DEVELOPMENT OF NUCLE AR MICROSATELLITE MARKERS FOR PINUS ALBICAULIS ENGELM. (PINACEAE) , A SPECIES OF CONSE RVATION CONCERN: APP LICATION TO POPULATIONS PREVIOUS LY ASSESSED FOR GENE TIC DIVERSITY by MARIAN VIRGINIA LEA B.S. , University of Wisconsin Madison , 20 12 A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Master of Science Biology Program 2017
ii This thesis for the Master of Science degree b y Marian Virginia Lea h as been approved for the Biology Program by Diana F. Tomback , Chair Leo P. Bruederle Jennifer R. Neale Date: December 16, 2017 11 01
iii Lea, Marian V irginia (M.S., Biology Program ) Development of nuclear microsatellite mar kers for Pinus albicaulis Engelm. (Pinaceae), a species of conservation concern: application to populations previously assessed for genetic diversity Thesis directed by Professor Diana F. Tomback ABSTRACT Genetic diversity helps maintain forest health b y provid ing resilience against pests, pathogens, and environmental fluctuations. High genetic diversity also correlate s with overall population fitness, and reduced genetic diversity has been linked to local extirpation and overall extinction risk for thre atened species. Nuclear microsatellite single sequence repeat s (nSSR s ) are a suitable tool to study genetic diversity , because they can be used with degraded DNA and tend to be highly polymorphic, codominant, abundant, easily reproducible, and relatively inexpensive to use . W hitebark pine ( Pinus albicaulis ), a widely distributed western North American tree, is a candidate for listing under the U.S. Endangered Species Act and has experienced widespread declines . Whitebark pine depends on a bird, n ut cracker ( Nucifraga columbiana ) for seed dispersal , which potential influences population structure . My objectives were to develop reliable nSSR markers to assess population genetic diversity and structure in whitebark pine, and to apply them to two popul ations to determine the utility of these markers. Samples of 20 individuals each were collected from Henderson Mountain, Custer Gallatin National Forest, MT and Mount Washburn, Yellowstone National Park, WY. A total of 308 potential novel whitebark pine nS SRs determined from Illumina sequencing and 49 nSSRs from related species were screened using DNA amplified from
iv these whitebark pine samples, of which 10 and 13 , respectively , were found to be dependable . Transferred loci had higher numbers of alleles and expected heterozygosities than novel loci, and samples from Henderson Mountain and Mount Washburn had similar averages. Seven loci deviated from Hardy Weinberg Equilibrium, and both populations had large positive F IS values, indicating heterozygote defici ency. However, inbreeding coefficients were likely inflated by null alleles in the transferred loci. geographic distances at Henderson Mountain, but a positive correlation at Mount Washburn. au tocorrelation. AMOVA suggests that 91 % of genetic variability is held within populations and 9 % among populations. Overall, these results are similar to microsatellite findings for the Eurasian nutcracker consistent with results from previous allozyme analysis of these populations. The results highlight the utility of testing for locus transfer ability when establishing microsatellites in pines. The nSSRs presented here can be applied to characterize genetic diversity across the range of whitebark pine, identify seed transfer zones for tree screening for genetic resistance to pests and pathogens, and indicate populations most in need of management intervent ion, especially as white pine blister rust continues to spread. Studies assessing genetic diversity based on these nSSR loci can help guide future conservation efforts and restoration plans for whitebark pine. The form and content of this abstract are app roved. I recommend its publication. Approved: Diana F. Tomback
v TABLE OF CONTENTS CHAPTER I. LITERATURE REVIEW .................................. .................. ... ................................ ...............................1 Background ................ ............ ......... .......................... ........................................ ...... ................ ...............2 Taxonomy of Whitebark Pine ................................................. ........... ...... ............. .................. 2 White ................ ............................... ......... .. ................... 5 Threats ....................................... ................. ...................... ......................... ..................... ............. . .... 6 The Greater Yellowstone Area .................. ..................... ......................... .......... .............. ....... 9 Genetic Diversity .................................................... .......... ....................... ............. .......... ........... ........ 1 0 Population Genetic Structure of Plants ........... ....................... .......................... ................ 12 Molecular Markers...................................................................................... .. ............ ..... ........... 16 Objectives ................................. ............... ... ........................... .......................... .... .. .......................... ..... 19 Tables and Figures .... ................................................... .......... ...... ............................................ ........ 20 II. DEVELOPMENT OF NUCLE AR MICROSATELLITE MARKERS FOR PINUS ALBICAULIS E N G E L M . (PINACEAE) , A SPECIES OF CONSE RVATION CONCERN ..... ............................................................... ........ ...................................... ........... ............ .... 2 5 Introduction............................................................... ........ ...................................... ........... ............ .... 2 5 Methods ................... ........................................................ ... .... ....................................................... ... ... 31 Results...................................................................................... .... .......................... ......................... ......3 4 Discussion.................. ............. ................. ........... ... ................................... . .......................................... 3 6 Management Applications..................................................................................................... 41 Tabl es and Figures................... .................... ................ .................... .. ........ . .................... ................. 4 4 .................................................................................................... . ..................... .............5 5
vi LIST OF TABLES TABLE 1.1 Classification of the genus Pinus ....................................................................................................... 20 1.2 Fine scale genetic structure in whitebark pine and related species .................................. 21 2.1 List of 49 primers screened for transferability to whitebark pine .................................... 44 2.2 Diameter at breast height of sampled whitebark pine ........................................ .................... 45 2.3 Information on novel whitebark pine microsatellite loci ...................................................... 45 2.4 Genetic diversity metrics for novel and transferred microsatellite loci . ........... ..............46 2.5 Summary o f microsatellite locus transferability success .......................................................47 2.6 Genetic diversity measurements summarized by locus source and population ........ 47 2.7 n ......................................47
vii LIST OF FIGURES FIGURE 1.1 Distribution of whitebark pine in western North America ................................................... 22 1.2 Whitebark pine ................................................ ......................................................................................... 2 3 1.3 ............................................................................................................................. ....... 2 3 1.4 Map of change in fire risk from 1920 to 2010 ............................................................................. 24 2.1 Map of sample collection locations ................................................................................................. . 48 2.2 Phylogenies of whiteb ark pine and nearest neighbors........................................................... 49 2.3 Histograms of the number of alleles for 23 micros atellite loci............................................ 50 2.4 Difference in mean number of alleles and expected heterozygosities and 95% confidenc e intervals between populations.................................................................................. 51 2.5 Analysis of Molecular Variance within and among populations ......... ................................ 52 2.6 Plot of pairwise geographic and genetic distances by population ..................................... 53 2.7 Frequency distribution of random R xy versus the observed R xy test, by population ............... .................................................................................................................. 54
viii ACKNOWLEDGMENTS I thank Custer Gallatin National Forest and Yellowstone National Park for permission to sample whitebark pine (Yell owstone research permit number SCI 6064) . In addition to support from my advisor and committee, I had guidance from Richard Cronn (US Forest Service, Pacific Northwest Research Station, PNW) and John Syring (Linfield College, OR) for the steps in marker d evelopment. I also thank Andrew Boddicker, Bhargavi Ramanathan, Aaron Wagner, Elizabeth Pansing, Andres Andrade, and Amy Halbach for assisting with sample collection and Dr. Mitchell McGlaughlin from the University of Northern Colorado for assisting with the fragment analysis preparation protocols, and the Arizona State University DNA Lab for all of the fragment analysis results . I am grateful to the Wyoming Native Plant Society and the U.S. Forest Service (PNW) for financial support of this project.
1 C HAPTER I LITERATURE REVIEW Whitebark pine ( Pinus albicaulis Engelm. [Pinaceae] ) is a foundation and keystone species that provides ecosystem functions and services at higher elevations throughout the western United States and Canada (Tomback and Achuff 20 10). It has the largest and most northern dis tribution of five needle white pines (Fig. 1), but is restricted to a narrow elevational band from upper subalpine zone to the alpine treeline ecotone (Arno and Hoff 1990; Tomback and Achuff 2010). It is often o ne of the first tree species to regenerate in high elevation areas following fires, with new seedlings establishing on all aspects within a few years (Tomback and Linhart 1990; Tomback et al. 2001a). Whitebark pine Nucifra ga columbiana Wilson) for seed dispersal (Tomback 1982). Whitebark pine tolerates cold, dry, and windy environments, persisting as climax stands in these harsh areas. At treeline elevations, it also functions as a tree island initiator, facilitating infil ling and upward expansion of the alpine treeline ecotone (Resler and Tomback 2008; Tomback et al. 2014). However , it is a slow growing species and, while individuals can be reproductively mature by 30 years, they generally do not begin producing large cone crops until at least 60 years of age (Arno and Hoff 1990). Further, seedlings and saplings are moderately shade intolerant and are outcompeted by more tolerant tree species as succession progresses on less harsh subalpine sites (Arno 2001). Recently, whi tebark pine has experienced widespread decline , and is now a candidate for listing in the United States under the Endangered Species Act (U.S. Fish
2 and Wildlife Service, 2011). It is susceptible to white pine blister rust ( Cronartium ribicola J.C. Fisch . ), which is currently spreading, and is a host for mountain pine beetle ( Dendroctonus ponderosae Hopkins), which erupted in large scale outbreaks in the Rocky Mountains and elsewhere in the west over the last 20 years (McDonald and Hoff 2001; Tomback et al. 2001b; Tomback 2 008; Tomback and Achuff 2010). Population persistence is further endangered by fires , which are projected to increase in frequency , scale, and severity across the Western United States (Westerling et al. 2011; Moritz et al. 2012) . As such, managers need efficient, cost effective tools to study genetic changes in population as these threats persist and increase . Background Taxonomy o f W hitebark P ine Whitebark pine belongs to the five needled white pines of genus Pinus L. , subgenus Strobus (D. Don) Lemmon, section Quinquefoliae Duhamel, s ubsection Strobus Loudon (Gernandt et al. 2005). This genus includes 111 species in two subgenera , with 40 species in two sections of subgenus Strobus (Table 1). Within section Quinquefoliae there are 25 specie s in three subsections, including 21 in subsection Strobus (Table 1) . classified in subsection Cembrae . The five stone pines have large wingless seeds and indehiscent cones , which have scale s that do not separate at maturity. Pine t axa in general have undergone a series of taxonomic and phylogenetic reclassifications in recent decades, as genetic studies have revealed polyphyletic relationships, especially at the subsection level.
3 Historicall y, classification of subgenus Strobus or soft pines ( originally classified in section Haploxylon ) relied heavily on the presence or absence of seed wings, and whether cones are dehiscent or indehiscent at maturity. The combinations of these traits formed t he basis for classification of species in subsection Cembra into three groups: Strobi (winged seeds, dehiscent cones), Flexiles (wingless seeds, dehiscent cones), and Cembrae (wingless seeds, indehiscent cones) (Shaw 1914). However, classification subseque ntly removed Flexiles , placing those species into the Strobi group (Shaw 1924), thus originally using cone dehiscence as a monophyletic trait to differentiate the groups. Species in these groups are geographically widespread, spanning Europe, Asia, and Nor th America. The taxonomy was revised, placing these species in subgenus Strobus section Strobus , with the former groups recognized as subsections (Little and Critchfield 1969). In addition to whitebark pine, the Cembrae subsection cons isted of the other st one pines: European and west Asian species Swiss stone pine ( P. cembra L.) and Siberian pine ( P. sibirica Du Tour), and east Asian species dwarf Siberian pine ( P. pumila (Pall.) Regel) and Korean pine ( P. koraiensis Siebold & Zucc.) (Little and Critchfield 1969). Silviculture hybrid viability studies of the white pines called into question the classical categorization of subsections Strobus and Cembrae . Unlike hard pines (subgenus Pinus ), white pines lack geographic hybridization inviability , with hybrids r eadily arising from crosses within and between eastern and western hemisphere species (Critchfield 1975). The successes and failures of hybridization seemed to refute the monophyly of cone dehiscence, instead indicating a polyphyletic trait representi ng co nvergent evolution with three origins one for P. pumila ; one for P. cembra, P.
