PHYLOGEOGRAPHY and GENETIC STRUCTURING of MOOSE (ALCES ALCES) POPULATIONS in ONTARIO, CANADA a Dissertation Submitted to The

PHYLOGEOGRAPHY AND GENETIC STRUCTURING OF MOOSE (ALCES ALCES) POPULATIONS IN ONTARIO, CANADA

A Dissertation Submitted to the Committee on Graduate Studies in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in the Faculty of Arts and Science

TRENT UNIVERSITY Peterborough, Ontario, Canada

Abstract Phylogeography and Genetic Structuring of Moose (Alces alces) populations in Ontario, Canada Glynis N. R. Price

Moose are an iconic species, known for their large size and impressive antlers. Eight subspecies are classified in circumpolar regions of the planet - four in North America.

Two subspecies are similar in shape and size, the north-western moose (Alces alces andersoni) and the eastern moose (Alces alces americana). It was previously believed that these two subspecies meet in northern Ontario. Earlier genetic population studies used a small number of samples from Ontario, primarily in broad studies covering all of

North America.

A comprehensive genetic study of moose populations in Ontario has not previously been conducted. We examined the genetic diversity and population structure at 10 polymorphic loci using 776 samples from Ontario, as well as outgroups from representative populations – Manitoba/Cape Breton, representing A. a. andersoni, and

New Brunswick/Nova Scotia, representing A. a. americana. Results indicated three genetic populations in the province, in north-western Ontario, north-eastern Ontario and south-central Ontario. RST values, compared against both FST and Jost’s D values for phylogenetic analyses, indicated no phylogenetic pattern which suggests no subspeciation present in the province.

Population movement patterns in Ontario were studied. Gene flow was estimated using genetic and spatial data. Isolation by distance was only seen within the first distance class of 100 kilometres and then not seen again at further distances, indicating that moose display philopatry. There were very few migrants travelling across the province, with a greater number moving gradually north and west, towards better habitat and food sources.

A forensic database in the form of an allele frequency table was created. Three loci showed very low levels of heterozygosity across all three populations. Probability of identity was calculated for the three populations and quantified. Samples with known geographic origins were run against the database to test for sensitivity, with identification of origin occurring at an accuracy level between 87 and 100%.

Within Ontario, there are not two different subspecies, as previously believed, but two different populations of the same subspecies meeting in northern Ontario. The genetic data does not support previous research performed in Ontario. The sample sizes in our research also provide a more comprehensive view of the entire province not seen in any previous studies. The comprehensive research enabled the building of a reliable forensic database that can be used for both management and forensic purposes for the entire province.

Keywords: Moose, Ontario, phylogeography, subspecies, genetic diversity, Alces alces, migration, forensic database

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Preface

This dissertation is written in article format. Chapters 2-4 are in preparation for peer- reviewed journals. Experimental design, analyses, and manuscript preparation were all carried out by the Ph.D. candidate. Dr. Cornelya Klutsch helped with the DAPC analyses.

Acknowledgements

“I can no other answer make but thanks, and thanks, and ever thanks.” William Shakespeare, Twelfth Night, Act 3, Scene 3

I am grateful to many people for supporting me through this journey. First, I would like to thank my supervisory committee, Jeff Bowman and Dennis Murray. Thanks to my supervisors: Barry Saville, for helping me finish my thesis; Paul Wilson, for providing me with this opportunity and for his financial contributions; and Marie-Josée Fortin, for her support, valuable feedback and courage along the way. Thanks to all of my funders, along with the many hunters and MNRF conservation officers who enthusiastically provided me with the numerous samples. I would also like to thank Cornelya Klutsch for her help with some of my statistical analyses.

I would like to thank my parents for their endless support, patience, editing skills and understanding. It has been quite the journey, and they have made the entire trip with me.

Thanks to Glen Hawkins and Joseph Warren for their interest and advice. A huge thankyou to Linda Cardwell for her support, as well as being a conscientious advocate for her students. I also want to acknowledge the Happy PhD Club: Eunice, Megan and

Andrea - the final piece of our puzzle is now in place. To Karen McQuade Smith,

Lindsay Thompson and Sharon Beaucage-Johnson - thanks to all of you for lending me your ears and giving me some great suggestions. There are many others to whom I owe thanks for their support over the years: to Brandie Bugiak for always being there, letting me vent and helping me laugh. Thanks to Mandy Juby-Livings and Bill Juby for

v providing distractions and encouragement, both in person and across the country, and to my sisters, Adrienne and Megan, and my extended family and friends.

Finally, a huge thankyou to Derek Winterhalt, for being my resident supporter, my sounding board, an avid fan of “Meese” . . . and for keeping me grounded.

Funders NSERC Ontario Ministry of Natural Resources and Forestry Ontario Federation of Anglers and Hunters Trent University

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Table of Contents Abstract ...... ii Preface ...... iv Acknowledgements ...... v Funders ...... vii List of Commonly used Abbreviations and Acronyms ...... x List of Tables ...... xi List of Figures ...... xii Chapter 1: General Introduction ...... 1 References ...... 13 Chapter 2 - Genetic structure of moose (Alces alces) in Ontario, Canada ...... 16 Abstract ...... 16 Introduction ...... 17 Methods ...... 20 Results...... 23 Discussion ...... 26 Conclusion ...... 29 References ...... 31 Figure Legend ...... 37 Chapter 3 – Spatial and genetic structure of moose (Alces alces) populations in Ontario, Canada: Implications for Management...... 42 Abstract ...... 42 Introduction ...... 43 Methods ...... 50 Results...... 53 Discussion and Conclusions...... 54 References ...... 58 Figure Legend ...... 63 Chapter 4 – Resolution of regional populations of moose (Alces alces) in Ontario - formation of forensic genetic database ...... 70 Abstract ...... 70 Introduction ...... 71 Methods ...... 78

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Results...... 82 Discussion and Conclusions...... 86 References ...... 91 Chapter 5 - Synthesis ...... 100 References ...... 107 Appendix A – Ontario Wildlife Management Unit Maps...... 108 Appendix B – Genotypic database for all Ontario individuals ...... 110 Appendix C – Wildlife Management Unit (WMU) Centroid latitude and longitude values...... 136

List of Commonly used Abbreviations and Acronyms

WMU – Wildlife Management Unit STR – Short Tandem Repeat PCA – Principal Component Analysis DAPC – Discriminant Analysis of Principal Components IBD – Isolation by Distance NW - Northwest NE - Northeast SC – South Central A.a – Alces alces DNA – Deoxyribonucleic Acid OMNR – Ontario Ministry of Natural Resources OMNRF - Ontario Ministry of Natural Resources and Forestry Man - Manitoba NB – New Brunswick NS – Nova Scotia CB – Cape Breton PE – Probability of Exclusion PD – Probability of Discrimination

PID – Probability of Identity RMP – Random Match Probability LR – Likelihood Ratio

List of Tables

Table 2.1 Page 33 Sample size, average number of alleles (NA) at 10 loci, expected and observed heterozygosity (HE, HO), and FIS averaged over seven populations sampled in Central and Eastern Canada.

Table 2.2 Page 34 Genetic diversity measurements across the ten microsatellite (STR) loci in Ontario.

Table 2.3 Page 35 Pairwise FST, RST and Jost’s D values calculated based on microsatellite data set across all populations.

Table 2.4 Page 36 Analysis of molecular variance (AMOVA) results for moose population assignments in Ontario, Canada.

Table 4.1 Page 94 Forensic Database - observed allele distributions (as %) for 10 STR loci in three population groups separated by locus with observed and expected heterozygosity for each locus.

Table 4.2 Page 98 Summary table of probabilities for 10 STR loci in the three Ontario population groups (PID stands for probability of identity, PIDsib stands for probability of identity of a sibling, PD stands for probability of discrimination, PDsib stands for probability of discrimination of a sibling and PEcomb stands for the combined probability of exclusion).

Table 4.3 Page 99 Database Sensitivity Testing (numbers indicate individuals unless otherwise denoted)

List of Figures

Figure 2.1 Page 38 STRUCTURE assignment plots for K=2 (A) to K=6 (E). Manitoba and NW Ontario are consistently grouped together. The Maritime populations all group together, consistent with the sheer geographic distance between them and the central Canada populations. NE and SC Ontario are grouped together for K=2 but become more and more defined as separate populations for greater values of K. NE Ontario does not show one single population assignment but rather an admixed population of the NW assigned values and the SC assigned values.

Figure 2.2 Page 39 Estimated number of populations (K) from the program STRUCTURE (Pritchard et al. 2000) for moose in Ontario. On the left, the log-likelihood values for each estimated number of populations, shown by the dotted line (Pritchard et al. 2000). A plateau in the values indicates the most likely number of populations. On the right, the rate of change in log-likelihood values, shown by the solid line (Evanno et al. 2005). The maximum ΔK indicates the most likely number of populations.

Figure 2.3 Page 40 PCA results for Central and Eastern Canadian moose populations. In the analysis, 74.65% of the variation is accounted for. Cape Breton clusters off by itself; this is supported by the genetic measures. The other two Maritime Provinces cluster off together, as expected due to geographic distance. NW Ontario and Manitoba, as supported by all other analyses, cluster off together. NE and SC Ontario are closest to each other with NE Ontario being between SC and NW Ontario, as expected based on geographic distance and the genetic measures.

Figure 2.4 Page 41 DAPC graph showing clustering of populations across Central and Eastern Canada (Clusters 1 and 2 – NE and SC Ontario, Cluster 3 – NS and NB, Cluster 4 – Man, CB and NW Ontario). Ellipses indicate 95% confidence intervals around each cluster. Two of the Maritime populations (NS and NB) cluster out away from all other populations as expected. NE and SC Ontario are almost completely superimposed, confirming the close genetic relationship between the two. Part of the NE population also overlaps with the NW Ontario/Manitoba/Cape Breton cluster (cluster 4). The larger spread of cluster 4 can be explained by the inclusion of the Cape Breton population. The Cape Breton population originated in Elk Island Park in Alberta and was transplanted to Cape Breton due to low population numbers. Time and the geographic distance would account for the spread of the cluster.

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Figure 3.1 Page 64 TESS assignment plots for K=2 (A) to K=6 (E) with no admixture using wildlife management unit (WMU) centroid latitudes and longitudes for spatial data. Data are ordered based on WMU from west to east. NW= NW Ontario, NE=Northeast Ontario, SC= South Central Ontario. NE and SC Ontario group together when K=2 but split into two populations at greater K values. NW Ontario remains a distinct population at all K values.

Figure 3.2 Page 65 TESS assignment plots for K=2 (A) to K=6 (E) with admixture. Data are ordered based on WMU from west to east. NW= NW Ontario, NE=Northeast Ontario, SC= South Central Ontario. NE Ontario is paired with SC Ontario for K=2 but is split into two areas of admixture – one more similar to NW Ontario and one more similar to SC Ontario with higher K values. NW Ontario and SC Ontario remain separate populations for K=3 to K=6.

Figure 3.3 Page 66 STRUCTURE results for K=3 showing admixed region in NE Ontario for comparison with TESS assignments. Pattern is very similar to both TESS K=3 figures.

Figure 3.4 Page 67 Province of Ontario map with pie charts showing population assignments by wildlife management unit (WMU). We see distinct population assignments for both SC and NW Ontario. The NE population does not show up as a distinct population but rather an admixed population comprised of the NW and SC Ontario populations. This supports an admixed population (NE Ontario) between two distinct populations (SC and NW Ontario).

Figure 3.5 Page 68 Mantel Isolation By Distance (IBD) correlograms using Nei’s genetic distance for (A) the entire population, (B) the female population and (C) the male population. There is a small IBD pattern within the first 150-200 kilometres but very little seen after that for all three runs. Males show a slightly higher value than the females, likely due to the greater dispersion levels seen in males versus females.

Figure 3.6 Page 69 Map of Ontario showing the level of migration between regions of Ontario. Yellow indicates northwestern Ontario, green indicates northeastern Ontario and blue indicates south-central Ontario. A greater number of migrants are heading in a north and west direction (SC-NE and SC- NW) than heading south and east. There are even amounts of movement between SC and NE Ontario suggesting a free flow of individuals between the two regions.

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Chapter 1: General Introduction

Moose (Alces alces) are the largest members of the family Cervidae and are only found in the northern hemisphere in a circumpolar distribution. They live primarily in the boreal forests and mountain ranges, as well as in the transition zone between the Prairies and the northern forests of North America and Eurasia (Peterson 1955, Bubenik 1997).

Originating in Eurasia, they migrated across the 90 km-long Bering land bridge between

70,000 and 10,000 years ago and dispersed southward along ice-free corridors and across

North America (Bubenik 1997, Hundtermark et al. 2002). An expansion of the ice cap during the Wisconsinan glaciations pushed the North American population into four separate refugia (Peterson 1955). Peterson speculated that these 4 refugia became centre of origin of the 4 defined modern subspecies of moose in North America. Fracturing and retreat of ice sheets across North America during a phase of global warming around

14,000 years ago flooded the land bridge, separating North American moose from their

Eurasian relatives (Bubenik 1997). Once the ice sheets started retreating, new habitat was formed, and previously isolated, potentially genetically distinct populations came into contact through northward dispersal.

North America is considered to have 4 distinct moose subspecies due to these separate refugia – Alces alces gigas (A.a.gigas), Alces alces shirasi (A.a.shirasi), Alces alces andersoni (A.a.andersoni) and Alces alces americana (A.a.americana).

The tundra moose, A. a. gigas, is the largest of the subspecies. Cows weigh up to 500 kg while males can be in excess of 700 kg. Their coat colour, unlike the other subspecies,

1 changes between winter and summer – from a unisex brown in summer to colours in winter that depend on age, sex and range. Comparatively, their antlers develop at a faster rate than the other subspecies. Breeding occurs in harems, with the females gathering in a bull’s range. They are primarily found in Alaska and the Yukon with some distribution down into northern British Columbia (Peterson 1955, Bubenik 1997).

The Yellowstone moose, A. a. shirasi, are considered the smallest in size of the 4 subspecies. The bulls weigh up to 370 kg with the cows weighing below that. The coat colour along the top of the back is a rusty, yellowish-brown with dusky hair tips. Their colouring can be affected by age and sex as well. Their antlers are also the smallest among the subspecies. While harem mating does occur, serial mating – when the bull travels from female home range to female home range, is also common. These moose are found along the Rocky Mountain Range in both Canada and the United States – southern

Alberta, southern British Columbia, western Montana, western Wyoming, Idaho, northeastern Utah and Colorado (Peterson 1955, Bubenik 1997).

The taiga, or boreal forest, moose are comprised of the remaining two subspecies, A. a. andersoni and A. a. americana. The taiga moose subspecies are very similar to each other. Adults weigh between 360-600kg and have similar colouring based on sexual maturity (darker faces when post-prime and the females have obvious vulva patches).

Winter colouring for both is age, sex and social rank dependent. Antlers average 140 cm for both subspecies, and rarely exceed 165 cm. One difference documented between the two is the shape of the palate, which in A. a. andersoni is wider than in A. a. americana,

2 but palates in both subspecies are narrower than in the other two subspecies. Breeding in both taiga subspecies occurs as pairs with the female controlling the rut. The estrus of the cows of both subspecies is not synchronized as seen in a harem. A. a. americana are found from central Ontario eastward through the Maritimes in Canada and the northeastern United States – primarily in Maine. A. a. andersoni are found from central

Ontario westward across the Canadian Prairies, up to the Territories and through the north Midwestern United States (Michigan, Minnesota and the Dakotas) (Peterson 1955,

Bubenik 1997).

Moose are considered relative newcomers to Ontario. Peterson (1955) noted that the majority of the sightings on record for the area north of Lake Superior are from the twentieth century. Documented sightings for areas in southeastern Ontario and west and south of Lake Superior go back to the seventeenth century and early explorers. Peterson hypothesized that two subspecies evolved in two of the glacial refugia in central and eastern North America: A. a. andersoni in the Midwestern States (current day Michigan,

Wisconsin, Minnesota, Illinois area) and A. a. americana in the northeastern States

(current day Pennsylvania and New York area). Once the glaciers retreated, both populations re-populated the newly available areas. Over time, they dispersed into

Ontario from the northwest and the southeast until the populations met in northeastern

Ontario, where it is theorized that the two subpopulations integrated (Peterson 1955).

There is some debate in the field as to the classification of the taiga moose as two separate subspecies. The concept of subspecies is subjective and controversial.

Subspecies have been classified in numerous ways over the last 40 years: as a group of phenotypically similar populations inhabiting a geographic subdivision of the range of a species (Mayr 1970); or as a group where phylogenetic distinction among subspecies is required (monophyly) and where phylogenetic differences should consist of multiple independent genetic traits (Avise & Ball 1990); or where designation of subspecies relies on analysis of multiple criteria, rather than merely on analysis of genetic data (Cronin

1993). That being said, in North America, there has been little genetic evidence found showing differences between the A.a.andersoni and A.a.americana subspecies. Since all moose subspecies classification in North America was determined using morphological differences and considering the few differences between A.a.andersoni and

A.a.americana, with only the palate size difference documented, it is easy to understand why there is debate. It has been suggested that the subspecies of North American moose should be re-evaluated (Bubenik 1997, Franzmann 2000).

Phylogeography is the study of the historical processes that may be responsible for the geographic distributions of individuals. This is accomplished by considering the various relationships between phylogenetics and the geographic distribution of individuals.

The discipline was first reviewed in 1987 by John Avise and colleagues (Avise et al.

1987). This review provided a broad look at the origin of new genetic lineages (new species or subspeciation) with reference to geological or geographical events, such as a new river or a new mountain range. Phylogeography attempts to understand the

4 intraspecific history of a species by studying the genetic diversity across the species’ geographic range. This is the general definition of the term.

For the purposes of this study, however, we use a more focused definition of the term, namely the study of different genetic measures to allow the comparison of historical separation with more recent separation of populations, based on genetic indices.

Since there have been suggestions of subspeciation in Ontario, we have used the presence or absence of a phylogenetic signal, based on genetic indices, to arrive at a determination, not of the presence of two subspecies, but, instead, simply to test the hypothesis that there are two different populations of the same subspecies meeting in the province.

There have been several studies done across North America looking at moose distribution and genetic diversity. Cronin (1992) found no variation within North American moose using restriction fragment length polymorphisms (RFLP) in the mitochondrial region of the genome which suggested no support for the subspeciation in North America that had been advocated by Peterson in 1955. Another study discovered that morphological variation is clinal (dependent on latitude) and therefore is not a basis for subspecific recognition (Geist 1998). Hundertmark et al. (2002) studied mitochondrial cytb variation in moose across their circumpolar range – North America, Europe and Asia. They studied 55 individuals from North America representing all four subspecies. They only identified a single haplotype, or set of DNA variations that are inherited together from a parent, in North America, showing a relative lack of genetic diversity in moose across the

5 continent. A second study was published, this time studying the L hypervariable domain of the control region of the mitochondria (Hundertmark et al. 2003). Within their study,

Hundertmark et al. used only 35 individuals classified as A.a.andersoni and only 13 classified as A.a.americana subspecies. There were 6 haplotypes identified in

A.a.andersoni, with 5 considered unique to the subspecies. A.a.americana had 2 haplotypes identified, with one considered unique to the subspecies. The data from this study was consistent with the subspecies characterization delineated by Peterson in 1955.

Mitochondrial DNA was used in these two studies as it is considered informative for intraspecific phylogeography (Avise et al. 1987) and can be used to study population history. Hundertmark et al. proposed that further investigations using nuclear loci, particularly in contact zones between the subspecies, are necessary to come to a final conclusion on subspeciation of moose in North America.

Wilson et al. (2003) studied moose in central and eastern Canada to assess genetic variation, gene flow and population structure, using 5 nuclear microsatellite markers and the MHC-DRB locus. Approximately 200 samples were analyzed, from selected locales in Ontario, as well as out-groups representing the two subspecies from Manitoba, New

Brunswick and Newfoundland. FST and RST values were compared to study relatedness between populations and to study possible historical separations. This same method was used in a study of domestic and bighorn sheep to determine historical separation and population isolation. This method indicated that FST is more sensitive to allopatry and semi-isolation, while RST is more sensitive to longer historical separations (Forbes et al.

1995). Wilson et al. (2003) determined that there was a high level of gene flow from a

6 central Ontario population to all corners of the province, and suggested that differences across the province were from isolation by distance across a continuous range. There were a few island populations that were isolated from the main Ontario population, such as Isle Royale – an island with a population introduced 100 years ago, and little movement of individuals between it and the mainland, and Riding Mountain National

Park (RMNP) in Manitoba which was classified as a prairie “island” population. Isle

Royale has long been used for ecological and genetic studies, due to the nature of the wildlife populations introduced to an island ecosystem. RMNP is the closest land population to Ontario’s western border and considered an out-group of the A.a.andersoni subspecies. RMNP was shown to be isolated from its nearest geographic neighbour, NW

Ontario, which is consistent with a non-migratory prairie “island” population (Karns

1998, Wilson et al. 2003).

A comprehensive, large-scale, genetic study of moose populations in Ontario has not been performed before now. By studying moose populations in Ontario, a population survey can be done and, if it is found to be needed, a new management and sustainability model can be designed for the species. Effectively managing moose populations is not only important for conservation purposes but for economic reasons as well. Moose are an integral part of a complex ecosystem. They are an important source of food and contribute economically at both provincial and national levels. Management of such an important species is essential to long-term survival and population health.

The Ontario Ministry of Natural Resources and Forestry (OMNRF) divides up the province into wildlife management units, with each unit having specific hunting quotas for all harvested species (Appendix A). Under current harvest conditions, the management units are based primarily on the deer populations. A population that ranges across several units will be more susceptible to over-hunting than those that live in a single unit because that population is available for hunting in all units. Each management unit has its own quota on the number of bull and cow moose that can be harvested. A smaller population can often mean that there is a greater chance of losing the largest and fittest animals as trophies during the harvest (Timmerman & Buss 1998, Coltman et al.

2008). Male-biased harvesting occurs under the assumption that a population with a female-biased sex ratio will be more productive (Milner et al. 2006). The older males are often hunted as trophies because of their larger antler size. This can create a population with much younger males and can affect reproductive success as younger males generally do not have the ability to inseminate as many females as the older males do (Mysterud et al. 2002, Coltman et al. 2003, Hard et al. 2005, and Proaktor et al. 2007). Small populations are also at risk of losing genetic diversity. While wildlife management units are used for hunting area designations, biological units, or populations of genetically related individuals, show the true distribution of animals across a region without imposed limits. Biological units can be characterized in one of two ways, demographic and genetic. There are numerous definitions of a demographic unit, and it is generally based on a zone of synchronous population dynamics. A genetic unit is based on the genetic composition of the population in an area and is determined by studying the genetic structure. Knowledge of the genetic structure is important, because it can delineate the

8 true genetic distribution of population groups across the studied landscape, as well as gene flow across the landscape.

Gene flow is the transfer of genetic information through the migration and breeding of an individual and the contribution of its genes from one sub-population to another. Gene flow can help maintain genetic diversity that may be lost through selective adaptation

(Saccheri et al. 1998), inbreeding in small populations and genetic drift. Barriers to gene flow are generally due to geographic features such as mountain ranges, bodies of water, anthropogenic factors (habitat loss due to human interaction), roads and distances between populations beyond the normal dispersal of a species. If there are low levels of migration or gene flow, or complete isolation, then populations will show genetic distinction, or population structure, through genetic drift (Ryman et al. 1980, Broders et al. 1999, Coulon et al. 2006, Cushman et al. 2006, and Epps et al. 2007). Descriptive statistics can be used to calculate the amount of population structure by allelic distributions and probability-based models (Weir and Cockerham 1984, Paetkau et al.

1995, Pritchard et al. 2000).

Traditional barriers to gene flow are generally geographic features, including distance, and anthropogenic factors. The boreal forest range of moose is being reduced from the south as development moves north (Thompson & Stewart 1998, Karns 1998). Unlike many species, moose can thrive in areas that have been clear-cut and have experienced wildfires. These areas of new growth can provide rich browse for the moose quite soon after burns. Many forestry companies have adapted their cutting policies to harvest logs in specific patterns, often leaving little patches of habitat intact within the harvest tract or

9 cutting in irregular shapes to help with wildlife habitat management (Thompson &

Stewart 1998).

Some anthropogenic factors affecting gene flow and populations are not caused by human expansion into moose habitat, but by the harvest of animals. Not all animals that are hunted are obtained through legal means (permits, culls, first nations hunting rights) and hence identifiable by permits and other records. Animals may be hunted illegally, and identification of these animals and the responsible poachers is important for effective population management. This identification is achieved using forensic genetics methods and by creating provincial and regional databases to identify the location where an animal is likely to have originated. Wildlife forensics encompasses different facets, but some of the most important are tissue identification and tracking poachers of endangered and protected species (White et al. 2012, Johnson 2010, Jobin et al. 2008, NAWEG 2000).

Merchants can also sell exotic and endangered animal parts, claiming they are from unprotected sources, to escape criminal charges of hunting protected animals (Coghlan et al. 2012, Woolfe & Primrose 2004). In addition to legal investigations, wildlife data are used to regulate the harvest of non-endangered species by aiding the determination of the yearly hunting quotas. While still relatively new, wildlife DNA databases are commonly used, not only for legal investigations, but also for conservation and management purposes (White et al. 2012). Investigators tracking wildlife populations, determining parentage in small or endangered populations, or identifying admixed populations and landscape genetic inquiries, all commonly use wildlife DNA databases for their analyses.

The databases can be built at differing geographic scales depending on the study needs

(broad scale – regions, or finer scale – management units or individual geographic

10 regions such as islands, tracts of land, ponds and rivers). The genetic health of a population can also be examined and assessed by using the database individuals in the population to determine inbreeding levels and levels of fixation at different loci.

OBJECTIVE

The goals of this research are (i) to identify the genetic structure of the moose populations in Ontario using genetic profiles, (ii) to identify possible barriers to gene flow by associating the genetic data with landscape attributes across Ontario, and (iii) to develop a forensic database for individual identification of moose for the Province of Ontario.

There are numerous factors that can influence genetic structure in wild populations.

Assignment of individuals to particular subpopulations can be affected by their genetic profile. With species that cover large ranges, genetic structure can be a result of geographic distances between populations. If there is no evident genetic structure, the population is considered to be a panmictic population. A panmictic population is a population in which mating is completely random (as opposed to selective mating between certain adults in the population) and unstructured, where all individuals are potential partners. Landscape features that are present do not act as a barrier to random mating, and distance plays no part in determining the genetic composition of the population.

Calculating isolation by distance involves plotting the genetic similarity, or genetic distance, between two populations as a function of the geographic distance between those populations. Some plots can assess whether more distant population pairs are more

11 different genetically, reveal the importance of specific barriers to gene flow and may help separate the effects of population history from ongoing gene flow (Fortin & Dale 2005,

Carr et al. 2007, and Storfer et al. 2007).

Maintaining sustainable moose population is important for ecological and economic reasons (Environment Canada 2003). By studying the moose population in Ontario and looking at the genetic structure and factors that influence gene flow across the province, a more accurate, informative population survey can be done. If the results of the study warrant it, a new management and sustainability model can be designed for the province.

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Hundertmark KJ, Shields GF, Udina IG, Bowyer RT, Danikin AA, Schwartz CC. 2002. Mitochondrial Phylogeography of Moose (Alces alces): Late Pleistocene Divergence and Population Expansion. Mol. Phylo. Evol. 22(3):375-387

Karns PD. 1998. Population Distribution, Density and Trends In Ecology and Management of the North American Moose. Franzmann and Schwartz Eds. Smithsonian Institution Press. Washington, DC. pp 125-139

Mayr E. 1970. Populations, species, and evolution. Harvard University Press. Cambridge, Massachusetts.

