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Article

Population Subdivision in the Gopher Frog (Rana capito) across the Fragmented Longleaf Pine-Wiregrass Savanna of the Southeastern USA

by
Thomas J. Devitt
1,*,
Kevin M. Enge
2,
Anna L. Farmer
2,3,
Peter Beerli
4,
Stephen C. Richter
5,
Jeffrey G. Hall
6 and
Stacey L. Lance
7
1
Department of Integrative Biology, The University of Texas at Austin, Austin, TX 78712, USA
2
Lovett E. Williams Jr. Wildlife Research Laboratory, Fish and Wildlife Research Institute, Florida Fish and Wildlife Conservation Commission, Gainesville, FL 32601, USA
3
Department of Psychology, University of Florida, Gainesville, FL 32611, USA
4
Department of Scientific Computing, Florida State University, Tallahassee, FL 32306, USA
5
Division of Natural Areas and Department of Biological Sciences, Eastern Kentucky University, Richmond, KY 40475, USA
6
North Carolina Wildlife Resources Commission, Raleigh, NC 27699, USA
7
Savannah River Ecology Laboratory, University of Georgia, Aiken, SC 29802, USA
*
Author to whom correspondence should be addressed.
Diversity 2023, 15(1), 93; https://doi.org/10.3390/d15010093
Submission received: 21 December 2022 / Revised: 8 January 2023 / Accepted: 8 January 2023 / Published: 11 January 2023
(This article belongs to the Special Issue Amphibian Ecology in Geographically Isolated Wetlands)
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Abstract

:
Delineating genetically distinct population segments of threatened species and quantifying population connectivity are important steps in developing effective conservation and management strategies aimed at preventing extinction. The gopher frog (Rana capito) is a xeric-adapted, pond-breeding species endemic to the Gulf and Atlantic coastal plains of the southeastern United States. This species has experienced extensive habitat loss and fragmentation in the formerly widespread longleaf pine-wiregrass savanna where it lives, resulting in individual abundance declines and population extinctions throughout its range. We used individual-based clustering methods along with Bayesian inference of historical migration based on almost 1500 multilocus microsatellite genotypes to examine genetic structure in this taxon. Clustering analyses identified panhandle and peninsular populations in Florida as distinct genetic clusters separated by the Aucilla River, consistent with the division between the Coastal Plain and peninsular mitochondrial lineages, respectively. Analysis of historical migration indicated an east–west population divergence event followed by immigration to the east. Together, our results indicate that the genetically distinct Coastal Plain and peninsular Florida lineages should be considered separately for conservation and management purposes.

1. Introduction

Genetic diversity is a central component of biodiversity, one that underpins the ability of populations to adapt to changing environmental conditions [1,2]. Although the species category is usually the focus for decision makers tasked with environmental policy and conservation prioritization, most species are not genetically homogeneous, and many show strong structure among populations or contain historically isolated, intraspecific genealogical lineages [3]. For threatened taxa, genetically distinct populations or lineages may warrant special consideration for conservation and management purposes (i.e., conservation units), and thus, accurate delimitation of these units is critical for effective conservation strategies aimed at species persistence long term [4,5,6].
The North American Coastal Plain of the southeastern United States is a global biodiversity hotspot and home to the highest amphibian richness in North America [7,8]. The longleaf pine (Pinus palustris) savanna ecosystem that is home to much of this diversity has been reduced to an estimated 2% of its former ~360,000 km2 historic range due to land-use change [9], making it one of the most endangered ecosystems in the United States [10]. Many longleaf savanna specialist species have experienced abundance declines and population extirpations throughout the southeastern Coastal Plain [11]. One such species is the gopher frog (Rana capito) (Frost et al. [12] placed this species in the genus Lithobates, but this creates paraphyly in other genera as shown by Yuan et al. [13]), a species that breeds in geographically isolated wetlands of the fragmented longleaf savanna and has declined throughout its range [14].
Characterizing genetic variation within declining populations of longleaf savanna endemics like the gopher frog is an important step in designing measures to maximize their evolutionary potential for recovery [15]. A range-wide phylogeographic study of the gopher frog using mtDNA identified three distinct mitochondrial lineages, one Coastal Plain lineage and two on the Florida peninsula [16]. Florida panhandle populations belong to the Coastal Plain lineage. The Coastal Plain lineage was estimated to have diverged 1.9–2.3 million years ago (mya), and the northern and southern peninsular Florida lineages 1.1–1.3 mya [16].
Here, we use a multilocus approach using microsatellites to examine population structure across the range of Rana capito, with an emphasis on understanding population structure in Florida (Figure 1).
We quantify hierarchical patterns of population subdivision to test the hypothesis that alleles are drawn from the same distribution in all populations. We also test models of migration to infer the history of population divergence between Coastal Plain and peninsular Florida populations. Our results provide support for genetically distinct Coastal Plain and peninsular Florida lineages that should be recognized for effective conservation and management of this taxon.

2. Materials and Methods

2.1. Sampling and Genotyping

Tissue samples in Florida were obtained primarily by dipnetting tadpoles from ponds and excising a small piece (ca. 5 mm) from the tip of the tail. Most other samples were obtained by collecting 1–2 whole eggs from egg masses, although in some instances, tissue samples were collected by clipping a single toe from newly metamorphosed gopher frogs. Sample sizes were uneven among ponds due to site-specific variation in habitat quality and/or environmental conditions (mean 16, range 1−50; Table S1, supporting information). Tissue samples were stored in 95% ethanol. DNA was extracted from tissues using the DNeasy blood and tissue extraction kit (QIAGEN, Inc., Venlo, The Netherlands), and 10 microsatellite loci were amplified and genotyped following Nunziata et al. [17] at the University of Georgia’s Savannah River Ecology Laboratory (SREL), Aiken, South Carolina.