4 sibirica , and P. koraiensis ; and one for P. albicaulis ( Critchfield 1986). However, subsections Strobi and Cembrae were still widely accepted over a decade later (Price et al. 1 998). With the rise of molecular marker use, evidence has repeatedly failed to support the monophyly of section Cembrae . I sozyme research instead suggested two evolutionary origins for cone indehiscence once for P. cembra , P. sibirica , and P. albicaulis , and once for P. pumila and P. koraiensis (Belokon et al. 1998). Nuclear ribosomal DNA internal transcribed spacer (nrITS) region sequences support isozyme work, separating P. pumila from P. cembra and P. albicaulis , al though P. sibirica and P. koraiensis w ere not included as part of the study (Liston et al. 1999). Based on chloroplast DNA (cpDNA) sequences, w hitebark pine has been phylogenetically related to Japanese white pine ( P. parviflora Siebold & Zucc.) and sugar pine ( P. lambertiana Douglas), while t he other Cembrae pine s grouped with Eurasian species (Gernandt et al. 2005), su ggesting typical stone pine morphology has evolved at least twice . This also resulted in phylogenetic revision , merging subsection Cembrae with subsection Strobus , and placing b oth in section Quinquefoliae . More recent work, using nuclear genes to resolve phylogenetic uncertainty from nrITS and cpDNA results, also supports nonmonophyly of the Cembrae pines, al though whitebark pine is grouped in a clade with the east Asian pines, not the European pines. Further, those conflicting results point toward incomplete lineage sorting, the persistence of ancestral alleles following speciation events, to explain failures to converge on monophyly at the species level (Syring et al. 2007). No rthern populations of sugar pine show evidence of historical hybridization and introgression with whitebark pine, from species paraphyly of cpDNA
5 haplotypes, further complicating relationships among species (Liston et al. 2007). Most recently, phylogenetic trees constructed from chloroplast, mitochondrial, and nuclear DNA support this trend, tying allelic nonmonophyly in section Quinquefoliae to ancient and recent hybridization and introgression ( Tsutsui et al. 2009; Hao et al. 2015). Whitebark Pine a nd Cla utcracker Whitebark pine (Fig. 2) functions in ecosystems as a foundation and keystone species. Foundation species define community structure and control ecosystem processes, by creating stable environments for other species (Ellison et al. 2005). In contrast, k eystone species foster and maintain high levels of biodiversity in communities (Tomback et al. 2001b), which whitebark does by providing wildlife habitat and providing large, energy rich seeds that are used by numerous birds and mammals (Tombac k and Kendall 2001; Tomback et al. 2011). Whitebark pine facilitates the development of forest communities after disturbance and provides an essential wildlife food source for animals ranging from birds and squirrels to grizzly bears (Mattson et al. 2001) . The i mportance of the seeds is due both to their large size (about 180 mg each, 13 60 times the size of most other conifer seeds) (Mattson et al. 2001) and high nutritional content (52% fat, 21% carbohydrates, and 21% protein) (Lanner and Gilbert 1994). Whitebark pine also performs ecosystem services, reducing soil erosion and shading snow, which protracts snowmelt (Tomback and Achuff 2010). Whitebark pine has wingless seeds that are dispersed by an avian mu tualist, the (Fig. 3) , wherea s most other pines are wind dispersed (Tomback and Linhart 1990). Because c ones are indehiscent, the birds harvest seeds directly from the cones by prying the scales apart (Tomback 1982). In fall , most of the seeds they collect
6 are then cached for future u se as food estimated between 32,000 98,000 seeds per bird every year (Hutchins and Lanner 1982; Tomback 1982). Some of these caches are retrieved, but any excess or unretrieved caches have the potential to germinate. For whitebark pine, this is an obliga te mutualism , because the nutcracker is the most effective and frequent nutcracker preferentially selects whitebark pine seeds due to their size and energy rich content (Tomback 2001), they are also k nown to eat and cache seeds of other species including limber pine, pinyon pine ( Pinus edulis Engelm.), singleleaf pinyon ( Pinus monophylla Torr. and FrÃ©m.) (Tomback and Linhart 1990), and sugar pine ( Pinus lambertiana Dougla s) (Murray and Tomback 2010), a mong others. Threats Fire, a major disturbance factor in western forests, has been increasing in scale and intensity as a result of prolonged drought and warming temperatures (Schoennagel et al. 2005). Over the past century, fire risk has been increasing i n west ern North America and decreasing in the east (Fig. 4) . This may result from fire suppression, which has led to more mesic ecosystems in the east that are less likely to burn, while in the west has led to in an increase in biomass , but no reduction in burn probability . Climate warming has led to longer fire seasons from earlier snow melt and later snow accumulation. Over the next century, fire risk is expected to increase across North America. This increase is predicted to be higher in west ern North America, resulting from greater increases in temperature , decreases in precipitation , and altered hydrology (Moritz et al. 2012) . The amount of time needed to recover genetic diversity in a post fire regenerating forest is a question few have addressed, an d is an issue that
7 will become more pressing as fire intervals decrease due to climate change , especially in western North America . Fire intervals have been projected to undergo drastic reduction over the next century as fire probability increases (Westerl ing et al. 2011; Moritz et al. 2012), which is predicted to lead to population collapses and increase extirpation and extinction of woody plants (Bowman et al. 2014; Enright et al. 2015). Further, over the next 100 years, climate modeling has projected a 2 5 50% increase in total area burned in the United States, with a 10 30% increase in the seasonal severity rating of fires (Dale et al. 2001). For post fire recovery, one study of Aleppo pine ( Pinus halepensis Mill.) provides a range of 30 40 years (Schill er et al. 1997) for overall recovery of forest composition and structure after fires, but did not address recovery of genetic diversity relative to unburned stands. Black spruce ( Picea mariana (Mill.) Britton, Sterns and Poggenb. [Pinaceae]) and Canary Isl and pine ( Pinus canariensis C.Sm.) populations fully recovered genetic diversity when measured respectively 25+ and 60+ years after both natural and artificial disturbances, but neither study explored the time it took to achieve those levels of gene tic rec overy (Rajora and Pluhar 2003; NavascuÃ©s and Emerson 2007). It is essential to investigate the timeline of recovery of genetic diversity if we are to understand future fores ts in light of projected reduction in fire interv als and increased fire severity. W ill recovery occur rapidly enough to preclude serious loss of diversity and, thus, evolutionary potential? White pine blister rust is an invasive fungal pathogen native to Eurasia, with a complicated lifecycle that involves alternation between two hosts white pines and shrubs in the genus Ribes (McDonald and Hoff 2001). The fungus indiscrimina tely
8 infects trees of all sizes and , after infection , hyphae grow throughout the phloem. Eventually , ular tissue. This leads to branch or stem death above the point of infection, which eventually may kill the tree. In reproductively mature trees that survive infection, death of branches greatly reduces future cone production (McDonald and Hoff 2001). Whit ebark pine is susceptible to white pine blister rust, and has limited resistance to the pathogen . The blister rust infection kills both reproductively mature trees, reducing future regeneration, and seedlings and saplings, reducing recruitment. In Yellowst one National Park, infection rates are estimated at 20% , with 10% mortality (Jean et al. 2011). Mountain pine beetle is a bark beetle native to western North America that targets large, mature trees to infest and reproduce, typically killing the host (Sch wandt et al. 2010). Lodgepole pine ( Pinus contorta Douglas ex Loudon ) is the primary host, but all pines are suitable. Females lay eggs in the phloem of trees, and the larvae feed on the phloem, eventually cutting off transport of materials through this v ascular tissue (Gibson et al. 2008). Mountain pine beetles are limited by cold temperatures, but recent temperature increases, especially in minimum temperature, have enabled the beetles to survive further north and at higher elevations. Milder winters and warmer summers have increased the frequency and severity of beetle outbreaks (Logan et al. 2010). In the Yellowstone area, extensive outbreaks are occurring in high elevation whitebark pine forests, where in the past outbreaks have been infrequent (Logan et al. 2010). Mortality due to mountain pine beetle in whitebark pine stands was found to range from 38 96% for trees over 5 inches DBH in Montana and Yellowstone National Park
9 during an outbreak in the mid nal Park, whitebark pine infestation by mountain pine beetles is relatively high, wi th mortality between 38 52% for trees >30 cm DBH (Jean et al. 2011; Barringer et al. 2012) . Mountain pine beetles attack large, mature trees to maximize reproductive out put. This progressively reduces the number of cone producing individuals, depleting the genetic diversity of future generations (Raffa et al. 2008). Further, beetle outbreaks are predicted to intensify in response to climate change, and invasions will occu r at increasingly higher elevations and latitudes (Logan and Bentz 1999; Logan and Powell 2001; Logan et al. 2003). Mountain pine beetle outbreaks have been occurring throughout the Greater Yellowstone Ecosystem, and are now causing destruction of reproduc tively mature whitebark pine, exceeding that of past outbreaks (Tomback 2008; Logan et al. 2010). In contrast, white pine blister rust infects trees of all size s and, depending on the point of infection, can lead to the death of individual branches, the cr own, or the entire individual, all resulting in decreased cone production (McDonald and Hoff 2001). The Greater Yellowstone Area The area burned by the 1988 fires in the Greater Yellowstone Area provides a suitable location to investigate the recovery of genetic diversity in populations following a major disturbance. In 1988, 250,000 hectares of the Greater Yellowstone Area burned the most extensive fires in the region since the establishmen t of Yellowstone National Park in 1872 (Turner et al. 2003). The s ummer of 1988 was exceptionally warm and dry, and Yellowston e National Park experienced extreme
10 drought and high winds (Christensen et al. 1989). These weather conditions were the primary cause for the large scale fires , which resulted from lightning ignit ions and human sources starting in June, and were ultimately halted by snowfall in September ( Christensen et al. 1989; Turner et al. 2003). Subalpine forests in the Greater Yellowstone Area have historically been maintained by stand replacing fire regimes, characterized by high intensity canopy replacing fires and long return intervals (Turner e t al. 2003). These forests have fi re return intervals between 100 500 years, so with no major fires since at least the founding of Yellowstone National Park over 1 00 years prior, the 1988 fires were predicted from the natural fire interval for this ecosystem (Romme 1982; Romme and Despain 1989). Fires create a heterogeneous landscape of burned and unburned patches (Christensen et al. 1989). In the Greater Yellowston e Area, severely burned areas are in close proximity to unburned or lightly burned areas (Turner et al. 1994), facilitating quick reestablishment of plant communities post fire. Whitebark pine is an important tree species in the Greater Yellowstone Area (L ogan et al. 2010), and new seedlings were reported in the burned areas by 1991 (Tomback et al. 2001a). Now, the Greater Yellowstone Area is also predicted to experience shorter intervals between fires, projected at fewer than 30 years by 2099, down from th e historical average (Westerling et al. 2011). As such, it is essential to find out whether this important species will be able to recover genetic diversity rapidly enough to avoid bottlenecking. Genetic D iversity As populations regenerate following a dist urbance, the recovery of community structure and genetic diversity is extremely important (Mosseler et al. 2003; Rajora and
11 Pluhar 2003). Genetic diversity helps maintain forest health and facilitates adaptation , providing the variation upon which natural selection can act (Mosseler et al. 2003), and high genetic diversity in outcrossing species enables adaptation to changing environments. High genetic diversity, including heterozygosity, is also correlated with overall population fitness (Reed and Frankham 2003). In several species of conifers, individual heterozygosity has been found to correlate positively with fitness measures, such as height and tree diameter (Ledig et al . 1983; Mosseler et al. 2003). Reduced genetic diversity has been linked to local e xtirpation and overall extinction risk for threatened species (Speilman et al. 2004). Over time, loss of heterozygosity can decrease fitness and threaten long term population and species persistence (Lande 1995 , 1998). Genetic diversity in whitebark pine m ay be impacted by declines in seed production from recent increases in infection by white pine blister rust and outbreaks of mountain pine beetle, but these potential genetic effects have not yet been studied in this species. In western white pine ( Pinus m onticola Douglas ex D.Don), populations with low blister rust infection rates have higher levels of heterozygosity and more private alleles than populations with high infection rates (Kim et al. 2003). In whitebark pine, there has been some circumstantial evidence that heterozygotes are more tolerant of blister rust (Krakowski et al. 2003). Across multiple species of western bark beetles, it is speculated that reduction of reproductively mature host trees may decrease genetic diversity in future generations (Raffa et al. 2008), and this mechanism may be at work within whitebark pine populations. These declines in population size may be reducing genetic diversity, and could thus already be reducing population fitness and increasing
12 extirpation risk on localiz ed scales. Further, whether post fire regeneration of whitebark pine is impacted by increasing beetle and blister rust mortality needs to be investigated. Population Genetic Structure of Plants Populations of plants can be genetic ally structured both spati ally and temporally, and the structure of a population can impact genetic diversity and fitness through inbreeding and genetic drift (Mosseler et al. 2003). The nonrandom distribution of individual genotypes across spatial scales is known as spatial geneti c structure. Because plants are stationary, there are limitations to gene flow , frequently resulting in spatial genetic structure within and among populations (Vekemans and Hardy 2003). This pattern depends on pollen and seed dispersal distances; as distan ce from an individual parent tree increases, there is an exponentially negative relationship with the amount of pollen and seed dispersed and, thus, relatedness among individuals decreases (Epperson and Chung 2001; Bonnet et al. 2005). One underlying mecha nism of spatial genetic structure is isolation by distance, wherein individuals are more likely to reproduce with nearby members of the population than with distant members. Old growth populations of eastern white pine ( Pinus strobus L.) have weakly positi ve spatial genetic structure, fitting an isolation by distance model (Epperson and Chung 2001; Marquardt and Epperson 2004; Walter and Epperson 2004). This pattern was not observed in post logging second growth populations, so disturbances may affect spati al structuring. However, the seedlings in these second growth populations exhibit ed spatial structuring consistent with old growth populations (Epperson and Chung 2001). In maritime pine ( Pinus pinaster Aiton), fine scale spatial structure has been found i n
13 seedling regeneration, with an excess of parent offspring matches found around each tree. However, this was not reflected in relatedness among mature trees (GonzÃ¡lez MartÃnez et al. 2002). Animal dispersed species also exhibit genetic isolation by dista nce and spatial structure, with the magnitude of seed dispersal decreasing as distance from seed sources increases (Nathan and Muller Landau 2000). Prunus mahaleb L. (Rosaceae), an animal dispersed tree species, showed high levels of gene flow, with a disc ernable isolation by distance pattern (Jordano and Godoy 2000). The bird dispersed species Neolitsea sericea (Blume) Koidz. (Lauraceae) has a nearly random spatial distribution (Chung et al. 2000). However, Juniperus communis L. (Cupressaceae), which is th oug ht to be bird dispersed, was found to have highly differentiated populations in England (Van der Merwe et al., 2000). Further, if seed sources are stable, population diversity should be consistent among individuals of different ages. In populations of p onderosa pine ( Pinus ponderosa Douglas ex C. Lawson), genetic diversity among ten age classes was found at levels consistent with genetic diversity within the populations (Hamrick et al. 1989). In Neolitsea sericea , both genetic diversity and structure wer e homogeneous across each of five age classes (Chung et al. 2000). G enetic s tructure in p ines Pines that are wind dispersed have high differentiation among population s, with clear isolation by distance related to pollen and seed dispersal distances ( Benk man 1995 ; Bruederle et al. 1998 ). They also la ck fine scale structure (Table 2 ). Eastern white pine, for example, has weak genetic structure characteristic of isolation by distance (Epperson and Chung 2001; Marquardt and Epperson 2004; Walter and Epperso n
14 2004). Chinese pine ( Pinus tabuliformis CarriÃ¨re) has clear population differentiation consistent with isolation by distance. (Wang and Hao 2010). Lodgepole pine has nearly random distributions of genotypes within populations, with extremely high gene fl ow, preventing the development of related patches (Epperson and Allard 1989). Among populations, no isolation by distance was found, likely from very high rates of gene flow (Epperson and Allard 1989). Slight fine scale clustering has been found within pop ulations of both lodgepole pine (Knowles 1984) and jack pine ( Pinus banksiana Lamb.) (Xie and Knowles 1990), possibly a result of serotiny, which would result in related individuals germinating in close spatial proximity. Other studies in lodgepole pine ha ve not found similar structuring (Epperson and Allard 1989). In contrast, pondero sa pine populations have a mixed genetic structure, tending to be composed of clusters of closely related family groups that are genetically distinct from nearby groups (Linha rt et al. 1981). There is evidence suggesting that long distance dispersal in Gymnorhinus cyanocephalus Wied Neuwied) (Lesser and Jackson 2013), which may be contributing to a more rando m structure. Bird dispersed pines differ from wind dispersed species with respect to population structure (Table 2 ). They are expected to have less differentiation among populations, owing to long seed dispersal distances, and more fine scale differentiat ion within populations from the family structure of tree clusters. Some geographic structuring has been found in populations of Swiss stone pine ( Pinus cembra L.), which is dispersed by the Eurasian nutcracker ( N. caryocatactes Linnaeus), likely from genet ic drift as a result of barriers to pollen and seed dispersal distance (Lendvay et al. 2014).
