250 m; Jones 40882 (COLO) 1965, Cook County, MN: 47.544347000000002, -90.891818000000001, 2250 m; Lakela 2700 (MPM) 1938, Lake County, MN: 47.142195000000001, -91.458791000000005, 800 m; Mackenzie 2864 (MO) 1907, Middlesex County,
NJ: 40.494245999999997, -74.434548000000007, 500 m; Mackenzie s.n. (IND) 1907, Middlesex County, NJ: 40.495938000000002, -74.424318, 2200 m; Dougan s.n. (MO) 1916, Middlesex County, NJ: 40.507511999999998, -74.463409999999996, 400 m; Glenn 8544 (MO) 2003, Morris County, NJ: 40.788333000000002, -74.725555999999997, 100 m; Fink s.n. (MO) n.d., Tompkins County, NY: 42.541167999999999, -76.604442000000006, 1000 m; Radford 13891 (COLO) 1956, Alexander County, NC: 35.832678000000001, -81.277012999999997, 2250 m; Clark 8583 (MOR) 1966, Ashe County, NC: 36.391022, -81.571539999999999, 2200 m; Lee 52 (MOR) 1978, Clay County, NC: 35.078971000000003, -83.608418, 5600 m; Godfrey 51479 (MICH) 1951, Clay County, NC: 35.082098000000002, -83.601423999999994, 1200 m; Godfrey 51479 (RM) 1951, Clay County, NC: 35.082814999999997, -83.604890999999995, 1000 m; Noell 4 (MO) 1936, Haywood County, NC: 35.49456, -83.115549999999999, 6000 m; Reed 17302 (MO) 1949, Adams County, OH: 38.772773999999998, -83.401255000000006, 500 m; Wilson 1510 (WIS) 1928, Lucus County, OH: 41.694705999999996, -83.679382000000004, 600 m; s.n. (COLO) 1896, Richland County, OH: 40.758389999999999, -82.515446999999995, 6500 m; Dreisbach 1852 (MICH) 1923, Bucks County, PA: 40.061036000000001, -74.963645999999997, 1200 m; Montgomery s.n. (DBG) 1959, Clinton County, PA: 41.343223999999999, -77.693916000000002, 5500 m; Park 321 (USFS) 1935, Forest County, PA: 41.466585000000002, -79.304738999999998, 1000 m; Reed 29712 (MO) 1952, Fulton County, PA: 39.732183999999997, -78.365151999999995, 1000 m; Eby s.n. (MO) 1895, Lancaster County, PA: 40.039264000000003, -76.430796000000001, 1000 m; Thomas 2962 (WIS) 1982, Lawrence County, PA: 41.098424000000001, -
73


80.153520999999998, 1800 m; Moldenke 27232 (WIS) 1973, Perry County, PA: 40.427863000000002, -77.011269999999996, 2000 m; Davis s.n. (MO) 1920, Anderson County, SC: 34.503439, -
82.650132999999997, 2000 m; Davis 1708 (NEB) 1920, Anderson County, SC: 34.503439, -
82.650132999999997, 6300 m; Bell 10382 (MICH) 1957, Spartanburg County, SC: 35.036973000000003, -81.849509999999995, 2500 m; Krai 58659 (MO) 1976, Cumberland County, TN: 36.027931000000002, -84.809256000000005, 2250 m; Krai 40460 (MO) 1970, Fentress County, TN: 36.353513999999997, -84.727975999999998, 500 m; Channell V52 (MO) 1961, White County, TN: 35.838565000000003, -85.329831999999996, 5000 m; Curtiss 1749? (MO) 1871, Bedford County, VA: 37.315145000000001, -79.524180000000001, 30000 m; Fosberg 18420 (MO) 1941, Fairfax County, VA: 38.857604000000002, -77.208915000000005, 1000 m; Seymour 24610 (MO) 1966, Fairfax County, VA: 38.907494999999997, -77.322153999999998, 350 m; Bartsch s.n. (MO) 1911, Fauquier County, VA: 39.005346000000003, -77.951702999999995, 1500 m; Fosberg 23608 (MO) 1945, Page County, VA: 38.555672000000001, -78.401880000000006, 300 m; Reed 100424 (MO) 1975, County, VA: 38.004897999999997, -79.536017999999999, 1000 m; Steele 170 (MO) 1901, County, VA: 38.598216000000001, -78.373231000000004, 100 m; Berkley 1812 (MO) 1930, Mercer County, WV: 37.422341000000003, -81.016199, 2000 m; Berkley 1135 (MO) 1931, Summers County, WV: 37.677778000000004, -80.696708000000001, 500 m; Berkley 633 (MO) 1930, Tyler County, WV: 39.471265000000002, -80.871486000000004, 1500 m; Tans s.n. (WIS) 1971, Ashland County, Wl: 47.035212000000001, -90.431008000000006, 5400 m; Beals AP177 (WIS) 1957, Bayfield County, Wl: 46.982374999999998, -90.949211000000005, 2700 m; Smith 7572 (WIS) 1922, Crawford County, Wl: 43.051650000000002, -91.141239999999996, 3800 m; Fassett 22558 (WIS) 1938, Door County, Wl: 45.064993000000001, -87.124274, 3000 m; Cochrane 10454 (WIS) 1984, Door County, Wl: 45.091287000000001, -87.052394000000007, 1000 m; Leitner 1413 (MPM) 1988, Fond Du Lac County, Wl: 43.611853000000004, -88.173475999999994, 440 m; Rickett 1476-A (MO) 1937, Juneau County, Wl: 43.756872000000001, -89.854844, 14000 m; litis 9603 (WIS) 1957, Lafayette County, Wl: 42.520088000000001, -90.381395999999995, 2300 m; Seymour 14693 (WIS) 1952, Lincoln County, Wl: 45.147222999999997, -89.608750999999998, 2000 m; Lapham s.n. (WIS) n.d., Milwaukee County, Wl: 43.038902, -87.906474000000003, 20000 m; McIntosh c-619 (MPM) 1939, Oconto County, Wl: 44.935792399999997, -88.600862199999995, 5000 m; Leitner 1834 (MPM) 1988, Ozaukee County, Wl: 43.439137000000002, -88.023702, 250 m; Phillips 273 (WIS) 1973, Portage County, Wl: 44.506872000000001, -89.589911999999998, 2000 m; Davis s.n. (WIS) 1878, Racine County, Wl: 42.726131000000002, -87.782852000000005, 7000 m; Musselman 1497 (WIS) 1967, Rock County, Wl: 42.558838000000002, -89.046379000000002, 800 m; Smith 8103 (WIS) 1922, Sauk County, Wl: 43.241101, -89.81429, 1300 m; Kruschke s.n. (MPM) 1940, Sheboygan County, Wl: 43.638164000000003, -87.990476999999998, 1200 m; Cook C127 (MPM) 1938, Sheboygan County, Wl: 43.862772, -87.956760000000003, 1000 m; Potzger 8714 (WIS) 1940, Vilas County, Wl: 46.047311999999998, -89.652437000000006, 4200 m; Umbach 32736 (WIS) 1909, Walworth County, Wl: 42.572794000000002, -88.533989000000005, 3000 m; Smith 207 (WIS) 1928, Waupaca County, Wl: 44.272480999999999, -88.769831999999994, 2300 m; Cochrane 7597 (WIS) 1976, Waupaca County,
Wl: 44.299442999999997, -88.778969000000004, 650 m. category 3: Ohlendorf s.n. (MO) 1884, Cook County, IL: 41.830810999999997, -87.817858000000001, 2000 m; Mills s.n. (NEB) 1944, La Salle County, IL: 41.319974000000002, -89.000326999999999, 4500 m; Deam 34148 (IND) 1921, Franklin County, IN: 39.334643, -85.278807, 800 m; Deam 31721 (IND) 1920, Jasper County, IN: 41.070585000000001, -87.168296999999995, 1300 m; Deam 32973 (IND) 1920, Knox County, IN: 38.443593999999997, -
74


87.729434999999995, 1500 m; Wilhelm 20692 (MOR) 1992, Lake County, IN: 41.600433000000002, -87.446682999999993, 1200 m; Wilhelm 6351 (MOR) 1978, LaPorte County, IN: 41.697066, -86.858654999999999, 2300 m; Deam 36583 (IND) 1922, Pulaski County, IN: 40.975011000000002, -86.497416999999999, 1000 m; Deam 54054 (IND) 1933, Tippecanoe County, IN: 40.390725000000003, -87.079543999999999, 3700 m; Deam 60096B (IND) 1940, Warren County, KY: 37.084538999999999, -86.561284999999998, 2000 m; Greenman 2976 (MO) 1896, Middlesex County, MA: 42.375096999999997, -71.111444000000006, 4000 m; Schipper 1064 (MICH) 2017, Allegan County, Ml: 42.552132, -85.977841999999995, 250 m; Appel 421 (MICH) 1980, Antrim County, Ml: 44.963673999999997, -85.007225000000005, 250 m; Gates 10710 (MO) 1917, Emmet County, Ml: 45.552128000000003, -84.797244000000006, 1500 m; Parmelee 3339 (MICH) 1953, Genesee County, Ml: 43.102836000000003, -83.858123000000006, 4000 m; Kolar UP-6 (MOR) 1978, Gogebic County, Ml: 46.708826000000002, -89.972723999999999, 250 m; Voss 7461 (MICH) 1958, Hillsdale County, Ml: 41.837985000000003, -84.520407000000006, 4000 m; Bailey 5340 (MICH) 1959, Keweenaw County, Ml: 47.939416999999999, -89.167422000000002, 1500 m; Voss 12775 (MICH) 1968, Mackinac County, Ml: 45.860317999999999, -84.870988999999994, 950 m; Voss 13691 (MICH) 1971, Ontonagon County, Ml: 46.805669999999999, -89.823226000000005, 2000 m; Darlington s.n. (MICH) 1923, Ontonagon County, Ml: 46.806024000000001, -89.761407000000005, 13000 m; Billington s.n. (MICH) 1919, Washtenaw County, Ml: 42.225822999999998, -83.592067, 5000 m; Steyermark 23140 (MO) 1937, Christian County, MO: 36.901497999999997, -93.094337999999993, 1500 m; Cusick 3640 (RM) 1910, County, Oregon: 45.710396000000003, -118.352138, 15000 m; Berkley 1429 (MO) 1930, Pocahontas County, WV: 38.222493, -80.092134000000001, 1500 m; Stahmann 120 (WIS) 1973, Adams County, Wl: 44.028387000000002, -89.951789000000005, 27000 m; Peters 90 (WIS) 1958, Columbia County, Wl: 43.306984999999997, -89.706198000000001, 1700 m; Palmer 28786 (MO) 1925, Door County, Wl: 45.284863999999999, -87.050081000000006, 1000 m; Davis s.n. (WIS) 1913, Door County, Wl: 45.356650000000002, -86.930673999999996, 3000 m; Leitner 1524 (WIS) 1988, Fond du Lac County,
Wl: 43.612217999999999, -88.172873999999993, 2300 m; Tutton s.n. (WIS) 1959, Jefferson County, Wl: 42.877788000000002, -88.586212000000003, 2300 m; Schlising 133 (WIS) 1952, Lincoln County, Wl: 45.147222999999997, -89.608750999999998, 2000 m; 739 (MO) 1875, Milwaukee County, Wl: 43.034134000000002, -87.909221000000002, 7000 m; Schlising 988 (WIS) 1959, Oconto County, Wl: 45.157913000000001, -88.466119000000006, 1400 m; Cutler 433 (WIS) 1935, Ozaukee County, Wl: 43.385947999999999, -88.009292000000002, 2600 m; Leitner 1957 (WIS) 1988, Ozaukee County, Wl: 43.435496999999998, -88.018451999999996, 500 m; Wills s.n. (WIS) 1957, Richland County, Wl: 43.423037000000001, -90.242903999999996, 2300 m; Cook C127 (MICH) 1938, Sheboygan County, Wl: 43.862772, -87.956760000000003, 3000 m; Wilson 3036 (WIS) 1932, Vilas County, Wl: 46.054389, -89.655257000000006, 3200 m.
75


APPENDIX C
Histogram of follicle pubescence in Physocarpus opulifolius (L.) Maxim. s.I. (Rosaceae)
350
(O
C
£
'u
Q.
(O
Si
£
3
300
250
200
150
100
50
0
56.4% <
C?
21.7% C?
117
13.9%
75 3% 5% 16 27 &
Glabrous or sutural pubescence (categories 1 & 2)
Sutural and surface pubescence (category 3)
Uniformly pubescent (category 4)
Follicle pubescence category
Figure S2. Statistical distribution of follicle pubescence categories in the Physocarpus opulifolius (L.) Maxim. s.I. (Rosaceae) complex, where category 1 = follicles glabrous, category 2 = ventral sutures of follicles sparsely pubescent or glabrescent, category 3 = ventral sutures of follicles pubescent with perisutural pubescence on abaxial surface, and category 4 = abaxial surface of follicles uniformly pubescent. The subset of specimens that could not be easily distinguished between categories 3 and 4 is identified as "Transitional”.
76


APPENDIX D
R script for bioclimatic variables, pairwise Pearson's correlations (Figs. S4-S6) and PCA
The following script was run with R (version 3.5.2, R Core Team, 2018) in RStudio (version 1.1.463; RStudio Team, 2016) after downloading bioclimatic data from WorldClim.org.
#1 - Load the necessary R libraries. library("maptools", lib.loc="~/R/win-library/3.5") library("raster", lib.loc="~/R/win-library/3.5") library("sp", lib.loc="~/R/win-library/3.5") library("labdsv", lib.loc="~/R/win-library/3.5") library("ggbiplot", lib.loc="~/R/win-library/3.5") library("tidyverse", lib.loc="~/R/win-library/3.5") library("dplyr", lib.loc="~/R/win-library/3.5") library("spThin", lib.loc="~/R/win-library/3.5")
#2 - Set the working directory to the directory that contains the bioclimatic variables. setwd("C:/Users/Cyn/Desktop/Audrey/Thesis/Modeling/Raw_files_2.5min/centralNA")
#3 - Set the study extent. e<-extent(-125,-62,23,53)
#4 - Load current-climate rasters into R, crop to the study extent, and re-save the cropped files.
biol <- raster("wc2.0_bio_2.5m_01.tif) bio2 <- raster("wc2.0_bio_2.5m_02.tif) bio3 <- raster("wc2.0_bio_2.5m_03.tif) bio4 <- raster("wc2.0_bio_2.5m_04.tif) bio5 <- raster("wc2.0_bio_2.5m_05.tif) bio6 <- raster("wc2.0_bio_2.5m_06.tif) bio7 <- raster("wc2.0_bio_2.5m_07.tif") bio8 <- raster("wc2.0_bio_2.5m_08.tif) bio9 <- raster("wc2.0_bio_2.5m_09.tif) biolO <- raster("wc2.0_bio_2.5m_10.tif) bioll <- raster("wc2.0_bio_2.5m_ll.tif) biol2 <- raster("wc2.0_bio_2.5m_12.tif) biol3 <- raster("wc2.0_bio_2.5m_13.tif) biol4 <- raster("wc2.0_bio_2.5m_14.tif) biol5 <- raster("wc2.0_bio_2.5m_15.tif) biol6 <- raster("wc2.0_bio_2.5m_16.tif) biol7 <- raster("wc2.0_bio_2.5m_17.tif)
biol8 <- raster("wc2.0_bio_2.5m_18.tif")
biol9 <- raster("wc2.0_bio_2.5m_19.tif)
biolc <- crop(biol, extent(e)) bio2c <- crop(bio2, extent(e)) bio3c <- crop(bio3, extent(e)) bio4c <- crop(bio4, extent(e)) bio5c <- crop(bio5, extent(e)) bio6c <- crop(bio6, extent(e)) bio7c <- crop(bio7, extent(e)) bio8c <- crop(bio8, extent(e)) bio9c <- crop(bio9, extent(e)) biolOc <- crop(biolO, extent(e)) biollc <- crop(bioll, extent(e)) biol2c <- crop(biol2, extent(e)) biol3c <- crop(biol3, extent(e)) biol4c <- crop(biol4, extent(e)) biol5c <- crop(biol5, extent(e)) biol6c <- crop(biol6, extent(e)) biol7c <- crop(biol7, extent(e)) biol8c <- crop(biol8, extent(e)) biol9c <- crop(biol9, extent(e))
77


writeRaster(biolc, "biolc.asc", format="ascii") writeRaster(bio2c, "bio2c.asc", format="ascii") writeRaster(bio3c, "bio3c.asc", format="ascii") writeRaster(bio4c, "bio4c.asc", format="ascii") writeRaster(bio5c, "bio5c.asc", format="ascii") writeRaster(bio6c, "bio6c.asc", format="ascii") writeRaster(bio7c, "bio7c.asc", format="ascii") writeRaster(bio8c, "bio8c.asc", format="ascii") writeRaster(bio9c, "bio9c.asc", format="ascii") writeRaster(biolOc, "biolOc.asc", format="ascii") writeRaster(biollc, "biollc.asc", format="ascii") writeRaster(biol2c, "biol2c.asc", format="ascii") writeRaster(biol3c, "biol3c.asc", format="ascii") writeRaster(biol4c, "biol4c.asc", format="ascii") writeRaster(biol5c, "biol5c.asc", format="ascii") writeRaster(biol6c, "biol6c.asc", format="ascii") writeRaster(biol7c, "biol7c.asc", format="ascii") writeRaster(biol8c, "biol8c.asc", format="ascii") writeRaster(biol9c, "biol9c.asc", format="ascii")
#5 - Repeat for mid-Holocene and LGM climate data.
biomidlc <- crop(biomidl, extent(e)) biomid2c <- crop(biomid2, extent(e)) biomid3c <- crop(biomid3, extent(e)) biomid4c <- crop(biomid4, extent(e)) biomid5c <- crop(biomid5, extent(e)) biomid6c <- crop(biomid6, extent(e)) biomid7c <- crop(biomid7, extent(e)) biomid8c <- crop(biomid8, extent(e)) biomid9c <- crop(biomid9, extent(e)) biomidlOc <- crop (biomidi 0, extent(e)) biomidllc <- crop(biomidll, extent(e)) biomidl2c <- crop(biomidl2, extent(e)) biomidl3c <- crop(biomidl3, extent(e)) biomidl4c <- crop(biomidl4, extent(e)) biomidl5c <- crop (biomidi 5, extent(e)) biomidl6c <- crop(biomidl6, extent(e)) biomidl7c <- crop(biomidl7, extent(e)) biomidl8c <- crop(biomidl8, extent(e)) biomidl9c <- crop(biomidl9, extent(e))
biomidl <- raster("ccmidbil.tif) biomid2 <- raster("ccmidbi2.tif) biomid3 <- raster("ccmidbi3.tif) biomid4 <- raster("ccmidbi4.tif) biomid5 <- raster("ccmidbi5.tif) biomid6 <- raster("ccmidbi6.tif) biomid7 <- raster("ccmidbi7.tif) biomid8 <- raster("ccmidbi8.tif) biomid9 <- raster("ccmidbi9.tif) biomidlO <- raster("ccmidbil0.tif) biomidl 1 <- raster("ccmidbill.tif) biomidl2 <- raster("ccmidbil2.tif) biomidl3 <- raster("ccmidbil3.tif) biomidl4 <- raster("ccmidbil4.tif) biomidl5 <- raster("ccmidbil5.tif) biomidl6 <- raster("ccmidbil6.tif) biomidl7 <- raster("ccmidbil7.tif) biomidl8 <- raster("ccmidbil8.tif) biomidl9 <- raster("ccmidbil9.tif)
writeRaster(biomidlc, "biomidlc.asc", format="ascii") writeRaster(biomid2c, "biomid2c.asc", format="ascii") writeRaster(biomid3c, "biomid3c.asc", format="ascii") writeRaster(biomid4c, "biomid4c.asc", format="ascii")
78


writeRaster(biomid5c, "biomid5c.asc", format="ascii") writeRaster(biomid6c, "biomid6c.asc", format="ascii") writeRaster(biomid7c, "biomid7c.asc", format="ascii") writeRaster(biomid8c, "biomid8c.asc", format="ascii") writeRaster(biomid9c, "biomid9c.asc", format="ascii") write Raster (biomidl Oc, "biomidl Oc.asc", format="ascii") write Raster(biomidllc, "biomidl lc.asc", format="ascii") writeRaster(biomidl2c, "biomidl2c.asc", format="ascii") writeRaster(biomidl3c, "biomidl3c.asc", format="ascii") writeRaster(biomidl4c, "biomidl4c.asc", format="ascii") writeRaster(biomidl5c, "biomidl5c.asc", format="ascii") write Raster (biomidl 6c, "biomidl6c.asc", format="ascii") writeRaster(biomidl7c, "biomidl7c.asc", format="ascii") write Raster (biomidl 8c, "biomidl 8c.asc", format="ascii") writeRaster(biomidl9c, "biomidl9c.asc", format="ascii")
bioLGMl <- raster("cclgmbil.tif) bioLGM2 <- raster("cclgmbi2.tif) bioLGM3 <- raster("cclgmbi3.tif) bioLGM4 <- raster("cclgmbi4.tif) bioLGM5 <- raster("cclgmbi5.tif) bioLGM6 <- raster("cclgmbi6.tif) bioLGM7 <- raster("cclgmbi7.tif) bioLGM8 <- raster("cclgmbi8.tif) bioLGM9 <- raster("cclgmbi9.tif) bioLGMIO <- raster("cclgmbil0.tif) bioLGMll <- raster("cclgmbill.tif") bioLGM12 <- raster("cclgmbil2.tif) bioLGM13 <- raster("cclgmbil3.tif) bioLGM14 <- raster("cclgmbil4.tif) bioLGM15 <- raster("cclgmbil5.tif) bioLGM16 <- raster("cclgmbil6.tif) bioLGM17 <- raster("cclgmbil7.tif) bioLGM18 <- raster("cclgmbil8.tif) bioLGM19 <- raster("cclgmbil9.tif)
bioLGMlc <- crop(bioLGMl, extent(e)) bioLGM2c <- crop(bioLGM2, extent(e)) bioLGM3c <- crop(bioLGM3, extent(e)) bioLGM4c <- crop(bioLGM4, extent(e)) bioLGM5c <- crop(bioLGM5, extent(e)) bioLGM6c <- crop(bioLGM6, extent(e)) bioLGM7c <- crop(bioLGM7, extent(e)) bioLGM8c <- crop(bioLGM8, extent(e)) bioLGM9c <- crop(bioLGM9, extent(e)) bioLGMIOc <- crop (bioLGMIO, extent(e)) bioLGMllc <- crop (bioLGMll, extent(e)) bioLGM12c <- crop(bioLGM12, extent(e)) bioLGM13c <- crop(bioLGM13, extent(e)) bioLGM14c <- crop(bioLGM14, extent(e)) bioLGM15c <- crop(bioLGM15, extent(e)) bioLGM16c <- crop(bioLGM16, extent(e)) bioLGM17c <- crop(bioLGM17, extent(e)) bioLGM18c <- crop(bioLGM18, extent(e)) bioLGM19c <- crop(bioLGM19, extent(e))
writeRaster(bioLGMlc, "bioLGMlc.asc", format="ascii") writeRaster(bioLGM2c, "bioLGM2c.asc", format="ascii") writeRaster(bioLGM3c, "bioLGM3c.asc", format="ascii") writeRaster(bioLGM4c, "bioLGM4c.asc", format="ascii") writeRaster(bioLGM5c, "bioLGM5c.asc", format="ascii") writeRaster(bioLGM6c, "bioLGM6c.asc", format="ascii") writeRaster(bioLGM7c, "bioLGM7c.asc", format="ascii") writeRaster(bioLGM8c, "bioLGM8c.asc", format="ascii") writeRaster(bioLGM9c, "bioLGM9c.asc", format="ascii") writeRaster(bioLGM10c, "bioLGMIOc.asc", format="ascii")
79


writeRaster(bioLGMllc, "bioLGMllc.asc", format="ascii") writeRaster(bioLGM12c, "bioLGM12c.asc", format="ascii") writeRaster(bioLGM13c, "bioLGM13c.asc", format="ascii") writeRaster(bioLGM14c, "bioLGM14c.asc", format="ascii") writeRaster(bioLGM15c, "bioLGM15c.asc", format="ascii") writeRaster(bioLGM16c, "bioLGM16c.asc", format="ascii") writeRaster(bioLGM17c, "bioLGM17c.asc", format="ascii") writeRaster(bioLGM18c, "bioLGM18c.asc", format="ascii") writeRaster(bioLGM19c, "bioLGM19c.asc", format="ascii")
#6 - Download SRTM 90-meter (30-second) altitude data getData('alt', country='USA', mask=TRUE)
USA <- raster("USAl_msk_alt.grd") getData('alt', country='CAN', mask=TRUE)
CAN <- raster("CAN_msk_alt.grd") getData('alt', country='MEX', mask=TRUE)
MEX <- raster("MEX_msk_altgrd") alt <- merge(USA,CAN,MEX)
#7 - Increase the spatial resolution of the altitude layer to match that of the bioclimatic layers. alt2.5m <- aggregate (alt, fact=5)
#8 - Crop the altitude layer to the study extent and save. alt2.5m <- crop(alt2.5m, extent(e)) writeRaster(alt2.5m, "alt2.5m.asc", format="ascii")
#9 - Extract values of environmental variables at presence points and create correlation matrix for each taxon:
file <- read.csv(file="C:/Users/Cyn/Desktop/Audrey/Thesis/Modeling/P.monogynus.csv", header=TRUE)
monogynus_points <- file[,2:3]
predictors <- stack(biolc, bio2c, bio3c, bio4c, bio5c, bio6c, bio7c, bio8c, bio9c, biolOc, biollc, biol2c, biol3c, biol4c, biol5c, biol6c, biol7c, biol8c, biol9c, alt) presvals <- raster::extract(predictors, monogynus_points, df=TRUE) cor <- cor(presvals)
write.csv(cor, "correlationallvariablesjnonogynus.csv")
file <- read.csv(file="C:/Users/Cyn/Desktop/Audrey/Thesis/Modeling/P.intermedius.csv", header=TRUE)
intermedius_points <- file[,2:3]
predictors <- stack(biolc, bio2c, bio3c, bio4c, bio5c, bio6c, bio7c, bio8c, bio9c, biolOc, biollc, biol2c, biol3c, biol4c, biol5c, biol6c, biol7c, biol8c, biol9c, alt) presvals <- raster::extractoredictors, intermedius_points, df=TRUE) cor <- cor(presvals)
write.csv(cor, "correlationallvariablesjntermedius.csv")
80


file <- read.csv(file="C:/Users/Cyn/Desktop/Audrey/Thesis/Modeling/P.opulifolius.csv",
header=TRUE)
opulifolius_points <- file[,2:3]
predictors <- stack(biolc, bio2c, bio3c, bio4c, bio5c, bio6c, bio7c, bio8c, bio9c, biolOc, biollc, biol2c, biol3c, biol4c, biol5c, biol6c, biol7c, biol8c, biol9c, alt) presvals <- raster: :extract(predictors, opulifolius_points, df=TRUE) cor <- cor(presvals)
write.csv(cor, "correlationallvariables_opulifolius.csv")
#10 - PCA: first, create a samples-with-data (SWD) .csv file for all occurrence points combined, occurrences <- read.csv(file=
"C:/Users/Cyn/Desktop/Audrey/Thesis/Modeling/occurrences_all.csv", header=TRUE) occurrences_points <- occurrences[,2:3]
predictors <- stack(biolc, bio2c, bio3c, bio4c, bio5c, bio6c, bio7c, bio8c, bio9c, biolOc, biollc, biol2c, biol3c, biol4c, biol5c, biol6c, biol7c, biol8c, biol9c, alt) occurrences_presvals <- raster::extract(predictors, occurrences_points, df=TRUE) occurrences <- tibble::rowid_to_column(occurrences, "ID")
SWD_all <- full_join(occurrences,occurrences_presvals,by="ID") colnames(SWD_all)[2] <- "species" write.csv(SWD_all, file=" SWD_all.csv")
#11 - PCA: second, run PCA and create a biplot with ellipses for each taxon ("groups”). SWD_all$species <- as.factor(SWD_all$species)
occurrences.pca <- prcomp(occurrences[,c(4:23)], center = TRUE,scale. = TRUE) ggbiplot(occurrences.pca,ellipse=TRUE,groups=occurrences$species) print(occurrences.pca, digits = 3, cutoff = 0.2, sort = TRUE)
#12 - Thin the occurrences for each taxon in preparation for niche modeling.
monogynus <- read.csv(file="C:/Users/Cyn/Desktop/Audrey/Thesis/Modeling/monogynus.csv",
header=TRUE)
thin(monogynus, latcol = "LAT", long.col = "LONG", spec.col = "species", 20,10, locs.thinned.listreturn = FALSE, write.files = TRUE, max.files = 5,
"C:/Users/Cyn/Desktop/Audrey/Thesis/Modeling/thinned", outbase = "mono", write.log.file = TRUE, log.file = "thin_log.txt", verbose = FALSE)
intermedius <- read.csv(file="C:/Users/Cyn/Desktop/Audrey/Thesis/Modeling/intermedius.csv", header=TRUE)
thin(intermedius, latcol = "LAT", long.col = "LONG", spec.col = "species", 20,10, locs.thinned.listreturn = FALSE, write.files = TRUE, max.files = 5,
"C:/Users/Cyn/Desktop/Audrey/Thesis/Modeling/thinned", outbase = "inter", write.log.file = TRUE, log.file = "thin_log.txt", verbose = FALSE)
opulifolius <- read.csv(file="C:/Users/Cyn/Desktop/Audrey/Thesis/Modeling/opulifolius.csv", header=TRUE)
thin(opulifolius, latcol = "LAT", long.col = "LONG", spec.col = "species", 20,10, locs.thinned.listreturn = FALSE, write.files = TRUE, max.files = 5,
"C:/Users/Cyn/Desktop/Audrey/Thesis/Modeling/thinned", outbase = "opul", write.log.file = TRUE, log.file = "thin_log.txt", verbose = FALSE)
81


