Abstract Phylogeographic Analysis of the Prairie Vole

Home , Alexandromys, California vole, Common vole, Field vole, Jinshaia, Lepturichthys

ABSTRACT

PHYLOGEOGRAPHIC ANALYSIS OF THE PRAIRIE VOLE (Microtus ochrogaster)

by Joshua Joseph Robinson

The prairie vole (Microtus ochrogaster) is a monogamous rodent currently classified into seven subspecies across central United States and south-central Canada. However, conflicting data from recent morphological and nuclear DNA analyses suggest further phylogeographic study is warranted. The primary objective of my study was to conduct the first phylogeographic analysis of prairie voles using mitochondrial DNA sequence data. I sequenced an approximately 505 base pair region of cytochrome b from 40 individuals across the species range that represented six of the seven current subspecies. A spatial genetic cluster analysis yielded three clusters. The largest cluster contained 73% of the individuals sequenced including representatives from the six subspecies. A haplotype network and phylogenetic reconstruction suggested further substructuring within the smaller clusters. Overall, my results, and a prior analysis of nuclear data, suggest panmixia with high gene flow throughout the species range. Interestingly, the genetic structure from the mitochondrial data does not match the structure from the nuclear data. This discordance is likely caused by incomplete lineage sorting due to recent range expansion. Furthermore, the mitochondrial genetic structure is not consistent with the currently accepted geographic ranges of the seven described subspecies and suggests that past subspecies classifications need to be reevaluated.

PHYLOGEOGRAPHIC ANALYSIS OF THE PRAIRIE VOLE (Microtus ochrogaster)

Thesis

Submitted to the

Faculty of Miami University

in partial fulfillment of

the requirements for the degree of

Master of Science

Joshua Joseph Robinson

Miami University

Oxford, Ohio

2020

Advisor: Brian Keane

Advisor: Nancy Solomon

Reader: David Berg

Reader: Meixia Zhao

This Thesis titled

PHYLOGEOGRAPHIC ANALYSIS OF THE PRAIRIE VOLE (Microtus ochrogaster)

Joshua Joseph Robinson

has been approved for publication by

The College of Arts and Science

and

Department of Biology

______Brian Keane

______Nancy Solomon

______David Berg

______Meixia Zhao

Table of Contents Introduction……………………………………………………………..1 Prairie Vole Subspecies……………………………………………..2 Objective, Hypothesis, and Predictions...………………………...... 4 Methods……………………………………………………………....…5 Tissue Samples………...……….…………………………………...5 Sanger Sequencing..………………………………………………...6 Mitochondrial Analyses………………...…………………………..7 Nuclear and Subspecies Comparison Analysis……………………..8 Morphological and Ecological Data from Literature……………….8 Results…………………………………………………………………..9 Mitochondrial Data………………….……………………………...9 Comparisons to Nuclear Data and Subspecies Designations….…...10 Morphological and Ecological Data…………………………...…...11 Discussion ……………………………………………………………...12 Phylogeography of Prairie Voles…………………………………..12 Comparisons to Nuclear Data and Subspecies Designations….…...15 Caveat and Conclusion……………………………………………..17 Literature Cited…………………………………………………………20 Figures and Tables……………………………………………………...32 Appendix I. Mantel Distance Matrices……………………………...….58 Appendix II. Expanded Phylogenetic Reconstruction………………….70 Appendix III. Contingency Tables……………………………………...71 Appendix III. Failure of Samples………………………………….……74

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List of Tables Table 1- Success of sequencing Table 2- Museum specimen details Table 3- BLAST results Table 4- Cytochrome b diversity measures Table 5- AMOVA results Table 6- Comparison of clusters between mitochondrial and nuclear loci Table 7- Mantel test results Table 8- Summary morphology data from literature Table 9- Litter size data from literature Table 10- Home range size data from literature

List of Figures Figure 1- Distribution of Microtus ochrogaster subspecies Figure 2- Cytochrome b amplification and sequencing strategy Figure 3- Distribution of mitochondrial clusters Figure 4- Haplotype network Figure 5- Phylogenetic reconstruction Figure 6- Frequency of pairwise differences plot for subspecies Figure 7- Frequency of pairwise differences plot for clusters Figure 8- Isolation-by-distance plot for clusters Figure 9- Isolation-by-distance plot for current subspecies Figure 10- Total body length summary data from VertNet Figure 11- Total body length range Figure 12- Tail length range Figure 13- Hindfoot length range

Acknowledgements

First, I would like to thank Nicole Adams for laying the groundwork for this project. She collected most of the samples used in our analyses and performed the nuclear analysis that I reference throughout my thesis. Her thesis and paper were amazing resources that I always had open on my computer for reference. She did an excellent job with her analysis, and I am greatly indebted to her. I cannot imagine how long it took for her to collect and prepare all the samples that were used in our projects. I do not know how I could have done it without her pioneering work. Next, I would like to acknowledge everyone who made this project logistically possible. The many museums that graciously allowed us to use their specimens include University of Colorado Museum of Natural History, Museum of Natural History and Science, Cincinnati Museum Center, Sternberg Museum of Natural History, Fort Hays State University, Illinois Natural History Survey, National Museum of Natural History, Museum of Biological Diversity, The Ohio State University, University of Illinois Museum of Natural History, University of Kansas Museum of Natural History, University of Nebraska State Museum, and University of Wisconsin Stevens Point Museum of Natural History. Connor Lambert and other members of the Solomon- Keane Lab collected a few more samples that I used in my analysis including the positive controls. While I am mentioning the other Solomon-Keane Lab members, I also want to thank Connor Lambert, James Licther, and Kyle Smith (thanks for all the thoughtful brainstorming lunches) for constant assistance in and around the lab. The Miami University Biology Department provided the funding and assistantships that allowed this work to get done. I acknowledge and thank the staff (Dr. Andor Kiss & Ms. Xiaoyun Deng) of the Center for Bioinformatics & Functional Genomics (CBFG) at Miami University for instrumentation and computational support. While we could not crack the puzzle, I greatly appreciate all of Dr. Kiss’ hard work when we attempted next-generation sequencing. I also want to acknowledge the numerous individuals that I have asked for advice including Dr. Michael Hughes for helping with some of the statistics, Dr. Alexander Ophir for providing me with some much-needed morphological data, and the amazing librarians at Miami University. I want to thank my wonderful advisors: Dr. Brian Keane and Dr. Nancy G. Solomon. Their patience, vast knowledge, friendliness, and dedication made this all possible. They were always ready with an answer and advice throughout my many questions about the project, school, and life. They pushed and challenged me to make this the best project that it could be. I am going to miss working with them. I feel inspired by their hard work to keeping moving forward as a scientist. I also want to thank my other committee members: David Berg and Meixia Zhao for their help during the project. Lastly, I want to acknowledge the many other supporters in my life. Specifically, Lauren Fockler supported me throughout this entire journey with our menagerie of creatures at home. I would not have my sanity this without her or our three dogs, Jasper, Graham, and Vader (thanks for being the best Grad School Pup). While my mom did not fully understand what I was doing, I wanted to thank her for her many encouragements. I wish you could have seen the final product of all my hard work.

Introduction

Genetic structure, or the subdivision of genetic variation and gene frequency in or among populations, is caused by the interplay of historic and contemporary genetic drift, mutations, gene flow, and natural selection (Wright 1950, Slatkin 1987, Eckert et al. 2008, Adams and Hadley 2010). Drift, mutation, and selection lead to genetic differentiation among populations while gene flow between populations typically prevents this differentiation (Slatkin 1987). Thus, restriction of gene flow between populations can result in genetic differentiation and eventually the development of genetic structure (Laurence et al. 2011, Adams and Burg 2015). On larger temporal and geographic scales, phylogeographic patterns, or the spatial distribution of genetic lineages, result from this restriction of gene flow (Avise et al. 1987, Adams and Hadley 2010). Barriers that restrict or prevent gene flow between populations can include topographic features, unsuitable habitat, and anthropogenic disturbance (Adams and Burg 2015). Geographic distance between populations also can influence the amount of gene flow and may cause individuals to become less genetically similar as the geographic distance between individuals increases, which is referred to as isolation-by-distance (Wright 1943). Additionally, geophysical events such as glaciation and intrinsic biological factors such as mobility, sex-biased dispersal, philopatry, and the reproductive patterns of the species can have major impacts on gene flow (Laurence et al. 2011). Understanding phylogeography of a species can provide valuable insight into the evolutionary history, patterns of phenotypic variation, and conservation of that species (Metcalf et al. 2001, Wang and Summers 2010, Zamudio et al. 2016). To assess genetic structure and phylogeographic patterns, both mitochondrial and nuclear genetic markers have been commonly used (Fallon 2005, Bradley and Baker 2006, Toews and Brelsford 2012). Mitochondrial DNA (mtDNA), particularly the cytochrome b locus (Parson et al. 2000) is the most popular marker for assessing molecular diversity in animal studies (Galtier et al. 2009, Ogden et al. 2009). Most population genetic and molecular systematic studies of animal species use mtDNA at some point (Galtier et al. 2009). The reasons for the popularity of using mtDNA include the relative ease of amplification due to the large numbers of copies in each cell, conservation of gene content across animal taxa, high variability within and between natural populations due to high mutation rate, and variable regions flanked by highly conserved regions, which allows for the development of primers for polymerase chain reaction (PCR) analyses (Galtier et al. 2009). Additionally, the mitochondrial genome is haploid and inherited maternally in most animals. Therefore, the effective population size for mtDNA is fourfold smaller than that of nuclear DNA (nDNA, Galtier et al. 2009, Toews and Brelsford 2012). The smaller effective population size causes more rapid fixation of mutations and completion of lineage sorting than nDNA, which simplifies analyses (Avise et al. 1987, Galtier et al. 2009, Toews and Brelsford 2012). However, nuclear genes also should be examined to provide an independent line of evidence to assess the species’ evolutionary history (Prychitko and Moore 2000). Since both mitochondrial and nuclear genetic markers have attributes that make them useful for assessing genetic structure, phylogenetic relationships, and phylogeographic structure, several studies utilize a combination of multiple mitochondrial and nuclear molecular markers (Prychitko and Moore 2000, Brown et al. 2007, Rout et al. 2012, Inoue et al. 2014, Platt et al. 2015, Johnson et al. 2016). While most studies that analyze mitochondrial and nuclear markers show concordant phylogeographic patterns, several studies report mito-nuclear discordant patterns in a variety of animal taxa (Toews and Brelsford 2012). Mito-nuclear discordance can be caused by incomplete lineage sorting, adaptive selection, and demographic processes like

hybridization and sex-biased dispersal (Prychitko and Moore 2000, Toews and Brelsford 2012). In balitorine loaches (Lepturichthys fimbrita and L. dolichopterus), mito-nuclear discordant patterns were hypothesized to be a result of introgressive hybridization within the sympatric balitorine loach genus, Jinshaia, and incomplete lineage sorting (Tang et al. 2012). Mito-nuclear discordance due to hybridization also was observed in leopard frogs (Rana blairi and R. pipiens, Candia and Routman 2007), Burmese and Indian pythons (Python bivittatus and P. molurus, Hunter et al. 2018), and two lineages of California voles (Microtus californicus, Lin et al. 2017). In eastern yellow robins (Eopsaltria australis), the mito-nuclear discordance was hypothesized to be due to female-linked environmental selection (Pavlova et al. 2013). Prairie voles, Microtus ochrogaster (Wagner 1842), have become a valuable model organism for studying proximate factors underlying social and genetic monogamy (Hammock and Young 2005, Solomon et al. 2009, McGraw and Young 2010, Streatfeild et al. 2011). Social and genetic monogamy in prairie voles varies within and between populations (Cushing et al. 2004, Streatfeild et al. 2011, Solomon and Keane 2018, but see Ophir et al. 2007). For example, field studies have found populations of prairie voles in Kansas to be more socially and genetically monogamous than those from Indiana (Streatfeild et al. 2011). Prairie voles from Kansas also have been reported to be less monogamous than those from Illinois (Cushing et al. 2004) and Tennessee (Ophir et al. 2007). While the intraspecific variation in monogamy between the Kansas and Indiana populations may be explained by differences in ecological factors such as population density and vegetation (Streatfeild et al. 2011), some of this variation is known to be associated with genotypic differences including differences at loci encoding the vasopressin 1a receptor (Solomon et al. 2009) and estrogen receptor alpha (Cushing et al. 2004). Thus, differences in genetic structure and phylogeography may explain some of the observed intraspecific variation in prairie vole monogamy. Only two studies (Modi 1993, Adams et al. 2017), both using nuclear data, have examined the genetic structure and phylogeography of the prairie vole. Specifically, Adams et al. (2017) found extensive gene flow throughout the species range while Modi (1993) did not find genetic differences between two of the three examined subspecies. Due to the limitations of using only one type of molecular marker, the use of mitochondrial data to assess the phylogeography across the range of the prairie vole will add to our understanding of the interpopulational variation in social behavior, observed phenotypic variation in morphology, and the relationship between this species’ phylogeography and its current subspecies designations.

Prairie Vole Subspecies The prairie vole is thought to have originated approximately 191 to 130 thousand years ago in the central United States within the tallgrass prairie of the Great Plains, and then the species distribution expanded eastward and westward (Choate and Williams 1978, Zakrzewski 1985). Fossil evidence suggests that the species range also formerly extended southward along the Texas coast during the late Pleistocene, but the species receded from Texas as the region became drier during the Holocene (Raun and Laughlin 1972, Stangl et al. 2004). Following its expansion across the central United States and south-central Canada, taxonomists identified seven subspecies (Hall 1981, Figure 1). Six of the seven subspecies including M. o. ochrogaster (Wagner 1842), M. o. haydenii (Baird 1858), M. o. minor (Merriam 1888), M. o. ohionensis (Bole and Moulthrop 1942), M. o. taylori (Hibbard and Rinker 1943), and M. o. similis (Severinghaus 1977) form a contiguous distribution while the seventh subspecies, M. o. ludovicianus (Bailey 1900), is geographically separated from the other subspecies (Figure 1).

