A First Approximation of Genetic Diversity and Population Structure to Identify Management Units for Western North Carolina Timber Rattlesnakes (Crotalus horridus)

doi:10.21203/rs.3.rs-4909311/v1

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A First Approximation of Genetic Diversity and Population Structure to Identify Management Units for Western North Carolina Timber Rattlesnakes (Crotalus horridus)

https://doi.org/10.21203/rs.3.rs-4909311/v1

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The Timber Rattlesnake (Crotalus horridus) is the only rattlesnake species inhabiting the temperate deciduous forests of Eastern North America and faces conservation challenges across its range. In North Carolina, the Timber Rattlesnake is listed as a Species of Special Concern, with habitat loss and fragmentation from human encroachment its primary threats. To effectively manage and conserve Timber Rattlesnake populations, land managers must first understand their genetic diversity and population structure. This study utilized 19 microsatellite markers to estimate genetic diversity parameters from 125 individuals sampled opportunistically across a broad geographic area in Western North Carolina. Our findings revealed robust genetic diversity and identified three management units, which will assist land managers in formulating effective conservation action plans.

Crotalus horridus

Genetic diversity

Management unit

Microsatellite

North Carolina

The Timber Rattlesnake (Crotalus horridus) plays a crucial ecological role in the temperate deciduous forests of eastern North America, but faces numerous conservation challenges across its range (Brown, 1993; Martin, 1993). In North Carolina, the Timber Rattlesnake is listed as a Species of Special Concern and classified as Vulnerable (S3) by NatureServe (NatureServe Explorer, 2024). Historically, Timber Rattlesnakes were distributed statewide in North Carolina (Palmer and Braswell, 1995), however, their habitat has been drastically reduced and fragmented due to anthropogenic changes. Development and the construction of roads create significant migration barriers, which and are thought to severely limit or eliminate gene flow among adjacent populations and contribute to high mortality rates (Brown, 1991; Bushar et al., 2014; Herrmann et al., 2017; Tipton et al, 2023). Direct consequences of these anthropogenic threats include loss of genetic diversity due to population isolation and inbreeding caused by reductions in population size from road mortality (Rudolph et al., 1998; Clark et al., 2011).

The unique biology of Timber Rattlesnakes including the dependance on hibernacula (over wintering sites) and gestation sites, exacerbates the threats of development and increases the risk of extirpation. The species is active for approximately five months of the year, from late May to October (Aldridge and Brown, 1995). During this period, male rattlesnakes disperse up to 2 km from the group hibernacula, while non-gravid females generally travel half that distance, and gravid females travel even shorter distances (Eckert & Jesper, 2024). This difference in movement creates a sexually dimorphic home range. Despite these varying distances, an individual typically returns to the same hibernaculum each year (Reinert & Rupert, 1999). Studies have shown that snakes relocated by humans to new environments with suitable hibernacula have a lower survival rate than those native to the area, suggesting strong fidelity to a home range (Reinert & Rupert, 1999). Hibernacula are typically located on south to west-facing rock outcrops, slopes, and bluffs, at elevations ranging from 200 to 1200 meters and snakes are often found basking near these sites (Jesper et al., 2024). These communal hibernacula usually consist of animal burrows, cracks in rock faces, hollowed-out trees, and root holes. These sites need to maintain a relatively constant temperature, slightly higher than the surrounding environment, to provide adequate protection during the winter months. Loss of community hibernacula is a critical threat to Timber Rattlesnakes range-wide, and efforts should be made to identify and protect communal hibernacula.

Recent studies have shown that male migration is crucial for gene flow between hibernacula in Timber Rattlesnakes (Royer et al., 2024). The search behavior of males seeking females during the mating season results in larger home ranges (Eckert & Jesper, 2024). This activity creates connections between hibernacula, likely forming a meta-population structure. These findings suggest that Timber Rattlesnake populations should not be defined by individual hibernacula but rather by groups of hibernacula interconnected by male migration during reproduction. Conserving these groups of hibernacula (management units) and ensuring connectivity between them should be the primary focus for effective species management.

Reductions in genetic diversity (allelic diversity and heterozygosity) are critical factors influencing a population’s likelihood of persistence (Frankham et al., 2010). In North Carolina, the effects of anthropogenic threats on Timber Rattlesnake genetic diversity are currently unknown. The goal of this research is to evaluate the current genetic diversity of Western North Carolina populations and determine management units to assist in formulating effective conservation action plans.