APPENDIX E
Pearson's correlation coefficients used in variable selection process
Figure S3. Pearson’s correlation matrix for Physocarpus monogynus (Torr.) Coult. (Rosaceae).
82


un in un un
10 (Jl (Jl oo
.y .y .y .y .y .y .y .y .y o
'o 'o 'o 'o 'o 'o 'o 'o 'o 'o ~z.
Figure S4. Pearson’s correlation matrix for Physocarpus intermedius (Rydb.) Schneid. (Rosaceae).
83


Figure S5. Pearson’s correlation matrix for Physocarpus opulifolius (L.) Maxim. (Rosaceae).
84


APPENDIX F
Maxent Response Curves
Physocarpus
intermedius
Rasponsa of intarmadius to bioSc
ho5t
Response of intermedius to bio14c
bio14c
Physocarpus
opulifolius
Response of opulifolius to biolc
b i o 1 c
Response of opulifolius to bio17c
biol7c
Figure S6. Maxent response curves for the top two highest-contributing variables for each taxon. Physocarpus monogynus (Torr.) Coult.: altitude and Bio9 (Mean Temperature of Driest Quarter); P. intermedius (Rydb.) Schneid.: Bio5 (Maximum Temperature ofthe Warmest Month) andBiol4 (Precipitation of the Driest Month); P. opulifolius (L.) Maxim.: Biol (Mean Annual Temperature) and Biol7 (Precipitation of the Driest Quarter).
85


APPENDIX G
Occurrences of examined Physocarpus opulifolius (L.) Maxim. s.I. (Rosaceae) specimens
versus all occurrences available on iDigBio
• Vetted occurrences
• iDigBio occurrences Elevation (m)
r -171 â–  4275
Figure S7. Geographic distribution of Physocarpus opulifolius (L.) Maxim. s.I. (Rosaceae) occurrences for which species identification was confirmed via physical examination, versus all occurrences available for download via Integrated Digitized Biocollections (iDigBio; idigbio.org). Although the overall range was represented, there are gaps (e.g, western Pennsylvania, St. Lawrence River valley) that need to be addressed in any future research.
86


Full Text

PAGE 1

i HISTORICAL PHYTOGEOGRAPHY OF FRONT RANGE PHYSOCARPUS (ROSACEAE) by AUDREY DIGNAN B.S., University of Richmond, 2011 A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requiremen ts for the degree of Master of Science Environmental Science s Program 2019

PAGE 2

ii This thesis for the Master of Science degree by Audrey Dignan has been approved for the Environmental Sciences Program by Christy Briles, C hair Leo P. Bruederle, Adviso r Peter Anthamatten Date: May 18, 2019

PAGE 3

iii Dignan, Audrey (M.S., Environmental Sciences Program ) Historical Phytogeography of Front Range Physocarpus (Rosaceae) Thesis directed by Professor Emeritus Leo P. Bruederle ABSTRACT Species distribution patterns t oday provide insight into historical drivers like climatic conditions. Glacial interglacial fluctuations throughout the Pleistocene have resulted in multiple re organizations of vegetation communities and complex bio geographic histories of many North Ameri can plants, including Physocarpus (Cambess.) Raf. (Rosaceae), an Arcto Ter ti ary relict. These changes have resulted in broad disjunctions within distributions , as well as distributional overlap among taxa with disparate biogeographic histories. An example of these phenomena can be observed in the isolated populations of eastern North American plants the so called eastern woodland prairie flora which co occur with the Cordilleran flora of the S outhern Rock ies i n the Front Range of Colorado. Here, I inve stigate the historical phytogeography of a member of the eastern woodland prairie element, Physocarpus opulifolius (L.) Maxim sensu lato and its Cordilleran congener, P. monogynus (Torr.) Coult. First, I clarif y the taxonomy of the P. opulifolius s.l. spec ies complex . To do this, I assess variation for follicle pubescence, a diagnostic character for the complex , with respect to geographic distribution . This approach serves to reveal cryptic variation across the full range of the complex and to reduce taxono mic uncertainty in occurrence data derived from herbarium specimens . Second, I examine the historical distribution s of P. opulifolius s.l. and P. monogynus by modeling their climatic niches during the Last Glacial Maximum (ca. 21,000 yrs ago), mid Holocene (ca. 6,000 yrs ago), and today . These models suggest that modern distribution s of the study taxa likely result from a complex history of multiple biogeographic events . The form and content of this abstract are approved. I recommend its publication. Approv ed: Leo P. Bruederle

PAGE 4

iv ACKNOWLEDGEMENTS support and guidance, as well as his assistance with examining many of the specimens included in this study . Thank you to Christy Briles for gui ding me on this thesis journey from the beginning . Thank s to Peter Anthamatten for his invaluable cartographic support . This study was funded by the Colorado Mountain Club Foundation ; many thanks to Paula Cushing and the grant review board. Thank you to Me lissa Islam and the Kathryn Kalmbach Herbarium at the Denver Botanic Gardens for providing workspace and equipment and for housing a large loan of s pecimens from the Missouri Botanical Garden ; many thanks to Dr. James Solomon for providing the MO loan, and to all personnel from the remaining nine herbaria for providing access to specimens and digitized specimen data . To Ryan, thank you for providing balance, light, encouragement, and tea . Finally, Mom and Dad : thank you for showing me the beauty of nature from a young age and for providing enthusiastic guidance while letting me find my own winding way.

PAGE 5

v TABLE OF CONTENTS CHAPTER I. INTRODUCTION ................................ ................................ ................................ ................................ ......................... 1 North American Paleoclimate and Vegetation ................................ ................................ ............................ 1 Environment and the Flora of Colorado ................................ ................................ ................................ ......... 2 Physocarpus ................................ ................................ ................................ ................................ ................................ .. 5 Objectives ................................ ................................ ................................ ................................ ................................ ...... 7 II. THE GEOGRAPHY OF FOLLICLE PU BESCENCE IN PHYSOCARPUS OPULIFOLIUS S.L. , WITH IMPLICATIONS FOR TA XONOMY ................................ ................................ ................................ ...... 10 Introduction ................................ ................................ ................................ ................................ .............................. 10 Materials and Methods ................................ ................................ ................................ ................................ ......... 13 Results and Discussion ................................ ................................ ................................ ................................ ......... 15 Conclusions ................................ ................................ ................................ ................................ ................................ 16 III. CLIMATIC NICHE MODELING OF FRONT RANGE PHYSOCARPUS WITH IMPLICATIONS FOR BIOGEOGRAPHIC ORIGINS OF COLORADO FLORA ................................ ................................ ...... 21 Introduction ................................ ................................ ................................ ................................ .............................. 21 Methods ................................ ................................ ................................ ................................ ................................ ....... 23 Results ................................ ................................ ................................ ................................ ................................ .......... 27 Discussion ................................ ................................ ................................ ................................ ................................ .. 30 IV. CONCLUSIONS ................................ ................................ ................................ ................................ .......................... 46 Taxonomy of Physocarpus opulifolius s.l. ................................ ................................ ................................ ..... 46

PAGE 6

vi Contributions to the Flora of Colorado ................................ ................................ ................................ ........ 47 Limitations ................................ ................................ ................................ ................................ ................................ . 48 Future Directions ................................ ................................ ................................ ................................ .................... 50 REFERENCES ................................ ................................ ................................ ................................ ................................ ............. 52 APPENDI CES A ................................ ................................ ................................ ................................ ................................ ...................... 60 B ................................ ................................ ................................ ................................ ................................ ...................... 62 C ................................ ................................ ................................ ................................ ................................ ....................... 76 D ................................ ................................ ................................ ................................ ................................ ...................... 77 E ................................ ................................ ................................ ................................ ................................ ...................... 82 F ................................ ................................ ................................ ................................ ................................ ....................... 85 G ................................ ................................ ................................ ................................ ................................ ...................... 86

PAGE 7

vii LIS T OF TABLES TABLE 3.1 . Candidate variables for climatic niche modeling. ................................ ................................ ............................... 34 3 .2 . Climatic variables used to train niche models ................................ ................................ ................................ ...... 38 3.3 . Model validation for niche models. ................................ ................................ ................................ ............................ 43 3.4 . PCA covariates matrix ................................ ................................ ................................ ................................ ...................... 45

PAGE 8

viii LIST OF FIGURES FIGURE 1.1. Estimated extent o f glaciation in North America during the Last Glacial Maximu m ............................ 5 1.2. EPA Level III Ecoregions of Colorado ................................ ................................ ................................ ......................... 6 2.1. Categories of follicle pubescence in Physocarpus opulifolius s.l . ................................ ................................ . 18 2.2. Geographic distribution of follicle pubescence in Physocarpus opuli folius s.l . ................................ ..... 19 2. 3. Prevalence of follicle pubescence in Physocarpus opulifoli us s.l. by EPA Ecoregion ......................... 2 0 3.1. Eastern woodland prairie and Cordilleran floristic element s ................................ ................................ .... 32 3.2. Inputs and model construction with Maxent ................................ ................................ ................................ ....... 33 3.3. Study extent ................................ ................................ ................................ ................................ ................................ ......... 35 3.4. E ffect of background selection on preliminary niche models of Physocarpus monogynus ............ 36 3.5. Occurrence dataset used to train climatic niche models ................................ ................................ ............... 37 3.6. Maxent niche models of occurrence probability trained on current climat e ................................ ....... 39 3 .7. Hindcast Maxent ni che models for Physocarpus opulifolius ( L .) Maxim. ................................ ................ 42 3 . 8 . Hindcast Maxent niche models for Physocarpus monogynus (Torr.) Coult . ................................ .......... 40 3 . 9 . Hindcast Maxent niche models for Physocarpus intermedius ( Rydb .) Schneid. ................................ .. 41 3.10. PCA biplot of variance in climati c conditions among occurrence data ................................ ................ 44

PAGE 9

1 CHAPTER I INTRODUCTION Species distributions are intricately linked to underlying drivers , including biotic and abiotic factors that may be ongoing or historical . Modern distribution patterns are indicative of pas t conditions, while e stimations of past distributions provide insight into the biogeographical origins of modern patterns and assemblages . Past distributions and drivers can be used to understand how contemporary distribution patterns might have developed . Disjunctions, in particular, have long been of interest to biogeographers, evolutionary biologists, and conserva tionists (e.g., theory of island biogeography) . Further, i ncreasing our understanding of current distributions through estimations of those pas t helps inform our understanding of future patterns (Fitzpatrick et al., 2018) . North American Paleoclimate and Vegetation Last Glacial Maximum. During the LGM, t he presence of continental ice sheets across much of North America (Fig. 1.1) influenced regio nal atmospheric circulation patterns and paleoclimate conditions . The elevated surface of the two mile thick ice mass divert ed upper level atmospheric flow , splitting the polar jet stream (Bartlein et al., 1998) . The strong southern arm of the jet stream flow ed along the southern ice margin, across the southern Great Lakes and southern New Engl and region s , while a weaker northern branch flowed north of the ice sheet (Bartlein et al., 2011). C old d ense air and h igh pressure developed over the ice sheet , wh ile a low pressure zone developed over the warmer ice free landmass to the south . Th e temperature differential created a steep pressure gradient that resulted in strong clockwise (anticyclone ) surface winds and prevailing cold d ry easterly winds across the continental interior (Schaetzl et al., 2016 ). Further , the southward shift of subtropical highs and moist maritime s torms further contribut ed to the cold and dry conditions in the continental interior during the LGM (Bartlein et al., 1998 ; Pausata et al., 2011 ; Schaetzl et a l. , 2016 ).

PAGE 10

2 The LGM v egetation patterns across unglaciated regions of eastern North America generally organized into broad latitudinal bands . C old dry sedge dominated communities occupied a relatively narrow strip along the south of the ice margin in the s outhern Great Lakes and southern New England region s. Boreal forest occurred farther south, while temperate deciduous forest was restricted south of northern Georgia. To the west, c ool dry Picea parkland community occurred along the sou thern ice margin (e. g., northern Montana ; Po wer et al., 2011; Yansa, 2006). Mid Holocene. Climatic shifts during the Holocene were largely driven by changes in incoming solar radiation ( insolation ) ; seasonality increased t hrough t he mid Holocene as s ummer insolation reached a maximum and winter inso lation a minimum after the LGM (Bartlein et al., 2011) . This resulted in significant ice melt and consequently t he glacial anticyclone diminished as the ice sheet s re treated , resulting in diminished cool dry wind s into the continent al interior. In addition, the warming c ontinent facilitated the flow of atmospheric moisture from the Pacific Ocean and the Gulf of Mexico into the continental interior . Th is led to the development of the current North American monsoona l regime (Bartlein e t al. , 1998) . Late glacial to post glac ial changes in vegetation in eastern North America largely followed changes in climat e , generally shifting northward tracking glacial retreat. During the early Holocene, oaks invaded the boreal spr uce fores ts near the former glacial margin (Delc ourt & Delcourt, 1993). Warm, moist air masses flowing into the eastern continental interior from the Gulf of Mexico supported the northward expansion of the temperate eastern deciduous forest community from southern glacial refugia. During the insolation maximum of the middle Holocene (ca. 9000 4000 yr B.P. ), the temperate prairie reached its easternmost extent into Illinois due to warm, dry Pacific air masses that penetrated far into the eastern continenta l interior (Delcourt & Delcourt, 1993). Environment and the Flora of Colorado Physiography and c limate . The diverse plant communities of Colorado result from complex topography and highly variable climat ic conditions , as well as a history of natural and h u man

PAGE 11

3 disturbance. Colorado has a continental climate, with warm summers and cool winters. One of t he most notable aspect s of the climate is the extreme variation in precipitation and temperature , which often occur over r elatively short temporal and spatial scales (Mutel & Emerick, 1992). Colorado straddles a transition between the Great Plains to the east and the Cold Deserts of the Great Basin to the west. Four primary ecoregions comprise this transition: High Pla ins, Southwestern Tablelands, Sout h ern Rockies, and Colorado Plateaus. In addition to these, smaller portions of two additional ecoregions the Arizona/New Mexico Plateau and the Wyoming Basin extend into the state from the south and northwest, respecti vely (Fig. 1. 2 ; EPA, 2013). High P lains . The High Plains ecoregion represents the highest elevation and westernmost extent of the Great Plains . This shortgrass prairie ecosystem is characterized by a cooler and drier climate than the mixed grass and tallgrass prairies to the east. T his ec o region occurs in the eastern and northeastern regions of Colorado, and comprise s semi arid flat to moderate relief plains with rolling sand sage prairie along river valleys (EPA, 2013) . Southwestern T ablelands. The Southwestern Tablelands are a vast semi a rid landscape of dissected plains and tablelands that cover the southwestern portion of the state . The terrain is more variable with greater relief than the High Plains to the north . As a result, clim ates tend to range from hot and dry in the central port i on of the ecoregion , between Pueblo and Las Animas, to cool and moist at higher elevations a long the base of the foothills to the west (EPA, 2013) . The vegetation is primarily shortgrass prairie , and pine oak woodlands interspersed with grasslands occur a l ong the Palmer Divide , which separat es the Arkansas River basin to the south from the South Platte River basin to the north. Pinyon juniper woodlands occur at higher elevation s in the foothills in the south west ern portion of the ecoregion (EPA, 2013) . So u thern Rockies. T he Southern Rockies ecoregion, the largest of the six, bisect s the stat e . T he Continental Divide ic and hydrological regime s ;

PAGE 12

4 prevailing westerlies carrying moisture from the Pacific rele a se precipitation as they pass over the mountains , creat ing a semi arid rain shadow to the east. T his ecoregion is characterized by long, cold, snowy winters ( EPA, 2013 ) . Pleistocene g laciation in a lpine and subalpine zones creat ed rugged mountain s with la r ge elevational gradients (EPA, 2013) . Colorado Plateaus . To the west of the Southern Rockies lie s the Colorado Plateau , characterized by semi arid rolling plains and basins interspersed with high relief features ( e.g., mesas, cliffs, arches, and canyons ) . Closed canopy forest is rare in this ecoregion; the vegetation here comprises sparse desert shrubland and sagebrush steppe . Semi arid w oodland communities , typically characterized as pinyon juniper or scrub oak woodland, can be found at warm dry sites in the foothills (EPA, 2013 ) . Arizona/New Mexico Plateau . A small portion of the Arizona/New Mexico Plateau ecoregion extends north into Colorado, comprising the San Luis Valley . The Colorado portion of the ecoregion is bounded by the Sangre de Cristo Mounta i ns to the east, La Garita Mountains to the northwest, and San Juan Mountains to the west. The lowest mean annual precipitation levels in the state ( 6 8 inches per year ) occur in the Salt Flats of central San Luis Valley, where vegetation is very sparse du e to alkaline soils (EPA, 2013) . In the alluvial flats and wetlands of the Rio Grande watershed to the west , mountain runoff and a high water table have provided a water supply suitable for irrigated cropland. As a result, m ost of the natural shrubland veg e tation has been replaced by irrigated agriculture (EPA, 2013). Wyoming Basin . The southernmost portion of the Wyoming Basin ecoregion extends into northwestern Colorado . Th is vast landscape comprises rolling s agebrush s teppe among plains, alluvial fans , and mesas , and arid s alt d esert shrubland interspersed with playas and sand dunes . Flora. The complex topography and highly variable climatic conditions have developed a mosaic of diverse plant communities and overlapping floristic elements , or groups of s pecies with similar distribution patterns ( Weber & Wittmann, 2011) . Within Colorado lies t he western limit of

PAGE 13

5 the Great Plains, northern limit of the Southwestern Tablelands and Arizona/New Mexico Plateaus, eastern limit of the Colorado Plateau, and south e rn limit of the Wyoming Basi n . As a result, t he Colorado flora includes contributions from surrounding regions, including the Great Basin, Rio Grande Valley, Chihuahuan D esert, Northern Rockies, and Great Plains (Weber & Wittmann, 2011). The Southern Rock i es have acted btoh as a physical barrier to westward dispersal for many temperate mesic pl ants (e.g., Physocarpus intermedius (Rydb.) Schneid.; Chapter III, this paper) , as well as a dispersal corridor, allowing arctic species to disperse far to the south of their core ranges in Canada ( e.g., Juncus biglumis L. [ Juncaceae ] ; Marr et al., 2012; Mutel & Emerick, 1992) . Species associated with the Andes and mountains of Central America have also been found in the Colorado Rockies, suggesting that the Rockies m a y have facilitated northward dispersal , as well (Weber & Wittmann, 2011) . Physocarpus Physocarpus (Cambess.) Raf. (Rosaceae) is a genus of deciduous shrubs comprising 6 20 species ( Alexander, 2014; Cuizhi & Alexander, 2003 ; Oh , 2015 ) . It is one of two g e nera in the Neillieae Maxim. ( Maximovich , 1879; Oh 2015 ) , and is differentiated from the second genus Neillia D. Don by the presence of stellate trichomes and corymbose inflorescences (Oh, 2015). Several Physocarpus species are commonly cultivated, in c luding P. capitatus (Pursh) Kuntze and P. opulifolius (L.) Maxim. (Newhall, 1891 ) ; the latter is known to escape from cultivation and has become naturalized in parts of Europe ( Alexander, 2014) . Arcto Tertiary E lement. Like many other genera in the Rosace a e (Potter et al., 2007) , Physocarpus occupies an intercontinental disjunction , occurring predominantly in North America with at least one species in eastern Asia ( P. amurensis [ Maxim. ] Maxim. ; Cuizhi & Alexander, 2003; Oh, 2015). This distribution pattern i s indicative of an Arcto Tertiary Geoflora ( Wen et al., 2010 ) . The paleoflora record suggests that temperate forest extended across the Bering land bridge , which connected the two continents and facilitated migration during the mid Tertiary (Thorne, 1993 ;

PAGE 14

6 Wen et al., 2010). As the climate became colder and drier through the late Tertiary, temperate species became restricted to lower latitudes of eastern Asia and North America and related taxa consequently became isolated on separate continent s (Axelrod & R aven, 1985; Weber 1965 ; Wen et al., 2010 ). The age of the most recent common ancestor (MRCA) of Neillieae was approximately early Miocene (~20.6 ± 0.4 mya; Oh & Potter, 2005) . This period has been shown to be important for the development of the eastern A s ian North American intercontinental disjunction in other taxa (Wen et al., 2010). During this time, the MRCA of Neillieae likely occupied a widespread distribution between the two continents via the Bering land bridge (Oh & Potter, 2005) . Subsequently, Ph y socarpus is thought to have undergone speciation in western North America (Oh & Potter, 2005). F ossil evidence . T here is limited evidence of Physocarpus in the paleo flora record. A total of four pollen grains identified as Physocarpus have been reported t o the Neotoma database ( neotomadb.org ; Goring et al., 2015 ) , including two from Frying Pan Lake in Utah (Shafer, 1989) , one from Crane Lake in Arizona (Shafer, 1989) , and one from Massachusetts (Oswald et al., 2018) . However, ther e may be some uncertain ty regarding the identification of these pollen grains due to the difficulty of identifying Rosaceae pollen below the family leve l ( C Briles , personal comm.) . Several macrofossils have also been reported. Physocarpus leaves dating to ca. 3 million years ago ( mya ) were found in the Canadian Arctic Archipelago at three Beaufort Formation sites (Banks Island, Prince Patrick Island, and Meighen Island) and o ne site on Ellesmere Island (Matthews and Ovenden, 1990). Physocarpus leaves were a lso discovered in the middle Eocene chert in Republic, WA (Wehr and Hopkins, 1994), but they lack the stellate trichomes that are diagnostic in extant species of Physocarpus (Oh and Potter, 2005). In addition, these fossilized leaves pre date the MRCA

PAGE 15

7 of t he Neillieae by about 20 25 million years, so they are unlikely to be true Physocarpus leaves (Oh and Potter, 2005). Objectives The research presented in the following chapters aims to clarify the taxonomy and recent evolutionary history of the P. opulifo l ius s ensu l ato complex and to shed light on the biogeographic origins of the Colorado flora. In the following chapters, I will r efer to this taxon at the specific level, following the taxonomic treatment of Alexander (2014) in Flora of North America, Nort h of Mexico (FNA). Chapter II will present a taxonomic review of the P. opulifolius s.l. complex . Specifically, I aim to clarify the understanding and the geographic distribution of follicle pubescence, a key diagnostic trait for discriminating subspecific variation in P. opulifolius s.l. Chapter III builds on those findings and examines the historical phytogeography of P. opulifolius s.s . and P. intermedius , as well as a third congener, P. monogynus (Torr.) Coult . Findings from this chapter shed light on t h e origins of the Colorado flora , specifically the western disjunction of the so called eastern woo dland prairie elemen t and the Cordilleran element. These findings also have broader implications for understanding glacial refugia . The conclusions from both chapters will be summarized , synthesized, and discussed in Chapter IV.

PAGE 16

8 Figure 1.1 . Estimated extent of glaciation in North America during the Last Glacial Maximum (est. 21,000 years before present). Ice extent determined by Dyke (2004 ) and made availa b le by the Geological Survey of Canada ( https://github.com/awickert/North American Ice Sheets ).

PAGE 17

9 Figure 1.2 . EPA Level III Ecoregions of Colorado (EPA, 2013) .

PAGE 18

10 CHAPTER II THE GEOGR A PHY OF FOLLICLE PUBESCENCE IN PHYSOCARPUS OPULIFOLIUS S.L. , WITH IMPLICATIONS FOR TAXONOMY Introduction Precise t axonomic identification is a critical prerequisite for ecological, taxonom ic , and systematics research (e.g., niche modeling, floristics, phy l ogenetics). Despite this, taxonomic revisions are common and disagreement among taxonomists is frequent. Furthermore, phenotypic or genotypic variation that may be undetected and uncaptured ( hereafter called cryptic variation can confound our understan d ing of systematic relationships (e.g., Mastin et al., 2018), as well as many other aspects of the biology of taxa (e.g., sexual reproduction, Menz et al., 2015; conservation, Duarte et al., 2014). C ryptic variation has long confounded the taxonomy of Phy s ocarpus (Cambess.) Raf. (Rosaceae). V ariable vegetative morphology (e.g., leaf shape) , inconsistent interpretation of morphological traits (e.g., follicle pubescence) , and geographically res tricted taxonomic treatments have all contributed to taxonomic co n fusion, particularly in eastern North America. C onflicting circumscriptions predominate in the P. opulifolius (L.) Maxim. sensu lato species complex. Morphological variation within this complex has been variously recognized as P. intermedius (Rydb.) Schne i d., P. opulifolius var. intermedius (Rydb.) B.L. Rob. , or as a not e under P. opulifolius (e.g., Gleason and Cronquist, 1991; McGregor et al., 1986). As a result, P . intermedi us has been variously recognized at the level of species or variety , or has been e xcluded entirely. This lack of clarity continues today; t he most recent floristic keys for Colorado either do not recognize the P. opulifolius s.l. complex (Ackerfield, 2015) or they attribute the morphological variation to local hybridization with P. mon o gynus (Torr.) Coult. (Weber & Wittmann, 2011). This issue has clouded un derstanding of the distribution of P. opulifolius s.l. complex across its range , which extends from the n ortheast ern US to the southern Appalachians and west to the eastern Great Plai n s, with

PAGE 19

11 disjunctions in the Sand Hills of Nebraska, the Black Hills of S outh Dakota, and the Front Range of Colorado. Although some specimens have been collected from the northwestern US, P. opulifolius s.l. does not occur west of the Rocky Mountains . Ryd b erg ( 1908b ) Pacific coast in the range of O. opulifolius glabrat e form of O. capitatus (Pursh) Kuntze . Physocarpus opulifolius and P. intermedius have undergone many taxonomic and nomenclatural changes at both t he genus and species level, result ing in a complex history. Car o l us Linnaeus publi shed the basionym Spiraea opulifolia L. in Species Plantarum (1753) . Jacques Cambessèdes later subdivided Sp iraea , assigning solely S. opulifolia to Spiraea L. subsect. Phys o carp u s Cambess. ( as Physocarpos ; Cambessèdes , 1824 ) . Constantine Samuel Rafinesque (1838) spelled it Physocarpa . Although a uthorship is given to Cambessèdes and Rafinesque, the first to publish the conserved spelling Physocarpus was Karl Maximovich (1879) . spelling was later retained by Camillo Karl Schneider (1906) in his publication o f P. intermedius . In 1865, George Bentham and Joseph Dalton Hooker moved S. opulifolia to Neillia D. Don ( N. opulifolia Benth. & Hook .) . M aximovich then moved i t to Physocarpus (as P. opulifolia ; Maximovich , 1879) . In Volume I of his Revisio Generum Plantarum ( 1891a) , Otto Kuntze originally referred to it as P. opulifolius , following but he later moved the species to Opulaster Medik. ex Ku n tze in Volume II ( Opulaster opulifolius [ L. ] Kuntze ; 1891b ), recognizing that Opulaster antedated Physocarpus (Oh, 2015) . Although a nomen nudum today, Opulaster was widely used in reference to several Physocarpus species for another 20 30 years (Oh, 2015 ) . Per Axel Rydberg was the first to publish the specific epithet intermedius , assigning it to Opulaster ( O. intermedius Ry db.; 1901) . Shortly thereafter, it was moved to Physocarpus by Schneider ( P. intermedius [Rydb.] Schneid.; 1906). Finally, in 1908 , B e njamin Lincoln Robinson published the combination P. opulifolius (L.) Maxim. var. intermedius (Rydb .) B.L. Rob. At least 14 synonyms for the taxa comprising the P. opulifolius s.l. Species Plan t arum , and P. opulifolius has been placed in four different genera: Spiraea , Neillia , Opulaster , and Physocarpus (Appendix A) .