Initial published descriptions differentiated prairie vole subspecies by body size and pelage color and texture and were associated with distinct geographic ranges (Hall 1981). The prairie vole, first described in 1842 by Johann Andreas Wagner, was said to weigh between 37 and 48 grams with coarse pelage, dorsal pelage that was light gray to “dark blister” with a peppery appearance, ventral pelage that was “neutral” gray or “washed with whitish or pale cinnamon,” and a sharply bicolored tail (Hall 1981). The original species description closely matches that of the first identified subspecies M. o. ochrogaster, which is described as dark in color with “grizzled” dorsal pelage and “buffy” ventral pelage (Bole and Moulthrop 1942). The range of this subspecies, commonly named the middle-western prairie vole or common prairie vole, extends throughout most of the mid-western United States (Bole and Moulthrop 1942, Long 1990, Hall 1981, Figure 1). Within this subspecies, natural variation in pelage color has been documented including xanthochromism (Getz and Pizzuto 1987, Hoffmeister 1989), melanism, albinism (Stalling 1974), patches of silver hair, and silver ventral pelage (Mumford and Whittaker 1982). Two additional subspecies were identified by the end of the 19th century. In 1858, M. o. haydenii, was identified as being longer in total body length than M. o. ochrogaster and having a very pale pelage with a “grizzled” dorsal and “buffy” ventral pelage (Baird 1858). The subspecies was commonly named the western prairie vole because its range extended from Kansas and Nebraska to the edge of the Rocky Mountains (Bole and Moulthrop 1942, Hall 1981, Figure 1). In 1888, Merriam examined “upland” meadow mice in Minnesota and eastern North and South Dakota and classified the previously unidentified species as a subspecies of the prairie vole, the least prairie vole. The least prairie vole, or M. o. minor, is the smallest subspecies of prairie vole but has a similar appearance to M. o. haydenii with an even more “grizzled” and more “buffy” ventral pelage than M. o. haydenii (Bole and Moulthrop 1942). Long (1990) also concluded that M. o. minor was shorter in total body length and skull size, had shorter and more narrow nasal bones, and had a lighter ventral pelage than M. o. ochrogaster. This subspecies occupies the northern region of the species range, extending into Canada (Hall 1981, Figure 1). Four additional subspecies were designated from the 1940s through the 1970s using distinct morphological features and geography. In 1942, Bole and Moulthrop identified a unique subspecies of M. ochrogaster in Ohio. The Ohio prairie vole, or M. o. ohionensis is the only subspecies with a white ventral pelage rather than being “buff-bellied” (Bole and Moulthrop 1942). Additionally, the subspecies is longer in total body length and has shorter hindfeet than M. o. ochrogaster, has dark dorsal pelage, and is very similar in initial appearance to M. pennsylvanicus (meadow vole) but has a more grizzled appearance than the uniform-colored pelage of M. pennsylvanicus (Bole and Moulthrop 1942, DeCoursey 1957, Gottschang 1981). A distinct subspecies, named M. o. taylori, was identified within the range of M. o. haydenii in 1943 in Kansas and described as having darker pelage than M. o. haydenii and longer total body length than M. o. ochrogaster (Hibbard and Rinker 1943). Another subspecies, M. o. similis, was detected within the range of M. o. haydenii in 1977 in Montana and Wyoming (Severinghaus 1977). Microtus ochrogaster similis was described as being smaller than M. o. haydenii and M. o. taylori based on external and cranial morphometric measurements, longer in total body length and lighter in dorsal pelage than M. o. ludovicianus, M. o. minor, and M. o. ohionensis, and was described as having a longer hindfoot and lighter dorsal pelage than M. o. ochrogaster (Severinghaus 1977). The last subspecies, M. o. ludovicianus, the Louisiana prairie vole, was originally classified as its own species, M. ludovicianus (Bole and Moulthrop 1942). Lowery (1974) determined that

M. ludovicianus lacked morphological differences from M. ochrogaster and classified it as a subspecies. Microtus ochrogaster ludovicianus, was described as having dark pelage, a narrow, elongated skull, “pinkish” dorsal pelage, large molars, and black incisors (Bole and Moulthrop 1942). This subspecies was found in the coastal tall grass prairies of southeastern Texas and southwestern Louisiana, isolated from the other six subspecies (Lowery 1974, Figure 1). Since the type specimens of M. o. ludovicianus were found in Louisiana in 1900 and several more individuals in Texas in 1905, there have been no additional reports of the subspecies in either region. Lowery (1974) concluded that the subspecies was extirpated or very rare, after approximately 30 years of trapping in the type locality of southwestern Louisiana with no success. The subspecies may have been a remnant population after the species receded from the area, and human habitat modification may have resulted in the subspecies’ extirpation (Raun and Laughlin 1972, Stangl et al. 2004). A morphological study of prairie voles by Choate and Williams (1978) failed to find consistent differences among subspecies. Choate and Williams (1978) examined various morphometric features in M. o. haydenii, M. o. ochrogaster, M. o. similis and M. o. taylori from the Central Plains but could not find consistent differences between M. o. haydenii, M. o. ochrogaster, and M. o. taylori. Microtus ochrogaster taylori was hypothesized to belong to a population that was geographically isolated during the droughts of the Dust Bowl era (1930 to 1936). Following the end of the drought, either the type locality of M. o. taylori was extirpated and recolonized by M. o. haydenii or M. o. haydenii genetically swamped M. o. taylori (Choate and Williams 1978). In contrast to Choate and Williams’ conclusions, when examining populations from the southwestern portion of the species range, Stangl et al. (2004) did find consistent differences between M. o. taylori and M. o. haydenii and supported the M. o. taylori designation. Choate and Williams (1978) also concluded that M. o. ochrogaster and M. o. haydenii were not distinct within Kansas and Nebraska, and that the species contained a large degree of variability in pelage color and texture within and among populations. The variation generally changed gradually across the landscape, but some populations resembled geographically distant populations more than neighboring populations (Choate and Williams 1978). The two studies of prairie vole nDNA also failed to detect distinct genetic differences among subspecies. In a Southern blot analysis using a 160 base pair tandem satellite array (MSAT- 160), samples from M. o. ochrogaster and M. o. similis yielded identical results while those from M. o. minor did not, which suggests no genetic differentiation between M. o. ochrogaster and M. o. similis (Modi 1993). This study only had samples from four populations from three subspecies. The most recent phylogeographic analysis of prairie voles utilized length polymorphisms at six nuclear microsatellite loci using tissue samples from 36 populations across the species range, representative of all seven subspecies (Adams et al. 2017). When M. o. ludovicianus was excluded because only two samples were available, the results suggested that the contiguous distribution of prairie voles comprised three genetic lineages (Adams et al. 2017), which is not consistent with the six currently accepted subspecies.

Objectives/Goals My primary objective was to conduct the first study to investigate genetic relationships and genetic diversity among individuals of prairie voles throughout the species range using DNA sequence data from the mitochondrial locus cytochrome b. I chose to sequence cytochrome b because the gene has been extensively sequenced and studied in various taxa (Kocher et al. 1989,

Irwin et al. 1991, Johns and Avise 1998) including studies of interspecific and intraspecific variation in mammalian taxa (Demboski et al. 1998). Additionally, several phylogeographic studies in various families of rodents have used either the complete or partial cytochrome b gene to determine phylogenetic relationships (Lessa and Cook 1998, Demboski et al. 1998, Jaarola and Searle 2002, Triant and DeWoody 2007, Moncrief et al. 2010). The cytochrome b sequence data will be used to document phylogeographic patterns across the species range via genetic diversity, cluster, haplotype network, phylogenetic reconstruction, and isolation-by-distance analyses. The secondary objectives of this study were to compare the resulting mitochondrial phylogeography to the genetic structure obtained from the nuclear microsatellite analysis from Adams et al. (2017) and to the current subspecies designations for M. ochrogaster. I will identify the similarities and differences between the mitochondrial and nuclear phylogeographies, which will provide two independent line of evidence for determining and understanding the overall phylogeography of the prairie vole. This comparison of mitochondrial sequence and nuclear microsatellite data is commonly used in phylogeographic studies (Inoue et al. 2014, Fennessy et al. 2016, Johnson et al. 2016). If the current subspecies consist of unique lineages and accurately reflect the current genetic structure, the intrasubspecific variation should be less than the intersubspecific variation since organisms within a subspecies would be more genetically similar than organisms of different subspecies. Also, each current subspecies should form a distinct lineage or cluster, and isolation-by-distance should not be observed across subspecies boundaries. This comparison will assess the validity of the current subspecies designations. To further assess the validity of the current subspecies designations, morphological and ecological data were collected through the literature.

Methods

Tissue Samples I obtained tissue samples from 340 putative M. ochrogaster collected from 48 populations throughout the species range that included all seven described subspecies (Figure 1). Samples from 45 of these populations were acquired from museum collections from animals trapped between 1895 and 2010 and stored as either preserved skins or tissues in ethanol (Adams et al. 2017). Museum specimens have been commonly used in phylogenetic and phylogeographic studies of rodents (Conroy and Neuwald 2008, Moncrief et al. 2010, Rowe et al. 2011, Galan et al. 2012, Platt II et al. 2015). In my study, museum specimens provided samples from a variety of locations that would be difficult and expensive to collect today, including samples from the potentially extirpated M. o. ludovicianus. Frozen tissue samples from three additional populations were collected from Douglas County, Kansas, Champaign County, Illinois, and Monroe County, Indiana between 2006 and 2007. Populations were defined as individuals trapped within the same county or parish as in Adams et al. (2017). Since some subspecies designations have changed over time, a priori current subspecies designations were assigned to each sample based on the county of origin and the geographic subspecies boundaries described in Hall (1981). The varying ages of the samples should not be problematic if the subspecies designations reflect genetic lineages with separate evolutionary pathways. Once a subspecies becomes a unique genetic lineage, any further drift and mutation that occurs in that subspecies since divergence should only act to increase differentiation between subspecies. Overall, the samples represent individuals from all seven current subspecies: M. o. haydenii (n = 97 samples,

14 populations), M. o. ludovicianus (n = 6 samples, 1 population), M. o. minor (n = 56 samples, 8 populations), M. o. taylori (n = 18 samples, 2 populations), M. o. ochrogaster (n = 83 samples, 13 populations), M. o. ohionensis (n = 31 samples, 5 populations), and M. o. similis (n = 49 samples, 5 populations, Table 1). I attempted to sequence at least one individual from each of 48 populations, except for M. o. ludovicianus for which I attempted to sequence two individuals due to only one population being available for this subspecies (Table 1).

Sanger Sequencing I sequenced 505 of 1,143 total base pairs from the cytochrome b gene using two degenerative primer sets (Irwin et al. 1991, Ozawa et al. 1997, Triant and DeWoody 2007). I used PCR to amplify the two degenerative regions of the gene, termed fragment 1 (primer set: L14841 (5'- CGAAGCTTGATATGAAAAACCATCGTTG-3', Irwin et al. 1991) and H15149 (5'- AACTGCAGCCCCTCAGAATGATATTTGTCCTCA-3', Kocher et al. 1989) and fragment 2 (primer set: L15144 (5'-ATAGCCACAGCMTTCATAGGMTAYGTCCT-3', Ozawa et al. 1997) and H15347 (5'-GGGTTRTTKGATCCTGTTTCGTG-3', Ozawa et al. 1997, Figure 2). Initial PCRs contained 10x PCR buffer, 2.0 mM dNTPs, 1.5 mM MgCl, 20 µM of the forward and reverse primer, GoTaq Flexi (0.375 units, Promega, Madison, WI), template DNA (3 to 150 ng), and water in a total reaction volume of 15 µL. The PCR conditions were as follows: initial denaturing at 95°C for 3 minutes, 45 cycles of denaturing at 93°C for 30 seconds, annealing at 48°C for 30 seconds, and extension at 72°C for 2 minutes, and a final extension at 72°C for 5 minutes (Irwin et al 1991). The PCR products were run on a 2% agarose gel via electrophoresis. DNA extracted from frozen tissue from individuals of Microtus ochrogaster ochrogaster captured in 2017 were included in each PCR as a positive control. Amplified bands of the appropriate base pair (bp) length were physically extracted and gel-purified using an E.Z.N.A. Gel Extraction Kit (Life Science, St. Petersburg, FL). Each DNA fragment was sequenced in both directions. Cycle sequencing reactions were carried out using the BigDye Terminator cycle sequencing kit (Applied Biosystems, Waltham, MA) in a TGRADIENT thermocycler (Biotmetra, Göttingen, Germany). Initial cycle sequencing reactions contained 10x PCR buffer, 1 µL of neat BigDye, 20 µM of the forward or reverse primer, DNA (2 to 20 ng), and water in a total reaction volume of 10 µL. Products were purified, and DNA pellets isolated via ethanol precipitation using 3 M NaOAc, 125 mM EDTA, and 100% and 70% EtOH. The DNA pellets with Hi-Di were run on an ABI 3730 automated DNA sequencer in the Center for Bioinformatics and Functional Genomics (CBFG) at Miami University, Oxford, OH. Sequence files were aligned, trimmed, and checked for primers using CLC Main Workbench (QIAGEN Bioinformatics, Redwood City, CA). Consensus sequences of DNA were produced from the forward and reverse sequences of every individual from both regions to build the approximately 500 bp region, and any conflicts between sequences were resolved to the most likely nucleotide. Alignment of the sequences was conducted through MUSCLE (Edgar 2004) and were visually confirmed. Additionally, I checked for evidence of nuclear mitochondrial pseudogene DNA inserts (numts) by aligning sequences and building a phylogeny with the confirmed cytochrome b numt in M. ochrogaster (GenBank accession number DQ432007.1, Triant and DeWoody 2007) and examining substitution patterns (Adams and Hadly 2010). No sequences grouped with the cytochrome b numt in the phylogeny. No unexpected stop codons occurred in any sequence, most substitutions (81%) occurred in the 3rd codon position, and only seven of 168 (4%) amino acids changed. To ensure samples were identified correctly as M.

ochrogaster, the sequences were run through the National Center for Biotechnology Information’s (NCBI) basic local alignment search tool (BLAST, https://blast.ncbi.nlm.nih.gov/Blast.cgi). The results of a BLAST search provide a list of sequences in the NCBI database that most closely align with the submitted sequence. Associated with each sequence in the list are identity scores, which indicates the extent that two sequence have the same residues at the same positions in the alignment and are expressed as percentages (NCBI 2019). Any sequence that did not align and have the highest sequence identity score with M. ochrogaster was removed from further analysis.

Mitochondrial Analyses To explore relationships of genetic diversity across the species range, measures of genetic diversity and analysis of molecular variance (AMOVA, Excoffier et al. 1992) were conducted at the current subspecies level using Arlequin v3.5.2.2 (Excoffier and Lischer 2010). The measures included number of haplotypes, number of segregating sites (s), mean genetic distances measured from the Kimura 2-parameter model of evolution (Kimura 1980), and nucleotide diversity (π), or the mean number of nucleotide differences per site, at the species level, current subspecies level, and with any identified clusters. AMOVA, conducted in Arlequin, uses haplotype frequency and number of mutations to analyze the amount of variance in genetic diversity that best explains genetic structure of the data (Excoffier et al. 1992). Primarily, intersubspecific variation and intrasubspecific variation in the species were analyzed. In addition, variation among groupings of subspecies also were analyzed to determine if a different clustering method of the subspecies besides the seven current subspecies would better describe the genetic structure of the species. Identification of unique genetic lineages or clusters within the species was found through cluster analysis, construction of a haplotype network, and phylogenetic reconstruction and confirmed through mismatch distributions. To determine spatial genetic clustering across the species range, a spatial clustering analysis was performed using Bayesian Analysis of Population Structure (BAPS) v.6 (Corander et al. 2008, Cheng et al. 2013) with the geographic center of each county serving as the coordinates of each sequence. The minimum spanning haplotype network at a 95% confidence level was constructed using TCS v1.21 (Clement et al. 2000) to identify haplotypes and specific relationships between sequences and clusters. Additional links were built when the confidence level restriction was relaxed in TCS. Phylogenetic reconstructions through maximum likelihood trees were conducted in MEGA v7 (Kumar et al. 2015) with consensus tree topologies produced by bootstrap values derived from 1,000 replicates. Bootstrap values indicate the likelihood that a node would be present if the tree was remade 100 times with 70% being accepted as the cut-off for accepted nodes (Hillis and Bull 1993). The Tamura 3-parameter model (T92, Tamura 1992) using a discrete Gamma distribution (+G) with invariant sites (+I) was selected as the most appropriate substitution model with the lowest BIC (Bayesian Information Criterion, 3195) values. Microtus californicus (accession number KU686854) and M. xanthognathus (accession number AF163907) were chosen as outgroups because these two species are closely related to M. ochrogaster in the phylogenies of the Microtus genus based on the cytochrome b locus developed by Jaarola et al. (2004). The resulting phylogeny would describe specific evolutionary relationships between clusters and sequences. Lastly, pairwise differences were plotted against their frequencies within and between subspecies or clusters. If subspecies or clusters are reproductively isolated, a unimodal distribution for each subspecies or cluster comparisons would be expected, and differences

within subspecies or cluster comparison should be relatively lower than differences between subspecies comparisons. To determine the role of geographic distance on genetic variation, genetic distances (Appendix I, Table 1) were plotted against geographic distances (Appendix I, Table 2) for each sample and a Mantel test with 999 permutations was conducted in GenAlEx v6.5 (Peakall and Smouse 2006, Peakall and Smouse 2012) to evaluate isolation-by-distance across the contiguous species range, within each of the 5 subspecies with more than two individuals, and within clusters identified in the haplotype network with more than two individuals. The geographic center of each county was used to calculate distances between individuals, and genetic distance was calculated via pairwise differences between sequences. Within cluster or subspecies comparisons are expected to have fewer pairwise differences than between cluster or subspecies comparisons if reflective of true genetic structure. If isolation-by-distance is the main explanation for the genetic structure in the species, a negative relationship between genetic similarity and distance would exist when all samples were considered. If the clusters identified in the study or current subspecies reflect the true genetic structure, a strong relationship would occur within the clusters or subspecies but a weak or no relationship between clusters or subspecies assuming isolation-by-distance does exist in the species. Standard deviations were calculated with each mean.