Milner JM, Nilsen EB, Andreassen HP. 2006. Demographic Side Effects of Selective Hunting in Ungulates and Carnivores. Conservation Biology. 21(1): 36-47

Mysterud A, Coulson T, Stenseth NC. 2002. The role of males in the dynamics of ungulate populations. Journal of Animal Ecology. 71: 907-915

Paetkau D, Calvert W, Stirling I, Strobeck C. 1995. Microsatellite analysis of population structure in Canadian polar bears Mol. Ecol. 4:347-354

Peterson RL. 1955. North American Moose. University of Toronto Press. Toronto, ON. 280pp

Pritchard JK, Stephens M, Donnelly P. 2000. Inference of Populations Structure Using Multilocus Genotype Data. Genetics. 155:945-959

Proaktor G, Coulson T, Milner-Gulland EJ. 2007. Evolutionary responses to harvesting in ungulates. Journal of Animal Ecology. 76:669-678

Rannala B, Mountain JL. 1997. Detecting immigration by using multilocus genotypes. Proc. Natl. Acad. Sci. USA 94:9197-9201

Raymond M, Rousset F. 2001. An exact test for population differentiation. Evolution. 49:1280-1283

Ryman N, Reuterwall C, Nygren K, Nygren T. 1980. Genetic Variation and Differentiation in Scandinavian Moose (Alces alces): Are Large Mammals Monomorphic? Evolution. 34(6): 1037-1049

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Chapter 2 - Genetic structure of moose (Alces alces) in Ontario, Canada

Abstract

Moose (Alces alces) are one of North America’s largest ungulates. Studies have suggested that two of the four North American moose subspecies meet in the northern part of the province of Ontario: the eastern moose (A. a. americana), which are descended from a population of moose from a southern refugium in the eastern continental US, and the north-western moose (A. a. andersoni), which are descended from a population of moose from a southern refugium in the central plains of the US.

Although several previous studies have looked at localized sample sites in Ontario, a comprehensive study of moose in the province has not previously been undertaken to determine the genetic diversity of the population, or the landscape factors affecting the population structure. The phylogeographic history of moose in Ontario should be studied to determine if there are potential consequences for landscape genetics analyses, contemporary population connectivity or current management strategies. To do so, we examined the population structure and genetic diversity of 776 individual moose (n=776) from across Ontario, using 10 polymorphic microsatellites. Individuals known to represent the north-western moose (Manitoba and Cape Breton) and individuals known to represent the eastern moose (New Brunswick and Nova Scotia) were compared to the

Ontario individuals to investigate the phylogeography of the Ontario samples. The overall FST of all of the populations indicated genetic differentiation (p<0.005) with

FST=0.161. The overall RST between the provinces studied corroborates this result with

16 moderate genetic differentiation (RST=0.124). Results of analyses from the program

STRUCTURE indicated two populations, one in north-western Ontario, and one in southern Ontario, with a third population composed of admixed individuals in between, in north-eastern Ontario. Phylogenetic comparisons, using RST values between A. a. americana (New Brunswick and Nova Scotia mainland) and A. a. andersoni (Manitoba and Cape Breton Island) samples and the Ontario samples, indicated that there is no phylogeographic pattern present in Ontario. The New Brunswick and mainland Nova

Scotia populations are distinct from the Cape Breton and other central Canadian samples, and the two eastern Ontario populations (north-eastern and south central), are much closer to each other than they are to any other population. We conclude that population structure in Ontario is being influenced by the convergence of two separate populations from the same subspecies, but there is no phylogenetic evidence that they are from two different genetic subspecies.

Introduction

Harvested species are often partitioned into wildlife management units (hereafter WMUs) for the purpose of setting quotas (Charlier et al. 2008). However, the method by which these WMUs are delineated, based on biological parameters, can vary. Furthermore, the models used to estimate population parameters and to set hunting quotas often assume closed systems (Vucetich & Peterson 2004, Coulon et al. 2006, Proaktor et al. 2007).

The use of DNA molecular markers to assess population structure provides a more accurate biological estimation of population boundaries.

The moose is an important harvested species in Canada and the northern US states.

Although WMU delineation varies among provinces and states, the Ontario Ministry of

Natural Resources and Forestry (OMNRF) even identifies wildlife WMUs differently across the province, with southern units being based on white-tailed deer populations and northern units being based on moose populations (Ontario Game and Fish Act 1975,

Appendix A). Most of the boundaries of the units are based on features such as rivers, roads and county lines. These boundaries have implications for management of moose throughout their distribution in Ontario - in particular at their southern range margin, where they are most susceptible to anthropogenic factors such as harvest, habitat fragmentation and climate change (Murray et al. 2012). Certainly the management of moose is further complicated by the historical assumption of two subspecies of moose in the province, which normally would be factored into further studies delineating effective management boundaries, as divergent lineages and regions with contact between subspecies might have different management requirements.

It has been suggested that the geographic boundaries of two of these subspecies, the eastern moose, Alces alces americana (A. a. americana) and the north-western moose,

Alces alces andersoni (A. a. andersoni), are in the process of meeting in northern Ontario

(Peterson 1955, Hundertmark et al. 2002). The distribution of samples for these earlier analyses, however, was limited to a putative zone of contact between the subspecies. A. a. andersoni has been proposed to have evolved in a southern refugium in the central plains of the US during the Pleistocene era, while A. a. americana is thought to have evolved at the same time in a southern refugium in the eastern continental US, indicating

18 different adaptive potentials in the separate distributions (Bubenik 1998). A genetic study of moose population structure across eastern Canada was undertaken by Wilson et al.

(2003) based on 10 markers and indicated broad-scale genetic structuring consistent with the proposed subspecies designations. However, phylogeographic differences were not explicitly tested using known subspecies reference specimens from outside the zone of contact.

The objective of our study was to assess the population structure of Ontario moose within a hierarchical framework of previously suggested subspecific distribution, subspecific contact zones and hybridization, and genetic structure within each subspecies. We tested the hypothesis that the phylogeography would drive the highest-order population structure in Ontario, and that we would see areas of potential admixture and additional substructure between and within the two subspecies ranges, respectively. Our hypothesis was based on previous studies by Peterson (1955), Hundertmark et al. (2002), and Wilson et al. (2003). All three studies suggested a phylogeographic pattern in Ontario with the two subspecies (A. a. andersoni and A. a. americana) meeting in northern Ontario over

Lake Superior. This pattern might have resulted from an expansion event from separate refugia following the most recent Pleistocene glaciations. We predicted that we would see higher RST values than either FST or Jost’s D values, due to RST’s greater sensitivity to longer historical separations. This would support the two subspecies meeting in Ontario.

An alternate hypothesis suggests separate expansion events from two different refugia, but instead of two subspecies meeting in northern Ontario, the meeting is instead the meeting of two populations of the same subspecies. The slightly larger size of animals

19 documented in northern Ontario could be due to differing diets, hunting and anthropomorphic pressures, or to the western refugium having been further north than the eastern refugium (since moose size increases as the latitude increases - OMNR 2009) as opposed to the presence of two distinct subspecies.

These determinations, in addition to providing a more accurate population genetic characterization of moose in Ontario, will further characterize the phylogeographic patterns of moose subspecies (Palsbøll et al. 2007). If phylogenetic patterns are present, they should be taken into account for landscape genetics analysis, contemporary population connectivity, gene flow and management unit characteristics. Failure to account for population history may give rise to errors in identifying any of these variables

(Dyer et al. 2010). Given the previous research in Ontario, a prediction of a visible phylogenetic signal was expected to support two separate subspecies meeting in northeastern Ontario.

Methods

Samples

Samples from Manitoba (Man), New Brunswick (NB), mainland Nova Scotia (NS) and

Cape Breton (CB) were used as known subspecies populations to investigate the area of potential convergence in Ontario. Individuals in Cape Breton were introduced from

Alberta in the late 1940s and so originated from the A. a. andersoni subspecies like the

Manitoba samples. Individuals from New Brunswick and mainland Nova Scotia are considered to be a continuous genetic population from the A. a. americana subspecies as

20 per the current models of taxonomy (Hundertmark et al. 2002). Small-sized tissue samples (n=776) were collected from across the province, spanning a 6-year period from

2002-2008 (no samples in 2005), by OMNR conservation officers, by researchers and by hunters during the annual fall moose hunt using supplied collection kits. Four female control samples and two male control samples were provided by the Centre of Veterinary

Sciences at the University of Guelph. Samples obtained from Manitoba (n=29), Nova

Scotia (n=31), New Brunswick (n=19) and Cape Breton (n=27) were also used in the analysis.

DNA Extraction

DNA samples were extracted using the Qiagen Tissue Extraction kit (Qiagen Inc.). DNA concentration in final elutions was quantified using Picogreen (Molecular Probes), and then diluted to a final concentration of 2.5ng/µL.

Genetic Profiling

DNA samples were profiled at 10 nuclear microsatellite loci in two multiplexes

(multiplex 1 - Map2C, BM4513, BM1225, RT9 and RT24, multiplex 2 - BM888,

BM848, FCB193, RT30 and BL42 (Ball et al. 2011)). The reaction conditions were

94°C for 5 minutes, 29 cycles of 94°C for 30 seconds, 56-60°C for 1 minute, and extension at 72°C for 1 minute, with a final extension at 60°C for 45 minutes. The samples were run on an ABI 3730 (Applied Biosystems, Foster City, CA) and genotypes were determined using GeneMarker v1.7 (SoftGenetics LLC, State College, PA). Ontario samples were compared to samples from Manitoba and Cape Breton (A. a. andersoni),

21 and from mainland Nova Scotia and New Brunswick (A. a. americana). These comparisons were used to determine if any genetic differentiation could be explained by the previously suggested meeting of two North American subspecies within Ontario.

We used a lower threshold of 80% for usable profiles - the profiles that were used for analysis were required to have a minimum of 8 out of 10 loci amplified. Individual profiles were used to establish genetic relationships between populations. The program

MicroChecker 2.2.3 (van Oosterhout et al. 2013) was used to test for pairwise linkage disequilibrium, null alleles and allelic dropout, and deviations from Hardy-Weinberg equilibrium. The level of gene flow and migration was calculated. FSTAT (Goudet 1995) produced standard diversity indices for each locus (HO, HE, PIC) as well as the inbreeding coefficient (FIS). However, the F statistic (FST) requires that individuals are assigned to a population. Due to unequal sampling across Ontario WMUs, testing was done to determine if there was a bias in population assignment. Five runs, with samples randomly removed from the WMUs with larger sample sizes (n>30), were performed, and FST was calculated for each. Population sub-structure was assessed using the program STRUCTURE 2.3.1 (Pritchard et al. 2000), performing three independent runs of

K=1-6, using a burn-in of 100,000 and a MCMC chain of 106 steps. The Evanno et al.

(2005) method was used to visualize the rate of change of log likelihood (log (K)) values from STRUCTURE. RST and FST was calculated using SPAGEDI v. 1.3d (Hardy&

Vekemans 2002) and a permutation test was done to determine if FST or RST is the more appropriate metric to use based on phylogenetic signal. Jost’s D was also calculated, using the program SPADE (Chao & Chen 2009).

An analysis of molecular variance (AMOVA) was run in Arlequin v 3.5(Excoffier &

Lischer 2010) to determine whether the assigned populations were statistically significant and whether there was more variation between populations or within individuals.

Principal Component Analysis (PCA) was performed using PCA-GEN v1.2 (Goudet

2005) on the Ontario populations, as well as all of the provincial populations. A

Discriminant Analysis of Principal Components (DAPC) was performed in adegenet in R with an alpha score suggesting that 27 principal components should be retained for analysis.

Results

Although there was disparate sampling of individuals across the province, no bias due to sample size was discerned after randomly removing samples from the WMUs with greater sample numbers to even out the sample distribution in five separate tests. With an opportunistic sampling design, a bias could have skewed the results, and extra samples in certain areas could have driven the STRUCTURE results. Without a bias, however, all samples were available for use in the analyses. This is supported by comparing the allelic richness to sample sizes in Ontario. While the largest number of alleles was found in the population with the largest sample size, it did not follow that the smallest sample size also had the lowest number of alleles (Table 2.1). The NW Ontario population, with the fewest number of samples (n=162) had an average number of alleles of 8.80 over 10 loci.

The NE Ontario population, with the largest number of samples (n=319) had an average

23 number of alleles of 9.50 over 10 loci. The SC Ontario population (n=295) had the lowest number of alleles with an average of only 7.20 over 10 loci.

Ontario

Genotyping of individuals resulted in genotypes for all of the 776 individual moose

(Appendix B). The mean number of alleles per locus was 10.5, ranging from 7 to 17

(Table 2.2). Mean expected and observed heterozygosity were 0.585 and 0.534 respectively. The data showed moderate genetic differentiation across the province when three populations were assumed (FST = 0.070). MicroChecker showed that putative populations were in HWE and there was no evidence of linkage disequilibrium. One locus, RT24, was flagged as possibly having null alleles present. Ontario populations were compared with samples from the Maritime provinces as well as the Manitoba samples. The number of alleles are significantly lower in the Maritime provinces than

Ontario and Manitoba, showing a lower level of genetic variability (Table 2.1).

STRUCTURE assigned individuals to several populations (Figure 2.1) with K=2 showing a grouping of north-western Ontario with the mainland Maritimes, Cape Breton and

Manitoba and north-eastern and southern Ontario grouping together. As the value of K increased, north-western Ontario and Manitoba remain grouped, but north-eastern and southern Ontario split off into their own group, as did the Maritimes and Cape Breton.

Results of Evanno et al. (2005) showed a ΔK maximal at K=2, and STRUCTURE results showed a ln(K) plateau at K=3 (Figure 2.2). FST values between the five randomly sampled tests showed little variation (FST = 0.056-0.060) suggesting a negligible bias due to sample size. FST values were calculated for both two and three assigned populations to

24 determine the level of genetic variation between populations. When K=2, there is moderate genetic differentiation between the populations. When K=3, population one

(NW Ontario) showed mild genetic differentiation from population three (SC Ontario) and little to no genetic differentiation between populations one and two (NW Ontario and

NE Ontario) and between populations two and three (NE Ontario, SC Ontario) (Table

2.3). The pattern of genetic variation was similar when RST values were used, although the RST values suggested lower levels of genetic differentiation than FST values. Jost’s D values, though generally higher, followed the same pattern as both RST and FST (Table

2.3). When K=3, population one (NW Ontario) showed moderate genetic differentiation from populations two (NE Ontario) and three (SC Ontario), but there was little to no genetic differentiation between populations two and three (NE Ontario, SC Ontario)

(Table 2.3).

Subspecies comparison

Individuals from the NW Ontario population showed little genetic differentiation from the Manitoba population and moderate genetic differentiation from all three maritime populations. Individuals from the NE and SC Ontario populations showed moderate genetic differentiation from all of the other populations (Table 2.3). FST values were consistently higher than RST values across all populations, with the exception of the comparisons between Nova Scotia and most other populations, which difference can be explained by a genetically isolated population with little immigration into the population.

The Cape Breton population was also distinct from the rest of the populations as expected

25 of an isolated, island population founded by only 18 animals from Elk Island National

Park in Alberta 70 years ago.

The results were statistically significant. The AMOVA generated a higher fraction of variability within individuals (79.95%) than among individuals within populations

(14.47%)(Table 2.4).

The DAPC results showed 4 clusters. The first and second clusters overlapped and were composed of NE and SC Ontario populations, the third cluster was made up of Nova

Scotia and New Brunswick and the fourth cluster was made up of the NW Ontario,

Manitoba and Cape Breton populations (Figure 2.4).

Discussion

STRUCTURE results indicated a genetic separation between western and eastern

Ontario. Eastern Ontario was further split into two genetic groups, north-eastern and southern, which showed low genetic differentiation. The putative null allele at RT24 could be due to several reasons: non-random mating, gene flow, selection or a population recently formed by mixing between two (or more) populations and equilibrium that has not yet been reached for that locus. The low level of genetic differentiation within the north-eastern assigned population can be explained by admixture between the individuals from north-western Ontario and those from south-central Ontario. Over 10% of all the individuals studied did not assign to the population they were geographically close to.

Most of those individuals were admixed individuals with an equal representation from

26 each of the Ontario populations. The remaining individuals were assigned to another population but were geographically isolated from the remainder of that population.

Although there is similarity between the NE Ontario and both NW and SC Ontario, NE

Ontario is identified as a separate population.

Based on our findings, we propose that the individuals in Ontario do not fall into the two subspecies geographic designations that have been suggested by previous literature

(Pederson 1955, Hundtermark et al. 2002, Feldhamer et al. 2003, Wilson et al. 2003).

The RST values do not support the hypothesis that two distinct subspecies meet over Lake

Superior. Both FST and D values are greater than the RST values, suggesting that phylogeography is not having an effect on the genetic structure seen across Ontario. If

RST values had been higher than both the FST and Jost’s D values, our initial hypothesis would have been supported. Instead, we saw low to moderate FST values across the province that are more in line with two populations from the same subspecies repopulating the province from the west and from the east and meeting in NE Ontario over Lake Superior. An area of admixture between the two populations in NE Ontario in fact supports our alternate hypothesis. This population delineation is also supported by our significant statistical result.

Samples from Ontario were compared to samples from Manitoba, Cape Breton, New

Brunswick and mainland Nova Scotia. The earlier literature describing the subspecies boundary suggested that the Maritimes are represented by A. a. americana and that

Manitoba and Cape Breton are represented by A. a. andersoni, and that the subspecies

27 meet in Ontario having expanded from their separate glacial refugia (Hundertmark et al.

2002, Feldhamer et al. 2003). For the majority of our population comparisons, the FST and D values were either similar or higher than the RST values. This pattern reflects postglacial recolonization from different refugia, human-mediated introduction of individuals from another region, or some locally occurring hybridization between the populations. The FST value between New Brunswick and Nova Scotia was slightly higher than the RST value, suggesting that distance and low levels of gene flow between the populations is a factor. There is only a roughly 30-kilometres land border between the provinces, bottlenecking the available area to move between the two, which may be contributing to this. The overwhelming geographic distance between the populations overshadows the effect that phylogeography may have. We are not discounting a meeting of the two subspecies somewhere in Eastern Canada, but there is no evidence to support their meeting in Ontario as previously proposed.

The PCA results also support our findings (Figure 2.3). The Cape Breton Island population is equidistant in the graph from the other populations, and shows signs of separation from the others, which confirms an isolated population as previously published

(Ball et al. 2011). However, the CB population is clustered with Manitoba and NW

Ontario in the DAPC graph, highlighting its origins from the prairies (Figure 2.4).

We did see a slightly higher level of relatedness between the SC population and the NE population. This is supported by the DAPC results (Figure 2.4). While there were a small number of NW and Manitoba individuals represented in the overlapping clusters 1

28 and 2, they were primarily made up of NE and SC individuals. This can be explained by a larger area of excellent habitat and forage in NW Ontario allowing for a greater carrying capacity, with moose not dispersing and showing a higher rate of philopatry. In SC

Ontario, anthropogenic factors, such as decreasing habitat quality and increased competition from white-tailed deer, could be causing the population to move north, interacting with the NE population at an increased rate.

Conclusion

There is no support for a phylogeographic meeting of subspecies in Ontario as previously suggested. There is still measurable genetic differentiation between the populations within Ontario, suggesting instead that two different populations of the same subspecies have met and admixed over Lake Superior. There may be a subspecies boundary in

Eastern Canada and the Northeastern United States. However, there is no evidence that such a boundary occurs in Ontario. Research, using samples from Quebec, Maine, and increased numbers of samples from the Maritimes, may be able to determine if a meeting point of these two, very similar subspecies occurs elsewhere.

For Ontario, other factors may influence the differentiation. Further study of the Ontario populations is needed to determine if the three populations can be further broken down to smaller genetic population groups. If there is measurable isolation by distance across the province, or if there are any barriers to movement, and therefore to gene flow, across the province, such research will help pinpoint where more localised population boundaries lie across the Province, will give increased insight into the genetic structure of moose

29 populations in Ontario and will inform any required changes to current management strategies. With no significant phylogenetic signal however, our results support our alternate hypothesis that there are individuals from the same subspecies from two different refugia meeting in NE Ontario, and not two separate subspecies as previously believed.

References

Ball MC, Finnegan LA, Nette T, Broders HG, Wilson PJ. 2011. Wildlife forensics: "Supervised" assignment testing can complicate the association of suspect cases to source populations. Forensic Science International: Genetics. 5(1): 50-56

Bubenik AB. 1998. Evolution, Taxonomy and Morphophysiology. Ecology and Management of the North American Moose. Franzmann & Schwartz Eds. Smithsonian Press. p. 77-123

Charlier J, Laikre L, Ryman N. 2008. Genetic Structure and Evidence of a Local Bottleneck in Moose in Sweden. Journal of Wildlife Management. 72(2): 411-415

Dyer RJ, Nason JD, Garrick RC. 2010. Landscape modelling of gene flow: improved power using conditional genetic distance derived from the topology of population networks. Molecular Ecology. 19(17): 3746-3759

Excoffier L, Lischer HEL. 2010. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Molecular Ecology Resources. 10: 564-567.

Feldhamer GA, Thompson BC, Chapman JA. 2003. Wild Mammals of North America. JHU Press. 1216pp.

Ontario Ministry of Natural Resources. 1975. Ontario Game and Fish Act.

Paetkau D, Calvert W, Stirling I, Strobeck C. 1995. Microsatellite analysis of population structure in Canadian polar bears. Mol. Ecol. 4: 347-354

Peterson RL. 1955. North American Moose. University of Toronto Press. Toronto, ON. 280pp

Proaktor G, Coulson T, Milner-Gulland EJ. 2007. Evolutionary responses to harvesting in ungulates. Journal of Animal Ecology. 76: 669-678

Rannala B, Mountain JL. 1997. Detecting immigration by using multilocus genotypes. Proc. Natl. Acad. Sci. USA 94: 9197-9201

Vucetich JA, Peterson RO. 2004. The influence of top-down, bottom-up and abiotic factors on the moose (Alces alces) population of Isle Royale. Proceedings of the Royal Society B. 271: 183-189

Table 2.1 Sample size, average number of alleles (NA) over 10 microsatellite loci, expected and observed heterozygosity (HE, HO), and FIS averaged over seven populations sampled in Central and Eastern Canada.

Population n NA HE HO FIS

Ontario NW 162 8.80 0.6512 0.5860 0.112

Ontario NE 319 9.50 0.5751 0.5367 0.062

Ontario SC 295 7.20 0.5131 0.4810 0.063

Cape Breton 27 5.20 0.6267 0.5838 0.070

New Brunswick 19 3.70 0.4638 0.4196 0.098

Nova Scotia 31 4.70 0.5459 0.4281 0.219

Manitoba (RMNP) 29 6.30 0.6982 0.7014 0.005

Table 2.2 Genetic diversity measurements across the ten microsatellite loci in Ontario. No. of Locus H H F PIC alleles E O IS Map2C 7 0.647 0.635 0.008 0.578 BM4513 9 0.762 0.689 0.042 0.681 BM1225 13 0.556 0.542 0.002 0.491 RT9 11 0.710 0.620 0.068 0.625 RT24 17 0.761 0.639 0.156 0.712 BM888 7 0.268 0.258 0.064 0.261 BM848 7 0.649 0.618 0.051 0.592 FCB193 8 0.512 0.446 0.120 0.452 RT 30 12 0.164 0.141 0.243 0.177 BL42 14 0.821 0.753 0.079 0.786 Mean 10.5 0.585 0.534 0.072 0.535

Table 2.3 Pairwise FST, RST and Jost’s D values calculated based on microsatellite data set across all populations. Man=Manitoba, NW=Northwest, NE=Northeast, SC=South Central, NB= New Brunswick, NS=Nova Scotia, CB=Cape Breton. NW NE SC Man NB NS CB RST Ontario Ontario Ontario Man - NW 0.052 - Ontario NE 0.091 0.026 - Ontario SC 0.21 0.093 0.02 - Ontario NB 0.089 0.01 0.048 0.121 - NS 0.184 0.076 0.111 0.16 0.096 - CB 0.021 0.128 0.153 0.293 0.228 0.39 -

NW NE SC F Man NB NS CB ST Ontario Ontario Ontario Man - NW 0.03 - Ontario NE 0.082 0.067 - Ontario SC 0.152 0.147 0.019 - Ontario NB 0.212 0.129 0.165 0.269 - NS 0.118 0.081 0.081 0.136 0.063 - CB 0.163 0.184 0.259 0.331 0.414 0.272 -

NW NE SC Jost's D Man NB NS CB Ontario Ontario Ontario Man - NW 0.052 - Ontario NE 0.124 0.106 - Ontario SC 0.187 0.187 0.018 - Ontario NB 0.345 0.289 0.337 0.378 - NS 0.253 0.247 0.236 0.245 0.052 - CB 0.253 0.243 0.259 0.314 0.402 0.302 -

Table 2.4 Analysis of molecular variance (AMOVA) results for moose population assignments in Ontario, Canada. Source of Degrees of Sum of Variance Percentage P-value Variation freedom (df) Squares Components Variation Among 2 181.163 0.17474 5.60119 0.00003 population Among individuals 2 2624.780 0.45069 14.44684 0.04688 within populations Within 775 1935.500 2.49420 79.95196 0.00750 individuals

Figure Legend

Figure 2.1 STRUCTURE assignment plots for K=2 (A) to K=6 (E). Manitoba and NW Ontario are consistently grouped together. The Maritime populations all group together, consistent with the sheer geographic distance between them and the central Canada populations. NE and SC Ontario are grouped together for K=2 but become more and more defined as separate populations for greater values of K. NE Ontario does not show one single population assignment but rather an admixed population of the NW assigned values and the SC assigned values.

Figure 2.2 Estimated number of populations (K) from the program STRUCTURE (Pritchard et al. 2000) for moose in Ontario. On the left, the log-likelihood values for each estimated number of populations, shown by the dotted line (Pritchard et al. 2000). A plateau in the values indicates the most likely number of populations. On the right, the rate of change in log-likelihood values, shown by the solid line (Evanno et al. 2005). The maximum ΔK indicates the most likely number of populations.

Figure 2.3 PCA results for Central and Eastern Canadian moose populations. In the analysis, 74.65% of the variation is accounted for. Cape Breton clusters off by itself, this is supported by the genetic measures. The other two Maritime Provinces cluster off together, as expected due to geographic distance. NW Ontario and Manitoba, as supported by all other analyses, cluster off together. NE and SC Ontario are closest to each other with NE Ontario being between SC and NW Ontario, as expected based on geographic distance and the genetic measures.

Figure 2.4 DAPC graph showing clustering of populations across Central and Eastern Canada (Clusters 1 and 2 – NE and SC Ontario, Cluster 3 – NS and NB, Cluster 4 – Man, CB and NW Ontario). Ellipses indicate 95% confidence intervals around each cluster. Two of the Maritime populations (NS and NB) cluster out away from all other populations as expected. NE and SC Ontario are almost completely superimposed, confirming the close genetic relationship between the two. Part of the NE population also overlaps with the NW Ontario/Manitoba/Cape Breton cluster (cluster 4). The larger spread of cluster 4 can be explained by the inclusion of the Cape Breton population. The Cape Breton population originated in Elk Island Park in Alberta and was transplanted to Cape Breton due to low population numbers. Time and the geographic distance would account for the spread of the cluster.

A K=2

B K=3

C K=4

D K=5

E K=6

Figure 2.1

-18500 900.00 -19000 800.00 -19500 700.00 -20000 600.00

-20500 500.00 K

Δ L(K) -21000 400.00 -21500 300.00 -22000 200.00 -22500 100.00 -23000 0.00 1 2 3 4 5 6 K

Figure 2.2

Axis 1 (47.42%) 0 NW Ont Man

NE Ont

SC Ont Axis 2 (27.23%)

Figure 2.3

Figure 2.4

Chapter 3 – Spatial and genetic structure of moose (Alces alces) populations in Ontario, Canada: Implications for Management.

Abstract

The management of animals is a critical component in the maintenance of healthy populations, low levels of inbreeding and the success of any commercialization of the species (i.e. wildlife watching, hunting). A major component of wildlife management is monitoring animal movement. Monitoring this movement helps with many parts of population regulation, while at the same time decreasing inbreeding in the population by increasing the gene flow between populations.

To determine moose genetic structure across Ontario, genetic profiles of individual moose (n=776) were used to estimate gene flow. The program TESS was used, showing two defined populations (NW and SC Ontario) with an admixed population in NE

Ontario. The correlograms for three groups were estimated (entire population, and male and female populations) and showed a pattern of isolation by distance (IBD) up to the first distance class - 100 kilometres (E=0.11099, M=0.10665, F=0.08607), but then showed no IBD pattern in further distance classes. The patterns seen in the population graph overlay and the correlograms suggest more local genetic differences than cross- provincial differences. There were few migrants moving between areas in the context of the entire Ontario population, but a greater number of those migrants heading north and west (NE-NW=6.698, SC-NW= 5.016) than south and east (NW-NE=3.089, NW-

SC=1.430).