PAGE 20

12 Several characters have been used to differentiate between P. opulifolius and P. intermedius , including carpel number and leaf pu b escence . The most useful diagnostic character , however, has been pubescence of mature follicles. Alexander (2014) recently summarized his observations on pubescence in both carpels and mature follicles , and used this trait to discriminate between P. opuli f olius and P. intermedius . In his treatment, he described the car pels and follicles of the former glabrous or sparsely stellate hairy, glabrescent densely stellate hairy (sometimes only on sutures) Geographic v ariation in this trait has been noted by several eminent botanists. In Flora of the Black Hills of South Dakota (1896) , for example, Rydberg commented on the morphological difference between specimens identified as O. opulifolius from Colorado and northern Nebraska in comparison to those from the eastern US . He described the western specimens as having permanently pubescent follicles , while the eastern plants had glabrous follicles (Rydberg, 1896). Five years l ater, Rydberg as cribed t h e pubescent western form to O. intermedius , contrasting it with O. opulifolius (L.) Kuntze to which he ascribed glabrous follicles ( Rydberg, 1901 ) . Aven Nelson (1902) also not ed the difference between Rocky Mountain and eastern Un i ted States O. opulifolius specimens . Shortly after Rydberg (1901) published O. intermedius , Nelson (1902) described a new species O. Ramaleyi Nelson which bears close ption of O. intermedius . In a footnote, he stated, h is is O. opulifolius probably, in so far as Rocky mountain specimens have been so named . It is not the O. opulifolius Schneider (1906) also described P. intermedius as being immediately distinguishable from P. op u lifolius by the pubescent fruits . In 1907, Francis Potter Daniels described two new Physocarpus species from the Midwest one with stellate pubescent follicles ( P. missouriensis Daniels) and one with glabrous follicles ( P. michiganensis Daniels) albeit reluctantly: Physocarpus

PAGE 21

13 are described as new species, but t he limitation of P. opulifolius (L.) Maxim. by recent authorities to the more glabrate forms, seems to leave no other recourse. Desp i te this lack of clarity and the presumed importance of this character , few treatments have critically examined variation a cross the range of the complex . Most treatments are regional or local in scale and, thus, lack detailed understanding of morphologica l or genetic variation across the complex (Oh, 2015). At the genus level, this limited scope and lack of clarity has also r esulted in the description of numerous species, rendering taxonomic relationships unclear and discrimination among taxa challenging ( O h, 2015). Here, I critically assess ed follicle pubescence across the range of the P. opulifolius s.l. , species complex with the goal of clarifying this trait and its natural distribution in North America. T his will contribute toward clarify ing the taxono m y of this species complex, which has significance for local and regional floras. The following questions were addressed: To what extent do P. opulifolius s ensu stricto and P. intermedius differ, in terms of the geographic distribution of follicle pubescen c e? Does this variation merit taxonomic recognition? To achieve this goal, I examined follicle morphology of P. opulifolius s.l . herbarium specimens from across the geographic range of the species complex . Subsequently , I examined the spatial distribution o f the follicle pubescence trait to assess potential geographic patterns in m orphological variation . Materials and Methods Reproductive morphology. T o assess variation for follicle pubescence across the natural range of P. opulifolius s.l . , I created four c ategories that qualitatively capture the range in variation (Fig. 2.1 ): 1 = follicles glabrous, 2 = ventral sutures of follicles sparsely pubescent or glabrescent, 3 = ventral sutures of follicles pubescent with perisutural pubescence on abaxial surface, a nd 4 = abaxial surface of follicles uniformly densely pubescent. Foll icle pubescence was assessed in over 580 fruiting specimens from 11 herbaria that were physically examined by m e or thesis advisor LP Bruederle (Appendix B) . Each accession was assigned t o one of the morphological categories

PAGE 22

14 outlined above (Fig. 2.1). The following herbaria were selected in order to maximize geographic coverage while minim izing travel and inter herbarium loans (abbreviations follow Thiers, 2018): Kathryn Kalmbach Herbariu m of Vascular Plants, Denver Botanic Gardens (KHD); University of Colorado Boulder (COLO); Colorado State University (CS); Rocky Mountain Herbarium, University of Wyoming (RM); Missouri Botanical Garden (MO); Indiana University (IND); University of Michiga n (MICH); University of Wisconsin Madison (WIS); University of N ebraska Lincoln (N D EB); Morton Arboretum (MOR) ; and Milwaukee Public Museum (MIL) . Material that appeared to be cultivated , as determined by information included on specimen labels (e.g., c ollected from college campuses, botanic gardens or arboreta ) , was excluded, as P. opulifolius has been widely cultivated since 1891 ( Newhall, 1891 ) and is known to escape from cultivation (Alexander, 2014) . Duplicate accessions were also excluded . The ori g inal dataset included several specimens from the northwestern US ; however, these were likely cultivated or misidentified and were therefore omitted . Digitized occurrence records were downloaded from SEINet (swbiodiversity.org) and the Consortium of Midwe s t Herbaria (midwestherbaria.org). Data on specimen sheets (e. g., original labels , annotation labels) were verified or entered manually into a project spreadsheet . These data will be returned to the herbaria listed above at the end of the project to suppor t digitization of the accessions examined . Geographic distribution. A ccessions that did not include geographic coordinates on the original label were georeferenced using GeoLocate ( geo locate.org; Rios & Bart, 2010) or Earth Point ( earthpoint.us ), when lab e l information was adequate for doing so. Although many digitized herbarium specimens were georeferenced previously, these data are often problematic (e.g., comput erized batch processing has been shown to result in inflated estimates of spatial accuracy ; B l oom et al., 201 8 ; Smith et al., 2016). Finally, a ccessions were mapped in QGIS 3.4.2 (QGIS

PAGE 23

15 Development Team, 2019) according to pubescence morphology to determine if there was a geographical component to the distribution of this character. Results and Di s cussion Reproductive morphology. Although P. opulifolius has been described as having glabrous follicles, very few accessions were observed with fruits that were truly glabrous . Upon closer examination, sutural stellate hairs were found on many accessions that were initially placed in category 1, which render ed discrimination between categories 1 and 2 challenging. F urther, s everal accessions could not be definitively placed in either category 3 or 4 . T hese formed a gradient of pubescence morphology , rangi n g from very spars ely pubescen t on perisutural abaxial surface to densely pubescent on perisutural abaxial surface and extending across much of the abaxial surface. This transitional group may indicate phenotypic plasticity or hybridization and warrants fu r ther examination. Overall, f ollicle p ubescence displayed two primary forms: a) follicle s glabrous to pubescent along ventral suture s , and b) follicles uniformly and densely pubescent across the entire abaxial surface (Appendix C) . Geographic distribution . The final dataset comprised 526 georeferenced accessions distributed across 29 US states , two Canadian provinces, and one Mexican state (Fig. 2. 2 ). Physocarpus opulifolius s.l. is more or less continuously distributed across the Appa la chian Mountain s and G reat Lakes south to the h ighland forests of the Ozark and Ouachita Mountains , with d isjunct ions in the Sand Hills of Nebraska, the Black Hills of South Dakota, and the Front Range of Colorado (Fig. 2. 2 ) . Within the complex, a spatial pattern with respect to follicle pubescence can be observed (Fig. 2. 2 ). T he two primary morphotypes described above 1) glabrous to pubescent along ventral sutures and 2) uniformly and densely pubescent generally occur in different regions of the US with overlap in the wes t ern Great Lakes region . The glabrous morphotype occupies an eastern distribution and occur s in eastern temperate forests from the southern Appalachians north to the

PAGE 24

16 Atlantic Highlands of New England and west to the mixed woods plains of the Great Lakes (F i g s . 2. 2 , 2.3 ) . T he pubescent morphotype , on the other hand, occupies a more western distribution . A lthough broadly sympatric with glabrous and glabrescent forms in the Upper Midwest, it occurs most commonly and almost to the exclusion of the glabrous and g labrescent form s in the Interior Highlands of Missouri, Arkansas , Oklahoma, and Kansas and the Drif tless Area of Iowa, Minnesota, Wisconsin, and Illinois, with d isjunct occurrences in the Black Hills of South Dakota and the Front Range of Colorado , as wel l as the Sand Hills of Nebraska (Fig s . 2. 2 , 2.3 ) . The transitional accessions were collected from the southern Great Lakes area, a region of overlap between the more glabrous form and the more pubescent form (Fig. 2. 2 ). These accessions may therefore indic a te hybridization between the two forms or phenotypic plasticity of this trait . Con clusion s T he findings presented above support recognition of two taxa, as first suggested by Rydberg ( 1901 ). B ased on the pattern s observed in both the histogram (Appendix C ) and the geographic distribution of this trait (Fig. 2.2) , P. opulifolius and P. intermedius appear to be distinct taxa and thus merit taxonomic recognition (see Chapter IV for further discussion). Following original descriptions re c ent treatment for FNA ( 2014) , accessions with fruits in categories 1 through 3 were annotated as P. opulifolius , while those with category 4 fruits were annotated as P. intermedius . Follicle pubescence is a complicated character that has often been misint e rpreted in the literature . Cry ptic variation for this trait within the P. opulifolius s.l. complex has led to the misidentification of western disjunct populations , including those on the Front Range . A critical examination of historical taxonomy, reprodu c tive morphology, and geographic distribution reveal that the western populations are, in fact, P. intermedius . Further, P. opulifolius s.s. does not occur in Colorado. It should be noted that putativ e hybrids between P. intermedius and P. monogynus were o b served where the two are syntopic ( occur ring at the same point ) .

PAGE 25

17 These findings underscore the importance of considering taxa across the ir full range . Geographic restriction to a subset of the distribution might fail to capture the full range of morpholog i c al variation or phenotypic expression . As in the P. opulifolius s.l. complex, this practice perpetuates taxonomic uncertainty and conflicting treatments . A t worst, this could lead to misaligned conservation priorities (Duarte et al., 2014) . Taxonomic dis a greements between treatments are by no means limited to Physocarpus ; they can be quite common. The methodology presented here that is, mapping the variation of a can be applied to other taxa requiring similar clarification. Further, this approach can be used to reduce taxonomic uncertainty in species occur rence data , which has been shown to produce misleading results in downstream research approaches including niche modeling (Ensin g et al., 2013).

PAGE 26

18 Figure 2.1 . Categories of follicle pubescence in Physocarpus opulifolius (L.) Maxim. s.l. (Rosaceae) : a) category 1: follicles glabrous, b) category 2: ventral sutures of follicles sparsely pubescent, c) category 3: ventral sutures o f follicles pubescent with perisutural pubescence on abaxial surface, and d) category 4: abaxial surface of foll icles uniformly densely pubescent .

PAGE 27

19 Figure 2. 2. Geographic distribution of follicle pubescence in Physocarpus opulifolius (L.) Maxim. s.l. ( R osaceae) , where 1 = follicles glabrous, 2 = ventral sutures of follicles sparsely pubescent or glabrescent , 3 = ventral sutures of follicles pubescent with perisutural pubescence on abaxial surface, and 4 = abaxial surface of follicles uniformly densely p u bescent .

PAGE 28

20 Figure 2. 3. Prevalence of follicle pubescence in Physocarpus opulifolius s.l. (Rosaceae), by EPA Level III Ecoregion (EPA, 2013), where 1 = follicles glabrous, 2 = ventral sutures of follicles sparsely pubescent or glabrescent, 3 = ventral s utures of follicles pubescent with perisutural pubescence on abaxial surface, and 4 = abaxi al surface of follicles uniformly densely pubescent.

PAGE 29

21 CHAPTER III CLIMATIC NICHE MODELING OF FRONT RANGE PHYSOCARPUS WITH IMPLICATIONS FOR BIOGEOGRAPHIC ORIGINS OF C OLORADO FLORA Introduction Modern distribution patterns of plants have been shaped by past climat ic conditions. Throughout the Quaternary, g lacial interglacial fluctuations resulted in widespread re organizations of floristic communities (Delcourt and Del c ourt, 1993). Historical phytogeography examines spatial and tempor al patterns in the geographical distributions of plants and investigates the drivers of those patterns. One such influence is climate, which is linked to resource availability ( e.g., soil m o isture, sunlight) and mediat es not only which species can grow in a given location at a given time, but also how distributions change through time (Elith et al., 201 0 ) . Climate conditions thus provide a framework for investigating historical phytogeograph y . Further, c limatic niche is a n important and complex character that delimits the area in which a given plant species is likely to survive and reproduce (Franklin, 2010). Relationships between taxa can be explored by modeling the geographic distribution o f their climatic niche or clima tic envelope, the quantifiable set of climatic conditions that support survival (Franklin, 2010). Due to its complex topography, t he Southern Rocky Mountain region harbors a wide variety of plant communities . S teep environmen t al gradients created by local topography allow plant species with disparate biogeographic histories to co occur ; as a consequence , m ultiple floristic elements have assemble d in to a mosaic a long the Front Range of Colorado (Nelson, 2010; Weber, 1965; Weber and Wittmann, 201 1 ). Here, d isjunct populations of several eastern North American species , referred to by Weber (1965) as the eastern woodland prairie element, intermingle with the flora of the Southern Rocky Mountains , or the Cordilleran element ( Fig. 3. 1 ; Weber, 1965) . The eastern woodland prairie element include s temperate deciduous trees and shrubs (e.g., Corylus cornuta Marshall), eastern woodland herbs (e.g. Aralia nudicaulis L., Sanicula marilandica

PAGE 30

22 L.,), and tallgrass prairie species (e.g., Sporob o lus heterolepis [ A. Gray ] A. Gray, Hesperostipa spartea [ Trin. ] Barkworth) . They occur in protected mesic sites s uch as north facing slopes and cool ravines (Cooper, 1984; Hogan, 1989; Livingston, 1952; Nelson, 2010; Weber, 1965, 1976; Weber and Wittmann, 2011). The Front Range transitions abruptly out of the High Plains, particularly in Boulder county, where the landscape rises nearly 5,000 feet (1500 m) fr om grassland s to the alpine peaks of the Continental Divide over less than 20 miles (Mutel & Emerick , 1992). Th e steep gradient and topographic heterogeneity contribute to highly localized climatic conditions that are capable of satisfy ing the humidity and soil moisture requirements of mesic eastern woodland prairie plants (Cooper, 1984; Weber, 1965). T h e biogeogra phic origin of disjunct ions within the eastern woodland prairie distribution is unclear . T he prevailing hypothesis proposes that disjunct populations may be relicts of a widespread eastern temperate forest community that reach ed as far west as t he foothills of the Southern Rockies during the Last Glacial Maximum (Weber, 1965). During the Holocene, it was postulated that the forest retreated back towards the east , leaving relict populations in isolated refugia (Weber, 1965) . I ref er to this hypot h contraction hypothesis . While the eastern woodland prairie flora is dominated by mesic temperate species, t he Cordilleran flora is adapted to a relatively harsh continental climate characterized by short growing seasons, cold winte r s, and warm summers (Axelrod & Raven, 1985). The Cordilleran element is distributed across the Rocky Mountains, from British Columbia to northern New Mexi co (Fig. 3.1 ). This group includes conifers common to the Rocky Mountain s (e.g., Picea engelmannii Pa r ry ex Engelm. var. engelmannii , Pseudotsuga menziesii [ Mirbel ] Franco var. glauca [ Beissn. ] Franco, Pinus contorta Douglas ex Loud. var. latifolia Engelm. ) as well as several montane deciduous trees and shrubs (e.g., Populus angustifolia James; Axelrod & R aven, 1985; Weber, 1976). Physocarpus (Camb.) Raf. (Rosaceae) , a predominantly North American genus, includes two species that represent the Cordilleran and eastern woodland prairie floristic elements ,

PAGE 31

23 respectively: Physocarpus monogynus (Torr.) Coult. an d P. intermedius (Rydb. ) Schneid . The western disjunct populations of P. intermedius have been variously attributed to a broader complex, P. opulifolius (L.) Maxim. sensu lato . As discussed in the previous chapter , however, v ariable morphology and inconsis t ent taxonomic interpretations of P. opulifolius and P. intermedius have resulted in misidentifications and conflicting circumscriptions (cf. Ackerfield, 2015; Weber and Wittmann, 2011 ; see Chapter II for further discussion of taxonomy ). A critical examina t ion of a putative diagnostic trait ( i.e., follicle pubescence) reveals that western populations of P. opulifolius s.l. take the pubescent form and should be recognized as P. intermedius . Thus, P. intermedius not P. opulifolius s.s. is represent ative o f a predominantly eastern woodland prairie element in the Southern Rocky Moun tains . Here, I investigate the historical phytogeography of P. opulifolius s.l. and P. monogynus by comparing climatic niche models through time, including the LGM (22,000 yr BP) , mid Holocene (6,000 yr BP), and today. In order to understand the relationships between these taxa, I will address t he following questions: W h at is the biogeographical origin of sympatry involving the eastern woodland prairie and the Cordilleran floristi c elements along the Front Range? What regions acted as glacial refugia during the LGM? How have th e distributions of the study taxa shifted since the LGM? Furthermore, I interpret the distribution patterns predicted by niche n sion the eastern woodland prairie disjunction. To support this hypothesis, I would expect to see three spatial patterns in the modeled distribution of P. intermedius : 1) connection between eastern North America an d the Southern Rocky Mountains during the LGM, 2) overlap with P. monogynus during the LGM , and 3) development of a disjunct pattern after the LGM . Methods The objectives and research questions outline d above were approached using climatic niche modeling. T his approach quantifies niche by comparing climatic conditions at known occurrence

PAGE 32

24 points with conditions at background points generated by randomly sampling across the landscape of interest (Elith et al. , 2011; Merow et al. , 2013). The correlative model i s then applied to geogr aphic space to create a spatial model of continuous probability of presence (Fig. 3. 2 ). Occurrence records. Occurrence data were generated from georeferenced herbarium accessions from KHD , COLO , CS , RM , MO , IND , MICH , WI S, N D EB , MOR , and MIL ( abbreviations follow Thiers, 2018) . These herbaria were selected to maximize coverage o f the range of P. opulifolius s.l. and P. monogynus . The s patial accuracy of occurrence records was quantified using the coordinate uncertainty radius method ( Wieczorek et al., 2004) . The dataset compiled in the previous c hapter was refined by limiting the uncertainty radius to 2300 m or less, in order to match the resolution of the environmental la yers (approximately 4600 m at the equator ) . Furthermore, e nsuri n g spatial independence of presence points through spatial thinning has been shown to reduce model sensitivity to overfitting from using too many environmental variables as inputs (Kramer Schad t et al. , 2013). To minimize spatial autocorrelation and avoid a rtificial inflation of accuracy measures (Veloz, 2009) , presence points were spatially thinned by applying a 20 km nearest neighbor distance in R ( version 3. 5.2 , R Core Team, 2018 ) using the p ackage spThin ( v0.1.0 ; Aiello Lammens et al., 2014 ) . Environme n tal variables . A total of 20 variables were considered for niche modeling : 19 bioclimatic variables derived from monthly temperature and precipitation data , as well as altitude ( Table 3.1 ) . These variables are commonly used in niche model ing and are consi d ered biologically relevant summary data that represent annual climatic trends, seasonal variability, and measures of extreme conditions Bioclimatic variables were down loaded from Wo r ldClim (worldclim.org ; Hijmans et al., 2005 ) at a spatial resolution of 2.5 arc minutes (~4.6 km at the equator) for current and historical (LGM and mid Holocene) climates. Current climate data are derived from 30 year normals, averaged over 1970 2000 . H i s torical data derived from the Community Climate System Model (CCSM4; Gent et al.,

PAGE 33

25 2011) w ere downloaded for mid Holocene (6,000 yr BP) and LGM (22,000 yr BP) climatic conditions. CCSM4 has been used in similar studies to project paleodistributions of spec i es across large geographic ranges (Schorr et al., 2013; Waltari et al., 2007). The LGM and mid Holocene were chosen for hindcasting as they are important bench marks for evaluation of m odels involved in projects such as the Palaeoclimate Modelling Intercom p arison Project (PMIP ; Bartlein et al., 2011). Although additional time points would strengthen the analysis, I am not aware of other sources of hindcast climate raster products that ar e available at an appropriately fine spatial resolution (i.e., 2.5 arc m inutes or less) . Finally, a ltitude was derived from SRTM 90 meter digital elevation models (DEMs) downloaded and resampled to 2.5 arc minutes in R ( ver. 3.5.2 ). All v ariables were cropped in R (ver. 3.5.2) to encompass the full distributional range s of th e study taxa (23° to 53° N latitude, 62° to 125° W longitude; Fig. 3. 3 ; Appendix D ). To capture the different climatic niches occupied by the study taxa, e ach was considered separately in the following variable selection process. First, strongly c orrelat e coefficient r > | 0. 7 5 | ) were eliminated by extracting values of all 20 environmental variables at presence points and con struct ing a pairwise correlation matrix (Heikkinen et al., 2006) . T hose that were strongly correlat e d with at least three other s were eliminated (Appendix E ) . Exploratory n iche models were constructed with the remaining variables, and the relative variable contribution s and jackknife tests were used to guide selection of the final variable set. To exami n e variance in climatic conditions between occurrences, principal components analysis (PCA) was condu cted in R (ver. 3.5.2 ) using the ggbiplot package ( v0.55 ; Vu, 2011 ; Appendix D ). Niche models . Climatic niche models were constructed with Maxent (ver sion 3 .4.1; Phillips et al. , 2006). Maxent is a free and open source species distribution modeling program designed to predict suitable habitat using presence only data and , thus , is well suited for modeling plant distributions using occurrence records from her b arium specimens (Elith et al., 2011; Phillips et al., 2006). Maxent has been widely used in a variety of ecological, conservation, and evolutionary

PAGE 34

26 applications (Elith et al. , 2011; Phillips, 2008) , includ ing mapping current distributions for use in conse r vation planning (e.g., Tinoco et al. , 2009), hindcasting paleodistributions to investigate co ntemporary distributional patterns (e.g., Carnaval & Moritz, 2008), evaluating invasion risk (e.g., Peterson et al. , 2003), or predicting future response to clima t e change (e.g., Pérez Navarro et al. , 2018; Yates et al., 2010). Maxent was run with the and only hinge feature s , which simplif ies the underlying functions and avoids overfitting ( Elith et al. , 2011; Phillips & Dudi k , 2008) . Default settings were used for a ll other model parameters (e.g., regularization m ultiplier = 1 , maximum number of background points = 10000, maximum iterations = 500, prevalence = 0.5 ) . The body of literature that addresses background extent in M a xent niche models (e.g., Barve et al. , 2011; Elith et al. , 2010, 2011; Fourcade et al. , 2014; Merow et al. , 2013; Phillips, 2008; Phillips et al. , 2009 ; Yates et al. , 2010) emphasizes the importance of limiting background to an extent t hat is relevant to t he study system and to the questions being asked . However, p reliminary niche model s for P. monogynus indicated that the restriction of background to states in which occurrences were located resulted in models that failed to accurately predict the known di s tribution of the species (Fig. 3.4 ) . For this reason, background was not restricted in the final model procedure. To identify where suitable environments were likely to occur in the past, model s trained with current climate data were hindcast to mid Holoc e ne and LGM climate data. The three sets of models were compared to examine approximate distributional shifts through time. Model validation. M odels were 10 fold cross validated to assess statistical uncertainty in model fit (Elith et al. , 2011; Merow et a l . , 2013). To assess model performance, AUC is generally accepted in ecological modeling (Merow et al., 2013), but because it may be misleading or inappropriate for modeling with presence only data ( Jiménez Valverde, 201 2 ; Lobo et al., 2008), the true skill statistic (TSS) was also calculated . TSS is equal to sensitivity + specificity 1, where sensitivity is the proportion of all presences tha t are correctly predicted (absence of omission

PAGE 35

27 error) an d specificity is proportion of all absences that are correctly predicted ( absence of commission error) ( Allouche et al., 2006; Liu et al. , 2015 ; Phillips et al. , 2006 ) . While AUC is a threshold independent discrimination metric , TSS is threshold dependent (Allouche et al., 2006) . The threshold value used to calculate TSS was determined by maxim i zing the sum of sensitivity and specificity (max SSS) , which has been shown t o be an objective threshold selection method for presence only data (Liu et al. , 2015) . TSS was calculated for each of the 10 cross validated runs . Finally, the 10 TSS scores were averaged and reported as the mean TSS value for each study taxon. R esults Occurrence records . A total of 1,02 4 herbarium specimens were examined and categorized by follicle pubescence (Chapter II) . After refining the dataset by limiting the uncertainty radius and applying a spatial thin function, the final dataset comprised 470 occurrence points , with 129 P. opulifolius occurrences, 151 P. intermedius occurrences, a nd 39 P. monogynus occurrences (Fig. 3.5 , Appendix B ) . Environmental variables . A di fferent set of variables was used to model each study tax on , reflecting the differences between occupied climatic niche s . Of the 20 candidate variable s, seven were selected for P. intermedius models , while six were selected for P. opulifolius and P. monogynus models (Table 3.2 ) . Bio5 (maximum summer temperature, measured as maximum temperature of the warmest month) contributed most to the P. intermedius model (34.5% contribu tion), Bio17 (precipitation of the driest quarter) contributed most to the P. opulifoli us model (46.4%), and altitude had the highest percent contribution to the P. monogynus model (60.1%). Niche models. Both model validation metrics AUC and TSS indic ate d high levels of performance, with all three taxa scoring above 0.9 AUC and above 0.7 TSS (Table 3.3 ). Climatic niche models trained on contemporary climate normals can provide an estimation of current geographic distribution (Soberón & Peterson, 2005) . The current climate niche model for P. monogynus was aligned with its known distribution and , therefore , confirm ed that it is

PAGE 36

28 representative of the Cordilleran floristic element (Fig. 3.6 a ) . Likewise, t he current climate niche model for P. intermedius dis play ed the distribution pattern of the eastern woodland prairie element , occur ring primarily in the eastern temperate forests and temperate prairie woodlands of the east central US with disjunct populations along the easternmost ranges of th e Rockies, from the Black Hills to the Sierra Madre Oriental (Fig. 3.6b ). Physocarpus opulifolius s.s. , by comparison , is a n eastern temperate forest species : t he current climate model indicate d that it occurs in the forests of the Appalachian Mountains and coastal Great Lakes (Fig. 3.6 c ) . Further, models hindcast to past climatic conditions indicate d that it has not dispersed west of the Great Lakes region since the LGM . Historical niche models. Multivariate environmental similarity surfaces ( MESS ) were used to understa nd where model predictions were projected outside the training range (i.e., where no analog climate conditions were predicted to occur during the mid Holocene and LGM; Elith et al., 2010) . Across all three taxa, MESS indicate d that LGM climatic conditions were outside of the contemporary climate envelope, especially for the northern half of the study region. Only one model , the P. opulifolius mid Holocene model, predicted occurrence in a region of high uncertainty due to novelty. The mid Holocene model for P. opulifolius (Fig. 3. 7 , top ) predicted an isolated distribution in the western Sierra Madres. However, the MESS and MoD (most dissimilar variable; i.e., the variable with projections that were most outside the train ing range) indicated high levels of unc ertainty in that region due to climate novelty in Bio18 (precipitation of warmest quarter). LGM niche m odels predict ed southward displace ment compared to present for all three taxa . Specifically, P. opulifolius s.s. appears to have occupied an Atlantic Co ast glacial refugium (Fig. 3. 7 , bottom ) , which has also been recognized for other temperate deciduous species, including two species of hickory ( Carya cordiformis ( Wangenh. ) K.Koch and C. ovata ( Mill. ) K.Koch ; Bemmels & Dick, 2018 ) , American beech ( Fagus g ra n difolia Ehrh ; Morris et al., 2010 ) , and flowering dogwood ( Cornus florida L. ; Call et al., 201 6 ) . P hysocarpus monogynus and P. intermedius , on the other hand, we re predicted to have occupied southwestern US highlands and portions of the Sierra Madres in

PAGE 37

29 Mexico during the LGM (Fig . 3. 8 , 3. 9 ) , a region that was warmer and moister than the arid, windy continental interior . The modern disjunct ion in the distribution of P. intermedius with core range in the eastern US and isolated populations in the Front Range (CO), Black Hills (SD), and Sand Hills (NE) wa s not a feature of the predicted LGM distribution, but it wa s predicted for the mid Holocene (Fig . 3. 9 ). Thus, this disjunction appear ed to have become established between the LGM and mid Holocene . It r emain s unclear, however, whether the disjunction wa s the result of a vicariance event or recolonization via dispersal along two different routes nort h into the Southern Rockies and northeast to the Great Lakes region from a common southwestern US refug ium. Response curves . Maxent r esponse curves were used to understand t he relationship between the probability of occurrence and values of a given environmental variable ( Appendix F ) . P hysocarpus monogynus wa s most likely to occur at altitudes between appro ximately 1500 m (~5000 ft) and 3680 m (~12,000 ft) in regions where th e mean temperature of the driest quarter was be tween approximately 10 ° C and 20 ° C . Physocarpus intermedius wa s most likely to occur in regions where the maximum temperature of the warmes t month f ell between approximately 25 ° C and 33 ° C , and precipitation of the driest month f ell between approximately 10 mm and 60 mm. Physocarpus opulifolius wa s most likely to occur in regions where the annual mean temperature fell between approximately 3 ° C and 13 ° C and where precipitation of the driest q uarter wa s greater than approximately 70 mm . PCA. Th e first two principal components explain 74% of the variance in the occurrence data (Fig. 3.1 0 , 3.11 ) . PC1 is negatively correlated with Bio1 (Annual Mean Temperature), Bio12 (Annual Precipitation), Bio14 (Precipitation of the Driest Month), Bio17 (Precipitation of the Driest Quarter), and Bio19 (Precipitation of the Coldest Quarter) (Table 3.4 ). PC2 is negatively correlated with altitude, Bio2 (Mean Diurna l Temperature Range), and Bio 3 (Isothermality), and positively correlated with Bio4 (Temperature Seasonality) and Bio7 (Annual Temperature Range) (Table 3.4 ).

PAGE 38

30 PC1 can therefore be considered a measure of coldness and dryness ( climate minimums ), while PC2 c an be viewed as a measure of temperature variability with altitude. Discussion The biogeographical history of the intersection of the eastern woodland prairie floristic element and Cordilleran element was investigated by constructing c limatic niche models for three Physocarpus species that have at various times been attributed to one of the two floristic element s . The disjunct eastern woodland prairie distribution pattern is hypothesized to be the remnant of a Pleistocene distribution of eastern temperate f orest that reached its maximum extent during the LGM (Weber, 1965) . Specifically, Weber (1965) states that this distribution This period of expansion was purportedly foll owed by a subsequent period of constriction into eastern North America, leaving relictual populations in isolated refugial habitats (Weber, 1965) . Three spatial patterns in the distribution of P. intermedius contraction hypoth esis : LG M continuity, LGM overlap with P. monogynus , and post glacial isolation of western populations. Although the first pattern is not observed, the second and third patterns are observed . T he LGM P. intermedius model indicates low occurrence probabili t y throug hout the study area. This may be a result of the modeling process, or it may indicate that P. intermedius did not diversi fy until sometime after the LGM. Where LGM occurrence is predicted, however, it overlaps with that of P. monogynus . This inter action can be interpreted as the eastern taxon encountering the Cordilleran flora and provides support for the second spatial pattern . As for the third pattern , the distribution of P. intermedius appear s to have become disjunct sometime before the mid Holo cene . This comparison may suggest either a vicariance event resulting in disjunction or bimodal distribution trajectories north and east from a putative glacial refugium. The o supported nor refuted . Re gardless, the findings presented here shed light on the development of the eastern woodland prairie and

PAGE 39

31 Cordilleran flora of Colorado as well as the southwestern United States . T here is likely a more complex explanation for the bio geographic history of the P. opulifolius s.l . complex . This biogeographical hypothesis may have involv ed 1) southward dispersal from B eringia in response to early Pleistocene climat ic cool ing , 2) divergence into western and eastern components that then followed different post glac ial dispersal trajectories. The western component ( P. intermedius or ancestor ) appears to have occupied a glacial refugium in southwestern US highlands and the Sierra Madres of Mexico . As the continental ice sheets retreated and the climate warmed through the mid Holocene, this taxon appears to have followed two post glacial disper sal routes: a) north along the eastern flank of the Rocky Mountain s, and b) northeast into the Interior Highla nds and Great Lakes regions. Th e eastern component ( P. o pulifolius s. s . or ancestor ) likely dispersed north ward along the Appalachian highlands and we st into the Great Lakes region from an Atlantic Coast glacial refugiu m. Future directions. Further in sight may be gained from a focus on ecological rather than strictly climat ic niche models by includ ing variables that may be more directly influential on species survival, such as effective moisture ( e.g., topographic wetness index , evapotranspiration , vapor pressure deficit , snow persistence , etc. ) , canopy density ( e.g., based on distributional information included in taxonomic treatments, P. intermedius appears to occupy sites with less dense canopy cover) , or edaphic properties (Beauregard & de Blois, 2014) . Given the difficulty of hindcasting t hese variables, however, predict ions would be limited to current climate . Another recommendation for future re search is to take an ensemble modelling approach and repeat the procedure presented here with multiple species belonging to the eastern woodland prairie element. In addition, qua ntifying niche overlap between study taxa would address to what extent they oc cupy the same niche or similar niches. Finally, phylogenetic work at the species level may provide additional information regarding the historical biogeography and systematics of these taxa .