Nuclear and Subspecies Comparison Analysis Since Adams et al. (2017) mostly utilized the same set of samples in their analysis, their dataset of six nuclear microsatellites length polymorphisms (AV13, MOE2, MSMM2, MSMM3, MSMM5, and MSMM6) amplified across 170 specimens and representing 36 populations, were reanalyzed. First, any populations that had individuals that did not have the highest sequence identity score with M. ochrogaster in the BLAST analysis were removed. Then, a spatial clustering analysis of the remaining individuals was performed in BAPS v.6 to determine the optimal number of clusters in the nuclear data. To determine if associations existed between mitochondrial clusters, nuclear clusters, and current subspecies designations and since a three-way contingency analysis was not possible due to low expected cell frequencies, three two-way Fisher’s exact tests for contingency tables were conducted in R v.3.3.2 (R Core Team 2016).

Morphological and Ecological Data from Literature To further assess the validity of the subspecies designations, data on average and range of total body length (i.e., length from the snout to the tip of the tail), tail length, hindfoot length, litter size, and home range size for prairie voles across the species range was collected from the literature. Additionally, raw data on total body length for M. ochrogaster was downloaded from 22 different datasets through the VertNet portal (Schmidt Museum of Natural History 2015, TTU 2015, UAFMC 2015, UAZ 2016, University of Connecticut, Biodiversity Research Collections 2016, SUI 2016, CRCM 2017, HSU 2017, Museum of Comparative Zoology 2017, San Noble Oklahoma Museum 2017, CUMV 2018, T.L. Hankinson Vertebrate Museum 2018, CHAS 2019, DMNS 2019, KUBI 2019, LACM 2019, MSB 2019, MVZ 2019, UAM 2019, UCM 2019, UWBM 2019, WNMU 2019). A one-way ANOVA of total body length by subspecies was conducted from this raw data to determine any differences in total body length between the current subspecies. Since little raw data existed in the database for tail length or hindfoot length, this analysis was not repeated for those two measurements.

Results

Mitochondrial Data I was able to sequence a 505 bp region of cytochrome b for 40 individuals from the six contiguous subspecies (Table 2). For six additional individuals including two samples from M. o. ludovicianus, I was only able to sequence a 202 bp region of the gene (Table 2). One individual from M. o. minor and one individual from M. o. ochrogaster did not amplify for either region. For the 505 bp region, 34 of 40 (85%) sequences aligned most closely with M. ochrogaster sequences with  94% sequence identity in the BLAST analysis and were considered to be M. ochrogaster (Table 3). Six of 40 (15%) sequences aligned most closely with either M. longicaudus (n = 3) or M. pennsylvanicus (n =3) with  92% sequence identity and were not considered to be M. ochrogaster (Table 3). For the 202 bp region, three of seven (42.9%) sequences aligned most closely with M. ochrogaster with  95% identity and four of seven (57.1%) sequences aligned most closely with M. longicaudus with  95% identity including the two putative M. o. ludovicianus and a M. o. haydenii from Custer County, South Dakota (Table 3), which is the type locality of M. longicaudus (Bailey 1900). During a preliminary analysis, almost all sequences, including the three sequences that were only sequenced for the 202 bp region, grouped into one cluster and did not contain enough variation to warrant a separate set of analyses with the 202 bp region. For the 34 sequences of the 505 bp region that aligned closest with M. ochrogaster in the BLAST analysis, the genetic diversity values were as follows: 26 haplotypes, 59 segregating sites, mean genetic distance of 14.03 ± 6.45%, and mean nucleotide differences of 0.026 ± 0.014 across the locus for nucleotide diversity (Table 4). This mean genetic distance is greater than that expected for the entire length of cytochrome b in rodents (0.00% - 6.29%, Bradley and Baker 2001). The mean genetic distances also were higher than expected at the subspecies level (0.00% - 1.87%, Bradley and Baker 2001) for M. o. haydenii (7.44 ± 3.84%, M. o. ochrogaster (14.69 ± 7.13%), M. o. minor (14.77 ± 7.99%), M. o. similis (22.94 ± 12.89%), and M. o. taylori (2.00 ± 1.74%). From the AMOVA examining the seven current subspecies, total intersubspecific molecular variation (Фsc) significantly explained 16.47% of the total variation (df = 5, SS = 58.983, variance among subspecies [Va] = 1.138, p = 0.012) present in the sequences while intrasubspecific variation (Фst) explained the remaining 83.53% (df = 28, SS = 161.635, variance among individuals within subspecies [Vb] = 5.773, Table 5). Regardless of method used to cluster subspecies, no significant genetic structure could be detected in the intersubspecific variation or variation among groups (Фct), and the only significant explanation for molecular variation for any grouping method was intrasubspecific (> 60%, Table 5). For example, when M. o. minor was treated as a unique group, as suggested by Modi (1993), the only significant explanation for the variance in molecular variation was intrasubspecific variation at 72% (p = 0.012, df = 28, SS = 161.635, Vb = 5.773). The spatial clustering analysis identified three clusters (log of marginal likelihood = - 611.010). The largest cluster, termed primary cluster, included 25 sequences from the six contiguous subspecies: M. o. ochrogaster (n = 8), M. o. minor (n = 2), M. o. ohionensis (n = 3), M. o. taylori (n = 2), M. o. similis (n = 2), and M. o. haydenii (n = 8, Table 6). Also, the primary cluster has a distribution that includes most of the contiguous species range, except the north- central region that includes most of the range of M. o. minor and some of the range of M. o haydenii (Figure 3). The primary cluster contained a mean genetic distance of 3.34 ± 1.77% with a nucleotide diversity of 0.001 ± 0.004 nucleotide differences (Table 4). A second cluster,

termed minor cluster, contained samples from M. o. minor (n = 3), M. o. haydenii (n = 1), and M. o. ochrogaster (n = 1) and was mostly distributed within the north-central region of the species range (Figure 3). The minor cluster contained a mean genetic distance of 8.23 ± 4.60% with a nucleotide diversity of 0.016 ± 0.011 nucleotide differences (Table 4). The third cluster, termed similis cluster, contained samples from M. o. similis (n = 2) and M. o. ochrogaster (n = 2, Table 6). The similis cluster contained a mean genetic distance of 18.19 ± 10.29% with a nucleotide diversity of 0.036 ± 0.024 nucleotide differences (Table 4). The remaining subspecies, M. o. ludovicianus, was not present in any clusters due to both sequenced samples being a potentially different species than M. ochrogaster. In contrast to the results of the spatial clustering analysis, the 95% parsimonious network produced six clusters of haplotypes and only two shared haplotypes (Figure 4). The largest cluster consisted of the same samples as the primary cluster from the spatial clustering analysis and included the two shared haplotypes. The largest shared haplotype was composed of eight sequences from four subspecies: M. o. taylori (n = 1), M. o. haydenii (n = 3), M. o. ochrogaster (n = 2), and M. o. ohionensis (n = 2). The second shared haplotype was one mutational step from the large shared haplotype and included M. o. ochrogaster (n = 1) and M. o. ohionensis (n = 1). Other samples produced a star-shape around the large shared haplotype. The other individual from M. o. taylori was two mutational steps and another individual from M. o. ohionensis was one mutational step from the large shared haplotype. A second cluster was similar to the minor cluster from the spatial clustering analysis but did not contain the sample from M. o. ochrogaster in Madison County, Alabama, which was distantly connected outside the parsimonious network. When excluding the sample from Madison, Alabama, the minor cluster contained a mean genetic distance of 5.74 ± 3.47% with a nucleotide diversity of 0.011 ± 0.008 nucleotide differences (Table 4). Similarly, a third cluster was similar to the similis cluster from the spatial clustering analysis but did not contain the samples from M. o. ochrogaster in Lancaster County, Nebraska and Alexander County, Illinois which were distantly connected outside the parsimonious network. The phylogenetic reconstruction clustered all sequences together rather than the outgroups (Figure 5, Appendix II). An early evolutionary node with a bootstrap value of 95% separated sequences from the primary and minor clusters from the similis cluster of the spatial clustering analysis. Sequences from the minor cluster then diverged from the primary cluster at a node with a bootstrap value of 73%. Contrary to the cluster analysis and haplotype network, most sequences within the minor and similis clusters were unique branches. Distributions of pairwise differences within subspecies comparisons were not unimodal and were not fewer than between subspecies comparisons (Figure 6). Distributions within the primary and the minor cluster had fewer pairwise differences than between cluster comparisons (Figure 7). However, the primary cluster had one main mode and a smaller mode created by comparisons with the specimen from Big Horn County, Montana. The minor and similis clusters did not appear unimodal, but sample sizes were small. No relationship existed between pairwise differences and geographic distance for all 34 samples, within any identified cluster, or within the tested current subspecies (Table 7). Comparisons made within clusters had fewer pairwise differences than comparisons made between clusters (Figure 8) while comparisons within subspecies did not have fewer pairwise differences than comparisons made between subspecies (Figure 9).

Comparisons to Nuclear Data and Current Subspecies Designations After removing the six populations that were not M. ochrogaster (Licking County, Ohio, Larimer County, Colorado, Calcasieu Parish, Louisiana, Wingard County, Saskatchewan, Jefferson County, Indiana, and Clay County, Minnesota), the nuclear microsatellite dataset consisted of 153 samples, representing 30 populations. The spatial clustering analysis grouped these populations into seven clusters (log of marginal likelihood = -3660.653). Twenty-nine individuals from this study were also the same as those in the study by Adams et al. (2017, Table 6). The three mitochondrial clusters based on findings of this study (primary, minor, similis) were compared to the seven nuclear clusters derived from the spatial clustering analysis, which are listed as clusters 1 through 7. The mitochondrial primary cluster contained 22/29 (76%) total individuals while the remaining seven are separated into the other two clusters (Table 6). The Fisher’s exact test for a 3 (mitochondrial clusters) x 7 (nuclear clusters) contingency table (Appendix III, Table 1) between the mitochondrial and nuclear clusters did not find evidence of an association (p = 0.216). Additionally, the Fisher’s exact test for a 6 (current subspecies designations) x 3 (mitochondrial clusters) contingency table (Appendix III, Table 2) between the current subspecies and mitochondrial clusters did not find evidence of an association (p = 0.131). Lastly, the Fisher’s exact test for a 6 (current subspecies designations) x 7 (nuclear clusters) contingency table (Appendix III, Table 3) between the nuclear clusters and subspecies designation found an association between the two categorizations of the samples (p = 0.020).

Morphological and Ecological Data The dataset obtained through VertNet contained 1,642 records with total body lengths (Figure 10). The overall mean total body length was 143.5 ± 14.2 mm. Since only one record with total body length existed for M. o. taylori and no records existed with total body length for M. o. ludovicianus, these subspecies were not included in the analysis. Therefore, the ANOVA was run between total body lengths of M. o. haydenii, M. o. ochrogaster, M. o. ohionensis, M. o. minor, and M. o. similis. There were statistically significant differences between the means of total body length for the five subspecies (F4, 1636 = 4.788, p = 0.001). Specifically, Tukey’s honest significant difference test (Tukey-Kramer method) revealed that the total body lengths significantly differed between M. o. haydenii and M. o. minor (p = 0.004) and M. o. minor and M. o. ochrogaster (p = 0.025). Specifically, the mean total body length of M. o. minor (134.9 ± 12.3) was smaller than M. o. haydenii (145.0 ± 15.3) and M. o. ochrogaster (143.3 ± 13.9). However, since few total body lengths (< 26) were reported for M. o. similis, M. o. minor, and M. o. ohionensis, other potential differences between subspecies may not have been detected. The external morphological measurements of the species found in the literature reveal total body lengths ranging from 116 to 190 mm, tail lengths ranging from 19 to 53 mm, and hindfoot lengths ranging from 15 to 36 mm (Table 8, Figure 11, Figure 12, Figure 13). For M. o. minor, M. o. ludovicianus, and M. o. ohionensis, measurements for only one population were found for each subspecies. Overall, the ranges of the morphological measurements do not appear to differ between any of the subspecies (Figure 11, Figure 12, Figure 13). Litter size, sampled in utero or post-parturition, in M. o. haydenii, M. o. ochrogaster, M. o. similis, and M. o. ohionensis mostly ranges from 3 to 4 pups (Table 9). Only data for home range size of M. o. ochrogaster and M. o. haydenii were found through the literature search. Home range size appears to vary widely between location, sex, and study (Table 10). In M. o. haydenii, home range size was reported to range from 67 and 488 m2 in northeastern Colorado and had an average of 890 m2 in Scotts Bluff County, Nebraska. In M. o. ochrogaster, home

range sizes in Illinois, Indiana, Kansas, and Kentucky were reported to range from 81 to 1,133 m2 (Table 10). Additionally, some of the variation in home range size between studies could be due to different methods of calculating home range or the time span of the study. For instance, Swihart and Slade (1989) and Streatfeild et al. (2011) calculated vastly different home range sizes while trapping in Douglas County, Kansas (Table 10). This discrepancy may be due to the different methodology used in these two studies. Specifically, Swihart and Slade (1989) trapped prairie voles for about a decade for generally a 3 day-period per month and calculated home range via home range lengths while Streatfield et al. (2011) trapped prairie voles for four week periods from May to June across two years and calculated home range via 95% home range kernels.