15 However, there is neither genetic differentiation among populations nor isolation by distance, owing to recent retraction of the species range, and the long dista nce pollen dispersal by wind and seed dispersal by nutcrackers (Tomback et al. 1993; Lendvay et al. 2014). In tree clusters of Swiss stone pine, limber pine ( Pinus flexilis E. James) , and whitebark pine, multiple genetically distinct individuals are found growing together, which are the result of multiple seeds germinating at the same time, rather than clonal growth (Linhart and Tomback 1985). These species have family structure within tree clusters; individuals in the same tree cluster are related on avera ge between half and full sibling, while individuals in different clusters are unrelated (Schuster and Mitton 1991; Carsey and Tomback 1994; Rogers et al., 1999; Schuster and Mitton, 2000). This is likely the result of nutcrackers collecting seeds from only one or a few trees before caching, and different nutcrackers harvesting seeds from different trees but caching in the same area (Furnier et al. 1987; Carsey and Tomback 1994). Whitebark pine has further been found to lack family structure among clusters, with individuals in nearby clusters no more genetically similar than individuals in distant clusters, likely due to randomness in caching. However, it has also been proposed that birds return to caches, recover seeds, and re cache them elsewhere (Furnier e t al. 1987; Rogers et al. 1999). Bird dispersal influences both the genetic diversity and population structure of P . a l b i c a u l i s . Individual nutcrackers tend to collect seeds from a small number of source trees at a time, but then disperse these seeds acro ss many different areas, traveling long distances. In addition, multiple birds are likely to cache seeds in the same area (Tomback and Linhart 1990). The combination of these two effects results in a more random distribution of genotypes than fo und in wind dispersed species, with
16 n eighboring trees no more likely to be related to each other than to distant trees (Furnier et al . 1987) . Compared to wind dispersed pines, whitebark pine has lower levels of genetic diversity and less population differentiation in the Greater Yellowstone Ecosystem and range wide (Jorgensen and Hamrick 1997; Bruederle et al. 1998). However, a common garden experiment on seedlings range wide found moderate to strong geographic differentiation among populations for growth rate and col d adaptive traits (Bower and Aitken 2007). Range wide, populations have distinct mitochondrial DNA haplotypes, but very little differentiation in chloroplast DNA (Richardson et al. 2002). Among watershe ds in the eastern Sierra Nevada , there is almost no ge netic differentiation (Rogers et al. 1999). Seed s from the seven Inland West whitebark pine seed zones show high genetic diversity, but low differentiation among zones with some differentiation broadly (Mahalovich and Hipkins 2011). Populations in the Casc ades and northern Rocky Mountains have moderate genetic differentiation (Krakowski et al. 2003). Molecular M arkers In pines, nuclear microsatellites (highly polymorphic simple sequence repeats [SSRs] of 1 6 base pairs) have been used to characterize gene tic structure within and among populations with increasing frequency since the 1990s (Rajora et al. 2000; Mariette et al. 2001; Walter and Epperson 2004; Iwasaki et al. 2013; Chhatre and Rajora 2014; Lendvay et al. 2014; Oreshkova et al. 2014; Villalobos A rÃ¡mbula et al. 2014). RAPDs (amplifications of random segments of DNA using non specific primers), AFLPs (amplification of restriction fragments using specific primers), and minisatellites (repetitive sequences of 10 60 base pairs) have been used over the past decades to
17 study genetic diversity (Mosseler et al. 1992; Latta and Mitton 1997; Mariette et al. 2001; Lee et al. 2002; Ribeiro et al. 2002; Kim et al. 2003; Godbout et al. 2005; Tian et al. 2008). In whitebark pine , population genetics have been exam ined using allozymes (different forms of an enzyme), mitochondrial DNA introns, and chloroplast DNA microsatellites (Linhart and Tomback 1985; Furnier et al. 1987; Bruederle et al. 1998; Jorgensen and Hamrick 1999; Rogers et al. 1999; Richardson et al. 200 2; Krakowski et al. 200 3; Mahalovich and Hipkins 2011 ). SNPs single nucleotide polymorphisms, where a gene sequence differs by one nucleotide have recently been examined in whitebark pine, and have been used to characterize populations in the Sierra Nevada and Pacific Northwest (Liu et al. 2016; Lind et al. 2017). Currently, microsatellites and SNPs are the two most common markers used to study population genetics. Microsatellites In 1988, a PCR method that led to more precise analysis of small nucleotide sequences was pioneered (Saski et al. 1988). This technique enabled effective use of microsatellites as a method of geneti c analysis (Litt and Luty 1989) . The short length of repeating sequences means that microsatellite alleles are less than 1kb long with very little size variability, which makes these loci easy to assay with just PCR and gel electrophoresis (Bruford and Wayne 1993), though they are now more commonly fluorescently tagged and run on a sequencer. The small size also enables analysis with ver y small amounts of DNA, and even makes it possible to analyze highly degraded DNA or extractions with many impurities (Bruford and Wayne 1993). Further, primers that have been developed for one species are often transferable to other related species (Oresh kova et al. 2014; Villalobos ArÃ¡mbula et al. 2014).
18 Previous whitebark pine studies have included three microsatellite loci in chloroplast DNA (Richardson et al. 2002). Mahalovich and Hipkins (2011) applied these to a common garden approach. However, in p ines, chloroplast DNA has uniparental, paternal inheritance and, as such, it is not ideal when exploring genetic diversity (Neale and Sederoff 1989). These chloroplast loci were identified from a microsatellite study of Pinus thunbergii (Vendramin et al. 1 996) in subgenus Pinus . While nuclear microsatellites have not yet been developed for whitebark pine, the success in using chloroplast loci from a distantly related pine species in whitebark pine suggests the possibility of adapting nuclear microsatellites from closely related subgenus Strobus pines, for which numerous loci have been developed and used. Illumina sequencing using hybridization based capture probes for forty eight individuals from across the range of whitebark pine has identified 4,452 genes that are candidates for microsatellite loci (Syring et al. 2016) . SNPs Single nucleotide polymorphisms (SNPs) are a frequently used molecular marker in plant genetics, as they are numerous within genomes of almost all species (Kumar et al. 2012). They invo lve the difference of one nucleotide within a gene sequence, which can result in the production of a different protein and may reflect adaptation. SNPs are processed using next generation sequencing. The development of high throughput next generation seque ncing techniques over the past 15 years has both greatly reduced the cost of sequencing and increased the daily volume of throughput (Kircher and Kelso 2010). This has resulted in an expanded use of SNPs in plant genetics, where many genome sizes had previ ously been prohibitively large
19 (Kumar et al. 2012). In whitebark pine, SNP discovery has been successful with at least several thousand markers accessible ( Liu et al. 2016 ). Objectives My first and primary objective was the identification of nSSR loci as r eliable markers to assess genetic diversity in whitebark pine. This would enable the use of nuclear microsatellites as a viable tool to describe and monitor genetic diversity of populations , particularly as threats to species persistence continue to grow. My secondary objective was to provide an application of the utility of these microsatellites by comparing genetic diversity in two populations previously studied in the Greater Yellowstone Ecosystem using allozymes (Bruederle et al. 1998). The development of nuclear SSR markers will provide a much needed tool to study changes in genetic diversity within this declining keystone species.
20 Tables and Figures Table 1 : Classification of the genus Pinus , from Gernandt et al. 2005 .
21 Table 2 : Fine scale genetic structure in whitebark pine ( Pinus albicaulis ) and similar species, from Bruederle et al. 2001. 1 F ST = genetic diff erentiation 2 R = Rogers genetic distance 3 r = relatedness within clusters 4 D e = gene diversity among elevational zones 5 Autocor. = autocorrelation analysis
22 Fig. 1: Distribution of whitebark pine ( Pinus albicaulis ) in western North America. Spatial data from the Whitebark Pine Ecosystem Foundation (2014).
23 Fig. 2: Mature whitebark pine ( Pinus albicaulis ) at Henderson Mountain, Custer Gallatin National Forest, MT. P hoto by Marian Lea. Fig. 3 Nu cifraga columbiana ), seed disperser for whitebark pine ( Pinus albicaulis ). Photo by Liana Boggs.
24 Fig. 4 : F ire risk maps of suitable habitat, based on elevation, slope, aspect, precipitation, maximum temperature, and vegetation type. A) change in fire risk from 1920 to 2010 B) risk ma p for 1920 from raster addition, and C) risk map for 2010 from raster addition .