PAGE 40

32 Figure 3.1 . E astern woodland prairie and Cordilleran floristic elements . Representative species listed were compiled from Weber & Wittmann (2011) and Axelrod & Raven (1985). Occurrence data downloaded from SEINet ( swbiodiversity.org/seinet ). Eastern woodland prairie species : Physocarpus opulifolius (L.) Maxim. s.l. (Rosaceae) Corylus cornuta Marshall (Betulace ae) Aralia nudicaulis L. ( Araliaceae ) Sanicula marilandica L. ( Api aceae ) Sporobolus heterolepis (A. Gray) A. Gray (Poaceae) Hesperostipa spartea (Trin.) Barkworth (Poaceae) Cordill eran species : Picea engelmannii Parry ex. Engelm. var. engelmannii ( Pinacea e ) Pseudotsuga menziesii ( M irb.) Franco var. glauca (Beissn.) Fran co ( Pin aceae) Pinus contorta Douglas ex Loud. var. latifolia Engelm. (Pin aceae ) Physocarpus monogynus (Torr.) Coult. (Ros aceae ) Populus angustifolia James (Salicaceae)

PAGE 41

33 Figure 3. 2 . Inputs and model construction with Maxent . P redictor variables mapped across a landscape of interest (left); occurrence (presence) data , as well as randomly sampled background points (center left); probability distributions in environmenta l space (center right); and predictive model in geographic space (right). Maxent samples environmental layers at known species occurrences as well as randomly sampled background points. Figure modified from Elith et al., 2011.

PAGE 42

34 Table 3 .1 . Candidate variab les for climatic niche modeling of Physocarpus opulifolius (L.) Maxim. s.l. (Rosaceae) and P. monogynus (Torr.) Coul t. Modifie d from worldclim.org/bioclim (Hijmans et al . , 2005) . Name Variable Description Alt Altitude bio1 Annual Mean Temperature Overall energy inputs to the system bio2 Mean Diurnal Range Mean of monthly (maximum temperature minimum temperature)) bio3 Isothermality Mean diurnal temperature range (bio2)/an nual temperature range (bio7) * 100 Measure of diurnal temperature fluctuations relative to annual variation bio4 Temperature Seasonality Standard deviation * 100 bio5 Max Temperature of Warmest Month Measure of m aximum summer temperature bio6 Min Temp erature of Coldest Month Measure of minimum winter temperature bio7 Temperature Annual Range Maximum temperature of the warmest month minimum temperature of the coldest month bio8 Mean Temperature of Wettest Quarter bio9 Mean Temperature of Driest Qu arter bio10 Mean Temperature of Warmest Quarter Measure of mean summer temperature bio11 Mean Temperature of Coldest Quarter Measure of mean winter temperature bio12 Annual Precipitation Overall bio13 Precipitation of Wettest Month Maximum annual pre cipitation bio14 Precipitation of Driest Month Minimum annual precipitation bio15 Precipitation Seasonality Coefficient of variation bio16 Precipitation of Wettest Quarter bio17 Precipitation of Driest Quarter bio18 Precipitation of Warmest Quarter Summer precipitation bio19 Precipitation of Coldest Quarter Winter precipitation

PAGE 43

35 Figure 3. 3 . Study extent . x (longitude) maximum = 125 W , x minimum = 62 W , y maximum = 53 N, y minimum = 23N. The bounding box appears warped due to cartographic proje ction (North America Lambert Conformal Conic, EPSG: 102009).

PAGE 44

36 Figure 3.4 . E ffect of background selection on preliminary niche models of Physocarpus monogynus (Torr.) Coult. (Rosaceae) . Models were constructed by training on (a) background extent restric ted to states where it is known to occur, then projected to full study extent, and (b) background sampled across the full study extent (no projection is required in this case). Warmer colors indicate higher probabilities of presence. The known distribution of P. monogynus is shown in (c), where green indicates presence at county lev el (light green) and state level (dark green), yellow indicates locally rare populations, and brown indicates regions outside of the species distribution (BONAP , 201 4 ). (a) ( b ) ( c )

PAGE 45

37 Figu re 3. 5 . Occurrence dataset used to train climatic niche models. Occurrences sh own in colored circles: green = Physocarpus monogynus (Torr.) Coult. (Rosaceae), pink = P. intermedius (Rydb.) Schneid., and yellow = P. opulifolius (L.) Maxim.

PAGE 46

38 Table 3.2 . Clim atic variables used to train niche models for Physocarpus monogynus (Torr.) Coult. (Rosaceae), P. intermedius (Rydb.) Schneid., and P. opulifolius (L.) Maxim . Relative variable importance to each model is indicated by percent contribution and by jackknife test results: variables with the highest gain when used in are isol ation shown in bold ; variables with the most decrease in gain when omitted are italicized . Physocarpus monogynus Physocarpus intermedius Physocarpus opulifolius s.s. Altitude (60.1%) Bio9: Mean Temp of Driest Quarter (16.6%) Bio13: Precip of Wettest Month (9.4%) Bio2: Mean Diurnal Temp Range (8.1%) Bio11: Avg Winter Temp (Mean Temp of Coldest Quarter) (5.7%) Bio3: Isothermality (0.1%) Bio5 : Max Summer Temp (Max Temp of Warmest Month) (35.7 %) Bio14: Precip of Driest Month (28.1%) Altitude (12.6%) Bio8: Mean Temp of Wettest Quarter (11.8%) Bio13: Precip of Wettest Month (6.9%) Bio3: Isothermality (2.4%) Bio7: Annual Temp Range (2.3%) Bio17 : Precip of Driest Quarter (46.4%) Bio1 : Annual Mean T emp (30.6%) Bio18: Summer Precip (Precip of Warmest Quarter) (9.4%) Bio9: Mean Temp of Driest Quarter (6.8%) Bio7: Annual Temp Range (4.1%) Bio2: Mean Diurnal Temp Range (2.6%)

PAGE 47

39 Figure 3 .6 . Maxent niche models of occurrence probability trained on current climatic conditions for the study taxa : a) Physocarpus monogynus (Torr.) Coult. (Rosaceae), b) P. intermedius (Rydb.) Schneid . , and c) P. opulifolius (L.) Maxim. Warmer colors indicate greater probability of occurrence. Black dots indicate occurren ce data. a. b. c.

PAGE 48

40 Figure 3.7. Maxent niche models for Physocarpus opulifolius (L.) Maxim. (Rosaceae) hindcast to the mid Holocene (ca. 6,000 yr BP; top) and LGM ( ca. 21,000 yr BP; bottom). M ultivariate environmental similarity surfaces (MESS ; top right) and map s of the limiting factor (most dissimilar variable, MoD; bottom right) . MESS indicates where predictions are being projected outside the training range (red regions) and provide insight into the spatial distribution of no analog climatic conditions, relati ve to input variables. MoD maps indicate which variable is most influential over the model at any given point.

PAGE 49

41 Figure 3 . 8 . Maxent niche models for Physocarpus monogynus (Torr.) Coult. (Rosaceae) hindcast to the mid Holocene (ca. 6,000 yr BP; top) and L GM (ca. 21,000 yr BP; bottom). Multivariate environmental similarity surfaces (MESS; top right ) and maps of the limiting facto r (most dissimilar variable, MoD; bottom right). MESS indicates where predictions are being projected outside the training range ( red regions) and provide insight into the spatial distribution of no analog climatic conditions, relative to input variables. MoD maps indicate which variable is most influential over the model at any given point.

PAGE 50

42 Figure 3. 9 . Maxent niche models for Phy socarpus intermedius (Rydb .) Schneid. (Rosaceae) hindcast to the mid Holocene (ca. 6,000 yr BP; top) and LGM (ca. 21,000 yr BP; bottom). M ultivariate environmental similarity surfaces (MESS ; top right ) and maps of the limiting factor (most dissimilar varia ble, MoD; bottom right) . M ESS indicates where predictions are being projected outside the training range (red regions) and provide insight into the spatial distribution of no analog climatic conditions, relative to input variables. MoD maps indicate which variable is most influenti al over the model at any given point.

PAGE 51

43 Table 3.3 . Model validation metrices for niche models of Physocarpus monogynus (Torr.) Coult. (Rosaceae), P. intermedius (Rydb.) Coult., and P. opulifolius (L.) Maxim. AUC: mean cross valida ted area under the r eceive r operating characteristic curve ; TSS: true skill statistic ( sensitivity + specificity 1 ) ; maxSSS: threshold values derived from maximizing the sum of sensitivity and specificity. Study t ax on Mean AUC (standard deviation) TSS m axSSS threshold (training omission rate) P. monogynus 0.982 (0.010) 0.831 0.206 (0.026) P. intermedius 0.936 (0.014) 0.761 0.367 (0.132) P. opulifolius 0.919 (0.021) 0.734 0.259 (0.047)

PAGE 52

44 Figure 3.1 0 . PCA biplot of variance in climatic conditions among occurrence data for the study taxa . Occurrence points represented by solid dots; climatic niche space summarized by ellipses. G reen = Physocarpus monogynus (Torr.) Coult. (Rosaceae), red = P. intermedius (Rydb.) Schneid., blue = P. opulifolius (L.) Maxim.

PAGE 53

45 Tabl e 3.4. PCA c ovariate matrix . T op 5 score s for each principal component shown in bold. Variable PC1 (47.1%) PC2 ( 26.9%) Bio1 0.273153 0.03717 Bio2 0.062814 0.32466 Bio3 0.000269 0.41144 Bio4 0.091230 0.40084 Bio5 0.214825 0.01062 B io6 0.255123 0.22475 Bio7 0.114367 0.31623 Bio8 0.030470 0.22418 Bio9 0.237237 0.22413 Bio10 0.229912 0.11798 Bio11 0.243440 0.23647 Bio12 0.304695 0.11135 Bio13 0.266396 0.11417 Bio14 0.291159 0.01617 Bio15 0.246106 0.02940 Bio16 0. 260235 0.15057 Bio17 0.301766 0.00181 Bio18 0.202195 0.27096 Bio19 0.289583 0.02969 Altitude 0.163691 0.34462

PAGE 54

46 CHAPTER IV CONCLUSIONS The phytogeographic history of the eastern woodland prairie floristic element was examined to better unders tand the association between disjunct western populations of this and the Cordilleran floristic element in the Southern Rocky Mountains of Colorado. The taxonomic review presented in Chapter II served to clarify the taxonomy of the P. opulifolius s.l. comp lex through a range wide examination of critical morphological variation. Approximately 21% of herbarium specimens were misidentified, according to t he treatment described in Chapter II. By carefully defining and verifying the identification of each specim en through meticulous examination of follicle morphology , taxonomic uncertainty was considerably reduced . Ultimately, this served to increase confide nce in conclusions drawn from climatic niche models. The vegetation niche modeling process often assumes th at species identifications and coordinate data are accurate. However, this and other studies (e.g., Bloom et al., 2018) have shown that these are fal se assumptions which can considerably influence results. Further, p aleodistribution modeling of vegetation often excludes an examination of the taxonomy of the species being modeled. The approach used herein, however, bridged th e gap between botany and pal eo climatic modeling. Taxonomy of Physocarpus opulifolius s.l. Historical treatments, original publications , morphotype frequency, and geographic distribution patterns indicate two primary forms within the P. opulifolius s.l. complex: nearly glabrous or glabrescent and uniformly pubescent. I conclude that P. intermedius merits taxonomic recognition and that t wo Physocarpus taxa are present in eastern North America: P. opulifolius and P. intermedius . T he latter also occurs in d isjunct habitats on the Front Range of Colorado, the Black Hills of South Dakota, and the Sand Hills of Nebraska. Despite this evidence, h owever, variation within a single character typically is not sufficient evidence to support segregation at the speci es

PAGE 55

47 leve l. S ubspecific recognition is recommended. The follicle morphology character should allow identification of flowering as well as frui ting plants (LP Bruederle, personal comm.). T hese findings illustrate the importance of taxonomic identification when constructing niche models of plant species. Taxonomic uncertainty and cryptic variation can substantially affect model predictions (Ensing et al., 2013) . T he clarification of morpholo gical variation between P. opulifolius and P. intermedius in Chapter II played an important role in the construction of climatic niche models in Chapter III and had a considerable influence regarding the phytoge ographic histories of the se taxa. Although th e topic of taxonomic uncertainty has been explored in the zoological literature ( e.g., Zhang et al., 2014), it has received relatively little attention in botanical research (Ensing et al., 2013). To minimize ta xonomic uncertainty, q ualification ( and often , quantification ) of diagnostic traits across the range of the taxon of interest will be required. If differences between diagnostic character states cannot be observed in digital photographs, rigorous physical examination of herbarium specimens will be required. Similarly, t hese findings underline the importance of considering the full range of the taxon of interest. Just as overlooked variation can result in conflicting taxonomic treatments, niche models train ed on occurrences drawn from a subset of t he species geographic range and thus a subset of the realized niche do not take into account the full environmental envelope and are limited in their implications for that species (Merow et al. , 2013; Phillips et al. , 2006). Contributions to t he Flora of Colorado P revious accounts have assigned populations of P. opulifolius s.s. in Colorado to the eastern woodland prairie element (Weber & Wittmann, 2011 ) . However, unexamined morphological variation across the fu ll geographic range had prevented accurate taxonomic identification ; in fact, P . intermedius is representative of the eastern woodland prairie element. Despite previously conflicting taxonomic treatments ( cf. Ackerfield, 2015; Weber & Wittmann, 2011 ), thes e findings sho w that P. opulifolius does not occur west of Lake Superior, and certainly does not occur in

PAGE 56

48 Colorado. Thus, the two species of Physocarpus on the Front Range are P. intermedius and P. monogynus . Overlap in geographic space (Figs. 2.2, 3.6) a s well as environmental space ( Fig. 3.10) exists between two pairs of study taxa : P. monogynus and P. intermedius co occur on the Front Range, while P. intermedius and P. opulifolius s.s. co occur in the Great Lakes region . These regions may indicate zones of sympatry, where hybridization may occur . Indeed, the accessions from these regions tend to exhibit morphologically ambiguous character states (e.g., follicle pubescence category 3 discussed in Chapter II) . Anecdotally, some of the western P. intermediu s accessions appear to be hybrids, with follicles resembling P. monogynus in size, but not in pubescence or number. This observation has been noted previously (Weber & Wittmann, 2011 ). P hysocarpus intermedius ( comprising f ollicle pubescence category 4 ) ove rlap s with P . opulifolius ( comprising categories 1 through 3 ) in the Great Lakes region. Niche models indicate additional spatial overlap between these two taxa, with the predicted occurrence of P. opulifolius extending we st and south to eastern Kansas. Ad ditional research focusing on these potential zones of hybridization is recommended . Limitations Several limitations affected the analysis and findings presented above. First , there were gaps in the occurrence dat aset (Appendix G) . Although the dataset was not 100% co mplete, I would not expect the inclusion of additional occurrence poi nts to substantially change the conclusions presented herein , as the current samp le spans the majorit y of the range and was considered t o be ecologically repres entat ive . The pro cess of g eoreferenc ing herbarium specimens adds further uncertainty to the o ccurrence data. The n iche modeling approach assumes that the input coordinates are both accurate and precise , assumption s whi ch are often false (Bloom et al., 201 8 ) . I attempted to reduce t he error from this assumption by a) including a large sample of herbarium specimens from across the full

PAGE 57

49 range , b) georeferencing speci mens myself to ensure that the same procedure and consider ations were appl ied when t ranslati ng qua litative descripti ons i nto spatial co ordinates , and c) limiting the uncertainty radius. T h e limited availability of hindcast climate data at an appropriately fine spatial resolution prevented a more robust study of shifts in predicted distributions. Ideally, paleo dis tributiona l shifts would be interpreted from niche models constructed for mult iple regularly spaced intervals. In the absence of these data, niche models were constructed for just three time points over the past 21,000 years . C onclusions can only be drawn regarding distri butions at those three s napshot time poin ts . T his limit s the scope o f and certainty in t he ana lysis of biogeographical origins , as no information is pro vide d regarding distribution changes during the interve ning periods . Further, the niche modeling approach assumes that the niche itself has not changed since the LGM (assumption of niche conservatism; Araújo and Guisan, 2006 ; Warren et al., 2008) . This approa ch also a ssumes that the distributions of the three s tudy taxa are most sensitive to climate ( in other words, climate is most influenti al) without consideration of other factors (e.g., edaphic propertie s ; Beauregard & de Blois, 2014 ) . E cological (rather than strictly climatic) niche models constructed with variables that may be more directly influ ential on species survival, such as effective moisture (e.g., topographic wetness index, eva potranspiration, vapor pressure deficit, etc.), canopy density, or edaphic properties , are likel y t o provide mor e r obust res u lts . Given the difficulty of hindcastin g these variables at an appropriate spatial scale , however, resulting predictions are limited to current climate mo dels . D ecisions regardin g mode l inputs and parameters are known to significantly affect niche mod els (Elith et al., 2011; Merow et al., 2013 ) . For example, background points were randomly sampled across the f ull study region . On one hand, t he decision n ot to limit back g round extent ensured adequate representation of environ ments across the study range and req uired less ext rapolation in the res ulting predictive models (Elith et al., 2011) . On th e other hand, it as sumes

PAGE 58

50 that the true lik elih ood of presence is even ly distri but ed ac ross the la ndscap e ; in other words, the chance of encou ntering the species is the same at every point ( Elith et al. , 2 011). This underl ying assu mption is often in accurate, as geographic al (e.g ., human development) or ecological barriers (e.g., l ow pollinator prevalence ) may pre ven t dispersal into areas predicted to have suitable climates (El ith et al ., 2011) . Unrestricted back ground is therefore considered to be eco logically unrealistic (Elith et al., 2011) and sh ould be examined close ly in future model ing . Finally, p resence only o ccurrence dat a derive d from herbarium specim ens is often assum e d to be spatially biased , as s ampling tech ni ques var y across collectors and are rarely linked to d igitized specimen data (Elith et al., 2011) . Although this bias is likely present in the input occurrence data, no correction for this bias could be made , as no information was known about sampling stra teg y . Future Directions T hese findings provide insight into the biogeographical history of the Colorado flora, and support the theory that there isn e flora appears to have a complex b iogeographical history. Future research should focus on hybridization between P. monogynus and P. intermedius along the Front Range and in the Black Hills. Additi onally, research investigating common cultivation practices relevant to P. opulifolius s.s. , as well as the history of cultivation , will help understand the extent to which specimens collected from natur alized horticultural escapees might affect distributi onal model s. Further, m ulti method approaches are becoming more prevalent and important to ga ining multidimensional perspectiv es on complex concepts. T he integration of niche modeling with other traditional plant science methods, such as phylogenetics and demograph ics , is promising (e.g., Call et al., 2016 ; Morris et al. , 2010; Schorr et al., 2013 ). Chapter III presented a new biogeographical hypothesis for the origin of the disjunct P. opulifoliu s s.l . distribution pattern. Future research should address t his new hypothesis using a complementary methodological approach . G enetic analysis would be e specially beneficial ;

PAGE 59

51 p hylogenetic work at the species level is expected to provide additional information regarding the phylo geography and systematics of these taxa . In addition, f urther examin ation of the geographic range of morphological variation spe cifically related to Level III Ecoregions (Fig. 2. 3 ) will help better understand the ec ology of th is group. S pecies distributio ns derived f rom current climate models should be further investigated . Exploration of non climate variables is recommended, esp ec ia lly those that may be more directly influential on species survival (e.g., effective moisture , canopy density, or edaphic properties ) . Fin ally, f u ture research sh ould q uantify niche differences between P. opulifolius s.s . and P. intermedius (e.g., nich e overlap , niche breadth ) , as these metrics have be en linked to ecological and phylogenetic distances among closely related taxa (Warren et al., 2008).

PAGE 60

52 REFERENCES Ackerfield J. 2015. Flora of Colorado . Fort Worth, Texas, USA : BRIT Press . Aiello Lammens M E , Boria RA , Radosavljevic A , Vilela B and Anderson RP . 201 5 . spThin: An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography . 38(5) : 541 545. doi:10.1111/ecog.01132 . Alexander C. 2014. Physocarpus . In: Flora of North America Editorial Committee [eds.], Flora of North Ame rica North of Mexico (V ol. 9 , pp. 347 348 ) . New York and Oxford. Allouche O, Tsoar A and Kadmon R . 2006 . Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS) . Journal of Applied Ecology . 43 (6 ) : 1223 1232 . doi:10.1111/j.1365 2664.2006.01214.x . Araújo MB and Guisan A. 2006 . Five (or so) challenges for species distribution modelling. Journal of Biogeography . 33 : 1677 1688 . doi:10.1111/j. 1365 2699.2006.01584.x. Axelrod DI and Raven PH. 1985 . Origi ns of the Cordilleran Flora . Journal of Biogeography . 12 ( 1 ): 21 47 . doi:10.2307/2845027 . Bartlein PJ, Anderson KH , Anderson PM , Edwards ME , Mock CJ , Thompson RS , Webb RS , Webb III T and Whitlock C . 1998. Paleoclimate simulations for North America over the past 21,000 years: Features of the simulated climate and comparisons with paleoenvironmental data. Quaternary Science Reviews 17(6): 549 585. doi:10.1016/S0277 3791(98)00012 2 . Bartlein PJ, Harris on SP, Brewer S, Connor S, Davis BAS, Gajewski K, Guiot J, H arrison Prentice TI, Henderson A, Peyron O, Prentice IC, Scholze M, Seppä H, Shuman B, Sugita S, Thompson RS, Viau AE, Williams J and Wu H. 2011 . Pollen based continental climate reconst ructions a t 6 and 21 ka: a global synthesis. Climate Dynamics . 37: 775 802. doi:10.1016/j.ecolmodel.2011.02.011 . Barve N, Barve V, Jiménez Valverde A, Lira Noriega A, Maher S, Peterson AT, Soberón J and Villalobos F . 2011 . The crucial role of the accessibl e area in ecological niche modeling and species distribution modeling . Ecological Modelling . 222: 1810 1819. Beauregard F and de Blois S. 2014. Beyond a climate centric view of plant distribution: Edaphic variables add value to distribution models. PLoS ONE . 9(3): e 92642. doi:10.1371/journal.pone.0092642 . Bemmels JB and Dick CW. 2018. Genomic evidence of a widespread southern distribution during the Last Glacial Maximum for two eastern North American hickory species . Journal of Biogeography . 45: 1739 1750. doi:10.111 1/jbi.13358 . Bentham G and Hooker JD . 1865. Genera Plantarum ( Vol. 1 Part 2 ) p. 612. London: Lovell Reeve & Co. Retrieved from https://www.biodiversitylibrary.org/item/146 83#page/182/mode/1up . Biota of North America Program (BONA P). 201 4 . [ D istribution m ap for Physocarpus monogynus (Torr.) Coult. (Rosaceae) ]. Taxonomic Data Center . Retrieved 23 January 2019 from http://bonap.net/MapGallery/County/Physocarpus%20monogynus.p ng . Bloom TDS, F lower A and D eChaine EG . 2018. Why georefere ncing matters: Introducing a practical protocol to prepare species occurrence records for spatial analysis. Ecology and Evolution . 8: 765 777. doi:10.1002/ece3.3516 .

PAGE 61

53 Call A, Sun YX, Yu Y, Pearman PB, Thomas DT, Trigiano RN, Carbone I a nd Xiang QY. 201 6 . Ge netic structure and post glacial expansion of Cornus florida L. (Cornaceae): integrative evidence from p hylogeography, population demographic history, and species distribution modeling . Journal of Systematics and Evolution . 54(2): 136 151 . doi:10.1111/jse. 12171 . Cambessèdes M J. 1824. Monographie du Genera Spiraea . In: Audouin, Brongniart, and Dumas (Eds.), Annales des sciences naturelles (Vol. 1 : 2 25 245 , 385 387 ) . Paris: Imprimerie de J. Tastu. Re trieved from https://www.biodiversitylibrary.org/item/28623# page/247/mode/1up . Carnaval AC and Moritz C. 2008 . Historical Climate Modelling Predicts Patterns of Current Biodiversity in the Brazilian Atlantic Forest . Journal of Biogeography . 35 ( 7 ): 1187 1 20 1 . doi:10.1111/j.1365 2699.2007.01870.x . Consortium of Midwe st Herbaria. 2019. http//:midwestherbaria.org/portal/index.php. Accessed on February 19, 2019. Cooper DJ (Ed.) . 1984 . Ecological survey of the city of Boulder, Colorado Mountain Parks. Coulter JM and Nelson A. 1909. New Manual of Botany of the Central Roc ky Mountains (Vascular Plants) . Cincinnati : American Book Company . pp. 247 248. Retrieved from https://www.biodivers itylibrary.org/item/116743#page/252/mode/1up . Cuizhi G and Alexander C . 2003. Ph ysocarpus . In: Flora of China (Vol. 9 , p. 76 ) . Retrieved fro m http://flora.huh.harvard.edu/china/PDF/PDF09/Physocarpus.PDF . Cambridge, MA : Missouri Botanical Garden, St. Louis, MO & Harvard University Herbaria. Daniels FP . 1907. The Flora of C olumbia Missouri and Vicinity. University of Missouri Studies, Science Series . 1 (2) : 149 150. Columbia, MO: E. W. Stephens Publishing Company. Retrieved from https://www.biodiversitylibrary.or g/item/99297#page/330/mode/1up . Delcourt PA and Delcourt HR . 1993 . Chapter 4: Paleoclimates, Paleovegetation, and Paleofloras of North America North of Mexico During the Late Quaternary. In : Flora of North America Editoria l Committee [eds.], Flora of North America North of Mexico (V ol 1 ) . New York and Oxford . Duart e M, Guerrero PC, Carvallo G and Bustamante RO . 2014 . Conservation network design for endemic cacti under taxonomic uncertainty . Biological Conservation . 176 : 236 242 . doi:10.1016/j.biocon.2014.05 .028 . Dyke A S. 2004 . An outline of N orth A merican deglaciation with emphasis on central and northern C anada. In: Ehlers J a nd Gibbard P L (Ed s .) : Quaternary Glaciations Extent and Chronology, Part II. North America. Developments in Quaternary S cience 2 : 3 73 424 . Elith J, Kearney M and Phillips S . 2010. The art of mo delling range shifting species . Methods in Ecology and Evolution . 1 (4) : 330 342 . doi:10.1111/j.2041 210X.2010.00036.x . Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE and Yates CJ . 2011 . A stat istical explanation of MaxEnt for ecologists . Diversity and Di stributions . 1 7 ( 1 ): 43 57 . doi:10.1111/j.1472 4642.2010.00725.x . Ensing DJ, Moffat CE and Pither J. 2013 . Taxonomic identification errors generate misleading ecological niche model predictions o f an invasive hawkweed. Botany . 91: 137 147. dx.doi.org/10.113 9/cjb 2012 0205. Fitzpatrick MC, Blois JL, Williams JW, Nieto Lugilde D, Maguire KC and Lorenz DJ. 2018. How will climate novelty influence ecological forecasts? Using the Quaternary to assess f uture reliability. Global Change Biology . 24(8) : 3575 3586. do i:10.1111/gcb.14138 .