Discussion

Phylogeography of the Prairie Vole The phylogeography created from the mitochondrial data reveals that the prairie vole has high gene flow with few barriers across its range. The spatial genetic cluster analysis grouped the 34 successfully sequenced specimens into three clusters. The primary cluster containing 25 specimens (73% of total) is widespread throughout the contiguous species range without any clear geographic pattern (Figure 3). This cluster also contained the most common haplotype, which consisted of eight specimens that were widespread throughout the species range and included individuals from the current subspecies of M. o. haydenii, M. o. taylori, M. o. ohionensis, and M. o. ochrogaster. While the minor and similis clusters are smaller than the primary cluster, these clusters also appear to be geographically widespread. For instance, the similis cluster contains individuals in Illinois, Nebraska, Wyoming, and South Dakota, and the minor cluster contains individuals in Wisconsin, Minnesota, South Dakota, and Alabama (Figure 3). The genetic distances, haplotype network, and phylogeny suggest further substructuring within these smaller mitochondrial clusters. The lack of phylogeographic patterns and isolation- by-distance suggests panmixia with high gene flow throughout the species range. This panmixia is consistent with the nuclear microsatellite results from Adams et al. (2017); although, evidence for isolation-by-distance was found using nuclear microsatellites. The lack of extensive phylogeographic structuring may be due to recent range expansion. After the glacial cycles of the Pleistocene (~2.6 million years ago to 11.7 thousand years ago) and subsequent climate warming of the Holocene, the glaciers retreated from North America, and many species expanded their range from ice-free Pleistocene refugia (Lessa et al. 2003, Hewitt 2004, Barton and Wisely 2012). For species like the North American rat snake (Elaphe obsoleta) and striped skunk (Mephitis mephitis), dispersal patterns from these refugia and subsequent reduction of gene flow between clades caused genetic structuring throughout the species range (Burbrink et al. 2000, Barton and Wisely 2012). In contrast, the eastern fox squirrel (Sciurus niger) and western harvest mouse (Reithrodontomys megalotis) lack extensive phylogeographic structuring across their wide distributions due to recent range expansion and incomplete lineage sorting after postglacial expansion (Moncrief et al. 2010, Nava-García et al. 2016). Similarity, in Nearctic continental avian species, insufficient evolutionary time after postglacial expansion resulted in low phylogenetic differentiation between subspecies (Phillimore and Owens 2006). Species in the Arvicolinae subfamily, which contains the Microtus genus, also have undergone recent global range expansion and ongoing divergence in the last 1.2 to 2 million years (Fink et al. 2010). Thus, the prairie vole may have had insufficient

evolutionary time to develop extensive genetic structure after expanding into the grasslands of central North America. Other species of central North America including the swift fox (Vulpes velox), the Woodhouse’s toad (Bufo woodhousii), and prairie grouse (a species complex, genus Tympanuchus) also show evidence of recent range expansion (Ellsworth et al. 1994, Masta et al. 2003, Schwalm and Waits 2014), but only the prairie grouse lacks extensive genetic structuring, which was entirely attributed to insufficient evolutionary time (Ellsworth et al. 1994). The lack of genetic structuring in the prairie vole also may be related to its high gene flow, which may be partially attributed to the species’ ability to readily disperse across potential barriers in its range. In wide-ranging species of North America like the striped skunk, deer mouse (Peromyscus maniculatus), and black-capped chickadee (Poecile atricapillus), barriers to gene flow including mountain ranges, rivers, and unsuitable habitat resulted in extensive genetic structuring (Dragoo et al. 2006, Barton and Wisely 2012, Adams and Burg 2015). Similarly, within the grasslands of central United States, natural and anthropogenic sources of habitat fragmentation reduced gene flow between populations of the swift fox and produced extensive genetic structure (Schwalm and Waits 2014). However, gene flow in prairie voles does not appear to be deterred by these barriers. For instance, while rivers influence the genetic structure of some small mammals such as the little ground squirrel (Spermophilus pygmaeus, Ermakov et al. 2018) and white-footed mouse (Peromyscus leucopus, Marrotte et al. 2014), the genetic structure of the prairie vole is not associated with rivers within the species range, which also has been reported for other small mammals such as the eastern fox squirrel (Moncrief et al. 2010) and Namaqua rock mouse (Micaelamys namaquensis, Russo et al. 2010). Additionally, prairie voles appear to be less susceptible to reduced gene flow from habitat fragmentation because prairie voles can occasionally disperse large distances (> 400 m), even across unsuitable habitats including roads, forest, and fragmented habitat (Diffendorfer et al. 1995, Diffendorfer and Slade 2002). Similarly, another vole species, the water vole (Arvicola terrestris), has been reported to maintain high gene flow across fragmentated habitat in Eurasia (Aars et al. 2006). This high vagility may allow the prairie vole to cross bodies of water due to the lack of structuring around rivers. Also, prairie voles can reproduce year-round if weather conditions are suitable, have a 21-day gestation period, and can mate as early as 31 days of age, resulting in a short generation time (Richmond and Conway 1969, Getz et al. 1987a, Solomon 1991). Thus, high dispersal ability and large effective population size from rapid reproduction can produce multiple migrants per generation preventing geographic isolation and genetic differentiation between populations and current subspecies (Adams et al. 2017). The California vole (M. californicus) also shows evidence of maintaining high gene flow through large effective population sizes (Adams and Hadley 2010). Therefore, the lack of effective geographic barriers between the clusters and high gene flow from high reproductive rates and high dispersal ability despite high juvenile mortality (Getz et al. 1987a) may prevent long-term isolation and differentiation between clusters regardless of evolutionary time. Despite the lack of structuring and high gene flow, the prairie vole contains an overall high genetic distance (14.03 ± 6.45%), which appears to be driven by certain divergent individuals like those from Madison County, Alabama and Alexander County, Illinois. Therefore, some barriers must exist to reduce gene flow to these divergent individuals. The only apparent barrier to this high gene flow among the contiguous distribution of the species, according to the nuclear microsatellite data, is isolation-by-distance (Adams et al. 2017). Isolation-by-distance in prairie voles is likely because long-distance dispersals are rare events (Diffendorfer and Slade 2002). Under conditions of extreme habitat fragmentation, populations can become isolated and

differentiated without consistent gene flow (Gaines et al. 1997). In an analysis of fine-scale genetic structure based on allozymes and mtDNA restriction fragment length polymorphisms, habitat fragmentation in four populations of prairie voles within Douglas County, Kansas resulted in high genetic variation, which suggests isolation and differentiation from reduced gene flow (Gaines et al. 1997). This habitat fragmentation may occur in the periphery of the species range where populations become more isolated and genetic drift increases from smaller effective population size (Vucetich and Waite 2003). Isolation-by-distance also has been reported for a variety of species in North America including the muskrat (Ondatra zibethicus, Laurence et al. 2011), swift fox (Schwalm and Waits 2014), striped skunk (Barton and Wisely 2012), Woodhouse’s toad (Masta et al. 2003), California mouse (Peromyscus californicus, Lion et al. 2018), but not all species such as the black-capped chickadee (Adams and Burg 2015), North American rat snake (Burbrink et al. 2000), and Northern Baja deermouse (Peromyscus fraterculus, Lion et al. 2018). However, no evidence for isolation-by-distance was observed in the prairie vole mitochondrial data. The discrepancy of the presence or absence of isolation-by-distance between the mitochondrial and nuclear data could be a result of differences between evolutionary rates of nuclear and mitochondrial genes (Platt II et al. 2015). The rapid evolution of mtDNA causes mitochondrial markers to reflect more recent evolutionary relationships and may not accurately reflect the entire evolutionary history of the species (Toews and Brelsford 2012, Platt II et al. 2015). For instance, in the codons of mitochondrial protein-coding genes like cytochrome b, the first and second positions are highly conserved while the third position is biased toward cytosine and adenine, which may lead to higher levels of homoplasy (Prychitko and Moore 2000). Nuclear genes more accurately reflect past evolutionary relationships and may better depict earlier evolutionary events like hybridization and lineage sorting (Toews and Brelsford 2012, Platt et al. 2015). Thus, the nuclear microsatellites could describe isolation-by-distance earlier in evolutionary time while the mitochondrial sequence data suggests that this trend had disappeared in recent evolutionary history. Alternatively, mtDNA may be less likely to detect isolation-by- distance than microsatellite data due to fewer differences between generations and variation- reducing selection in mtDNA and may be unsuitable for this kind of analysis (Teske et al. 2018). Nonetheless, the lack of isolation-by-distance in the mitochondrial data suggests the barrier of geographic distance no longer exists in the prairie vole but may be a historic driver of differentiation between populations. Thus, some individuals and the smaller clusters, may have differentiated due to this historic barrier to gene flow resulting in the high genetic variation. This genetic variation detected for a 505 bp segment at the species level (14.03 ± 6.45%) is greater than that typically found for the entire length of cytochrome b in rodents (0.00% - 6.29%, Bradley and Baker 2001). The field vole (M. agrestis) also has high levels of mtDNA genetic diversity but has distinct phylogeographic structure across its range in Eurasia (Jaarola and Searle 2002). Other vole species like the meadow vole (M. pennsylvanicus, Donavan 2016), common vole (M. arvalis, Stojak et al. 2016) and long-tailed vole (M. longicaudus, Conroy and Cook 2000) have distinctive phylogeographic structure across their ranges too. The high level of genetic variation in the prairie vole could indicate cryptic species as suggested by Bouarakia et al. (2018) when observing a genetic distance of 10.7% between two parapatric lineages of pygmy gerbils (Gerbillus henleyi). Similarly, despite the presence of 17 currently recognized subspecies based on morphology in the California vole, only two phylogeographic groups could be identified (Conroy and Neuwald 2008). These groups were suggested to belong to sister species based on average cytochrome b genetic distance of 4.46% (Conroy and Neuwald 2008). Thus,

multiple cryptic species or lineages could be possible within the prairie vole. When examined independently, the primary cluster has a genetic distance of 3.34 ± 1.77% while the minor cluster excluding Madison, Alabama has a genetic distance of 5.74 ± 3.47%, which both fall into the typical genetic distance at the species level for rodents. These two clusters along with the other individuals could belong to unique lineages of the prairie vole. Alternatively, the prairie vole may naturally have higher genetic variability than other rodent species. Even within the primary cluster, the genetic distance is higher than the mean genetic distance at the species level for rodents (2.09%, Bradley and Baker 2001). The high level of genetic variation within the species would allow for diverse morphological phenotypes and potential phenotypic plasticity in response to changes in environmental conditions, which may have been mistaken for distinct genetic lineages and appear to contrast with the lack of phylogeographic structure. Other small mammals have been reported to lack extensive phylogeographic structure despite high observed morphological variation including the eastern fox squirrel (Moncrief et al. 2010) and western harvest mouse (Nava-García et al. 2016). This potential for morphological diversity could partially explain the apparent contradiction between genetic (Modi 1993, Adams et al. 2017) and morphological studies (Choate and Williams 1978, Stangl et al. 2004) and subspecies designations in the prairie vole. Thus, morphological differences reported by the subspecies designations of the prairie vole could be due to local adaptation and phenotypic plasticity. For instance, long total body lengths of prairie voles observed in the southwestern portion of the species range populations were suggested to be possibly attributed to local adaptation of peripheral populations, as observed in other arvicoline taxa, rather than distinct genetic lineages (Stangl et al. 2004). Similarly, observed clinal variation in cranial measurements between southeastern prairie vole populations suggest local adaptation rather than genetic differences between populations (Huggins and McDaniel 1984). Also, the high degree of genetic variation may provide the basis for the varying degrees of social and genetic monogamy observed in distinct populations of prairie voles (Streatfeild et al. 2011), allowing adaptation to local ecological factors such as population density and vegetative characteristics of the habitats.

Comparisons to Nuclear Data and Current Subspecies Designations The phylogeography based on mitochondrial data does not match the currently used subspecies designations. First, the intrasubspecific variation was much greater than the intersubspecific variation of the cytochrome b sequences. Only 16.5% of the total genetic variation present in the cytochrome b gene of the samples occurred between subspecies, suggesting little genetic differentiation and high gene flow between the current subspecies. Second, the mean genetic distance within M. o. haydenii, M. o. ochrogaster, M. o. minor, M. o. taylori, and M. o. similis are higher than expected at the subspecies level for rodents (0.00% - 1.87%, Bradley and Baker 2001), which suggests the individuals within these putative subspecies are more distantly related than typical subspecies. Third, specimens from the six successfully sequenced current subspecies did not cluster into six distinct lineages within the spatial genetic cluster analysis, haplotype network, or phylogeny. Additionally, the mismatch distributions of pairwise differences in subspecies comparisons do not support the existence of six reproductively isolated subspecies. Instead, individuals from all six contiguous subspecies grouped together in the primary cluster. However, the subspecies designations for M. o. similis and M. o. minor appear to be partially supported by the similis and minor clusters; although, the geographic ranges of those clusters do not fully match the ranges suggested by the currently used

subspecies map. For instance, the individuals from Hughes County, South Dakota and Madison County, Alabama belong to the minor cluster but are located outside the geographic range of M. o. minor, and the individuals from Winona County, Minnesota and Portage County, Wisconsin are located within the geographic range of M. o. minor but do not belong to the minor cluster. Fourth, isolation-by-distance does not occur within any of the current subspecies. Lastly, the results of the Fisher’s exact test revealed that the mitochondrial clustering was not consistent with the current subspecies designations. The mitochondrial clustering was also not consistent with the clusters found through the nuclear microsatellite analysis; although, the nuclear clusters were consistent with the current subspecies designations according to Fisher’s exact tests, which contrasts with the findings of Adams et al (2017). However, despite producing different patterns of clustering, both the mitochondrial and nuclear analyses showed evidence of high gene flow and panmixia throughout the species range. Mito-nuclear discordance also has appeared in other Microtus species. In the California vole, mito-nuclear discordance occurred across a recent contact zone between two lineages and resulted from mitochondrial introgression due to historic hybridization between the two lineages (Lin et al. 2017). The genetic clustering of the Mediterranean (M. duodecimcostatus) and Lusitanian pine vole (M. lustianicus) from nuclear microsatellites suggest two distinct lineages in agreement with morphological differences between the species (Bastos- Silveira et al. 2012). However, cytochrome b sequence data was in discordance with the nuclear microsatellites, which appears to be due to introgression from M. duodecimcostatus to M. lustianicus (Bastos-Silveira et al. 2012). Therefore, hybridization of potentially distinct lineages within the prairie vole could have caused the observed mito-nuclear discordance. Alternatively, the discordance may be due to incomplete lineage sorting, which better explains the lack of phylogeographic structure across the species range in both the nuclear and mitochondrial data (Toews and Brelsford 2012). Incomplete lineage sorting also has been identified to be a major cause of discordance between nuclear and mitochondrial data in East Asia voles (Alexandromys, Lissovsky et al. 2017). Additionally, male-biased dispersal, which has been observed in Kansas (Gaines et al. 1979, but see Diffendorfer and Slade 2002) and Illinois (Smith and Batzli 2006, but see McGuire et al. 1993), may contribute to the mito-nuclear discordance. Nonetheless, neither the nuclear nor mitochondrial studies support the current seven subspecies designation. Morphological data from the literature also do not indicate consistent differences among subspecies designations. The collected external morphological measurements from the literature reveals a large degree of variation, and the measurements do not support clear morphological separation between the subspecies. Additionally, the analysis of total body length collected from museum records of five subspecies only supported differences between M. o. haydenii and M. o. minor and between M. o. ochrogaster and M. o. minor. However, additional morphological data should be compared to confirm these differences between the subspecies because few data points (< 26) could be found for M. o. similis, M. o. minor, M. o. ohionensis, M. o. taylori, and M. o. ludovicianus through the museum records of VertNet. Other studies that have compared morphological data did not support clear differences in subspecies either. Choate and Williams (1978) did not find consistent morphological differences between M. o. ochrogaster, M. o. haydenii, and M. o. taylori (but see Stangl et al. 2004). Turner (1974) compared pelage color, total body length, tail length, hindfoot length, and cranial measurements between two populations representative of M. o. haydenii in South Dakota and four population later designated as M. o. similis in South Dakota and Wyoming but did not observe discernable differences. Also, in comparisons between M. o. ohionensis and M. o. ochrogaster,

Severinghaus (1977) did not observe a difference in the range of total body length while Gottschang (1981), using unpublished data, suggested that the differences in pelage color were only subtle changes and not enough to classification as distinct subspecies. The ecological data including activity patterns, litter size, and home range size do not indicate subspecific differences. Populations from M. o. haydenii, M. o. ochrogaster, and M. o. minor are reported to be crepuscular but active most hours (Barbour and Davis 1974, Choate 1989, Hoffmeister 1989, Long 1990, Armstrong 2011). However, individuals of M. o. ochrogaster in Douglas County, Kansas were reported to be crepuscular in the summer and shifted to diurnal activity in the winter, which indicates that activity may vary depending on time of year (Martin 1956). Ultradian rhythms are common among Microtus species (Madison 1985), and this activity pattern has been reported in prairie voles (Barbour 1963, Lewis and Curtis 2016). Populations from the contiguous subspecies other than M. o. taylori, have been reported to utilize runways and burrows (Stoner 1918, Hall 1955, DeCoursey 1957, Barbour and Davis 1974, Turner 1974, Carroll and Getz 1976, Sealander 1979, Bee 1981, Clark 1987, Choate 1989, Hoffmeister 1989, Long 1990, Armstrong 2011). Additionally, these subspecies are reproductively active throughout the entire year, but reproduction may be reduced in the summer or winter months (DeCoursey 1957, Corthum 1967, Jones 1964, Meserve 1971, Turner 1974, Sealander 1979, Clark 1987, Getz et al. 1987b, Hoffmeister 1989, Choate 1989, Long 1990, Armstrong 2011) depending on weather conditions. Litter size did not appear to vary among M. o. haydenii, M. o. ochrogaster, M. o. similis, and M. o. ohionensis. Home range size of M. o. haydenii appeared to widely vary between individuals and among different months in northeastern Colorado (Abramsky and Tracy 1980). Variation also occurred between sexes (Gaines and Johnson 1982) and different sites in M. o. ochrogaster (Kansas and Indiana, Streatfeild et al. 2011). Home range sizes of M. o haydenii and M. o. ochrogaster did not appear to be different. The lack of differences in activity patterns, litter size, or home range size do not support between subspecies. In contrast, habitat types occupied by prairie voles do appear to vary between some subspecies. Prairie voles from populations designated as M. o. ochrogaster, M. o. haydenii, M. o. ohionensis, and M. o. similis are reported to occupy well-drained upland grassland habitat (Stoner 1918, DeCoursey 1957, Jones 1964, Barbour and Davis 1974, Turner 1974, Bee 1981, Getz 1985, Choate 1989, Hoffmeister 1989, Armstrong 2011). However, specimens designated as M. o. ludovicianus were only collected in “damp” sites (Getz 1985) while populations designated as M. o. minor occupy prairies with thin soil glacial sands within Minnesota (Long 1990). For M. o. taylori, Hibbard and Rinker (1943) noted that the specimens were found in habitats wetter than traditionally occupied by M. o. haydenii including a bog at the type locality. However, these subspecies may occupy different habitats due to predation or interspecific competition rather than differences between subspecies. For instance, competition from M. pennsylvanicus appears to potentially exclude M. ochrogaster from bluegrass and tallgrass habitats in east-central Illinois (Getz 1987b).