2.2. Diversity, Differentiation, and Relatedness Estimation

To overcome any bias due to small or uneven sample sizes among ponds [18,19], we performed analyses on the entire dataset, as well as a subset of the data consisting of only those ponds with at least 30 sampled individuals (865 individuals from 27 ponds; Table S1). Each sampled pond was treated as a population a priori for calculating basic genetic diversity and differentiation statistics because that is the lowest level of genetic structure we would expect based on the life history of this species (i.e., a deme). We used the genepop [20] package for R to calculate allele frequencies, observed and expected genotype counts, exact tests for Hardy–Weinberg (HW) genotypic equilibrium, estimates of null allele frequencies [18], exact G-tests of population differentiation based on allelic frequencies [21], F-statistics based on allele identity [22], and F-statistic analogs for microsatellites based on allele size [23] using the method of Michalakis and Excoffier [24] (Data S1). We assessed the significance of exact tests for HW equilibrium using the Markov chain Monte Carlo (MCMC) simulation [25] with default values for the Markov chain (100 batches with 1000 iterations per batch and 1000 dememorization steps). Descriptive statistics per locus and averaged over all loci for populations with at least 30 samples (Data S2) were calculated using the hierfstat package for R [26]. We tested for relatedness within ponds using the estimator of Loiselle et al. [27] using the package demerelate [28] for R (Data S3). We chose this estimator because it corrects for small sample size and is more accurate than most unmodified relatedness estimators [29,30].

2.3. Population Structure

We analyzed overall population structure using two approaches. First, we performed an analysis of molecular variance (AMOVA) [31] to quantify within-population and among-population genetic diversity. Ten thousand permutations were performed to test the significance of the variance components and their associated F-statistic analogs under an infinite allele model implemented in GenoDive (Data S4).
Second, we used individual-based assignment methods for inferring population structure implemented in Structure [32,33]. We analyzed several different subsets of the full data to look for hierarchical population structure at different spatial scales and test for any small sample size effects [34]. For the range-wide dataset (N = 1487 samples), we examined values of K (the number of populations) from 1–40 (Data S5). We also analyzed peninsular Florida populations separately based on results from the range-wide analysis that clustered samples into Coastal Plain and peninsular groups. We examined peninsular Florida populations for values of K from 1–20 with N = 1057 samples (Data S6) and using a reduced dataset with only populations containing 30 or more samples for values of K from 1–15 (Data S7). Null alleles (population-specific double missing genotypes) were coded as missing. We used the correlated allele frequency model and assumed admixture among populations using the default settings. Analyses were performed both with and without sample group information as a prior [35]. Ten runs were conducted for each value of K in Structure, with each run consisting of 50,000 steps after a burn-in of 10,000 steps. We chose this burn-in value and assessed MCMC convergence by examining time series plots of parameters (𝛼, F, likelihood) along the run. We evaluated the “best” K by examining Evanno’s ΔK method [34], as well as the log probability of the data (lnPr(X|K)), following the software authors’ recommendation (Structure v2.3 manual, pp. 16–17), using the web program Structure Harvester [36]. Results were summarized and visualized using Clumpp [37] and Distruct [38] implemented in the package pophelper [39] for the R statistical software environment [40].

2.4. Migration Estimation

We estimated historical migration (on the order of thousands of years ago) across a genetic discontinuity between panhandle and peninsular Florida populations that was identified in preliminary Structure analyses, as well as from mtDNA sequence data [16]. Five subpopulations (sampled ponds) with at least 20 individuals each were pooled into 2 ‘populations’: panhandle (ponds EAFB101, ANFCL007, and ANF003) and peninsula (BBSCU and FWMP008; Figure 2). We tested seven models that specified different population histories (Figure 2) using the software Migrate 4.2.9 [41] to evaluate the different models based on their marginal likelihood (Data S8). Migrate uses Bayesian inference to calculate the probability of explicit population models in a coalescent framework. Our null hypothesis (and simplest case) is that sampled ponds belong to a single panmictic population (model 7). More complex models included a divergence event (models 1, 2, and 6) and various directional migration scenarios (models 3, 4, and 5; Figure 2).

3. Results

3.1. Sampling and Microsatellite Descriptive Statistics

After removing individuals with no scored loci, 1482 individuals from 94 ponds remained (Table S1). There was 5.11% missing data in the entire dataset (Figure S1) and 4.38% in the subset of the data containing only populations with at least 30 samples (Figure S2). Summary statistics for ponds with at least 30 samples are provided in the Supplementary Materials. The mean number of alleles averaged over all loci and populations was 24 (Figure S3). The effective number of alleles (the number of alleles weighted by their frequency) was five (Figure S3). We did not find a significant correlation between locus polymorphism and diversity partitioning statistics (Figure S4). Heterozygosity and gene diversity estimates were high (mean 0.8; Figure S5). Allelic differentiation among populations was significant based on exact G-tests (p < 0.001), allowing us to reject the hypothesis that alleles are drawn from the same distribution in all populations. Population differentiation statistics per locus and averaged over all loci for populations with at least 30 samples are shown in Table S2. Several populations had one or more private alleles (Table S3). Heterozygosity-based tests for Hardy–Weinberg equilibrium (GIS) were significant (p < 0.05) for 15 of 25 populations that had at least 30 samples (Table S4, Figure S6). Different point estimates of population differentiation show the same general pattern (Figure S7).

3.2. Relatedness

Several populations had a high mean within-population pairwise relatedness estimate (Figure S8). Considering sample sizes and confidence limits on point estimates, St. Sebastian River Preserve State Park populations (SSRP003, SSRP011, and SSRP020) contain putative full-sibling groups (r = 0.5). Populations with putative half-sibling groups (r = 0.25) include Apalachicola National Forest populations (ANF003 and ANFCL007); Big Bend Wildlife Management Area’s Spring Creek Unit (BBSCU002); Camp Blanding Military Reservation (CBMR009); Eglin Air Force Base (EAFB003); Etoniah Creek State Forest (ECSF007); private land near Highlands Hammock (HIGH001); and Jennings State Forest (JSF031). Other populations show a higher degree of relatedness than expected at random, but low sample sizes for some of these populations may bias relatedness estimates upwards.

