Legacy of historical disturbance shapes modern genetic diversity in a high-elevation salamander

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Wade, John A. Crawford, William E. Peterman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7623606/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Dec, 2025 Read the published version in Conservation Genetics → Version 1 posted 9 You are reading this latest preprint version Abstract Maintaining the genetic diversity of wildlife populations is important as reductions in genetic diversity can have negative consequences like reduced fitness caused by inbreeding depression or increased risk of extirpation. Anthropogenic disturbance can have long-lasting impacts on the genetic diversity of wildlife populations. Here, we evaluated how historic timber harvesting and settlement shape modern spatial patterns of genetic diversity in the narrow-range endemic red-cheeked salamander ( Plethodon jordani ) in the Great Smoky Mountains National Park (GSMNP). Using microsatellite genotypes from 549 individuals across 23 sites, we quantified genetic diversity, tested for isolation by distance, and assessed the relationship between genetic diversity and historic disturbance and elevation. We found spatial variation in genetic diversity and statistically significant isolation by distance. Sampling sites located near historically harvested or settled areas exhibited reduced genetic diversity, but this negative effect was moderated at high elevations where salamander densities are higher and microhabitats are more favorable. Additionally, historic disturbance was associated with reduced modern understory density, a habitat feature that positively influences salamander abundance. Our findings demonstrate that land-use legacies continue to shape both forest structure and the genetic diversity of a narrowly distributed amphibian nearly a century after large-scale timber harvest ceased. These results highlight the importance of incorporating historic landscape change into conservation planning, especially for high-elevation endemics whose long-term persistence may depend on maintaining genetic diversity and adaptive capacity. anthropogenic disturbance elevation heterozygosity microsatellites Plethodon jordani timber harvest Figures Figure 1 Figure 2 Figure 3 Introduction Habitat loss and fragmentation caused by anthropogenic activities are the primary factors driving modern biodiversity loss (Brooks et al. 2002 ; Caro et al. 2022 ). Human actions such as timber harvest have caused large scale declines in wildlife populations and left remaining populations isolated (e.g., Duguay et al. 2000 ; Karraker & Welsh Jr 2006 ; McIntyre et al. 2025 ). As populations become small and isolated, they are prone to rapid loss of genetic diversity through the natural process of genetic drift (Conner & Hartl 2004 ). Reduced levels of genetic diversity can further exacerbate biodiversity declines as affected populations have increased rates of inbreeding depression that leads to lower individual fitness and a higher probability of population extirpation (Booy et al. 2000 ; Reed & Frankham 2003 ; Vandewoestijne et al. 2008 ). Low standing genetic variation also means that populations may not have the adaptive capacity to respond to future environmental change (Jump et al. 2009 ; Markert et al. 2010 ). Therefore, assessing spatial patterns of genetic diversity and understanding how anthropogenic activities influence these patterns are primary goals in conservation genetics (Escudero et al. 2003 ; Allendorf et al. 2010 ). Population genetic analyses have consistently documented the negative effects of anthropogenic disturbance on genetic diversity of a variety of taxa (for thorough reviews see: Banks et al. 2013 ; Almeida-Rocha et al. 2020 ). The loss of forest cover due to timber harvest or other anthropogenic activities is a primary cause of the loss of population-level genetic diversity (Almeida‐Rocha et al. 2020). A major component of this loss occurs through the reduction of gene flow between habitat patches (e.g., Crawford et al. 2016 ; Gómez-Fernández et al. 2016 ; Jangjoo et al. 2016 ) and the importance of forest cover for maintaining connectivity and gene flow between isolated habitat patches has been well documented in the field of landscape genetics for a variety of taxa (e.g., Munshi‐South 2012; Khimoun et al. 2017 ; Wade et al. 2025 ). Timber harvest can also have both immediate and