PAGE 62

54 Fourcade Y, Engler JO, Rödder D and Secon di J . 2014 . Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessmen t of Methods for Correcting Sampling Bias . PLoS ONE . 9(5): e97 122. doi:10.1371/journal.pone.0097122 . Franklin J. 2010. Mapping species distributions: Spatial inference and prediction . New York : Cambridge University Press. Retrieved from https://ebookcentra l.proquest.com . Gent PR, Danabasoglu G, Donner LJ, Holland MM, Hunke EC, Jayne SR, Lawrence DM, Neale RB, Rasc h PJ, Vertenstein M, Worley PH, Yang ZL and Zhang M. 2011 . The Community Climate System Model Version 4 . Journal of Climate . 24: 4973 4991. https: //doi.org/10.1175/2011JCLI4083.1 . Gleason HA and Cronquist A. 1991 . Manual of vascular plants of northeastern United States and adjacent Canada ( 2 nd ed. ). Bronx, NY: The New York Botanical Garden. Goring S, Dawson A, Simpson GL, Ram K, Graham RW, Grimm E C and Williams J W. 2015. N eotoma: A Programmatic Interface to th e Neotoma Paleoecological Database . Open Quaternary . 1(1) : Art. 2. http://doi.org/10.5334/oq.ab . Heikkinen RK, Luoto M, Araújo MB, Virkkala R, Thuiller W and Sykes MT . 2006 . Methods and uncertai nties in bioclimatic envelope modelling under climate change. Progress in Physical Geography . 30 ( 6 ): 751 777 . doi:10.1177/0309133306071957 . Hijmans RJ , Cameron SE , Parra JL , Jones PG and Jarvis A. 2005. Very high resolution interpolat ed climate surfaces fo r global land areas. International Journal of Climatology . 25: 1965 1978. doi:10.1002/joc.1276 . Hogan T . 1989. Survey of Plants of Special Concern in Long Canyon, Panther Canyon, Greenman Springs Area, and Tributary Canyons and Gulches in the City of Bould er Mountains Parks, Boulder, Colorado. Jiménez Valverde A. 2 01 2. Insights into the area under the receiver o perating characteristic curve (AUC) as a discrimination measure in species distribution modelling . Global Ecology and Biogeography . 21 : 498 507 . d oi:10.1111/j.1466 8238.2011.00683.x . S, Niedball a J, Pilgrim JD, Schröder B, Lindenborn J, Reinf elder V, Stillfried M, Heckmann I, Scharf AK, Augeri DM, Cheyne SM, Hearn AJ, Ross J, Macdonald DW, Mathai J, Eaton J, Marshall AJ, Semiadi G, Rus tam R, Bernard H, Alfred R, Samejima H, Duckworth JW, Breitenm C, Belant JL, Hofer H and Wilting A . 2013 . The importance of correcting for sampling bias in MaxEnt species distribution models . Diversity and Distributions . 19 ( 11 ) : 1366 1379. doi :10.1111/ddi.12096 . Kuntze O . 1891 a . Revisio Generum Plantarum (Vol. 1, pp. 218 219). Retrieved from http://bibdigital.rjb.csic.es/ing/Libro.php?Libro=5479 . Kuntze O. 1891b. Revisio Generum Plantarum (Vol. 2 , p. 949 ) . Retrieved from http://bibdigital.rjb.c sic.es/ing/Libro.php?Libro=5480 . Linnaeus C. 1753. Species Pla ntarum (Vol. 1 , p. 489) . Holmiæ Stockholm: Impensis Laurentii Salvii . Retrieved from https://www.biodiversitylibrary.org/item/121860#page/522/mode/1up . Liu C, Newell G and White M . 2015 . On the selection of thresholds for predicting species occurrence wit h presence only data . Ecology and Evolution . 6(1): 337 348 . doi: 10.1002/ece3.1878 .

PAGE 63

55 Livingston RB . 1952. Relict True Prairie Communities in Central Colorado . Ecology . 33 ( 1 ): 72 86 . doi:10.2307/1 931253 . Lobo JM, Jiménez Valverde A and Real R. 2008 . AUC: A M isleading Measure of the Performance of Predictive Distribution Models . Global Ecology and Biogeography . 17 ( 2 ): 145 151 . doi:10.1111/j.1466 8238.2007.00358.x . McGregor RL , Barkley TM and Great P lains Flora Association. 1986. Flora of the G reat P lains . Law r ence, KS : University Press of Kansas. Marr KL , Hebda RJ and MacKenzie W H. 2012. New alpine plant records for B ritish C olumbia and a previously unrecognized biogeographical element in western N or th A merica. Botany . 90(6) : 445 455. doi:10.1139/b2012 009 . Mas tin J , Luebke N, Anthamatten P and Bruederle LP. 2018 . Evidence for genetic allopolyploidy in Eutrema edwardsii (Brass icaceae): implications for conservation . Plant Systematics and Evolution . 30 4(1) : 133 141 . doi:10.1007/s00606 017 1447 2 . Maximovich CJ. 1 879. Adnotationes de Spiraeaceis. In: Trudy Imperatorskago S. Peterburgskago Retrieved from https://www.biodiversitylibrary.org/item/53600#page/1/mode/1u p . Medi kus FK . 1799. Beytr ge zur Pflanzen Anatomie, Pflanzen Physiologie und einer neuen Cha rakteristik der B ume und Str ucher . Leipzig: Ben Heinrich Gr ff . p. 109 . Retrieved from https://www.biodiversitylibrary.org/item/220807#page/5/mode/1up . Menz MHM , Phillips RD, Anthony JM, Bohman B, Dixon KW and Peakall R . 2015 . Ecological and genetic evidence for cryptic ecotypes in a rare sexually deceptive orchid, Drakaea elastica . Botanical Journal of the Linnean Society . 177 (1) : 124 140. doi:10.1111/boj.12230 . Merow C, Smith MJ and Silander, Jr., JA . 2013 . A practical guide to MaxEnt distr ibutions: what it does, and why inputs and settings matter . Ec ography . 36: 1058 1069 . doi: 10.1111/j.1600 0587.2013.07872.x Morris AB, Graham CH, Soltis DE and Soltis PS . 2010 . Reassessment of phylogeographical structure in an e astern North American tree u sing Monmonier's algorithm and ecological niche modelling . Jou rnal of Biogeography . 37 ( 9 ): 1657 1667 . doi:10.1111/j.1365 2699.2010.02315.x . Mutel CF and Emerick JC . 1992. From G rassland to G lacier: The natural history of Co lorado and the surrounding region (2nd ed.). Boulder: Johnson Books. Retrieved from http://aura rialibrary.idm.oclc.org/login?url=http://search.ebscohost.com/login.aspx?direct=true& db=nlebk&AN=93 . Nelson A . 1902. Contributio ns from the Rocky Mountain Herbarium. Botanical Gazette . 34(5): 36 7 . Retrieved from https://www.biodiversitylibrary.org/page/288 73052#page/2/mode/1up . Nelson JK. 2010 . Vascular flora of the Rocky Flats Area, Jefferson County, Colorado, USA. Phytologia 92(2 ) : 121 150. Newhall CS. 1891 . The trees of northeastern America: T he shrubs of northeastern America . pp. 88 90. Putnam, New York : G.P. Putnam's sons . Retrieved from https://babel.hathitrust.org/cgi/pt?id=nyp.33433006568343;view=1up;seq=9 .

PAGE 64

56 MS and Ignizio DA. 2012 . Bioclimatic Predictors for Supporting Ecological Applications in the Conterminous United States: U.S. Geologic al Survey Data Series 691 . Retrieved from https://pubs.usgs.gov/ds/691/ . Oh SH. 2015. Taxonomy of tribe Neillieae (Rosaceae): Physocarpus . Korean Journal of Plant Taxonomy . 45(4): 332 352. doi : 10.11110/kjpt.2015.45.4.332 . Oh SH and Potter D . 2005. Molecul ar phylogenetic systematics and biogeography of tribe Neillieae (Rosaceae) using DNA sequences of cpDNA, rDNA, and LEAFY. American Journal of Botany . 92(1): 179 192. doi:10.3732/ajb.92.1.179 . O swald WW , Foster DR , Shuman BN , Doughty ED , Fai so n EK , Hall BR , Hansen BCS, Lindbladh M, Marroquin A and Truebe SA . 2018. Subregional variability in the response of N ew E ngland vegetation to postglacial climate change. Journal of Biogeography . 45(10) : 2375 2388. doi:10.1111/jbi.13407 . Pausata FSR, Li C, Wettstein JJ , Kageyama M and Nisancioglu KH. 2011. The key role of topography in altering North Atlantic atmospheric circulation during the last glacial period . Climate of the Past 7(4) : 1089 1101. doi: 10.51 94/cp 7 1089 2011 . Pérez Navarro MA, Sapes G, Batllori E, Serr a Diaz JM, Esteve MA and Lloret F . 201 9 . Climatic Suitability Derived from Species Distribution Models Captures Community Responses to an Extreme Drought Episode. Ecosystems . 22(1) : 77 90. doi:1 0.1007/s10021 018 0254 0 . Peterson AT , Papes M and Kluz a DA . 2 003. Predicting the potential invasive distributions of four alien plant species in N orth A merica. Weed Science . 51(6) : 863 868. doi:10.1614/P2002 081 . Phillips SJ. 2008 . Transferab ility, sample selection bias and background data in presence only modelling : a response to Peterson et al. (2007). Ecography . 31: 272 278 . doi: 10.1111/j.2007.0906 7590.05378.x Phillips SJ, Anderson RP and Schapire RE . 2006 . Maximum entropy modeling of species geograph ic distributions . Ecological Modelling . 190 : 231 259 . doi:10.1 016/j.ecolmodel.2005.03.026 . Phillips SJ and Dud í k M. 2008 . Modeling of Species Distributions with Maxent: New Extensions and a Comprehensive Evaluation . Ecography . 31 ( 2 ): 161 175 . doi: 10.1111/ j.2007.0906 7590.05203.x . Phillips SJ, Dud í k M, Elith J, Graha m CH, Lehmann A, Leathwick J and Ferrier S . 2009 . Sample Selection Bias and Presence Only Distribution Models: Implications for Background and Pseudo Absence Data . E cological Applications . 19 ( 1 ) : 181 197 . Retrieved from http://www.jstor.org/stable/27645958 . Potter D, Eriksson T, Evans RC, O h S, Smedmark JEE , Morgan DR, M. Kerr , Robertson KR, Arsenault M, Dickinson TA and Campbell CS. 2007. Phyl ogeny and classification of R osaceae. Plant Systematics and Ev olution . 266(1/2) : 5 43. doi:10.1007/s00606 007 0539 9 . Power MJ , Whitlock C and Bartlein P J. 201 1 . Postglacial fire, vegetation, and climate history acro ss an elevational gradient in the N orthe rn R ocky M ountains, USA and C anada. Quaternary Science Reviews . 30(19) : 2520 2533. doi:10.1016/j.quascirev.2011.04.012 . QGIS Development Team. 2019. QGIS Geographic Information System (Version 3.4.2) [computer software] . Open Source Geospatial Foundation P roject. Available from http:// www. qgis.org .

PAGE 65

57 R Core Team . 201 8 . R: A language and environment for statistica l computing ( version 3.4.1 ) [software program] . Vienna, Austria : R Foundation for Statistica l Computing. Retrieved from http://www.R project.org/. Ra finesque CS . 1838. New flora and botany of North America, or, A supplemental flora, additional to all the botanical works on North America and the United States. Containing 1000 new or revised specie s (Part 3 ) . Philadelphia . Retrieved from https://www.biodiversitylibrary.org/item/187964#page/275/mode/1up . Rehde r A . 1920. New species, varieties and combinations from the herbarium and the collections of the Arnold Arboretu m. Journal of the Arnold Arboretum . 1(4): 256. Rios NE and Ba rt HL . 20 10. GEOLocate (Version 3.22) [Computer software]. Belle Chasse, LA: Tulane University Museum of Natural History. Retrieved from https://www.geo locate.org . Robinso n BL . 1908. Notes on the Vascular Plants of the No rtheastern United States. Rhodora . 10(100): 32. Retrieved from https://www.biodiversitylibrary.org/p age/563475#page/35/mode/1up . RStudio Team . 2016. RStudio: Int egrated Development for R [software program] . Boston, MA : RStudio, Inc . Retrieved from www.rstudio.com. Rydberg PA . 1896. Flora of the Black Hills of South Da kota. Contributions from the U.S. Na tional Herbarium . 3(8): Washington: Gov ernment Print ing Off ice . Retrieved from https://www.biodiversitylibrary.org/item/32247#page/5/mode/1up . Rydberg PA. 1901. Rosaceae . In: NL Britton, Manual of the Flora of the northern States and Canada (pp. 490 514). New York: Henry Holt and Company. Retrieved from https://www.biodiversitylibrary.org/item/122523#page/505/mode/1up . Rydberg PA . 1906. Flora of Colorado. Bulletin of the Agricultural Experiment Station of the Colorado Agricultural C ollege . 100. Fort Collins , CO : Experiment Station . Retrieved from https://www.biodiversitylibrary.org/item/20094#page/7/mode/1up . Rydberg PA . 1908 a . Rosaceae . In : NL Britton, North American Flor a (Vol. 22 , Part 3 , pp. 2 39 292) . New York: New York Botanical Garden. Retrieved from https://www.biodiversitylibrary.org/item/15436#page/1/mode/1up . Rydberg PA. 1908b. No tes on Rosaceae I . Bulletin of the Torrey Botanical Club . 35 ( 1 1 ): 535 542 . doi : 10.2307/2479109 . Rydberg PA . 1917. Flora of the Rocky Mountains and Adjacent Plains . New York. Retrieved from https://www.biodiversitylibrary.org/item/3219 9#page/5/mode/1up . Schaetzl RJ , Krist Jr . FJ , Lewis CFM , Luehmann MD and Michalek MJ . 2016. Spits formed in G lacial L ake A lgonquin indicate strong easterly winds over the L aurentian G reat L akes during late P leistocene. Journal of Paleolimnology . 55(1) : 49 65. doi:10.1007/s10933 015 9862 2 . SEINet Portal Network. 2019. http//:swbiodiversity.org/seinet/index.php. Schneider CK . 1906. Illustriertes Handbuch der Laubholzkunde (Vol. 1, pp. 44 2 446). J ena: Gustav Fischer. Retrieved from https://www.biodiversityli brary.org/item/5886#page/466/mode/1up .

PAGE 66

58 Schorr G, Pearman PB, Guisan A and Kadereit JW . 2013 . Combining palaeodistribution modelling and phylogeographical approaches for identifying glacial refug ia in Alpine Primula. Journal of Biogeography . 40 (10) : 1947 19 60. doi:10.1111/jbi.12132 . Shafer DS. 1989. T he timing of late Quaternary monsoon precipitation maxima in the Southwest United States [dissertation] . University of Arizona : UA Campus Repository , University Libraries . Retrieved from http://hdl.handle.net/10 150/184766 . Small JK. 1903. Flora of the Sout heastern United States . p. 33. Lancaster, PA: The New Era Printing Company. Retrieved from https://www.biodiversitylibrary.org/item/3397#page/523/mod e/1up . Smith B E, Johnston MK and Lücking R. 2016. From GenBank to GBIF: Phylogeny based predictive niche modeling tests accuracy of taxonomic identifications in large occurrence data repositories. PloS One . 11(3) : e0151232. doi:10.1371/journal.pone.0151232 . Soberón J and Peterson AT. 2005. Interpretation of Models of Fundamental Ecological Niches and Biodiversity Informatics . 2 : 1 10 . doi:10.17161/bi.v2i0.4 . Thiers B. 201 9. Index Herbariorum: A global directory of public herba ria and a ssociated staff. New York Botanical Garden's Virtual Herbarium. http://sweetgum.nybg.org/science/ih/. Thorne RF. 1993 . Chapter 6: Phytogeography of North America North of Mexico. In : Flora of North America Editorial Committee [eds.], Flora of Nort h America North of Mexico (V ol. 1 ) . New York and Oxford. Retri eved from http://floranorthamerica.org/Volume/V01/Chapter06 . Tinoco BA, Astudillo PX, Latta SC and Graham CH . 2009 . Distribution, ecology and conservation of an endangered A ndean hummingbird: Th e V iolet throated M etaltail ( M etallura baroni ). Bird Conservat ion International . 19(1) : 63 76. doi:10.1017/S0959270908007703 . Tropicos.org. 2019. Missouri Botanical Garden. http://www.tropicos.org U.S. Environmental Protection Agency (EPA). 2013 . Level III ecoregions of the con tinental United States: Corvallis, Oregon, U.S. EPA National Health and Environme ntal Effects Research Laboratory, map scale 1:7,500,000 . Retrieved from https://www.epa.gov/eco research/level iii and iv ecoregions continental united s tates . Veloz SD. 2009 . Spatially Autocorrelated Sampling Falsely Inflates Measures of Accuracy for Presence Onl y Niche Models . Journal of Biogeography . 36 ( 12 ): 2290 2299 . do i:10.1111/j.1365 2699.2009.02174.x . Vu V Q. 2011. ggbiplot: A ggplot2 based biplot . [ R package version 0.55 ] . Retrieved from http://github.com/vqv/gg biplot Waltari E, H ijmans RJ , Peterson AT , Nyári ÁS , Perkins SL and Guralnick RP . 2007. Locating Pleistocene Refugia: Comparing Phylogeographic and Ecological Niche Model Predictions. PLoS One . 2(7): e563. doi:10.1371/journal.pone.0000563 . Warren DL, Glor RE and Turelli M. 2008. Environmental Niche Equivalency versu s Conservatism: Qua ntitative Approaches to Niche Evolution. Evolution . 62(11): 2868 2883. doi:10.1111/j.1558 5646.2008.00482.x . Weber WA. 1965 . Plant Geography in the Southern Rocky Mountains. I n : HE Wright and DG Frey [ E ds.], The Quaternary of the United States: A review vo lume for the VII Congress of the International

PAGE 67

59 Association for Quaternary Research (pp. 453 468 ) . Princeton, New Jersey, USA : Princeton University Press . Weber WA and Wittmann RC . 2011. Colorado Flora: Eastern Slope (4th ed . ). Boulder, C olo: University Press of Colorado. Wehr WC and Hopkins DQ. 1994. The Eocene orchards and gardens of Republic, Washington . Washington Geology . 22 ( 3 ): 27 34. Retrieved from http://www.dnr.wa.gov/P ublications/ger_washington_geology_1994_v22_no3.pdf . Wen J, Ic kert Bond S, Nie Z L and L i R . 2010. Timing and modes of evolution of eastern Asian North American biogeographic disjunctions in seed plants. Darwin's Heritage Today: Proceedings of the Darwin 2 00 Beijing International Conference . 252 269. Wieczorek J, Qin ghua G and Hijmans RJ . 2004 . The point radius method for georeferencing locality descriptions and calculating associated uncertainty. International Journal of Geographical Information Science . 1 8(8): 745 767. doi:10.1080/13658810412331280211 . Y ansa CH . 200 6. The timing and nature of Late Quaternary vegetation changes in the northern Great Plains, USA and Canada: A re assessment of the spruce phase. Quaternary Science Reviews . 25 (3) : 263 281. doi: 10.1016/j.quasc irev.2005.02.008 . Yates CJ, Elith J, Latimer AM , L e M aitre D, M idgley GF , S churr FM and West AG . 2010 . Projecting climate change impacts on species distributions in megadiverse South African Cape and Southwest Australian Floristic Regions: O pportunities and challenges . Austral Ecology . 35(4) : 374 391. doi:10.1111/j.1442 9993.2009.02044.x . Zhang Y, Chen C, Li L, Zhao C , Chen W and Huang Y. 2014. Insights from ecological niche modeling on the taxonomic distinction and niche differentiation betw tokay geckoes ( Gekko gecko ). Ecology and Evolution . 4(17) : 3383 3394. doi:10.1002/ece3.1183 .

PAGE 68

60 APPEND I X A Nomenclatural History of Physocarpus opulifolius (L.) Maxim. s.l. (Rosaceae) Table S1. Synonyms of Physocarpus opulifolius (L.) Maxim. s.s. (Rosaceae) and P. intermedius (Rydb.) Schneid . C ompiled from the Integrated Taxonomic Information System (ITIS; itis.gov), The Flora of North America North of Mexico (FNA; Alexander, 2014), Tropicos (tropicos.org), and The Plan t List (theplantlist.org). P hysocarpus opulifolius P hysocarpus intermedius Basionym: Spiraea opulifolia L. (Linnaeus, 1753) Basionym: Opulas ter intermedius Rydb. ( Rydberg , 1901) Opulaster opulifolius (L.) Kuntze (Kuntze, 1891 b ) Physocarpus opulifolius (L .) Maxim. var. intermedius (Rydb.) B.L. Rob. (Robinson, 1908) Opulaster australis Rydb. ( Rydberg , 1908 a ) Opulaster alabamensis Rydb. (Rydber g, 1908 a )* Physocarpus australis (Rydb.) Rehder (Rehder, 1920) Opulaster ramaleyi A. Nels. (Nelson, 1902) Physoca rpus michiganensis Daniels (Daniels, 1907) Physocarpus ramaleyi A. Nels. ( Coulter & Nelson, 1909) Opulaster bullatus Medik. (Medikus, 1799) Opulaster stellatus Rydb. ( Small , 1903)* Physocarpus stellatus (Rydb. ex Small) Rehder (Rehder, 1920) Physocarp us missouriensis Daniels (Daniels, 1907) Note: Names marked with an asterisk (*) are listed as synonyms of P. opulifolius by the Interagency Taxonomic Information System (ITIS), but are described as having pubescent follicles and are thus treated as synon yms of P. intermedius .

PAGE 69

61 Figure S1. Nomenclatural history of Physocarpus opulifolius (L.) Maxim. s.l. (Rosaceae).

PAGE 70

62 APPENDIX B A ccessions e xamined for taxonomic review (Chapter II) Physocarpus intermedius (Rydb.) Schneid. Mexico: Hinton 22964 ( WIS ) 1993 , Nuevo Leon : 23.99315 , 99.714956000000001 , 2300 m. United States: Earle s.n. ( MO ) 189? , Lee County, AL : 32.608699999999999 , 85.4783790000 00004 , 2250 m; s.n. ( MO ) n.d. , Benton County, AR : 36.335911000000003 , 94.460770999999994 , 1500 m; Demaree 4884 ( M O ) 1927 , Benton County, AR : 36.448405000000001 , 93.974089000000006 , 1000 m; Demaree 4884 ( WIS ) 1927 , Benton County, AR : 36.448405000000001 , 93.974089000000006 , 2300 m; Palmer 44454 ( MO ) 1937 , Benton County, AR : 36.448405000000001 , 93.974089000000006 , 10 00 m; Palmer 4385 ( MO ) 1913 , Carroll County, AR : 36.401181999999999 , 93.737971000000002 , 5000 m; Palmer 26578 ( MOR ) 1924 , Hot Springs County , AR : 34.449057000000003 , 92.875575999999995 , 2000 m; Thomas 20078 ( WIS ) 1970 , Independence County, AR : 35.8980930 00000003 , 91.7033490 00000003 , 2300 m; Thomas 15088 ( COLO ) 1969 , Independence County, AR : 35.904877999999997 , 91.681726999999995 , 250 m; Dem aree 21297 ( MO ) 1940 , Logan County, AR : 35.165858999999998 , 93.630341999999999 , 1500 m; Palmer 24121 ( MOR ) 1923 , L ogan County, AR : 35.168405999999997 , 93.631581999999995 , 4700 m; Kim 83.5 ( MOR ) 2004 , Logan County, AR : 35.171532999999997 , 93.651799999999 994 , 300 m; Palmer 5982 ( MO ) 1914 , Marion County, AR : 36.261074999999998 , 92.538606999999999 , 1000 m; Demaree 3794 4 ( M O ) 1955 , Montgomery County, AR : 34.556736000000001 , 93.635075999999998 , 7000 m; Bates 10448 ( MO ) 1990 , Montgomery County, AR : 34.681891 , 93.582954999999998 , 350 m; Redfearn, Jr. 27532 ( MO ) 1971 , Newton County, AR : 35.843000000000004 , 93.389815999999 996 , 450 m; Iltis 5468 ( WIS ) 1955 , Newton County, AR : 35.947299000000001 , 93.077395999999993 , 1600 m; Hess 6889 ( MOR ) 1992 , Pope County, AR : 35.549002000000002 , 93.160916999999998 , 1800 m; Weber 11554 ( COLO ) 1963 , Boulder County, CO : 39.9 86649300000003 , 105.2860276 , 1300 m; Ramaley 2504 ( RM ) 1906 , Boulder County, CO : 39.996893999999998 , 105.301751 , 500 m; Denham 85449 ( CS ) 1985 , Boulder Cou nty, CO : 39.997287999999998 , 105.27967 , 500 m; Ramaley 874 ( COLO ) 1901 , Boulder County, CO : 39.999487999999999 , 1 05.389087 , 8500 m; Clokey 4157 ( MO ) 1921 , Boulder County, CO : 40.005927999999997 , 105.406142 , 100 m; Weber 18069 ( COLO ) 1990 , Boulder County , CO : 40.013018000000002 , 105.297774 , 500 m; Penfound s.n. ( COLO ) 1924 , Boulder County, CO : 40.014986 , 105.270546 , 8000 m; Ackerfield 2061 ( CS ) 2004 , Boulder County, CO : 40.236666669999998 , 105.3197222 , 500 m; Scully 263 ( COLO ) 2007 , Boulder County, CO : 40.247773000000002 , 105.300646 , 1000 m; Bates s.n. ( NEB ) 1919 , Clear Creek County, CO : 39.742488000000002 , 105.5 13608 , 2300 m; Marriage s.n. ( CS ) 1936 , El Paso County, CO : 38.739488999999999 , 104.87908899999999 , 1500 m; Glatfelter s.n. ( MO ) 1905 , El Pa so County, CO : 38.785288999999999 , 104.874647 , 500 m; Christ 1242 ( CS ) 1935 , El Paso County, CO : 38.79272999999999 9 , 104.88525 , 1500 m; Clokey 4159 ( RM ) 1921 , El Paso County, CO : 39.123080000000002 , 104.91488699999999 , 1000 m; Bessey s.n. ( NEB ) 1895 , El Paso County, CO : 38.790289999999999 , 104.86621599999999 , 1800 m; Murdock 281 ( COLO ) 1995 , Jefferson County, CO : 3 9.9043657 , 105.2073967 , 2250 m; Rondeau 97 088 ( COLO ) 1997 , Larimer County, CO : 40.526113000000002 , 105.18230200000001 , 300 m; Bates 7125 ( NEB ) 1919 , Larimer County, CO : 40.397761000000003 , 105.07498 , 6000 m; Swink 9043 ( MOR ) 1989 , Cook County, IL : 41.5 20159 , 87.691243 , 500 m; Swink 6691 ( MOR ) 1986 , Cook County, IL : 41.836227000000001 , -

PAGE 71

63 87.838674999999995 , 800 m; Hedborn s.n. ( MOR ) 1978 , Jo Daviess County, IL : 42.351404000000002 , 90.182830999999993 , 3000 m; Chase 1481 ( MICH ) 1907 , Marshall County, IL : 41.018489000000002 , 89.521889999999999 , 5000 m; Deam 30075 ( IND ) 1919 , Decatur County, IN : 39.426125999999996 , 85.609543000000002 , 1000 m; Deam 45179 ( IND ) 1927 , Henry County, IN : 40.072732999999999 , 85.365326999999994 , 1000 m; 196 ( IND ) 1895 , Kosciusk o County, IN : 41.396236999999999 , 85.694063999999997 , 3700 m; Deam 55102 ( IND ) 1934 , La Porte County, IN : 41.511932000000002 , 86.5431930000 00002 , 2000 m; Otto 26 ( MOR ) 1982 , Lake County, IN : 41.605787999999997 , 87.265669000000003 , 3800 m; Deam 18113 ( IN D ) 1915 , Newton County, IN : 41.152593000000003 , 87.448515 , 600 m; Umbach 40127, 5614 ( WIS ) 1912 , Porter County, IN : 41.661569999999998 , 87. 054522000000006 , 6000 m; Deam 15152 ( IND ) 1914 , Starke County, IN : 41.213724999999997 , 86.58663 , 2300 m; Swink 582 1 ( MOR ) 1985 , Starke County, IN : 41.2361 , 86.665862000000004 , 1000 m; Deam 29455 ( IND ) 1919 , Warren County, IN : 40.398017000000003 , 87. 3297 74999999998 , 1500 m; Wood 29 ( WIS ) 1964 , Wayne County, IN : 39.817231 , 84.842949000000004 , 1500 m; Deam 1785 ( IND ) 1906 , Wells County, IN : 40.830370000000002 , 85.253208999999998 , 850 m; Pammel 1805 ( MO ) 1898 , Boone County, IA : 42.036549000000001 , 93.9316 70999999994 , 20000 m; van der Linden 1995 17 ( MOR ) 1995 , Clayton County, IA : 43.007522999999999 , 91.16838199999999 4 , 1800 m; Tolstead s.n. ( NEB ) 1935 , Clayton County, IA : 43.018318000000001 , 91.182630000000003 , 2300 m; Shinck s.n. ( WIS ) 1932 , Van Buren C ounty, IA : 40.753228999999997 , 91.949986999999993 , 26000 m; Tolstead NA ( NEB ) 1933 , Winneshiek County, IA : 43.2906 78999999997 , 91.843704000000002 , 30000 m; Hitchcock s.n. ( MO ) n.d. , Black Hawk County, IA? : 42.492764000000001 , 92.342962999999997 , 10000 m ; Oyster s.n. ( MO ) 1886 , Miami County, KS : 38.572234999999999 , 94.879472000000007 , 2000 m; Switzenberg s.n. ( MICH ) 1955 , Alger County, MI : 46.354979999999998 , 86.401655000000005 , 1500 m; Hickman G794 ( MOR ) 2000 , Berrien County, MI : 41.878281999999999 , 8 6.605321000000004 , 350 m; Lammers 12751 ( MICH ) 2010 , Delta County, MI : 45.902555999999997 , 86.577918999999994 , 250 m; Dieterle 1228 ( MICH ) 1954 , Grand Traverse County, MI : 44.675925999999997 , 85.391373000000002 , 2300 m; Parmelee 3315 ( MICH ) 1953 , Ingham County, MI : 42.733074000000002 , 84.312859000000003 , 5300 m; Hanes s.n. ( MICH ) 1948 , Kalamazoo County, MI : 42.36436 9000000003 , 85.356407000000004 , 1000 m; Smith 4034 ( MICH ) 1998 , Lenawee County, MI : 41.794324000000003 , 84.242442999999994 , 700 m; McVaugh 8886 ( MICH ) 1947 , Luce County, MI : 46.675764000000001 , 85.455982000000006 , 5000 m; Farwell 2738 ( MICH ) 1914 , Oakla nd County, MI : 42.680588 , 83.133820999999998 , 2300 m; Dodge s.n. ( MICH ) 1915 , Schoolcraft County, MI : 45.935420999999998 , 85.93138000000000 4 , 2300 m; Pennington s.n. ( MICH ) 1910 , Van Buren County, MI : 42.413733000000001 , 86.268319000000005 , 2000 m; Emer son 19 ( MICH ) 1924 , Washtenaw County, MI : 42.271253 000000002 , 83.648566000000002 , 5000 m; Almendinger s.n. ( MICH ) 1860 , Washtenaw County, MI : 42.298302999999997 , 83.742846 , 5000 m; Smith 27404 ( MO ) 1998 , Anoka County, MN : 45.200527999999998 , 93.2072 , 35 0 m; Mell s.n. ( MO ) 1903 , Crow Wing County, MN : 46.364655999999997 , 94.191902999999996 , 2000 m; Sandberg 380 ( CS ) 1891 , Goodhue County, MN : 44.510827999999997 , 92.905998999999994 , 2250 m; Smith 12418 ( MO ) 1986 , Wabasha County, MN : 44.256943999999997 , 91 .991667000000007 , 500 m; Fassett 31 03 ( WIS ) 1926 , Winona County, MN : 44.032235999999997 , 91.641738000000004 , 2200 m; Steyermark 77387 ( MO ) 1 954 , Audrain County, MO : 39.337260000000001 , 91.814914999999999 , 1000 m; Palmer 66426 ( WIS ) 1957 , Barry County, MO : 36.672896999999999 , 93.689316000000005 , 1800 m; Davidse 37934 ( MO ) 2001 , Barry County, MO : 36.684119000000003 , 93.596179000000006 , 1500 m ; Palmer 66426 ( MO ) 1957 , Barry County, MO : 36.684182 , 93.642976000000004 , 2250 m; Bush 15544 ( MO ) 1936 , Barry Cou nt y, MO :