3.3. Analysis of Molecular Variance (AMOVA)

AMOVA results are shown in Table 1. Within-population variation accounted for most (82%) of the total variance, with among-population variation capturing 18%.

3.4. Clustering Analyses

Time series plots of summary statistics and relevant parameters showed MCMC convergence well before the 10,000 steps excluded as burn-in. Mean values of the tuning parameter r indicated that the sample group was informative regarding ancestry [35]. Our preferred model assumed admixture and correlated allele frequencies among populations, using the sample group as prior information to assist clustering. Under this model, the mean log probability of the data for the range-wide dataset was greatest for K = 22, though the rate of change in the log probability of the data ΔK was greatest from 1 to 2 clusters (Figure S9). At K = 2, peninsular Florida populations clustered separately from the rest of the range-wide dataset (Figure 3). For peninsular populations alone, the mean log probability of the data was greatest for K = 17, though ΔK was greatest from K = 1 to K = 2 and nearly as great as from K = 2 to K = 3 (not shown). At K = 2, individuals from St. Sebastian River Preserve State Park (SSRP) formed a cluster separate from other peninsular populations. Removing this divergent cluster from the analysis to explore further subdivision did not reveal additional structure within the peninsula. These results were consistent whether the entire peninsular dataset was used or only populations with at least 30 samples (not shown).

3.5. Analysis of Migration

Based on the model probabilities, the best model specifies an east–west population divergence event with immigration to the east (i.e., from the panhandle to the peninsula). All other tested models are improbable (Table 2).

4. Discussion

Genetic diversity within populations of gopher frogs is high overall; the within-population component of genetic variation contributes the most to total genetic diversity. The among-population variance component is relatively small, but significant allelic differentiation exists among populations, allowing us to reject the hypothesis that alleles are drawn from the same distribution in all populations. While FST values are low (overall FST = 0.105), the observed range of FST is always less than HS, and the range of possible FST values becomes small when HS is large [42]. For example, when HS = 0.8, the maximum value of FST = 0.2, a value that would indicate the maximum possible differentiation between populations. Estimators that do not depend on within-population diversity (as FST does) indicated stronger genetic population structure than was estimated by FST values alone.
Given the observed allelic differentiation among populations, we applied model-based clustering in Structure to identify groups of populations with distinct allele frequencies. In general, it was difficult to detect population structure under the admixture model, even when sample information was included as a prior to assist in detecting weak population structure. Rigorous estimation of K is a difficult statistical problem even when assumptions of the underlying inference model are met [43]. Nonetheless, two clusters were always identified that corresponded to Coastal Plain and peninsular Florida populations separated across the Aucilla River Basin. Within peninsular Florida, St. Sebastian River Preserve State Park (SSRP) populations formed a separate cluster, a pattern most likely due to the high degree of relatedness among sampled individuals rather than any barriers to gene flow. Although it is often assumed that removing putative siblings is necessary prior to subsequent population genetic analyses, doing so may create unnecessary problems [44]. Siblings are present in all naturally occurring populations. Because their frequency is inversely related to effective population size, removing siblings from analyses can upwardly bias population size estimates, while reducing sample size, and thus, decreasing statistical power [45].
Although our Structure analysis provided some idea of the present genetic structure, that analysis does not provide any insight into the processes that resulted in the pattern. Our Migrate analysis revealed that an east–west population divergence event with subsequent immigration to the east (i.e., from the panhandle to the peninsula) was by far the most probable of the seven demographic models we tested. The location of this divergence is generally concordant with the mitochondrial break identified by Richter et al. [14] that is estimated to have occurred during the late Pliocene to early Pleistocene 2.5–3 mya. Our sampling narrowed the location of this split to an area spanning about 70 km in a region of low relief (the Gulf Coastal Lowlands; [46,47]) lacking habitat spanning the Aucilla River Basin. Several other species show phylogeographic breaks across this same region [48,49,50,51].
A brief discussion of the complicated taxonomy of Rana capito is warranted (for a full taxonomic history, see [52]) because it bears on the conservation status of this species. This taxon was described in 1855 [53] soon after classified as a subspecies of R. areolata [54]. Over the next century, various workers described additional subspecies that were later elevated to species level, synonymized, and/or combined into new taxa. Populations from Louisiana and Mississippi were described as a distinct species R. sevosa in 1940 [55] but later subsumed under R. capito and then R. areolata. Young and Crother [56] elevated R. sevosa back to species level based on a single fixed difference at one allozyme locus and did not recognize any subspecies within R. capito populations to the east, a conclusion followed by Richter et al. [14]. Some workers, however, continue to recognize two subspecies of R. capito, the Carolina gopher frog (R. capito capito) and the Florida gopher frog (R. c. aesopus). Because these subspecies were described based on apparent morphological differences, applying these names to the genetic lineages identified here would make little sense unless phenotypic differences are found, at least in so far as subspecies designations have been used traditionally (see, e.g., [57,58]).