Collections:

Specimens utilized for this analysis were opportunistically collected in Western North Carolina by a large volunteer network of State Park, National Park, and North Carolina Wildlife Resources Commission employees as well as amateur herpetologists. Permits were obtained from North Carolina Wildlife Resources Commission (#21-ES00592), Blue Ridge Parkway (#BLRI-2021-SCI-0009), and North Carolina State Parks (#2021_0520). Collections occurred from 2019–2022 and included scale clips, muscle, liver, or shed skin tissues from nuisance animals within state parks, animals found dead due to road mortality, or animals in gestation sites. All tissues, except shed skins, were stored in 95% ethanol at -20°C until DNA extraction. Location information was recorded with each collection, and GPS coordinates were later extracted from Google Maps.

DNA isolation:

DNA was extracted with a PureLink Genomic DNA mini-kit (Invitrogen, Carlsbad, California, USA) following the manufacturers protocol with minor modifications. Resulting DNA was examined for quality with a Nano-Drop 1000 (Thermo Fisher Scientific, Waltham, MA, USA) and a 1% agarose gel. High quality DNA samples were diluted to 30 ng/µl and randomly arrayed into 96 well plates with both positive and negative (ddH₂O) controls to ensure the accuracy of genotyping.

Microsatellite analysis:

A total of 35 previously published microsatellites markers were screened against a small number of individuals to identify polymorphic loci that easily amplified (Villarreal et al., 1996, Holycross et al., 2002, Pozarowski et al., 2012). These 35 primer pairs were modified to include an M13 tag (5’-CACGACGTTGTAAAACGAC-3’) on the 5’ end of the forward primer to facilitate fluorescent tagging of PCR products with FAM, VIC, NED, or PET fluorophores (Life Technologies, Grand Island, NY, USA) (Schuelke, 2000). A total of 19 markers with diverse repeat motifs were utilized to genotype samples including 7–87, CW_A14, CW_A29, CW_B23, Ca120, Ca122, Ca131, Ca139, Ca227, Ca238, Ca264, Ca274, CS2322, CS234, CS2340, CC1110, CC2210, CC227, CC2333 (Table 1). PCR reactions were prepared in 10µL volumes consisting of GoTaq Flexi Buffer, 2.5 mM MgCl₂, 800 µM dNTPs, 0.5 µM of reverse primer, 0.25 µM of tagged forward primer, 0.25 µM of a M13 fluorescent labeled primer, 0.5 units of GoTaq Flexi DNA Polymerase, and ~ 30ng of DNA (Promega, Madison, Wisconsin, USA). A touchdown PCR protocol was employed to increase specificity and reduce stutter on an Eppendorf Mastercycler thermal cycler (Eppendorf, Hauppauge, NY, USA) (Korbie and Mattick, 2008). Uniquely tagged PCR products were then multiplexed with HI-DI and a GeneScan Liz 500 size standard (Applied Biosystems, Foster City, California, USA) for separation on an ABI 3730. The chromatograms were scored in Geneious Prime 2023.2.1 using the microsatellite plug-in (Biomatters, Auckland, NZ) (Kearse et al., 2012).

Table 1

Microsatellite markers utilized in this study. Including original marker name, publication, Fluorophore utilized, the observed size range and microsatellite repeat motif.
Marker name	Publication	Fluorophore	Observed product sizes (bp)	Repeat motif
7–87	Villarreal et al., 1996	NED or PET	161–181	CA
CW_A14	Holycross et al., 2002	NED	153–187	AC
CW_A29	Holycross et al., 2002	VIC	166–190	AC
CW_B32	Holycross et al., 2002	PET	220–266	TG_AG
Ca120	Pozarowski et al., 2012	FAM	270–320	AG
Ca122	Pozarowski et al., 2012	VIC	187–213	TG
Ca131	Pozarowski et al., 2012	NED	227–263	TCT
Ca139	Pozarowski et al., 2012	PET	335–395	TTA(TTG)₂(TTA)₂
Ca227	Pozarowski et al., 2012	VIC	300–486	TCC
Ca238	Pozarowski et al., 2012	NED	300–360	AGGC
Ca264	Pozarowski et al., 2012	PET	325–413	AAAG
Ca274	Pozarowski et al., 2012	FAM	294–300	GT
CS2322	Pozarowski et al., 2012	VIC	431–559	GATA
CS234	Pozarowski et al., 2012	NED	424–512	GAAA
CS2340	Pozarowski et al., 2012	PET	256–278	TG - Complex
CC1110	Pozarowski et al., 2012	FAM	270–326	CTTT - Complex
CC2210	Pozarowski et al., 2012	VIC	314–358	GAAA
CC227	Pozarowski et al., 2012	NED	221–293	CTTT
CC2333	Pozarowski et al., 2012	PET	423–483	CTA

Management unit determination analysis:

The study’s sampling strategy utilized did not allow for a priori determination of the population of origin for different samples. Therefore, we investigated population structure using a discriminant analysis of principal coordinates (DAPC) and STRUCTURE analysis with the R package poppr and STRUCTURE software (Kamvar et al., 2014; Pritchard et al., 2000).