Introduction

Movement is an essential component of the regulation of populations, reduction of competition, maximization of survival, and maintenance of habitat resources, while decreasing inbreeding by increasing gene flow between populations (Hoffman et al.

2006). Populations can be genetically structured at different spatial scales (Legendre &

Fortin 1989, Hardy & Vekemans 2002, Manel et al. 2003, Mora et al. 2010 ) and this genetic structure can be associated with geographic distance, i.e. isolation-by-distance

(Slatkin 1987, Bockelmann et al. 2003, Skribinšek et al. 2012, Storch et al. 2012), phylogeographic patterns, sex-biased processes (Ginsberg & Milner-Gulland 1994,

Hewison & Gaillard 1999, Gobush et al. 2009, Palazy et al. 2012), and natural and non- natural barriers on the landscape (Bolger et al. 2008, Crispo et al. 2011, Naidoo et al.

2012, Nater et al. 2012).

Accurate delineation of genetically structured populations can be a critical component of identifying workable biological units for management and conservation efforts (Moritz &

Faith 1998, Bolger et al. 2008, Sawyer et al. 2009, Johnson et al. 2010, Heller et al.

2012). If there are low levels of dispersal and gene flow, or complete isolation, then populations will show genetic distinction resulting from subsequent genetic drift (Ryman et al. 1980, Cushman et al. 2006, Coulon et al. 2006, Epps et al. 2007) and possibly local adaptations. Population genetic structure-based allelic distributions and probability-based models are commonly used to determine the degree of isolation by distance (Weir &

Cockerham 1984, Paetkau et al. 1995). If sampling is opportunistic across the study area,

43 that fact needs to be taken into account when estimating isolation by distance, to avoid the creation of false patterns arising from the sampling regime. The relationship between genetic distances and geographical distances can tell us whether more distant population pairs are more different genetically than pairs that are nearby, revealing the importance of specific barriers to gene flow. Under natural conditions, isolation by distance is frequently the default expectation. Wright’s theory of isolation by distance (Wright

1943) predicts that individuals will be more genetically similar to other individuals that are close geographically than to those that are farther away. Such spatial relationships will also help separate the effects of population history from ongoing gene flow (Carr et al. 2007, Storfer et al. 2007). Gene flow helps maintain genetic diversity that may be lost through selective adaptation (Saccheri et al. 1998), inbreeding in small populations or genetic drift (Couvet 2002, Keller & Waller 2002, Schwartz & Mills 2005, Ortego et al.

2011).

There are two main categories of movement in mammals – dispersal and migration.

Dispersal is the establishment of new home ranges by young animals and colonization of new habitats. Migration is the regular movement of animals between seasonal ranges that evolved as a strategy to minimize the deleterious effects of food resources that may be seasonally limited in particular habitats (Baker 1978).

Dispersal can have a dramatic effect on population dynamics due to changes in population size, spatial distribution, colonization of new habitats and gene flow

(Hundertmark 1998). Greenwood (1980) suggested four main types of dispersal: (1)

44 natal – where the animal moves from its natal range to where it will potentially breed, (2) breeding – where the animal moves between successive breeding sites, (3) gross - where the animal moves away permanently from their natal range, but may or may not breed, and (4) effective – where the animals who exhibit gross dispersal successfully breed.

Dispersal has further been categorized as adaptive and non-adaptive. Adaptive dispersal includes both presaturation dispersal, i.e. below carrying capacity but with a growing population, and ambient dispersal, which is low-level dispersal that occurs independently of population density and is usually composed of young, healthy and reproducing active adults (Hundertmark 1998). Non-adaptive dispersal involves animals that have been forced from their natal ranges or established home ranges by social factors in a high- density population and are generally non-reproductive adults and juveniles who face an increased probability of mortality (Hundertmark 1998).

While dispersal may see animals wander at random in search of new home ranges, migration is directional in nature and thus predictable. Bull moose exhibit increased migration between seasonal ranges, particularly during the autumn rut, which is generally considered to be due to the polygamous nature of males searching for mates. Migrating moose move with purpose, because their migration is recognized as a learned and traditional behaviour (Mytton & Keith 1981). The proportion of migrants versus non- migrants in a stable population will reach an equilibrium, at which time the benefits of leaving are equal to the benefits of staying (Sinclair 1983). Moose must achieve a benefit from migration, and so, if snow depths and available forage available across an area are uniform, the population will not be expected to migrate (Hundertmark 1998).

There are numerous management implications for moose, when animal movement is taken into consideration. In determining the areas occupied by a population, the identification of home ranges is useful, when accounting for the effects of anthropogenic- related habitat alteration and when studying the determination of critical habitat areas and reserves (Hundertmark 1998). Movement also must be considered in the construction of new physical barriers that may block traditional migration routes, disrupt population dynamics and increase incidental mortality. Another consideration is the potential for the over-harvesting of certain groups. Younger moose (under 2 years old) are up to twice as vulnerable to harvest as older moose (Boer 1998) because of increased movements they exhibit during dispersal and establishment of home ranges. Mature bulls are also more vulnerable during the rut (which generally coincides with the hunt) because their heightened activity, including movement, is likely to bring them into contact with hunters at a time when the animals’ innate wariness is diminished. By regulating hunter access and methods of harvest, hunting mortality can be maintained at a level that is proportional to the surplus available in the local populations, not to the regional estimates, thereby tailoring the harvesting to the actual numbers in the specific area (Cederlund & Okarma

1988).

In Ontario, the overarching policy, under which moose management falls, is the Cervid

Ecological Framework (CEF). This framework was put in place to ensure ecologically sustainable populations of the four cervid species present in Ontario: moose, woodland caribou, American elk and white-tailed deer. It separates the province into nine cervid

46 ecological zones (CEZs) and provides broad landscape-level population and habitat management guidelines for each cervid species within each zone (OMNR 2009).

The Ontario Ministry of Natural Resources and Forestry (OMNRF) conducts surveys of

Ontario hunters, and those results along with ecological and landscape studies determined the framework for the 2009 Moose Management Policy and the Moose Harvest

Management Guidelines. These policies and guidelines replaced policies from 1980 and set out a new direction for a landscape and ecologically-based approach to moose management. The goal of the Policy is to ensure that moose populations, and ecosystems on which they rely, are sustainable and allow for the continuous provision of ecological, cultural, economic and social benefits for the people of Ontario (OMNR 2009). The

OMNRF developed two main objectives for moose management, each containing multiple strategies to achieve these objectives. The first objective is to manage moose populations sustainably according to the broad, overarching direction set out by the provincial CEF. Strategies for this objective are provided under legislation and policy, population objectives, population management, population assessment and habitat management. The second objective is to provide an optimal mix of benefits from appropriate harvest allocation of moose. The management of activities related to moose strategies for this objective fall under many different sections: allocation, non- consumptive uses, enforcement, education and human-moose conflict.

One of the main focuses of moose management in Ontario is harvest management. The

OMNRF manages the harvest at the wildlife management unit (WMU) level with a three- step process:

(1) planning the moose harvest,

(2) managing the harvest, using management strategies, and

(3) assessing the effectiveness of harvest strategies in achieving allowable harvest.

The first step involves the calculation of the allowable harvest and is directly linked to the population objective set for the WMU at that time, using factors such as productivity, net recruitment, previous harvest numbers and other sources of information on significant mortalities. Once this number has been determined, the number of tags available for each

WMU is calculated (OMNR 2009). The second step, the moose harvest management strategies, falls into five categories:

(a) Selective Harvest System – type of moose hunted,

(b) Seasons – timing of moose hunts,

(d) Gear - use of firearms and types allowable, and

(e) Hunter Management – managing party hunting (i.e. – permits per group and

rules regulating who can hunt based on party numbers).

These categories and their effectiveness, acceptability and feasibility were all considered by the OMNR, based on suggestions garnered from the public through the 2008 Ontario

Moose Program Review. Continuing assessment of the management of the moose harvest is based on current widespread provincial policies, such as the hunter harvest

48 surveys, review of the hunt to detect any oddities that may necessitate changes to the following year’s hunt, and the mandatory reporting required in some WMUs.

A greater understanding of moose movement patterns and the effect of these patterns across the province is important for management of the population. The characterization of the genetic structure and the phylogeographic history of moose populations can be used to adjust hunting regulations or quotas, and can also be used for forensic identification of individuals in illegal hunting cases. Both knowledge of population genetic structure, and any phylogeographic effects that may be present, are essential for the development of databases that can be used for management purposes, conservation efforts and forensic identification.

Our objective with this research was to determine the effect that the genetic makeup of the moose population has on current management strategies in Ontario, and to suggest options to maintain sustainable populations across the province using genetic data, while still adhering to the Moose Management Policy and the Cervid Ecological Framework

Policy. To do this, an assessment of the possible barriers to moose movement across the province of Ontario was done. Previous studies have classified distance as the greatest barrier to gene flow (Peterson 1955, Broders et al. 1999, Wilson et al. 2003) due to the philopatric nature of home range choices of moose.

There are a few studies in other jurisdictions showing individual moose dispersing distances greater than 100km across their North American range (Bowles and Gladfelter

1980, Mytton and Keith 1981, Ballard et al. 1991, Hoffman et al. 2006). In Ontario, recent studies by Murray et al. (2012) and Lowe et al. (2010) have shown that female moose in South Central Ontario exhibit little mobility, staying within their home ranges with calves setting up in nearby home ranges.

Based on these earlier studies, our hypothesis is that the main feature of moose population structure in Ontario is that of related individuals remaining in closer proximity geographically, with little long range dispersal predicted. These findings would determine if geographic distance is affecting moose genetic structure in Ontario and would identify regions where gene flow is minimal or not occurring, allowing for further analyses of possible barriers to movement in the province. Any barriers, either IBD or physical, will have an effect on management of the species and will need to be taken into account when developing management strategies for that area of the province. We initially predicted that we would see evidence of isolation by distance in our first distance class confirming the philopatric nature of moose, as well as evidence in further distance classes, showing possible physical barriers, the possible extent of individual moose dispersal in Ontario and a potential difference between the sexes, since females are more likely to stay close to the home range of their mother than males.

Methods

Genetic Analyses

We collected samples from 776 moose across Ontario from 2002-2008 (no samples in

2005) from conservation officers, OMNR scientists and hunters. Samples were extracted

50 using the Qiagen Tissue Extraction kit and profiled at 10 nuclear microsatellite loci in two multiplexes (multiplex 1 - Map2C, BM4513, BM1225, RT9 and RT24, multiplex 2 -

BM888, BM848, FCB193, RT30 and BL42). A total reaction volume of 10 µL per tube was used, containing 5 ng of genomic DNA, 200 μM dNTPs, 10× buffer, 1.5 mM MgCl2, labelled primers (0.2 mM – 0.5mM), 3 mg/mL of bovine serum albumin (BSA) and 0.5 U of Taq polymerase (Invitrogen). The reaction conditions were 94°C for 5 minutes, 29 cycles of 94°C for 30 seconds, 56-60°C for 1 minute, and extension at 72°C for 1 minute with a final extension at 60°C for 45 minutes. Samples were run on an ABI 3730

(Applied Biosystems, Foster City, CA), and genotypes were determined using

GeneMarker v1.7 (SoftGenetics LLC, State College, PA).

Data Analyses

Only samples with a minimum of 8 out of 10 loci amplified were used for analysis. The genetic relationship between populations was studied using the individual profiles, and the level of gene flow was calculated. Individuals were assigned to the populations where they were most likely to occur (Paetkau et al. 1995, Rannala & Mountain 1997).

Genotypes and spatial co-ordinates of the WMU centroids (Appendix C) were run through TESS 2.3 (Durand et al. 2009), and results were compared to previously run

STRUCTURE results at K=3 to determine if the addition of the spatial information allowed for greater visualization of the genetic structure across the province. The three populations across Ontario were confirmed by this comparison.

We had previously run Micro-Checker 2.2.3 software (Van Oosterhout et al. 2004) to detect genotyping problems such as null alleles, allelic dropout and possible scoring errors due to stuttering or replication. Analysis of null alleles suggested possible null alleles in a 2 loci in different populations. Given that the possible null alleles were not systematic across all three populations, we were satisfied that it was unlikely that the null alleles were due to non-amplified alleles and continued with our data analyses. FSTAT

2.9.3.2 (Goudet 1995) had also been previously run and had produced standard diversity indices for each locus as well as the inbreeding coefficient (FIS).

We used Genetic Studio (Dyer 2009) to calculate Nei’s genetic distance (Nei 1972) between all population pairs. As the program does not accept populations (or WMUs in this case) with less than 5 individuals such WMUs were omitted and only 740 individuals were used for the analysis. The genetic population assignments and the WMU centroid latitude and longitude values (Appendix C) were mapped in ArcMap 10.3 as pie charts depicting the population makeup of each WMU. (Figure 3.4).

PASSaGE v.2 (Rosenberg and Anderson 2011) was then used to determine if isolation by distance was present and to build Mantel correlograms for the entire population, as well as for the male and female populations using the Nei’s genetic distance matrix calculated in Genetic Studio. Both sexes were run separately, not because of an expected sex bias, but to determine if the sexes had different patterns that could require specialized management schemes. Ten distance classes were designated with an even number of individuals in each.

MIGRATE v.3.2.16 (Beerli 2012) was used to identify migration rates between the three identified population areas (two defined populations, one admixed population).

Results

Our TESS results showed two defined populations (SC and NW) with an admixed population in NE Ontario (Figures 3.1 and 3.2). Both the admixture and no admixture models in TESS showed this. The TESS results, which included spatial information

(Appendix C), supported our use of K=3 in our previously run STRUCTURE output

(Figure 3.3). The major difference between the admixture and no-admixture models was the delineation between NW Ontario and NE Ontario. The no-admixture TESS model showed the split where the STRUCTURE model placed it, while the admixture model showed it a little further to the west. Since there was only one WMU difference between the two and there was less than 120 km between the centroids of these units, the difference in placement was not significant and likely due to program parameters and the use of centroids for the spatial data.

A provincial map with population assignments shown in pie chart format for each WMU corroborated the population makeup of the province (Figure 3.4).

This pattern was also seen in the Mantel correlograms (Figure 3.5). The Mantel correlograms for all three runs performed (entire, male and female populations) showed

IBD within the first 100 kilometres (E=0.11099, M=0.10665, F=0.08607) and then showed no IBD pattern in farther distance classes.

The number of migrants between the three different regions of Ontario is shown in Figure

3.6. There are a greater number of migrants heading north and west (NE-NW=6.698, SC-

NW= 5.016) than there are heading south and east (NW-NE=3.089, NW-SC=1.430).

There is an almost identical number of migrants between NE and SC (NE-SC=8.338 and

SC-NE=8.303), which is supported by the lower level of genetic distance between the two populations.

Discussion and Conclusions

The patterns seen in the population assignment figure (Figure 3.4) and the Mantel correlograms (Figure 3.5) suggest more local differences than cross-province differences.

This reinforces previous conclusions that the genetic differences in Ontario moose populations are due to two populations still expanding into new territory from their respective glacial refugia and meeting in NE Ontario.

There are few migrants moving between areas in the context of the entire Ontario population, but the largest move we are seeing is from the south and the east towards the north and west. There is free movement between the SC and NE populations, supported by the smaller genetic distance between them as seen in the genetic results from both

TESS and STRUCTURE. The smaller number of migrants out of the northwest suggests that, although there are still small amounts of expansion for the NW population, the

54 habitat is prime moose habitat with sufficient food and protection, and that the area has not yet reached carrying capacity. With the encroachment of anthropogenic factors in the south, more animals are moving north in an effort to find good forage and safe habitat.

Ontario bases its wildlife management units (WMUs) on moose populations in northern

Ontario and on deer populations in central and southern Ontario, with hunting quotas based on previous years’ harvests (OMNR) (Appendix A). At the southern edge of moose range, this may create management issues over time. With populations spanning multiple WMUs, overharvesting is a serious threat to the productivity of the populations.

The result of overharvesting could be fractured populations with sink-source patterns forming around Algonquin Provincial Park, where hunting by the general public is prohibited. The park would serve as the source for smaller populations in the surrounding areas leading to decreased genetic variability and increased fixation of alleles within the population.

Given that moose in Ontario are generally limited dispersers, management of low-density areas, especially if attempting to promote recovery, is very important. If the need should arise in the future, given the growing area of admixture and the low levels of genetic differentiation across the province, translocation of individuals could be effected to add some new genetic variability to other areas of the province.

One issue in Ontario is that annual harvest rates are only an estimate, as hunters (with the exception of tourists and all hunting within five specific WMUs) are not required to

55 register their kills. This makes management a challenge, as the actual number of harvested animals versus issued harvest tags cannot be determined, forcing the OMNR to decide on the following year’s harvest based on a voluntary annual hunter survey or guess-work. A solution to this would be to have all moose hunters in the province report or register their kills. This would give a fast and accurate portrait of the tag filling rate

(used for estimates) and allow for quick detection of possible areas of concern. If the number of tags filled rises or drops significantly in an area over the course of a few years, a population health survey can be done, and management plans implemented as needed.

This real-time approach to monitoring the provincial population can be added to the strategies suggested in the Moose Management Harvest Guidelines. Designating selective harvest systems as needed to maintain population objectives, shortening or redesigning the hunting season, monitoring areas where populations may be more vulnerable (e.g. cutover areas) and restricting hunting in vulnerable areas as needed

(OMNR 2009), will all help to maintain a sustainable harvest for years to come.

Overharvesting and a general decrease of population numbers will result in a decrease in the numbers of available hunting tags. While poaching levels are not currently high for moose in Ontario, the restriction of hunting could lead to increased illegal harvesting. A forensic database designed with population allele frequencies from the delineated populations would provide a key to easier identification of poached animals and would increase the chances of connecting them to the poachers. Smaller, localized population delineation would also help to narrow down the location of origin for the poached animals. Statistics similar to those being used in human forensics would also aid in

56 prosecution by numerically expressing the chances of seeing a particular genotype in the population. Statistics convey the relative support for the weight to be given to the DNA evidence and would perhaps provide a sounder basis of probability to prove animal identity “beyond a reasonable doubt”. With no phylogeographic structure and no significant barriers to gene flow in the province to take into account, it is the logical next step to create such a database for the entire province.

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Figure Legend

Figure 3.1 TESS assignment plots for K=2 (A) to K=6 (E) with no admixture using wildlife management unit (WMU) centroid latitudes and longitudes for spatial data. Data are ordered based on WMU from west to east. NW= NW Ontario, NE=Northeast Ontario, SC= South Central Ontario. NE and SC Ontario group together when K=2 but split into two populations at greater K values. NW Ontario remains a distinct population at all K values.

Figure 3.2 TESS assignment plots for K=2 (A) to K=6 (E) with admixture. Data are ordered based on WMU from west to east. NW= NW Ontario, NE=Northeast Ontario, SC= South Central Ontario. NE Ontario is paired with SC Ontario for K=2 but is split into two areas of admixture – one more similar to NW Ontario and one more similar to SC Ontario with higher K values. NW Ontario and SC Ontario remain separate populations for K=3 to K=6.

Figure 3.3 STRUCTURE results for K=3 showing admixed region in NE Ontario for comparison with TESS assignments. Pattern is very similar to both TESS K=3 figures.

Figure 3.4 Province of Ontario map with pie charts showing population assignments by wildlife management unit (WMU). We see distinct population assignments for both SC and NW Ontario. The NE population does not show up as a distinct population but rather an admixed population comprised of the NW and SC Ontario populations. This supports an admixed population (NE Ontario) between two distinct populations (SC and NW Ontario).

Figure 3.5 Mantel Isolation By Distance (IBD) correlograms using Nei’s genetic distance for (A) the entire population, (B) the female population and (C) the male population. There is a small IBD pattern within the first 150-200 kilometres but very little seen after that for all three runs. Males show a slightly higher value than the females, likely due to the greater dispersion levels seen in males versus females.

Figure 3.6 Map of Ontario showing the level of migration between regions of Ontario. Yellow indicates northwestern Ontario, green indicates northeastern Ontario and blue indicates south- central Ontario. A greater number of migrants are heading in a north and west direction (SC-NE and SC-NW) than heading south and east. There are even amounts of movement between SC and NE Ontario suggesting a free flow of individuals between the two regions.

A NW NE SC

K=2

NE B NW SC

K=3

C NW NE SC

K=4

D NW NE SC

K=5

E NW NE SC

K=6

Figure 3.1

A NW NE SC

K=2

B NW NE SC

K=3

C NW NE SC

K=4

D SC NW NE

K=5

E NW NE SC

K=6

Figure 3.2

Figure 3.3

Figure 3.4

All animals

Females only

Males only

Figure 3.5

Figure 3.6

Chapter 4 – Resolution of regional populations of moose (Alces alces) in Ontario - formation of forensic genetic database

Abstract

DNA-based applications for individual identification in poaching cases have long been established in wildlife forensic science. However, identifying the location where an individual was living at time of death can be important for improving disease identification and control and wildlife management purposes, as well as successful prosecution of poaching cases. Genetic databases are a commonly-used tool in wildlife conservation and management studies and in forensic investigations. There are standards in place for how to construct human forensic databases, but there are no guidelines for determining the statistical validity and the limitations of wildlife forensic databases.

Here we investigate the creating of a database of moose genotypes using ten microsatellite loci, for the purpose of creating a forensic database. We evaluated the resolution of statistical estimates of individual identification and population assignment to the region where they are most likely to have been residing. Random match probabilities, likelihood ratios, probability of identity, probability of discrimination and probability of exclusion were calculated for each locus within each of the three identified populations (NW, NE and SC Ontario) and a combined probability of exclusion was calculated for each population across the ten loci.

Three of the ten loci showed low levels of heterozygosity across all three regions –

BM888, FCB193 and Rt30, with some alleles at near fixation levels. The remaining loci showed moderate to high levels of heterozygosity, ranging from 0.5 to 0.7. Locus BL42 showed the greatest level of heterozygosity across the province (NW - 79.5%, NE -

74.4% and SC - 71.9%). NW Ontario, with the highest level of overall heterozygosity,

-11 also had the smallest probability of identity (PID) at 2.34x10 . NE Ontario was next at

-10 PID=2.54x10 and SC Ontario, with the lowest level of heterozygosity, had the largest

-9 PID at 4.09x10 . The decreasing probability of exclusion (PE) across the province is consistent with the decreasing heterozygosity gradient from the north-west through the north-east down to south central Ontario. Analyzing samples, cross-referenced using geographic location, against the database, showed correct identification of population of origin 87-100% of the time (3 groups – male, female and whole population). Since moose are considered a species with low overall genetic diversity, these findings provide a framework for the minimal requirements for establishing wildlife forensic databases and further provide an assessment of the resolution of population identification.

Introduction

Many species across North America are facing population declines due to over-hunting

(Channell and Lomolino 2000). The effective management of moose populations is therefore important not only for conservation purposes, but for economic reasons as well.

Indeed, hunting licences contribute to Ontario income, and park viewing fees contribute to both provincial and federal incomes. In the most recently-published data from Ontario in 1996, residents of Ontario spent $4.3 billion on nature-related activities, of which

71 almost $2.9 billion was spent on outdoor activities in natural areas. In particular, wildlife viewing expenditures were estimated at $410.9 million. Expenditures for recreational fishing amounted to $762.2 million, and $200.6 million was spent on hunting wildlife.

Since not all animals hunted are obtained through legal means (permits, culls, first nations hunting rights), they may not be identifiable by permits or other records. Animals may be hunted illegally, and identification of these animals and the responsible poachers is important for effective population management. This identification is achieved using forensic genetics methods and by creating provincial and regional databases to identify the location where an animal is likely to have originated. Here we created a forensic genetic database for moose in Ontario that allowed for identification of geographical location of origin of individuals at a regional (NW, NE and SC Ontario) level, and at a finer (WMU) level.

Databases form the basis for both individual identification and population identification applications. The databases amalgamate the genetic information, either mitochondrial or nuclear, of individuals in a single location and range in size from very few to thousands of individual genetic profiles. Other demographic information, such as sex, age and location are frequently also included in databases to create a more complete picture of the individuals within them. Notwithstanding the increased use of genetic databases, there are no concrete rules or even suggested methods for constructing a genetic database. If researchers wishes to collate their database with a larger, global database, such as the

National Centre for Biotechnology Information (NCBI) GenBank, the Gene Expression

Omnibus (GEO), or the single nucleotide polymorphism database (dbSNP), among

72 others, there are certain formatting rules, but there are no guidelines for creating the original database.

Furthermore, the absence of validation of public forensic databases can pose challenges for evidentiary admissibility in court. In 2010, Walsh et al. determined a method of modeling forensic DNA database performance. While this model was helpful in determining the most cost-efficient way of building and maintaining databases, it required homogenous data sets and focused on human forensic offender and crime scene databases. The study compared ethical costs of DNA databases with the monetary cost of processing the profiles. There is little research on the physical building or determining the limitations of a forensic database. As a general rule, genetic databases are constructed in a similar fashion to each other, with the majority being built simply to accommodate the samples collected, and any database limitations are not determined or tested for statistical validity.

These genetic databases are then used to create one of two types of forensic DNA databases. The first type of forensic database is a database of genotyped alleles that unknown or evidentiary samples can be run against to determine similarities or to eliminate suspects. The second is a database of allele frequencies that evidentiary samples can be run against to determine statistical probabilities of inclusion or exclusion of an individual. Forensic investigations primarily rely on the second type of forensic

DNA database to produce the statistics required in the court system. Forensic DNA databases have been in use since the late 1990s. The first and best known human forensic

DNA database was the U.S. National DNA Index System, now known as the Combined

DNA Index System, or CODIS (Butler 2009). Forensic databases are created using individuals with a known genetic background – subspecies, breeds, races, even known hybrids or different geographic locations of the same breed or race. For humans, this can be complicated, as racial background is generally self-reported and racial profiling is considered a serious ethical issue (Dumitrescu et al. 2010, Chow-White & Duster 2011).

Scientists will not always run race-specific tests to validate the self-reported responses due to cost. For human databases there are standards in place that require a core set of 13

STR loci to be used. These loci are used for all races following the Budowle et al. paper in 1999 that created the database used in the United States. Additional databases using different ethnic groups (both globally and regionally defined) have been constructed using the Budowle et al. (1999) method. There are numerous quality assurance (QA) and quality control (QC) regulations in place for processing human samples, as well as regulations for the availability of samples to the databases. The human forensic field is rife with the legal and social implications of DNA being stored and databases being created. Privacy concerns, and the public’s concern that DNA profiles could be misused, have created strict guidelines that delineate how human DNA evidence and samples can be used (Technical Working Group on DNA Analysis Methods 1995, Federal Bureau of

Investigation 2000).

Animal forensics is an emerging discipline in the field of non-human forensics. Animal forensics focuses on domestic animals, or animals that live in close contact with humans.

The main use of databases within animal forensics focuses on the involvement of

74 domestic animals in human crime scene investigations. Some recommendations have been made for identity testing in domestic animals (Budowle et al. 2005). In 2015, the

American Pet Products Manufacturers Association (APPMA) reported that approximately

65% of households in the United States owned at least one cat or dog. These domestic animals can provide important information to the investigator by the transfer of trace evidence (APPMA 2015, Halverson & Basten 2005, Clark & Vandenberg 2010,

Scharnhorst & Kanthaswamy 2011). Animal hairs are easily transferred from one location to another and can be left at crime scenes by perpetrators as well as victims

(Halverson & Basten 2005). With the advent of forensic databases, such as the canine database, precise identification of breed is possible (Halverson & Basten 2005, Clark &

Vandenberg 2010, Scharnhorst & Kanthaswamy 2011). Another important sub-discipline of animal forensics is pedigree verification and breeding programs. Purebred horses are bred for speed and strength, and domestic cattle are bred for increased milk production or particular carcass traits (Applied Biosystems, Dimsoski 2003, Womack 2005, Frkonja et al. 2012). The offspring of top animals can command high prices, and consumers and breeders are becoming more and more cautious about the product they are buying.