5. Conclusions

Much of the concern about the Earth’s current extinction crisis continues to focus on the loss of species, even though global extinction usually represents the endpoint of a series of population declines and extirpations [59,60,61]. These population losses contribute to the erosion of ecosystem functioning and services through complex interactions among species [62,63,64]. Results presented here provide evidence for two genetically distinct lineages of gopher frogs that should be recognized by decision makers tasked with conservation prioritization to prevent the extinction of one or both. Gopher frog populations in the Coastal Plain lineage appear to be at greater risk of extinction in the near term compared to the peninsular Florida lineage. Estimates indicate that Coastal Plain gopher frogs are still present in 65% of the 75 sites where they once occurred, whereas peninsular Florida gopher frogs occupy 93% of 108 known sites [65]. The number of populations is estimated at five in South Carolina, seven in North Carolina, 15 in Georgia, 19 in the Florida panhandle, and three in Alabama [65]. Modeling predicts that only 9–26 of the 49 extant Coastal Plain populations would persist through 2050 under current management levels [65]. Although Peninsular Florida has the greatest number of remaining populations, many of which are found on conservation lands [66], they are increasingly isolated due to habitat fragmentation. These isolated populations face a higher likelihood of extinction due to decreased fitness caused by low genetic diversity resulting from reduced migration [67]. Habitat fragmentation has also been shown to increase susceptibility to pathogens in amphibians [68], and although there have been few reports of disease in gopher frogs, a mass mortality event following an infectious disease outbreak at a protected area was recently documented in this species [69].
Given the evidence of population declines and habitat loss, the Coastal Plain lineage should be prioritized to prevent its extinction. Conservation measures should include upland and wetland habitat acquisition and restoration, as well as headstarting and translocation to areas of suitable habitat that were formerly occupied (to preserve genetic structure and diversity, translocations between the Coastal Plain and peninsular Florida lineages should be avoided). These measures are being implemented by conservation managers in several states, but the scale and scope of management should be expanded.
A petition to list Rana capito under the U.S. Endangered Species Act was filed in 2012 [70], and in 2015, the U.S. Fish and Wildlife Service found that the petition presented substantial scientific evidence that listing may be warranted [71]. The current listing status remains under review at the time of publication. Based on our findings, we recommend that Coastal Plain and peninsular Florida lineages be considered separately for listing as “distinct population segments”.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/d15010093/s1, Table S1: Sampling information. Table S2: population differentiation statistics per locus and averaged over all loci for range-wide dataset with at least 30 samples per population. Table S3: private alleles by locus and population for Florida populations. Table S4: Significance (p < 0.05) of heterozygosity-based tests for Hardy Weinberg equilibrium for populations with at least 30 samples. Figure S1: missing data for the entire range-wide dataset. Figure S2: missing data for range-wide dataset with at least 30 samples per population. Figure S3: number of alleles per locus and rarefied allelic counts per locus for Florida populations with at least 30 individuals. Figure S4: scatterplots of diversity partitioning estimators vs. locus polymorphism for Florida populations with at least 30 samples. Figure S5: summary of genetic diversity and differentiation estimates per locus for range-wide dataset with at least 30 samples per population. Figure S6: significance of tests for Hardy–Weinberg equilibrium for range-wide dataset with at least 30 samples per population. Figure S7: comparison of point estimates of population differentiation measures. Figure S8: relatedness estimates for ponds with at least 30 samples in the range-wide dataset. Figure S9: results from structure analysis of the range-wide dataset showing mean log probability of the data and the rate of change in the log probability of the data (ΔK). Data S1–S8: Data S1: Genotypes file for the entire rangewide dataset in GenePop format. Data S2: Genotypes file for ponds with at least 30 samples in Genepop format. Data S3: demerelate input file. Data S4: GenoDive input file. Data S5: Genotypes file for rangewide dataset in Structure format. Data S6: Genotypes file for peninsular Florida populations in Structure format. Data S7: Genotypes file for peninsular Florida populations with at least 30 samples in Structure format. Data S8: Input file for Migrate analysis.

Author Contributions

Conceptualization, T.J.D., K.M.E., A.L.F., S.C.R. and S.L.L.; methodology, T.J.D., K.M.E., A.L.F., S.C.R. and S.L.L.; formal analysis, T.J.D., P.B. and S.L.L.; investigation, T.J.D., K.M.E., A.L.F., S.C.R., J.G.H. and S.L.L.; data curation, T.J.D.; writing—original draft preparation, T.J.D.; writing—review and editing, T.J.D., A.L.F., K.M.E., S.C.R., J.G.H., P.B. and S.L.L.; funding acquisition, T.J.D., K.M.E., A.L.F., S.C.R. and S.L.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Florida Fish and Wildlife Conservation Commission’s and the U.S. Fish and Wildlife Service’s State Wildlife Grants program (Data Gaps Projects Grant Cycle 2012 T-30, F13AF00983), the Georgia Department of Natural Resources project number 10-21-RR267-321, and is based upon work supported by the Department of Energy Office of Environmental Management under Award Numbers DE-FC09-07SR22506 and DE-EM0005228 to the University of Georgia Research Foundation.

Institutional Review Board Statement

All tissues samples were collected under state permits (if required) as follows: South Carolina Department of Natural Resources permit numbers SC-39-2013, SC-04-2014, SC-05-2015, and SC-02-2016. Permits were not required in North Carolina, Georgia, Alabama, or Florida because samples were collected by state wildlife biologists.

Data Availability Statement

The data presented in this study are available in as files. Data S1–S8 included with Supplementary Material.

Acknowledgments

Grant assistance was provided by Andrea Alden, Ginger Gornto, and Robyn McDole. Jamie Barichivich, Glenn Bartolotti, Bob Simons, Travis Blunden, Jim Blush, Joe Burgess, Brian Camposano, Garrett Craft, Jason DePue, Nancy Dwyer, Erik Egensteiner, Carolyn Enloe, Mel Gramke, Christopher Haggerty, Allan Hallman, Stephen Harris, Emma Knight, Joe Mansuetti, Jonathan Mays, Ryan Means, Paul Moler, Vince Morris, Dwight Myers, Jennifer Myers, Charlie Pedersen, Ralph Risch, Jonathan Roberts, Emily Rushton, Carrie Sekerak, Sarah Reintjes-Tolen, Jordan Schmitt, Jen Stabile, Travis Thomas, the late Courtney Tye, Lindsay Wagner, and Graham Williams helped collect samples. David Scott, John Jensen, Mark Bailey, Dirk J. Stevenson, John N. Macey, and staff from the Fort Benning Military Installation, The Nature Conservancy of Georgia, and the North Carolina Wildlife Resources Commission all provided samples. Jason O’Bryhim assisted with laboratory analyses.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Disclaimer

This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, make any warranty, express or implied, or assume any legal liability or responsibility for the accuracy, completeness or usefulness of any information, apparatus, product, or process disclosed, or represent that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States.