25 CHAPTER II DEVELOPMENT OF NUCL EAR MICROSATELLITE MARKERS FOR PINUS ALBICAULIS ENGELM . (PINACEAE) , A SPECIES OF CONSE RVATION CONCERN: APP LICATION TO POPULATIONS PREVIOUS LY ASSESSED FOR GENE TIC DIVERSITY Introduction Genetic diversity helps maintain forest health and facilitates adaptat ion , providing the variation upon which natural selection can act (Mosseler et al. 2003), with high genetic diversity in outcrossing species enabling adaptation to changing environments. High genetic diversity, and therefore high frequ ency of heterozygosit y, is also correlated with overall population fitness (Reed and Frankham 2003). Reduced genetic diversity has been linked to local extirpation and overall extinction risk for threatened species (Spielman et al. 2004). Over time, loss of heterozygosity can decrease fitness and threaten long term population and s pecies persistence (Lande 1995, 1998). Genetic diversity also provides much needed resilienc e against pests, pathogens, and environmental fluctuations, especially with respect to climate change. Thus , monitoring genetic diversity and heterozygosity may provide information on the persistence of populations and conservation of species. North American forest trees have been subject to a number of unprecedented health challenges, driven in part more rece ntly by warming temperatures. These include outbreaks of native insects, such as bark beetles (Raffa et al. 2008), mortality of older ag e classes from drought (V an Mantgem et al. 2009 ), and the accidental introduction and spread of destructive exotic pest s and pathogens (e.g., Lovett et al. 2016). In fire prone communities in the western United States, intervals between fires
26 are projected to decline over the next century as climate warms (Westerling et al. 2011; Moritz et al. 2012). This may lead to the extirpation and extinction of some woody plant species (Bowman et al. 2014; Enright et al. 2015). As populations regenerate following a disturbance, the recovery of community structure and genetic diversity is extremely important (Mosseler et al. 2003; Raj ora and Pluhar 2003). In general, the continued refining of methods that expedite the assessment of genetic diversity in species of conservation concern will enable us to detect declines and implement restoration treatments. Pinus albicaulis , a widely dist ributed western North American tree species of subalpine and treeline elevations, ranging from about 37Â° to 55Â° N latitude, is a candidate for listing under the U.S. Endangered Species Act (U.S. Fish and Wildlife Service 2011). Pinus albicaulis relies on t influences its ecology and population biology (Tomback 1982, Hutchins and Lanner 1982, Tomback and Linhart 1990, Tomback 2005). It is a slow growing species and, although individuals can be reproductively mat ure by 30 years, they generally do not begin producing large cone crops until at least 60 years of age (Arno and Hoff 1990). The species tolerates cold, dry, and windy environments, persisting as climax stands on these harsh sites. Successional P. albicaul is communities depend on disturbance, primarily wildland fire, for renewal (e.g., Arno 2001). Seedlings and saplings are moderately shade intolerant, and mature trees are outcompeted by more rapidly growing species as succession progresses on less harsh s ubalpine sites (Arno 2001). Pinus albicaulis is often one of the first tree species to regenerate in high elevation areas following fires, with new seedlings establishing on all aspects within a few years
27 (Tomback et al. 1990, 1993, 2001a). At treeline ele vations, it functions as a tree island initiator, facilitating infilling and upward expansion of the alpine treeline ecotone (Resler and Tomback 2008; Tomback et al. 2014, 2016). Given that P. albicaulis has experienced widespread declines, it is a specie s of management and conservation concern (Tomback and Achuff 2010; Kean e et al. 2012 ). Developing relative easy and cost efficient methods to study levels and apportionment of genetic diversity in P. albicaulis populations would expedite con servation actions, including the assessment of local genetic diversity, recovery of genetic diversity after fire to levels equivalent to unburned stands , and establishment of seed transfer zones across its range (e.g., Mahalovich and Hipkins 2011, Leirfall om et al. 2015). Pinus albicaulis is a host for mountain pine beetle, which erupted in large scale outbreaks in the Rocky Mountains and elsewhere in the West over the last 20 years ; and , it is susceptible to white pine blister rust , which continues to spread geographically (Tomback et al. 2001b; Tomback and Achuff 2010; Schwandt et al. 2010). Both tree mortality from pine beetles and mortality and canopy damage by blister rust progre ssively reduce the number of cone producing individuals, depleting genetic diversity for future g enerations (Raffa et al. 2008); blister rust also infects and kills young trees, reducing regeneration (McDonald and Hoff 2001). In P. albicaulis , some evidenc e suggests that heterozygotes are more tolerant of blister rust (Krakowski et al. 2003). With declines in cone production and high mortality, highly damaged P. albicaulis stands may be experiencing various degrees of inbreeding or pollen limi tation.
28 Microsatellites, or simple sequence repeats (SSRs), have been a common tool for genetic studies over the past two decades, because they can be used with degraded DNA, and tend to be highly polymorphic, codominant, abundant, easily reproducible, and relatively inexpensive to use (Bruford and Wayne 1993; Kalia et al. 2011). SNPs (single nucleotide polymorphisms) are an increasingly common tool to assay genetic diversity, as they are more abundant within genomes than SSRs and can be resolved with highl y degraded DNA. However, SNP mutation rates are orders of magnitude lower than in SSRs, and loci exhibit less polymorphism (typically only two alleles), so more markers are needed to examine genetic diversity (Butler et al. 2007). Although sequencing costs continue to decline, it can still be prohibitively expensive to perform genetic diversity studies using SNPs, especially as funding opportunities become more scarce. Nuclear microsatellites (nSSRs) have been a key tool for characterizing genetic diversity in species of conservation concern. The y have been used to differentiate populations ( pequi, Caryocar brasiliense Camb.), map the recovery trajectory of genetic diversity following near extirpation ( brown bear , Ursus arctos Linnaeus, 1758 ) , and monitor fo r hybridization between rare and widespread taxa (Colorado hookless cactus , Sclerocactus glaucus [K. Schumann] L.D. Benson ) ( Waits et al. 2000; Collevatti et al. 2001; Schwabe et al. 2015). They have also been successfully used to describe genetic diversit y in threatened and endangered species of pines. In ( P. krempfii Lecomte ) , Phong et al. (2015) w ere able to differenti ate populations that were separated by between 200 meters and 50 kilometers, and documented an excess of homozygotes and d eficiency of heterozygotes in one of the populations (Phong et al.
29 2015). In ( P. merkusii Jungh. & de Vriese ) and Guangdong white pine ( P. wangii Hu & Cheng ) , Nurtjahjaningsih et al. ( 2005 ), Dou et al. ( 2013 ), and Thao et al. ( 2013 ) found low genetic variabilit y and an excess of homozygotes . I n Monterey pine ( P. radiata D. Don ) , Moraga Suazo et al. (2014) screened for pathogen resistance using loci transferred from loblolly pine ( P. taeda L. ) , and mapped inheritance of t raits to both parent tr ees . Thus, nuclear microsatellites should be a suitable, useful tool to study genetic diversity in P. albicaulis and to monitor changes in populations. To facilitate finding useful nSSR markers, ma ny investigators look to near relatives , or sister species, for potential transfer (Barbara et al. 2007; Moraga Suazo et al. 2014; Oreshkova et al. 2014; Villalobos ArÃ¡mbula et al. 2014). In P. albicaulis , Gernandt et al. (2005) identified the closest relatives as P. parviflora and P. lambertiana . Ho wever, t he phylogenetic relationships of P. albicaulis and its nearest relatives within Section Quinquefoliae are unresolved (Gernandt et al. 2005; Syring et al. 2005). Sec t ion Quinquefoliae includes a recent merger of 21 species in two Str obus pine subsections Strobus and Cembrae , which were historically separated based on cone morphology and other traits (Shaw 1914, 1924). It is now hypothesized that cone indehiscence has arise n more than once in white pines, but this is not entirely clear , and may be the result of incomplete lineage sorting, resulting in the persistence of ancestral alleles (Syring et al. 2007), and events of hybridization and introgression across section Quinquefoliae (Liston et al. 2007; Tsutsui et al. 2009). The most r ecent phylogenies of subgenus Strobus constructed from nuclear, mito chondrial, and chloroplast gene sequences variously group P. albicaulis with species from North America, Asia, and Europe (Hao et al. 2015).
30 G enetic diversity in P. albicaulis has been previously examined using different genetic markers. Population surveys across the species range using allozymes found low mean numbers of alleles per locus (2.19) and expected heterozygosity (0.102) within 30 populations (Jorgensen and Ha mrick 1997). However, allozymes bias samples by selecting for proteins that are highly soluble, and species tend to contain a small number of markers, and as such are insufficient for continued monitoring (Lee et al. 2002). SNPs recently developed for P. a lbicaulis have been used to characterize populations in the Sierra Nevada and Pacific Northwest (Liu et al. 2016; Lind et al. 2017). As expected with this tool, t he populations exhibit a small number of mean alleles per locus (1.93), but much higher expected heterozygosity (0.35) th an allozymes (Liu et al. 2016). SNPs have low levels of polymorphism, requiring many loci to resolve genetic differences, and must be sequenced for genotyping, which can be prohibitively expensive (Butler et al. 2007). Mitochondria l and chloroplast SSRs applied to P. albicaulis nearly range wide reveal that regional populations have distinct mitochondrial DNA haplotypes but very little differentiation in chloroplast DNA (Richardson et al. 2002). However, as these are haploid and uniparentally inherited, they are not an ideal tool to monitor genetic diversity and structure of populations (Neale and Sederoff 1989). Despite the utility of nSSRs for surveys of genetic diversity in species of conserva tion concern, especially other pines, nuclear microsatellites have not yet been successfully developed for Pinus albicaulis . Pinus albicaulis has a large, highly repetitive genome, which has confounded efforts to develop single locus polymor phic
31 microsatellites. In addition, microsatellite transfer among pine species has been inconsistent (GonzÃ¡lez MartÃnez et al. 2004). Here, my first and primary objective was the development of reliable nSSR markers to assess genetic diversity in P. a lbicaulis , in order to provide an efficient , informative, and inexpensive tool to monitor genetic diversity of populations as threats to t h e i r persistence continue to grow. My secondary objective was to determine the utility of these micros atellites by comparing genetic diversity in two populations previously studied in the Greater Yellowstone Ecosystem (Bruederle et al. 1998). To maximize the number of nSSRs, I combined screening from Illumina sequencing for novel locus development with scr eening of loci from related pine species for potential transferability. Methods Microsatellite markers were compiled from two sources novel locus development in P. albicaulis and transfer of loci developed in other species to screen for ampli fication. For the novel development, Illumina sequencing conducted by Syring et al. (2016) using hybridization based capture probes , identified 4,452 potential nSSR markers based on samples from forty eight individuals from across the range of P. alb icaulis (Syring et al. 2016). All identified tetranucleotide repeat primers were selected for additional screening. For the dinucleotide repeat primers, all primer pairs that included a GC ratio of <35% or >60% in either primer were cut, as were primer pairs producing an amplicon smaller than 125bp in length. For loci developed for other species, t he most recent paper with published phylogenies of subgenus Strobus was used to guide locus selection to test for transferability, and I focused specifically on
32 phyloge nies formed using nuclear genes, because chloroplast and mitochondrial DNA have uniparental inheritance in pines and are thus not ideal for exploring genetic diversity and population structure (Fig. 2; Neale and Sederoff 1989; Hao et al. 2015). The nSSR loci selected to assess for transferability included 14 loci from P. cembra L. (Swiss stone pine) (Salzer et a l. 2009; Lendvay et al. 2014), three loci from P. parviflora Siebold & Zucc. (Japanese white pine) (Iwasaki et al. 2013), 13 loci from P. koraiensis Siebold & Zucc. (Korean pine) (Yu et al. 2012), and 19 loci from P. strobus L. (eastern white pine) (Echt et al. 1996). A total of 49 primers were ordered and screened for transferability (Table 1). DNA was extracted from silica dri ed needles using SDS based protocols optimized for use with Pinus species , modified with mercaptoethanol per extraction (Chang et al. 1993). DNA concentrations were determined using a Thermo Scientific NanoDrop 2000 microspectrophotom eter. polymerase chain reaction (PCR) mixtures lifications proS using the following cycling conditions: denaturation for 5 min. at 95Â°C, 35 cycles of 45 s each at 95Â°C, 45 s at 58Â°C, and 15 s at 72Â°C, followed by a final annealing cycle of 72Â°C for 5 min. PCR products were visualized on 2% agarose gel using GelRed (Biotium, #41002) and a low molecular weight DNA ladder (New England BioLabs, #N3233L). (Boutin Ganache et al. 2001). Microsatellite locus amplifications for fragment analysis
33 were created using the Fluorescent Tag Microsatellite PCR Protocol (Glenn 2006), with (School of Life Sciences, 427 E. Tyler Mall, Tempe AZ 85287 4501) for fragment analysis. Fragment analysis ou tputs were visualized and genotyped using the software program GeneMarker V2.6.2 (SoftGenetics). In association with an on going ecological study, and to compare with past population genetics research in the region, two populations were selected in the Gr eater Yellowstone Ecosystem. Henderson Mountain, Cust er Gallatin National Forest, MT and Mount Washburn, Yellowstone National Park, WY, 50 kilometers southwest of Henderson Mountain (Fig. 1), were originally described in Tomback et al. (2001a). The two sub alpine forest stands selected for this study comprise cone bearing P . a l b i c a u l i s , P . c o n t o r t a , Picea engelma n nii Parry ex Engelm. , and Abies lasiocarpa (Hooker) Nuttall . They are 0.5 5.0 km from post fire stud y areas established in the Greater Yellowstone E cosystem in 1990, so selected stands represent putative seed sources for areas regenerating following the 1988 Yellowstone fires. P. albicaulis s amples from Mt. Washburn were collected within a 13.3 hectar e (0.13 km 2 ) area ranging from 2810 3005 m elevation in north central Yellowstone National Park, on the north and west facing slopes, 7 9 km north of Canyon Junction. Samples from Henderson Mountain were collected within a 15.7 hectare (0.16 km 2 ) area ra nging from 2690 2785 m elevation on south and east facing slopes, 3 km northeast of Cooke City, MT, and 5 7 km
34 northeast of Yellowstone Park, within Custer Gallatin National Forest. For both populations, 20 individuals, each neare st a random ly generated point, were sampled for DNA extraction , a n d a few extra random points and individuals were sampled in case of failed DNA amplification . From each individual, five fascicles were collected and silica dried for extended storage. Trees were measured for diameter at breast height (DBH). Populations had similar ranges in DBH, although Henderson Mountain had a higher mean DBH (21.7 cm) than Mt. Washburn (17.2 cm) (Table 2), and past tree core sampling at Hender son Mountain revealed a maximum age of 316 yea rs at this site . Eight individuals were screened for the initial locus testing, and then 20 were used for the population assessment. Because of some failed amplifications, a total of 26 individuals from He nderson Mountain and 22 individuals from Mt. Washburn were sampled to bring the total sample size up to 20 genotypes for each locus. Standard genetic diversity measures, Hardy Weinberg equilibrium, inbreeding coefficient, and population differenti ation were calculated by population using GenAlEx 6.5 (Peakall and Smouse 2006, 2012). Histograms of average number of alleles per locus were created in the program R to characterize differences between populations (R Development Core Team 2016). Differenc es between mean number of alleles per locus and expected heterozygosity were found by subtracting the values for Henderson Mountain from the val ues for M t . Washburn. These differences in means were plotted in the program R , along with 95% confidence interv als around those differences. Analysis of molecular variance (AMOVA) was performed on genetic distances with 9,999 permutations in GenAlEx 6.5. Isolation by distance within each population and between populations was 9,999 permutations (Mantel 1967). This
35 test used linear genetic distance ( number of alleles ) a nd linear geographic distance ( meters ) to directly correlate the two (Peakall and Smouse 2006, 2012) Results Of the potential microsatellite loci identif ied through the Illumina sequencing, the selection criteria reduced the number of primers to screen down to 308 options (J. Syr ing, R. Cronn, pers. communication) . Of those, a total of ten novel P. albicaulis derive d loci (Tables 3 and 4) amplified successfully and were polymorphic in the two screened populations. In addition, 13 of the 42 transferred loci (Table 4 ) amplified successfully following extensive screening. Although transfer success rate was highly variab le, overall nearly 27% of loci transferred well. Loci developed for P . koraiensis had the highest rate of transferability, with almost 62% of tested loci amplifying in P. albicaulis (Table 5 ). This was followed by P. parviflora (33%), P. strobus (16%), and finally P. cembra (7%). Loci developed for P . koraiensis averaged 7.5 alleles , while those for P. strobus averaged 6.7 alleles per locus , P. parviflora had eight alleles, and P. cembra had five alleles (T able 5 ). For novel loci ( developed from P. albica ulis ), the total number of alleles (N A ) ranged from 2 9 in the combined populations , with an average of 3.3 alleles per locus. Observed heterozygosity (H O ) ranged from 0.02 0.50 ( = 0.18 ) , and expected heterozygosity (H E ) ranged from 0.02 0.40 ( = 0.18 ) . All loci conformed with Hardy Weinberg E quilibrium (HWE) (Table 4 ). For transferred loci, the N A ranged from 3 15 ( = 7.2 ) . H O ranged from 0. 1 8 to 0.56 ( = 0.39 ) , and H E ranged from 0.22 to 0.89 ( = 0.60 ) . Seven loci deviated from HWE (Table 4 ).