Parentage tests of offspring and confirmation of semen from stud animals are being done on a more regular basis to verify identity (Applied Biosystems, Dimsoski 2003).

Databases have been created for most domestic animals for both identification and pedigree verification purposes by both private and, in the U.S., state-run DNA laboratories.

Wildlife forensics encompasses different objectives, of which the most important are tissue identification and tracking poachers of endangered and protected species (NAWEG

2000, Jobin et al. 2008, Johnson 2010, W hite et al. 2012). Merchants may also sell exotic and endangered animal parts, claiming they are from domestic sources, to escape criminal charges of hunting and trafficking protected animals (Woolfe & Primrose 2004,

Coghlan et al. 2012). In addition to legal investigations, wildlife data is used to regulate the harvest of non-endangered species by determining the yearly hunting quotas, as well as for conservation and management purposes (White et al. 2012). Tracking wildlife populations, determining parentage in small or endangered populations, identifying admixed populations and landscape genetic inquiries, all commonly use wildlife DNA databases for analysis.

The world of forensic DNA analysis is rife with databases that have been created for determining statistics for legal proceedings. In human forensics, the idea of producing a random match probability, a likelihood ratio, a probability of exclusion or any number of other statistics is the accepted standard. In animal or wildlife forensics, the trend is to follow the lead of human forensics and create databases to use for livestock verification and breeding, wildlife poaching and illegal importation of endangered species (Jobin et al. 2008, Ogden 2010, Scharnhorst & Kanthaswamy 2011, White et al. 2011).

One complication with database creation is acquiring a representative sampling of the population, which can be difficult due to sample collecting schemes, geographic location of the population to be sampled and sampling restrictions due to low population numbers

76 or endangered status. Another consideration when creating a database is determining the appropriate number of individuals to be sampled for the database. Many jurisdictions use whatever samples are available, on the assumption that any database is better than none.

This practice inevitably raises questions about the statistical validity of these databases.

It is an important first step to determine if there are enough individuals within a designated population that are genetically assigned to that population at a level greater than eighty percent.

A number suggested by some authorities for an appropriate number of samples is 10% of the entire population. While a worthy goal, the collection and processing of that volume of samples with a large population is simply prohibitive. If working with a small population (n<1000), then collecting 10% may be feasible (Butler 2009). In reality, the number of samples required will depend on the population dynamics. A panmictic population, due to the genetic similarity between the individuals, will require fewer samples than a highly structured population. When determining the appropriate number of samples, allele determination is also important. Collecting enough samples to identify all of the alleles per locus within the population needs to be balanced with oversampling and finding rare alleles that are found in less than 1% of the population. While informative at an individual identification level, these rare alleles are not informative at a population identification level.

To ensure statistical soundness of the database, a study of the levels of individual assignment to each population must be undertaken. The program STRUCTURE

(Pritchard et al. 2000) provides these assignments for each run. The assignment table for the appropriate number of populations (K value) should be studied and a determination should be made of how many individuals assign to the population with a value greater than 0.8 (80%). A more stringent threshold value of 0.9 (90%) could be used, if there were highly divergent populations.

Our forensic database was of the second type mentioned earlier, a common allele table determined from samples collected, and genotyped, from across the province. Testing of the database determined the scale limits across the province. Random match probabilities and likelihood ratios gave statistical value and levels of certainty to the assignment of origin. This chapter presents allele distribution data in NW Ontario, NE Ontario and SC

Ontario at ten STR loci. The data demonstrates that these loci are useful for providing estimates of the frequency of a DNA profile in forensic identity testing. We predicted that we would not be able to break the populations down into smaller genetic units within the three delineated populations due to the low overall genetic variability we have seen in the province, but that a statistically sound and viable database could nevertheless be designed for those three populations.

Methods Sample Preparation We collected samples from OMNR conservation officers, researchers and hunters from across Ontario spanning a 6-year period (2002-2008 – no samples were collected in

2005). Control samples, both for use during genotyping and analyses were provided by the Centre of Veterinary Sciences at the University of Guelph. All samples were

78 extracted using the Qiagen Tissue Extraction kit (Qiagen Inc.). DNA concentration in final elutions was quantified using Picogreen (Molecular Probes) and diluted to a final concentration of 2.5ng/µL. All of these samples, 776 in total, were successfully genotyped at minimum 8 out of 10 microsatellite loci and wereused for analysis.

Sex Determination

A previously-developed protocol (Gilson et al. 1998) was used for sex determination.

The reactions were duplex reactions with both primer sets being run together. The protocol consisted of an 11-minute initial denaturation at 94°C, followed by 29 cycles of

45 seconds of denaturation at 94°C, 45 seconds of annealing at 60°C, 1 minute of extension at 73°C, followed by a final extension at 72°C for 15 minutes. Agarose gel electrophoresis was run on the amplified products to visualize the bands representing the

X and Y chromosomes. Amplification products were evaluated on a 2% agarose gel

(BioShop Canada Inc, Burlington, ON). Four microliters of Low DNA Mass Ladder

(Invitrogen, Carlsbad, CA) was added to the first and last well of each comb row. The gels were loaded with 4μl of PCR product, mixed with 2μl of loading dye. The amplified products were electrophoresed at 100V for one hour. Gels were visualized using a mid- wavelength UV transilluminator and digitally photographed.

STR Amplification

Samples were profiled at 10 nuclear microsatellite loci in two multiplexes (multiplex 1 -

Map2C, BM4513, BM1225, RT9 and RT24, multiplex 2 - BM888, BM848, FCB193,

RT30 and BL42). A total reaction volume of 10 µL per tube was used, containing 5 ng of

79 genomic DNA, 200 μM dNTPs, 10× buffer, 1.5 mM MgCl2, labelled primers (0.2 mM –

0.5mM), 3 mg/mL of bovine serum albumin (BSA) and 0.5 U of Taq polymerase

(Invitrogen). The reaction conditions were 94°C for 5 minutes, 29 cycles of 94°C for 30 seconds, 56-60°C for 1 minute, and extension at 72°C for 1 minute with a final extension at 60°C for 45 minutes. Samples were run on an ABI 3730 (Applied Biosystems, Foster

City, CA), and genotypes were determined using GeneMarker v1.7 (SoftGenetics LLC,

State College, PA).

Statistical Analysis

Genetic profiles were used to establish population delineation. Gene flow was calculated using FSTAT (Goudet 1995), which produced standard diversity indices for each locus

(observed heterozygosity (HO), expected heterozygosity (HE) and polymorphism information content (PIC)) as well as the inbreeding coefficient (FIS). Provincial population sub-structure was assessed using the program STRUCTURE 2.3.1 (Pritchard et al. 2000), performing three independent runs of K=1-6 using a burn-in of 100,000 and a

MCMC chain of 106 steps. Results showed two distinct populations, with a third population comprised of admixed individuals. They were geographically-delineated populations that were given the designations North-western Ontario, North-eastern

Ontario and South-central Ontario (NW, NE and SC respectively)(Appendix B). Within the identified provincial population regions, STRUCTURE was used to investigate the possibility of small population pockets within the larger geographic areas, to determine if further regional breakdown was possible for forensic analyses. The delineated populations were used to calculate allele frequencies, HO, HE and PIC, using the

Microsatellite Toolkit for Microsoft Excel (Park 2001). The probability of identity (PID), probability of discrimination (PD) and the probability of exclusion (PE) were calculated for each locus within each population. A combined probability of exclusion was calculated for each of the three populations across the 10 loci. The probability of identity

(sibling) and probability of discrimination (sibling) was also calculated for all three populations to determine the conservative upper limit of the probability of identity (Waits et al. 2001).

Database Testing

All of the 776 samples genotyped were of known geographic origin and were used to determine the strength of the forensic database (Appendix B). To test the database further, samples with eight of ten loci amplified (n=15) and complete profiles (n=15) were randomly chosen from the data set. To these samples were added incomplete profiles with seven of ten loci amplified from individuals outside the original sample set that had been previously genotyped, but not included in any database because of not meeting the standard eight out of ten loci threshold. These samples were used to determine the limits of the database. Random match probability and likelihood ratios were calculated for all these samples using the NW, NE and SC population allele frequencies, following NRC II formulae 4.4a and 4.4b with a theta (Θ) correction value of

0.03 (small or isolated population) due to the genetic similarity of the populations. The theta value is defined as the probability that two alleles in different animals, in the same subpopulation, are identical by descent (NRC 1996). The most conservative value obtained between the three populations was determined to be the most likely population

81 of origin, and results were compared to the known location of origin to determine the strengths and limitations of the database.

Results

STRUCTURE results of runs of each of the three populations (run independently of the other two identified populations), to determine if they could be further divided into smaller population units, showed no discernible sub-population divisions. Further tests were only performed on the three previously identified populations (NW, NE and SC)

(Appendix B). Allele tables were built using the results of testing for all three populations (Table 4.1). Both observed and expected heterozygosities and homozygosities were calculated for each locus and region. Three loci showed low levels of heterozygosity across all three regions – BM888, FCB193 and Rt30. SC Ontario showed near fixation of allele 196 at Rt30 with only 3.1% heterozygosity. The heterozygosity increased slightly for NE Ontario (14.6%) and is more than 8 times higher in NW Ontario (24.6%) – although the population still showed high levels of homozygosity, with allele 196 being prominent. FCB193 also showed a heterozygous gradient across the province, ranging from 59.9% in NW Ontario, down to 39.9% in NE

Ontario and 34.1% in SC Ontario. BM888 showed low heterozygosity across the province (NW-31.2%, NE-22.5% and SC-23.8%) (Table 4.1).

Alleles from locus BM1225 ranged greatly across the province. In NW Ontario, locus

BM1225 showed high levels of heterozygosity at 70.2%, moderate heterozygosity in NE

Ontario at 56.5% and low levels of heterozygosity in SC Ontario at 35.8%. The

82 remaining loci showed moderate to high levels of heterozygosity ranging from the mid-

50s% to the high 70s%. Locus BL42 showed the greatest level of heterozygosity across the province (NW-79.5%, NE-74.4% and SC-71.9%). Provincially, NW Ontario showed the greatest level of heterozygosity, followed by NE Ontario. SC Ontario showed the least amount of heterozygosity in the province (Table 4.1).

Table 4.2 shows calculated probabilities that are commonly used in forensic cases, the probability of identity, probability of discrimination and probability of exclusion. The probability of identity, or the probability of two unrelated individuals having the same profile, was very low across the province (NW = 2.34x10-11, NE= 2.54x10-10 and SC =

4.04x10-9). With an estimated population in Ontario of just over 100,000 moose (OMNR

2009), these values show a very low chance of finding two unrelated individuals with the same profile. The probability of identity for siblings, or the probability of two siblings having the same profile, is a much more conservative number and considered the upper boundary for identification. These values were 5.97x10-4, 1.97x10-3 and 3.84x10-3 for

NW, NE and SC respectively. These values correspond to a probability of discrimination between siblings, or the probability that two siblings have a different profile, of 0.9994

(99.94%) for NW, 0.9980 (99.8%) for NE and 0.9962 (99.62%) for SC. The levels of heterozygosity contributed to a high combined probability of exclusion (PE) across the province (Table 4.2). NW Ontario, with the highest level of heterozygosity also had the largest PE at 0.982. NE Ontario was next at PE=0.969 and SC Ontario, with the lowest level of heterozygosity had the smallest PE at 0.940. For forensic analysis, this means that 98.2%, 96.9% and 94.0% of the time, a randomly-chosen individual from the

83 respective population would be excluded as the source of the DNA evidence. PE provides an estimate of the population that will have a genotype made up of at least one allele that was not observed in the source sample. These probabilities have implications for identification of location of origin for samples collected in both forensic cases and sample collection for population genetics.

The full profiles (allele calls at all 10 loci) were the most consistent when testing the database (Table 4.3). Each of the six control samples was identified as correctly being consistent with the region it originated from. The randomly-chosen fifteen full profiles also showed a high level of correct calls – with 13/15 correctly identified using the entire population allele frequencies, 15/15 correctly identified using the male allele frequencies and 14/15 correctly identified using the female allele frequencies. The number of correctly identified locations of origin decreased with a decrease in the number of successfully called loci. With 2 missing loci (calls at only 8/10 loci), 13/15 correctly identified using the entire population allele frequencies, only 10/15 correctly identified using the male allele frequencies and 13/15 correctly identified using the female allele frequencies in the RMP calculation and 14/15 were correctly identified using the female allele frequencies with the LR calculation (Table 4.3).

The correct location calls were significantly reduced by testing profiles that only had 7 out of 10 loci successfully called. Although more samples were used (n=33), only 18/33 were correctly identified using the entire population allele frequencies; only 18/33 were correctly identified using the male allele frequencies; and 15/33 were correctly identified

84 using the female allele frequencies (Table 4.3). Another issue identified along with the missing loci was a very high level of homozygosity among the 7/10 profiles. This indicates a possibility of allelic dropout, accounting for the incorrect calls. Within the full profile and 2 loci missing samples, the majority of incorrectly called individuals originated from the adjacent population to the population they had been genetically assigned to.

In studying the database, the numbers of assigned individuals were tallied within the population assignments of 0.9 and 0.8 (9/10 and 8/10 successfully amplified loci). At an assignment level of 0.8, 75% of the expected NW population actually assigned to the NW population, however only 15% and 26% assigned to the expected NE and SC populations respectively. Given that the NE Ontario population has been shown to be admixed, the assignment values of 0.7, 0.6 and 0.5 were also counted for statistical purposes. The number of individuals legitimately assigned to their expected population increased in all three regions resulting in 90%, 53% and 66% for NW, NE and SC. This supports a greater level of admixture in both the NE and the SC. A high level of admixture was expected in the NE, but the SC population, due to genetic differences from the NW, had been assumed to be a separate and not highly admixed population. This could be due to a higher than expected level of migration between the NE and SC populations.

The number of individuals being assigned to other populations was minimal at an assignment level of 0.9 and 0.8 (< 1.26% and <5.07%) with all assignments being between neighbouring regions - NW↔NE and NE↔SC. One problem with an

85 assignment level of 0.8 in forensics is that there is only a level of certainty of origin of

80% when a certainty level of <99.9% would be preferred (Manel et al. 2002). Once the assignment level dropped to 0.7 and below, some cross-province assignments were seen

(NW expected but SC assigned), but at a relatively low level (0.68% - 1.35%). At an assignment level of 0.7, up to 11.82% of the population was assigned to a different population (NE expected but SC assigned). At 0.6 that number rose to 20.27% and increased further to 29.73% at an assignment level of 0.5. This latter figure is almost a third of the population and supports the idea that an assignment level less than 0.8 should not be considered for use when creating or assembling a database for forensic use.

Discussion and Conclusions

With the ability to exclude most of the population in the region, the certainty of the statistics used for identification is well-founded. The PID values across the province are consistent with the decreasing heterozygosity gradient from the north-west through the north-east down to south-central Ontario. With low levels of heterozygosity, the chances of finding two individuals with the same profile increases. PID is very helpful as well in paternity determination when both the calf and cow’s genetic profiles are known. This can be useful in determining the number of bulls that are successfully mating within a region and has implications for the physical and genetic health of the population. While moose are not considered threatened in Ontario, they are elsewhere in Canada and application of these procedures could also be used for other species to aid in population remediation and monitoring of conservation plans for threatened or endangered species.

Ball et al. (2010) studied population source assignment for several moose poaching cases in Nova Scotia. Individuals in Cape Breton were introduced in late 1940s from Alberta, so originated from the A. a. andersoni, or western moose subspecies. Individuals from

New Brunswick and mainland Nova Scotia are considered to be a continuous genetic population from the A. a. americana, or eastern subspecies (Hundertmark et al. 2002).

Hunting on mainland Nova Scotia is banned as the moose population there is considered to be endangered. The 30-kilometre land border with New Brunswick allows for movement between the two provinces, and little genetic difference was seen between the two, suggesting gene flow (Ball et al. 2010). In the study, samples from New Brunswick

(NB), mainland Nova Scotia (NS) and Cape Breton Island (CBI) were taken and compared. Once distinct genetic differences between the mainland samples (NB and NS) and CBI were identified, the assignment of individuals to these populations could be studied. Less than 30% of samples from NB and NS were able to be assigned to one population or the other, instead showing a large proportion of admixed individuals. All of the CBI individuals assigned to the CBI population. This difference allowed for testing to be done on three moose poaching case samples. The hunter maintained the animals were hunted on CBI, where hunting moose is permitted. However upon genetic testing, all three animals were assigned to the mainland population and excluded from the

CBI population. In this case, Ball et al. concluded that due to the island nature of CBI with only a small highway causeway to connect the island from the mainland, movement between the populations was highly unlikely. The genetic nature of the CBI population makes it an ideal population to use in creating a forensic database, because the individuals all assigned to it, and it is genetically very different from the surrounding

87 populations. CBI’s differences from the mainland population, where hunting is prohibited (NS) or limited (NB), allow for greater statistical certainty in forensic cases.

With no distinct linear genetic and geographic boundaries between regions, it is important to report the statistics across the regions in their geographic context, as both the random match probability (RMP) and likelihood ratio (LR) numbers were very close. Wildlife forensic laboratories use both RMP and LR as statistics for cases, but often the databases used are comprised of small sample groups made up of previous case samples or randomly collected samples from wildlife enforcement officers.

For all three populations in our database, the probability of discrimination is greater than

0.999, allowing for a high level of confidence in regional identification of animals. With only just over 100,000 moose in Ontario according to the last OMNR count, the probability of identity also allows for a high level of confidence. These values can be used along with RMP and LR values for court forensic probabilities.

In conclusion, we have created a forensic database for north-western, north-eastern and south central Ontario at 10 polymorphic loci. While laboratories may have smaller, local databases that they use, a database of this scale has not previously been available in

Ontario. The co-operation of researchers, conservation officers and hunters, for which we are grateful, from across the region made this database possible. It is important that all stakeholders in the process of conservation and management work together for the system to function.

DNA databases are becoming desirable due to their multiple applications in both forensics and in population studies. They are helpful for improving the speed of analyses and for increasing the level of knowledge about genetic populations. The databases will enable the identification of the most probable race or species of an individual from a

DNA sample and present the result statistically based on known samples. They will allow identification of region of origin for an individual based on genetic similarities found in different regions. They can be built at differing geographic scales depending on the study needs (broad scale for regions, or finer scale for management units or individual geographic regions such as islands, tracts of land, ponds and rivers). The genetic health of a population can be examined and assessed by using the database individuals in the population to determine inbreeding levels and levels of fixation at different loci.

In Ontario, the forensic database strength has been tested, using samples of known geographic origin. The samples with only seven amplified loci fared the worst, with between 45% and 55% success in correctly determining location of origin. With eight amplified loci, that number jumped to 87% and ranged from 87-100% with full profiles.

Databases will also be useful for populations that may not assign fully to one distinct group or another. Scientists need to take care, when creating forensic databases, not only to check the assignment values, but to test the database using samples with known linkage to the populations groups that are defined in the database. If a database does not correctly identify the region or population of origin, it will be of no use for analysis.

However, a database that can accurately identify the region or population of origin, even

89 if the individuals in those populations may not be fully assigned to one or another, can still be used for analyses and can create cogent and usable statistics. It is also important to recognize the limitations of the database. If the accuracy levels drop below a usable threshold (80%, for instance, since it is a standard in the field for population assignment and also for amplified loci in a usable genetic profile), then the database should be re- designed, more samples should be added, or it should be used as a reference only, and detailed statistical analyses should not be performed using its data. Databases will continue to be built, re-designed and added to since they are an important tool for scientists studying everything from humans to animals and wildlife, plants to plankton and everything in between. Creating universally-accepted standards for genetic database creation may be complicated, considering the breadth of database subjects, but it is still possible.

Access to larger databases made up of individuals from all provincial jurisdictions allow for more accurate identification. Any addition of individuals to the database allows for greater differentiation between populations and greater statistical power in reporting region of origin of individual animals.

References

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Table 4.1 Forensic Database - observed allele distributions (as %) for 10 STR loci in three population groups separated by locus with observed and expected heterozygosity for each locus.

NW Ontario NE Ontario SC Ontario Map2C (n=162) (n=318) (n=296) 101 0.630 1.270 2.550 103 13.130 21.430 27.040 105 20.630 21.430 30.270 107 56.560 54.440 39.970 109 3.750 0.480 0.170 111 5.310 0.950 0.008 Homozygosity (Obs.) 0.381 0.397 0.316 Homozygosity (Exp.) 0.382 0.388 0.324 NW Ontario NE Ontario SC Ontario BM4513 (n=162) (n=318) (n=296) 115 0.015 0.320 0.170 117 19.620 23.240 27.990 119 0.015 1.120 0.008 125 0.630 0.320 0.680 127 5.060 1.920 0.510 129 32.910 11.540 2.560 131 31.010 23.400 25.090 133 5.700 1.760 2.050 135 5.060 36.380 40.960 Homozygosity (Obs.) 0.335 0.272 0.324 Homozygosity (Exp.) 0.249 0.254 0.309 NW Ontario NE Ontario SC Ontario BM1225 (n=162) (n=318) (n=296) 224 0.310 0.480 0.008 226 0.015 0.008 3.920 228 41.300 62.700 77.820 230 2.800 0.950 0.340 232 0.015 0.008 0.170 234 38.820 19.210 4.780 240 0.015 0.008 0.340 242 0.620 0.320 0.340 244 0.310 0.008 0.008 246 5.900 1.430 0.340 248 4.040 1.430 0.170 250 5.900 13.490 11.770

Homozygosity (Obs.) 0.298 0.435 0.642 Homozygosity (Exp.) 0.329 0.448 0.623 NW Ontario NE Ontario SC Ontario RT9 (n=162) (n=318) (n=296) 108 1.270 0.640 0.008 114 0.950 0.480 0.170 116 0.630 4.470 9.900 118 0.015 1.120 3.750 120 8.540 50.800 56.830 122 8.230 3.190 3.240 124 31.650 29.230 24.910 126 31.330 6.390 0.850 128 16.460 3.510 0.340 130 0.630 0.160 0.008 132 0.320 0.008 0.008 Homozygosity (Obs.) 0.367 0.371 0.403 Homozygosity (Exp.) 0.237 0.351 0.396 NW Ontario NE Ontario SC Ontario RT24 (n=162) (n=318) (n=296) 222 0.310 0.008 0.008 224 0.310 0.008 0.008 226 0.620 0.008 1.390 228 14.200 15.720 7.490 236 0.000 0.160 0.008 238 0.620 0.940 2.090 240 3.090 8.020 6.970 242 45.370 34.910 34.670 244 0.310 0.310 1.050 246 0.015 0.008 0.170 250 0.015 0.160 0.170 252 2.160 0.790 1.740 254 13.270 23.580 35.890 256 13.580 14.470 8.190 258 5.250 0.940 0.170 260 0.930 0.008 0.008 Homozygosity (Obs.) 0.377 0.308 0.397 Homozygosity (Exp.) 0.264 0.229 0.266 NW Ontario NE Ontario SC Ontario BM888 (n=162) (n=318) (n=296) 172 0.015 0.810 0.008 174 0.960 0.008 0.008

176 0.320 0.810 2.550 178 78.030 86.810 86.730 180 11.780 9.450 10.710 182 8.920 1.950 0.008 186 0.008 0.160 0.008 Homozygosity (Obs.) 0.688 0.775 0.762 Homozygosity (Exp.) 0.630 0.763 0.764 NW Ontario NE Ontario SC Ontario BM848 (n=162) (n=318) (n=296) 351 0.630 0.008 0.008 353 10.060 1.860 0.510 355 0.940 1.520 1.370 357 38.990 47.640 47.950 359 14.150 8.780 11.640 361 27.040 38.010 38.530 363 8.180 2.200 0.008 Homozygosity (Obs.) 0.333 0.368 0.445 Homozygosity (Exp.) 0.260 0.379 0.391 NW Ontario NE Ontario SC Ontario FCB193 (n=162) (n=318) (n=296) 103 0.015 0.170 0.008 105 4.740 3.260 0.170 107 43.800 16.490 1.190 109 0.730 0.000 0.170 111 1.820 1.200 1.370 113 37.960 70.270 78.160 115 9.120 8.080 15.870 117 1.820 0.520 3.070 Homozygosity (Obs.) 0.402 0.601 0.659 Homozygosity (Exp.) 0.345 0.528 0.637 NW Ontario NE Ontario SC Ontario RT30 (n=162) (n=318) (n=296) 190 0.370 0.008 0.008 194 0.750 0.340 1.030 196 80.970 90.510 98.450 198 4.100 1.690 0.340 200 5.600 0.850 0.170 202 0.015 0.340 0.008 204 0.750 0.680 0.008 206 0.750 0.510 0.008 208 0.750 0.170 0.008

210 5.970 4.070 0.008 212 0.015 0.850 0.008 Homozygosity (Obs.) 0.754 0.854 0.969 Homozygosity (Exp.) 0.663 0.821 0.969 NW Ontario NE Ontario SC Ontario BL42 (n=162) (n=318) (n=296) 250 0.850 0.200 0.390 252 8.120 0.600 0.590 254 6.410 3.000 0.008 256 4.700 25.200 31.050 258 9.400 2.200 7.030 260 32.050 25.000 19.920 262 12.390 15.600 13.280 264 8.120 20.600 22.460 266 7.260 6.400 4.690 268 8.120 0.800 0.590 270 1.280 0.400 0.008 272 0.430 0.008 0.008 274 0.850 0.000 0.008 Homozygosity (Obs.) 0.205 0.256 0.281 Homozygosity (Exp.) 0.155 0.197 0.210

Table 4.2 Summary table of probabilities for 10 microsatellite loci in the three Ontario population groups (PID stands for probability of identity, PIDsib stands for probability of identity of a sibling, PD stands for probability of discrimination, PDsib stands for probability of discrimination of a sibling and PEcomb stands for the combined probability of exclusion).

NW Ontario NE Ontario SC Ontario (n=162) (n=318) (n=296) -11 -10 -9 PID 2.34x10 2.54x10 4.09x10 -4 -3 -3 PIDsib 5.97x10 1.97x10 3.84x10 PD 1.00 1.00 0.999

PDsib 0.999 0.998 0.996

PEcomb 0.982 0.969 0.940

Table 4.3 Database Sensitivity Testing (numbers indicate individuals unless otherwise denoted). RMP = Random Match Probability, LR= Likelihood Ratio.

Total Male Female Percentage Samples n RMP LR RMP LR RMP LR Total Male Female Known Controls 6 6 6 6 6 6 6 100% 100% 100% Full Profile Random 15 13 13 15 15 14 14 87% 100% 93% 2 Loci Missing (8/10) Random 15 13 13 10 10 13 14 87% 67% 87-93% 3 Loci Missing (7/10) Random 33 18 18 18 18 15 15 55% 55% 45%

Chapter 5 - Synthesis

Based on the findings of this thesis, the moose in Ontario do not fall into the two subspecies geographic designations that have been suggested by previous literature

(Peterson 1955, Hundtermark et al. 2002, Feldhamer et al. 2003, Wilson et al. 2003).

Samples from Ontario were compared to samples from Manitoba and Cape Breton, New

Brunswick and mainland Nova Scotia. The earlier literature describing the subspecies boundary suggests that the Maritimes (excluding Cape Breton) are represented by

A.a.americana and that Manitoba and Cape Breton (animal translocation from Alberta) are represented by A.a.andersoni, and that the subspecies meet in Ontario having expanded from their separate glacial refugia (Hundertmark et al. 2002, Feldhamer et al.

2003, Wilson et al. 2003).

The results indicate a distinct genetic separation between western and eastern Ontario.

Although there is a suggestion of a third genetic population in north-eastern Ontario, the low level of genetic differentiation within the north-eastern assigned population can be explained by a level of admixture between the individuals from north-western Ontario and those from south-central Ontario. There was a slightly higher level of relatedness between the SC population and the NE (or admixed) population. There are few migrants moving between areas in the context of the entire Ontario population, with the largest move being from the south and the east towards the north and west. This supports the expansion of the population from the south up into northern and northwestern Ontario.

There is free movement between the SC and NE populations, supported by the data. The smaller number of migrants out of the northwest suggests that, although there are still

100 small amounts of expansion for the NW population, the habitat is prime moose habitat with sufficient food and protection, and that the area has not yet reached carrying capacity. With the encroachment of anthropogenic factors in the south, more animals are moving north in an effort to find good forage and undisturbed habitat.