References

  1. Lande, R.; Shannon, S. The Role of Genetic Variation in Adaptation and Population Persistence in a Changing Environment. Evolution 1996, 50, 434. [Google Scholar] [CrossRef]
  2. DeWoody, J.A.; Harder, A.M.; Mathur, S.; Willoughby, J.R. The Long-standing Significance of Genetic Diversity in Conservation. Mol. Ecol. 2021, 30, 4147–4154. [Google Scholar] [CrossRef]
  3. Coates, D.J.; Byrne, M.; Moritz, C. Genetic Diversity and Conservation Units: Dealing with the Species-Population Continuum in the Age of Genomics. Front. Ecol. Evol. 2018, 6, 1–13. [Google Scholar] [CrossRef] [Green Version]
  4. Moritz, C. Defining ‘Evolutionarily Significant Units’ for Conservation. Trends Ecol. Evol. 2003, 9, 373–375. [Google Scholar] [CrossRef] [PubMed]
  5. Funk, W.C.; McKay, J.K.; Hohenlohe, P.A.; Allendorf, F.W. Harnessing Genomics for Delineating Conservation Units. Trends Ecol. Evol. 2012, 27, 489–496. [Google Scholar] [CrossRef] [Green Version]
  6. Shaffer, H.B.; Gidiş, M.; McCartney-Melstad, E.; Neal, K.M.; Oyamaguchi, H.M.; Tellez, M.; Toffelmier, E.M. Conservation Genetics and Genomics of Amphibians and Reptiles. Annu. Rev. Anim. Biosci. 2015, 3, 113–138. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Noss, R.F.; Platt, W.J.; Sorrie, B.A.; Weakley, A.S.; Means, D.B.; Costanza, J.; Peet, R.K. How Global Biodiversity Hotspots May Go Unrecognized: Lessons from the North American Coastal Plain. Divers. Distrib. 2015, 21, 236–244. [Google Scholar] [CrossRef]
  8. Jenkins, C.N.; Pimm, S.L.; Joppa, L.N. Global Patterns of Terrestrial Vertebrate Diversity and Conservation. Proc. Natl. Acad. Sci. USA 2013, 110, E2602–E2610. [Google Scholar] [CrossRef] [Green Version]
  9. Frost, C. History and Future of the Longleaf Pine Ecosystem. In The Longleaf Pine Ecosystem; Jose, S., Jokela, E.J., Miller, D.L., Eds.; Springer: New York, NY, USA, 2006; pp. 9–48. ISBN 978-0-387-30687-2. [Google Scholar]
  10. Noss, R.F.; LaRoe, E.T.; Scott, J.M. Endangered Ecosystems of the United States: A Preliminary Assessment of Loss and Degradation; Biological Report 28; U.S. Department of the Interior: Washington, DC, USA, 1995; pp. 4–58. [Google Scholar]
  11. Means, D.B. Vertebrate Faunal Diversity of Longleaf Pine Ecosystems. In The Longleaf Pine Ecosystem; Jose, S., Jokela, E.J., Miller, D.L., Eds.; Springer: New York, NY, USA, 2006; pp. 157–213. ISBN 978-0-387-30687-2. [Google Scholar]
  12. Frost, D.R.; Grant, T.; Faivovich, J.; Bain, R.H.; Haas, A.; Haddad, C.F.; Sa, R.O.D.; Channing, A.; Wilkinson, M.; Donnellan, S.C. The Amphibian Tree of Life. Bull. Am. Mus. Nat. Hist. 2006, 2006, 1–291. [Google Scholar] [CrossRef]
  13. Yuan, Z.-Y.; Zhou, W.-W.; Chen, X.; Poyarkov, N.A., Jr.; Chen, H.-M.; Jang-Liaw, N.-H.; Chou, W.-H.; Matzke, N.J.; Iizuka, K.; Min, M.-S.; et al. Spatiotemporal Diversification of the True Frogs (Genus Rana): A Historical Framework for a Widely Studied Group of Model Organisms. Syst. Biol. 2016, 65, 824–842. [Google Scholar] [CrossRef]
  14. Jensen, J.B.; Richter, S.C. Rana capito Le Conte, 1855. In Amphibian Declines: The Conservation Status of United States Species; Lannoo, M.J., Ed.; University of California Press: Berkeley, CA, USA, 2005; pp. 536–538. [Google Scholar]
  15. Yoder, A.D.; Poelstra, J.W.; Tiley, G.P.; Williams, R.C. Neutral Theory Is the Foundation of Conservation Genetics. Mol. Biol. Evol. 2018, 35, 1322–1326. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Richter, S.C.; O’Neill, E.M.; Nunziata, S.O.; Rumments, A.; Gustin, E.S.; Young, J.E.; Crother, B.I. Cryptic Diversity and Conservation of Gopher Frogs across the Southeastern United States. Copeia 2014, 2014, 231–237. [Google Scholar] [CrossRef]
  17. Nunziata, S.O.; Richter, S.C.; Denton, R.D.; Yeiser, J.M.; Wells, D.E.; Jones, K.L.; Hagen, C.; Lance, S.L. Fourteen Novel Microsatellite Markers for the Gopher Frog, Lithobates capito (Amphibia: Ranidae). Conserv. Genet. Resour. 2012, 4, 201–203. [Google Scholar] [CrossRef]
  18. Struebig, M.J.; Comber, S.C.L.; Rossiter, S.J. Overcoming the Issue of Small Sample Sizes in Fragmentation Genetics. Mol. Ecol. 2012, 21, 2850–2851. [Google Scholar] [CrossRef]
  19. Nazareno, A.G.; Jump, A.S. Species-Genetic Diversity Correlations in Habitat Fragmentation Can Be Biased by Small Sample Sizes. Mol. Ecol. 2012, 21, 2847–2849. [Google Scholar] [CrossRef]
  20. Rousset, F. Genepop’007: A Complete Re-implementation of the Genepop Software for Windows and Linux. Mol. Ecol. Resour. 2008, 8, 103–106. [Google Scholar] [CrossRef]
  21. Goudet, J.; Raymond, M.; de Meeüs, T.; Rousset, F. Testing Differentiation in Diploid Populations. Genetics 1996, 144, 1933–1940. [Google Scholar] [CrossRef]
  22. Weir, B.S.; Cockerham, C.C. Estimating F-Statistics for the Analysis of Population Structure. Evolution 1984, 38, 1358–1370. [Google Scholar] [CrossRef]