The STRUCTURE analysis began by searching for the optimal number of clusters (K). We tested initial K values ranging from 1 to 10, with burn-in periods set to 100,000 and MCMC repetitions set to 500,000 (Pritchard et al., 2000). The resulting values were examined using the pophelperShiny app to identify the optimal K value utilizing the Evanno method (Evanno et al., 2005; Francis, 2017).

Once the optimal K was identified, STRUCTURE was rerun using this K value, with burn-in periods set to 500,000 and MCMC repetitions set to 1,000,000. Samples were then grouped into management units based on the Q-matrix score. Georeferenced points for each sample were mapped using ArcGIS Pro, and an Inverse Distance Weighted (IDW) interpolation model was employed through the Spatial Analyst Toolbox to predict the distribution of management units across the collection area. (ArcGIS Pro, 2023).

Genetic diversity estimates:

The resulting genotype data were analyzed using the R package poppr and the Excel-based package GenAlEx 6.51b2 (Peakall and Smouse, 2012; Kamvar et al., 2014; R Core Team, 2021). A genotype accumulation curve was generated to verify the power of the markers in distinguishing unique individuals. Subsequently, we calculated several genetic diversity metrics by locus: the number of alleles observed, Simpson's Index of Diversity, expected heterozygosity, and evenness.

Finally, using management unit designations from the STRUCTURE analysis Q-matrix score, we calculated: the number of multi-locus genotypes, mean number of alleles per locus, mean number of effective alleles per locus, observed and expected heterozygosity, Shannon-Weiner Diversity Index, Simpson's Index, and evenness for each group. Classic measures of population differentiation (F_ST, G_ST) and estimates of the number of migrants were also calculated.

Genetic bottleneck test

We further examined the Q-matrix score management unit designations for signs of a recent genetic bottleneck. To do this, we used BOTTLENECK version 1.2.02 (Piry et al., 1999) to test for deviations from the mutation-drift equilibrium model. The analysis was conducted using two different mutational models: the stepwise mutation model (SMM) and the two-phase model (TPM). We employed a sign test, a standardized differences test and a Wilcoxon signed-rank test to assess significance under both models. Additionally, we examined allele frequency distributions to identify the L-shaped curve expected under the mutation-drift equilibrium model (Luikart et al., 1998).

Management unit determination analysis

The discriminant analysis of principal coordinates identified 80 principal coordinates (Figure S1) and two eigenvalues (Figure S2), resulting in three distinct genetic clusters within the samples (Fig. 1a). The STRUCTURE analysis, using the log probability (ln[Pr(X|K)]) correction (Evanno et al., 2005), also indicated a maximum at ΔK = 3, suggesting three genetic groups (Fig. 1b). A bar plot of the resulting Q-matrix scores illustrates the probability of assignment to the three clusters and shows some admixture among the groups (Fig. 1c). Additionally, mapping the genetic clusters in GIS with an interpolation model provides an estimate of the geographic locations of the three clusters in Western North Carolina (Fig. 1d).

Genetic diversity estimates

The microsatellite markers produced a total of 280 alleles across the 125 individuals genotyped, with an average of 14.74 alleles per locus (ranging from 4 to 27 alleles) (Table 2). Both Simpson’s Index of Diversity (0.806) and heterozygosity (0.812) were high, indicating robust genetic diversity. Alleles were relatively evenly distributed, with an average evenness score of 0.74 (ranging from 0.54 to 0.89), suggesting balanced genotype diversity within loci without a single dominant genotype. Additionally, the genotype accumulation curve demonstrated significant power to distinguish among individuals (Figure S3).