For the majority of the population comparisons, the FST and Jost’s D values are either similar or higher than the RST values. The RST values do not support the hypothesis that two distinct subspecies meet over Lake Superior, as we would expect the RST values to be significantly higher than the values of our other genetic measures. Rather, the values of our genetic measures suggest the meeting of two populations of the same species. The greater FST and Jost’s D values suggest that phylogeography is not having an effect on the genetic structure seen across Ontario. We saw low to moderate FST values across the province that are more in line with two populations (NW and SC) from the same subspecies repopulating the province from the west and from the east and meeting in NE

Ontario over Lake Superior.

This suggests that a re-evaluation of the subspecies classification of A.a. andersoni and

A.a. americana should be performed as there is no genetic evidence of differentiation.

The patterns seen in the results suggest more local differences than cross-province differences. In Peterson’s 1955 tome, North American Moose, he discusses population studies done as far back as the mid-1600s, where moose were seen sporadically in SC

Ontario. Not until over 100 years later were moose spotted, by European explorers,

101 further north. Scientific documentation did not really begin until the late 1800s, where a dispersal of moose from Michigan up into Ontario was documented and moose numbers started increasing in NW and SC Ontario. There is more documentation available of moose movement from east to west, due to the pattern of human settlement in Ontario, than there is from west to east, but the evidence provided by over a century of documentation and discovery is that the two populations of moose have expanded their ranges and have met north of Lake Superior, with initial contact possibly around 1900

(Peterson 1955). This is supported by the continued ability to distinguish between the two populations in NW Ontario and SC Ontario and also by the area of admixture in NE

Ontario and the diminishing overall genetic structure across the province. Given time, a large area of panmixia will likely occur in Ontario with indistinguishable genotypic separation between the populations.

Human development in the province has been both beneficial and detrimental to moose populations. While small to midscale logging operations can be beneficial by providing new growth and fresh browse, urban development, highway expansion and increased habitat fracturing diminish available resources and disrupt home ranges and traditional migration routes. Such human interference forces moose into urban areas in search of food and often results in the euthanizing of the animals. Increased traffic on provincial highways also increases the mortality rate of moose, with collisions causing loss of animal and human life as well as millions of dollars in damage. Fencing along major highways and wildlife corridors over or under the highway have been moderately successful in many jurisdictions in reducing moose (and human) collision mortalities.

102

Mitigation of this development and close monitoring can help with the management of moose populations in Ontario, particularly in Southern Ontario, where anthropogenic factors are greater than elsewhere in the Province.

A forensic database has been established for NW, NE and SC Ontario at 10 polymorphic loci. With the ability to exclude most of the population in a region, the certainty of the statistics used for identification is well-founded. The results across the province are consistent with the decreasing heterozygosity gradient from the north-west through the north-east down to south-central Ontario. With low levels of heterozygosity, determining the chances of finding two individuals with the same profile is helpful in paternity determination when both the calf and cow’s genetic profiles are known. Paternity determination is useful in determining the number of bulls that are successfully mating within a region and has implications for the physical and genetic health of the population.

With only just over 100,000 moose in Ontario, according to the last OMNRF count, the probability of identity also allows for a high level of confidence in the statistical results.

While moose are not considered threatened in Ontario, they are threatened in other parts of Canada, and application of these techniques can also be used across multiple species to aid in population remediation and monitoring of conservation plans for threatened or endangered species. It is important that all stakeholders in the process of conservation and management work together for that process to function.

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DNA databases are very desirable due to their multiple applications in both forensics and in population studies. The forensic database enables the identification of the most probable race, species or region of origin of an individual from a DNA sample and presents the result statistically based on known samples and genetic similarities found in different regions. Forensic databases can be built at differing geographic scales depending on the study needs (broad scale for whole regions, or finer scale for management units or individual geographic regions such as islands, tracts of land, ponds and rivers). Access to larger forensic databases made up of individuals from all provincial jurisdictions allow for more accurate identification. Any addition of individuals to the database allows for greater differentiation between populations and greater statistical power in reporting region of origin of individual animals. The genetic health of a population can be examined and assessed by using the database individuals in the population to determine inbreeding levels and levels of fixation at different loci.

A more focused database may be required where there is a lower level of assignment due to high levels of admixture in the population, or other issues such as low sample sizes, sink-source populations with one population feeding another, or the presence of high levels of inbreeding. In such cases, in-depth testing of the database can be used. The strength of our database has been tested, using numerous samples of known geographic origin.

One issue in Ontario is that annual harvest rates are only an estimate, as hunters (with the exception of tourists and within five WMUs) are not required to register their kills. This

104 can make management a challenge, as the actual number of harvested animals versus issued harvest tags cannot be determined, forcing the OMNRF to decide on the following year’s harvest based on a voluntary annual hunter survey. A solution to this would be to have all moose hunters in the province report or register their kills as a term of their licence. This real-time approach to monitoring the population, could be added to the strategies suggested in the Moose Management Harvest Guidelines. Designating selective harvest systems as needed to maintain population objectives, shortening or redesigning the hunting season and monitoring areas where populations may be more vulnerable (cutover areas) and restricting hunting in those areas as needed (OMNR 2009), will all help to maintain a sustainable harvest for the years to come. A result of overharvesting and the general decrease of population numbers would be a decrease in the numbers of available hunting tags. While poaching levels are not currently high for moose in Ontario, the restriction of hunting could lead to increased illegal harvesting.

The forensic database, designed with population alleles from the three delineated populations, provides a key to identification of poached animals and their connection to the alleged poachers.

Ontario currently bases its wildlife management units (WMUs) on moose populations in northern Ontario and on deer populations in central and southern Ontario, with hunting quotas based on previous years’ harvests (OMNR). At the southern edge of moose range, this may create management issues over time. With populations spanning multiple

WMUs, overharvesting is a serious threat to the productivity of the populations. The result of overharvesting could be fractured populations with sink-source patterns forming

105 around the Algonquin Provincial Park, where hunting by the general population is prohibited. Management of low-density areas, especially if attempting to promote recovery, is very important. If the need should arise in the future, with the same subspecies and little genetic difference within the three regions of Ontario, translocation of individuals is a possibility. This translocation would increase genetic variability across the province.

Our results do not support the presence of two subspecies in Ontario, as proposed by earlier writers. Previous studies relied on morphological differences in palate size and primarily mitochondrial studies, and were all conducted with limited samples. Based on genetic measure comparisons, our discriminant analysis of principal components and our analyses of genetic and spatial information, we have shown that the moose in Ontario are from populations of a single subspecies meeting after the most recent glacial retreat with no significant isolation by distance past 100 kilometres. This allowed us to construct a reliable database for both forensic and management purposes.

106

References

Feldhamer GA, Thompson BC, Chapman JA. 2003. Wild Mammals of North America. JHU Press. 1216pp.

Ontario Ministry of Natural Resources (OMNR). 2009. Harvest Management Guidelines. 24p. Online : http://www.mnr.gov.on.ca/stdprodconsume/groups/lr/@mnr/@fw/documents/document/2 63995.pdf

Peterson R.L. 1955. North American Moose. University of Toronto Press. Toronto, ON. 280pp

107

Appendix A – Ontario Wildlife Management Unit Maps.

A = Northern Ontario WMUs. B= Southeastern Ontario WMUs. As no moose samples were taken from any WMU above 60A, Southwestern Ontario WMU map was not used.

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Appendix B – Genotypic database for all Ontario individuals - showing WMU of origin, sample identification name, sex, population assignment and genotypes at 10 loci (missing loci denoted with 0). M=Male, MC=Male calf, F=Female, FC=Female calf. NW=Northwest, NE=Northeast, SC=South Central. Locus 1 Locus 2 Locus 3 Locus 4 Locus 5 Locus 6 Locus 7 Locus 8 Locus 9 Locus 10 WMU Sample Name Sex Pop. Map2C BM4513 BM1225 RT9 RT24 BM888 BM848 FCB193 RT30 BL42 1C 660 F NW1 105 107 117 135 228 228 120 120 242 242 178 178 357 357 113 113 196 196 256 264 2 20756 M NW2 107 109 129 131 228 246 122 126 242 254 178 178 363 363 107 113 196 210 0 0 2 19897 F NW3 107 109 131 133 234 234 124 126 228 228 178 182 353 353 0 0 196 196 0 0 3 NB140 F NW4 105 105 117 127 230 246 124 126 242 254 178 178 357 361 109 111 196 210 252 262 3 20848 M NW5 105 107 117 131 234 246 126 126 242 242 178 182 357 357 107 107 196 200 260 260 3 96423 M NW6 105 107 129 131 234 248 126 128 242 242 182 182 353 363 107 113 196 200 262 268 3 96454 M NW7 103 105 131 131 228 234 124 124 242 254 178 182 357 357 117 117 196 196 260 262 3 97031 M NW8 107 109 131 131 234 250 124 126 228 256 180 182 361 363 107 113 196 200 258 260 3 19392 M NW9 105 107 117 129 234 246 128 128 242 242 178 182 361 361 107 113 196 196 260 260 3 20893 F NW10 103 107 0 0 228 234 126 126 242 242 178 180 353 357 107 107 196 196 258 260 3 97147 F NW11 105 107 131 131 228 228 124 124 242 242 178 178 353 363 113 115 196 196 0 0 3 02-017 F NW12 109 109 131 131 228 228 124 124 254 254 178 178 357 363 107 113 196 196 0 0 3 260046 M NW13 107 111 131 131 228 228 126 126 242 242 178 182 361 363 105 107 0 0 254 270 3 20329 M NW14 105 109 131 133 228 246 126 126 242 242 178 182 357 359 0 0 198 198 258 260 3 19279 M NW15 103 107 129 131 228 234 124 130 256 256 182 182 357 361 105 113 0 0 0 0 3 19583 M NW16 107 107 131 131 234 234 126 126 242 260 178 182 0 0 107 113 196 196 0 0 4 96102 F NW17 103 107 117 129 228 228 120 126 240 256 178 178 357 359 113 113 196 196 260 272 4 260053 M NW18 103 105 117 129 228 248 122 124 228 242 178 178 357 363 107 113 196 196 252 266 4 07-023 M NW19 105 107 131 131 228 228 124 126 242 254 178 178 357 361 107 107 196 196 258 258 4 07-037 M NW20 105 107 129 131 228 234 124 126 242 252 178 178 357 363 107 107 196 198 262 264 4 08-001 M NW21 105 107 129 131 228 228 120 124 242 242 178 178 357 357 107 115 196 196 252 264 4 08-002 M NW22 103 107 129 129 228 250 120 124 228 242 178 178 357 359 113 113 196 196 252 252 4 19910 F NW23 105 107 129 129 234 234 124 128 228 256 178 182 357 361 107 113 0 0 256 264 4 21258 F NW24 107 107 129 131 228 250 126 126 242 242 178 180 359 363 107 107 0 0 258 260 4 20954 M NW25 103 107 117 131 228 234 124 124 254 254 178 180 353 357 0 0 200 200 258 264 4 21241 M NW26 105 111 129 129 228 228 126 128 254 254 178 178 361 361 0 0 0 0 264 268 4 21449 M NW27 107 111 131 133 228 230 108 126 256 256 178 178 353 361 0 0 0 0 258 268 5 259958 F NW28 107 109 127 131 228 234 126 128 228 256 178 180 357 357 107 113 190 196 260 260

110

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5 Locus 6 Locus 7 Locus 8 Locus 9 Locus 10 WMU Sample Name Sex Pop. Map2C BM4513 BM1225 RT9 RT24 BM888 BM848 FCB193 RT30 BL42 5 NB251 M NW29 107 107 127 133 234 234 124 124 228 258 178 178 359 359 107 113 196 196 252 262 5 Hwy17N004 M NW30 107 107 117 127 234 246 126 128 242 252 178 182 357 361 107 107 196 196 266 268 5 NB119 M NW31 103 107 117 117 228 234 124 124 256 258 180 182 351 357 107 107 196 210 252 260 5 07-076 F NW32 107 107 117 131 228 246 126 126 242 254 178 178 357 357 107 113 196 196 0 0 5 21005 M NW33 107 109 117 117 228 228 0 0 242 256 178 180 357 361 107 113 196 196 254 266 5 21029 M NW34 105 107 117 129 228 234 126 128 254 256 178 178 357 361 0 0 0 0 258 258 5 260077 M NW35 107 107 117 133 228 250 126 126 242 258 178 178 361 361 107 107 0 0 0 0 7 NB234 M NW36 105 107 131 133 228 234 126 126 228 228 178 178 357 363 113 117 196 196 252 268 7B 260206 F NW37 103 107 129 131 228 234 126 126 242 254 178 182 357 357 113 113 196 210 254 260 7B NB133 M NW38 105 107 131 131 228 234 126 128 252 256 178 180 357 361 113 113 196 210 258 260 8 02_M08A F NW39 107 107 117 133 234 234 124 126 242 254 178 178 357 357 113 115 196 196 258 262 8 07-053 F NW40 103 107 131 131 234 246 124 126 242 242 178 178 359 359 107 107 196 196 260 260 8 786 M NW41 103 105 117 135 228 234 120 120 240 242 178 180 357 361 113 113 196 196 256 262 8 260107 M NW42 107 111 131 131 234 234 128 132 242 242 182 182 361 361 113 113 196 196 260 262 8 07-010 M NW43 107 107 129 131 234 234 126 128 228 242 178 182 357 359 107 113 196 196 260 268 8 07-011 M NW44 107 107 129 133 228 246 126 126 242 254 178 180 357 357 113 113 206 210 252 274 8 07-012 M NW45 103 107 129 131 228 230 126 126 254 258 178 178 353 361 115 115 196 210 260 266 8 07-035 M NW46 107 107 117 131 228 234 124 126 242 242 178 178 359 361 107 115 196 198 260 266 8 96157 F NW47 105 107 129 133 228 234 120 126 242 256 180 180 357 357 0 0 196 196 0 0 9A 07-064 F NW48 103 107 127 129 228 234 120 124 228 242 178 178 353 363 107 113 196 196 254 258 9A NB136 F NW49 103 107 117 129 228 234 126 130 254 256 178 178 359 361 0 0 196 196 0 0 9A NB137 F NW50 105 107 131 131 228 228 124 124 242 256 0 0 357 361 107 107 196 196 0 0 9B 04-9B01 F NW51 107 107 117 129 228 234 124 124 242 258 178 182 359 363 107 113 196 196 260 264 9B 20428 M NW52 103 107 129 129 234 248 126 128 242 256 178 178 359 363 107 107 196 210 260 260 11A 97017 F NW53 107 107 117 129 228 228 124 128 228 242 178 178 359 361 115 115 196 196 254 258 11A 97062 F NW54 103 107 117 129 234 248 124 126 242 242 178 178 359 361 107 115 196 196 0 0 11A 08-009 M NW55 107 107 129 131 228 250 120 124 242 256 178 178 357 357 107 113 196 196 0 0 11A NB127 M NW56 105 107 117 129 234 234 122 124 228 242 178 178 357 357 0 0 196 210 0 0 11B Hwy17n059 M NW57 107 107 117 131 228 234 122 124 242 254 178 178 353 361 105 107 200 210 262 266 11B 20930 F NW58 107 111 131 131 234 234 0 0 242 242 178 180 353 361 113 113 196 196 260 262 11B 21036 F NW59 103 105 129 129 228 234 120 128 242 242 178 182 359 361 107 113 0 0 260 268 11B 21418 F NW60 103 107 129 129 228 234 108 122 242 242 178 178 353 357 107 113 0 0 266 266

111

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5 Locus 6 Locus 7 Locus 8 Locus 9 Locus 10 WMU Sample Name Sex Pop. Map2C BM4513 BM1225 RT9 RT24 BM888 BM848 FCB193 RT30 BL42 11B 260190 M NW61 103 105 117 135 234 234 124 126 242 256 178 180 353 361 107 113 0 0 264 264 11B NB232 M NW62 0 0 117 129 0 0 126 128 242 242 178 178 353 357 107 115 196 196 260 266 12A 21159 M NW63 103 109 131 131 228 234 108 126 242 254 178 178 359 363 113 113 0 0 262 262 12A 21340 M NW64 107 107 117 131 228 234 122 124 242 252 178 178 357 357 105 113 0 0 260 262 12A 260008 M NW65 105 105 117 129 234 234 126 128 242 256 178 178 357 361 107 113 0 0 260 268 12A NB120 M NW66 107 107 129 131 234 250 124 126 256 256 180 180 361 361 105 107 196 210 0 0 12A 96089 F NW67 107 107 129 131 228 228 122 122 242 254 178 178 359 359 0 0 196 196 0 0 12A 96430 M NW68 107 107 129 131 228 234 124 126 254 256 178 178 359 361 0 0 196 198 0 0 12B 02_M12 F NW69 105 107 117 133 228 234 116 126 238 252 178 180 357 359 111 113 196 196 260 262 12B 02_M12B F NW70 105 107 117 133 228 234 116 126 240 254 178 178 357 359 113 113 196 196 260 262 12B 07-028 F NW71 105 107 117 131 228 234 124 126 228 242 178 180 357 361 107 107 196 210 254 262 12B 19408 M NW72 103 103 117 129 234 234 124 124 242 242 178 178 357 361 107 117 196 196 252 254 12B 07-015 M NW73 105 107 129 131 228 234 124 126 242 242 178 178 359 361 107 107 196 196 260 266 12B 21289 F NW74 107 107 129 129 228 234 126 128 240 242 178 180 357 357 113 113 0 0 258 260 12B 21401 M NW75 107 107 129 131 228 228 122 124 240 242 178 182 357 361 107 107 0 0 260 260 12B 19460 F NW76 107 107 127 127 228 234 124 124 256 256 178 178 353 357 0 0 0 0 258 260 12B 19729 F NW77 107 107 131 131 228 234 0 0 228 228 178 178 361 361 107 113 196 196 0 0 13 07-033 F NW78 107 109 129 129 234 246 124 126 228 256 178 178 353 363 113 115 196 196 250 262 13 NB112 M NW79 107 107 129 129 228 234 126 126 242 254 176 178 357 357 111 111 194 194 260 262 13 07-029 M NW80 105 107 117 117 230 234 120 126 228 254 178 178 363 363 113 115 196 196 260 268 13 07-039 M NW81 103 107 131 131 228 228 124 124 240 252 182 182 357 361 107 113 196 196 260 264 13 19958 F NW82 105 107 129 129 248 250 124 126 242 256 178 178 359 361 107 113 196 196 0 0 13 96010 F NW83 107 111 117 127 230 248 128 128 228 260 178 178 361 361 0 0 196 198 252 256 13 20947 M NW84 103 111 127 131 228 234 120 124 242 242 178 178 357 357 107 107 0 0 254 260 13 97345 F NW85 107 111 131 135 234 248 124 124 228 254 0 0 357 357 113 113 196 196 0 0 13 19903 M NW86 103 107 117 129 228 234 126 128 242 256 178 178 361 361 0 0 196 198 0 0 13 20114 M NW87 103 107 129 129 224 230 126 126 222 238 178 178 357 357 0 0 196 196 0 0 14 NB230 F NW88 105 107 129 129 228 234 124 124 254 254 178 180 361 361 105 107 196 198 260 262 14 07-060 M NW89 103 107 117 129 228 246 126 126 228 242 178 178 357 359 107 115 196 196 256 260 14 97178 F NW90 101 105 125 125 228 234 120 120 228 228 178 178 357 357 107 107 196 200 0 0 14 21227 M NW91 103 107 129 131 246 250 108 126 242 242 178 180 357 363 113 113 0 0 252 258 14 259965 M NW92 103 107 127 131 234 234 124 124 242 258 178 180 357 359 107 113 0 0 256 268

112

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5 Locus 6 Locus 7 Locus 8 Locus 9 Locus 10 WMU Sample Name Sex Pop. Map2C BM4513 BM1225 RT9 RT24 BM888 BM848 FCB193 RT30 BL42 15A 97864 M NW93 107 107 131 131 234 234 122 124 242 258 178 178 357 361 109 115 196 196 260 266 15A 20664 M NW94 105 107 129 131 228 234 124 128 242 256 180 180 353 363 107 113 200 200 252 260 15A 97840 M NW95 105 107 127 129 228 228 120 126 242 252 178 182 357 359 107 113 196 196 0 0 15A 260084 M NW96 105 105 129 131 228 242 124 124 228 242 178 178 357 359 107 113 0 0 260 262 15A 20169 M NW97 105 107 117 129 228 228 124 126 254 256 178 182 357 361 107 113 0 0 260 260 15A 20466 M NW98 105 107 117 129 234 246 126 128 242 254 178 178 353 361 105 113 0 0 262 268 15A 20565 M NW99 109 109 135 135 234 246 0 0 242 242 178 182 353 353 105 113 196 200 252 268 15A 20442 M NW100 105 107 117 117 234 234 124 124 242 242 178 178 357 363 113 113 0 0 0 0 15A 96027 M NW101 105 107 117 129 234 246 122 122 228 228 178 178 359 363 0 0 196 196 0 0 15A 97161 M NW102 105 111 131 131 228 234 124 124 228 242 0 0 361 361 107 107 196 206 0 0 15B NB254 F NW103 107 107 117 117 228 228 126 126 242 242 178 178 361 361 107 107 196 196 252 260 15B 97734 F NW104 107 107 127 133 234 234 126 128 256 256 178 178 357 357 107 113 196 196 260 260 15B 02-003 F NW105 107 107 117 129 234 248 124 124 242 242 178 178 357 357 107 113 196 204 264 266 15B NB146 F NW106 107 111 129 131 228 228 124 124 228 242 178 178 357 361 107 113 196 196 260 260 15B NB174 M NW107 103 107 129 129 228 248 124 126 254 254 178 180 357 363 107 113 196 196 258 260 15B NB231 M NW108 107 107 117 129 234 250 126 128 242 242 178 182 357 359 107 107 196 196 260 270 15B 21388 M NW109 103 107 129 131 234 246 124 126 256 256 178 178 357 363 107 107 196 196 260 262 15B 97055 M NW110 107 111 131 131 228 246 120 126 242 242 178 178 353 357 107 115 196 196 252 270 15B 150125 M NW111 107 107 129 131 234 234 126 128 256 258 178 178 357 361 107 113 196 196 252 260 15B 07-004 M NW112 107 107 129 133 228 234 126 126 228 254 178 178 357 357 107 115 196 196 260 264 15B 07-006 M NW113 103 107 127 129 234 250 124 128 242 256 178 178 359 361 107 115 196 196 260 266 15B 08-074 M NW114 105 105 129 131 228 234 120 126 228 258 178 178 361 363 107 107 196 196 260 262 15B NB116 M NW115 107 111 129 131 234 234 122 124 242 254 178 178 353 361 107 113 196 196 262 262 15B NB123 M NW116 107 107 117 131 228 234 120 128 228 258 178 178 361 361 107 107 196 198 254 258 15B NB84 M NW117 103 107 129 131 248 248 122 124 242 242 178 178 357 361 107 107 196 196 262 266 15B NB94 M NW118 105 107 129 131 228 234 126 128 242 242 178 178 355 355 107 113 196 196 260 266 15B 19651 F NW119 103 103 131 133 234 250 124 128 242 244 178 178 357 357 107 113 196 196 0 0 15B NB13 M NW120 107 107 0 0 228 234 124 128 240 240 178 180 357 359 107 113 196 196 260 262 15B 20107 M NW121 105 107 117 131 228 234 128 128 242 242 178 178 357 357 105 113 196 196 0 0 15B 21319 M NW122 107 107 0 0 228 228 128 128 242 242 178 180 357 361 113 113 196 196 252 258 15B 21395 M NW123 103 105 129 129 234 234 124 126 242 242 178 178 353 353 0 0 196 196 268 268 15B 97758 M NW124 107 107 131 133 228 234 122 126 228 242 178 180 353 361 107 113 196 210 0 0

113

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5 Locus 6 Locus 7 Locus 8 Locus 9 Locus 10 WMU Sample Name Sex Pop. Map2C BM4513 BM1225 RT9 RT24 BM888 BM848 FCB193 RT30 BL42 15B NB93 F NW125 107 111 129 129 228 234 124 126 242 242 174 174 353 361 0 0 196 196 0 0 15B 19712 M NW126 107 107 129 129 228 234 122 122 228 242 178 178 357 357 113 115 0 0 0 0 15B 20886 M NW127 105 107 117 129 228 228 128 128 242 254 178 178 353 359 0 0 0 0 260 260 15B 20923 M NW128 105 105 129 131 230 250 128 128 256 256 178 180 361 361 107 107 0 0 0 0 15B 96447 M NW129 103 107 129 129 228 228 124 126 242 258 0 0 357 357 107 107 196 196 0 0 15B 97109 M NW130 107 107 117 117 228 228 122 124 242 254 178 178 353 357 0 0 196 196 0 0 15B NB139 M NW131 105 107 131 131 228 234 122 128 228 256 178 180 357 361 0 0 208 208 0 0 16A 07-062 M NW132 103 105 127 129 228 234 126 126 226 254 178 178 357 357 107 115 196 196 268 268 16B 07-025 M NW133 105 107 129 131 234 242 124 124 242 258 178 178 361 361 107 113 196 196 264 274 16C 07-059 F NW134 105 107 131 135 228 230 124 126 228 242 178 178 357 361 113 115 196 200 254 264 17 07-045 M NW135 105 107 117 129 234 234 124 126 242 242 178 178 357 363 113 113 196 204 254 268 18A NB122 F NW136 107 107 129 129 228 234 122 124 242 242 178 178 357 361 107 107 196 196 260 260 18A 260015 M NW137 105 107 129 131 228 234 126 128 228 242 178 178 357 361 107 113 196 196 254 256 18A NB82 M NW138 103 111 129 129 228 234 120 124 228 242 178 178 359 361 0 0 196 196 0 0 18B NB259 M NW139 105 107 133 135 234 248 120 124 228 242 178 178 359 361 107 113 196 196 256 268 19 NB233 M NW140 107 107 117 127 228 250 128 128 228 254 178 178 359 361 105 107 196 196 254 260 19 96881 M NW141 101 103 129 131 228 248 126 128 228 242 178 178 359 361 107 113 196 196 254 254 19 07-007 M NW142 0 0 133 135 228 234 114 120 242 254 178 178 357 361 113 113 196 196 260 260 19 08-006 M NW143 107 107 129 131 228 228 120 124 228 240 178 178 359 361 107 113 196 210 0 0 19 NB76 M NW144 107 107 117 131 228 234 120 128 242 254 180 180 357 359 107 115 200 200 0 0 21A 20497 F NW145 107 107 127 129 228 234 120 124 242 242 178 178 357 359 105 111 200 200 250 258 21A 20602 F NW146 107 107 129 131 228 228 124 128 228 258 178 178 357 357 105 113 196 196 256 264 21A 07-042 F NW147 107 107 117 131 228 234 124 124 224 254 178 178 357 361 107 115 196 196 260 268 21A NB110 F NW148 107 107 131 135 228 250 122 128 240 258 178 178 357 359 113 113 196 196 260 260 21A 20909 M NW149 105 105 129 129 230 250 128 128 256 256 178 180 357 361 107 107 196 196 256 266 21A 97048 M NW150 107 111 129 129 234 234 120 124 228 242 178 178 353 357 107 115 196 196 260 260 21A 260039 M NW151 107 107 131 131 228 234 126 126 242 256 178 178 357 357 107 113 196 196 262 264 21A 07-049 M NW152 105 107 117 135 228 228 122 124 228 242 178 178 359 361 107 113 196 196 256 266 21A NB118 M NW153 107 107 117 131 228 250 124 126 242 254 178 178 355 359 115 115 196 196 262 264 21A 21470 F NW154 103 105 129 129 228 234 120 128 242 242 178 180 357 361 113 113 0 0 260 264 21A 97888 F NW155 105 107 117 131 234 246 124 126 242 242 178 178 353 357 107 107 196 210 0 0 21A 20916 M NW156 107 107 0 0 234 250 124 126 256 256 178 180 353 361 113 113 196 196 258 264