  23. Rousset, F. Equilibrium Values of Measures of Population Subdivision for Stepwise Mutation Processes. Genetics 1996, 142, 1357–1362. [Google Scholar] [CrossRef]
  24. Michalakis, Y.; Excoffier, L. A Generic Estimation of Population Subdivision Using Distances between Alleles with Special Reference for Microsatellite Loci. Genetics 1996, 142, 1061–1064. [Google Scholar] [CrossRef]
  25. Guo, S.W.; Thompson, E.A. Performing the Exact Test of Hardy-Weinberg Proportion for Multiple Alleles. Biometrics 1992, 48, 361–372. [Google Scholar] [CrossRef] [PubMed]
  26. Goudet, J. HIERFSTAT, a package for R to compute and test hierarchical F-statistics. Mol. Ecol. Notes 2005, 5, 184–186. [Google Scholar] [CrossRef] [Green Version]
  27. Loiselle, B.A.; Sork, V.L.; Nason, J.; Graham, C. Spatial Genetic Structure of a Tropical Understory Shrub, Psychotria officinalis (Rubiaceae)). Am. J. Bot. 1995, 82, 1420–1425. [Google Scholar] [CrossRef]
  28. Kraemer, P.; Gerlach, G. Demerelate: Calculating Interindividual Relatedness for Kinship Analysis Based on Codominant Diploid Genetic Markers Using R. Mol. Ecol. Resour. 2017, 17, 1371–1377. [Google Scholar] [CrossRef]
  29. Wang, J. Estimating Pairwise Relatedness in a Small Sample of Individuals. Heredity 2017, 119, 302–313. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Vekemans, X.; Hardy, O.J. New Insights from Fine-scale Spatial Genetic Structure Analyses in Plant Populations. Mol. Ecol. 2004, 13, 921–935. [Google Scholar] [CrossRef] [PubMed]
  31. Excoffier, L.; Smouse, P.E.; Quattro, J.M. Analysis of Molecular Variance Inferred from Metric Distances among DNA Haplotypes: Application to Human Mitochondrial DNA Restriction Data. Genetics 1992, 131, 479–491. [Google Scholar] [CrossRef] [PubMed]
  32. Pritchard, J.K.; Stephens, M.; Donnelly, P. Inference of Population Structure Using Multilocus Genotype Data. Genetics 2000, 155, 945–959. [Google Scholar] [CrossRef]
  33. Falush, D.; Stephens, M.; Pritchard, J.K. Inference of Population Structure Using Multilocus Genotype Data: Linked Loci and Correlated Allele Frequencies. Genetics 2003, 164, 1567–1587. [Google Scholar] [CrossRef] [PubMed]
  34. Evanno, G.; Regnaut, S.; Goudet, J. Detecting the Number of Clusters of Individuals Using the Software Structure: A Simulation Study. Mol. Ecol. 2005, 14, 2611–2620. [Google Scholar] [CrossRef]
  35. Hubisz, M.J.; Falush, D.; Stephens, M.; Pritchard, J.K. Inferring Weak Population Structure with the Assistance of Sample Group Information. Mol. Ecol. Resour. 2009, 9, 1322–1332. [Google Scholar] [CrossRef] [Green Version]
  36. Earl, D.A.; vonHoldt, B.M. STRUCTURE HARVESTER: A Website and Program for Visualizing STRUCTURE Output and Implementing the Evanno Method. Conserv. Genet. Resour. 2012, 4, 359–361. [Google Scholar] [CrossRef]
  37. Jakobsson, M.; Rosenberg, N.A. CLUMPP: A Cluster Matching and Permutation Program for Dealing with Label Switching and Multimodality in Analysis of Population Structure. Bioinformatics 2007, 23, 1801–1806. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Rosenberg, N.A. Distruct: A Program for the Graphical Display of Population Structure. Mol. Ecol. Notes 2003, 4, 137–138. [Google Scholar] [CrossRef]
  39. Francis, R.M. Pophelper: An R Package and Web App to Analyse and Visualize Population Structure. Mol. Ecol. Resour. 2017, 17, 27–32. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2022; Available online: https://www.R-project.org/ (accessed on 7 January 2023).
  41. Beerli, P.; Palczewski, M. Unified Framework to Evaluate Panmixia and Migration Direction among Multiple Sampling Locations. Genetics 2010, 185, 313–326. [Google Scholar] [CrossRef] [Green Version]
  42. Meirmans, P.G.; Hedrick, P.W. Assessing Population Structure: FST and Related Measures. Mol. Ecol. Resour. 2010, 11, 5–18. [Google Scholar] [CrossRef] [PubMed]
  43. Lawson, D.J.; van Dorp, L.; Falush, D. A Tutorial on How Not to Over-Interpret STRUCTURE and ADMIXTURE Bar Plots. Nat. Commun. 2018, 9, 1–11. [Google Scholar] [CrossRef] [Green Version]
  44. Waples, R.S.; Anderson, E.C. Purging Putative Siblings from Population Genetic Data Sets: A Cautionary View. Mol. Ecol. 2017, 26, 1211–1224. [Google Scholar] [CrossRef] [Green Version]
  45. Hendricks, S.; Anderson, E.C.; Antao, T.; Bernatchez, L.; Forester, B.R.; Garner, B.; Hand, B.K.; Hohenlohe, P.A.; Kardos, M.; Koop, B.; et al. Recent Advances in Conservation and Population Genomics Data Analysis. Evol. Appl. 2018, 11, 1197–1211. [Google Scholar] [CrossRef]
  46. Cooke, C.W. Scenery of Florida Interpreted by a Geologist; Florida Geological Survey Bulletin: Tallahassee, FL, USA, 1939; Volume 17. [Google Scholar]
  47. Clark, W.Z.; Zisa, A.C. Physiographic Map of Georgia: Georgia Geologic Survey. Ga. Geol. Surv. 1976, OM-8. [Google Scholar]
  48. Avise, J.C. Phylogeography; Harvard University Press: Cambridge, MA, USA, 2000; ISBN 9780674666382. [Google Scholar]