Caveat and Conclusion A caveat to this study is that museum samples can degrade over time and may have hindered an accurate analysis. Until recently, museums did not preserve specimens adequately for molecular studies, which has made studies involving museum specimens challenging (Rowe et al. 2011). For instance, some preservation methods, such as unbuffered formalin, can degrade DNA while others, like hard tissues and preserved skins, may result in lower yields of

extractable DNA (Burrell et al. 2015). While the DNA contained in museum specimens have become useful in evolutionary and biogeographic studies, the DNA may be of low quantity and poor quality, especially for poorly preserved older specimens (Besnard et al. 2016). Amplification of even short DNA segments (< 400 base pairs) remains unpredictable (Besnard et al. 2016). Failed amplifications can be caused by small amounts of total DNA, contamination, and DNA degradation including fragmentation and/or nucleotide damage from crosslinks between DNA strands and other molecules, oxidation, and hydrolysis (Burrell et al. 2015, Sproul and Maddison 2017). Specifically, nucleotide damage can result from exposure to ultraviolet light, which can deaminate cytosine and cause deterioration of primer binding sites preventing the sequence from being amplified (Rowe et al. 2011). However, the most significant problem with DNA from museum specimens is that the DNA often becomes sheared into small fragments typically only a few hundred base pairs in length at most (Rowe et al. 2011). Even if samples are successfully amplified and sequenced, some uncertainty exists with the resulting contiguous sequences. Potential sequence errors can occur from damaged nucleotides or nucleotide misincorporation during amplification, which has been reported for ancient DNA (> 2000 years old, Stiller et al. 2006), but not confirmed for archival DNA (< 200 years old, Lee and Prys- Jones 2008). Additionally, museum collections rely on information on species identity and location of collection recorded by collectors, but these labels may have errors (Rasmussen and Prys-Jones 2003). While a BLAST analysis can confirm species identity, not ever study sequences and confirms species identity, such as the microsatellite analysis in Adams et al. (2017). Thus, some genetic analyses may have inaccuracies due to this mislabeling. Evidence does exist that suggests that the museum specimens used in this study did contain potential nucleotide degradation and shearing. First, only a 505 bp region of the mitochondrial cytochrome b could be produced from most samples despite multiple attempts to sequence additional regions of the gene, which could be attributed to the age, source, or tissue type of the specimens (Appendix IV). Additionally, attempts to sequence two nuclear genes, to use repair enzymes, and to conduct next-generation sequencing utilizing restriction-associated DNA tags failed. These failures likely indicate that samples had too little intact DNA with primer binding sites to amplify in the samples. Only mtDNA existed in enough quantities to be amplified, but the shearing present in the mtDNA only allowed for a small region to be amplifiable. Second, potential nucleotide damage could be indicated by the mean genetic distances being larger for the 505 bp region than expected for the entire length of the cytochrome b for rodents (Bradley and Baker 2001. However, the museum specimens in this study do not appear to have significantly hindered the analysis despite potential nucleotide degradation. The BLAST analysis aligned the sequences with M. ochrogaster or other related species with high certainty (> 90% similarity), and any potential damage to the sequences did not hinder alignment. The conversion from a M. ochrogaster to M. longicaudus or M. pennsylvanicus through nucleotide damage or sequencing errors seems unlikely considering the phylogenetic distance of these species in the Microtus species tree (Jaarola et al. 2004). Sequences that were identified as M. longicaudus or M. pennsylvanicus using BLAST are most likely due to mislabeling upon capture or later during collection management since M. ochrogaster, M. longicaudus, and M. pennsylvanicus share overlapping ranges (Hall 1981) and may appear similar when using morphological characteristics to identify them. Also, while the sequences in this study contained 59 variable sites (12% of total base pairs in the sequences) resulting in only 4% of the amino acids being changed, no unexpected stop codons, insertions, or deletions were present. Additionally, the

cytochrome b gene and similar base pair regions from museum specimens have been used in other rodent phylogeographic studies (Lessa and Cook 1998, Moncrief et al. 2010). Overall, the genetic, morphological, and ecological data do not consistently support the current subspecies designations, and the mitochondrial and nuclear genetic structures are in discordance with each other. However, additional nuclear and mitochondrial loci along with further morphological and ecological data should be examined before determining if the subspecies designations of M. ochrogaster are warranted. The current genetic data is limited with six nuclear microsatellite length polymorphisms and a 505 bp region of the mitochondrial cytochrome b both produced from highly degraded museum samples, which has limited the amount of data that could be collected and has produced results with higher than expected genetic variation. Potentially, other next-generation sequencing methods may be able to produce viable coverage from these museum specimens. Also, fresh samples across the species range could be collected and tissue samples analyzed. Additionally, more comprehensive comparisons of morphological and ecological data from populations across the species range, especially populations outside of M. o. ochrogaster and M. o. haydenii, would be beneficial to resolve the subspecies designation, which can help in understanding the mechanisms underlying intraspecific variation and population behavioral differences of prairie voles. For instance, the geographic differences in behavior identified by Streatfeild et al. (2011) between prairie vole populations from Douglas County, Kansas and Monroe County, Indiana are not likely the result of genetic differences between subspecies or lineages since both individuals representing these populations belong to the same subspecies and mitochondrial haplotype. Beyond developing a better understanding of this species, phylogeographic information collected from prairie voles can be extrapolated to other species or habitats since they are a widely distributed species (Lion et al. 2018). For instance, prairie voles could serve as a model to better understand genetic structure, barriers to gene flow, and variation in populations vulnerable to local extinction from the disappearance of grasslands in North America caused by anthropogenic activities. In North America, 99% of the original temperate grasslands have been destroyed or altered for agricultural purposes (Olechnowski et al. 2009). Grassland habitats that are preserved typically face other disturbances such as fragmentation, industrial development in adjacent areas, drought, extreme weather events, unmanaged grazing, loss of native grazers, changes in fire frequency, and invasive vegetation (Van Dyke et al. 2004, Spencer et al. 2016). Therefore, prairie voles may serve to model the impacts of these disturbances on small mammals in grassland habitats. This study also adds to the growing number of contradictions that have been found between prior subspecies designations and subsequent molecular data across a variety of avian (Zink et al. 2001, Scribner et al. 2003, Lovette et al. 2010) and mammalian (Johnson et al. 1999, Cullingham et al. 2008, Latch et al. 2009, Laurence et al. 2011) species. In the past, subspecies were designated using morphological data under the morphological and biological species concepts (Mayr 1982, Padial et al. 2010). However, morphological differentiation may not be the best predictor of unique genetic lineages. For instance, phenotypic plasticity, sexual dimorphism, or developmental stages can cause organisms of the same species to appear morphologically distinct (Vane-Wright and Tennet 2011, Galan et al. 2012), and cryptic species lack morphological differentiation despite comprising distinct evolutionary histories (Bickford et al. 2007, Padial et al. 2010). Additionally, in the past, arbitrary morphological characteristics were often used to designate subspecies while multivariate characteristics are currently favored (Zink 2004). With the advent of molecular techniques, researchers have shifted from designating a

subspecies using morphological characteristics to molecular data (Padial et al. 2010). The resulting contradictions suggest that past subspecies classifications may need to be reevaluated (Phillimore and Owens 2006). The prairie vole is one of these species that need revaluation according to this study and prior genetic and morphological studies.

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Table 1. Summary data for all specimens putatively from Microtus ochrogaster in this study including the number of specimens per subspecies (ns), the number of populations per subspecies (np), and the number and percentage of populations that had an individual successfully sequenced per subspecies for the cytochrome b fragment 1 (307 bp) and fragment 2 (202 bp, Figure 2). All specimens that were successfully sequenced for fragment 1 also were successfully sequenced for fragment 2.

np (%) sequenced for np (%) sequenced for Subspecies ns np cytochrome b fragment 1 cytochrome b fragment 2 haydenii 97 14 11 (79%) 14 (100%) ohionensis 31 5 4 (80%) 5 (100%) ochrogaster 83 13 12 (92%) 12 (92%) minor 56 8 7 (88%) 8 (100%) taylori 18 2 2 (100%) 2 (100%) ludovicianus* 6 1 0 (0%) 1 (100%) similis 49 5 4 (80%) 4 (80%) Total 340 48 40 (83%) 46 (96%)

* For M. o. ludovicianus, two individuals from the same population were successfully sequenced for cytochrome b fragment 2.

Table 2. Details of the specimens putatively from Microtus ochrogaster in this study that were successfully sequenced for the cytochrome b gene (n=47) and the bp length sequenced, either 505 bp or a 202 bp subset of the longer fragment. Abbreviations for the sources are as follows: CMM = University of Colorado Museum of Natural History, CMNH = Museum of Natural History and Science, Cincinnati Museum Center, FHSM = Sternberg Museum of Natural History, Fort Hays State University, INHS = Illinois Natural History Survey, NMNH = National Museum of Natural History, OSU = Museum of Biological Diversity, The Ohio State University, UIMNH = University of Illinois Museum of Natural History, UKMNH = University of Kansas Museum of Natural History, UNSMZM = University of Nebraska State Museum, UWSP = University of Wisconsin Stevens Point Museum of Natural History. Tissues were either from museum study skins, organ tissue preserved in ethanol, or frozen organ tissue.

Sample Current Year Tissue Cytochrome Source ID State/Province County/Parish ID Subspecies Collected Type b length (bp) CMM 484 haydenii Colorado Boulder 2010 preserved 505 CMM 714 haydenii Colorado Larimer 2010 preserved 505 FHSM 5820 haydenii Kansas Ellis 1965 study skin 505 FHSM 37950 haydenii Kansas Chautauqua 2007 preserved 505 INHS 47933 haydenii Nebraska Cherry 1973 study skin 505 INHS 47986 haydenii Nebraska Scotts Bluff 1973 study skin 505 UNSMZM 17484 haydenii Nebraska Antelope 1989 study skin 505 UNSMZM 23162 haydenii Nebraska Sioux 1989 study skin 505 NMHM 23194 haydenii Oklahoma Canadian 1889 study skin 202 UKMNH 101492 haydenii North Dakota Billings 1965 study skin 202 UKMNH 122944 haydenii North Dakota Bowman 1970 study skin 505 UINHM 57600 haydenii South Dakota Hughes 1979 study skin 505 UINHM 59479 haydenii South Dakota Gregory 1981 study skin 505 NMHM 19067 haydenii South Dakota Custer 1888 study skin 202 OSU 3766 ohionensis Ohio Brown 1971 study skin 505 CMNH M788 ohionensis Ohio Gallia 1984 study skin 505 OSU 1294 ohionensis Ohio Clermont 1942 study skin 505 OSU 292 ohionensis Ohio Shelby 1921 study skin 202 OSU 2867 ohionensis Ohio Licking 1970 study skin 505 CMNH M1814 ochrogaster Indiana Jefferson 1954 study skin 505 CMNH M1900 ochrogaster Indiana Ripley 1952 study skin 505

N/A N/A ochrogaster Indiana Monroe 2007 frozen 505 FHSM 5681 ochrogaster Kansas Dickinson 1965 study skin 505 FHSM 38425 ochrogaster Kansas Washington 2007 preserved 505 N/A N/A ochrogaster Kansas Douglas 2006 frozen 505 FHSM 38624 ochrogaster Kansas Greenwood 2008 preserved 505 N/A N/A ochrogaster Illinois Champaign 2007 frozen 505 UWSP 3696 ochrogaster Wisconsin Sauk 1972 study skin 505 UINHM 10180 ochrogaster Alabama Madison 1955 study skin 505 UINHM 59062 ochrogaster Illinois Alexander 1981 study skin 505 UNSMZM 29065 ochrogaster Nebraska Lancaster 2002 study skin 505 CMNH M1828 minor Minnesota Winona 1960 study skin 505 UINHM 48162 minor Minnesota Clay 1973 study skin 505 UKMNH 48132 minor Minnesota Sherburne 1927 study skin 505 NMNH 75329 minor Saskatchewan Wingard 1895 study skin 505 NMNH 248632 minor Wisconsin Clark 1927 study skin 505 NMNH 209136 minor North Dakota McKenzie 1915 study skin 202 UWSP M941 minor Wisconsin Portage 1968 study skin 505 UWSP 9623 minor Wisconsin Monroe 2009 preserved 505 FHSM 3399 taylori Kansas Hamilton 1964 study skin 505 FHSM 29514 taylori Kansas Meade 1993 study skin 505 NMNH 96632 ludovicianus Louisiana Calcasieu 1899 study skin 202 NMNH 96633 ludovicianus Louisiana Calcasieu 1899 study skin 202 NMNH 214251 similis Montana Big Horn 1916 study skin 505 UKMNH 27653 similis Wyoming Natrona 1948 study skin 505 UKMNH 20745 similis Wyoming Niobrara 1947 study skin 505 UKMNH 113520 similis South Dakota Fall River 1967 study skin 505

Table 3. BLAST results of the specimens putatively from Microtus ochrogaster in this study that were successfully sequenced for the cytochrome b gene (n=47) and base pair length sequenced, either 505 bp or a 202 bp subset of the longer fragment. For each sample, the species that the sample aligned most closely with and the associated sequence identity (%) from the BLAST analysis are listed.