114

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5 Locus 6 Locus 7 Locus 8 Locus 9 Locus 10 WMU Sample Name Sex Pop. Map2C BM4513 BM1225 RT9 RT24 BM888 BM848 FCB193 RT30 BL42 21A 07-041 M NW157 111 111 117 117 244 246 114 114 226 228 178 180 0 0 105 117 198 198 252 260 21A NB105 F NW158 103 107 117 135 228 234 122 126 258 258 178 178 351 359 0 0 196 196 0 0 21A NB78 F NW159 105 107 117 135 228 250 122 122 242 242 178 180 353 357 0 0 196 196 0 0 21A 19668 M NW160 105 105 131 135 228 234 128 128 256 260 174 178 0 0 107 113 196 196 0 0 21A 97307 M NW161 107 107 135 135 228 250 122 128 242 254 178 178 357 357 0 0 196 210 0 0 21A 96775 M NW162 107 107 131 133 228 228 124 124 254 258 0 0 359 361 113 113 196 196 0 0 21B NB222 F NE1 105 107 117 131 228 228 120 126 240 254 178 178 361 361 107 113 196 196 254 266 21B 20121 F NE2 105 107 117 135 234 246 120 124 242 256 178 178 357 363 113 113 196 198 260 262 21B 20596 F NE3 103 103 131 135 228 228 120 124 242 258 178 178 357 357 107 107 196 196 264 266 21B 96416 F NE4 107 107 135 135 234 250 120 126 228 242 178 178 357 361 105 115 196 210 260 260 21B 97741 F NE5 103 103 129 129 228 228 120 124 242 242 178 178 359 363 107 113 196 204 256 264 21B 21074 M NE6 103 107 117 131 228 234 120 124 242 254 178 180 357 363 113 115 196 196 256 260 21B NB187 M NE7 103 107 127 133 228 234 120 128 240 240 178 180 359 361 107 107 196 196 260 266 21B NB257 M NE8 103 107 129 131 228 234 120 124 228 242 178 178 359 359 113 113 196 196 264 266 21B 19385 M NE9 105 105 117 131 228 234 124 124 242 242 178 178 357 361 107 113 196 204 254 256 21B 20046 M NE10 107 107 131 131 228 234 124 124 242 258 178 178 357 361 107 107 196 196 254 260 21B 20275 M NE11 105 107 117 131 228 234 120 124 242 242 178 180 357 361 107 113 196 198 264 270 21B 20336 M NE12 107 107 117 129 234 248 120 120 242 256 178 178 361 361 107 107 196 210 260 260 21B 20374 M NE13 101 103 115 133 228 228 124 124 228 242 172 182 355 357 105 115 204 204 260 260 21B 259941 M NE14 107 107 117 131 228 234 120 120 228 242 178 178 357 361 107 113 196 198 256 260 21B 02-001 M NE15 105 107 117 135 228 228 126 126 252 252 178 186 357 357 105 115 196 200 262 264 21B 07-009 M NE16 105 107 117 131 228 250 120 126 242 242 178 178 355 357 113 115 196 206 260 266 21B 07-051 M NE17 107 107 131 131 228 234 120 128 242 254 178 182 357 361 115 115 210 210 256 264 21B 21081 M NE18 103 107 117 135 234 250 124 124 242 254 178 178 357 359 113 113 0 0 260 262 21B 150040 M NE19 107 111 117 129 228 250 120 124 228 256 178 178 357 357 107 113 0 0 256 260 21B 02-006 M NE20 103 103 0 0 228 234 120 126 242 242 178 178 361 361 113 113 196 196 260 262 21B Hwy17N015 M NE21 103 107 127 135 228 234 120 124 242 254 178 180 357 361 107 107 0 0 260 262 21B 19859 M NE22 103 111 135 135 234 242 120 124 228 254 178 178 357 361 0 0 196 210 0 0 21B 19927 M NE23 103 103 127 127 228 230 114 118 228 242 180 180 357 357 0 0 196 196 0 0 21B 20176 M NE24 107 107 117 129 228 234 120 120 242 242 178 178 357 361 0 0 196 196 0 0 21B 20510 M NE25 105 107 131 135 228 234 116 120 242 252 178 178 357 357 0 0 196 196 0 0 22 20404 F NE26 105 107 131 135 234 234 120 120 242 254 178 178 357 357 113 113 196 196 262 268

115

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5 Locus 6 Locus 7 Locus 8 Locus 9 Locus 10 WMU Sample Name Sex Pop. Map2C BM4513 BM1225 RT9 RT24 BM888 BM848 FCB193 RT30 BL42 22 20459 M NE27 105 105 129 133 234 246 124 124 228 242 178 182 357 359 107 113 196 196 258 260 22 96461 M NE28 103 107 133 135 228 234 116 124 254 254 178 180 357 363 107 107 196 196 256 268 22 Hwy17N002 M NE29 105 107 117 131 228 228 120 126 242 258 178 178 357 361 113 115 196 196 256 262 22 NB126 M NE30 105 105 131 135 228 228 120 124 228 256 178 180 361 361 113 113 196 196 256 256 22 19293 F NE31 105 107 131 135 228 250 120 120 240 256 178 178 359 361 105 107 196 196 0 0 22 20817 F NE32 103 105 135 135 228 228 120 124 242 242 178 180 353 361 113 113 196 196 0 0 22 19477 M NE33 105 105 135 135 228 228 124 124 256 256 178 178 359 361 0 0 200 200 262 264 22 Hwy17N007 M NE34 105 107 117 127 228 228 120 120 242 242 178 178 353 357 113 113 0 0 260 264 22 Hwy17N013 M NE35 107 107 117 135 228 250 120 120 242 242 178 178 361 361 113 113 196 196 0 0 22 Hwy17N010 F NE36 105 107 117 131 228 228 108 124 254 254 180 180 0 0 113 113 196 196 0 0 22 96348 M NE37 105 107 127 131 228 228 120 124 228 240 178 178 353 361 0 0 196 196 0 0 22 96546 M NE38 105 105 129 135 228 228 118 122 228 254 178 178 353 357 0 0 196 198 0 0 22 Hwy17N014 M NE39 107 107 0 0 228 228 120 122 242 242 178 178 359 359 107 115 196 196 0 0 22 19224 M NE40 105 107 131 135 228 228 120 126 256 256 178 178 357 357 115 115 0 0 0 0 22 20060 M NE41 103 105 125 127 234 234 116 116 242 254 178 178 0 0 113 113 196 196 0 0 24 NB100 M NE42 107 107 135 135 228 250 120 120 254 254 178 180 357 357 113 113 196 196 256 260 25 19514 M NE43 107 107 117 131 228 228 108 124 254 256 178 178 357 361 113 113 196 196 256 256 25 96584 M NE44 107 107 129 131 228 234 120 124 242 254 178 180 357 363 107 107 196 196 256 260 26 Dube1_1 M NE45 107 107 117 117 228 234 120 126 242 254 178 178 357 357 113 113 196 196 256 260 26 Dube2-2 F NE46 103 107 131 135 228 228 120 120 242 242 178 178 359 359 0 0 196 196 254 266 26 Dube1_2 M NE47 0 0 117 117 228 234 120 126 242 254 178 178 357 357 113 113 196 196 256 260 26 C134-06 M NE48 105 107 0 0 228 234 0 0 240 254 178 178 357 357 113 115 196 196 258 258 27 C113-04 F NE49 107 107 117 135 228 228 124 126 242 242 176 178 357 357 113 113 196 196 262 264 27 07-068 M NE50 105 107 131 135 228 250 120 120 240 254 178 178 355 357 113 113 196 196 256 262 27 636 M NE51 105 107 129 135 228 234 120 120 242 242 178 178 357 359 113 113 196 196 252 264 27 637 M NE52 103 107 117 133 228 228 124 126 254 254 178 178 357 357 107 113 196 196 260 264 27 C134-03 M NE53 105 107 129 131 228 228 122 124 256 256 178 178 357 361 113 113 196 196 256 264 27 C134-05 F NE54 105 107 129 135 228 250 120 120 242 242 178 178 359 361 113 115 196 196 0 0 27 C113-02 M NE55 107 107 117 135 228 250 120 126 242 242 178 178 357 359 113 113 0 0 256 262 27 C134-02 M NE56 103 107 117 131 234 250 0 0 240 240 178 178 359 361 113 113 196 196 260 264 28 P15.2.09.9TC F NE57 107 107 117 131 228 228 120 120 242 254 178 178 357 357 113 115 196 196 256 270 28 P16.2.09.8TC F NE58 107 107 117 135 228 228 120 120 228 256 178 178 357 357 113 113 196 196 256 264

116

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5 Locus 6 Locus 7 Locus 8 Locus 9 Locus 10 WMU Sample Name Sex Pop. Map2C BM4513 BM1225 RT9 RT24 BM888 BM848 FCB193 RT30 BL42 28 P24.2.09.4NR F NE59 103 107 117 131 228 250 124 124 242 242 178 180 357 361 113 113 196 196 262 266 28 P7.2.09.5NR F NE60 103 105 131 135 228 250 120 124 254 256 178 178 359 361 113 113 196 196 256 264 28 P7.2.09.5TC F NE61 103 105 129 135 228 228 120 120 228 242 178 178 357 361 113 113 196 196 256 260 28 P7.2.09.6NR F NE62 103 107 117 117 228 234 120 128 240 254 178 178 357 361 113 113 196 196 256 260 28 07-048 F NE63 107 107 117 129 228 228 120 124 242 254 178 178 357 357 113 113 196 196 264 264 28 07-073 F NE64 105 107 131 131 250 250 124 124 256 256 178 178 361 361 107 107 196 196 262 264 28 08-008 F NE65 107 107 131 131 250 250 124 124 228 256 178 178 359 363 113 113 194 196 256 256 28 08-014 F NE66 105 107 131 135 228 228 120 124 254 256 178 178 357 361 113 113 196 196 260 260 28 08-022 F NE67 103 107 135 135 234 250 120 120 242 256 178 180 357 361 113 113 196 196 256 268 28 08-034 F NE68 105 107 117 131 228 234 120 124 240 256 178 178 357 361 113 113 196 196 256 260 28 08-038 F NE69 103 107 135 135 228 228 120 124 242 256 178 180 357 361 107 113 196 196 260 260 28 08-039 F NE70 103 107 129 129 228 228 120 124 254 254 178 178 357 357 107 113 196 196 256 262 28 08-040 F NE71 103 105 129 129 228 250 124 124 254 256 178 178 357 361 107 107 196 196 256 256 28 08-044 F NE72 103 103 131 131 228 228 120 124 242 242 178 178 357 361 113 113 196 196 260 264 28 08-045 F NE73 107 107 135 135 228 248 120 124 254 256 178 178 355 357 107 113 196 196 256 260 28 08-046 F NE74 105 107 131 135 228 228 120 120 228 256 178 178 355 357 113 115 196 196 260 264 28 08-048 F NE75 107 107 133 135 228 228 122 124 228 256 178 178 357 361 107 113 196 196 264 264 28 08-054 F NE76 105 107 117 135 228 250 120 120 242 242 178 178 357 357 113 113 196 196 256 264 28 08-055 F NE77 103 105 117 135 228 234 124 124 242 254 178 178 357 361 105 113 196 196 260 260 28 08-056 F NE78 107 107 117 131 234 250 116 120 228 242 178 180 357 361 113 113 196 196 256 264 28 08-057 F NE79 107 107 131 135 228 228 124 124 240 242 178 178 361 361 113 113 196 196 264 264 28 P12.2.09.3TC F NE80 103 105 117 129 228 250 120 120 228 256 178 182 357 359 107 113 196 196 260 260 28 P13.2.09.2NR F NE81 103 105 127 135 228 246 116 120 228 242 178 178 361 361 113 113 196 196 262 262 28 P15.2.09.9NR F NE82 105 107 135 135 228 234 120 126 228 238 178 178 361 361 107 113 196 196 256 264 28 P17.2.09.5TC M NE83 105 107 129 135 228 234 120 124 228 242 178 178 357 361 113 113 196 196 262 264 28 P9.2.09.11TC M NE84 105 107 117 129 234 234 120 126 242 254 178 180 357 361 113 115 196 196 254 264 28 P9.2.09.4NR M NE85 107 107 135 135 228 228 120 120 240 258 178 178 357 357 107 113 196 196 256 260 28 NB60 M NE86 107 107 117 131 228 228 120 124 228 254 178 182 357 357 113 113 196 196 256 264 28 05-001 M NE87 105 107 129 131 228 228 116 120 242 242 178 180 357 357 107 113 196 210 262 262 28 07-030 M NE88 105 107 133 135 228 228 118 124 240 242 178 178 357 361 113 113 196 196 260 260 28 07-032 M NE89 103 107 129 135 228 228 120 120 254 254 178 178 357 357 113 113 196 196 256 266 28 07-077 M NE90 107 107 131 135 228 228 120 120 228 242 178 178 357 361 113 113 196 196 256 258

117

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5 Locus 6 Locus 7 Locus 8 Locus 9 Locus 10 WMU Sample Name Sex Pop. Map2C BM4513 BM1225 RT9 RT24 BM888 BM848 FCB193 RT30 BL42 28 07-079 M NE91 105 107 117 135 228 228 120 120 242 242 178 178 359 361 113 113 196 196 264 264 28 08-007 M NE92 105 107 135 135 228 228 120 120 254 254 178 178 357 361 113 113 196 196 262 264 28 08-013 M NE93 105 107 135 135 234 250 120 124 228 242 178 178 357 361 113 113 196 196 260 262 28 08-015 M NE94 103 107 117 135 228 250 120 128 240 256 178 178 353 357 113 113 196 196 256 264 28 08-020 M NE95 105 107 135 135 228 228 120 124 240 242 178 178 357 359 113 113 196 196 260 260 28 08-035 M NE96 107 107 131 135 228 250 120 120 228 256 178 180 357 357 113 113 196 196 262 262 28 08-037 M NE97 105 105 117 135 250 250 122 124 240 256 178 178 357 361 107 113 196 196 260 264 28 08-042 M NE98 101 105 129 135 228 228 120 120 242 256 178 178 357 359 107 113 196 196 260 260 28 08-047 M NE99 103 105 117 117 228 250 124 124 240 254 178 178 357 359 107 113 196 196 256 266 28 08-050 M NE100 107 107 135 135 228 250 122 124 238 242 178 180 359 361 107 113 196 196 256 256 28 08-052 M NE101 107 107 117 135 228 228 120 120 240 256 178 180 357 361 105 107 196 196 260 260 28 08-058 M NE102 103 103 117 117 228 228 124 124 242 254 178 178 357 357 107 113 196 196 260 262 28 08-059 M NE103 105 107 135 135 234 250 120 124 228 242 178 178 357 361 113 113 196 196 260 262 28 08-061 M NE104 103 107 117 135 228 234 120 120 254 254 178 178 361 361 113 113 196 210 256 264 28 08-062 M NE105 103 107 129 135 228 250 120 120 242 254 178 178 355 361 113 113 196 196 256 266 28 277-03 M NE106 103 107 127 135 228 228 120 124 254 256 178 180 361 361 113 115 196 196 260 264 28 P12.2.09.8NR M NE107 103 107 135 135 228 234 120 124 228 254 178 178 361 361 113 113 196 196 264 264 28 P15.2._09.11TC M NE108 105 105 117 131 228 228 120 120 242 254 178 180 357 359 105 113 196 196 264 264 28 P16.2._09.1TC F NE109 107 107 129 129 228 228 120 124 242 256 178 178 357 357 113 113 196 196 0 0 28 P16.2._09.3TC F NE110 103 107 119 135 228 234 120 120 254 254 178 178 357 357 113 113 196 196 0 0 28 P7.2.09.14TC F NE111 107 107 117 117 0 0 120 124 254 256 178 178 357 361 113 113 196 196 256 260 28 P9.2._09.5NR F NE112 103 103 117 135 234 250 116 120 254 254 178 178 357 361 113 113 196 196 0 0 28 08-017 F NE113 103 107 117 131 228 228 124 124 242 254 178 178 357 357 107 115 196 196 0 0 28 08-018 F NE114 105 107 117 131 228 234 120 120 228 242 178 178 355 357 113 113 196 198 0 0 28 08-021 F NE115 103 107 133 135 228 250 120 120 228 228 178 178 357 361 113 113 196 196 0 0 28 08-023 F NE116 103 107 135 135 228 250 120 124 242 256 178 178 357 357 113 113 196 196 0 0 28 08-026 F NE117 103 103 129 131 228 250 124 124 242 254 178 180 357 361 113 113 196 196 0 0 28 08-043 F NE118 103 105 117 131 234 250 124 124 228 242 178 178 361 361 107 113 196 196 0 0 28 08-049 F NE119 103 107 135 135 228 228 120 124 254 254 178 180 357 361 113 113 196 198 0 0 28 08-060 F NE120 103 103 135 135 228 250 120 124 254 256 178 178 361 361 107 113 196 196 0 0 28 08-063 F NE121 107 107 131 135 228 228 120 124 242 256 178 178 361 361 107 113 196 196 0 0 28 08-066 F NE122 105 107 117 135 228 228 120 124 242 254 178 178 355 361 113 113 196 196 0 0

118

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5 Locus 6 Locus 7 Locus 8 Locus 9 Locus 10 WMU Sample Name Sex Pop. Map2C BM4513 BM1225 RT9 RT24 BM888 BM848 FCB193 RT30 BL42 28 08-067 F NE123 105 107 133 135 228 250 120 124 228 256 178 178 357 357 105 113 196 196 0 0 28 08-069 F NE124 105 107 131 131 228 228 120 124 242 254 178 178 357 361 105 113 196 196 0 0 28 08-070 F NE125 105 107 117 135 228 250 116 124 242 254 178 178 357 361 105 111 196 196 0 0 28 08-071 F NE126 103 107 131 135 228 250 124 124 240 242 178 178 361 361 113 113 196 196 0 0 28 277_02 F NE127 107 107 117 135 228 250 120 120 240 240 178 180 353 357 113 113 196 196 0 0 28 P12.2._09.12NR F NE128 103 107 119 135 228 234 120 126 228 238 178 178 359 361 113 113 196 196 0 0 28 P16.2._09.6TC M NE129 103 105 117 135 250 250 120 122 256 256 178 178 357 361 113 113 196 196 0 0 28 P24.2._09.1TC M NE130 107 107 131 131 228 250 122 124 240 242 178 178 361 361 113 113 196 196 0 0 28 NB224 M NE131 107 107 0 0 228 228 120 124 240 254 178 178 359 361 113 113 196 196 264 266 28 19804 M NE132 105 107 117 135 228 228 122 124 242 256 178 178 357 359 113 113 0 0 258 260 28 08-024 M NE133 103 107 131 135 228 228 120 124 242 254 178 178 353 361 113 113 196 196 0 0 28 08-041 M NE134 107 107 129 135 228 234 124 124 242 254 178 178 361 361 113 113 196 196 0 0 28 08-051 M NE135 105 107 135 135 228 228 116 120 242 254 178 178 357 359 113 113 196 196 0 0 28 08-053 M NE136 0 0 131 131 228 234 114 118 240 254 178 178 357 361 113 113 196 198 254 264 28 08-072 M NE137 103 107 131 135 228 250 116 120 254 256 178 178 359 361 105 105 196 196 0 0 28 P12.2._09.6NR M NE138 103 107 119 131 228 228 120 120 242 254 178 178 357 357 113 113 196 196 0 0 28 P15.2._09.2TC F NE139 107 107 119 135 228 228 120 120 240 242 178 178 357 361 0 0 196 196 0 0 28 P25.2.09.4TC F NE140 105 105 131 135 228 242 124 124 242 242 0 0 357 361 0 0 196 196 256 264 28 P9.2.09.3TC F NE141 103 107 131 135 228 228 120 120 254 254 0 0 0 0 113 113 196 196 260 264 28 19767 F NE142 103 107 135 135 234 250 120 120 242 242 178 178 361 363 0 0 0 0 256 256 28 08-033 F NE143 107 107 131 131 250 250 124 124 228 256 178 178 359 363 0 0 196 196 0 0 28 277_04 F NE144 105 107 117 131 228 228 124 124 228 256 178 178 0 0 113 113 196 196 0 0 28 19798 M NE145 103 105 117 135 228 234 120 124 242 242 178 178 353 361 113 113 0 0 0 0 28 96119 M NE146 107 107 117 135 228 234 120 126 228 254 178 178 357 361 0 0 196 196 0 0 28 08-025 M NE147 105 107 117 117 228 228 120 124 256 256 178 178 357 357 0 0 196 196 0 0 28 08-029 M NE148 107 107 117 135 228 234 120 124 242 242 178 178 357 361 0 0 196 196 0 0 28 08-032 M NE149 103 103 117 129 234 250 124 128 242 254 178 178 361 361 0 0 196 196 0 0 28 08-065 M NE150 107 107 131 135 228 234 120 120 228 228 178 178 361 361 0 0 196 196 0 0 28 08-068 M NE151 107 107 117 135 228 250 120 120 228 242 178 178 357 357 0 0 196 196 0 0 28 P13.2._09.1TC M NE152 103 105 0 0 228 228 120 120 254 254 178 180 357 357 113 113 196 196 0 0 29 631 F NE153 105 107 117 117 228 234 120 124 240 242 178 178 357 361 113 113 196 196 256 260 29 633 F NE154 107 107 117 135 228 234 116 122 256 256 178 178 361 361 113 113 196 196 262 264

119

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5 Locus 6 Locus 7 Locus 8 Locus 9 Locus 10 WMU Sample Name Sex Pop. Map2C BM4513 BM1225 RT9 RT24 BM888 BM848 FCB193 RT30 BL42 29 258012 F NE155 105 107 135 135 228 228 120 124 242 254 178 178 357 361 107 115 196 196 262 264 29 07-005 F NE156 107 109 131 135 228 228 120 120 228 242 178 180 357 359 113 115 196 196 250 264 29 NB58 M NE157 107 107 135 135 228 234 120 120 252 252 178 178 357 357 113 115 196 196 256 260 29 635 M NE158 107 107 117 135 228 234 116 124 254 256 176 178 361 361 111 113 196 196 262 264 29 257961 M NE159 103 107 131 135 228 228 116 128 242 254 172 172 361 361 111 111 196 196 260 264 29 257978 M NE160 103 107 131 135 228 234 120 124 228 240 178 178 357 361 107 113 196 196 260 260 29 08-073 M NE161 107 107 135 135 234 250 120 124 228 256 178 178 357 357 113 113 196 196 260 260 29 19835 M NE162 103 107 129 129 228 234 108 122 242 242 178 178 361 361 113 113 0 0 256 264 29 257992 M NE163 107 107 129 135 228 228 120 124 228 242 178 178 357 361 107 113 0 0 260 260 29 258005 M NE164 105 107 117 131 228 228 120 124 228 242 178 180 355 357 107 113 0 0 260 264 29 19774 F NE165 103 107 127 135 228 228 120 124 242 254 178 180 357 359 113 113 0 0 0 0 29 258029 M NE166 107 111 117 131 228 234 120 120 240 254 178 178 357 361 107 115 0 0 0 0 30 630 F NE167 105 107 117 135 228 234 120 124 242 242 178 178 357 359 113 113 196 196 260 264 30 632 F NE168 107 107 117 135 228 234 116 124 256 256 178 178 361 361 113 113 196 196 262 264 30 258036 F NE169 103 107 117 135 228 228 116 124 254 256 178 178 357 357 107 113 196 196 258 264 30 634 M NE170 105 107 131 135 228 250 118 120 254 256 178 178 357 357 113 113 196 196 256 264 30 97925 M NE171 103 105 131 135 228 246 124 126 228 228 178 178 357 357 113 115 196 196 266 266 30 07-057 M NE172 105 105 135 135 234 234 120 124 254 254 178 178 357 359 113 113 196 196 262 264 30 NB138 M NE173 105 107 131 135 228 228 120 120 240 254 178 178 359 361 113 113 196 198 264 266 30 97871 M NE174 103 107 117 135 228 250 120 124 228 254 178 178 357 357 113 113 196 196 0 0 31 P10.1.09.1TC F NE175 105 107 117 131 228 228 120 128 228 242 178 178 357 357 113 113 196 196 264 264 31 P10.1.2009.4NR F NE176 107 107 117 135 228 228 120 124 254 254 178 178 357 357 113 115 196 196 256 256 31 P11.1.09.2TC F NE177 103 107 129 135 248 250 120 120 242 254 178 178 357 361 113 113 196 210 256 264 31 P12.1.09.2NRA F NE178 101 105 129 133 228 228 128 128 228 242 178 178 359 361 113 113 196 196 260 262 31 P12.1.09.3TC F NE179 103 105 117 135 224 228 120 120 228 242 178 178 361 363 113 113 196 196 256 256 31 P15.1.09.1TC F NE180 103 107 127 135 228 228 120 120 242 254 178 178 357 359 107 113 196 196 262 264 31 P17.1.09.1NR F NE181 103 107 117 135 228 250 120 120 228 254 178 178 357 361 113 113 196 198 260 262 31 P17.1.09.2TC F NE182 103 103 117 131 228 228 124 126 242 242 178 178 361 361 113 113 196 196 256 260 31 P18.1.09.10NR F NE183 103 105 131 131 228 228 116 120 228 242 178 180 361 361 113 117 196 198 260 260 31 P18.1.09.6NR F NE184 105 107 131 135 228 234 120 124 242 256 178 178 359 361 107 115 196 196 256 260 31 P18.1.09.9NR F NE185 107 107 131 135 228 234 120 126 242 254 178 180 357 361 113 113 196 196 266 266 31 P19.1.09.11NR F NE186 107 107 117 135 228 234 120 120 228 242 178 182 357 359 113 113 196 196 256 262

120

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5 Locus 6 Locus 7 Locus 8 Locus 9 Locus 10 WMU Sample Name Sex Pop. Map2C BM4513 BM1225 RT9 RT24 BM888 BM848 FCB193 RT30 BL42 31 P19.1.09.3NR F NE187 107 107 135 135 228 250 120 122 228 228 178 178 357 357 113 113 196 196 260 262 31 P19.1.09.7TC F NE188 107 107 129 131 228 250 120 124 242 256 178 178 357 361 113 113 196 196 254 264 31 P2.3.09.1TC F NE189 103 105 117 129 228 228 120 124 254 256 178 180 357 357 113 115 196 196 260 264 31 P3.3.09.4NR F NE190 105 107 117 129 228 230 120 120 228 242 178 178 357 357 113 117 196 196 256 260 31 P5.2.09.1TC F NE191 103 103 129 131 228 228 116 120 228 254 178 178 357 361 113 113 196 196 256 260 31 P5.2.09.5TC F NE192 107 107 117 135 228 234 120 126 240 254 178 178 357 361 115 115 196 196 260 260 31 P6.2.09.1TC F NE193 103 105 117 135 228 234 120 124 242 254 178 178 357 361 113 113 196 196 256 266 31 P6.2.09.7TC F NE194 107 107 117 135 228 234 116 124 228 228 176 178 359 361 107 113 196 196 254 256 31 P6.2.09.9TC F NE195 103 107 117 135 228 250 120 128 242 242 178 178 361 361 113 113 196 196 256 262 31 P7.1.09.3NRF F NE196 107 107 131 135 228 234 116 120 240 242 178 178 359 361 113 115 196 196 256 264 31 P7.1.2009.2NR F NE197 103 107 117 117 228 228 120 124 242 254 178 178 361 361 113 113 196 196 264 264 31 P7.1.2009.7DP F NE198 103 107 117 133 228 250 120 120 228 242 178 180 357 357 107 113 196 196 260 260 31 P9.1.2009.2TC F NE199 107 107 131 135 228 234 116 120 242 254 178 178 357 359 113 113 196 196 264 264 31 97789 F NE200 103 103 131 131 228 228 120 120 228 242 178 178 357 357 113 113 196 212 256 260 31 P17.1.09.3TC M NE201 103 107 117 129 228 228 120 126 228 242 178 178 357 361 113 113 196 196 256 262 31 P19.1.09.10NR M NE202 103 107 131 135 228 230 120 126 240 256 178 178 357 361 113 113 196 196 256 256 31 P19.1.09.2TC M NE203 103 103 117 135 246 250 124 126 242 242 178 182 357 357 113 115 196 196 252 254 31 P19.1.09.5TC M NE204 101 101 135 135 250 250 120 120 228 256 178 178 357 357 113 113 196 196 254 260 31 P19.1.09.6NR M NE205 105 107 135 135 228 234 124 124 228 242 178 178 357 357 113 113 196 196 260 266 31 P19.1.09.9NR M NE206 103 107 131 135 228 230 120 126 240 256 178 178 357 361 113 113 196 196 256 256 31 P4.2.09.3TC M NE207 107 107 119 135 228 250 120 120 238 256 178 178 357 357 107 113 196 196 256 262 31 P5.2.09.4TC M NE208 103 107 117 117 228 228 120 128 228 228 178 180 357 359 113 115 196 196 260 266 31 P6.2.09.14TC M NE209 105 107 117 135 228 230 120 124 228 242 178 178 357 359 113 115 196 196 262 264 31 P7.1.09.3NRM M NE210 103 107 131 131 228 250 120 120 228 240 178 178 357 361 113 113 196 196 256 264 31 P7.1.2009.1TC M NE211 107 107 131 135 228 234 120 124 254 254 178 178 357 357 113 113 196 196 262 266 31 P7.1.2009.3DP M NE212 105 107 117 135 228 234 124 124 228 242 178 180 357 357 113 113 196 196 262 266 31 P7.1.2009.4NR M NE213 107 107 117 131 228 234 120 124 228 254 178 178 357 361 113 113 196 210 260 262 31 P8.1.2009.2NR M NE214 105 107 117 131 228 228 120 128 228 254 178 178 357 361 113 115 196 196 264 266 31 P8.1.2009.4NR M NE215 107 107 131 135 228 228 116 120 238 256 178 178 357 361 113 115 196 196 260 260 31 257947 M NE216 105 107 135 135 228 228 120 124 240 256 178 178 359 361 107 113 196 196 260 262 31 260114 M NE217 105 107 117 135 228 228 124 128 240 240 178 178 357 361 107 113 196 210 256 260 31 260138 M NE218 105 107 117 135 228 228 124 128 240 240 178 178 357 361 107 113 196 210 256 260