36 T he overall range of alleles per locus was similar for both pop ulations, but M t . Washburn had only tw o loci with more than six alleles, whereas the number of alleles per locus for Henderson Mountain was more normally distributed (Fig. 3). Henderson Mountai n ha d a higher average number of alleles and expected heterozygosity per locus (Fig. 4). N A for Hender son Mountain ranged from 2 9 ( = 4.6 ), with an average of 1.3 0 private alleles (present in one population and not the other) per locus. H O ranged from 0.04 0.56 ( = 0.29 ), and H E ranged from 0.04 0.84 ( = 0.43) (Table 4 ). N A for M t . Washburn ranged from 1 12 ( = 4.1 ), with an a verage of 0.91 private alleles per locus. H O ranged from 0 0. 6 5 ( = 0.32 ), and H E rang ed from 0 0.90 ( = 0.41) (Table 6 ). Seven loci deviated from Hardy Weinberg equilibrium between the two populations (Table 4 ). The i nbreeding coefficient (F IS ) for He nderson Mountain was 0.32, and 0.24 for Mt. Washburn. However, the loci developed for P. albicaulis had a small coefficient (0.007) , while those transferred from other species had a large coefficient (0.291) (Table 5 ). Total population differ entiation (F ST ) was estimated at 0.016 and was consistent across loci. AMOVA identified 91 % of va riance within populations, and 9 % among populations (Fig. 5) . The spatial d istance between samples at Henderson Mountain was larger (range = 8 to 538 m, = 231) than at M t . Washburn (range=7 to 348 m, = 173) (Table 7 ). Mantel s test within populations showed no correlation between genetic and geographic distances at Henderson Mountain (r x,y = 0.120, p = 0.121 ), indicating that individuals are no more closely related than expected with a random spatial pattern (Fig. 6a , 7 a) . A positive correlation at M t . Washburn (r x,y = 0.162, p = 0.05 1) indicated that individuals may be more closely related genetically to those
37 nearby spatially than random (Fig 6b , 7 b) . Bet ween the two populations , there wa s no spatial autocorrelation (r x,y = 0.029 , p = 0.1 49 ) (Table 7 ). Discussion Our primary objective, the development of reliable nSSR markers to assess genetic diversity in P. albicaulis , was accomplished with t he identification and application of ten novel and 13 transferred microsatellite loci for use in P. albicaulis population genetic assessments. O f the 308 novel microsatellites identified from Illumina sequencing of P. albicaulis , 150 were selected for further screening based on initial results from J. Syring (unpublished data ) . O nly ten polymorphic, variable, repeatable loci were ultimately developed for marker use a success rate of around 3%. The success rate of SSR marker transfer from other white pines to P. albicaulis was 13 out of 49 almost 27% nearly ten times that of novel loci. Given that the average number of alleles per locus of the transferred microsatellites (7.2 ) is more than twice that o f the nov el microsatellites (3.2 ), the transferred loci will likely better show genetic diversity and structure within and among populations. Notably, transferability of loci from the two east Asian species ( P. koraiensis and P. parviflora ) was substantially highe r than for the European ( P. cembra ) and North American ( P. strobus ) species tested. Although nuclear SSR transferability does not directly cor relate to phylogeny, as non coding sequences are not predictably conserved among related species (Ellis and Burke 2007), current P. albicaulis phylogenies do relate P. albicaulis to these other species . Pinus albicaulis is closely related to P. koraiensis according to the phylogenies using sequenced nuclear LEA like genes and mitochondrial DNA, and closely related to P. parviflora using chloroplast and
38 mitochondrial DNA. Pinus albicaulis is also related to P. strobus using nuclear LEAFY and LEA like genes, and mitochondrial DNA, and P. cembra using nuclear LEAFY genes (Hao et al. 2015). These differences may support the hypothesis of influence of incomplete lineage sorting, and ancient and recent events of hybridization and introgression , on P. albicaulis genetics, and may also indicate that these events have had some impact on the transferability of loci to P. albicaulis . Application to previously studied populations Our secondary objective was to provide a case example of the utility of these microsatellites by comparing genetic diversity in two p reviously studied populations. Despite the relatively small geographic distance between them, the developed nSSR markers indicated that both Henderson Mountain and M t . Washburn had private alleles, averaging 1.3 and 0.9 per locus , respectively . Compared t o other tools for measuring genetic diversity in P. albicaulis , the microsatellite loci described here have a higher mean number of alleles per locus, as would be expected with this class of genetic markers . Henderson Mo untain and M t . Washburn populations were previously sampled for genetic diversity and population structure with allozymes (Bruederle et al. 1998), and results using microsatellites reveal similar trends genetic diversity . Henderson Mountain averaged 1 2 % more alleles per SSR locus than M t . Wa shburn (4.6 versus 4.1 ) (Table 6 ), which shows a greater difference than allozyme results for these populations (1.7 versus 1.6). However, the 95% confidence interval of this difference overlaps zero, so there may b e no true difference in allelic diversity between these populations (Fig. 4A).
39 Expected heterozygosity for the novel loci was higher than that found with allozymes , but lower than found with SNPs for other populations ; and , for the transferred loci , exp ected heterozygosity was 2 4 times greater than both other methods (Bruederle et al. 1998; Liu et al. 2016). Looking at only Henderson Mountain and M t . Washburn populations, the average expected heterozygosity reported using allozymes was 1/3 that of present microsatellite work on those same sites (0.164 and 0.145 versus 0.429 and 0.414, respectively). But, once again the 95% confidence interval of this difference overlaps zero, so there may be no true difference in expected heterozygosity between thes e populations (Fig. 4B). All violations of HWE are from an excess of homozygous genotypes, not heterozygotes, and may result from population structure, not the amplification of multiple genes by a given primer set. Most of the loci that violate HWE are tra nsferred, which may also indicate imperfect conservation of primer binding sites in P. albicaulis , resulting in the presence of null alleles. These null alleles would fail to amplify, leading to decreased observed heterozygosity and apparent reaction failure in null homozygous individuals (DeWoody et al. 2006). The potential impact of null alleles may be shown in F IS values , which are large and positive for both populations, indicating moderate inbreeding that individuals are more related tha n expected with random mating . Henderson Mountain exhibited higher inbreeding than Mt. Washburn, but l ooking at only the novel loci developed for P. albicaulis , the inbreeding coefficient is very small, conforming with expectations of random mating . The d ifference between inbreeding coefficients for novel and transferred loci may reflect the higher proportion of transferred loci exhibiting homozygote excess, which again may be an artifact of null alleles. Loci with large
40 positive F IS values may have hetero zygote deficiency from nonrandom mating within populations, but the wide range and correlation with violations of HWE are inconclusive. The effect of null alleles on the fragment analysis results will need to be further studied using the program M L NullFreq t o e s t i m a t e t h e i r f r e q u e n c y ( Kalinowski and Taper 2006 ) . L o c i t h a t d i v e r g e f r o m H W E a n d s h o w e v i d e n c e o f n u l l a l l e l e s s h o u l d b e u s e d w i t h c a u t i o n . Total F ST over all loci indicates low genetic differentiation among populations and high gene flow, and that most of the genetic variation is within the populations. This lack of differentiation is also supported by the AMOVA, with 91% of genetic different iation contained within populations, and Mantel results , with no correlation between geographic distance and genetic relatedness among populations . Within Mountain, with indivi duals no more or less closely related genetically to those nearby geographically than expected at random. There is some evidence for i solation by distance in the M t . Washburn population, with a positive Mantel correlation indicating that individuals are mo re closely related to those nearby geographically. However, samples were collected from a steep elevational gradient, which may res trict pollen and seed movement and affect the correlation. The F IS would indicate much higher inbreeding than found in these populations two d ecades earlier, but the coefficient for novel loci is similar to past research (Bruederle et al. 1998). F ST values are consistent with past research on these populations, and are consistent with the long distance pollen dispersal by wind a nd seed dispersal by nutcrackers observed in this species (Tomback et al. 1990; Bruederle
41 et al. 1998). Bird dispersal influences both the population genetic diversity and structure of P. albicaulis . Individual nutcrackers tend to collect see ds from a small number of source trees at a time, but then disperse these seeds across many different areas, traveling long distances. In addition, multiple birds are likely to cache seeds in the same area ( Furnier et al. 1987; Tomback and Linhart 1990). T he combination of these two effects results in a more random distribution of genotypes than found in wind dispersed species. Neighboring trees are no more likely to be related than distant trees (Furnier et al. 1987). Compared to wind dispersed pines, P. a lbicaulis has lower levels of genetic diversity and less population differentiation in the Greater Yellowstone Area and range wide (Jorgensen and Hamrick 1997; Bruederle et al. 1998). Overall, these nSSR results are simil ar to microsatellite findings for Eurasian stone pine populations , with recent SSR research on P. cembra , P. sibirica , and P. koraiensis revealing 4.5 7.6 alleles per locus and expect ed heterozygosities of 0.511 0.570 (Yu et al. 2012; Lendvay et al. 2014; Oreshkova et al. 2014). Both number of alleles per locus and expected heterozygosity are lower than in wind dispersed pines, with recent SSR research on P. strobus , P. sylvestris , P. taeda , and P. parviflora finding 10.8 18.5 alleles per locus and expect ed heterozygositie s of 0.603 0.873 (Iwasaki et al. 2013; Chhatre and Rajora 2014; Grattapaglia et al. 2014; Pazouk et al. 2016). That trend is consistent with allozyme results for P. albicaulis , and in line with the more recent evolution of bird dispersed pines, and relatively derived morphological traits including wingless seeds and indehiscent cones (Bruederle et al. 1998). These results highlight the utility of testing for locus transfer ability when developing microsatellites in pines, because this process for marker identification is
42 more efficient in both cost and time compared to novel nSSR development. Other work, however, has found cross species amplification to be unsuccessful in Pinus species (Echt and May Marquardt 1997; GonzÃ¡lez MartÃnez et al. 2004; Yu et al. 2012). It may be the case that the loci I tested for transfer were , by chance , more often located at stable sites in the P. albicaulis genome those near centromeres, in regions of low rec ombination, or in tight linkage with conserved genes than loci tested in previous studies. Management a pplications These 23 loci provide a useful low cost method to survey P. albicaulis genetic diversity, population structure, and gene flow. Work with isozymes and chloroplast DNA microsatellites established five seed zones in the inland west (Mahalovich and Hipkins 2011). However, this leaves a large portion of the species range in need of more focused genetic characterization, in order to det ermine likely seed zones for the remaining P. albicaulis range. Common garden approaches have been used to map preliminary seed zones for the Pacific Northwest portion of the range based on morphology (Aubrey et al. 2008; Hamlin et al. 2011 ), and SNPs have been used to characterize seed families from sub seed zones (Liu et al. 2016). However, comprehensive genetic surveys of populations in situ will help to further refine the delineations. These zones are necessary to form the basis for bliste r rust resistance screening in P. albicaulis for restoration projects involving seedling planting, and for the exploration of climate change mitigation (e.g., Keane et al. 2012, 2017) . It is also imperative to determine what genetic diversity will remain i n populations as they decline from white pine blister rust infection and mountain pine
43 beetle outbreaks , especially for trees remaining as seed sources after disturbances. Further, the time needed to recover genetic diversity following wildland fire distur bances will be an essential future research question as time intervals between fires decrease. The Greater Yellowstone Ecosystem is likely to experience reduced intervals between repeated wildfire; these are projected to decline to fewer than 30 years by 2 099 in this region, down from the historical average of 100 500 years (Westerling et al. 2011). Given this trend, it is essential to predict whether P. albicaulis will be able to recover genetic diversity rapidly enough to avoid bottlenecking . Given the need to monitor population changes and accomplish conservation goals in P. albicaulis , nSSR markers, with much higher numbers of alleles, may be the best available option to resolve genetic diversity and population structure. The nuclear microsatellites presented here can be applied to characterize genetic diversity across the range of P. albicaulis , which would provide baseline population parameters. This may help determine regions to focus screening for resistance to pests and pathogens, and indicate populations most in need of management intervention, especially as white pine blister rust continues to spread. Studies assessing genetic diversity based on these SSR loci can help guide future conservation efforts and restoration plans for P. albicaulis .