PAGE 72

64 36.817841000000001 , 93.776171000000005 , 20500 m; Palmer 35969 ( MO ) 1929 , Benton County, MO : 37.502155000000002 , 93.589886000000007 , 6000 m; Steyermark 10726 ( MO ) 1936 , Benton County, MO : 38.238807999999999 , 93.143956000000003 , 1000 m; Palmer 36 772 ( MO ) 1930 , Benton County, MO : 38.246501000000002 , 93.386104000000003 , 2250 m; Palmer 26345 ( MOR ) 1924 , Benton County, MO : 38.45844100000 0001 , 93.205229000000003 , 2000 m; Palmer 26345 ( MO ) 1924 , Benton County, MO : 38.460183000000001 , 93.2017 940000000 07 , 2000 m; Ryan 1338 ( MO ) 1989 , Boone County, MO : 38.874274999999997 , 92.313028000000003 , 500 m; Rickett s.n. ( MO ) 1929 , Boone County, MO : 38.911645 , 92.339564999999993 , 10000 m; Dunn 16975 ( MO ) 1969 , Boone County, MO : 39.13373 , 92.322620999999998 , 500 m; Rickett s.n. ( MO ) 1927 , Boone County, MO : 39.13373 , 92.322620999999998 , 500 m; Steyermark 76678 ( MO ) 1954 , Callaway County, MO : 38.80278 5999999998 , 91.662142000000003 , 750 m; Steyermark 26101 ( MO ) 1937 , Callaway County, MO : 38 .814736000000003 , 91.91 5212999999994 , 1000 m; McVeigh s.n. ( MO ) 1933 , Callaway County, MO : 38.880837999999997 , 91.889011999999994 , 1000 m; Korschgen 1139 ( MO ) 1975 , Camden County, MO : 38.02704 , 92.766053999999997 , 35000 m; Kastler 58575 ( MO ) 1966 , Carter County, MO : 36.9424009 99999997 , 91.010338000000004 , 4000 m; Palmer 6183 ( MO ) 1914 , Carter County, MO : 36.995415000000001 , 91.014927999999998 , 1000 m; Palmer 6183 ( MICH ) 1914 , Carter County, MO : 36.998663000000001 , 91.008211000000003 , 3000 m; Stey ermark 12284 ( MO ) 1936 , Carte r County, MO : 37.074660000000002 , 91.049302999999995 , 1000 m; Steyermark 13547 ( MO ) 1934 , Cedar County, MO : 37.794440999999999 , 93.71221599 9999998 , 7000 m; Sikes 1 ( MO ) 1998 , Christian County, MO : 36.862408000000002 , 93.224862000000002 , 500 m; Sikes 1 ( MICH ) 1998 , Christian County, MO : 36.862777999999999 , 93.228333000000006 , 1000 m; Bush 3478 ( MO ) 1905 , Christian County, MO : 36.969571999999 999 , 93.188854000000006 , 28000 m; Bush 5892 ( MO ) 1909 , Cl ark County, MO : 40.521110999999998 , 91.635598999999999 , 1000 m; Bush 15534 ( MO ) 1936 , Cooper County, MO : 38.699876000000003 , 93.002212999999998 , 1500 m; Bush 13660 ( MO ) 1933 , Cooper County, MO : 38 .843539999999997 , 92.810119 , 27000 m; Ryan 2142 ( MO ) 1992 , Crawford County, MO : 37.714618000000002 , 91.1622700000 00007 , 1300 m; Feltz 44 ( MO ) 2009 , Crawford County, MO : 38.079526999999999 , 91.214106000000001 , 2000 m; Steyermark 15432 ( MO ) 1934 , Crawford County, MO : 38.116539000000003 , 91.463804999999994 , 1000 m; Palmer 54349 ( MO ) 1952 , Dade County, MO : 37.353827000 000003 , 93.789244999999994 , 2250 m; Palmer 51285 ( WIS ) 1950 , Dade County, MO : 37.497900000000001 , 93.843153999999998 , 2000 m; Palmer 51285 ( MO ) 1950 , Dade County, MO : 37.502439000000003 , 93.833432999999999 , 1500 m; Steyermark 5664 ( MO ) 1938 , Dade County , MO : 37.515887999999997 , 93.773932000000002 , 500 m; Steyermark 13693 ( MO ) 1934 , Dallas County, MO : 37.633898000000002 , 93.027398000000005 , 2000 m; Steyermark 12830 ( M O ) 1936 , Dent County, MO : 37.549425999999997 , 91.349639999999994 , 1500 m; Palmer 34958 ( MOR ) 1928 , Dent County, MO : 37.617778000000001 , 91.291667000000004 , 2000 m; Steyermark 12551 ( MO ) 1936 , Dent County, MO : 37.65918200000000 1 , 91.358134000000007 , 5500 m; Steyermark s.n. ( MO ) 1931 , Dent County, MO : 37.755049999999997 , 91.341623999999996 , 1000 m; Steyermark 14730 ( MO ) 1934 , Douglas County, MO : 36.865850999999999 , 92.485736000000003 , 1000 m; Taylor s.n. ( IND ) 1989 , Franklin C oun ty, MO : 38.459200000000003 , 90.831900000000005 , 2000 m; Taylor 5960 ( MO ) 1989 , Franklin County, MO : 38.46119099 9999999 , 90.829976000000002 , 1000 m; Steyermark 13306 ( MO ) 1934 , Hickory County, MO : 38.011775999999998 , 93.088117999999994 , 2000 m; Steyer mark 14414 ( MO ) 1934 , Howell County, MO : 36.592140000000001 , 92.035571000000004 , 2250 m; Steyermark 23571 ( MO ) 193 7 , Howell County, MO : 36.947642999999999 , 92.042972000000006 , 1000 m; Palmer 6231 ( MO ) 1914 , Howell County, MO : 36.992524000000003 , 9 1.9691 73999999995 , 2000 m; Trelease 140

PAGE 73

65 ( MO ) 1897 , Iron County, MO : 37.4664 , 90.648340000000005 , 20000 m; Steyermark 835 4 ( MO ) 1933 , Iron County, MO : 37.541539999999998 , 90.682875999999993 , 400 m; Trelease 138 ( MO ) 1897 , Jasper County, MO : 37.057453000000002 , 94.517356000000007 , 7000 m; Palmer 16016 ( MO ) 1918 , Jasper County, MO : 37.110365000000002 , 94.519130000000004 , 22 50 m; Palmer 6 ( MO ) 1901 , Jasper County, MO : 37.174720999999998 , 94.458094000000003 , 500 m; Palmer 24083 ( MO ) 1923 , Jasp er County, MO : 37.18 6407000000003 , 94.320536000000004 , 2000 m; Palmer 24083 ( MOR ) 1923 , Jasper County, MO : 37.187114999999999 , 94.310 222999999993 , 5000 m; Palmer 1455 ( MO ) 1908 , Jasper County, MO : 37.335298000000002 , 94.300836000000004 , 3000 m; Raven 27190 ( MO ) 1986 , Jeffe rson County, MO : 38.125112999999999 , 90.670610999999994 , 250 m; Raven 27190 ( MICH ) 1986 , Jefferson County, MO : 38. 125455000000002 , 90.671206999999995 , 400 m; Hitchcock s.n. ( MO ) 1891 , Jefferson Cou nty, MO : 38.178100000000001 , 90.525270000000006 , 1000 m; Hess 6837 ( MOR ) 1992 , Jefferson County, MO : 38.262301999999998 , 90.624859999999998 , 700 m; Steyermark 13892 ( MO ) 1934 , Laclede County, MO : 37.592432000000002 , 92.364264000000006 , 1000 m; Palmer 55728 ( MO ) 1953 , Lawrence County, MO : 36.946002 , 93.790336 999999994 , 500 m; Davis 4419 ( MO ) 1915 , Lincoln County, MO : 39.123288000000002 , 91.055552000000006 , 1500 m; Steyer mark 26029 ( MO ) 1937 , Lincoln County, MO : 39.145839000000002 , 91.031728999 999999 , 1000 m; Rowan 1326 ( MO ) 1994 , Madison County, MO : 37.33048 6000000001 , 90.442527999999996 , 500 m; Erickson 36 ( MO ) 1996 , Madison County, MO : 37.480441999999996 , 90.31012400 0000002 , 1500 m; Steyermark 15268 ( MO ) 1934 , Maries County, MO : 38.131027000000003 , 91.830015000000003 , 2000 m; Bush 10196A ( MO ) 1923 , McDon ald County, MO : 36.516151999999998 , 94.611419999999995 , 1000 m; Palmer 4172 ( MO ) 1913 , McDonald County, MO : 36.546 785999999997 , 94.483484000000004 , 1500 m; Steyermark 15815 ( MO ) 1934 , Montgomery County, MO : 38.771089000000003 , 91.576560000000001 , 400 m; Steyermark 13141 ( MO ) 1934 , Morgan County, MO : 38.272503999999998 , 92.945631000000006 , 4500 m; Palmer 52973 ( WIS ) 1951 , Newton County, MO : 37.040368999999998 , 94.561914000000002 , 1000 m; Palmer 52973 ( MO ) 1951 , Newton County, MO : 37.043618000000002 , 94 .563108 , 500 m; Palmer 6266 ( MO ) 1914 , Newton County, MO : 37.068893000000003 , 94.116286000000002 , 1500 m; Steyerma rk 14355 ( MO ) 1934 , Oregon County, MO : 36.7937 05000000003 , 91.343856000000002 , 1000 m; Steyermark 7814 ( MO ) 1933 , Ozark County, MO : 36.68720 2999999997 , 92.470912999999996 , 1000 m; Steyermark 25355 ( MO ) 1937 , Phelps County, MO : 37.92906 , 92.0086909999999 99 , 1500 m; Davis 3098 (dup) ( MO ) 1914 , Pike County, MO : 39.235878999999997 , 91.008191999999994 , 3000 m; Steyermark 24093 ( MO ) 1937 , Polk Co unty, MO : 37.784547000000003 , 93.418164000000004 , 1000 m; Steyermark 11815 ( MO ) 1936 , Ripley County, MO : 36.694989 999999997 , 91.031687000000005 , 1000 m; Drouet 1468 ( MO ) 1934 , Saint Charles County, MO : 38.854494000000003 , 90.902963999999997 , 500 m; Stey ermark 82168 ( MO ) 1956 , Saint Clair County, MO : 37.909694999999999 , 93.775820999999993 , 500 m; Steyermark 24460 ( M O ) 1937 , Saint Clair County, MO : 38.175026000000003 , 93.513565999999997 , 1500 m; Trelease 137 ( MO ) 1897 , Saint Francois County, MO : 37.84947 3000000003 , 90.517139 , 4000 m; Davidse 40877 ( MO ) 2013 , Saint Francois County, MO : 37.988861 , 90.514250000000004 , 500 m; Letterman s.n. ( MO ) 1911 , Saint Louis County, MO : 38.501382 , 90.672381000000001 , 1000 m; Christ s.n. ( MO ) 1931 , Saint Louis County, MO : 38.554763000000001 , 90.532419000000004 , 1000 m; Hitchcock s.n. ( MO ) 1890 , Saint Louis County, MO : 38.566994000 000001 , 90.411790999999994 , 1500 m; Steyermark 8788 ( MO ) 1933 , Sainte Genevieve County, MO : 37.857587000000002 , 90.217224999999999 , 5500 m; Trelease 978 ( MO ) 1898 , Sainte Genevieve County, MO : 37.884583999999997 , 90.373557000000005 , 4500 m; Bush 5098 ( M O ) 1908 , Shannon County, MO : 36.987051000000001 , 91.575441999999995 , 1500 m; Redfearn, Jr. 788 ( MO ) 1969 , Shannon County,

PAGE 74

66 MO : 37.03870200000 0001 , 91.605720000000005 , 1000 m; Miller 8142 ( MO ) 1993 , Shannon County, MO : 37.133333 , 91.466667000000001 , 2000 m; Redfearn, Jr. 10067 ( MO ) 1962 , Shannon County, MO : 37.133443999999997 , 91.477643999999998 , 1000 m; Redfearn, Jr. 569 ( MO ) 1969 , Shannon C ounty, MO : 37.154716999999998 , 91.440360999999996 , 500 m; Palmer 40901 ( MO ) 1933 , Shelby County, MO : 39.8753499999 99997 , 92.025434000000004 , 1000 m; Ladd 4405 ( MOR ) 1979 , St. Genevieve County, MO : 37.830564000000003 , 90.227688000000001 , 250 m; Ladd 4030 ( MOR ) 1979 , St. Genevieve County, MO : 37.842277000000003 , 90.266435999999999 , 1250 m; Bushnell 162 ( WIS ) 1969 , St . Louis County, MO : 38.638587000000001 , 90.668301 , 1000 m; Moore 243 ( WIS ) 1953 , Stone County, MO : 36.559646000000001 , 93.422864000000004 , 10000 m; Steyermark 22720 ( MO ) 1937 , Stone County, MO : 36.6 20719000000001 , 93.353091000000006 , 1000 m; Palmer 5793 ( MO ) 1914 , Stone County, MO : 36.805387000000003 , 93.462519999999998 , 1000 m; Moore 174 ( WIS ) 1953 , Taney County, MO : 36.597228000000001 , 9 3.307528000000005 , 2300 m; Palmer 23908 ( MO ) 1923 , Taney County, MO : 36.643397999999998 , 93.218513999999999 , 2250 m; Steyermark 14553 ( MO ) 1934 , TX County, MO : 37.059404999999998 , 91.686002000000002 , 1000 m; Palmer 60304 ( MO ) 1955 , TX County, MO : 37.3226 64000000003 , 92.007541000000003 , 3000 m; Palmer 60 304 ( WIS ) 1955 , TX County, MO : 37.327236999999997 , 92.007591000 000005 , 2300 m; Miller 8293 ( MO ) 1994 , Washington County, MO : 37.872974999999997 , 90.907926000000003 , 1000 m; Davidse 42433 ( MO ) 2015 , Washi ngton County, MO : 38.081277999999998 , 90.974417000000003 , 1000 m; Steyermark 7029 ( MO ) 1932 , Washington County, MO : 38.083784999999999 , 90.737832999999995 , 500 m; Steyermark 23686 ( MO ) 1937 , Wright County, MO : 37.088611 , 92.657777999999993 , 1500 m; Tols tead 41857 ( NEB ) 1 941 , Brown County, NE : 42.749907999999998 , 99.857144000000005 , 2300 m; Phillippe 42054 ( NEB ) 200 9 , Brown County, NE : 42.765155 , 99.886041000000006 , 550 m; Steinauer 1903 ( NEB ) 2003 , Cherry County, NE : 42.850555999999997 , 100.215833 , 25 0 m; Hitchcock 1025 ( NEB ) 1981 , Cherry County, NE : 42.858266 , 100.235613 , 2300 m; Churchill 12172 ( NEB ) 1982 , Cher ry County, NE : 42.859746000000001 , 100.238867 , 3500 m; Tolstead 534 ( NEB ) 1936 , Cherry County, NE : 42.887810999999999 , 100.31576099999999 , 1000 m; Clements s.n. ( NEB ) 1893 , Holt County, NE : 42.455711000000001 , 98.783844999999999 , 57000 m; Kiener 23721 ( NEB ) 1948 , Keya Paha County, NE : 42.759141 , 99.829094999999995 , 2300 m; Clements 2951 ( NEB ) 1893 , Keya Paha County, NE : 42.878829000000003 , 99.712350000000001 , 41000 m; Legler 5876 ( DBG ) 2007 , Colfax County, NM : 36.926715999999999 , 104.6815 , 75 m; Water fall 9198 ( COLO ) 1949 , Delaware County, OK : 36.408199000000003 , 94.802650999999997 , 34800 m; Over 15830 ( RM ) 1924 , Custe r County, SD : 43.580 261 , 103.439412 , 9500 m; Porter 6728 ( RM ) 1955 , Custer County, SD : 43.618886000000003 , 103.463319 , 2250 m; Mayer 243 ( RM ) 2001 , Custer County, SD : 43.840263 , 103.43944999999999 , 500 m; Jones s.n. ( COLO ) 1953 , Custer County, SD : 43.847034999999998 , 103. 628122 , 2000 m; Mayer 519 ( RM ) 2005 , Pennington County, SD : 43.874181 , 103.463035 , 750 m; Stoesz s.n. ( NEB ) 1937 , Pennington County, SD : 43.876913999999999 , 103.439691 , 400 m; Marriott 12093 ( RM ) 2000 , Pennington County, SD : 43.887062 , 103.530057 , 100 m ; Over 15828 ( RM ) 1924 , Pennington County, SD : 43.905296 , 103.535073 , 750 m; Salamun 565 ( MO ) 1947 , Pennington Cou nty, SD : 43.974138000000004 , 103.290362 , 1500 m; Severson 232 ( USFS ) 1971 , Pennington County, SD : 44.025300000000001 , 103.6392 , 500 m; Will iams s.n. ( MO ) 1891 , Pennington County, SD : 44.08005 , 103.231015 , 5000 m; Alverson 1632 ( WIS ) 1980 , Adams County, WI : 43.915655999999998 , 89.849356999999998 , 850 m; Brown 42 ( WIS ) 1948 , Adams County, WI : 43.989336999999999 , 89.668289000000001 , 5500 m; A lfieri s.n. ( WIS ) 1963 , Adams County, WI : 44.029490000000003 , 89.712784999999997 , 2300 m; Palmer 28539 ( MO ) 1925 , Buffalo County, WI : 44.132838999999997 , 91.713595999999995 , 1000 m; Fassett

PAGE 75

67 15457 ( MO ) 192 7 , Buffalo County, WI : 44.132838999999997 , 91.713 595999999995 , 1000 m; Fassett 15457 ( WIS ) 1927 , Buffalo County, WI : 44.133017000000002 , 91.715672999999995 , 2300 m ; Baird s.n. ( WIS ) 1916 , Burnette County, WI : 46.006611999999997 , 92.371305000000007 , 2000 m; Patman s.n. ( WIS ) 1959 , Chippewa County, WI : 4 5.046199999999999 , 91.27413 , 800 m; Lammers 14410 ( MICH ) 2013 , Clark County, WI : 44.459865000000001 , 90.679045000 000002 , 800 m; Goessl 1486 ( MPM ) 1915 , Clark County, WI : 44.559961 999999999 , 90.596249999999998 , 2250 m; Fassett s.n. ( WIS ) 1935 , Columbia C ounty, WI : 43.306984999999997 , 89.706198000000001 , 1700 m; Hess 7886 ( MO ) 1997 , Columbia County, WI : 43.6034050000 00002 , 89.157554000000005 , 100 m; Moore 3 ( WIS ) 1976 , Crawford County, WI : 43.049101 , 90.964911999999998 , 2000 m; Fassett 15475 ( WIS ) 1927 , Crawford County, WI : 43.051650000000002 , 91.141239999999996 , 3800 m; Stueber 23 ( WIS ) 1976 , Crawford County, WI : 43.051650000000002 , 91.141239999999996 , 3800 m; Peters 129 ( WIS ) 1973 , Crawford County, WI : 43.056165 , 91.114681000000004 , 700 m; R. H. Den niston s.n. ( WIS ) 1915 , Crawford County, WI : 43.246367999999997 , 91.056239000000005 , 1700 m; Nee 62467 ( WIS ) 2015 , Crawford County, WI : 43.320555599999999 , 90.894999999999996 , 200 m; Armstrong 34 88 ( WIS ) 1988 , Dane County, WI : 42.900367000000003 , 89.54 0656999999996 , 1500 m; Miller 322 ( WIS ) 1967 , Dane County, WI : 43.084631999999999 , 89.596284999999995 , 2300 m; Den niston s.n. ( WIS ) 1927 , Dunn County, WI : 44.692186999999997 , 92.011562999999995 , 2300 m; Kunz 65 ( WIS ) 1928 , Eau Claire County, WI : 44.76357 2000000003 , 91.277099000000007 , 2300 m; Iltis 10417 ( WIS ) 1957 , Grant County, WI : 42.684817000000002 , 90.49293199 9999996 , 2300 m; Roethke 78 ( WIS ) 1980 , Grant County, WI : 42.725490000000001 , 90.532390000000007 , 1500 m; Iltis 6531 ( WIS ) 1956 , Grant Count y, WI : 42.733719999999998 , 91.019574000000006 , 1600 m; Fassett 13454 ( WIS ) 1930 , Grant County, WI : 42. 783324999999 998 , 90.613738999999995 , 2300 m; Fassett 13462 ( WIS ) 1930 , Grant County, WI : 42.847490000000001 , 90.710682000000006 , 2200 m; Nee 61376 ( WIS ) 2014 , Grant County, WI : 42.951667 , 91.141110999999995 , 1000 m; Davis s.n. ( WIS ) 1913 , Grant County, WI : 43.03331 8000000001 , 90.930126999999999 , 2200 m; Penney 191 ( WIS ) 1961 , Grant County, WI : 43.044992999999998 , 90.535304999999994 , 2000 m; Fell 58 47 1 ( WIS ) 1958 , Green County, WI : 42.628534000000002 , 89.760468000000003 , 1800 m; Rice 1359 ( WI S ) 1972 , Green County , WI : 42.648842000000002 , 89.727887999999993 , 2000 m; Fell 58 519 ( WIS ) 1958 , Green County, WI : 42.649883000000003 , 89.396944000000005 , 210 0 m; Fell 58 266 ( WIS ) 1958 , Green County, WI : 42.733719999999998 , 91.019574000000006 , 1500 m; Cochrane 15340 ( WIS ) 2011 , IA County, WI : 42.972695000000002 , 89.859786 , 2000 m; Schall 43656 ( MOR ) 1991 , IA County, WI : 43.064436000000001 , 89.95057900000000 5 , 350 m; Brady s.n. ( WIS ) n.d. , IA County, WI : 43.081491 , 89.9711 27999999993 , 2000 m; Cross 107 ( WIS ) 1959 , IA Co unty, WI : 43.149566 , 90.045614999999998 , 1000 m; Smith 6856 ( WIS ) 1922 , Jackson County, WI : 44.294682999999999 , 90.851530999999994 , 3000 m; Grether 6384 ( WIS ) 1947 , Jackson County, WI : 44.414684000000001 , 90.730697000000006 , 2000 m; Sorensen 2899 ( WIS ) 1962 , Juneau County, WI : 43.752614999999999 , 89.985067000000001 , 2300 m; Steenis s.n. ( WIS ) 1932 , Juneau County, WI : 44.025584000000002 , 90 .074017999999995 , 10000 m; Pammel 48 ( MO ) 1887 , La Crosse County, WI : 43.790645599999998 , 91.199458199999995 , 2000 m; Schnurrer 17 ( WIS ) 1976 , La Crosse County, WI : 43.817860000000003 , 91.195870999999997 , 2000 m; Hansen 1089 ( WIS ) 1972 , Lafayette County, WI : 42.789706000000002 , 89.850773000000004 , 1500 m; Grassl 3190 ( MICH ) 1933 , Marinette County, WI : 45.07448099999 9999 , 87.680443999999994 , 5400 m; Iltis 6282 ( WIS ) 1956 , Marquette County, WI : 43.690438 , 89.394082999999995 , 500 m; Thompson 87 ( WIS ) 1956 , Marquette County, WI : 4 3.756915999999997 , 89.280911000000003 , 2000 m; Ritchie 25 ( WIS ) 1976 , Marquette County, W I : 43.773535000000003 , 89.448446000000004 , 800 m;

PAGE 76

68 Peters 11 ( WIS ) 1973 , Marquette County, WI : 43.976421000000002 , 89.379335999999995 , 550 m ; Iltis 8639 ( WIS ) 1957 , Monroe County, WI : 43.821640000000002 , 90.737520000000004 , 1750 m; Black 02 P15 ( WIS ) 200 2 , Pepin County, WI : 44.543236999999998 , 92.073612999999995 , 400 m; Davis s.n. ( WIS ) 1916 , Pierce County, WI : 44.562190000000001 , 92.307900 000000004 , 1300 m; Hansen 4036 ( WIS ) 1976 , Pierce County, WI : 44.632285000000003 , 92.587329999999994 , 2300 m; Fass ett 15454 ( WIS ) 1927 , Pierce County, WI : 44.751036999999997 , 92.803133000000003 , 27000 m; Baird s.n. ( WIS ) 1916 , Polk County, WI : 45.359572 , 92.633538999999999 , 3000 m; Baker 6412 ( COLO ) 1900 , Polk County, WI : 45.408017000000001 , 92.638648000000003 , 225 0 m; Sorensen 1788 ( WIS ) 1962 , Portage County, WI : 44.268312999999999 , 89.656835000000001 , 4000 m; Mauritz 634 ( WIS ) 1964 , Portage County, W I : 44.596795 , 89.268009000000006 , 1000 m; Nee 60161 ( WIS ) 2013 , Richland County, WI : 43.190556000000001 , 90.66500 0000000006 , 500 m; Huffman s.n. ( WIS ) 1968 , Richland County, WI : 43.202388999999997 , 90.256326000000001 , 6000 m; Nee 43755 ( MO ) 19 93 , Richla nd County, WI : 43.226847999999997 , 90.632265000000004 , 2250 m; Nee 16317 ( MO ) 1978 , Richland County, WI : 43.296684 999999997 , 90.337011000000004 , 500 m; Nee 37799 ( MO ) 1989 , Richland County, WI : 43.296684999999997 , 90.337011000000004 , 500 m; Nee 37799 ( W IS ) 1989 , Richland County, WI : 43.303617000000003 , 90.345149000000006 , 2000 m; Nee 16317 ( MPM ) 1978 , Richland Coun ty, WI : 43.304090000000002 , 90.339691000000002 , 1000 m; Fosberg 272 ( WIS ) 1948 , Richland County, WI : 43.329093 , 90.292722999999995 , 1100 m; Nee 2573 ( WIS ) 1969 , Richland County, WI : 43.331519 , 90.657972999999998 , 2000 m; Iltis 9901 ( WIS ) 1957 , Richland County, WI : 43.365192999999998 , 90.264280999999997 , 1000 m; Iltis 11847 ( WIS ) 1958 , Richland County, WI : 43.373038999999999 , 90.26734799999 9998 , 2300 m; Nee 6054 ( WIS ) 1973 , Richland County, WI : 43.525258000000001 , 90.354583000000005 , 2000 m; Nee s.n. ( WIS ) 1974 , Richland County, WI : 43.550457000000002 , 90.659516999999994 , 1500 m; Fell 57 1270 ( WIS ) 1957 , Rock County, WI : 42.533481000000002 , 89.278717999999998 , 2000 m; Stearns 886 ( WIS ) 1941 , Sauk County, WI : 43.241101 , 89.81429 , 1300 m; Hansen 1017 ( WIS ) 1932 , Sauk County, WI : 43.532480999999997 , 90.002626000000006 , 3700 m; Eggert s.n. ( MO ) 1903 , Sauk County, WI : 43.565275999999997 , 89. 860292999999999 , 2250 m; De Stefano 260 ( WIS ) 1980 , Shawano County, WI : 44.650505000000003 , 88.546476999999996 , 51 00 m; Iltis 7202 ( WIS ) 1956 , St. Croix County, WI : 45.164146000000002 , 92.271624000000003 , 7500 m; Taylor 3306 ( MPM ) 1977 , Trempealeau Count y, WI : 44.017741000000001 , 91.465699999999998 , 1800 m; Levins 212 ( MOR ) 1983 , Vernon County, WI : 43.54648499999999 7 , 90.906640999999993 , 1200 m; Nee 63860 ( WIS ) 2017 , Vernon County, WI : 43.625813000000001 , 91.218840999999998 , 1800 m; Kline s.n. ( WIS ) 19 74 , Vernon County, WI : 43.630892000000003 , 90.624459000000002 , 2300 m; Baird s.n. ( WIS ) 1920 , Vernon County, WI : 4 3.657747999999998 , 91.096519999999998 , 1500 m; Swink 9171 ( MOR ) 1989 , Wa lworth County, WI : 42.547099000000003 , 88.578609999999998 , 1200 m; Shannon 124 ( MO ) 1905 , Walworth County, WI : 42.821389000000003 , 88.320832999999993 , 500 m; Sheaffer s.n. ( WIS ) 195 7 , Waukesha County, WI : 43.116394 , 88.489444000000006 , 2300 m; Iltis 13531 ( WIS ) 1959 , Waupaca County, WI : 44.690893000000003 , 88.672653999 999994 , 600 m; Cochrane 10359 ( WIS ) 1984 , Waushara County, WI : 44.003326999999999 , 89.282195000000002 , 250 m; Fass ett 21345 ( MO ) 1941 , Waushara County, WI : 44.022089000000001 , 8 9.321331000000001 , 4000 m; Hess 4839 ( MOR ) 1979 , Waushara County, WI : 44.0481 04000000002 , 89.133374000000003 , 1000 m; Dennis s.n. ( WIS ) 1966 , Waushara County, WI : 44.232447999999998 , 89.1911 98 , 1000 m; Hanneman, Jr. 8 ( WIS ) 1964 , Wood County, WI : 44.363264999999998 , 89.750553999999994 , 1100 m. Physocarpus monogynus (Torr.) Coult .