121

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5 Locus 6 Locus 7 Locus 8 Locus 9 Locus 10 WMU Sample Name Sex Pop. Map2C BM4513 BM1225 RT9 RT24 BM888 BM848 FCB193 RT30 BL42 31 260268 M NE219 103 107 117 129 228 228 120 120 228 228 178 178 361 361 107 113 196 196 260 260 31 08-011 M NE220 103 107 117 129 228 234 116 126 242 254 178 178 357 361 113 113 196 196 256 262 31 P11.1._09.3TC F NE221 103 107 129 135 248 250 120 120 242 254 178 178 357 361 113 113 196 210 0 0 31 P12.1.09.1NR F NE222 103 107 135 135 228 234 120 124 242 254 178 178 0 0 107 113 196 200 256 262 31 P12.1.09.2NRB F NE223 107 107 129 135 228 228 120 124 240 242 176 178 0 0 107 113 196 196 256 262 31 P12.1.09.2TC F NE224 107 107 117 135 228 234 120 120 228 256 178 178 0 0 107 113 196 196 260 264 31 P18.1.09.5NR F NE225 105 107 131 135 228 234 120 124 242 256 182 182 359 361 0 0 196 196 254 258 31 P5.02.09.2TC F NE226 103 105 117 119 0 0 120 120 242 254 178 178 361 361 107 113 196 196 260 262 31 P6.2.09.2TC F NE227 107 111 131 131 228 234 0 0 242 254 178 178 357 361 113 113 196 196 256 266 31 P6.2.09.3TC F NE228 107 107 129 135 228 234 124 124 228 256 178 178 0 0 113 113 196 196 254 256 31 19439 F NE229 107 107 135 135 234 250 120 122 242 256 178 178 361 363 113 113 196 196 0 0 31 P19.1.09.2NR M NE230 105 107 117 131 228 228 120 128 240 254 178 178 0 0 107 113 196 196 260 264 31 P6.2.09.10TC M NE231 105 107 117 129 0 0 120 120 242 254 178 178 357 361 113 113 196 196 262 264 31 P6.2.09.8TC M NE232 107 107 135 135 228 228 124 124 242 242 0 0 357 357 113 113 196 196 256 256 31 97949 M NE233 103 107 131 135 228 246 120 124 228 254 178 178 357 361 113 113 196 196 0 0 31 97956 M NE234 105 105 131 135 228 246 120 122 242 254 178 178 357 357 113 113 196 210 0 0 31 P11.1.09.4TC F NE235 107 107 135 135 228 228 120 124 242 256 0 0 0 0 113 113 196 196 256 264 31 P15.1._09.1TC2 F NE236 105 107 129 131 228 234 120 120 240 240 178 178 0 0 113 113 196 196 0 0 31 P9.1.2009.1TC M NE237 101 101 115 135 234 248 114 130 236 250 0 0 0 0 107 107 200 208 264 264 31 P9.1._2009.3TC M NE238 107 111 131 135 234 234 120 120 242 254 178 178 0 0 113 113 196 196 0 0 31 R17.1._09.2TC M NE239 105 107 131 131 228 234 120 126 242 242 178 178 0 0 113 115 196 196 0 0 31 R17.1._09.5TC M NE240 103 107 117 129 228 228 120 126 228 242 178 178 0 0 113 113 196 196 0 0 32 260244 F NE241 105 105 117 135 228 228 120 122 228 256 178 180 361 361 107 113 196 196 254 256 32 20183 M NE242 105 107 131 135 228 248 120 122 228 240 178 178 361 361 107 113 196 196 260 264 32 20244 M NE243 105 107 129 135 228 234 120 128 242 256 178 180 357 357 107 113 196 196 258 264 32 260282 M NE244 107 107 117 117 228 228 120 120 242 256 178 180 357 357 107 113 196 196 264 264 32 20213 F NE245 103 105 129 135 234 250 122 124 228 254 178 180 357 361 107 113 0 0 260 260 32 19538 M NE246 103 107 117 131 248 250 120 124 242 242 178 178 357 361 113 115 0 0 254 268 32 19545 M NE247 103 107 117 131 248 250 120 124 242 242 178 178 357 361 113 113 0 0 0 0 32 20985 M NE248 103 103 135 135 248 250 0 0 256 256 178 178 353 357 111 111 0 0 262 264 33 07-058 F NE249 105 105 117 135 228 234 120 124 228 228 178 178 357 361 113 115 196 196 256 260 33 19446 M NE250 105 107 129 135 228 250 108 124 242 256 178 178 361 361 107 107 196 196 262 262

122

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5 Locus 6 Locus 7 Locus 8 Locus 9 Locus 10 WMU Sample Name Sex Pop. Map2C BM4513 BM1225 RT9 RT24 BM888 BM848 FCB193 RT30 BL42 33 19590 M NE251 107 107 131 135 228 250 120 124 242 254 178 182 357 361 113 113 196 210 256 260 34 19996 M NE252 105 107 129 131 228 250 120 124 254 254 178 178 357 357 113 113 196 196 256 262 35 P20.1.09.5TC F NE253 103 107 117 131 228 234 120 120 242 256 178 178 357 361 113 113 196 196 256 264 35 P20.1.09.6TC F NE254 103 107 117 131 228 234 120 120 242 256 178 178 357 361 113 113 196 196 256 264 35 P21.1.09.1NR F NE255 105 107 117 129 228 228 120 128 228 256 178 180 357 361 107 113 196 196 256 264 35 P21.1.09.5TC F NE256 105 107 117 129 228 250 120 120 228 256 178 178 357 361 113 113 196 196 260 264 35 P22.1.09.3TC F NE257 105 107 135 135 228 234 120 120 242 242 178 180 361 361 113 113 196 196 262 264 35 P24.1.09.2NR F NE258 107 107 129 135 234 250 120 124 240 242 178 178 357 357 105 113 196 196 256 264 35 P24.1.09.6NR F NE259 103 107 117 131 228 228 120 124 242 242 178 180 359 361 107 115 196 196 256 266 35 P24.1.09.7NR F NE260 103 107 117 129 228 234 120 124 242 254 178 180 357 361 107 113 196 196 256 260 35 P25.2.09.1NR F NE261 107 107 117 135 228 228 120 128 242 254 178 180 357 361 113 113 196 210 256 264 35 P27.1.09.3TC F NE262 107 107 117 135 228 234 120 120 228 256 178 178 361 361 113 113 196 210 262 266 35 P27.1.09.5TC F NE263 103 105 117 135 228 230 124 126 228 242 178 178 361 361 113 113 196 196 256 264 35 P28.1.09.4TC F NE264 103 107 131 135 228 234 120 124 242 254 178 178 357 361 107 113 196 196 256 260 35 P31.1.09.3NR F NE265 103 105 119 135 234 250 120 120 242 242 178 178 357 359 113 113 196 196 260 260 35 260336 F NE266 107 107 117 117 228 228 120 124 242 254 178 178 357 357 107 113 196 196 260 260 35 P1.3.09.6NR M NE267 103 107 131 131 228 228 120 126 254 254 178 178 357 361 107 113 196 196 262 264 35 P20.1.09.4NR M NE268 107 107 131 135 224 228 120 124 254 254 178 180 357 361 113 113 196 212 262 266 35 P20.1.09.5NR M NE269 107 107 131 135 228 234 120 124 254 254 178 180 359 361 113 113 196 212 262 266 35 P21.1.09.10TC M NE270 107 109 135 135 228 250 120 120 242 256 178 178 357 359 113 113 206 212 256 260 35 P22.1.09.4TC M NE271 101 103 129 129 228 228 118 118 242 242 178 178 361 363 115 115 196 196 256 264 35 P22.1.09.5TC M NE272 109 111 129 129 228 228 122 122 242 242 178 178 361 363 113 113 196 196 256 264 35 P22.1.09.6NR M NE273 103 107 131 135 228 234 120 128 238 254 178 178 357 357 113 115 196 196 256 262 35 P22.1.09.6TC M NE274 105 107 129 129 228 228 120 120 242 242 178 178 361 363 113 117 196 196 256 264 35 P22.1.09.7TC M NE275 105 105 117 129 228 250 120 126 228 242 178 178 361 361 107 113 196 196 260 264 35 P24.1.09.10TC M NE276 103 107 135 135 228 228 120 126 228 242 178 178 361 361 107 113 196 196 256 262 35 P25.1.09.3NR M NE277 103 107 117 129 228 234 124 126 242 254 172 172 357 361 105 111 194 210 256 260 35 P29.1.09.1TC M NE278 107 107 131 135 228 228 116 120 242 242 178 180 357 361 107 113 196 196 256 256 35 P29.1.09.5NR M NE279 107 107 131 135 228 228 116 120 242 242 178 180 357 361 113 113 196 196 256 256 35 P30.1.09.1NR M NE280 103 105 131 131 228 228 124 124 242 256 178 178 357 361 105 105 196 210 262 266 35 P30.1.09.2NR M NE281 103 105 131 131 228 228 124 124 242 256 178 178 357 361 113 113 196 210 258 262 35 260251 M NE282 107 107 131 135 246 250 116 120 240 254 178 180 353 361 107 113 196 196 262 264

123

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5 Locus 6 Locus 7 Locus 8 Locus 9 Locus 10 WMU Sample Name Sex Pop. Map2C BM4513 BM1225 RT9 RT24 BM888 BM848 FCB193 RT30 BL42 35 260275 M NE283 103 105 131 131 228 234 122 126 228 242 178 180 353 361 107 113 196 196 260 264 35 P20.1.09.10NR F NE284 107 107 131 135 228 228 120 124 254 254 0 0 357 361 113 113 196 212 262 266 35 P20.1.09.7NR F NE285 107 107 131 135 228 228 120 124 254 254 0 0 357 361 103 113 196 196 262 266 35 P21.1.09.2NR F NE286 107 107 127 129 228 250 124 128 242 256 178 178 357 361 0 0 196 196 260 260 35 P21.1.09.3TC F NE287 103 107 131 135 228 228 120 124 242 242 178 178 0 0 107 107 196 196 260 260 35 P24.1.09.11TC F NE288 105 107 131 135 228 228 124 124 228 240 178 178 0 0 113 113 196 210 258 260 35 P24.1.09.13TC F NE289 107 107 117 131 234 234 120 124 242 256 0 0 357 357 115 115 196 196 260 262 35 P28.1.09.5TC M NE290 105 107 117 117 228 234 120 124 228 258 0 0 361 361 113 113 196 206 256 258 35 P29.1.09.2NR M NE291 107 107 131 135 228 234 116 120 242 242 178 178 0 0 105 107 196 196 256 256 35 P30.1.09.5NR M NE292 103 105 125 131 228 228 124 126 228 256 178 178 357 357 0 0 196 196 254 260 35 P31.3.09.9NR M NE293 103 107 0 0 228 228 120 126 254 254 178 178 357 361 107 113 196 196 262 264 35 260312 M NE294 105 107 131 131 228 250 120 120 254 258 178 178 357 361 107 115 0 0 256 262 35 260329 M NE295 105 107 117 129 228 234 120 124 254 256 178 178 357 359 107 113 0 0 256 264 35 20237 F NE296 107 107 135 135 234 234 120 124 240 254 178 180 357 361 0 0 196 210 0 0 35 P20.1.09.20NR F NE297 107 107 131 135 228 228 120 124 254 254 0 0 357 361 105 113 0 0 262 266 35 P21.1._09.4TC F NE298 105 107 117 129 228 250 120 120 228 256 178 178 0 0 113 115 196 196 0 0 35 P24.1._09.5NR F NE299 103 103 117 129 228 234 120 124 242 254 178 178 357 361 0 0 196 196 0 0 35 P27.1.09.2TC F NE300 107 107 131 135 228 228 124 126 228 256 178 178 0 0 0 0 196 196 260 262 35 P22.1.09.10NR M NE301 103 107 117 131 228 228 120 126 254 254 0 0 357 361 0 0 196 196 256 262 35 19231 M NE302 107 107 131 135 228 234 120 120 242 256 178 180 357 361 0 0 0 0 260 260 35 P20.1.09.2NRA M NE303 107 107 117 135 234 246 120 126 242 242 178 182 357 357 0 0 196 196 0 0 35 P20.1.09.2NRB M NE304 107 107 131 135 224 228 120 124 254 254 176 178 0 0 113 113 0 0 256 262 35 P29.1.09.5TC M NE305 0 0 129 129 234 234 0 0 244 244 180 182 357 361 105 113 202 202 256 256 36 Huges1 M NE306 107 107 131 135 228 234 120 120 254 256 178 180 357 361 113 113 196 196 262 262 37 260220 M NE307 105 105 129 131 228 228 124 128 228 242 178 178 357 361 107 113 196 196 262 262 37 07-046 M NE308 103 107 129 131 228 228 120 120 242 256 178 178 357 361 113 115 196 196 262 262 38 260145 F NE309 107 107 117 135 228 228 120 120 228 254 178 178 357 361 107 113 196 210 256 256 38 260237 M NE310 103 107 117 135 228 234 120 120 228 242 178 178 357 361 113 113 210 210 256 256 38 260299 M NE311 107 107 117 117 228 250 120 120 228 242 178 178 357 361 107 113 196 196 256 264 38 260305 M NE312 103 105 117 135 228 250 124 124 254 254 178 178 357 357 107 113 196 196 256 264 38 92784 M NE313 107 107 117 131 228 228 120 120 240 242 178 178 0 0 113 113 196 196 0 0 40 07-063 M NE314 107 107 135 135 228 228 120 126 254 254 178 178 357 357 113 115 196 196 262 264

124

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5 Locus 6 Locus 7 Locus 8 Locus 9 Locus 10 WMU Sample Name Sex Pop. Map2C BM4513 BM1225 RT9 RT24 BM888 BM848 FCB193 RT30 BL42 40 07-070 M NE315 103 107 117 117 228 228 120 124 228 242 178 180 357 361 113 113 196 196 256 260 41 07-001 M NE316 105 107 117 131 228 234 124 128 242 256 178 178 357 361 113 113 196 196 260 260 42 07-016 M NE317 103 107 135 135 228 250 116 120 228 240 178 180 357 357 113 113 196 196 252 256 42 07-054 M NE318 105 107 135 135 228 228 120 124 242 254 178 178 357 361 113 113 196 196 260 266 46 07-020 F SC1 105 105 131 135 228 228 120 128 242 256 178 180 357 357 113 113 196 196 256 256 46 07-021 F SC2 105 105 131 135 228 250 120 120 242 242 178 178 357 357 113 113 196 196 256 264 46 260350 M SC3 107 107 135 135 228 228 120 124 254 254 178 178 357 361 113 113 196 196 262 264 47 643 F SC4 105 107 131 135 226 228 120 124 242 242 178 180 357 361 113 117 196 196 262 266 47 858 F SC5 103 103 131 135 228 228 120 120 242 254 178 180 361 361 113 113 196 196 256 264 47 04-4701 F SC6 103 103 131 135 228 228 120 120 242 254 178 180 361 361 113 113 196 196 258 264 47 07-018 F SC7 105 107 135 135 228 228 120 126 242 254 178 180 357 361 113 113 196 196 256 262 47 07-027 F SC8 105 107 131 135 228 228 120 126 244 254 178 180 357 359 113 113 196 196 256 262 47 258050 M SC9 105 107 131 131 228 250 116 120 228 228 178 178 357 361 107 113 196 196 260 262 47 07-002 M SC10 103 105 117 135 228 250 120 120 242 242 178 178 359 361 113 113 196 196 262 262 47 615 M SC11 105 107 117 135 234 250 120 120 254 256 178 180 361 361 113 113 196 196 262 264 48 202 F SC12 103 107 131 135 228 228 122 124 242 242 178 178 357 361 113 115 196 196 258 262 48 501 F SC13 103 107 117 131 228 234 116 120 254 254 178 178 357 361 113 113 196 196 260 264 48 788 F SC14 103 107 117 131 228 234 116 120 254 254 178 178 357 361 113 113 196 196 260 264 48 789 F SC15 101 103 131 135 226 228 120 124 254 254 178 180 357 359 113 113 196 196 256 256 48 790 F SC16 103 107 131 135 228 228 122 124 242 242 178 178 357 361 113 115 196 196 256 262 48 792 F SC17 105 107 129 131 226 228 120 124 242 254 178 178 357 357 113 115 196 196 256 262 48 902 F SC18 101 103 131 135 226 228 120 124 254 254 178 180 357 359 113 113 196 196 258 258 48 03-4806 F SC19 103 107 131 135 250 250 120 124 254 254 178 178 357 361 113 113 196 196 256 260 48 04-4801B F SC20 101 107 129 131 228 234 122 126 252 254 178 178 361 361 113 113 196 196 264 268 48 04-4802C F SC21 103 107 117 135 228 228 116 120 242 254 178 178 357 361 113 115 196 196 258 260 48 04-4804C F SC22 105 107 131 135 228 228 120 120 242 254 180 180 357 357 113 113 196 196 260 264 48 04-4806B F SC23 105 107 131 135 228 228 118 120 240 242 178 178 357 359 113 113 196 196 258 258 48 04-4807A F SC24 107 107 117 131 228 250 116 124 242 242 178 178 353 357 115 115 196 196 256 266 48 785 FC SC25 101 107 129 131 228 234 122 126 252 254 178 178 361 361 113 113 196 196 264 268 48 502 M SC26 101 101 117 131 228 228 116 120 242 254 178 178 359 361 113 113 196 196 258 262 48 503 M SC27 105 105 135 135 226 228 118 120 242 242 178 178 357 361 113 117 196 196 258 260 48 504 M SC28 107 107 117 129 228 228 120 124 240 242 178 178 361 361 113 113 196 196 260 264

125

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5 Locus 6 Locus 7 Locus 8 Locus 9 Locus 10 WMU Sample Name Sex Pop. Map2C BM4513 BM1225 RT9 RT24 BM888 BM848 FCB193 RT30 BL42 48 787 M SC29 103 103 135 135 228 228 120 124 242 254 178 178 361 361 113 113 196 196 256 264 48 794 M SC30 103 105 117 135 226 228 116 124 242 254 178 178 357 357 113 113 196 196 256 256 48 795 M SC31 103 105 117 131 228 228 116 120 242 254 178 178 359 361 113 113 196 196 256 262 48 796 M SC32 107 107 117 129 228 228 120 124 240 242 178 178 361 361 113 113 196 196 260 264 48 798 M SC33 105 105 135 135 226 228 118 120 242 242 178 178 357 361 113 117 196 196 256 260 48 901 M SC34 103 105 117 135 226 228 116 124 242 254 178 178 357 357 113 113 196 196 258 258 48 904 M SC35 105 107 129 131 226 228 120 124 242 254 178 178 357 357 113 115 196 196 258 262 48 03-4801 M SC36 103 107 131 131 228 250 120 120 242 242 178 178 357 359 113 113 196 196 256 256 48 03-4804 M SC37 105 107 135 135 228 228 120 120 254 254 178 180 361 361 113 115 196 196 256 262 48 03-4805 M SC38 105 107 131 135 228 228 120 120 242 242 178 178 357 361 113 113 196 196 262 262 48 04-4801C M SC39 103 103 135 135 228 228 120 124 242 254 178 178 361 361 113 113 196 196 258 264 48 04-4803A M SC40 103 105 133 135 226 228 116 122 242 254 178 180 361 361 113 115 196 196 258 260 48 04-4805C M SC41 103 105 117 135 228 228 120 120 256 256 178 178 357 357 113 113 196 196 258 262 48 408 F SC42 105 107 117 135 228 250 120 124 252 254 178 178 361 361 113 113 196 196 0 0 48 791 F SC43 103 107 117 117 228 228 124 124 242 254 178 178 357 357 113 113 196 196 0 0 48 793 F SC44 105 107 117 135 228 250 120 124 252 254 178 178 361 361 113 113 196 196 0 0 48 903 F SC45 103 107 117 117 228 228 124 124 242 254 178 178 357 357 113 113 196 196 0 0 48 208 M SC46 103 107 125 125 228 228 0 0 0 0 178 178 357 359 113 113 196 196 258 264 48 797 M SC47 103 107 125 125 228 228 0 0 0 0 178 178 357 359 113 113 196 196 256 264 49 612 F SC48 103 107 117 131 228 250 120 120 242 254 178 180 357 357 111 113 194 196 256 262 49 613 F SC49 103 105 117 135 228 234 120 120 240 242 178 178 357 359 113 113 196 196 258 260 49 614 F SC50 103 105 131 135 228 228 120 120 256 256 178 178 361 361 113 115 196 196 256 264 49 638 F SC51 105 107 117 117 228 228 124 124 228 228 178 178 357 357 107 113 196 196 260 264 49 99838 F SC52 105 107 117 135 228 228 120 120 256 256 178 180 357 361 113 115 196 196 256 262 49 04-hagerman02 F SC53 103 105 135 135 228 228 120 120 242 254 178 178 361 361 113 113 196 196 258 264 49 99840 F SC54 103 107 131 135 228 228 120 120 226 228 178 178 357 357 113 113 196 196 260 260 49 99841 F SC55 107 107 117 131 228 228 120 120 254 254 178 178 359 359 113 113 196 196 256 262 49 99843 F SC56 103 107 117 135 228 250 120 124 228 256 178 178 357 359 113 113 196 196 260 262 49 99845 F SC57 105 107 135 135 228 228 120 124 254 256 178 180 357 361 113 115 196 196 264 264 49 99849 F SC58 105 107 117 131 228 228 116 120 254 254 178 178 355 359 113 113 196 196 260 260 49 99851 F SC59 105 107 117 129 228 250 116 124 242 254 178 178 355 357 113 115 196 196 260 264 49 99852 F SC60 105 107 135 135 228 228 120 120 228 242 178 178 361 361 113 113 196 196 256 256

126

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5 Locus 6 Locus 7 Locus 8 Locus 9 Locus 10 WMU Sample Name Sex Pop. Map2C BM4513 BM1225 RT9 RT24 BM888 BM848 FCB193 RT30 BL42 49 99859 F SC61 105 107 117 135 228 250 116 120 242 254 178 178 361 361 113 113 196 196 260 266 49 99861 F SC62 107 107 117 131 228 228 120 120 254 254 178 178 357 361 113 113 196 196 260 264 49 99863 F SC63 103 107 135 135 228 228 120 120 254 254 178 180 357 361 113 113 196 196 256 258 49 99865 F SC64 103 103 135 135 228 228 120 120 242 254 178 178 361 361 113 113 196 196 256 256 49 89936 F SC65 107 107 131 135 228 228 120 120 242 254 178 180 357 359 113 113 196 196 258 264 49 93653 F SC66 105 107 135 135 228 250 120 124 242 254 178 178 359 361 113 115 196 196 260 264 49 93697 F SC67 105 107 131 135 228 228 120 124 254 254 178 178 359 361 113 113 196 196 256 262 49 93699 F SC68 103 105 117 131 234 250 120 120 242 242 178 178 361 361 113 115 196 196 260 260 49 98701 F SC69 107 107 117 135 228 228 118 120 254 254 178 178 357 361 115 117 196 196 256 264 49 98707 F SC70 103 107 131 135 228 234 120 120 254 254 180 180 357 361 113 113 196 196 252 252 49 98713 F SC71 105 107 117 131 228 228 116 120 256 256 178 178 357 361 113 115 196 196 256 260 49 98716 F SC72 105 105 117 135 228 228 120 120 242 254 178 180 357 357 113 115 196 196 260 260 49 98719 F SC73 105 107 117 135 228 250 118 124 240 242 178 178 361 361 113 113 196 196 256 256 49 98724 F SC74 103 105 117 135 228 228 120 120 254 256 178 178 357 357 113 113 196 196 260 262 49 98727 F SC75 103 107 135 135 228 234 120 124 240 256 178 178 357 361 113 113 196 196 256 262 49 98733 F SC76 105 107 117 135 228 228 120 120 228 256 178 180 357 361 113 115 196 196 250 250 49 98734 F SC77 105 107 117 131 228 234 120 124 228 254 178 178 361 361 113 115 196 196 262 262 49 98735 F SC78 107 107 131 135 228 228 120 120 254 254 178 178 357 361 113 113 196 196 256 256 49 99802 F SC79 103 107 135 135 228 228 124 124 240 242 178 180 357 359 113 113 196 196 258 260 49 99804 F SC80 103 105 117 131 250 250 122 124 242 256 178 178 357 357 113 113 196 196 262 264 49 99806 F SC81 103 103 131 135 228 228 120 124 228 228 178 178 357 361 113 113 196 196 258 266 49 99808 F SC82 103 105 131 135 228 228 120 120 256 256 178 178 361 361 113 113 196 196 256 264 49 99814 F SC83 103 105 135 135 228 250 120 124 254 254 178 178 357 357 113 113 196 196 262 266 49 99817 F SC84 103 107 117 131 228 228 120 120 242 242 178 178 357 361 113 113 196 196 266 266 49 99819 F SC85 105 107 135 135 228 228 120 124 240 242 178 178 357 359 113 117 196 196 256 260 49 99821 F SC86 107 107 117 131 228 228 120 124 228 254 178 178 357 357 113 113 196 196 256 266 49 99823 F SC87 103 107 135 135 228 234 120 120 228 256 178 178 357 361 113 113 196 196 256 262 49 99827 F SC88 103 107 117 117 228 228 120 124 228 242 178 178 357 361 113 113 196 196 264 264 49 99831 F SC89 103 105 131 131 228 234 120 124 242 254 178 178 357 357 115 115 196 196 256 262 49 99833 F SC90 107 107 117 135 228 250 120 120 228 254 178 178 357 357 113 113 196 196 256 264 49 99835 F SC91 105 107 131 135 228 228 116 120 254 254 178 178 357 361 113 115 196 196 260 262 49 99837 F SC92 105 107 131 135 228 228 120 124 256 256 178 178 359 361 115 115 196 196 262 262