44 Tables and Figures Table 1: Locus name, source species of primer development , and allele size range for 49 primers screened for transferability to whitebark pine. Bold entries successfully tran sferred to whitebark pine.
45 Table 2: Minimum, maximum, and mean diameter at breast height (DBH) in centimeters compared between whitebark pine ( Pinus albicaulis ) sampled at two populations (Henderson Mountain, Custer Gallatin National Forest, MT, an d Mount Washburn, Yellowstone National Park, WY ) Table 3: Locus name, primer sequences, repeat motif, and allele size range for the 10 microsatellite loci isolated for whitebark pine ( Pinus albicaulis ). Annealing temperature is 58 Â° C across all loci .
46 N a = total number of alleles; H o = combined observed heterozygosity; H e = combined expected heterozygosity; N A = number of alleles; A P = private alleles; H O = observed heterozygosity; H E = expected heterozygosity; HWE = Hardy Weinberg equilibriu m T able 4 : Source species of primer development, allele size range, and genetic diversity statistics for 10 microsatellite loci isolated for whiteba rk pine ( Pinus albicaulis ) and 12 microsatellite loci transferred , based on 20 from each population. (Henderson Mountain, Custer Gallatin National Forest, MT, and Mount Washburn, Yellowstone National Pa rk, WY.)
47 Table 5 : Results of microsatellite locus transferability screening. Number of loci tested per species, number that amplified in whitebark pine ( Pinus albicaulis ), percent of total loci tested that transferred successfully, and average number of alleles per locus. Primers were screened in two populations (Henderson Mountain, Custer Gallatin National Forest, MT, and Mount Washburn, Yellowstone National Park, WY). Table 6 : Microsatellite summary for whitebark pine ( Pinus albicaulis ). Average number of alleles (N A ), private alleles (A P ), observed heterozygosity (H O ), and expected heterozygosity (H E ) by SSR development (novel developed for whitebark pine, transferred deve loped in other pines) and population (Henderson Mountain, Custer Gallatin National Forest, MT, and Mount Washburn, Yellowstone National Park, WY). Table 7 : Minimum, maximum, and mean distance between whitebark pine ( Pinus albicaulis ) sampled at two p opulations ( Henderson Mountain, Custer Gallatin National Forest, MT, and Mount Washburn, Yellowstone National Park, WY ). R xy and P (rxy rand >= rxy data) isolation by distance and spatial autocorrelation, testing for ea ch population and for all individuals combined.
48 Fig. 1: Top. Sample collection locations for whitebark pine ( Pinus albicaulis ) . Henderson Mountain, Cu ster Gallatin National Forest, MT and Mt. Washburn , Yellowstone National Park, WY . Bottom. Geographic location of Yellowstone National Park, Central Rocky Mountains.
49 Fi g. 2 : Pinus phylogenies of whitebark pine ( Pinus albicaulis ) and nearest neighbors, based on nuclear LEAFY ( A ) and LEA like ( B ) genes. Adapted from Hao et al. 2015.
50 Fig. 3: Histograms of the number of alleles for all of 23 microsatellite loci for two p opulations of whitebark pine ( Pinus albicaulis ) A ) Henderson Mountain, Custer Gallatin National Forest, MT, and B ) Mount Washburn, Yellowstone National Park, WY A B
51 Fig. 4: A ) difference in mean number of alleles ( 0.74) and 95% confidence interval of the difference ( 2.29 to 0.81) and B ) difference in mean expected heterozygosity ( 0.015) and 95% confidence interval of the difference ( 0.178 to 0.148) of 23 microsatellite loci for two populations of whitebark pine ( Pinus albicaulis ): Henderson Mountain, Cu ster Gallatin National Forest, MT, and Mount Washburn, Yellowstone National Park, WY. Plotted points are the difference in means, Washburn minus Henderson, and the lines are the 95% confidence intervals around those points. A B
52 F ig. 5 : Analysis of Mole cular Variance (AMOVA) for genetic variation within and among two whitebark pine ( Pinus albicaulis ) populations at Henderson Mountain, Custer Gallatin National Forest, MT and Mount Washburn, Yellowstone National Park, WY .
53 F ig. 6 : Correlation between geog raphic distance and genetic distance, Pinus albicaulis ) populations. A ) Henderson Mountain, Custer Gallatin National Forest, MT showing no correlation between genetic and geographic distances (r x,y = 0.1 2 0, p = 0. 121 ) and B) Mount Washburn, Yellowstone National Park, WY showing a positive correlation (r x,y = 0.162, p = 0.05 1) .
54 F ig. 7 : Frequency distribution of random R xy versus the observed R xy (red line) pine ( Pinus albicaulis ) populations ( A . Henderson Mountain, Custer Gallatin National Forest, MT, and B . Mount Washburn, Yellowstone National Park, WY) . R xy is the correlation between geographic and genetic distance between individuals. A positive value in dicates individuals are more closely related genetically to those nearby geographically than random, and a negative value indicating individuals are less closely related to those nearby.
55 REFERENCES Arno SF (2001) Community types and natural disturbance processes. Whitebark Pine Communities: Ecology and Restoration. Edited by DF Tomback , SF Arno, and RE Keane. Island Press, Washington, DC: 74 88. Arno SF and Hoff RJ (1990) Pinus albicaulis Engelm. whitebark pine. Silvics of North America. Edited by Burns RM and Honkala BH . Washington, DC: USDA Forest Service: 268 279. transfer of nuclear microsatellite markers: potential and limitations. Molecular Ecology 16(18): 3759 37 67. Barringer LE, Tomback DF, Wunder MB, and McKinney ST (2012) W hitebark pine stand condition, tree abundance, and cone production as predictors of visitation by PLoS ONE 7(5): e37663. Benkman CW (1995) Wind dispersal capacity of pine seeds and the evolution of different seed dispersal modes in pine s. Oikos 73: 221 224. Bonnet VH, Schoettle AW, and Shepperd WD (2005) Postfire environmental conditions influence the spatial pattern of regeneration for Pinus ponderosa . Canadian Journal of Forest Research 35: 37 47. Bower AD and Aitken SN (2007) Genetic diversity and geographic differentiation in quantitative traits, and seed transfer guidelines for whitebark pine. USDA Forest Service R6 NR FHP 2007 01: 98 101. Bowman DMJS, Murphy BP, Neyland DLJ, Williamson GJ, and Prior LD (2014) Abrupt fire regime chan ge may cause landscape wide loss of mature obligate seeder forests. Global Change Biology 20: 1008 1015. Bruederle LP, Tomback DF, Kelly KK, and Hardwick RC (1998) Population genetic structure in a bird dispersed pine, Pinus albicaulis (Pinaceae). Canadian Journal of Botany 76(1): 83 90. Bruederle LP, Rogers DL, Krutovskii KV, and Politov DV (2001) Population genetics and evolutionary implications. Whitebark Pine Communities: Ecology and Restoration. Edited by DF Tomback, SF Arno, and RE Keane. Island Press , Washington, DC: 137 153 . Bruford MW and Wayne RK (1993) Microsatellites and their application to population genetic studies. Current Opinion in Genetics and Development 3(6): 939 943. Butler JM, Coble MD, and Vallone PM (2007) STRs vs. SNPs: thoughts on the future of forensic DNA testing. Forensic Science, Medicine, and Pathology 3(3): 200 205.
56 Carsey KS and Tomback DF (1994) Growth form distribution and genetic relationships in tree clusters of Pinus flexilis , a bird dispersed pine. Oecologia 98: 402 411 . Chang S, Puryear J, and Cairney J (1993) A simple and efficient method for isolating RNA from pine trees. Plant Molecular Biology Reporter 11(2): 113 116. Chhatre VE and Rajora OP (2014) Genetic divergence and signatures of natural selection in marginal populations of a keystone, long lived conifer, eastern white pine ( Pinus strobus ) from northern Ontario. PLoS ONE 9(5): e97291. Christensen NL, Agee JK, Brussard PF, Hughes J, Knight DH, Minshall GW, Peek JM, Pyne SJ, Swanson FJ, Thomas JW, Wells S, Willia ms SE, and Wright HA (1989) Interpreting the Yellowstone fires of 1988. BioScience 39(10): 678 685. Chung MG, Chung MY, Oh GS, and Epperson BK (2000) Spatial genetic structure in a Neolitsea sericea population (Lauraceae). Heredity 85(5): 490 497. Coart E, Vekemans X, Smulders MJ, Wagner I, Van Huylenbroeck J, Van Bockstaele E, and Malus sylvestris (L.) Mill.) in Belgium as revealed by amplified fragment length polymorphism and microsatell ite markers. Molecular ecology 12(4): 845 8 57. Collevatti RG, Grattapaglia D, and Hay JD (2001) Population genetic structure of the endangered tropical tree species Caryocar brasiliense , based on variability at microsatellite loci. Molecular ecology 10(2): 349 3 56. Critchfield WB (1975) Interspecific hybridization in Pinus: a summary review. In: Symposium on Interspecific and Interprovenance Hybridization in Forest Trees. [Ed] D.P. Fowler and C.Y. Yeatman. Proc. 14th Meeting, Canad. Tree Improv. Assoc., Par t II: p. 99 105. Critchfield WB (1986) Hybridization and classification of the white pines ( Pinus section Strobus ). Taxon 35(4): 647 656. Dale VH, Joyce LA, McNulty S, Neilson RP, Ayres MP, Flannigan MD, Hanson PJ, Irland LC, Lugo AE, Peterson CJ, Simberlo ff D, Swanson FJ, Stocks BJ, and Wotton BM (2001) Climate change and forest disturbances. BioScience 51(9): 723 734. Dou JJ, Zhou RC, Tang AJ, Ge XJ, and Wu W (2013) Development and characterization of nine microsatellites for an endangered tree, Pinus wan gii (Pinaceae). Applications in Plant Sciences 1(2): 1200134. Dyer RJ and Sork VL (2001) Pollen pool heterogeneity in shortleaf pine, Pinus echinata Mill. Molecular Ecology 10: 859 866. Dykhuizen D and Hartl DL (1980) Selective neutrality of 6PGD allozymes in E. COLI and THE effects of genetic background. Genetics 96(4): 801 817.