PAGE 77

69 United States: Blumer 1278 ( MO ) 1906 , Cochise County, AZ : 31.847064 , 109.291579 , 1000 m; Daniels 693 ( COLO ) 1906 , Boulder County, CO : 39.997925000000002 , 105.297826 , 50 0 m; Zobel s.n. ( DBG ) 1939 , Boulder County, CO : 40.131252000000003 , 105.51669099999 999 , 10000 m; Patterson 23 ( MO ) 1885 , Clear Creek County, CO : 39.733961999999998 , 105.684226 , 1500 m; Degener 1688 5 ( MO ) 1942 , Clear Creek County, CO : 39.742488000000002 , 105.513608 , 2250 m; Elliott 8575 ( COLO ) 1999 , Custer County, CO : 38.179712000000002 , 105.17367400000001 , 750 m; Islam 12 102 ( DBG ) 2012 , Douglas County, CO : 39.448810000000002 , 104.95601000000001 , 200 m; Brumback 75b ( DBG ) 1912 , El Paso County, CO : 38.838554999999999 , 105.04318600000001 , 5500 m; Clements 122 ( MO ) 1901 , El Paso County, CO : 38.849133000000002 , 104.961853 , 500 m; Van Schaack 3584 ( MO ) 1957 , El Paso County, CO : 38.917617999999997 , 1 04.955782 , 1000 m; Harrington 7524 ( CS ) 1954 , Fremont County, CO : 38.452235999999999 , 105.493431 , 2000 m; Baker 115 ( MO ) 1901 , Gunnison Coun ty, CO : 38.578498000000003 , 107.716832 , 1500 m; Miller 6718 ( MO ) 1991 , Jefferson County, CO : 39.407465999999999 , 105.171468 , 500 m; Duff 10 ( COLO ) 1992 , Jefferson County, CO : 39.657364000000001 , 105.248058 , 800 m; Imler 4051 ( CS ) 1937 , Jefferson County, CO : 39.736455999999997 , 105.242672 , 1300 m; Nunn 3970 ( RM ) 2001 , Larimer County, CO : 40.364806000000002 , 105.426 694 , 1000 m; Nunn 2459 ( RM ) 2001 , Larimer County, CO : 40.444876999999998 , 105.432385 , 1000 m; Nunn 3591 ( CS ) 2001 , Larimer County, CO : 40.67 5182999999997 , 105.46863999999999 , 850 m; Myers 37 ( CS ) 1972 , Larimer County, CO : 40.675814000000003 , 105.350908 , 1500 m; Nunn 3591 ( RM ) 2001 , Larimer County, CO : 40.677599999999998 , 105.4676 , 500 m; Osterhout 3432 ( COLO ) 1906 , Larimer County, CO : 40.69 9283999999999 , 105.58129599999999 , 3000 m; Weber 4859 ( COLO ) 1949 , Larimer County, CO : 40.762613000000002 , 105.17 9537 , 1000 m; Clark 604 ( COLO ) 1996 , Larimer County, CO : 40.797114000000001 , 105.30139200000001 , 1000 m; Rollins 1842 ( MO ) 1937 , Las Animas County, CO : 37.054934000000003 , 104.376896 , 800 m; Regensberg 1285 ( DBG ) 2013 , Pueblo County, CO : 38.1133499999999 97 , 104.94295 , 500 m; Werner s.n. ( DBG ) 1957 , Summit County, CO : 39.572099999999999 , 105.997201 , 3500 m; Lederer 4509 ( COLO ) 1995 , Weld Cou nty, CO : 40.913906799999999 , 103.6015522 , 2250 m; Ramaley 1371 ( COLO ) 1905 , County, CO : 38.945824999999999 , 105. 28943599999999 , 2250 m; Engelmann s.n. ( MO ) 1874 , County, CO : 39.742185999999997 , 105.709688 , 2000 m; Ginter 522 ( CS ) 1941 , Larimer County, CO : 40.173358999999998 , 105.37057799999999 , 2000 m; Owens 205 ( CS ) 1998 , Larimer County, CO : 40.531225999999997 , 105.184102 , 500 m; Denham 91020 ( CS ) 1991 , Larimer County, CO : 40.689965000000001 , 105.349079 , 500 m; Worthington 12190 ( COLO ) 1984 , Lincol n County, NM : 33.385970999999998 , 105.739812 , 1500 m; Reif 1369 ( COLO ) 2002 , Los Alamos County, NM : 35.93100799999 9999 , 106.34675 6 , 700 m; Hartman 77970 ( COLO ) 2003 , Sandoval County, NM : 35.89123 , 106.550704 , 500 m; Reif 7439 ( COLO ) 2003 , Santa Fe Count y, NM : 35.807909000000002 , 105.84642700000001 , 1000 m; Reif 7946 ( CS ) 2003 , Santa Fe County, NM : 35.82160300000000 3 , 105.79478400000001 , 500 m; Metcalfe 988 ( MO ) 1904 , Sierra County, NM : 32.994446000000003 , 107.83361499999999 , 43000 m; Metcalfe 277 ( RM ) 1903 , Soccoro (Catron? Label says Soccoro) County, NM : 33.199500999999998 , 108.470595 , 1000 m; W aterfall 10740 ( C OLO ) 1952 , Cimarron County, OK : 36.947217000000002 , 102.968362 , 1500 m; Over 15829 ( RM ) 1924 , Custer County, SD : 43.580261 , 103.439412 , 950 0 m; Marriott 11923 ( RM ) 2000 , Custer County, SD : 43.838948000000002 , 103.491467 , 500 m; Goodman 3320 ( MO ) 1941 , P ennington County, SD : 43.880795999999997 , 103.53086999999999 , 1500 m; Young s.n. ( MO ) 1916 , Culberson County, TX : 31.891480000000001 , 104.8 6056000000001 , 250 m; Goodding 1123 ( MO ) 1902 , Utah County, UT : 40.25412 , -

PAGE 78

70 111.55790500000001 , 50000 m; Osterhout s .n. ( COLO ) 1922 , County, UT : 40.634343000000001 , 111.689724 , 8000 m; Nelson 4567 ( RM ) 1940 , Albany County, WY : 41.004897 , 105.520371999999 99 , 15000 m; Letterman 134 ( MO ) 1884 , Albany County, WY : 41.131110999999997 , 105.398056 , 500 m; O'Brien 1132 ( RM ) 1982 , Albany County, WY : 41.936565700000003 , 105.3587003 , 1145 m; Hartman 7593 ( RM ) 1978 , Campbell County, WY : 43.739803000000002 , 105.9001 89 , 650 m; Hartman 40778 ( RM ) 1993 , Goshen County, WY : 41.879778999999999 , 104.613377 , 1000 m; Nelson 28140 ( RM ) 1 993 , Goshen County, WY : 42.398231000000003 , 104.641595 , 1000 m; Evert 8283 ( RM ) 1985 , Johnson County, WY : 44.553840000000001 , 106.940203 , 5 00 m; Fertig 16297 ( RM ) 1995 , Laramie County, WY : 41.16 2742000000001 , 105.206232 , 500 m; Hartman 55145 ( USFS ) 1996 , Park County, WY : 44.308548999999999 , 109.51046100000001 , 1000 m; Hartman 22017 ( RM ) 1985 , Park County, WY : 44.720593999999998 , 109.343723 , 850 m; Nelson 12977 ( RM ) 1985 , Park County, WY : 44.733651999999999 , 109.289506 , 500 m; Kademian 1579 ( USFS ) 1988 , Park County, WY : 44.949880999999998 , 109.312855 , 1500 m; Hartman 40717 ( RM ) 1993 , Platte County, WY : 41.876235000000001 , 104.780773 , 1000 m; Nelson 17170 ( RM ) 1989 , Platte County, WY : 42. 309525999999998 , 105.256849 , 1000 m; Porter 4898 ( COLO ) 1949 , Pl atte County, WY : 42.319943000000002 , 104.788974 , 5000 m; Nelson 498 ( MO ) 1894 , Platte County, WY : 42.327364000000003 , 104.725825 , 1000 m; E vert 8174 ( RM ) 1985 , Sheridan County, WY : 44.568894999999998 , 106.931826 , 400 m; Hartman 10681 ( RM ) 1979 , Sheridan County, WY : 44.617218000000001 , 107.08161699999999 , 1000 m; Solheim 471 ( RM ) 1931 , Sheridan County, WY : 44.681005999999996 , 107.214134 , 10 00 m; Dickson D 302 ( USFS ) 1933 , Sheridan County, WY : 44.756605 , 107.268475 , 2000 m; Nelson 2304 ( RM ) 1896 , Sherid an County, WY : 44.761217000000002 , 107.260294 , 1250 m; Hartman 9855 ( RM ) 1979 , Sheridan County, WY : 44.862583000000001 , 107.326277 , 1000 m; Fertig 17018 ( RM ) 1996 , Sheridan County, WY : 44.920532000000001 , 107.736459 , 1000 m; Nelson 8794 ( RM ) 1982 , Westo n County, WY : 43.687314000000001 , 104.050917 , 500 m; Marriott 8024 ( RM ) 1984 , Weston County, WY : 43.851599999999998 , 104.045058 , 800 m; Goo dman 4814 ( COLO ) 1948 , Cimarron County, OK : 36.943643999999999 , 102.972306 , 2250 m; Physocarpus opulifolius (L.) Maxim. Canada : category 1: Reznicek 3813 ( MICH ) 1973 , Ontario : 44.775784999999999 , 79.711485999999994 , 1500 m; Krotkov 9139 ( WIS ) 1934 , Onta rio: 44.942371000000001 , 81.009426000000005 , 4500 m; Grassl 2499 ( MICH ) 1932 , Ontario: 45.993485 , 81.916695000000 004 , 2300 m; Lechowicz s.n. ( WIS ) 1973 , Ontario: 46.730418999999998 , 84.349463999999998 , 2000 m; Voss 10639 ( MICH ) 1961 , Ontario: 47.2408870 00000001 , 84.650547000000003 , 500 m; Garton 1735 ( RM ) 1951 , Ontario: 48.456662999999999 , 89.181312000000005 , 1500 m; Iltis 3508C ( WIS ) 1947 , Quebec : 46.885326999999997 , 70.849106000000006 , 2300 m. category 2: Senn 5483 ( WIS ) 1950 , Ontario: 45. 5692110000 00003 , 81.967545999999999 , 9000 m; Soper 13401 ( MICH ) 1975 , Ontario: 46.537112 , 84.585751999999999 , 1000 m; Crins 9711 ( MICH ) 1993 , Ontario: 47.056454000000002 , 84.762572000000006 , 1000 m; Bartlett 542 ( MICH ) 1951 , Ontario: 47.242401000000001 , 84.64442 2000000006 , 300 m; Grassl 1258 ( MICH ) 1931 , Ontario: 48.044780000000003 , 85.954009999999997 , 350 m; Love 6952a ( CO LO ) 1955 , Ontario: 48.315364000000002 , 88.926299999999998 , 1700 m; Butters s.n. ( IND ) 1929 , Ontario: 48.7997769 99999999 , 87.935799000000003 , 14500 m; Garton 21283 ( MO ) 1982 , Ontario: 49.675763000000003 , 87.567216000000002 , 500 m; Garton 22734 ( MICH ) 198 3 , Ontario: 49.909892999999997 , 88.124404999999996 , 4300 m. category 3 : Garton 2001 ( RM ) 1952 , Ontario: 48.011783999999999 , -

PAGE 79

71 89.571658999999 997 , 1200 m; Garton 21470 ( MICH ) 1982 , Ontario: 49.600332999999999 , 87.961893000000003 , 5500 m; Oldham 36676 ( MICH ) 2009 , Ontario: 50.956350999999998 , 84.597183000000001 , 2300 m. United States: category 1 : C lark 5036C ( MOR ) 1966 , DeKalb County, AL : 34.35 2415000000001 , 85.676068000000001 , 800 m; Duncan 8760 ( MO ) 1948 , Bartow County, GA : 34.174799999999998 , 84.701999 999999998 , 500 m; Cronquist 5155 ( MO ) 1948 , Dade County, GA : 34.836655 , 85.482054000000005 , 500 m; Armstrong 2461 ( DBG ) 1964 , Cook County, I L : 41.642142 , 87.867262999999994 , 2000 m; Bennett 520 ( IND ) 1963 , Cook County, IL : 41.980761999999999 , 87.8525929 99999999 , 1850 m; Swink s.n. ( MOR ) 1946 , Cook County, IL : 41.99961900000 0003 , 87.784186000000005 , 1000 m; Evert 6410 ( MOR ) 1983 , Cook County , IL : 42.012889000000001 , 87.865399999999994 , 650 m; Laskowski 184 ( MICH ) 1960 , Cook County, IL : 42.03270299999999 8 , 88.011960000000002 , 2300 m; Lamp L336 ( MOR ) 1960 , Cook County, IL : 42.095874999999999 , 87.882182999999998 , 2700 m; Levins 101 ( MOR ) 1981 , Kane County, IL : 41.926409 , 88.348260999999994 , 1000 m; Harper s.n. ( MICH ) 1887 , Ogle County, IL : 42.12544100000 0002 , 89.263215000000002 , 2300 m; Deam 52614 ( IND ) 1932 , Crawford County, IN : 38.294331999999997 , 86.517043999999999 , 3000 m; Young s.n. ( M ICH ) 1876 , Jefferson County, IN : 42.256405000000001 , 90.279572999999999 , 2000 m; Reed 19698 ( MO ) 1950 , Carter Coun ty, KY : 38.361669999999997 , 83.111395999999999 , 2000 m; Reed 13881 ( MO ) 1948 , Carter County, KY : 38.361669999999997 , 83.111395999999999 , 50 0 m; Reed 118336 ( MO ) 1983 , Carroll County, MD : 39.590677999999997 , 77.022757999999996 , 300 m; Reed 21830 ( MO ) 195 0 , Garrett County, MD : 39.40 0995000000002 , 79.370088999999993 , 1000 m; Bailey s.n. ( MICH ) 1891 , Kent County, MI : 42.951298999999999 , 85.661 22 , 9000 m; Hermann 209 ( MICH ) 1926 , Keweenaw County, MI : 47.46996 , 87.890467000000001 , 1200 m; McFarlin 2002 ( MIC H ) 1930 , Keweenaw County, MI : 48.146514000000003 , 88.485724000000005 , 1000 m; Hazlett 3071 ( MICH ) 1985 , Leelanau County, MI : 45.484217000000 001 , 85.778999999999996 , 1200 m; Erlanson 766 ( MICH ) 1924 , Mackinac County, MI : 45.977344000000002 , 84.1930909999 99995 , 700 m; Mac Farlane 5023 ( MICH ) 1990 , Ontonagon County, MI : 46.651747999999998 , 89.151488000000001 , 200 m; Lundell 8312 ( MICH ) 1939 , Wa shtenaw County, MI : 42.298302999999997 , 83.742846 , 5000 m; Ahles 66806 ( NEB ) 1967 , Dutchess County, NY : 41.7810520 00000003 , 73.946315999999996 , 800 m; Biltmore 1282b ( WIS ) 1897 , Buncombe County, NC : 35.703482000000001 , 82.377489999999995 , 2300 m; Mosele y s.n. ( MICH ) 1921 , Ottawa County, OH : 41.654215999999998 , 82.820742999999993 , 1500 m; Selby 662 ( CS ) 18 99 , Wayne County, OH : 40.803237000000003 , 81.893601000000004 , 5000 m; Palmer 43585 ( MO ) 1937 , Adams County, PA : 39.903626000000003 , 77.09527900000000 5 , 1000 m; Berkheimer 18778 ( MICH ) 1958 , Bedford County, PA : 39.916530000000002 , 78.357688999999993 , 1000 m; Witte s.n. ( RM ) 1928 , Bucks County, PA : 40.489879999999999 , 75.219493999999997 , 1800 m; Kline s.n. ( MO ) 1952 , Cambria County, PA : 40.681223000000 003 , 78.582499999999996 , 2000 m; Fogg 21157 ( MICH ) 1953 , Montgomery County, PA : 40.236336000000001 , 75.1776749999 99994 , 500 m; Swartley 419 ( MO ) 1935 , Montgomery County, PA : 40.340864000000003 , 75.437241 , 500 m; Manning s.n. ( MO ) 1965 , Northumberland Co unty, PA : 40.836703 , 76.800877 , 1000 m; Grisez 221 ( USFS ) 1961 , Warren County, PA : 41.846094999999998 , 79.2889179 99999995 , 2000 m; Germplasm Unit 160 GU ( MOR ) 1989 , Augusta County, VA : 37.947203000000002 , 78.927960999999996 , 100 m; Reed 47074 ( MO ) 1960 , Augusta County, VA : 38.274875999999999 , 79.323150999999996 , 500 m; Fo sberg 23959 ( WIS ) 1945 , Fairfax County, VA : 38.790430000000001 , 77.055728000000002 , 1500 m; J.R.C. s.n. ( MO ) 1889 , Fairfax County, VA : 38.974432999999998 , 77.215305999999998 , 10000 m; Bartlett 2194 ( MICH ) 1910 , Fairfax County,

PAGE 80

72 VA : 38.997897999999999 , 77.286942999999994 , 4500 m; Fogg Jr. 14974 ( MO ) 1938 , Giles County, VA : 37.273693999999999 , 80.605166999999994 , 1000 m; Reed 83493 ( MO ) 1969 , Loudoun County, VA : 39.047964 , 77.437683000 000007 , 7500 m; Leys 21637 ( WIS ) 1974 , Nelson Co unty, VA : 37.858237000000003 , 78.984212999999997 , 2000 m; Sauleda 4337 ( MO ) 1980 , Nelson County, VA : 37.940626000000002 , 78.944096000000002 , 100 m; Reed 13843 ( MO ) 1948 , Page County, VA : 38.620345999999998 , 78.351225999999997 , 500 m; Musselman 4961 ( WIS ) 1976 , Tazewell County, VA : 37.125469000000002 , 81.38763299999999 4 , 2000 m; Reed 78752 ( MO ) 1968 , Berkeley County, WV : 39.389381999999998 , 78.110170999999994 , 500 m; Reed 140110 ( MO ) 1986 , Berkeley County, WV : 39.585476999999997 , 78.050392000000002 , 1000 m; Mackenzie 445 ( MO ) 1903 , Greenbrier County, WV : 37.7951460000 00003 , 80.299211 , 5000 m; Clarkson 2738 ( USFS ) 1959 , Greenbrier County, WV : 38.068469999999998 , 79.961117999999999 , 1500 m; Downs 8840 ( MO ) 1969 , Mineral County, WV : 39.599812999999997 , 78.798860000000005 , 1000 m; Reed 77685 ( MO ) 1968 , Randolph County, WV : 38.908693 , 79.701719999999995 , 500 m; Cheney 4944 ( WIS ) 1896 , Ashland County, WI : 46.652439999999999 , 90.730299000000002 , 3000 m; Taylo r 3586 ( MPM ) 1977 , Ashland County, WI : 46.916612999999998 , 90.547590999999997 , 1000 m; Seidlinger X 15 ( WIS ) 1975 , Dane County, WI : 43.041490000000003 , 88.922126000000006 , 1000 m; Rice 3157 ( WIS ) 1970 , Door County, WI : 42.764144999999999 , 87.77992700000 0001 , 2000 m; Broue tte P. o. 1 (b) ( WIS ) 1968 , Door County, WI : 45.150022999999997 , 87.217564999999993 , 2800 m; K och 7007 ( WIS ) 1971 , Douglas County, WI : 46.702958000000002 , 92.007758999999993 , 3500 m; Bedore 7 ( WIS ) 1985 , Kenosha County, WI : 42.5683909 99999998 , 88.059612999999999 , 22000 m; Seymour 15794 ( WIS ) 1954 , Lincoln County, WI : 45.183531000000002 , 89.74422 6999999995 , 1200 m; Schneider 1407 ( IND ) 1939 , Vilas County, WI : 46.046914000000001 , 89.667630000000003 , 3800 m; Waller DOB0358 ( W IS ) 2011 , Vilas County, WI : 46.047854999999998 , 89.664095000000003 , 750 m; Fassett 9413 ( WIS ) 1929 , Vilas County, WI : 46.061 131000000003 , 89.672779000000006 , 650 m. category 2: Clark 18234 ( MOR ) 1967 , Bibb County, AL : 32.992128000000001 , 87.133666000000005 , 1000 m; Duncan 6984 ( MO ) 1946 , Towns County, GA : 34.984478000000003 , 83.624105999999998 , 350 m; Armstrong 120 ( DBG ) 196 1 , Cook County, IL : 41.691057000000001 , 87.923158999999998 , 1000 m; Umbach 12433 ( WIS ) 1900 , Cook County, IL : 42.0 33363999999999 , 87.733393 000000007 , 4600 m; Johnson 2306 ( MOR ) 1995 , Will County, IL : 41.386999000000003 , 88.240128999999996 , 400 m; Mauger s.n. ( MOR ) 1994 , Will County, IL : 41.424737 , 87.575502999999998 , 7000 m; Clevenger 167 ( IND ) 1952 , Bartholomew County, IN : 39.2456419999999 97 , 85.710466999999994 , 3500 m; Deam 45812 ( IND ) 1928 , Cass County, IN : 40.836948 , 86.239874999999998 , 3000 m; De am 34384 ( IND ) 1921 , Fulton County, IN : 41.097749999999998 , 86.486895000000004 , 1800 m; Deam 22046 ( IND ) 1916 , Huntington County, IN : 40.721 676000000002 , 85.474907000000002 , 2500 m; Young s.n. ( IND ) 1877 , Jefferson County, IN : 38.714225999999996 , 85.473 571000000007 , 2000 m; Deam 9132 ( IND ) 1911 , Jennings County, IN : 38.999209 , 85.607900000000001 , 1000 m; Yuncker 10814 ( COLO ) 1941 , Lagrange County, IN : 41.695414999999997 , 85.313923000000003 , 1000 m; Armstrong 2460 ( DBG ) 1964 , Lake County, IN : 41.600617 , 87.261179999999996 , 2250 m; Deam 2421 ( IND ) 1907 , Madison County, IN : 40.1 18214000000002 , 85.683623999999995 , 3000 m; Deam 38001 ( IND ) 192 2 , Monroe County, IN : 39.025134000000001 , 86.555503000000002 , 1000 m; Umbach 32735, 3970 ( WIS ) 1909 , Porter County , IN : 41.661569999999998 , 87.054522000000006 , 6000 m; Deam 63050 ( IND ) 1944 , Randolph County, IN : 40.138381000000003 , 85.126485000000002 , 5 00 m; Price s.n. ( MO ) 1893 , Warren County, KY : 36.989772000000002 , 86.403090000000006 , 9000 m; Steyermark 889 ( MO ) 1929 , Hancock County, ME : 44.387087999999999 , 68.208022 , 2250 m; Reed 26815 ( MO ) 1951 , Allegany County, MD :

PAGE 81

73 39.701875000000001 , 78.5661539 99999997 , 1000 m; Reed 2116 ( MO ) 1940 , Baltimore County, MD : 39.393526999999999 , 76.766221000000002 , 9500 m; Reed 30036 ( MO ) 1952 , Baltimore County, MD : 39.39508 , 76.825460000000007 , 2250 m; Shull 117 ( MO ) 1902 , Harford County, MD : 39.510435999999999 , 7 6.134513999999996 , 500 m; Reed 71030 ( MO ) 1965 , Harford County, MD : 39.681230999999997 , 76.243556999999996 , 1700 m ; Downs 3164 ( MO ) 1968 , Washington County, MD : 39 .603366999999999 , 77.977281000000005 , 1200 m; Richardson s.n. ( MO ) 1963 , Middlesex County, MA : 42.456654 , 71.357647999999998 , 1500 m; Frisbie 8 ( USFS ) 1938 , Alger County, MI : 46.452249999999999 , 86.919289 000000006 , 2000 m; King s.n. ( MICH ) 1966 , Charlevoix A County, MI : 45.804720000000003 , 85.494534000000002 , 4500 m; Wood s.n. ( MICH ) 1912 , Ch ippewa County, MI : 46.766880999999998 , 84.975065999999998 , 2300 m; Ehlers 610 ( MICH ) 1917 , Emmet County, MI : 45.56 7979000000001 , 84.844898999999998 , 7000 m ; Ehlers 212 ( MICH ) 1916 , Emmet County, MI : 45.744055000000003 , 84.840986999999998 , 1500 m; Gates 10710 ( RM ) 1917 , Emmet (or Cheboygan) County, MI : 45.552197 , 84.797336999999999 , 1000 m; Davis s.n. ( MICH ) 1893 , G ratiot County, MI : 43.378920000000001 , 84.659727000000004 , 3000 m; Leisman EC 15 710 ( MICH ) 2015 , Kent County, MI : 43.111499999999999 , 85.6 29582999999997 , 350 m; Hall s.n. ( MOR ) 1951 , Keweenaw County, MI : 47.467308000000003 , 87.867312999999996 , 900 m; R eich R 266 79 ( MPM ) 1979 , Keweenaw County, MI : 47.476762999999998 , 88.008532000000002 , 1500 m; Bailey 5393 ( MICH ) 1959 , Keweenaw County, MI : 47.915554999999998 , 89.154501999999994 , 600 m; Bailey 5560 ( MICH ) 1959 , Keweenaw County, MI : 47.985702000000003 , 88.805339000000004 , 400 m; Zimmerman 928 ( MICH ) 1954 , Lapeer County, MI : 43.094749 , 83.072439000000003 , 6500 m; Voss 7306 ( MICH ) 1958 , Lape er County, MI : 43.126761000000002 , 83.368261000000004 , 1300 m; Nee 50977 ( MO ) 2000 , Mackinac County, MI : 45.908647 999999999 , 84.736644999999996 , 300 m; Grassl 2763 ( MICH ) 1933 , Menominee County, MI : 45.138131000000001 , 87.600678000000002 , 250 m; Jones 4 0882 ( COLO ) 1965 , Cook County, MN : 47.544347000000002 , 90.891818000000001 , 2250 m; Lakela 2700 ( MPM ) 1938 , Lake Co unty, MN : 47.142195000000001 , 91.458791000000005 , 800 m; Mackenzie 2864 ( MO ) 1907 , Middlesex County, NJ : 40.494245999999997 , 74.43454800000 0007 , 500 m; Mackenzie s.n. ( IND ) 1907 , Middlesex County, NJ : 40.495938000000002 , 74.424318 , 2200 m; Dougan s.n. ( MO ) 1916 , Middlesex County, NJ : 40.507511999999998 , 74.463409999999996 , 400 m; Glenn 8544 ( MO ) 2003 , Morris County, NJ : 40.788333000000002 , 74.725555999999997 , 100 m; Fink s.n. ( MO ) n.d. , Tompkins County, NY : 42.541167999999999 , 76.604442000000006 , 1000 m; Radford 13891 ( COLO ) 1956 , Alexander County, NC : 35.832678000000001 , 81.277012999999997 , 2250 m; Clark 8583 ( MOR ) 1966 , Ashe County, NC : 36.391022 , 81.571539999999999 , 2200 m; Lee 52 ( MOR ) 1978 , Clay County, NC : 35.0789710000 00003 , 83.608418 , 5600 m ; Godfrey 51479 ( MICH ) 1951 , Clay County, NC : 35.082098000000002 , 83.601423999999994 , 1200 m; Godfrey 51479 ( RM ) 1951 , Clay County, NC : 35.0 82814999999997 , 83.604890999999995 , 1000 m; Noell 4 ( MO ) 1936 , Haywood County, NC : 35.49456 , 83.115549999999999 , 6000 m; Reed 17302 ( MO ) 1949 , Adams County, OH : 38.772773999999998 , 83.401255000000006 , 500 m; Wilson 1510 ( WIS ) 1928 , Lucus County, OH : 41. 694705999999996 , 83.679382000000004 , 600 m; s.n. ( COLO ) 1896 , Richland County, OH : 40.758389999999999 , 82.515446 999999995 , 6500 m; Dreisbach 1852 ( MICH ) 1923 , Bucks County, PA : 40.061036000000001 , 74.963645999999997 , 1200 m; Montgomery s.n. ( DBG ) 1959 , Clinton County, PA : 41.343223999999999 , 77.693916000000002 , 5500 m; Park 321 ( USFS ) 1935 , Forest County, PA : 41.4 66585000000002 , 79.304738999999998 , 1000 m; Reed 29712 ( MO ) 1952 , Fulton County, PA : 39.732183999999997 , 78.365151999999995 , 1000 m; Eby s. n. ( MO ) 1895 , Lancaster County, PA : 40.039264000000003 , 76.430796000000001 , 100 0 m; Thomas 2962 ( WIS ) 1982 , Lawren ce County, PA : 41.098424000000001 , -