Table 2

Microsatellite loci summary statistics, including the number of alleles observed, Simpson’s Index of Diversity, expected heterozygosity, and evenness.
Marker name	# of alleles	D-1	H_e	Evenness
7–87	10	0.713	0.716	0.71
CW_A14	8	0.609	0.612	0.60
CW_A29	11	0.774	0.779	0.71
CW_B32	15	0.848	0.853	0.74
Ca120	13	0.726	0.733	0.54
Ca122	14	0.854	0.858	0.79
Ca131	11	0.831	0.842	0.78
Ca139	19	0.910	0.918	0.76
Ca227	27	0.950	0.963	0.87
Ca238	10	0.523	0.525	0.56
Ca264	20	0.881	0.885	0.73
Ca274	4	0.561	0.564	0.70
CS2322	27	0.945	0.953	0.83
CS234	21	0.911	0.916	0.75
CS2340	10	0.834	0.839	0.80
CC1110	15	0.887	0.891	0.82
CC2210	11	0.867	0.872	0.86
CC227	19	0.928	0.933	0.89
CC2333	15	0.767	0.773	0.57
Total	14.74	0.806	0.812	0.74

Next, we explored diversity estimates for the three management units identified using the DAPC and STRUCTURE analyses. Although sampling across the clusters was uneven, each individual had a unique multi-locus genotype (Table 3). Within the management units, we identified an average of 9.21 alleles per locus (range 5.84 to 12.0), with an average effective number of alleles of 5.49 (range 3.67 to 7.31). Observed heterozygosity was generally lower than expected. The Shannon-Weiner Diversity Index ranged from 3.37–3.97, reflecting high genetic diversity that is relatively evenly distributed. Both Simpson’s Index of Diversity and evenness were high, indicating robust genetic diversity across the management units. The overall F_ST was 0.072 (± 0.016) and G_ST was 0.047 (± 0.015), suggesting little differentiation among the management units. Additionally, the number of migrants was 5.25 (± 0.78), indicating that management units are connected via geneflow and that genetic patterns are unlikely to be due to genetic drift.

Table 3

Management unit summary statistics. N is the sample size, MLG is the number of multi-locus genotypes, N_a is the number of alleles, N_e is the effective number of alleles, H_o is the observed heterozygosity, H_e is the expected heterozygosity, H is Shannon-Weiner Diversity Index, 1-D is Simpsons Index of Diversity, E is evenness. Paratheses indicate standard error values.
Cluster	N	MLG	N_a	N_e	H_o	H_e	H	1-D	E
1	29	29	5.84 (0.59)	3.67 (0.33)	0.50 (0.06)	0.67 (0.03)	3.37	0.97	1
2	43	43	12.0 (0.97)	7.31 (0.91)	0.62 (0.04)	0.81 (0.02)	3.76	0.98	1
3	53	53	9.78 (0.91)	5.50 (0.71)	0.59 (0.05)	0.74 (0.03)	3.97	0.98	1
Mean			9.21 (0.58)	5.49 (0.44)	0.57 (0.02)	0.74 (0.02)

Genetic bottleneck analysis

To further explore the recent history of the three management units identified, we conducted a genetic bottleneck analysis. Cluster 1 showed marginal evidence for a recent bottleneck under the IAM model using the sign test (p = 0.051), but stronger support with the standardized differences test (p = 0.011) and the Wilcoxon test (p = 0.007) under the IAM model. The allele frequency distribution was L-shaped, suggesting no bottleneck. Cluster 2 showed no sign of a bottleneck in the sign test for either model, but significant results in the standardized differences test under both models (IAM: p = 0.002, SMM: p = 0.000). The Wilcoxon test was significant under the IAM model (p = 0.001) but not under the SMM model for Cluster 2. The allele frequency distribution was L-shaped, indicating no bottleneck. Cluster 3 showed strong evidence for a bottleneck across multiple tests: the sign test (IAM: p = 0.004, SMM: p = 0.004), the standardized differences test (IAM: p = 0.008, SMM: p = 0.000), and the Wilcoxon test (IAM: p = 0.005, SMM: p = 0.003). Despite this, the allele frequency distribution was L-shaped, suggesting no bottleneck.

Reductions in genetic diversity are critical factors influencing a population’s persistence (Frankham et al., 2010). Although habitat fragmentation is known to affect North Carolina’s Timber Rattlesnakes, the exact impacts of these anthropogenic threats are unknown. This research evaluated the genetic diversity of Timber Rattlesnakes in Western North Carolina and identified management units to aid in developing effective conservation plans.