57 Echt CS and May Marquardt P (1997) Survey of microsatellite DNA in pine. Genome 40: 9 17. Echt CS, May Marquardt P, Hseih M, Zahorchak R (1996) Characterization of microsatellite m arkers in eastern white pine. Genome 39(6): 1102 1108. Ellison AM, Bank MS, Clinton BD, Colburn EA, Elliott K, Ford CR, Foster DR, Kloeppel BD, Knoepp JD, Lovett GM, Mohan J (2005) Loss of foundation species: consequences for the structure and dynamics of forested ecosystems. Frontiers in Ecology and the Environment 3(9): 479 486. Enright NJ, Fontaine JB, Bowman DMJS, Bradstock RA, and Williams RJ (2015) Interval squeeze: altered fire regimes and demographic responses interact to threaten woody species per sistence as climate changes. Frontiers in Ecology and the Environment 13: 265 272. Epperson BK and Allard RW (1989) Spatial autocorrelation analysis of the distribution of genotypes within populations of lodgepole pine. Genetics 121(2): 369 377. Epperson B K and Li T (1996) Measurement of genetic structure within populations using Moran's spatial autocorrelation statistics. PNAS 93: 10528 10532. Epperson BK and Chung MG (2001) Spatial genetic structure of allozyme polymorphisms within populations of Pinus st robus (Pinaceae). American Journal of Botany 88(6): 1006 1010. Evanno G, Regnaut S, and Goudet J (2005) Blackwell Publishing, Ltd. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Molecular Ecology 14: 2611 2620. Furnier GR, Knowles P, Clyde MA, and Dancik BP (1987) Effects of avian seed dispersal on the genetic structure of whitebark pine populations. Evolution 41(3): 607 612. Gernandt D.S., LÃ³pez G.G., GarcÃa S.O., and Liston A. (2005). Phylogeny and classi fication of Pinus . Taxon 54(1): 29 42. Gibson K, Skov K, Kegley S, Jorgensen C, Smith S, and Witcosky J (2008) Mountain pine beetle impacts in high elevation five needle pines: current trends and challenges. USDA Forest Service R1 08 020. Glenn TC (2006) 5 Unpublished manual. Savannah River Ecology Lab, Department of Biological Sciences. University of South Carolina, Columbia. Godbout J, Jaramillo Correa JP, Beaulieu J, and Bosquet J (2005) A mito chondrial DNA minisatellite reveals the postglacial history of jack pine ( Pinus banksiana ), a broad range North American conifer. Molecular Ecology 14: 3497 3512.
58 GÃ³mez A, Vendramin GG, GonzÃ¡lez MartÃnez SC, and AlÃa R (2005) Genetic diversity and differen tiation of two Mediterranean pines ( Piuns halepensis Mill. and Pinus pinaster Ait.) along a latitudinal cline using chloroplast microsatelitte markers. Diversity and Distributions 11: 257 263. GonzÃ¡lez MartÃnez SC, Gerber S, Cervera MT, MartÃnez Zapater JM , Gil L, and AlÃa R (2002) Seed gene flow and fine scale structure in a Mediterranean pine ( Pinus pinaster Ait.) using nuclear microsatellite markers. Theoretical and Applied Genetics 104: 1290 1297. Gower JC (1967) Multivariate analysis and multidimension al geometry. Journal of the Royal Statistical Society. Series D (The Statistician) 17: 13 28. Grattapaglia D, do Amaral Diener PS, and Dos Santos GA (2014) Performance of microsatellites for parentage assignment following mass controlled pollination in a c lonal seed orchard of loblolly pine ( Pinus taeda L.). Tree Genetics & Genomes 10(6): 1631 16 43. Hamrick JL, Blanton HM, and Hamrick KJ (1989) Genetic structure of geographically marginal populations of ponderosa pine. American Journal of Botany 76(11): 155 9 1568. Hao ZZ, Liu YY, Nazaire M, Wei XX, and Wang XQ (2015) Molecular phylogenetics and evolutionary history of sect. Quinquefoliae ( Pinus ): Implications for Northern Hemisphere biogeography. Molecular Phylogenetics and Evolution 87: 65 79. Hutchins HE a nd Lanner RM (1982) The central role of Clark's nutcracker in the dispersal and establishment of whitebark pine. Oecologia 55: 192 201. Iwasaki T, Sase T, Takeda S, Ohsawa TA, Ozaki K, Tani N, Ikeda H, Suzuki M, Endo R, Tohei K, and Watano Y (2013) Extensi ve selfing in an endangered population of Pinus parviflora var. parviflora (Pinaceae) in the Boso Hills, Japan. Tree Genetics and Genomes 9: 693 705. Jean C, Shanahan E, Daley R, DeNitto G, Reinhart D, and Schwartz C (2011) Monitoring white pine blister ru st infection and mortality in whitebark pine in the Greater Yellowstone Ecosystem. USDA Forest Service Proceedings RMRS P 63 : 218 221 . Jordano PP and Godoy JA (2000) RAPD variation and population genetic structure in Prunus mahaleb (Rosaceae), an animal di spersed tree. Molecular Ecology 9(9): 1293 1305. Jorgensen SM and Hamrick JL (1997) Biogeography and population genetics of whitebark pine, Pinus albicaulis . Canadian Journal of Forest Research 27(10): 1574 1585.
59 Kalia RK, Rai MK, Kalia S, Singh R, and Dha wan AK (2011) Microsatellite markers: an overview of the recent progress in plants. Euphytica 177(3): 309 334. Kalinowski S T a n d Taper M L (2006) Maximum likelihood estimation of the frequency of null alleles at microsatell ite loci. Conservation Genetics 7(6) : 991 995. Keane RE, Tomback DF, Aubry CA, Bower AD, Campbell EM, Cripps CL, Jenkins MB, Mahalovich MF, Manning M, McKinney ST, Murray MP, Perkins DL, Reinh art DP, Ryan C, Schoettle AW, and Smith CM (2012) A range wide restoration strategy for whitebark pine ( Pinus albicaulis ). Gen. Tech. Rep. RMRS GTR 279. Fort Collins, CO: USDA, Forest Service, Rocky Mountain Research Station. 108 p. Kim MS, Brunsfeld SJ, M cDonald GI, and Klopfenstein NB (2003) Effect of white pine blister rust ( Cronartium ribicola ) and rust resistance breeding on genetic variation in western white pine ( Pinus monticola ). Theoretical and Applied Genetics 106: 1004 1010. Kircher M and Kelso J (2010) High throughput DNA sequencing concepts and limitations. Bioessays 32(6): 524 536. Knowles P (1984) Genetic variability among and within closely spaced populations of lodgepole pine. Canadian Journal of Genetics and Cytology 26(2): 177 184. Krak owski J, Aitken SN, and El Kassaby YA (2003) Inbreeding and conservation genetics is whitebark pine. Conservation Genetics 4: 581 593. Kumar S, Banks TW, and Cloutier S (2012) SNP discovery through next generation sequencing and its applications. Internati onal Journal of Plant Genomics 2012(831460): 1 15. Lande R (1995) Mutation and conservation. Conservation Biology 9(4): 782 791. Lande R (1998) Risk of population extinction from fixation of deleterious and reverse mutations. Genetica 102: 21 27. Lanner RM , and Gilbert BK (1994). Nutritive value of whitebark pine seeds, and the question oftheir variable dormancy. USDA Forest Service INT GTR 309 : 206 211. Latta RG and Mitton JB (1997) A comparison of population differentiation across four classes of gene mar ker in limber pine ( Pinus flexilis James). Genetics 146: 1153 1163. Ledig FT, Guries RP, and Bonefeld BA (1983) The relation of growth to heterozygosity in pitch pine. Evolution 37(6): 1227 1238.
60 Lee SW, Ledig FT, and Johnson DR (2002) Genetic variation at allozyme and RAPD markers in Pinus longaeva (Pinaceae) of the White Mountains, California. American Journal of Botany 89(4): 566 577. Leirfallom SB, Keane RE, Tomback DF, and Dubrowski S (2015) The effects of seed source health on whitebark pine ( Pinus al bicaulis ) regeneration density after wildfire. Canadian Journal of Forest Research 45:1597 1606. DOI: http://dx.doi.org/10.1139/cjfr 2015 0043. Lendvay B, HÃ¶hn M, Brodbeck S, MÃ®ndrescu M, and Gugerli F (2014) Genetic structure in Pinus cemb ra from the Carp athian Mountains inferred from nuclear and chloroplast microsatellites confirms post glacial range contraction and identifies introduced individuals. Tree Genetics and Genomes 10: 1419 1433. Levy M, Steiner EE, and Levin DA (1975) Allozyme genetics in perm anent translocation heterozygotes of Oenothera biennis Complex. Biochemical Genetics 13: 487 500. Lind BM, Friedline CJ, Wegrzyn JL, Maloney PE, Vogler DR, Neale DB, and Eckert AJ (2017) Water availability drives signatures of local adaptation in whitebark pine ( Pinus albicaulis Engelm.) across fine spatial scales of the Lake Tahoe Basin, USA. Molecular Ecology 26: 3168 3185. Linhart YB and Tomback DF (1985) Seed dispersal by nutcrackers causes multi trunk growth form in pines. Oecologia 67(1): 107 110. Lin hart YB, Mitton JB, Sturgeon KB, and Davis ML (1981) Genetic variation in space and time in a population of ponderosa pine. Heredity 46(3): 407 426. Liston A, Robinson WA, PiÃ±ero D, and Alvarez Buylla ER (1999) Phylogenetics of Pinus (Pinaceae) based on nu clear ribosomal DNA internal transcribed spacer region sequences. Molecular Phylogenetics and Evolution 11(1): 95 109. Liston A, Parker Defeniks M, Syring JV, Willyard A, and Cronn R (2007) Interspecific phylogenetic analysis enhances intraspecific phyloge ographical inference: a case study in Pinus lambertiana. Molecular Ecology 16: 3926 3937. Litt M and Luty JA (1989) A hypervariable microsatellite revealed by in vitro amplification of a dinucleotide repeat within the cardiac muscle actin gene. American Jo urnal of Human Genetics 44(3): 397 401. Little EL and Critchfield WB (1969) Subdivisions of the genus Pinus (pines). U.S. Department of Agriculture Miscellaneous Publication 1144: 7 9. Liu JJ, Sniezko R, Murray M, Wang N, Chen H, Zamany A, Sturrock RN, Sav in D, and Kegley A (2016) Genetic diversity and population structure of whitebark pine ( Pinus albicaulis Engelm.) in western North America. PloS One 11(12): e0167986.
61 Logan JA and Bentz BJ (1999) Model analysis of mountain pine beetle (Coleoptera: Scolytid ae) Seasonality. Environmental Entomology 28(6): 924 934. Logan JA and Powell JA (2001) Ghost forests, global warming, and the mountain pine beetle (Coleoptera: Scolytidae). American Entomologist 47(3): 160 173. Logan JA, RÃ©gniÃ¨re J, and Powell JA (2003) A ssessing the impacts of global warming on forest pest dynamics. Frontiers in Ecology and the Environment 1(3): 130 137. Logan JA, MacFarlane WW, and Willcox L (2010) Whitebark pine vulnerability to climate driven mountain pine beetle disturbance in the Gre ater Yellowstone Ecosystem. Ecological Applications 20(4): 895 902. Lovett GM, Weiss M, Liebhold AM, Holmes TP, Leung B, Lambert KF, Orwig DA, Campbell FT, Rosenthal J, McCullough DG, Wildova R (2016) Nonnative forest insects and pathogens in the United St ates: impacts and policy options. Ecological Applications 26(5): 1437 1455. Mahalovich MF and Hipkins VD (2011) Molecular genetic variation in whitebark pine ( Pinus albicaulis ) in the inland west. USDA Forest Service Proceedings RMRS P 63: 118 132. Mariett e S, ChagnÃ© D, LÃ©zier C, Patuszka P, Raffin A, Plomion C, and Kremer A (2001) Genetic diversity within and among Pinus pinaster populations: comparison between AFLP and microsatellite markers. Heredity 86: 469 479. Marquardt PE and Epperson BK (2004) Spati al and population genetic structure of microsatellites in white pine. Molecular Ecology 13(11): 3305 3315. Mattson DJ, Kendall KC, and Reinhart DP (2001) Whitebark pine, grizzly bears, and red squirrels. Whitebark Pine Communities: Ecology and Restoration . Edited by DF Tomback, SF Arno, and RE Keane. Is land Press, Washington, DC: 121 136. McCaughey WW and Tomback DF (2001) The natural regeneration process. Whitebark Pine Communities: Ecology and Restoration. Edited by DF Tomback, SF Arno, and RE Keane. Isla nd Press, Washington, DC: 105 120. McDonald GI, and Hoff RJ (2001) Blister rust: an introduced plague. Whitebark Pine Communities: Ecology and Restoration. Edited by DF Tomback, SF Arno, and RE Keane. Island Press, Washington , DC: 193 220. McKinney ST, Fie dler CE, and Tomback DF (2009) Invasive pathogen threatens bird pine mutualism: implications for sustaining a high elevation ecosystem. Ecological Applications 19(3): 597 607.