  49. Soltis, D.E.; Morris, A.B.; McLachlan, J.S.; Manos, P.S.; Soltis, P.S. Comparative Phylogeography of Unglaciated Eastern North America. Mol. Ecol. 2006, 15, 4261–4293. [Google Scholar] [CrossRef]
  50. Lyman, R.A.; Edwards, C.E. Revisiting the Comparative Phylogeography of Unglaciated Eastern North America: 15 Years of Patterns and Progress. Ecol. Evol. 2022, 12, e8827. [Google Scholar] [CrossRef] [PubMed]
  51. Marsico, T.D.; Sauby, K.E.; Brooks, C.P.; Welch, M.E.; Ervin, G.N. Phylogeographic Evidence for a Florida Panhandle–Peninsula Discontinuity in the Distribution of Melitara prodenialis Walker (Lepidoptera: Pyralidae), a Native Cactus-boring Moth. Insect Conserv. Divers. 2015, 8, 377–388. [Google Scholar] [CrossRef]
  52. Frost, D.R. Amphibian Species of the World 6.1, an Online Reference. Available online: https://amphibiansoftheworld.amnh.org/ (accessed on 13 December 2022).
  53. LeConte, J.E. Descriptive Catalogue of the Ranina of the United States. Proc. Acad. Nat. Sci. USA 1855, 7, 423–431. [Google Scholar]
  54. Cope, E.D. Check-List of North American Batrachia and Reptilia; with a Systematic List of the Higher Groups, and an Essay on Geographical Distribution. Based on the Specimens Contained in the U.S. National Museum. Bull. U. S. Natl. Mus. 1875, 1, 1–104. [Google Scholar]
  55. Goin, C.J.; Netting, M.G. A New Gopher Frog from the Gulf Coast, with Comments upon the Rana areolata Group. Ann. Carnegie Mus. 1940, 28, 137–168. [Google Scholar] [CrossRef]
  56. Young, J.E.; Crother, B.I. Allozyme Evidence for the Separation of Rana areolata and Rana capito and for the Resurrection of Rana sevosa. Copeia 2001, 2001, 382–388. [Google Scholar] [CrossRef]
  57. Mayr, E. Principles of Systematic Zoology; McGraw-Hill: New York, NY, USA, 1969; p. 428. [Google Scholar]
  58. Hillis, D.M. The Detection and Naming of Geographic Variation Within Species. Herpetol. Rev. 2020, 51, 52–56. [Google Scholar]
  59. Hughes, J.B.; Daily, G.C.; Ehrlich, P.R. Population Diversity: Its Extent and Extinction. Science 1997, 278, 689–692. [Google Scholar] [CrossRef] [Green Version]
  60. Ceballos, G.; Ehrlich, P.R. The Misunderstood Sixth Mass Extinction. Science 2018, 360, 1080–1081. [Google Scholar] [CrossRef]
  61. Dirzo, R.; Young, H.S.; Galetti, M.; Ceballos, G.; Isaac, N.J.B.; Collen, B. Defaunation in the Anthropocene. Science 2014, 345, 401–406. [Google Scholar] [CrossRef]
  62. Johnson, C.N.; Balmford, A.; Brook, B.W.; Buettel, J.C.; Galetti, M.; Guangchun, L.; Wilmshurst, J.M. Biodiversity Losses and Conservation Responses in the Anthropocene. Science 2017, 356, 270–274. [Google Scholar] [CrossRef] [PubMed]
  63. Estes, J.A.; Terborgh, J.; Brashares, J.S.; Power, M.E.; Berger, J.; Bond, W.J.; Carpenter, S.R.; Essington, T.E.; Holt, R.D.; Jackson, J.B.C.; et al. Trophic Downgrading of Planet Earth. Science 2011, 333, 301–306. [Google Scholar] [CrossRef] [PubMed]
  64. Åkesson, A.; Curtsdotter, A.; Eklöf, A.; Ebenman, B.; Norberg, J.; Barabás, G. The Importance of Species Interactions in Eco-Evolutionary Community Dynamics under Climate Change. Nat. Commun. 2021, 12, 4759. [Google Scholar] [CrossRef]
  65. Crawford, B.A.; Maerz, J.C. Strategic Conservation Management for Gopher Frogs at Site-Specific to Range-Wide Scales; Warnell School of Forestry & Natural Resources, University of Georgia: Athens, GA, USA, 2021. [Google Scholar]
  66. Enge, K.M.; Farmer, A.L.; Mays, J.D.; Castellón, T.D.; Hill, E.P.; Moler, P.E. Survey of Winter-Breeding Amphibian Species; Final Report; Florida Fish and Wildlife Conservation Commission, Fish and Wildlife Research Institute, Lovett E. Williams, Jr. Wildlife Research Laboratory: Gainesville, FL, USA, 2014. [Google Scholar]
  67. Cushman, S.A. Effects of Habitat Loss and Fragmentation on Amphibians: A Review and Prospectus. Biol. Conserv. 2006, 128, 231–240. [Google Scholar] [CrossRef]
  68. Belasen, A.M.; Bletz, M.C.; da Leite, D.S.; Toledo, L.F.; James, T.Y. Long-Term Habitat Fragmentation Is Associated with Reduced MHC IIB Diversity and Increased Infections in Amphibian Hosts. Front. Ecol. Evol. 2019, 6, 236. [Google Scholar] [CrossRef] [Green Version]
  69. Hartmann, A.M.; Maddox, M.L.; Ossiboff, R.J.; Longo, A.V. Sustained Ranavirus Outbreak Causes Mass Mortality and Morbidity of Imperiled Amphibians in Florida. Ecohealth 2022, 19, 8–14. [Google Scholar] [CrossRef] [PubMed]
  70. Giese, C.L.A.; Greenwald, D.N.; Curry, T. Petition to List 53 Amphibians and Reptiles in the United States as Threatened or Endangered Species under the Endangered Species Act. Cent. Biol. Divers. 2012, 454. [Google Scholar]
  71. Fish and Wildlife Service, Interior. Endangered and Threatened Wildlife and Plants; 90-Day Findings on 31 Petitions. Fed. Regist. 2015, 80 FR 37568, 37568–37579. [Google Scholar]