PAGE 82

74 80.153520999999998 , 1800 m; Moldenke 27232 ( WIS ) 1973 , Perry County, PA : 40.427863000000002 , 77.01126999 9999996 , 2000 m; Davis s.n. ( MO ) 1920 , Anderson County, SC : 34.503439 , 82.650132999999997 , 2000 m; Davis 1708 ( NEB ) 1920 , Anderson County, SC : 34.503439 , 82.650132999999997 , 6300 m; Bell 10382 ( MICH ) 1957 , Spartanburg County, SC : 35.036973000000003 , 81. 849509999999995 , 2500 m; Kral 58659 ( MO ) 1976 , Cumberland County, TN : 36.027931000000002 , 84.809256000000005 , 2250 m; Kral 40460 ( MO ) 1970 , Fentress County, TN : 36.353513999999997 , 84.727975999999998 , 500 m; Channell V52 ( MO ) 1961 , White County, TN : 35.8 38565000000003 , 85.329831999999996 , 5000 m; Curtiss 1749 ? ( MO ) 1871 , Bedford County, VA : 37.315145000000001 , 79.5 24180000000001 , 30000 m; Fosberg 18420 ( MO ) 1941 , Fairfax County, VA : 38.857604000000002 , 77.208915000000005 , 1000 m; Seymour 24610 ( MO ) 196 6 , Fairfax County, VA : 38.907494999999997 , 77.322153999999998 , 350 m; Bartsch s.n. ( MO ) 1911 , Fauquier County, VA : 39.005346000000003 , 77.951702999999995 , 1500 m; Fosberg 23608 ( MO ) 1945 , Page County, VA : 38.555672000000001 , 78.401880000000006 , 300 m; R eed 100424 ( MO ) 1975 , County, VA : 38.004897999999997 , 79.536017999999999 , 1000 m; Steele 170 ( MO ) 1901 , County, VA : 38.598216000000001 , 78.373231000000004 , 100 m; Berkley 1812 ( MO ) 1930 , Mercer County, WV : 37.422341000000003 , 81.016199 , 2000 m; Berkle y 1135 ( MO ) 1931 , Summers County, WV : 37.677778000000004 , 80.696708000000001 , 500 m; Berkley 633 ( MO ) 1930 , Tyler County, WV : 39.471265000000002 , 80.871486000000004 , 1500 m; Tans s.n. ( WIS ) 1971 , Ashland County, WI : 47.035212000000001 , 90.43100800000000 6 , 5400 m; Beals A P177 ( WIS ) 1957 , Bayfield County, WI : 46.982374999999998 , 90.949211000000005 , 2700 m; Smith 7572 ( WIS ) 1922 , Crawford County, WI : 43.051650000000002 , 91.141239999999996 , 3800 m; Fassett 22558 ( WIS ) 1938 , Door County, WI : 45.064993000000 001 , 87.124274 , 3000 m; Cochrane 10454 ( WIS ) 1984 , Door County, WI : 45.091287000000001 , 87.052394000000007 , 1000 m; Leitner 1413 ( MPM ) 1988 , Fond Du Lac County, WI : 43.611853000000004 , 88.173475999999994 , 440 m; Rickett 1476 A ( MO ) 1937 , Juneau County, WI : 43.756872000000001 , 89.854844 , 14000 m; Iltis 9603 ( WIS ) 1957 , Lafayette County, WI : 42.520088000000001 , 90.3 81395999999995 , 2300 m; Seymour 14693 ( WIS ) 1952 , Lincoln County, WI : 45.147222999999997 , 89.608750999999998 , 2000 m; Lapham s.n. ( WIS ) n.d. , Milwaukee County, WI : 43.038902 , 87.906474000000003 , 20000 m; McIntosh c 619 ( MPM ) 1939 , Oconto County, WI : 44.9 35792399999997 , 88.600862199999995 , 5000 m; Leitner 1834 ( MPM ) 1988 , Ozaukee County, WI : 43.439137000000002 , 88.023702 , 250 m; Phillips 273 ( WIS ) 1973 , Portage County, WI : 44.506872000000001 , 89.589911999999998 , 2000 m; Davis s.n. ( WIS ) 1878 , Racine Cou nty, WI : 42.726131000000002 , 87.782852000000005 , 7000 m; Musselman 1497 ( WIS ) 1967 , Rock County, WI : 42.558838000000002 , 89.046379000000002 , 800 m; Smith 8103 ( WIS ) 1922 , Sauk County, WI : 43.241101 , 89.81429 , 1300 m; Kruschke s.n. ( MPM ) 1940 , Sheboygan County, WI : 43.638164000000003 , 87.990476999999998 , 1200 m; Cook C127 ( MPM ) 1938 , Sh eboygan County, WI : 43.862772 , 87.956760000000003 , 1000 m; Potzger 8714 ( WIS ) 1940 , Vilas County, WI : 46.047311999999998 , 89.652437000000006 , 4200 m; Umbach 32736 ( WIS ) 1909 , Walworth County, WI : 42.572794000000002 , 88.533989000000005 , 3000 m; Smith 207 ( WIS ) 1928 , Waupaca County, WI : 44.272480999999999 , 88 .769831999999994 , 2300 m; Cochrane 7597 ( WIS ) 1976 , Waupaca County, WI : 44.299442999999997 , 88.778969000000004 , 65 0 m. category 3 : Ohlendorf s.n. ( MO ) 1884 , Cook County, IL : 41.8308109999999 97 , 87.817858000000001 , 2000 m; Mills s.n. ( NEB ) 1944 , La Salle County, IL : 41.319974000000002 , 89.000326999999999 , 4500 m; Deam 34148 ( IND ) 1921 , Franklin County, IN : 39.334643 , 85.278807 , 800 m; Deam 31721 ( IND ) 1920 , Jasper County, IN : 41.070585000000001 , 87.168296999999995 , 1300 m; Deam 32973 ( IND ) 1920 , Knox Co unty, IN : 38.443593999999997 , -

PAGE 83

75 87.729434999999995 , 1500 m; Wilhelm 20692 ( MOR ) 1992 , Lake County, IN : 41.6004330000 00002 , 87.446682999999993 , 1200 m; Wilhelm 6351 ( MOR ) 1978 , L aPorte County, IN : 41.697066 , 86.858654999999999 , 2300 m; Deam 36583 ( IND ) 192 2 , Pulaski County, IN : 40.975011000000002 , 86.497416999999999 , 1000 m; Deam 54054 ( IND ) 1933 , Tippecanoe County, I N : 40.390725000000003 , 87.079543999999999 , 3700 m; Deam 60096B ( IND ) 1940 , Warren County, KY : 37.084538999999999 , 86.561284999999998 , 2000 m; Greenman 2976 ( MO ) 1896 , Middlesex County, MA : 42.375096999999997 , 71.111444000000006 , 4000 m; Schipper 1064 ( M ICH ) 2017 , Allegan County, MI : 42.552132 , 85.97784199999 9995 , 250 m; Appel 421 ( MICH ) 1980 , Antrim County, MI : 44.963673999999997 , 85.00722 5000000005 , 250 m; Gates 10710 ( MO ) 1917 , Emmet County, MI : 45.552128000000003 , 84.797244000000006 , 1500 m; Parmel ee 3339 ( MICH ) 1953 , Genesee County, MI : 43.102836000000003 , 83.858123000000006 , 4000 m; Kolar UP 6 ( MOR ) 1978 , Gogebic County, MI : 46.70882 6000000002 , 89.972723999999999 , 250 m; Voss 7461 ( MICH ) 1958 , Hillsdale County, MI : 41.837985000000003 , 84.520407 000000006 , 4000 m; Bailey 5340 ( MICH ) 195 9 , Keweenaw County, MI : 47.939416999999999 , 89.167422000000002 , 1500 m; Voss 12775 ( MICH ) 1968 , Mac kinac County, MI : 45.860317999999999 , 84.870988999999994 , 950 m; Voss 13691 ( MICH ) 1971 , Ontonagon County, MI : 46. 805669999999999 , 89.823226000000005 , 2000 m; Darlington s.n. ( MICH ) 1923 , Ontonagon County, MI : 46.806024000000001 , 89.761407000000005 , 130 00 m; Billington s.n. ( MICH ) 1919 , Washtenaw County, MI : 42.225822999999998 , 83.592067 , 5000 m; Steyermark 23140 ( MO ) 1937 , Christi an County, MO : 36.901497999999997 , 93.094337999999993 , 1500 m; Cusick 3640 ( RM ) 1910 , County, Oregon : 45.710396000000003 , 118.352138 , 15000 m; Berkley 1429 ( MO ) 1930 , Pocahontas County, WV : 38.222493 , 80.092134000000001 , 1500 m; Stahma nn 120 ( WIS ) 1973 , Adams County, WI : 44.028387000000002 , 89.951789000000005 , 27000 m; Peters 90 ( WIS ) 1958 , Columbia County, WI : 43.30698499 9999997 , 89.706198000000001 , 1700 m; Palmer 28786 ( MO ) 1925 , Door County, WI : 45.284863999999999 , 87.050081000000 006 , 1000 m; Davis s.n. ( WIS ) 1913 , Door County, WI : 45.356650000000002 , 86.930673999999996 , 3000 m; Leitner 1524 ( WIS ) 1988 , Fond du Lac Co unty, WI : 43.612217999999999 , 88.172873999999993 , 2300 m; Tutton s.n. ( WIS ) 1959 , Jefferson County, WI : 42.8777880 00000002 , 88.586212000000003 , 2300 m; Schlising 133 ( WIS ) 1952 , Lincoln County, WI : 45.147222999999997 , 89.608750999999998 , 2000 m; 739 ( M O ) 1875 , Milwaukee County, WI : 43.034134000000002 , 87.909221000000002 , 7000 m; Schlising 988 ( WIS ) 1959 , Oconto Co unty, WI : 45.157913000000001 , 88.466119000000006 , 1400 m; Cutler 433 ( WIS ) 1935 , Ozaukee County, WI : 43.385947999999999 , 88.009292000000002 , 2600 m; Leitner 1957 ( WIS ) 1988 , Ozaukee County, WI : 43.435496999999998 , 88.018451999999996 , 500 m; Wills s.n. ( WIS ) 1957 , Richland County, WI : 43.423037000000001 , 90.242903999999996 , 2300 m; Cook C127 ( MICH ) 1938 , Sheboygan County, WI : 43.862772 , 87. 956760000000003 , 3000 m; Wilson 3036 ( WIS ) 1932 , Vilas County, WI : 46.05438 9 , 89.655257000000006 , 3200 m.

PAGE 84

76 APPEND IX C Histogram of f ollicle pubescence in Physocarpus opulifolius (L.) Maxim. s.l . (Rosaceae) Figure S 2 . Statistical d istribution of follicl e pubescence categories in the Physocarpus opulifolius (L.) Maxim. s.l. (Rosaceae) complex, where category 1 = foll icles glabrous, category 2 = ventral sutures of follicles sparsely pubescent or glabrescent, category 3 = ventral sutur es of follicles pubesc ent with perisutural pubescence on abaxial surface, and category 4 = abaxial surface of follicles uniformly pubesce nt. The subset of specimens that could not be easily distinguished between Follicle pubescence category Number of specimens 21.7% 1 3 . 9 % 56 . 4 % 3 % 5 %

PAGE 85

77 APPENDIX D R script for bioclimatic variables, pairwise s (Fig s . S 4 S 6 ) and PCA T he following script was run with R ( version 3. 5.2 , R Core Team, 2018 ) in RStudio ( ver sion 1.1.463 ; RStudio Team, 2016) after downloading bioclimatic data from World Clim.org. #1 Load the necessary R libraries. library("maptools", lib.loc="~/R/win library/3.5") library("raste r", lib.loc="~/R/win library/3.5") library("sp", lib.loc="~/R/win library/3.5") library("labdsv ", lib.loc="~/R/win library/3.5") library("ggb iplot", lib.loc="~/R/win library/3.5") library(" tidyverse ", lib.loc="~/R/win library/3.5") library(" dplyr ", lib.loc ="~/R/win library/3.5") library(" spThin ", lib.loc="~/R/win library/3.5") #2 Set t he working directory to the directory that contains the b ioclimatic variables. setwd("C:/Users/Cyn/Desktop/Audrey/Thesis/Modeling/Raw_files_2.5min/centralNA") # 3 Set th e study extent . e< extent( 125, 62,23,53) # 4 Load current climate rasters into R , crop to the study extent, and re save the cropped files. bio1 < raster("wc2.0_bio_2.5m_01.tif") bio2 < raster("wc2.0_bio_2.5m_02.tif") bio3 < raster("wc2.0_bio_2.5m_0 3.tif") bio4 < raster("wc2.0_bio_2.5m_04.tif") bio5 < raster("wc2.0_bio_2.5m_ 05.tif") bio6 < raster("wc2.0_bio_2.5m_06.tif") bio7 < raste r("wc2.0_bio_2.5m_07.tif") bio8 < raster("wc2.0_bio_2.5m_08.tif") bio9 < raster("wc2.0_bio_2.5m_09.tif") bio10 < raster("wc2.0_bio_2.5m_10.tif") bio11 < raster("wc2.0_bio_2.5m_11.tif") bio12 < raster("wc2.0_bio_2.5m_12.tif") bio13 < raster("wc2.0_bio _2.5m_13.tif") bio14 < raster("wc2.0_bio_2.5m_14.tif") bio15 < raster("wc2.0_bio_2.5m_15.tif") bio16 < raster("w c2.0_bio_2.5m_16.tif") bio17 < raster("wc2.0_bio_2.5m_17.tif") bio18 < raster ("wc2.0_bio_2.5m_18.tif") bio19 < raster("wc2.0_bio_2.5m_19.t if") bio1c < crop(bio1, extent(e)) bio2c < crop(bio2, extent(e)) bio3c < crop(bio3, extent(e)) bio4c < crop(bi o4, extent(e)) bio5c < crop(bio5, extent(e)) bio6c < crop(bio6, extent(e)) bio7c < crop(bio7, extent(e)) bio8c < crop(bio8, extent(e)) bi o9c < crop(bio9, extent(e)) bio10c < crop(bio10, extent(e)) bio11c < crop(bio11, extent(e)) bio12c < crop(bio12 , extent(e)) bio13c < crop(bio13, extent(e)) bio14c < crop(bio14 , extent(e)) bio15c < crop(bio15, extent(e)) bio16c < crop(bio16, extent( e)) bio17c < crop(bio17, extent(e)) bio18c < crop(bio18, extent(e)) bio19c < crop(bio19, extent(e))

PAGE 86

78 writeRaster( bio1c, "bio1c.asc", format="ascii") writeRaster( bio2c, "bio2c.asc", format="ascii") writeRaster(bio3c, "bio3c.asc", format="ascii") writeRast er(bio4c, "bio4c.asc", format="ascii") writeRaster(bio5c, "bio5c.asc", format="ascii") writeRaster(bio6c, "bio6c.as c", format="ascii") writeRaster(bio7c, "bio7c.as c", format="ascii") writeRaster(bio8c, "bio8c.asc", format="ascii") writeRaster(bio9c, "bio9c .asc", format="ascii") writeRaster(bio10c, "bio10c.asc", format="ascii") writeRaster(bio11c, "bio11c.asc", format=" ascii") writeRaster(bio12c, "bio12c.asc", format="ascii") writeRaster(bio13c, "bio13c.asc", format="ascii") writeRaster(bio14c, "bio14c.asc", format="ascii") writeRaster(bio15c, "bio15c.asc", format="ascii") writeRaster(bio16c, "bio16c.asc", format="ascii" ) writeRaster(bio17c, "bio17 c.asc", format="ascii") writeRaster(bio18c, "bio18c.asc", format="ascii") writeRaster(bio19c, "bio19c.asc", forma t="ascii") # 5 Repeat for mid Holocene and LGM climate data. biomid1 < raster("ccmidbi1.tif") biomid2 < raste r("ccmidbi2.tif") b iomid3 < raster("ccmidbi3.tif") biomid4 < raster("ccmidbi4.tif") biomid5 < raster("ccmidbi5.tif") biomid6 < raster("cc midbi6.tif") biomid7 < raster("ccmidbi7.tif") biomid8 < raster("ccmidbi8.tif") biomid9 < raster("ccmidbi9.tif") biomid10 < raster( "ccmidbi10.tif") biomid11 < raster("ccmidbi11.tif") biomid12 < raster("ccmidbi12.tif") biomid13 < raster("ccmidbi13.tif ") biomid14 < raster("ccmidbi14.tif") biomid15 < raster("ccmidbi15.tif") biomid16 < raster("ccmidbi16.tif") biom id17 < raster("ccmidbi17.tif") biomid18 < raster("ccmidbi18.tif") biomid19 < raster("ccmidbi19.tif") biomid1c < crop(biomid1, extent(e)) biomid2c < crop(biomid2, extent(e)) biomid3c < crop(biomid3, extent(e)) biomid4c < crop(biomid4, extent(e)) biom id5c < crop(biomid5, extent(e)) biomid6c < crop(biomid6, extent(e)) biomid7c < crop(biomid7, extent(e)) biomid8c < crop(biomid8, extent(e )) biomid9c < crop(biomid9, extent(e)) biomid10c < crop(biomid10, extent(e)) biomid11c < crop(biomid11, extent(e )) biomi d12c < crop(biomid12, extent(e)) biomid13c < crop(biomid13, extent(e)) biomid14c < crop(biomid14, extent(e)) biomid15c < crop(bio mid15, extent(e)) biomid16c < crop(biomid16, extent(e)) biomid17c < crop(biomid17, extent(e)) biomid18c < crop(b iomid18, extent(e)) biomid19c < crop(biomid19, extent(e)) writeRaster(biomid1c, "biomid1c.asc", format="ascii") writeRaster(biomid2c, "biom id2c.asc", format="ascii") writeRaster(biomid3c, "biomid3c.asc", format="ascii") writeRaster(biomid4c, "b iomid4c.as c", format="ascii")

PAGE 87

79 writeRaster(biomid5c, "biomid5c.asc", format="ascii") writeRaster(biomid6c, "biomid6c.asc", format="ascii") writeRaster(b iomid7c, "biomid7c.asc", format="ascii") writeRaster(biomid8c, "biomid8c.asc", format="ascii") writeRaster(biomid9c , "biomid9c.asc", format="ascii") writeRaster(biomid10c, "biomid10c.asc", format="ascii") writeRaster(biomid11c, "biomid11c.asc", format="asc ii") writeRaster(biomid12c, "biomid12c.asc", format="ascii") writeRaster(biomid 13c, "biomid13c.asc", format="ascii" ) writeRaster(biomid14c, "biomid14c.asc", format="ascii") writeRaster(biomid15c, "biomid15c.asc", format="ascii") writeRaster(biomid16c, "bio mid16c.asc", format="ascii") writeRaster(biomid17c, "biomid17c.asc", format="as cii") writeRaster(biomid18c, "biomid 18c.asc", format="ascii") writeRaster(biomid19c, "biomid19c.asc", format="ascii") bioLGM1 < raster("cclgmbi1.tif") bioLGM2 < raster("cclg mbi2.tif") bioLGM3 < raster("cclgmbi3.tif") bioLGM4 < raster( "cclgmbi4.tif") bioLGM5 < raster("cclgmbi5.tif") bi oLGM6 < raster("cclgmbi6.tif") bioLGM7 < raster("cclgmbi7.tif") bioLGM8 < raster("cclgmbi8.tif") bioLGM9 < raster("cclgmbi9.tif") bioLGM1 0 < raster("cclgmbi10.tif") bioLGM11 < raster("cclgmbi11.tif" ) bioLGM12 < raster("cclgmbi12.tif") bioLGM13 < ra ster("cclgmbi13.tif") bioLGM14 < raster("cclgmbi14.tif") bioLGM15 < raster("cclgmbi15.tif") bioLGM16 < raster("cclgmbi16.tif") bioLGM17 < raster("cclgmbi17.tif") bioLGM18 < raster("cclgmbi18.tif") bioLGM19 < raster("cclgmbi19.tif") bioLGM1c < crop(b ioLGM1, extent(e)) bioLGM2c < crop(bioLGM2, extent(e)) bioLGM3c < crop(bioLGM3, extent(e)) bioLGM4c < crop(bioLGM4, extent(e)) bioLGM5c < crop(bioLGM5, extent(e)) bioLGM6c < crop(bioLGM6, extent(e)) bioLGM7c < crop(bioLGM7, extent(e)) bioLGM8c < cro p(bioLGM8, extent(e)) bioLGM9c < crop(bioLGM9, extent(e)) bioLGM10c < crop(bioLGM10, extent(e)) bioLGM11c < crop(bioLGM11, extent(e)) bioL GM12c < crop(bioLGM12, extent(e)) bi oLGM13c < crop(bioLGM13, extent(e)) bioLGM14c < crop(bioLGM14, extent(e)) bi oLGM15c < crop(bioLGM15, extent(e)) bioLGM16c < crop(bioLGM16, extent(e)) bioLGM17c < crop(bioLGM17, extent(e)) bioLGM18c < crop(bioLGM18 , extent(e)) bioLGM 19c < crop(bioLGM19, extent(e)) writeRaster(bioLGM1c, "bioLGM1c.asc", format="ascii") writeRas ter(bioLGM2c, "bioLGM2c.asc", format="ascii") writeRaster(bioLGM3c, "bioLGM3c.asc", format="ascii") writeRaster(bioLGM4c, "bioLGM4c.asc", for mat="ascii") writeRaster(bioLGM5c, "bioLGM5c.asc", format="ascii") writeRaster(bioLGM6c, "bioLGM6c.asc", format="as cii") writeRaster(bioLGM7c, "bioLGM7c.asc", format="ascii") writeRaster(bioLGM8c, "bioLGM8c.asc", format="ascii") writeRaster(bioLGM9c, "b ioL GM9c.asc", format="ascii") writeRaster(bioLGM10c, "bioLGM10c.asc", format="ascii")

PAGE 88

80 writeRaster(bioLGM11c, "bioLGM11 c.asc", format="ascii") writeRaster(bioLGM12c, "bioLGM12c.asc", format="ascii") writeRaster(bioLGM13c, "bioLGM13c.asc", format="ascii") wr ite Raster(bioLGM14c, "bioLGM14c.asc", format="ascii") writeRaster(bioLGM15c, "bioLGM15c.asc", format="ascii") writeRas ter(bioLGM16c, "bioLGM16c.asc", format="ascii") writeRaster(bioLGM17c, "bioLGM17c.asc", format="ascii") writeRaster(bioLGM18c, "bioLGM18c. asc ", format="ascii") writeRaster(bioLGM19c, "bioLGM19c.asc", format="ascii") # 6 Download SRTM 90 meter (30 second) altitude data getData('alt', country='USA', mask=TRUE) USA < raster("USA1_msk_alt.grd") getData('alt', country='CAN', mask=TRUE) CAN < ra ster("CAN_msk_alt.grd") getData('alt', country='MEX', mask=TRUE) MEX < raster("MEX_msk_alt.grd") alt < merge(USA , CAN,MEX) # 7 Increase the spatial resolution of the altitude layer to match that of the bioclimatic layers . alt2.5m < aggregate(alt, fact= 5) #8 Crop the altitude layer to the study extent and save . alt 2.5m < crop(alt 2.5m , extent(e)) writeRaster(alt2 .5m, "alt2.5m.asc", format="ascii") # 9 E xtract values of environmental variables at presence points and create correla tion matrix for each taxon : file < read.csv(file="C:/Users/Cyn/Desktop/Audrey/Thesis/Modeling/P.monogynus.csv", header=TRUE) monogynus _points < file[,2:3] predictors < stack(bio1c, bio2c, bio3c, bio4c, bio5c, bio6c, bio7c, bio8c, bio9c, bio10c, bio11c, bio12c, bio13c, bio 14c, bio15c, bio16c, bio17c, bio18c, bio19c, alt) presvals < raster::extract(predictors, monogynus_points , df=TRUE ) cor < cor(presvals) write.csv(cor, "correlationallvariables _monogynus .csv") file < read.csv(file="C:/Users/Cyn/Deskt op/Audrey/Thesis/Mod eling/P. intermedi us.csv", header=TRUE) intermedius_points < file[,2:3] predictors < stack(bio1c, bio2c, bio3c, b io4c, bio5c, bio6c, bio7c, bio8c, bio9c, bio10c, bio11c, bio12c, bio13c, bio14c, bio15c, bio16c, bio17c, bio18c, bio19c, alt) presvals < ras ter::extract(predictors, intermedius_points , df=TRUE) cor < cor(presvals) write.csv(cor, "correlationallvariables _ intermedius .csv")

PAGE 89

81 file < read.csv(file="C:/Users/Cyn/Desktop/Audrey/Thesis/Modeling/P. opulifoli us.csv", header=TRUE) opulifolius_points < file[,2:3] predictors < stack(bio1c, bio2c, bio3c, bio4c, bio5c, bio6c, bio7c, bio8c, bio9c, bio10c, bio11c, bio1 2c, bio13c, bio14c, bio15c, bio16c, bio17c, bio18c, bio19c, alt) presvals < raster::extract(predictors, opulifolius_points , df=TRUE) cor < cor(presvals) write.csv(cor, "correlationallvariables _opulifolius .csv") # 10 PCA : first , create a samples with da ta (SWD) .csv file for all occurrence points combined. occurrences < read.csv(file= "C:/Users/Cyn/Desktop/Audrey/Thesis/Modeling/occurrences _all .csv", header=TRUE) occurrences_points < occurrences[,2:3] predictors < stack(bio1c, bio2c, bi o3c, bio4c, bio 5c, bio6c, bio7c, bio8c, bio9c, bio10c, bio11c, bio12c, bio13c, bio14c, bio15c, bio16c, bio17c, bio18c, bio19c , alt ) occurrences_presvals < raster::extract(predictors, occurrences_points, df=TRUE) occurrences < tibble::rowid_to_column(occu rrences, "ID") SWD_all < full_join(occurrences,occurrences_presvals,by="ID") colnames(SWD_all)[2] < "species" write.csv(SWD_all, file="SWD_all.csv") # 11 PCA SWD_all$species < as.factor(SW D_all$species) occurrences.pca < prcomp(occurrences[,c(4:23)], center = TRUE,scale. = TRUE) ggbiplot(occurrences.pca,ellipse=TRUE,groups=occ urrences$species) print(occurrences.pca, digits = 3, cutoff = 0.2, sort = TRUE) # 1 2 Thin the occu rrences for eac h taxon in preparation for niche modeling . monogynus < read.csv(file="C:/Users/Cyn/Desktop/Audrey/Thesis/Modeling/ monogynus .csv", header=TRU E) thin( monogynus , lat.col = "LAT", long.col = "LONG", spec.col = "species", 20, 10, locs.thinned.li st.return = FAL SE, write.files = TRUE, max.files = 5, "C:/Users/Cyn/Desktop/Audrey/Thesis/Modeling/thinned", out.base = " mono ", write.log.file = TRUE, log.f ile = "thin_log.txt", verbose = FALSE) intermedius < read.csv(file="C:/Users/Cyn/Desktop/Audrey/Thesis/Modeling/i ntermedius.csv", header=TRUE) thin(inter medius , lat.col = "LAT", long.col = "LONG", spec.col = "species", 20, 10, locs.thinned.list.return = FALSE, write.files = TRUE, max.files = 5, "C:/Users /Cyn/Desktop/Audrey/Thesis/Modeling/thinned", out.base = "inter" , write.log.file = TRUE, log.file = "thin_log.txt", verbose = FALSE) opulifolius < read.csv(file="C:/Users/Cyn/Desktop/Audrey/Thesis/Modeli ng/ opulifolius .csv", header=TRUE) thin( opulifolius , lat.col = "LAT", long.col = "LONG", spec.col = "species", 20, 1 0, locs.thinned.list.return = FALSE, write.files = TRUE, max.files = 5, "C:/Users/Cyn/Desktop/Audrey/Thesis/Modeling/thinned", out.base = " opul ", write.log.file = TRUE, log.file = "thin_log.txt", verbose = FALSE)

PAGE 90

82 APPENDIX E ients used in variable selection process Figur e S 3 . Physocarpus monogynus (Torr.) Coult. (Rosaceae).

PAGE 91

83 F igure S 4 . Physocarpus intermedius (Rydb.) Schneid. (Rosaceae).

PAGE 92

84 Figure S 5 . Pearso Physocarpus opulifolius (L.) Maxim. (Rosaceae).

PAGE 93

85 APPENDIX F Maxent Response Curves Physocarpus monogynus Ph ysocarpus intermedius Physocarpus opulifolius Figure S 6 . Maxent response curves for the top two highest contributing variables for each taxon. Physocarpus monogynus (Torr.) Coult. : altitude and Bio9 ( Mean Temperature of Driest Quarter ) ; P. inter me dius (Rydb.) Schneid. : Bio5 ( Maximum Temperature of the Warmest Mo nth ) and Bio14 ( Precipitation of the Driest Month ) ; P. opulifolius (L.) Maxim. : Bio1 ( Mean Annual Temperature ) and Bio17 ( Precipitation of the Driest Quarter ) .

PAGE 94

86 APPENDIX G Occurrences of ex amined Physocarpus opulifolius (L.) Maxim. s.l. (Rosaceae) specime ns versus all occurrences available on iDigBio Figure S 7 . Geographic d istribution of Physocarpus opulifolius (L.) Maxim. s.l. (Rosaceae) occurrences for which species identification was c onfirmed via physical examination , versus all occurrences availabl e for download via Integrated Digitized Biocollections ( iDigBio ; idigbio.org ) . Although the overall range was represented, there are gaps (e.g., western Pennsylva nia, St. Lawrence River vall ey) that need to be addressed in any future research.