62 McLane SC and Aitken SN (2012) Whitebark pine ( Pinus albicaulis ) assisted migrat ion potential: testing establishment north of the species range. Ecological Applications 22(1): 142 153. Moraga Suazo P, Orellana L, Quiroga P, Balocchi C, Sanfuentes E, Whetten RW, HasbÃºn R, and Valenzuela S (2014) Development of a genetic linkage map for Pinus radiata and detection of pitch canker disease resistance associated QTLs. Trees 28(6): 1823 1835. Moritz MA, Parisien MA, Batllori E, Krawchuk MA, Van Dorn J, Ganz DJ, and Hayhoe K (2012) Climate change and disruptions to global fire activity. Ecosp here 3(6): 49. Mosseler A, Egger KN, and Hughes GA (1992) Low levels of genetic diversity in red pine confirmed by random amplified polymorphic DNA markers. Canadian Journal of Forest Research 22(9): 1332 1337. Mosseler A, Major JE, and Rajora OP (2003) Ol d growth red spruce forests as reservoirs of genetic diversity and reproductive fitness. Theoretical and Applied Genetics 106(5): 931 937. cones. Western North American Natur alist 70(3): 413 414. Nathan R and Mueller Landau HC (2000) Spatial patterns of seed dispersal, their determinants and consequences for recruitment. Trends in Ecology and Evolution 15(7): 278 285. NavascuÃ©s M and Emerson BC (2007) Natural recovery of genet ic diversity by gene flow in reforested areas of the endemic Canary Island pine, Pinus canariensis . Forest Ecology and Management 244: 122 128. Neale DB and Sederoff RR (1989) Paternal inheritance of chloroplast DNA and maternal inheritance of mitochondria l DNA in loblolly pine. Theoretical and Applied Genetics 77: 212 216. Nurtjahjaningsih IL, Saito Y, Lian CL, Tsuda Y, and Ide Y (2005) Development and characteristics of microsatellite markers in Pinus merkusii . Molecular Ecology Resources 5(3): 55 2 553. O structure and differentiation of the bog and dry land populations of Pinus sibirica Du Tour based on nuclear microsatellite loci. Russian Journal of Genetics 50(9): 934 941 . Niinemets Ãœ (2016) Large within population genetic diversity of the widespread conifer Pinus sylvestris at its soil fertility limit characterized by nuclear and
63 chloroplast m icrosatellite markers. European Journal of Forest Research 135(1): 161 77. Peakall R. and Smouse P.E. (2006). GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes 6: 288 295. Peakall R. and Sm ouse P.E. (2012). GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research an update. Bioinformatics 28: 2537 2539. Phong DT, Lieu TT, Hien VT, and Hiep NT (2015) Genetic diversity of the endemic flat needle pine Pinus krempfii (Pinaceae) from Vietnam revealed by SSR markers. Genetics and Molecular Research 14(3): 7727 7739. Price RA, Liston A, and Strauss SH (1998) Phylogeny and systematics of Pinus . Ecology and Biogeography of Pinus. Edited by D.M. Richardson. Cambridg e University Press, Cambridge, MA: 49 68. Pritchard JK, Stephens M, and Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155: 945 959. R Core Team (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R project.org/. Raffa KF, Aukema BH, Bentz BJ, Carroll AL, Hicke JA, Turner MG, and Romme WH (2008) Cross scale drivers of natural disturbances prone to anthropogenic amplification: t he dynamics of bark beetle eruptions. Bioscience 58(6): 501 517. Rajora OP and Pluhar SA (2003) Genetic diversity impacts of forest fires, forest harvesting, and alternative reforestation practices in black spruce ( Picea mariana ). Theoretical and Applied G enetics 106(7): 1203 1212. Reed DH and Frankham R (2003) Correlation between fitness and genetic diversity. Conservation Biology 17(1): 230 237. Resler LM and Tomback DF (2008) Blister rust prevalence in krummholz whitebark pine: implications for treeline dynamics, Northern Rocky Mountains, Montana, U.S.A. Arctic, Antarctic, and Alpine Research , 40(1): 161 170. Ribeiro MM, Mariette S, Vendramin GG, Szmidt AE, Plomion C, and Kremer A (2002) Comparison of genetic diversity estimates within and among populatio ns of maritime pine using chloroplast simple sequence repeat and amplified fragment length polymorphism data. Molecular Ecolology 11: 869 877. Richardson BA, Brunsfeld SJ, and Klopfenstein NB (2002) DNA from bird dispersed seed and wind disseminated pollen provides insights into postglacial colonization
64 and population genetic structure of whitebark pine ( Pinus albicaulis ). Molecular Ecology 11(2): 215 227. Rogers DL, Miller CI, and Westfall RD (1999) Fine scale genetic structure of whitebark pine ( Pinus alb icaulis ): associations with watershed and growth form. Evolution 53(1): 74 90. Romme WH (1982) Fire and landscape diversity in subalpine forests of Yellowstone National Park. Ecological Monographs 52(2): 199 221. Romme WH and Despain DG (1989) Historical p erspective on the Yellowstone fires of 1988. Bioscience 39(10): 695 699. Saiki RK, Gelfand DH, Stoffel S, Scharf SJ, Higuchi R, and Horn GT (1988) Primer directed enzymatic amplification of DNA with a thermostable DNA polymerase. Science 239(4839): 487 491 . Salzer K, Sebastiani F, Gugerli F, Buonamici A, and Vendramin GG (2008) Isolation and characterization of polymorphic nuclear microsatellite loci in Pinus cembra L. Molecular Ecology Resources 9(3): 858 861. Schiller G, Ne'eman G, and Korol L (1997) Post fire vegetation dynamics in a native Pinus halepensis Mill. forest on Mt. Carmel, Israel. Israel Journal of Plant Sciences 45: 297 308. Schoennagel T, Veblen TT, Romme WH, Sibold JS, and Cook ER (2005) ENSO and PDO variability affect drought induced fire occurrence in Rocky Mountain subalpine forests. Ecological Applications 15(6): 2000 2014. Schuster WSF and Mitton JB (1991) Relatedness within clusters of a bird dispersed pine and the potential for kin interactions. Heredity 67: 41 48. Schuster WSF and Mi tton JB (2000) Paternity and gene dispersal in limber pine ( Pinus flexilis James). Heredity 84: 348 361. Schwabe AL, Neale JR, and McGlaughlin ME (2015) Examining the genetic integrity of a rare endemic Colorado cactus ( Sclerocactus glaucus ) in the face of hybridization threats from a close and widespread congener ( Sclerocactus parviflorus ). Conservation Genetics 16(2): 443 57. Schwandt JW, Lockman IB, Kliejunas JT, and Muir JA (2010) Current health issues and management strategies for white pines in the we stern United States and Canada. Forest Pathology 40: 226 250. Shaw GR (1914) The genus Pinus . Publications of the Arnold Arboretum 5: 26 36.
65 Shaw GR (1924) Notes on the genus Pinus . Journal of the Arnold Arboretum 5(4): 225 227. Speilman D, Brook BW, and F rankham R (2004) Most species are not driven to extinction before genetic factors impact them. PNAS 101(42): 15261 15264. Syring J, Willyard A, Cronn R, and Liston A (2005) Evolutionary relationships among Pinus (Pinaceae) subsections inferred from multipl e low copy nuclear loci. American Journal of Botany 92(12): 2086 2100. Syring J, Farrell K, Businsky R, Cronn R, and Liston A (2007) Widespread genealogical nonmonophyly in species of Pinus subgenus Strobus . Systematic Biology 56: 163 181. Syring JV, Tenne ssen JA, Jenning TN, Wegrzyn J, Scelfo Dalbey C, and Cronn R (2016) Targeted capture sequencing in whitebark pine reveals range wide demographic and adaptive patterns despite challenges of a large, repetitive genome. Frontiers in Plant Science 7. Smouse PE and Peakall R (1999) Spatial sutocorrelation analysis of individual multiallele and multilocus genetic structure. Heredity 82: 561 673. Tian S, Luo LC, Ge S, and Zhang ZY (2008) Clear genetic structure of Pinus kwangtungensis (Pinaceae) revealed by a plas tid DNA fragment with a novel minisatellite. Annals of Botany 102(1) 69 78. Thao DV, Widyatmoko AY, Guan L, Gotoh E, Watanabe A, and Shiraishi S (2013) Isolation and characterization of tetranucleotide microsatellite markers for Pinus merkusii . Conservatio n Genetics Resources 5(2): 433 436. mutualism hypothesis. Journal of Animal Ecology 51: 451 467. Whitebark Pine Commun ities: Ecology and Restoration. Edited by DF Tomback, SF Arno, and RE Keane. Island Press, Washington, DC: 89 104. Nutcracker Notes 15: 3. Tomback DF and Linhart YB (1990) The evolution of bird dispersed pines. Evoluti onary Ecology 4: 185 219. Tomback DF and Achuff P (2010) Blister rust and western forest biodiversity: ecology, values and outlook for white pines. Forest Pathology 40: 186 225.
66 Tomback DF, Hoffmann LA, and Sund SK (1990) Coevolution of whitebark pine and nutcrackers: implications for forest regeneration. General technical report INT (USA) 270. Tomback DF, Anderies AJ, Carsey KS, Powell ML, and Mellmann Brown S (2001a) Delayed seed germination in whitebark pine and regeneration patterns following the Yellow stone fires. Ecology 82(9): 2587 2600. Tomback DF, Arno SF, and Keane RE (2001b) The compelling case for management intervention. Whitebark Pine Communities: Ecology and Restoration. Edited by DF Tomback, SF Arno, and RE Keane. Island Press, Washington, DC : 3 25. Tomback DF, Achuff P, Schoettle AW, Schwandt JW, and Mastrogiuseppe RJ (2011) The magnificent high elevation five needle white pines: ecological roles and future outlook. USDA Forest Service Proceedings RMRS P 63 : 2 28. Tomback DF, Chipman KG, Resl er LM, Smith McKenna EK, and Smith CM (2014) Relative abundance and functional role of whitebark pine at treeline in the Northern Rocky Mountains. Arctic, Antarctic, and Alpine Research 46(2): 407 418 . Tomback DF, Resler LM, Keane RE, Pansing ER, Andrade A J, and Wagner AC (2016) Community structure, biodiversity, and ecosystem services in treeline whitebark pine communities: potential impacts from a non native pathogen. Forests 7(1): 21. Tsutsui K, Suwa A, Sawada KI, Kato T, Ohsawa TA, and Watano Y (2009) I ncongruence among mitochondrial, chloroplast and nuclear gene trees in Pinus subgenus Strobus (Pinaceae). Journal of Plant Research 122(5): 509 521. Turner MG, Hargrove WW, Gardner RH, and Romme WH (1994) Effects of fire on landscape heterogeneity in Yello wstone National Park, Wyoming. Journal of Vegetation Science 5(5): 731 742. Turner MG, Romme WH, and Tinker DB (2003) Surprises and lessons from the 1988 Yellowstone fires. Frontiers in Ecology and the Environment 1: 351 358. U.S. Fish and Wildlife Service . 2011. Endangered and threatened wildlife and plants; 12 month finding on a petition to list Pinus albicaulis as Endangered or Threatened with critical habitat. Federal Register 76: 42631 42654. Van der Merwe M, Winfield MO, Arnold GM, and Parker JS (2 000) Spatial and temporal aspects of the genetic structure of Juniperus communis populations. Molecular Ecology 9: 379 386.
67 Van Mantgem PJ, Stephenson NL, Byrne JC, Daniels LD, Franklin JF, FulÃ© PZ, Harmon ME, Larson AJ, Smith JM, Taylor AH, Veblen TT (200 9) Widespread increase of tree mortality rates in the western United States. Science 323(5913): 521 524. Vekemans X and Hardy OJ (2003) New insights from fine scale spatial genetic structure analyses in plant populations. Molecular Ecology 13: 921 935. Ven dramin GG, Lelli L, Rossi P, and Morgante M (1996) A set of primers for the amplification of 20 chloroplast microsatellites in Pinaceae. Molecular Ecology 5(4): 595 598. Villalobos ArÃ¡mbula AR, PÃ©rez de la Rosa JA, Arias A, and Rajora OP (2014) Cross speci es transferability of eastern white pine ( Pinus strobus ) nuclear microsatellite markers to five Mexican white pines. Genetics and Molecular Research 13(3): 7571 7576. Waits L, Taberlet P, Swenson JE, Sandegren F, and Franzen R (2000) Nuclear DNA microsatel lite analysis of genetic diversity and gene flow in the Scandinavian brown bear ( Ursus arctos ). Molecular Ecology 9(4): 421 4 31. Walter R and Epperson BK (2004) Microsatellite analysis of spatial structure among seedlings in populations of Pinus strobus (P inaceae). American Journal of Botany 91(4): 549 557 Wang MB and Hao ZZ (2010) Rangewide genetic diversity in natural populations of Chinese pine ( Pinus tabulaeformis ). Biochemical Genetics 48: 590 602. Weaver T (2001) Whitebark pine and its environment. W hitebark Pine Communities: Ecology and Restoration. Edited by DF Tomback, SF Arno, and RE Keane. Island Press, Washington, DC: 41 73. Westerling AL, Turner MG, Smithwick EAH, Romme WH, and Ryan MG (2011) Continued warming could transform Greater Yellowston e fire regimes by mid 21 st century. PNAS 108(32): 13165 13170. Whitebark Pine Ecosystem Foundation (2014) Whitebark pine and limber pine range maps. Available online from http: \ whitebarkfound.org [accessed: Nov. 5, 2017] Xin Z, Velten JP, Oliver MJ, and B urke JJ (2003) High throughput DNA extraction method suitable for PCR. BioTechniques 34: 820 826. Yu JH, Chen CM, Tang ZH, Yuan SS, Wang CJ, and Zu YG (2012) Isolation and characterization of 13 novel polymorphic microsatellite markers for Pinus koraiensis (Pinaceae). American Journal of Botany e421 e424.