Cytochrome Current Closet Alignment State/Province County/Parish b length Subspecies (Identity) (base pair) haydenii Colorado Boulder 505 M. ochrogaster (99%) haydenii Colorado Larimer 505 M. longicaudus (99%) haydenii Kansas Ellis 505 M. ochrogaster (99%) haydenii Kansas Chautauqua 505 M. ochrogaster (99%) haydenii Nebraska Cherry 505 M. ochrogaster (99%) haydenii Nebraska Scotts Bluff 505 M. ochrogaster (99%) haydenii Nebraska Antelope 505 M. ochrogaster (99%) haydenii Nebraska Sioux 505 M. ochrogaster (99%) haydenii North Dakota Bowman 505 M. longicaudus (92%) haydenii South Dakota Hughes 505 M. ochrogaster (96%) haydenii South Dakota Gregory 505 M. ochrogaster (99%) haydenii Oklahoma Canadian 202 M. ochrogaster (98%) haydenii North Dakota Billings 202 M. longicaudus (98%) haydenii South Dakota Custer 202 M. longicaudus (100%) ohionensis Ohio Brown 505 M. ochrogaster (100%) ohionensis Ohio Gallia 505 M. ochrogaster (99%) ohionensis Ohio Clermont 505 M. ochrogaster (99%) ohionensis Ohio Licking 505 M. pennsylvanicus (96%) ohionensis Ohio Shelby 202 M. ochrogaster (98%) ochrogaster Indiana Jefferson 505 M. longicaudus (92%) ochrogaster Indiana Ripley 505 M. ochrogaster (100%) ochrogaster Indiana Monroe 505 M. ochrogaster (99%) ochrogaster Kansas Dickinson 505 M. ochrogaster (99%) ochrogaster Kansas Washington 505 M. ochrogaster (99%) ochrogaster Kansas Douglas 505 M. ochrogaster (99%) ochrogaster Kansas Greenwood 505 M. ochrogaster (99%) ochrogaster Illinois Champaign 505 M. ochrogaster (100%) ochrogaster Wisconsin Sauk 505 M. ochrogaster (99%) ochrogaster Alabama Madison 505 M. ochrogaster (97%) ochrogaster Illinois Alexander 505 M. ochrogaster (93%) ochrogaster Nebraska Lancaster 505 M. ochrogaster (96%) minor Minnesota Winona 505 M. ochrogaster (99%) minor Minnesota Clay 505 M. pennsylvanicus (94%) minor Minnesota Sherburne 505 M. ochrogaster (96%)

minor Saskatchewan Wingard 505 M. pennsylvanicus (94%) minor Wisconsin Clark 505 M. ochrogaster (96%) minor Wisconsin Portage 505 M. ochrogaster (99%) minor Wisconsin Monroe 505 M. ochrogaster (96%) minor North Dakota McKenzie 202 M. ochrogaster (99%) taylori Kansas Hamilton 505 M. ochrogaster (99%) taylori Kansas Meade 505 M. ochrogaster (99%) similis Montana Big Horn 505 M. ochrogaster (98%) similis Wyoming Natrona 505 M. ochrogaster (94%) similis Wyoming Niobrara 505 M. ochrogaster (99%) similis South Dakota Fall River 505 M. ochrogaster (94%) ludovicianus Louisiana Calcasieu 202 M. longicaudus (100%) ludovicianus Louisiana Calcasieu 202 M. longicaudus (96%)

Table 4. Measures of genetic diversity from a 505 base pair region of cytochrome b from museum specimens confirmed as Microtus ochrogaster through BLAST. These represent 34 individuals (n) and six of the seven current subspecies. Genetic diversity was determined within each putative subspecies and the clusters determined by a spatial genetic cluster analysis in Bayesian Analysis of Population Structure (BAPS) v.6. Measures included number of haplotypes (nh), number of segregating sites (s), mean (± SD) genetic distance (%) calculated using a Kimura 2-parameter model of evolution, and nucleotide diversity (± SD, π).

Genetic Subspecies/Cluster n nh s π Distance (%) haydenii 9 7 31 7.44 ± 3.84 0.015 ± 0.009 ohionensis 3 2 1 0.67 ± 0.67 0.001 ± 0.002 ochrogaster 11 9 50 14.69 ± 7.13 0.029 ± 0.016 minor 5 5 25 14.77 ± 7.99 0.029 ± 0.018 taylori 2 2 2 2.00 ± 1.74 0.004 ± 0.005 similis 4 4 34 22.94 ± 12.89 0.045 ± 0.030 Primary Cluster 25 17 26 3.34 ±1.77 0.001 ± 0.004 minor Cluster 5 5 18 8.23 ± 4.60 0.016 ± 0.011 minor Cluster (excluding Madison, AL) 4 4 11 5.74 ± 3.47 0.011 ± 0.008 similis Cluster 4 4 32 18.19 ± 10.29 0.036 ± 0.024 Total 34 26 59 14.03 ± 6.45 0.026 ± 0.014

Table 5. Analysis of molecular variance (AMOVA) for a 505 base-pair region of cytochrome b from museum specimens confirmed as Microtus ochrogaster through BLAST and representing 34 individuals (n) and six of the seven putative subspecies. Labels correspond to the following: H = Microtus ochrogaster haydenii, M = M. o. minor, Oc = M. o. ochrogaster, Oh = M. o. ohionensis, S = M. o. similis, and T = M. o. taylori.

Variance Molecular p-value Grouping Tested df SS Component Variance (%) [Oc+Oh+H+M+T+S] Intersubspecific (Фsc) 5 58.983 1.13785 16.47 0.012 Intrasubspecific (Фst) 28 161.635 5.77267 83.53 N/A [Oc+Oh+H+T+S][M] Among Groups (Фct) 1 22.494 1.55440 19.52 0.348 Intersubspecific (Фsc) 4 36.489 0.63698 8.00 0.082 Intrasubspecific (Фst) 28 161.635 5.77267 72.48 0.012 [Oc+Oh+H+T][S][M] Among Groups (Фct) 2 51.308 3.16988 37.94 0.075 Intersubspecific (Фsc) 3 7.675 -0.58798 -7.04 0.866 Intrasubspecific (Фst) 28 161.635 5.77267 69.10 0.021 [Oc+Oh+T][H][S][M] Among Groups (Фct) 3 53.905 2.29751 31.81 0.156 Intersubspecific (Фsc) 2 5.078 -0.84822 -11.75 0.922 Intrasubspecific (Фst) 28 161.635 5.77267 79.97 0.025 [Oc+Oh][T][H][S][M] Among Groups (Фct) 4 55.825 1.82541 25.93 0.342 Intersubspecific (Фsc) 1 3.158 -0.55463 -7.87 0.865 Intrasubspecific (Фst) 28 161.635 5.77267 81.95 0.014 [Oc+Oh+T+H+M][S] Among Groups (Фct) 1 28.434 2.99571 32.89 0.170 Intersubspecific (Фsc) 4 30.548 0.33899 3.72 0.125 Intrasubspecific (Фst) 28 161.635 5.77267 63.38 0.015 [Oc+Oh+T+H][S+M] Among Groups (Фct) 1 26.569 1.38113 18.17 0.075 Intersubspecific (Фsc) 4 32.414 0.44728 5.88 0.208 Intrasubspecific (Фst) 28 161.635 5.77267 75.95 0.019

Table 6. The location and subspecies designation of 35 individuals from Microtus ochrogaster sequenced for a 505 base pair region of cytochrome b in this study and/or six nuclear microsatellites in Adams et al. (2017) and their respective cluster determined by a spatial genetic cluster analysis in Bayesian Analysis of Population Structure (BAPS) v.6.

County State/Province Subspecies Mitochondrial Cluster Nuclear Cluster Boulder Colorado haydenii Primary Cluster Cluster 1 Ellis Kansas haydenii Primary Cluster Cluster 1 Chautauqua Kansas haydenii Primary Cluster Cluster 1 Cherry Nebraska haydenii Primary Cluster Cluster 1 Scotts Bluff Nebraska haydenii Primary Cluster Cluster 1 Antelope Nebraska haydenii Primary Cluster Cluster 1 Sioux Nebraska haydenii Primary Cluster Cluster 1 Hughes South Dakota haydenii minor Cluster N/A Gregory South Dakota haydenii Primary Cluster Cluster 1 Brown Ohio ohionensis Primary Cluster Cluster 6 Gallia Ohio ohionensis Primary Cluster Cluster 7 Clermont Ohio ohionensis Primary Cluster Cluster 6 Ripley Indiana ochrogaster Primary Cluster Cluster 1 Monroe Indiana ochrogaster Primary Cluster N/A Dickinson Kansas ochrogaster Primary Cluster Cluster 1 Washington Kansas ochrogaster Primary Cluster Cluster 5 Douglas Kansas ochrogaster Primary Cluster N/A Greenwood Kansas ochrogaster Primary Cluster Cluster 1 Champaign Illinois ochrogaster Primary Cluster N/A Racine Wisconsin ochrogaster N/A Cluster 1 Sauk Wisconsin ochrogaster Primary Cluster Cluster 1 Madison Alabama ochrogaster minor Cluster Cluster 4 Alexander Illinois ochrogaster similis Cluster Cluster 2 Lancaster Nebraska ochrogaster similis Cluster Cluster 3 Winona Minnesota minor Primary Cluster Cluster 1 Sherburne Minnesota minor minor Cluster N/A Clark Wisconsin minor minor Cluster Cluster 1 Portage Wisconsin minor Primary Cluster Cluster 1 Monroe Wisconsin minor minor Cluster Cluster 1 Hamilton Kansas taylori Primary Cluster Cluster 1 Meade Kansas taylori Primary Cluster Cluster 1 Big Horn Montana similis Primary Cluster Cluster 2 Natrona Wyoming similis similis Cluster Cluster 1 Niobrara Wyoming similis Primary Cluster Cluster 1 Fall River South Dakota similis similis Cluster Cluster 1

Table 7. Results from Mantel tests comparing matrices of pairwise genetic differences and pairwise geographic distances (km) for 34 sequenced museum specimens of Microtus ochrogaster from a 505 base pair region of cytochrome b. Analyses were conducted on the entire species, five of the current subspecies, and the clusters identified in the spatial genetic cluster analysis. Additionally, mean (± SD) and range are listed for both the pairwise genetic differences matrix and geographic distance matrix.

Pairwise Differences Geographic Distance (km) Group n Mean (± SD) Range Mean (± SD) Range Mantel’s r p Microtus ochrogaster 34 13.02 ± 12.56 0 – 46 857 ± 531 0 – 2547 0.057 0.241 M. o. ochrogaster 11 11.64 ± 13.79 0 – 41 647 ± 476 0 – 1574 0.009 0.363 M. o. haydenii 9 6.38 ± 7.27 0 – 24 366 ± 270 0 – 872 0.028 0.355 M. o. ohionensis 3 0.33 ± 0.52 0 – 1 55 ± 73 0 – 163 -0.719 0.337 M. o. minor 5 10.40 ± 10.78 0 – 25 122 ± 117 0 – 355 -0.299 0.093 M. o. similis 4 13.10 ± 14.42 0 – 32 156 ± 155 0 – 382 -0.242 0.491 Primary Cluster 25 3.25 ± 2.84 0 – 14 824 ± 531 0 – 2294 -0.102 0.206 minor Cluster 5 6.60 ± 5.97 0 – 17 596 ± 680 0 – 1960 0.045 0.065 similis Cluster 4 10.50 ± 11.02 0 – 28 552 ±600 0 – 1618 0.576 0.150

Table 8. Total body length (i.e., length from the snout to the tip of the tail, mm), tail length (mm), and hindfoot length (mm) including specified locality, source of data, current subspecies based on locality, number of samples examined by the source (n), if given by the source, and mean (± SD if reported) and range of lengths if specified by the source from Microtus ochrogaster collected from various locations. Sources are as follows: 1 = Stoner 1918, 2 = Hibbard and Rinker 1943, 3= Jones 1964, 4 = Barbour and Davis 1974, 5 = Turner 1974, 6 = Severinghaus 1977, 7 = Choate and Williams 1978, 8 = Sealander 1979, 9 = Gottschang 1981, 10 = Choate 1989, 11 = Long 1990, 12 = Armstrong 2011, 13 = Ophir and Wolff, unpublished data.

Range of Mean Mean Total Mean Tail Range of Tail Range of County(s) State(s) Source Subspecies n Total Hindfoot Length Length Length Hindfoot Length Length Length Comanche OK 10 haydenii 51 142.8 ± 7.1 131 to 154 32.7 ± 2.9 26 to 43 19.2 ± 0.8 18 to 21 unspecified CO 12 haydenii ‒‒ ‒‒ 150 to 190 ‒‒ 33 to 45 ‒‒ 20 to 23 Cherry NE 7 haydenii 18 164.6 ± 5.7 137 to 186 37.3 ± 2.3 30 to 46 20.9 ± 1.4 18 to 23 Albany WY 7 haydenii 21 162.4 ± 2.6 148 to 178 40.6 ± 1.5 33 to 48 20.7 ± 0.7 19 to 22 Kimball and Scotts Bluff NE 7 haydenii 13 159.8 ± 5.6 146 to 170 38.9 ± 2.4 31 to 47 20.2 ± 1.0 19 to 21.5 Laramie and Platte WY Larimer CO 7 haydenii 20 171.7 ± 3.3 162 to 188 44.6 ± 1.7 39 to 53 21.4 ± 0.9 20 to 23 Dundy NE 7 haydenii 11 162.3 ± 2.7 155 to 171 36.4 ± 3.0 26 to 45 20.9 ± 0.7 20 to 22 Ellis KS 7 haydenii 29 154.5 ± 3.5 136 to 170 35.3 ± 1.9 25 to 46 19.9 ± 1.0 18 to 22 Harding SD 5 haydenii 20 158.8 ± 9.4 ‒‒ 36.2 ± 4.2 ‒‒ ‒‒ ‒‒ Bennett SD 5 haydenii 8 152.2 ± 8.4 ‒‒ 34.2 ± 4.8 ‒‒ ‒‒ ‒‒ Cherry NE 3 haydenii 26 165.6 153 to 186 37 33 to 45 21.1 20 to 23 Scotts Bluff NE 3 haydenii 24 157.7 147 to 168 38.6 33 to 43 20.5 19 to 22 Rawlins KS 2 haydenii 4 155.5 145 to 172 40 39 to 41 ‒‒ ‒‒ unspecified OK 13 haydenii 44 153.0 ± 7.6 139 to 169 33.0 ± 3.6 19 to 39 ‒‒ ‒‒ Custer and Fall River SD 7 similis 10 163.1 ± 6.4 148 to 176 38.3 ± 2.1 32 to 42 20.6 ± 1.0 19 to 22 Fall River SD 5 similis 44 158.1 ± 9.9 ‒‒ 37.5 ± 3.5 ‒‒ ‒‒ ‒‒ Campbell WY 5 similis 4 145.7 ± 3.9 ‒‒ 34.7 ± 5.0 ‒‒ ‒‒ ‒‒ Weston WY 5 similis 2 171.5 ± 7.8 ‒‒ 41.0 ± 0 ‒‒ ‒‒ ‒‒ Converse WY 5 similis 5 160.8 ± 4.2 ‒‒ 40.2 ± 4.3 ‒‒ ‒‒ ‒‒

Fremont, Mills, Montgomery, and Page IA Cass, Nemaha, Otoe, Pawnee, 7 ochrogaster 10 163.0 ± 6.0 142 to 174 38.3 ± 1.8 32.8 to 43 20.2 ± 1.0 18 to 21 Richardson, Sarpy, Saunders, NE and Washington Jewell and Republic KS 7 ochrogaster 17 156.8 ± 5.3 136 to 175 35.3 ± 2.1 30 to 43 19.9 ± 1.0 18 to 21 Atchison and Douglas KS 7 ochrogaster 2 167.0 164 to 170 36.0 35 to 37 21.0 ‒‒ Greenwood KS 7 ochrogaster 1 153.0 ‒‒ 37.0 ‒‒ 20.0 ‒‒ unspecified AR 8 ochrogaster 48 ‒‒ 116 to 172 ‒‒ 23 to 49 ‒‒ 15 to 22 Marion IA 1 ochrogaster ‒‒ 152.4 ‒‒ 33 ‒‒ 20.3 ‒‒ Fayette KY 4 ochrogaster ‒‒ ‒‒ 130 to 172 ‒‒ 24 to 41 ‒‒ 17 to 22 Posev IN 2 ochrogaster 7 154.8 141 to 169 29.2 27 to 34 21.2 20 to 22 Douglas KS 2 ochrogaster 19 150 142 to 165 34.4 29 to 40 ‒‒ ‒‒ Greenwood KS 2 ochrogaster 16 154.8 142 to 181 35 29 to 46 ‒‒ ‒‒ Crawford WI 11 ochrogaster 3 153 152 to 155 39 38 to 40 18.7 18 to 19 unspecified MN 13 ochrogaster 14 148 ± 13.0 116 to 172 ‒‒ ‒‒ ‒‒ ‒‒ unspecified MN 13 minor 23 120 ± 9.1 110 to 150 ‒‒ ‒‒ ‒‒ ‒‒ Ford, Hamilton, Kearney, and KS 7 taylori 7 164.4 ± 10.5 153 to 180 34.3 ± 6.7 22 to 42 25.7 ± 8.2 20 to 36 Meade Meade KS 2 taylori 22 160 141 to 180 35.4 30 to 42 21 20 to 22 unspecified OH 9 ohionensis 26 138 128 to 153 28 21 to 36 17 16 to 19 TX or Calcasieu 6 ludovicianus 1 143.1 ‒‒ ‒‒ ‒‒ 18.2 ‒‒ LA

Table 9. Mean and range of litter sizes, sampled in utero or post-parturition, of Microtus ochrogaster collected from various locations across the species range with specified locality, source of data, current subspecies based on locality, and number of litters examined by the source (n), if specified by the source.