127

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5 Locus 6 Locus 7 Locus 8 Locus 9 Locus 10 WMU Sample Name Sex Pop. Map2C BM4513 BM1225 RT9 RT24 BM888 BM848 FCB193 RT30 BL42 49 260428 M SC93 105 107 135 135 228 234 120 120 240 240 178 180 359 361 113 113 196 196 256 260 49 260466 M SC94 103 107 117 117 228 248 120 128 240 252 178 178 357 361 113 115 196 196 264 264 49 640 M SC95 103 107 117 131 228 246 120 120 226 228 178 178 357 359 107 113 196 196 260 264 49 641 M SC96 101 105 133 135 226 242 124 124 242 254 176 178 357 359 113 117 196 196 256 260 49 93667 M SC97 105 107 131 131 228 234 120 124 242 242 178 178 359 361 113 115 196 196 258 264 49 04-monteith01 M SC98 103 105 117 135 228 228 120 120 242 254 178 178 357 359 113 113 196 196 258 258 49 07-026 M SC99 107 107 117 135 228 232 120 126 228 256 178 178 361 361 113 115 196 196 256 260 49 07-061 M SC100 105 107 131 131 228 228 116 120 242 254 178 180 357 361 113 113 196 196 260 262 49 93677 M SC101 103 105 131 131 228 250 120 120 254 254 178 178 357 357 113 113 196 196 260 262 49 93698 M SC102 107 107 117 117 228 250 120 124 228 228 178 178 353 357 113 113 196 196 256 260 49 98680 M SC103 105 107 131 135 228 228 120 120 254 254 178 178 357 361 113 115 196 196 260 262 49 99882 M SC104 107 107 131 135 228 228 120 120 242 254 178 178 357 361 113 113 196 196 256 256 49 99870 F SC105 103 103 117 131 228 228 116 120 228 256 178 180 359 359 113 113 196 196 0 0 49 99825 F SC106 103 107 117 135 228 250 120 124 242 254 178 178 357 361 113 113 196 196 0 0 49 99829 F SC107 103 103 117 117 228 228 120 120 0 0 178 178 361 361 113 113 196 196 256 264 49 639 M SC108 107 107 117 129 228 228 116 124 0 0 178 178 357 359 107 113 196 196 260 264 49 260459 M SC109 103 103 117 135 228 250 120 120 242 254 178 178 357 361 107 113 0 0 264 264 49 04-hagerman01 M SC110 103 105 129 131 226 228 116 118 240 242 178 178 357 357 113 113 196 196 0 0 49 04-Spence01 M SC111 103 107 117 129 228 234 118 120 242 242 176 178 355 361 113 113 196 196 0 0 49 99888 M SC112 107 107 131 135 228 228 120 120 254 254 178 178 357 361 113 113 196 196 0 0 49 609 F SC113 105 105 0 0 0 0 120 122 256 256 178 178 357 357 113 113 196 196 260 264 49 93672 F SC114 103 103 117 135 228 228 120 120 0 0 178 178 357 359 107 115 196 196 0 0 49 99810 F SC115 103 105 117 117 228 228 120 120 254 256 178 178 357 361 0 0 196 196 0 0 49 99812 F SC116 107 107 117 131 234 250 120 124 240 242 178 178 359 361 0 0 196 200 0 0 50 644 F SC117 103 107 117 135 228 228 124 124 228 242 178 178 357 357 113 113 196 196 262 264 50 645 F SC118 103 107 117 135 228 250 116 124 242 242 178 178 357 357 113 113 196 196 262 264 50 646 F SC119 103 107 117 131 228 228 116 120 226 254 176 178 361 361 113 115 196 196 256 264 50 648 F SC120 103 105 133 135 228 234 116 118 256 256 178 178 357 357 111 113 194 196 266 266 50 649 F SC121 103 107 117 135 228 228 116 120 226 254 178 178 357 361 113 113 196 196 256 264 50 859 F SC122 103 105 117 135 228 234 120 120 240 242 178 180 357 361 113 113 196 196 256 262 50 04-5001 F SC123 103 105 117 135 228 234 120 120 240 242 178 180 357 361 113 113 196 196 258 262 50 07-019 F SC124 103 105 115 135 228 242 114 124 228 242 178 178 357 361 113 113 196 196 264 264

128

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5 Locus 6 Locus 7 Locus 8 Locus 9 Locus 10 WMU Sample Name Sex Pop. Map2C BM4513 BM1225 RT9 RT24 BM888 BM848 FCB193 RT30 BL42 50 642 M SC125 105 107 129 131 228 228 120 124 226 242 176 178 361 361 113 115 196 196 256 264 50 650 M SC126 105 107 133 135 228 250 116 124 226 242 178 178 357 359 113 115 196 196 260 264 50 07-022 M SC127 105 107 117 135 228 228 120 120 242 254 178 178 361 361 113 113 196 196 256 256 50 07-044 M SC128 105 107 131 131 228 234 120 120 242 242 178 180 357 357 113 113 196 196 256 264 50 07-052 M SC129 105 107 117 131 226 228 116 120 242 256 178 178 357 361 113 115 196 196 256 256 50 07-074 M SC130 107 107 117 129 228 228 120 120 242 254 178 178 357 361 113 113 196 196 256 256 50 08-010 M SC131 105 107 131 135 228 250 120 120 240 240 178 178 357 361 113 113 196 196 256 264 50 647 F SC132 103 107 117 135 228 250 0 0 240 256 176 178 361 361 113 115 196 196 256 264 51 799 F SC133 105 107 117 135 228 228 120 124 254 254 178 180 357 361 113 113 196 196 256 262 51 1001 F SC134 105 107 117 135 228 228 120 124 254 254 178 180 357 361 113 113 196 196 258 262 51 99867 F SC135 105 109 117 131 230 230 120 120 244 258 178 178 361 361 113 115 196 196 262 264 51 99828 F SC136 107 107 117 129 228 228 120 124 228 228 178 178 357 361 113 117 196 196 256 266 51 98712 F SC137 107 107 135 135 228 228 120 124 242 254 178 180 361 361 113 113 196 196 256 260 51 98721 F SC138 103 107 131 131 228 228 116 120 242 242 178 178 357 357 113 117 196 196 256 260 51 98722 F SC139 103 105 131 135 228 228 124 124 242 256 178 180 357 361 113 115 196 196 256 260 51 99507 F SC140 103 105 135 135 228 228 120 120 242 254 178 178 357 357 113 115 196 196 256 256 51 99508 F SC141 105 107 117 131 228 228 120 120 242 242 178 178 357 361 113 115 196 196 262 264 51 99513 F SC142 103 107 117 131 228 228 124 124 254 254 178 178 357 361 113 115 196 196 262 262 51 99514 F SC143 107 107 131 135 228 228 116 120 228 242 178 178 359 361 113 115 196 196 256 262 51 99803 F SC144 107 107 135 135 228 228 116 116 242 242 178 178 357 361 113 113 196 196 256 256 51 99807 F SC145 105 107 117 135 228 228 116 120 228 242 178 178 359 361 113 113 196 196 256 256 51 99809 F SC146 103 103 117 117 228 228 120 124 240 256 178 178 357 361 113 113 196 196 256 264 51 99811 F SC147 103 103 117 117 240 240 116 124 228 246 178 178 359 361 113 113 196 196 256 262 51 99813 F SC148 103 105 117 135 228 234 116 120 240 240 178 178 357 357 113 113 196 196 256 266 51 99815 F SC149 105 105 117 131 228 250 120 120 228 242 178 178 357 359 113 115 196 196 260 262 51 99816 F SC150 105 107 117 131 228 228 120 120 254 254 178 180 357 361 113 113 196 196 256 264 51 99818 F SC151 103 107 117 131 228 228 116 124 242 254 178 178 361 361 113 117 196 196 264 264 51 99820 F SC152 105 107 135 135 228 250 120 124 228 254 178 178 361 361 113 117 196 196 264 266 51 99824 F SC153 105 107 135 135 228 228 124 124 240 240 178 178 357 357 113 113 196 196 256 266 51 99826 F SC154 105 105 131 135 228 228 120 120 228 242 178 178 357 359 113 113 196 196 256 256 51 99830 F SC155 105 105 131 135 228 228 116 120 240 254 178 178 357 361 113 117 196 196 256 266 51 99836 F SC156 103 105 117 135 228 228 120 124 240 240 178 178 357 361 113 113 196 196 256 260

129

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5 Locus 6 Locus 7 Locus 8 Locus 9 Locus 10 WMU Sample Name Sex Pop. Map2C BM4513 BM1225 RT9 RT24 BM888 BM848 FCB193 RT30 BL42 51 99844 F SC157 103 107 131 135 228 228 124 124 254 254 178 178 357 357 113 113 196 196 256 260 51 99846 F SC158 103 105 117 135 228 250 120 124 242 254 178 178 357 361 113 113 196 196 256 256 51 99853 F SC159 105 107 135 135 228 228 120 120 228 242 178 178 359 361 113 115 196 196 260 260 51 99854 F SC160 103 105 131 135 228 228 120 120 238 242 178 180 357 359 113 113 196 196 260 260 51 99855 F SC161 105 105 117 131 228 228 120 124 242 256 178 178 357 359 113 113 196 196 262 262 51 99856 F SC162 103 103 117 117 228 234 116 120 242 254 178 178 361 361 113 113 196 196 256 264 51 99860 F SC163 105 107 131 135 228 228 120 124 254 254 178 178 357 361 113 113 196 196 262 264 51 99862 F SC164 103 107 117 117 228 228 120 124 242 254 178 178 359 359 113 113 196 196 256 260 51 99864 F SC165 105 107 135 135 228 228 120 124 254 254 178 178 357 357 113 115 196 196 260 264 51 99866 F SC166 107 107 135 135 228 228 120 120 242 254 178 178 357 361 113 117 196 196 256 262 51 99868 F SC167 103 107 117 135 228 228 120 120 242 256 178 180 357 361 113 113 196 196 256 260 51 Calf-57 M SC168 103 105 117 135 228 234 120 120 242 254 178 178 357 357 113 115 196 196 256 264 51 M130 M SC169 103 105 117 117 228 228 120 124 242 242 178 178 357 361 113 113 196 196 256 260 51 99510 M SC170 105 107 131 131 228 228 124 124 242 242 178 178 357 357 113 113 196 196 256 264 51 M016A F SC171 105 105 117 135 228 250 120 120 242 254 176 178 361 361 113 115 196 196 0 0 51 M016B F SC172 105 107 131 135 228 228 120 124 254 254 178 178 357 361 113 115 196 196 0 0 51 98718 F SC173 101 101 131 131 0 0 122 122 240 242 178 178 357 357 113 115 196 196 260 260 51 99224 F SC174 103 107 135 135 228 228 120 124 242 254 178 178 357 357 113 115 0 0 256 256 51 99834 F SC175 105 105 117 135 228 228 120 124 242 252 178 180 361 361 113 113 196 196 0 0 51 99858 F SC176 103 105 0 0 228 228 120 120 254 254 178 178 359 359 113 115 196 196 256 260 51 M131 M SC177 107 107 131 135 228 250 120 124 240 254 178 178 361 361 113 113 0 0 256 256 51 Wyse1 M SC178 0 0 117 117 228 228 120 120 242 242 178 180 357 357 115 115 196 196 0 0 51 M059 F SC179 101 107 117 131 228 228 120 120 254 254 0 0 357 357 113 113 196 196 0 0 51 M060 F SC180 103 107 117 131 228 250 120 124 254 256 178 178 353 357 111 111 0 0 0 0 51 99832 F SC181 105 107 117 131 228 228 124 124 242 254 176 178 357 357 0 0 196 196 0 0 51 20855 M SC182 103 103 0 0 228 228 120 124 242 256 178 178 361 361 111 111 0 0 256 256 51 Wyse1 M SC183 0 0 117 117 228 228 120 120 242 242 178 180 357 357 115 115 196 196 0 0 51 99509 M SC184 105 105 117 135 228 246 120 124 242 242 178 180 0 0 113 115 196 196 0 0 53A 651 F SC185 103 107 135 135 250 250 120 120 238 242 178 178 357 359 113 115 196 196 260 264 53A 07-003 F SC186 107 107 117 135 228 228 120 120 228 242 178 180 357 361 113 113 196 196 264 268 53A 07-013 F SC187 105 107 117 135 228 228 120 120 228 254 178 180 361 361 113 115 196 196 256 264 53A 07-014 F SC188 105 107 117 135 228 228 120 120 226 254 178 180 361 361 113 115 196 196 256 264

130

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5 Locus 6 Locus 7 Locus 8 Locus 9 Locus 10 WMU Sample Name Sex Pop. Map2C BM4513 BM1225 RT9 RT24 BM888 BM848 FCB193 RT30 BL42 53A 659 M SC189 103 107 131 135 228 234 120 120 254 254 178 178 357 361 113 113 196 196 262 264 54 656 F SC190 103 107 133 135 228 228 120 124 242 254 176 178 357 361 111 115 194 196 262 264 54 657 F SC191 105 107 133 135 228 250 116 120 238 242 178 178 357 361 113 115 196 196 256 260 54 07-038 F SC192 103 103 135 135 228 228 122 124 254 256 178 178 357 359 113 113 196 196 260 266 54 652 M SC193 103 107 117 135 226 228 116 118 238 242 178 178 357 357 113 113 196 196 256 262 54 654 M SC194 103 107 117 135 228 250 120 124 228 256 176 178 359 361 113 115 196 196 256 260 54 655 M SC195 107 107 117 117 228 250 120 120 238 242 176 178 357 361 111 115 194 196 264 264 54 07-024 M SC196 103 107 135 135 228 228 116 120 254 254 178 180 357 357 113 113 194 196 256 260 55A 619 F SC197 101 103 117 131 228 228 118 120 254 256 178 180 357 357 113 113 194 196 264 266 55A 800 F SC198 107 107 131 135 228 250 118 120 244 254 178 178 357 357 113 113 196 196 260 260 55A 801 F SC199 103 105 117 135 234 250 120 124 240 254 178 178 357 361 115 115 196 196 256 264 55A 802 F SC200 107 107 117 131 228 228 124 124 240 242 178 178 359 359 113 117 196 196 256 264 55A 543475 F SC201 107 107 117 131 228 228 124 124 240 242 178 178 359 359 113 117 196 196 258 264 55A 543484 F SC202 107 107 131 135 228 250 118 120 244 254 178 178 357 357 113 113 196 196 260 260 55A 616 M SC203 105 107 131 135 228 250 120 124 226 254 178 178 357 357 113 113 196 196 256 266 55A 621 M SC204 105 107 135 135 228 228 120 124 228 240 178 178 357 361 113 113 196 196 256 264 55A 622 M SC205 105 107 131 135 228 228 124 124 242 242 178 178 357 361 113 113 196 196 256 264 55A 627 M SC206 103 107 117 117 228 228 120 124 254 254 178 178 357 357 113 113 196 196 256 260 55A 803 M SC207 103 107 135 135 228 228 120 120 238 256 178 178 357 359 113 113 196 196 260 264 55A 805 M SC208 101 101 117 135 228 228 120 124 240 254 178 178 357 361 113 113 196 196 256 256 55A 806 M SC209 105 105 131 135 228 250 120 124 242 254 178 178 357 361 113 113 196 196 260 262 55A 807 M SC210 103 105 117 131 228 228 120 120 242 256 178 178 357 357 113 115 196 196 264 264 55A 808 M SC211 107 107 117 131 228 228 116 120 254 254 178 178 357 357 113 113 196 196 256 256 55A 809 M SC212 103 105 117 131 228 250 118 120 238 238 178 180 359 361 113 115 196 198 256 260 55A 810 M SC213 103 105 135 135 228 228 120 124 242 242 178 178 357 359 113 113 196 196 260 262 55A 811 M SC214 103 105 135 135 228 228 120 124 254 254 178 178 359 361 113 113 196 196 256 260 55A 498275 M SC215 103 105 135 135 228 228 120 124 242 242 178 178 357 359 113 113 196 196 260 262 55A 498442 M SC216 105 107 117 135 228 228 120 124 254 254 178 178 359 359 113 113 196 196 260 266 55A 498445 M SC217 101 101 117 135 228 228 120 124 240 254 178 178 357 361 113 113 196 196 258 258 55A 543480 M SC218 103 105 135 135 228 228 120 124 254 254 178 178 359 361 113 113 196 196 258 260 55A 543481 M SC219 103 105 117 131 228 228 120 120 242 256 178 178 357 357 113 115 196 196 264 264 55A 543482 M SC220 107 107 117 131 228 228 116 120 254 254 178 178 357 357 113 113 196 196 258 258

131

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5 Locus 6 Locus 7 Locus 8 Locus 9 Locus 10 WMU Sample Name Sex Pop. Map2C BM4513 BM1225 RT9 RT24 BM888 BM848 FCB193 RT30 BL42 55A 543483 M SC221 103 105 117 131 228 250 118 120 238 238 178 180 359 361 113 115 196 198 258 260 55A 543486 M SC222 105 105 131 135 228 250 120 124 242 254 178 178 357 361 113 113 196 196 260 262 55A 04-55A01 M SC223 103 107 135 135 228 228 120 120 238 256 178 178 357 359 113 113 196 196 260 264 55A 258043 M SC224 107 107 131 135 228 250 120 120 242 254 178 178 357 357 107 113 196 196 0 0 55A 804 M SC225 103 105 133 133 226 226 118 122 242 252 178 178 0 0 113 113 196 196 0 0 55A 498439 M SC226 103 105 133 133 226 226 118 122 242 252 178 178 0 0 113 113 196 196 0 0 55B 812 M SC227 105 107 117 135 228 228 120 124 254 254 178 178 359 359 113 113 196 196 260 266 56 DEPM01-08 F SC228 103 103 131 135 228 228 120 120 254 254 178 178 361 361 113 115 196 196 256 256 56 DEPM02-02 F SC229 107 107 117 135 228 228 120 124 242 254 178 180 357 361 113 117 196 196 256 262 56 NUGM01-00 F SC230 103 103 131 135 228 228 116 120 242 254 178 180 359 361 113 113 196 196 256 256 56 STAM01-01 F SC231 105 107 117 117 228 228 120 124 242 242 178 178 357 361 113 115 196 196 256 260 56 STAM01-02 F SC232 105 107 131 135 228 250 120 120 242 254 178 178 357 361 113 113 196 196 256 256 56 STAM02-02 F SC233 107 107 131 131 228 250 120 124 242 242 178 178 357 361 113 113 196 196 256 260 56 STAM03-01 F SC234 105 105 135 135 228 228 116 116 242 242 178 178 361 361 113 113 196 196 256 266 56 STOM01-01 F SC235 107 107 135 135 228 228 116 120 242 254 178 178 357 357 113 113 196 196 262 264 56 WILM01-01 F SC236 105 107 117 131 228 228 120 124 242 242 178 178 357 359 113 113 196 196 256 264 56 WILM02-03 F SC237 103 107 117 131 228 228 122 124 242 254 178 178 357 361 115 115 196 196 256 262 56 WILMO2-02 F SC238 107 107 117 135 250 250 120 120 242 242 178 178 361 361 113 113 196 196 260 260 56 07-047 M SC239 103 105 117 135 228 234 120 124 240 254 178 180 357 361 113 113 196 196 252 260 56 BLAM01_01 M SC240 103 105 135 135 228 228 120 124 228 254 176 178 357 357 113 115 196 196 264 264 56 BLAM01_02 M SC241 107 107 117 117 228 228 120 124 242 242 178 178 357 361 113 115 196 196 256 262 56 BONM01_01 M SC242 103 103 135 135 228 228 120 124 242 242 178 178 357 357 113 113 196 196 264 264 56 BONM02_02 M SC243 103 107 117 129 228 228 120 124 228 242 178 178 355 357 113 113 196 196 256 256 56 BUD01-02 M SC244 103 103 135 135 228 250 120 124 242 254 178 180 357 357 113 115 196 196 256 264 56 DEPM01-01 M SC245 107 107 131 131 228 228 120 122 254 254 178 178 357 361 113 115 196 196 256 262 56 DEPM01-02 M SC246 103 107 117 135 228 228 116 120 242 254 178 178 357 357 113 117 196 196 256 260 56 DEPM04-00 M SC247 107 107 131 135 228 250 120 124 254 254 178 178 357 361 115 115 196 196 256 264 56 DIAM01-00 M SC248 103 107 131 131 250 250 120 124 242 242 178 180 357 361 113 113 196 196 264 266 56 DIAM01-01 M SC249 103 105 135 135 228 228 124 124 242 254 178 178 357 357 113 113 196 196 256 266 56 HOLM01-08 M SC250 105 107 131 135 228 250 120 120 242 254 178 178 355 357 113 113 196 196 262 264 56 LOSM01-08 M SC251 105 107 117 135 228 250 120 124 242 254 178 178 357 361 113 113 196 196 256 264 56 NUGM01-02 M SC252 103 107 131 135 228 228 124 124 242 242 178 180 357 357 113 113 196 196 256 260

132

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5 Locus 6 Locus 7 Locus 8 Locus 9 Locus 10 WMU Sample Name Sex Pop. Map2C BM4513 BM1225 RT9 RT24 BM888 BM848 FCB193 RT30 BL42 56 STAM03-02 M SC253 103 107 117 131 228 228 116 124 242 242 178 178 361 361 113 113 196 196 256 264 56 STOM01-00 M SC254 107 107 131 131 228 250 120 120 242 256 178 178 361 361 113 113 196 196 260 266 56 STOM01-03 M SC255 105 107 117 135 228 234 120 120 242 254 178 178 357 357 113 117 196 196 260 264 56 WILM01-02 M SC256 105 107 135 135 228 228 120 122 242 254 180 180 357 361 113 113 196 196 256 260 56 DEPM01-00 F SC257 103 103 117 135 228 250 124 124 242 242 178 180 357 357 113 117 196 196 0 0 56 STAM04-00 F SC258 105 105 117 135 228 228 120 120 242 254 178 178 359 361 113 115 196 196 0 0 56 WILM03-02 F SC259 107 107 135 135 228 250 120 124 242 254 178 180 357 357 113 115 196 196 0 0 56 08-004 M SC260 103 105 135 135 228 234 120 120 242 254 178 178 357 361 113 113 196 196 0 0 56 BONM01_02 M SC261 103 107 129 131 228 228 116 124 228 228 178 178 357 357 113 113 196 196 0 0 56 LOSM01-00 M SC262 103 107 131 135 228 228 116 120 242 256 178 180 361 361 113 113 196 196 0 0 56 MILM02-02 M SC263 105 107 117 117 228 228 116 120 254 254 178 178 361 361 113 113 196 196 0 0 56 STAM03-00 M SC264 103 105 135 135 228 228 120 124 0 0 178 178 357 361 113 113 196 196 260 264 56 THIM01-02 M SC265 105 107 135 135 228 228 120 120 256 256 178 178 357 361 115 115 196 196 0 0 56 NUGM03-00 F SC266 105 107 131 135 228 228 120 120 242 242 0 0 355 357 113 115 196 196 0 0 56 THIM01-04 F SC267 103 105 117 117 228 228 120 120 0 0 178 178 357 357 113 113 196 196 0 0 56 STAM01-00 M SC268 107 107 117 131 0 0 120 120 0 0 178 178 357 361 113 115 196 196 256 256 56 THIM02-03 M SC269 107 107 117 131 228 228 120 124 254 254 178 178 357 357 105 113 0 0 0 0 57 628 F SC270 105 107 117 133 228 228 124 124 228 242 178 178 361 361 113 113 196 196 256 260 57 814 F SC271 105 107 117 131 226 228 118 120 254 254 178 178 357 357 113 115 196 196 256 264 57 498493 F SC272 105 107 117 131 226 228 118 120 254 254 178 178 357 357 113 115 196 196 258 264 57 617 M SC273 103 105 127 131 250 250 118 120 240 254 178 178 357 361 113 113 196 196 256 260 57 618 M SC274 103 107 117 135 226 228 122 124 250 254 178 178 357 361 113 113 196 196 256 260 57 620 M SC275 105 107 135 135 228 250 120 120 242 254 180 180 357 361 113 113 196 196 260 264 57 623 M SC276 107 107 131 135 228 228 120 122 242 254 178 178 357 361 109 113 196 196 256 260 57 625 M SC277 105 105 131 135 228 228 120 120 242 254 178 178 361 361 113 113 196 196 264 264 57 817 M SC278 103 105 117 127 226 228 118 120 240 254 178 178 361 361 113 115 196 196 264 264 57 818 M SC279 105 107 135 135 228 228 116 120 254 254 178 178 357 361 113 113 196 196 256 264 57 498429 M SC280 103 105 117 127 226 228 118 120 240 254 178 178 361 361 113 115 196 196 264 264 57 498440 M SC281 105 105 117 131 228 228 120 120 240 254 178 178 357 361 113 113 196 196 256 256 57 498494 M SC282 105 107 135 135 228 228 116 120 254 254 178 178 357 361 113 113 196 196 258 264 57 816 M SC283 105 105 117 131 228 228 120 120 240 254 178 178 357 361 113 113 196 196 0 0 57 819 M SC284 103 105 131 135 228 228 116 120 242 254 176 178 355 361 113 113 196 196 0 0

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Locus 1 Locus 2 Locus 3 Locus 4 Locus 5 Locus 6 Locus 7 Locus 8 Locus 9 Locus 10 WMU Sample Name Sex Pop. Map2C BM4513 BM1225 RT9 RT24 BM888 BM848 FCB193 RT30 BL42 57 498431 M SC285 103 105 131 135 228 228 116 120 242 254 176 178 355 361 113 113 196 196 0 0 57 498495 M SC286 103 103 135 135 228 228 120 120 0 0 178 180 357 361 113 113 196 196 260 260 57 498443 F SC287 105 107 117 131 228 228 116 122 244 244 178 178 0 0 113 113 196 196 0 0 60A 605 F SC288 105 107 135 135 228 228 120 124 254 254 176 178 357 357 113 115 196 196 264 264 60A 608 F SC289 107 107 117 131 228 228 124 124 242 242 178 178 357 357 113 113 196 196 260 264 60A 606 M SC290 103 105 133 135 228 228 118 122 252 252 176 178 357 357 113 115 196 196 264 264 60A 607 F SC291 103 107 131 135 228 250 120 120 242 254 178 178 361 361 113 115 196 196 0 0 61 601 F SC292 103 107 131 135 228 250 116 124 238 242 178 180 357 361 113 113 196 196 256 260 61 603 F SC293 105 105 135 135 228 250 116 120 228 242 178 180 357 361 113 113 196 196 256 264 61 07-050 F SC294 107 107 131 135 228 228 116 124 254 254 178 178 357 359 115 115 196 196 256 264 61 602 M SC295 103 107 117 131 228 228 120 120 228 242 178 178 357 361 113 113 196 196 256 256 61 604 MC SC296 105 107 135 135 228 228 116 124 228 254 178 178 357 359 113 113 196 196 260 262

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Appendix C – Wildlife WMU Longitude Latitude Management Unit (WMU) 25 -82.5514 51.1411 Centroid latitude and 26 -80.4437 50.2638 27 -80.3821 49.0844 longitude values. 28 -79.9962 48.1007 WMU Longitude Latitude 29A -81.2120 48.2098 01C -91.7190 53.1027 29B -81.1039 48.0396 2 -94.5732 51.3788 30 -81.9574 48.7929 3 -93.5114 51.1583 31 -82.4391 47.9394 4 -92.1310 50.7038 32 -84.3766 48.1738 5 -92.6295 50.1357 33 -85.7141 47.9552 07A -94.3902 49.3531 34 -84.9092 47.5100 07B -94.3620 49.5639 35 -83.8404 47.3784 8 -93.1094 49.6312 36 -84.2931 46.7971 09A -92.8816 49.2705 37 -82.8272 46.4506 09B -93.2130 48.9037 38 -82.4732 47.0534 11A -92.3906 48.5792 40 -80.3814 47.3115 11B -90.5877 48.5062 41 -80.0964 46.7031 12A -91.6190 49.1883 42 -81.0059 46.2092 12B -91.1738 48.8473 46 -80.2853 45.3639 13 -89.4353 48.5276 47 -79.8810 45.9450 14 -87.9007 48.4979 48 -78.3707 46.0998 15A -90.9540 49.7335 49 -79.7756 45.5054 15B -89.5118 49.5670 50 -79.1596 45.6134 16A -91.0775 51.4743 51 -78.3941 45.8131 16B -90.5129 50.6916 53a -79.2498 45.1004 16C -89.0910 50.8011 54 -78.5685 45.2760 17 -87.0248 51.2714 55A -77.9937 45.4944 18A -87.1656 50.4695 55B -77.4436 45.6506 18B -85.2257 50.4410 56 -78.7065 44.9552 19 -86.8560 49.9487 57 -77.8690 45.2045 21A -87.0375 48.9653 60A -78.1064 44.7290 21B -85.4818 49.2155 61 -77.4376 44.8638 22 -84.4235 49.1187 24 -82.6840 49.9428

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