Sampling natural populations of some vagile taxa can be challenging, which can affect our ability to accurately describe genetic diversity or identify management units. In this study, we collected tissues opportunistically from a diverse set of sources across a broad geographic range in Western North Carolina. These sources included nuisance animals within state parks, road-killed animals along the Blue Ridge Parkway or within state parks, and individuals from gestation sites. It is worth noting, that our sampling approach could have missed distinct populations based on their distance from roads or parks where sampling was focused, but did allow us to learn from animals that would have otherwise been destroyed. Our goal was to use this broad sampling strategy as a first approximation to identify management units on the landscape and assess the genetic diversity of these groups.

To identify management units within our samples, we first employed a Discriminant Analysis of Principal Components (DAPC), this multivariate approach that does not assume Hardy-Weinberg Equilibrium (Jombart et al., 2010). We then used the Bayesian clustering method STRUCTURE as an independent analysis to determine the appropriate number of groups using the Evanno method (Evanno et al., 2005). Both approaches identified three genetic clusters within our dataset, which we defined as management units. These units likely represent a series of natural populations (hibernaculum) connected by gene flow (Eckert and Jesper, 2024). Mapping the management unit assignments with GIS and an interpolation model allowed us to visualize the geographic extent of each unit (Fig. 1d). The Blue Ridge Parkway National Park crosses all three management units. DuPont State Recreational Forest and Gorges State Park share individuals from Cluster 1. Chimney Rock State Park mostly contains individuals from Cluster 2, although some admixture is evident in the STRUCTURE plot (Fig. 1c). Hanging Rock State Park, Pilot Mountain State Park, and Stone Mountain State Park all share individuals from Cluster 3. Understanding the geographic extent of these management units will allow land managers from different government units to coordinate their efforts to protect Timber Rattlesnakes in these regions.

Our analysis revealed robust genetic diversity within Timber Rattlesnakes in Western North Carolina, with a total of 280 unique alleles across 19 genetic loci. Expected heterozygosity, Simpson’s Diversity Index, and Evenness scores all indicate a substantial amount of genetic diversity that is fairly evenly distributed across the samples. Within each management unit, diversity estimates also indicated strong genetic diversity. Cluster 1, which had the fewest samples (N = 29), demonstrated the lowest diversity estimates for all measures but still showed a high Shannon-Weiner Index (3.37) and Simpson’s Index of Diversity (0.97). Cluster 2, containing 43 individuals, showed the highest diversity estimates for all measures except the Shannon-Weiner Index (3.76). Cluster 3 had the highest sample size (N = 53) and exhibited more diversity than Cluster 1 but less than Cluster 2. Overall, these diversity estimates suggest that each management unit possesses robust genetic diversity, indicating that management activities like genetic rescue or augmentation are not currently necessary for these units to persist into the future.

A genetic bottleneck analysis was also performed to assess if there were recent changes to the genetic diversity of these management units. Clusters 1 and 3 show significant signs of a recent bottleneck, particularly under IAM. Cluster 2 shows mixed results with some indication of genetic disturbance. The mode-shift indicator for all three clusters shows a normal L-shaped distribution, which is typically expected in stable populations under the mutation-drift equilibrium model (Luikart et al., 1998). Overall, the standardized differences test and Wilcoxon test provide some evidence for recent genetic bottlenecks in Clusters 1 and 3. This recent loss of diversity is likely caused by anthropogenic activity (barriers to dispersal, habitat destruction, etc.), but our current sampling strategy does not allow us to further explore the causes of these recent genetic bottlenecks.

Several studies have examined the genetic diversity of Crotalus horridus across its broad geographic range (Bushar et al., 1998; Clark et al., 2008; Clark et al., 2011; Bushar et al., 2014, Bushar et al., 2015, Royer et al., 2024). These studies utilized between 4 and 9 microsatellite markers, many of which are shared with this study (Villarreal et al., 1996; Holycross et al., 2002; Pozarowski et al., 2012). Comparing allelic diversity across studies can be misleading due to differences in sample size, but heterozygosity, which is not sensitive to sample size, provides a reliable measure for comparison (Allendorf et al., 2012). Early studies from Pennsylvania reported heterozygosity ranging from 0.25–0.56, with an average of 0.49, based on genotyping 32 individuals across 5 locations (Bushar et al., 1998). Populations from New York exhibited heterozygosity ranging from 0.55–0.66, with over 300 individuals sampled (Clark et al., 2008). An examination of the last remaining population in New Hampshire did not report heterozygosity but indicated very low allelic diversity, likely translating into low heterozygosity values (Clark et al., 2011). One of the largest studies examined 21 populations in New Jersey, Pennsylvania, and Virginia and found expected heterozygosity ranged from 0.41–0.69 and populations appeared to structure by physiographic province (Bushar et al., 2014). In a follow up study, heterozygosity ranged from 0.33–0.64 in populations from the New Jersey Pine Barrens (Bushar et al., 2015). The most recent study reported heterozygosity ranging from 0.62–0.69 in Pennsylvania (Royer et al., 2024). These new estimates are generally higher than earlier estimates from Pennsylvania where fewer genetic markers were utilized (Bushar et al., 1998, Bushar et al., 2014). All of these reported measures of heterozygosity are lower than what we calculated for the Western North Carolina management units. This suggests that the populations in Western North Carolina have greater genetic diversity and could potentially be used to augment northern populations that are failing, provided that local adaptation is not a concern.