County(s) State(s) Source Subspecies n Mean Range Comanche OK Choate 1989 M. o. haydenii 12 3.0 ‒‒ unspecified CO Armstrong 2011 M. o. haydenii ‒‒ 3.5 1 – 7 Fall River SD Turner 1974 M. o. similis 28 3.7 1 – 6 unspecified AR Sealander 1979 M. o. ochrogaster ‒‒ 4.0 1 – 9 Fayette KY Barbour and Davis 1974 M. o. ochrogaster ‒‒ 3.4 1 – 9 Jefferson KY Quick 1970 M. o. ochrogaster 31 3.3 1 – 6 Vigo IN Corthum 1967 M. o. ochrogaster 134 3.9 2 – 7 Monroe IN Keller and Krebs 1970 M. o. ochrogaster 160 3.3 ‒‒ Champaign IL Cole and Batzli 1978 M. o. ochrogaster 28 4.25 ‒‒ Champaign IL Cole and Batzli 1979 M. o. ochrogaster 30 5.0 ‒‒ Champaign IL Cole and Batzli 1979 M. o. ochrogaster 56 3.7 ‒‒ Champaign IL Cole and Batzli 1979 M. o. ochrogaster 19 3.7 ‒‒ Champaign IL Cole and Batzli 1979 M. o. ochrogaster 21 5.2 ‒‒ Champaign IL Cole and Batzli 1979 M. o. ochrogaster 9 3.7 ‒‒ Champaign IL Solomon et al. 2004 M. o. ochrogaster 9 4.8 3 – 6 southern IL Layne 1958 M. o. ochrogaster 11 3.5 2 – 5 Piatt IL Hoffmeister 1989 M. o. ochrogaster 94 3.7 1 – 8 Greene OH DeCoursey 1957 M. o. ohionensis 35 3.4 1 – 5 Douglas KS Jameson 1947 M. o. ochrogaster 58 3.4 1 – 7 Douglas KS Martin 1956 M. o. ochrogaster 65 3.2 1 – 6 Douglas KS Fitch 1957 M. o. ochrogaster 82 3.4 2 – 5 Douglas KS Rose and Gaines 1968 M. o. ochrogaster 181 3.4 ‒‒

Table 10. Home range size (m2) of Microtus ochrogaster collected from various locations specified locality, source of data, current subspecies based on locality, reported range or average home range size, male and female mean home range size ± SD (number of animals), and method used to calculate home range.

Reported Method Male Female County State Source Subspecies Range or Mean (n) Mean (n) Average unspecified CO Abramsky and Tracy 1980 M. o. haydenii 67 to 448 205 (143) 184 (169) Home range length Scotts Inclusive boundary strip NE Meserve 1971 M. o. haydenii 890 809 (13) 890 (26) Bluff Fayette KY Harvey and Barbour 1965 M. o. ochrogaster 81 to 728 445 (5) 81 (1) Modified minimum area Champaign IL Getz et al. 1987 M. o. ochrogaster ~300 ‒‒ ‒‒ Not specified 278 ± 64 183 ± 24 Minimum-convex-polygon Jasper IL Gaulin and FitzGerald 1988 M. o. ochrogaster ‒‒ (21) (26) Monroe IN Streatfeild et al. 2011 M. o. ochrogaster ‒‒ ~500 (20) ~250 (21) 95% home range Douglas KS Gaines and Johnson 1982 M. o. ochrogaster 108 to 767 447 239 Home range length Douglas KS Streatfeild et al. 2011 M. o. ochrogaster ‒‒ ~150 (12) ~125 (13) 95% kernel home range 812 ± 20 369 ± 15 Home range length Douglas KS Swihart and Slade 1989 M. o. ochrogaster 302 to 888 (319) (353) Douglas KS Martin 1956 M. o. ochrogaster 81 to 1133 567 486 Inclusive boundary strip

Figure 1. Distributions of the seven subspecies of Microtus ochrogaster within North America and locations where putative M. ochrogaster populations were sampled ( ). Subspecies designations: 1 = Microtus ochrogaster haydenii, 2 = M. o. ludovicianus, 3 = M. o. minor, 4 = M. o. ochrogaster, 5 = M. o. ohionensis, 6 = M. o. similis, and 7 = M. o. taylori. Adapted from Adams et al. (2017).

Figure 2. Amplification and sequencing strategy for the 1,143 base pair mitochondrial cytochrome b gene. Bars represent position consistent with the human mtDNA sequence, and the arrows represent primers named regarding the heavy or light strand and position of the 3' end of the oligonucleotide (Anderson et al. 1981, Irwin et al. 1991). Sequences from fragment 1 and fragment 2 were assembled into a 505 base pair consensus sequence for 40 samples. For 7 additional samples, only fragment 2 could be amplified into a 202 base pair consensus sequences.

Figure 3. Distribution of the 34 tissue samples from Microtus ochrogaster used in this study with colors indicating the mitochondrial cluster identified through the spatial genetic cluster analysis. Subspecies designations: 1 = Microtus ochrogaster haydenii, 2 = M. o. ludovicianus, 3 = M. o. minor, 4 = M. o. ochrogaster, 5 = M. o. ohionensis, 6 = M. o. similis, and 7 = M. o. taylori. Adapted from Adams et al. (2017).

Figure 4. Minimum spanning haplotype network of a 505 base-pair region of cytochrome b museum specimens confirmed as Microtus ochrogaster through BLAST and representing 34 individuals (n) and six of the seven putative subspecies. Each colored circle corresponds to a haplotype of the following subspecies designations: green = M. ochrogaster haydenii, red = M. o. minor, blue = M. o. ochrogaster, orange = M. o. ohionensis, purple = M. o. similis, and yellow = M. o. taylori. Empty circles represent haplotypes that are either extinct or were not sampled. Unless labeled, each haplotype only contains one individual. Shared haplotypes are split proportionally based on the number of individuals comprising a subspecies. Each solid branch represents one mutational step between haplotypes, which were constructed from a 95% confidence parsimony network using the program TCS v1.21 (Clement et al. 2000). The dashed lines represent additional links when this restriction is relaxed with the numbered boxes corresponding to the number of haplotypes between these additional links.

Figure 5. Phylogenetic reconstruction by maximum likelihood method bootstrap values derived from 1,000 replicates a 505 base-pair region of cytochrome b museum specimens confirmed as Microtus ochrogaster through BLAST and representing 34 individuals (n) and six of the seven putative subspecies. The Tamura 3-parameter model (Tamura 1992) with a discrete Gamma distribution was used to model evolutionary rate differences among sites (+G, parameter = 0.3376) with some sites evolutionarily invariable (+I, 56%). Current M. ochrogaster subspecies are labeled by locality. Since no nodes at least 70% appeared between the 25 sequences from the primary cluster identified in the spatial genetic cluster analysis, these sequences were grouped into one branch. Two species from Microtus served as outgroups with the related GenBank accession number shown.

Frequency of Individuals of Frequency

Number of Pairwise Differences Figure 6. Frequency plots of the number of pairwise differences within (blue) and between (orange) each of the six analyzed current subspecies of Microtus ochrogaster derived from 34 museum specimen sequences using a 505 base pair region of cytochrome b.

Frequency of Individuals of Frequency

Number of Pairwise Differences Figure 7. Frequency plots of number of pairwise differences within (blue) and between (orange) each cluster identified in the spatial genetic cluster analysis of Microtus ochrogaster derived from 34 museum specimen sequences using a 505 base pair region of cytochrome b.

50 Within Clusters 45 Between Clusters 40

15 No. of Pairwise No. Pairwise of Differences 10

0 0 500 1000 1500 2000 2500 Geographic Distance (km)

Figure 8. The number of pairwise differences and geographic distance (km) between 34 museum specimens from Microtus ochrogaster sequences representing six of the seven current subspecies sequences using a 505 base pair region of cytochrome b. Comparisons were made within and between clusters identified in the spatial genetic cluster analysis.

52 50 Within Subspecies 45 Between Subspecies 40

15 NumberPairwise of Differences 10

0 0 500 1000 1500 2000 2500 Geographic Distance (km)

Figure 9. Plot of number of pairwise differences and geographic distance (km) between 34 museum specimens from Microtus ochrogaster sequences representing six of the seven current subspecies sequences using a 505 base pair region of cytochrome b. Comparisons were made within and between current subspecies.

170

160

a b 150 a b a a

140 b a b

130

120

110

n = 438 n = 26 n = 1142 n = 21 n = 14 n = 1 100 M. o. haydenii M. o. minor M. o. ochrogaster M. o. ohionensis M. o. similis M. o. taylori

Figure 10. Mean total body length (i.e., length from the snout to the tip of the tail, mm) with standard deviation and number of records (n) from museum specimens of Microtus ochrogaster collected from various datasets through the VertNet portal and sorted by current subspecies. Letters above bars indicate a significant difference in means between subspecies (p < 0.05).

Figure 11. Total body length (i.e., length from the snout to the tip of the tail, mm) range for six of the seven current subspecies in Microtus ochrogaster found through a literature search and summarized from Table 8.

Figure 12. Tail length (mm) range for five of the seven current subspecies in Microtus ochrogaster found through a literature search and summarized from Table 8.

Figure 13. Hindfoot length (mm) range for five of the seven current subspecies in Microtus ochrogaster found through a literature search and summarized from Table 8.

Appendix I. Mantel Distance Matrices

Table 1. Matrix of genetic pairwise differences based on a 505 bp region of the mitochondrial cytochrome b for the 34 specimens of Microtus ochrogaster analyzed in this study and used for the Mantel test. Colors indicate current subspecies: green = M. o. haydenii, orange = M. o. ohionensis, blue = M. o. ochrogaster, red = M. o. minor, yellow = M. o. taylori, and purple = M. o. similis. Boulder, Ellis, Chautauqua, Cherry, Scotts Bluff, Antelope, CO KS KS NE NE NE Boulder, CO 0 Ellis, KS 2 0 Chautauqua, KS 3 5 0 Cherry, KS 0 2 3 0 Scotts Bluff, NE 5 7 8 5 0 Antelope, NE 0 2 3 0 5 0 Sioux, NE 3 5 6 3 8 3 Hughes, SD 19 19 21 19 23 19 Gregory, SD 2 4 5 2 7 2 Brown, OH 1 3 4 1 6 1 Gallia, OH 0 2 3 0 5 0 Clermont, OH 0 2 3 0 5 0 Ripley, IN 1 1 4 1 6 1 Monroe, IN 0 2 3 0 5 0 Dickenson, KS 2 2 5 2 8 2 Washington, KS 1 3 4 1 4 1 Douglas, KS 0 2 3 0 5 0 Greenwood, KS 1 3 4 1 6 1 Champaign, IL 1 1 4 1 6 1 Sauk, WI 3 5 6 3 6 3 Madison, AL 17 19 18 17 21 17 Alexander, IL 39 40 40 39 44 39 Lancaster, NE 19 21 22 19 20 19 Winona, MN 2 2 5 2 7 2 Sherburne, MN 22 24 23 22 21 22 Portage, WI 1 3 4 1 4 1 Monroe, WI 22 24 23 22 23 22 Clark, WI 24 25 24 24 20 24 Hamilton, KS 0 2 3 0 5 0 Meade, KS 2 4 5 2 7 2 Big Horn, MT 10 12 13 10 9 10 Natrona, WY 33 35 36 33 32 33 Niobrara, WY 4 6 7 4 3 4 Fall River, SD 27 30 31 27 25 27

Table 1. continued

Sioux, Hughes, Gregory, Brown, Gallia, Clermont, NE SD SD OH OH OH Boulder, CO Ellis, KS Chautauqua, KS Cherry, KS Scotts Bluff, NE Antelope, NE Sioux, NE 0 Hughes, SD 23 0 Gregory, SD 5 17 0 Brown, OH 4 18 3 0 Gallia, OH 3 19 2 1 0 Clermont, OH 3 19 2 1 0 0 Ripley, IN 4 19 3 2 1 1 Monroe, IN 3 19 2 1 0 0 Dickenson, KS 5 20 4 3 2 2 Washington, KS 4 19 3 2 1 1 Douglas, KS 3 19 2 1 0 0 Greenwood, KS 4 19 3 2 1 1 Champaign, IL 4 19 3 2 1 1 Sauk, WI 6 19 5 4 3 3 Madison, AL 19 10 17 18 17 17 Alexander, IL 41 22 39 38 39 39 Lancaster, NE 21 35 21 20 19 19 Winona, MN 5 17 4 3 2 2 Sherburne, MN 24 7 22 21 22 22 Portage, WI 4 19 3 2 1 1 Monroe, WI 25 7 22 21 22 22 Clark, WI 25 13 23 23 24 24 Hamilton, KS 3 19 2 1 0 0 Meade, KS 3 20 4 3 2 2 Big Horn, MT 13 20 12 9 10 10 Natrona, WY 37 37 36 33 33 33 Niobrara, WY 7 22 6 5 4 4 Fall River, SD 31 43 31 30 27 27

Table 1. continued

Ripley, Monroe, Dickenson, Washington, Douglas, Greenwood, IN IN KS KS KS KS Boulder, CO Ellis, KS Chautauqua, KS Cherry, KS Scotts Bluff, NE Antelope, NE Sioux, NE Hughes, SD Gregory, SD Brown, OH Gallia, OH Clermont, OH Ripley, IN 0 Monroe, IN 1 0 Dickenson, KS 1 2 0 Washington, KS 2 1 3 0 Douglas, KS 1 0 2 1 0 Greenwood, KS 2 1 3 2 1 0 Champaign, IL 0 1 1 2 1 2 Sauk, WI 4 3 5 4 3 4 Madison, AL 18 17 19 18 17 18 Alexander, IL 40 39 40 40 39 40 Lancaster, NE 20 19 21 20 19 20 Winona, MN 1 2 2 3 2 3 Sherburne, MN 23 22 24 23 22 23 Portage, WI 2 1 3 2 1 2 Monroe, WI 23 22 24 23 22 23 Clark, WI 24 24 25 22 24 24 Hamilton, KS 1 0 2 1 0 1 Meade, KS 3 2 4 3 2 3 Big Horn, MT 11 10 12 11 10 11 Natrona, WY 35 33 37 34 33 25 Niobrara, WY 5 4 6 5 4 5 Fall River, SD 30 27 31 28 27 30

Table 1. continued

Champaign, Sauk, Madison, Alexander, Lancaster, Winona, IL WI AL IL NE MN Boulder, CO Ellis, KS Chautauqua, KS Cherry, KS Scotts Bluff, NE Antelope, NE Sioux, NE Hughes, SD Gregory, SD Brown, OH Gallia, OH Clermont, OH Ripley, IN Monroe, IN Dickenson, KS Washington, KS Douglas, KS Greenwood, KS Champaign, IL 0 Sauk, WI 4 0 Madison, AL 18 18 0 Alexander, IL 40 37 29 0 Lancaster, NE 20 19 36 28 0 Winona, MN 1 5 17 39 21 0 Sherburne, MN 23 21 12 24 35 22 Portage, WI 2 2 18 38 20 3 Monroe, WI 23 23 12 23 35 22 Clark, WI 24 22 15 27 35 23 Hamilton, KS 1 3 17 39 19 2 Meade, KS 3 3 19 39 19 4 Big Horn, MT 11 9 21 37 23 12 Natrona, WY 35 32 41 19 15 36 Niobrara, WY 5 5 21 40 17 6 Fall River, SD 30 29 46 26 10 31

Table 1. continued

Sherburne, Portage, Monroe, Clark, Hamilton, Meade, MN WI WI WI KS KS Boulder, CO Ellis, KS Chautauqua, KS Cherry, KS Scotts Bluff, NE Antelope, NE Sioux, NE Hughes, SD Gregory, SD Brown, OH Gallia, OH Clermont, OH Ripley, IN Monroe, IN Dickenson, KS Washington, KS Douglas, KS Greenwood, KS Champaign, IL Sauk, WI Madison, AL Alexander, IL Lancaster, NE Winona, MN Sherburne, MN 0 Portage, WI 21 0 Monroe, WI 2 23 0 Clark, WI 6 22 6 0 Hamilton, KS 22 1 22 24 0 Meade, KS 24 3 24 25 2 0 Big Horn, MT 15 9 17 17 10 12 Natrona, WY 36 34 36 34 33 34 Niobrara, WY 18 3 20 19 4 6 Fall River, SD 44 29 42 40 27 29

Table 1. continued

Big Horn, Natrona, Niobrara, Fall River, MT WY WY SD Boulder, CO Ellis, KS Chautauqua, KS Cherry, KS Scotts Bluff, NE Antelope, NE Sioux, NE Hughes, SD Gregory, SD Brown, OH Gallia, OH Clermont, OH Ripley, IN Monroe, IN Dickenson, KS Washington, KS Douglas, KS Greenwood, KS Champaign, IL Sauk, WI Madison, AL Alexander, IL Lancaster, NE Winona, MN Sherburne, MN Portage, WI Monroe, WI Clark, WI Hamilton, KS Meade, KS Big Horn, MT 0 Natrona, WY 29 0 Niobrara, WY 6 31 0 Fall River, SD 32 7 26 0

Table 2. Matrix of geographic distances (km) between geographic centers of each county for the 34 specimens of Microtus ochrogaster analyzed in this study and used for the Mantel test. Colors indicate current subspecies: green = M. o. haydenii, orange = M. o. ohionensis, blue = M. o. ochrogaster, red = M. o. minor, yellow = M. o. taylori, and purple = M. o. similis.