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Figure 1. Sampled subpopulations of gopher frogs (Rana capito) in Alabama (AL), Florida (FL), Georgia (GA), North Carolina (NC) and South Carolina (SC). See Table S1 for further detail on sampling sites.
Figure 1. Sampled subpopulations of gopher frogs (Rana capito) in Alabama (AL), Florida (FL), Georgia (GA), North Carolina (NC) and South Carolina (SC). See Table S1 for further detail on sampling sites.
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Figure 2. Top: seven models tested in Migrate analysis. Model 1: split with immigration to the east; 2: split without immigration; 3: immigration to the east only; 4: immigration to the east and west; 5: immigration to the west only; 6: split with immigration to the west only; 7: panmixia. Orange circles represent populations combined into a panhandle group and blue circles represent peninsular populations that were combined.
Figure 2. Top: seven models tested in Migrate analysis. Model 1: split with immigration to the east; 2: split without immigration; 3: immigration to the east only; 4: immigration to the east and west; 5: immigration to the west only; 6: split with immigration to the west only; 7: panmixia. Orange circles represent populations combined into a panhandle group and blue circles represent peninsular populations that were combined.
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Figure 3. Results from structure analysis of the entire dataset assuming admixture with correlated allele frequencies among populations and using location information as a prior. At K = 2, Coastal Plain and peninsular Florida populations formed separate clusters.
Figure 3. Results from structure analysis of the entire dataset assuming admixture with correlated allele frequencies among populations and using location information as a prior. At K = 2, Coastal Plain and peninsular Florida populations formed separate clusters.
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Table
Table 1. Analysis of molecular variance (AMOVA) using an infinite allele model (reported statistics are equivalent to Rho). Significance was tested using 10,000 permutations. Standard deviations of F-statistics were obtained through jackknifing over loci. A total of 95% confidence intervals of F-statistics were obtained through bootstrapping over loci.
Table 1. Analysis of molecular variance (AMOVA) using an infinite allele model (reported statistics are equivalent to Rho). Significance was tested using 10,000 permutations. Standard deviations of F-statistics were obtained through jackknifing over loci. A total of 95% confidence intervals of F-statistics were obtained through bootstrapping over loci.
Variance Component% Total VarianceF-StatF-Valuep-Value
Within population0.816------
Among population0.184RhoST0.1840.000
Table
Table 2. Models of demographic history specified for the Florida panhandle/peninsula divergence tested in Migrate. The null hypothesis (and simplest case) is that sampled ponds belong to a single panmictic population (Model 7). The best model (Model 1) has log Bayes factor (LBF) of 0.0 because the best model is used as a reference in Migrate.
Table 2. Models of demographic history specified for the Florida panhandle/peninsula divergence tested in Migrate. The null hypothesis (and simplest case) is that sampled ponds belong to a single panmictic population (Model 7). The best model (Model 1) has log Bayes factor (LBF) of 0.0 because the best model is used as a reference in Migrate.
ModelLog(mL)LBFModel Probability
1: Split with immigration to the east−62,432.900.001.0000
2: Split without immigration−63,342.03−909.130.0000
3: Immigration to the east only−68,137.96−5705.060.0000
4: Immigration to the east and west−68,665.67−6232.770.0000
5: Immigration to the west only−68,752.47−6319.570.0000
6: Split with immigration to the west only−77,835.94−15,403.040.0000
7: Panmixia−120,335.72−57,902.820.0000
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Devitt, T.J.; Enge, K.M.; Farmer, A.L.; Beerli, P.; Richter, S.C.; Hall, J.G.; Lance, S.L. Population Subdivision in the Gopher Frog (Rana capito) across the Fragmented Longleaf Pine-Wiregrass Savanna of the Southeastern USA. Diversity 2023, 15, 93. https://doi.org/10.3390/d15010093

AMA Style

Devitt TJ, Enge KM, Farmer AL, Beerli P, Richter SC, Hall JG, Lance SL. Population Subdivision in the Gopher Frog (Rana capito) across the Fragmented Longleaf Pine-Wiregrass Savanna of the Southeastern USA. Diversity. 2023; 15(1):93. https://doi.org/10.3390/d15010093

Chicago/Turabian Style

Devitt, Thomas J., Kevin M. Enge, Anna L. Farmer, Peter Beerli, Stephen C. Richter, Jeffrey G. Hall, and Stacey L. Lance. 2023. "Population Subdivision in the Gopher Frog (Rana capito) across the Fragmented Longleaf Pine-Wiregrass Savanna of the Southeastern USA" Diversity 15, no. 1: 93. https://doi.org/10.3390/d15010093

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