The Timber Rattlesnake is one of 36 species within the genus Crotalus. Over the last decade, genetic diversity studies using microsatellite markers have examined three other Crotalus species (Sunny et al., 2015; Herrmann et al., 2017; Le et al., 2020). Heterozygosity values for the Mexican Dusky Rattlesnake (C. triseriatus) ranged from 0.53–0.59 (Sunny et al., 2015). For the Western Diamondback Rattlesnake (C. atrox), values ranged from 0.81–0.82 (Herrmann et al., 2017). The Eastern Diamondback Rattlesnake (C. adamanteus) exhibited heterozygosity values ranging from 0.53–0.92, with an average of 0.74 (Le et al., 2020). These findings suggest that the populations in Western North Carolina possess comparable or higher genetic diversity than other Crotalus species examined with similar marker systems, some of which are not considered threatened.

This study found a high level of genetic diversity within Timber Rattlesnake populations in Western North Carolina, surpassing values reported in regions such as Pennsylvania, New York, New Hampshire, New Jersey, and Virginia. This suggests that the Western North Carolina populations have a greater potential for long-term survival and adaptability. We identified three genetic clusters, considered management units, likely representing groups of natural populations (hibernacula) connected by gene flow. Mapping these units provides a valuable tool for land managers to coordinate conservation efforts and formulate targeted strategies to maintain genetic diversity and connectivity among populations.

However, early signs of a genetic bottleneck were detected in two of the management units, likely indicating the impacts of anthropogenic pressure. To mitigate these effects, augmentation or genetic rescue may be considered. Though a prior study concluded Timber Rattlesnakes do not typically transplant well (Reinert & Rupert, 1999), releasing male snakes at the right time could ensure their alleles are passed to the next generation, regardless of their survival post-transplant. While this recommendation may be contentious, it could help rescue failing populations in the northeastern part of the range, where options for habitat protection and connectivity are limited.

This work was supported by the Nobles Family Charitable Fund that were donated to the Estep Conservation Genetics lab at Appalachian State University and a small grant from the North Carolina Herpetological Society.

Acknowledgments:

The authors would like to thank the large network of people that collected samples, as this work would not have been possible without their efforts. These include Blue Ridge Parkway: Jonathan Bennett, Jimmy Burnette, Stephen Cravey, David Hines, Lillian McElrath, Conrad Shirk, Bambi Teague and Shalyn Yost. North Carolina Wildlife Resources Commission: Alan Cameron, Jeff Hall, Chris Kelly, Kabryn Mattison, Wanda Mugo, Lori Williams North Carolina State Parks: Jesse Anderson, Jason Anthony, Ed Corey, Sharon Bischof, Kevin Bischof, Jonathan Buie, Alyssa Coburn, Nathan Emerson, Joe Hiatt, Sam Koch, Rob McGraw, Robin Rittlebarger, Tim Robinson, Erin Staib, Carla Williams, Matt Windsor. We also thank Jeff Beane, John Sealy, JJ Apodaca and Michael Davis. The authors also thank Jen Rhode Ward, Lauren Gray and Kate Loughran for editorial feedback on the manuscript, Sarah Marshburn for improving the quality of figures. The Nobles Family Charitable Fund and the North Carolina Herpetological Society provided funding for this work.

Competing interests: The authors have no relevant financial or non-financial interest to disclose.

Author Contributions: All authors contributed to the study and approved the final manuscript. HK extracted DNA, scored chromatograms, and analyzed the data. AO generated the GIS map, ME extracted DNA, genotyped samples, scored chromatograms, analyzed the data, created figures and tables and wrote the 1^st draft of the manuscript.

Data Availability: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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A First Approximation of Genetic Diversity and Population Structure to Identify Management Units for Western North Carolina Timber Rattlesnakes (Crotalus horridus)

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