Boulder, Ellis, Chautauqua, Cherry, Scotts Bluff, Antelope, CO KS KS NE NE NE Boulder, CO 0 Ellis, KS 553 0 Chautauqua, KS 866 326 0 Cherry, KS 442 429 725 0 Scotts Bluff, NE 241 491 813 222 0 Antelope, NE 664 383 578 257 463 0 Sioux, NE 295 556 867 211 82 470 Hughes, SD 660 608 853 240 423 278 Gregory, SD 631 484 708 190 403 147 Brown, OH 1862 1339 1111 1512 1706 1254 Gallia, OH 1993 1466 1240 1642 1836 1380 Clermont, OH 1831 1306 1080 1482 1673 1222 Ripley, IN 1742 1216 992 1397 1588 1138 Monroe, IN 1634 1105 886 1289 1480 1034 Dickenson, KS 731 182 208 524 638 378 Washington, KS 711 213 308 448 586 264 Douglas, KS 889 349 210 642 778 435 Greenwood, KS 848 292 83 664 772 501 Champaign, IL 1413 912 746 1047 1249 793 Sauk, WI 1343 929 878 921 1138 684 Madison, AL 1781 906 922 1305 1689 1305 Alexander, IL 1443 895 612 936 1329 929 Lancaster, NE 754 314 414 420 592 191 Winona, MN 1212 846 879 860 1080 629 Sherburne, MN 1129 860 947 687 894 500 Portage, WI 1401 1028 988 972 1187 743 Monroe, WI 1290 911 848 777 997 552 Clark, WI 403 241 496 500 454 564 Hamilton, KS 548 197 361 587 579 584 Meade, KS 600 982 1289 587 489 824 Big Horn, MT 323 780 1100 478 296 735 Natrona, WY 330 639 956 276 152 538 Niobrara, WY 385 597 904 201 152 462 Fall River, SD 1321 966 968 875 1096 660

Table 2. continued

Sioux, Hughes, Gregory, Brown, Gallia, Clermont, NE SD SD OH OH OH Boulder, CO Ellis, KS Chautauqua, KS Cherry, KS Scotts Bluff, NE Antelope, NE Sioux, NE 0 Hughes, SD 376 0 Gregory, SD 386 143 0 Brown, OH 1723 1455 1372 0 Gallia, OH 1848 1575 1489 132 0 Clermont, OH 1690 1421 1329 33 163 0 Ripley, IN 1605 1344 1247 125 253 92 Monroe, IN 1497 1344 1144 231 361 201 Dickenson, KS 680 646 514 1157 1287 1124 Washington, KS 627 544 405 1153 1283 1119 Douglas, KS 817 711 575 991 1123 957 Greenwood, KS 826 780 636 1083 1214 1055 Champaign, IL 1258 992 898 467 589 434 Sauk, WI 1129 803 746 713 811 678 Madison, AL 1722 1559 1443 523 590 523 Alexander, IL 1365 1190 1068 519 645 503 Lancaster, NE 616 469 329 1118 1244 1083 Winona, MN 1063 726 680 805 899 770 Sherburne, MN 865 496 497 1085 1190 1055 Portage, WI 1170 823 785 780 857 743 Monroe, WI 988 646 597 872 968 835 Clark, WI 534 730 623 1566 1697 1540 Hamilton, KS 648 787 676 1455 1585 1431 Meade, KS 429 604 700 2054 2173 2022 Big Horn, MT 263 596 639 1988 2111 1951 Natrona, WY 83 403 438 1789 1912 1755 Niobrara, WY 85 320 356 1710 1834 1675 Fall River, SD 1079 720 693 871 961 833

Table 2. continued

Ripley, Monroe, Dickenson, Washington, Douglas, Greenwood, IN IN KS KS KS KS Boulder, CO Ellis, KS Chautauqua, KS Cherry, KS Scotts Bluff, NE Antelope, NE Sioux, NE Hughes, SD Gregory, SD Brown, OH Gallia, OH Clermont, OH Ripley, IN 0 Monroe, IN 110 0 Dickenson, KS 1036 927 0 Washington, KS 1031 921 108 0 Douglas, KS 869 762 168 195 0 Greenwood, KS 961 851 138 233 138 0 Champaign, IL 353 251 736 712 582 695 Sauk, WI 622 550 790 723 673 809 Madison, AL 506 497 1058 1101 907 935 Alexander, IL 422 338 712 744 553 608 Lancaster, NE 995 886 222 118 244 330 Winona, MN 705 638 780 700 679 809 Sherburne, MN 992 915 784 687 737 865 Portage, WI 692 632 893 819 782 923 Monroe, WI 772 690 733 648 637 774 Clark, WI 1447 1339 416 448 580 496 Hamilton, KS 1340 1232 328 397 482 376 Meade, KS 1941 1841 1099 1031 1221 1243 Big Horn, MT 1863 1761 932 882 1070 1065 Natrona, WY 1672 1568 768 704 897 908 Niobrara, WY 1592 1487 719 644 838 852 Fall River, SD 777 717 847 762 759 889

Table 2. continued

Champaign, Sauk, Madison, Alexander, Lancaster, Winona, IL WI AL IL NE MN Boulder, CO Ellis, KS Chautauqua, KS Cherry, KS Scotts Bluff, NE Antelope, NE Sioux, NE Hughes, SD Gregory, SD Brown, OH Gallia, OH Clermont, OH Ripley, IN Monroe, IN Dickenson, KS Washington, KS Douglas, KS Greenwood, KS Champaign, IL 0 Sauk, WI 329 0 Madison, AL 677 1002 0 Alexander, IL 383 693 372 0 Lancaster, NE 662 620 1119 756 0 Winona, MN 406 89 1086 765 601 0 Sherburne, MN 675 379 1350 994 565 292 Portage, WI 434 120 1115 810 716 115 Monroe, WI 453 165 1125 785 540 83 Clark, WI 1150 1164 1416 1101 547 1135 Hamilton, KS 1059 1121 1281 974 509 1094 Meade, KS 1593 1399 2120 1748 999 1317 Big Horn, MT 1523 1383 1984 1624 879 1312 Natrona, WY 1326 1180 1805 1442 694 1112 Niobrara, WY 1244 1097 1739 1375 623 1028 Fall River, SD 505 170 1173 865 657 122

Table 2. continued

Sherburne, Portage, Monroe, Clark, Hamilton, Meade, MN WI WI WI KS KS Boulder, CO Ellis, KS Chautauqua, KS Cherry, KS Scotts Bluff, NE Antelope, NE Sioux, NE Hughes, SD Gregory, SD Brown, OH Gallia, OH Clermont, OH Ripley, IN Monroe, IN Dickenson, KS Washington, KS Douglas, KS Greenwood, KS Champaign, IL Sauk, WI Madison, AL Alexander, IL Lancaster, NE Winona, MN Sherburne, MN 0 Portage, WI 355 0 Monroe, WI 223 184 0 Clark, WI 1071 1259 1076 0 Hamilton, KS 1066 1218 1041 151 0 Meade, KS 1058 1407 1243 940 1066 0 Big Horn, MT 1093 1415 1234 691 833 285 Natrona, WY 898 1216 1036 606 731 341 Niobrara, WY 814 1131 948 602 712 386 Fall River, SD 246 110 98 1194 1161 1303

Table 2. continued

Appendix II. Expanded Phylogenetic Reconstruction

Primary Cluster

minor Cluster

similis Cluster

Phylogenetic reconstruction by maximum likelihood method bootstrap values derived from 1,000 replicates a 505 base-pair region of cytochrome b museum specimens confirmed as Microtus ochrogaster through BLAST and representing 34 individuals (n) and six of the seven putative subspecies. The Tamura 3-parameter model (Tamura 1992) with a discrete Gamma distribution was used to model evolutionary rate differences among sites (+G, parameter = 0.3376) with some sites evolutionarily invariable (+I, 56%). Current subspecies of M. ochrogaster are labeled by locality. Two species from Microtus served as outgroups with the related GenBank accession number shown. Clusters identified in the cluster analysis are labeled.

Appendix III. Contingency Tables

Table 1. Contingency table for a Fisher’s exact test of the 29 individuals from Microtus ochrogaster used in both this study and Adams et al. (2017). The number of individuals in each nuclear cluster (cluster 1 through 7), identified using nuclear microsatellite length polymorphism data, are sorted by assignment into each mitochondrial cluster (primary, minor, and similis), identified using cytochrome b data. Clustering was determined by spatial genetic cluster analysis in Bayesian Analysis of Population Structure (BAPS) v.6.

Mitochondrial Clusters Nuclear Cluster No. in primary No. in minor No. in similis cluster cluster cluster cluster 1 17 2 2 cluster 2 1 1 0 cluster 3 0 1 0 cluster 4 0 0 1 cluster 5 1 0 0 cluster 6 2 0 0 cluster 7 1 0 0

Table 2. Contingency table for a Fisher’s exact test of the 34 tissue samples from Microtus ochrogaster used in this study. The number of individuals in each current subspecies are sorted by assignment into each mitochondrial cluster (primary, minor, and similis), identified using cytochrome b data. Clustering was determined by spatial genetic cluster analysis in Bayesian Analysis of Population Structure (BAPS) v.6.

Mitochondrial Clusters Subspecies No. in primary No. in minor No in similis cluster cluster cluster haydenii 8 1 0 minor 2 3 0 ochrogaster 8 1 2 ohionensis 3 0 0 taylori 2 0 0 similis 2 0 2

Table 3. Contingency table for a Fisher’s exact test of 30 individuals of Microtus ochrogaster from unique populations used in Adams et al. (2017). The number of individuals in each current subspecies are sorted by assignment into each nuclear cluster (cluster 1 through 7), identified using nuclear microsatellite length polymorphism data. Clustering was determined by spatial genetic cluster analysis in Bayesian Analysis of Population Structure (BAPS) v.6.

Nuclear Clusters Subspecies No. in No. in No. in No. in No. in No. in No. in cluster 1 cluster 2 cluster 3 cluster 4 cluster 5 cluster 6 cluster 7 haydenii 9 0 0 0 0 0 0 minor 4 0 0 0 0 0 0 ochrogaster 4 1 1 1 1 0 0 ohionensis 0 0 0 0 0 2 1 taylori 3 1 0 0 0 0 0 similis 2 0 0 0 0 0 0

Appendix IV. Failure of Samples In my thesis, I attempted to sequence one sample per population. Once one sample from a population was sequenced, I did not attempt to sequence any other samples from that population. For some populations, I successfully sequenced the first sample attempted from that population. For other populations, I had to run multiple samples or the same samples multiple times to successfully sequence one sample. Therefore, some populations had more samples that I attempted to sequence than others. For instance, I only needed to sequence one of ten samples from the Clermont County, Ohio population to be successful. The remaining nine samples were not attempted, even though some of the samples may not have been able to be sequenced. However, in the Natrona County, Wyoming population, I had to attempt to sequence all seven samples, but one sample was sequenced after three reruns. Across the 113 samples that I attempted to sequence, which is 33% of the total available samples (n = 340), only 40 samples were completely successful for the entire 505 bp region (35%) while 7 additional samples were only successful for a partial 202 bp region. Three possible factors may have contributed to the failure of successfully sequencing these samples: age, source of sample, or tissue type. A linear regression was conducted using age of sample and success of sequencing on an arcsine square root transformed proportion response to account for non-constant variance in residuals in proportions (Hughes, personal communication, 2019). While there was a negative correlation between the age of the sample and success rate, only 28% (R2 = 0.2755, p = 0.0024) of the variance in the success rate could be explained by the variance in the age of the sample (Figure 1). Sequencing of samples from some sources such as the National Museum of Natural History (17%, n = 23) and University of Kansas Museum of Natural History (16%, n =34) were less successful than other sources such as University of Colorado Museum of Natural History (100%, n = 2) and University of Wisconsin Stevens Point Museum of Natural History (100%, n = 3, Figure 2). Preserved tissue and frozen tissue appear to have higher success rates than study skins (Figure 3). The findings suggest that source and tissue type may have had an impact on the success rate of these samples. To better determine this impact, larger sample sizes should be used to focus an analysis on each variable while the other two variables are controlled. For instance, an analysis of age should use samples with the same source and tissue type, or an analysis of tissue type should use samples with the same source and age. Alternatively, with larger samples sizes, a multiple regression could be used to estimate the importance of the three variables.

3.5

2.5

2 y = -0.0173x + 3.0223 1.5 R² = 0.2755

0.5 Transformed Transformed Proportion of Success 0 0 20 40 60 80 100 120 140 Age of Sample (years)

Figure 1. The age (years) and arcsine square root transformed proportion of success of sequencing for a 505 base pair region of cytochrome b from 113 museum specimens. Age of samples from Microtus ochrogaster was determined by subtracting the year collected from 2018.

100 n=2 n=3 n=3 n=7 90 n=3 80 70 n=5 60 n=7 50 40 n=12 n=14 30

Precentage Precentage of Success (%) 20 n=23 n=34 10 0

Source of Tissue

Figure 2. The source and percentage of success of sequencing for a 505 base pair region of cytochrome b from 113 museum specimens from Microtus ochrogaster. Source of tissue refers to the museum or laboratory that initially housed these specimens. Abbreviations for the sources are as follows: CMM = University of Colorado Museum of Natural History, CMNH = Museum of Natural History and Science, Cincinnati Museum Center, FHSM = Sternberg Museum of Natural History, Fort Hays State University, INHS = Illinois Natural History Survey, NMNH = National Museum of Natural History, OSU = Museum of Biological Diversity, The Ohio State University, UIMNH = University of Illinois Museum of Natural History, UKMNH = University of Kansas Museum of Natural History, UNSMZM = University of Nebraska State Museum, UWSP = University of Wisconsin Stevens Point Museum of Natural History.

100 n=5

60 n=5

40 n=103

30 Precentage Precentage of Success (%) 20

0 frozen liver preserved study skin Tissue Type

Figure 3. The tissue type and percentage of success of sequencing for a 505 base pair region of cytochrome b from 113 museum specimens from Microtus ochrogaster. Tissues were either from museum study skins, organ tissue preserved in ethanol, or fresh organ tissue (frozen liver).