long-term consequences for the genetic diversity of wildlife populations. Timber harvest may negatively impact genetic diversity directly through large mortality events (and subsequent genetic bottlenecks) in tree species (André et al. 2008 ; Soares et al. 2019 ; Roque et al. 2023 ) and the animals that depend on them (Andersen et al. 2004 ; Alexander et al. 2011 ; González-Fernández et al. 2019 ). Additionally, timber harvesting may reduce the size of forest patches and alter key biotic and abiotic conditions, including plant community composition, soil moisture, leaf litter depth, understory density, and canopy cover (Matlack 1993 ; Hermy & Verheyen 2007 ; Knapp et al. 2013 ). These changes may in turn lead to lower carrying capacities of habitat patches and altered survival rates of organisms occupying these modified patches (e.g., Nupp & Swihart 1996 ; Peterman et al. 2011 ; Thornton et al. 2011 ), leading to a more rapid loss of genetic diversity (Frankham 1996 ). Studies assessing the impact of forest loss on genetic diversity and gene flow have often found evidence for a time lag between when the disturbance occurred and when the effect can be detected using genetic analyses (e.g., Richter et al. 2013 ; Reisch et al. 2017 ; Antunes et al. 2023 ). In other words, modern patterns of genetic diversity across the landscape may be best explained by historic disturbance patterns. Therefore, when attempting to ascertain the effect of timber harvest on genetic diversity, it is important to utilize historic disturbance data rather than estimates of modern forest cover that may be more readily available (e.g., from the National Land Cover Database). Amphibians are the most threatened vertebrate group on earth and are particularly sensitive to disturbances like loss of forest cover (Beebee & Griffiths 2005 ). Previous work has shown that forest loss poses a risk to amphibian abundance (e.g., Pough et al. 1987 ; Petranka et al. 1994 ; Semlitsch et al. 2007 ) and species diversity (e.g., Ernst et al. 2006 ; Konopik et al. 2015 ; Ribeiro Jr et al. 2018 ), but considerably less work has been devoted toward understanding how deforestation influences genetic diversity (but see: Curtis & Taylor 2004 ; Richter et al. 2013 ; Haugen et al. 2024 ). Among amphibians, plethodontid, or lungless, salamanders are particularly sensitive to land use changes as activities like timber harvesting can lead to drier microhabitats that are not conducive to cutaneous respiration. There has been debate concerning the extent to which timber harvest affects plethodontid salamander populations and the time to recovery (Petranka et al. 1993 ; Ash 1997 ; Petranka 1999 ). However, there is strong evidence salamander abundance is influenced by the disturbance history of the forests they inhabit (Herbeck & Larsen 1999 ; Hicks & Pearson 2003 ; Cosentino & Brubaker 2018 ), with detectable population impacts persisting > 100 years post-harvest (Connette & Semlitsch 2013 ). What is not well-understood is how the genetic diversity of plethodontid salamander populations is affected by timber harvesting. Global environmental change, such as shifting temperature and moisture regimes, severely threatens high-elevation endemic species as their potential for range shifts is limited by the elevation of mountains within their range (Dirnböck et al. 2011 ). Consequently, a key conservation priority is to maintain their adaptive potential by protecting and enhancing standing genetic variation (Ofori et al. 2017 ; Foden et al. 2019 ; Hyman et al. 2025 ). Here, we assessed spatial patterns of genetic diversity in a narrow-range endemic lungless salamander, the red-cheeked salamander ( Plethodon jordani ), within the Great Smoky Mountains National Park (GSMNP). Specifically, we utilized historic disturbance data to ask: (1) how are modern patterns of genetic diversity shaped by historic disturbance and (2) how might inferences change along an elevational gradient? We predicted that populations of red-cheeked salamanders in areas affected by historic timber harvesting would have lower genetic diversity, but these effects would be less pronounced at higher elevations where microhabitat conditions may be insulated from the effects of deforestation. Methods Study site and focal organism Our study took place in the GSMNP in Tennessee and North Carolina, USA. The GSMNP, ranging from 267–2025 m in elevation, encompasses many different forest types including cove hardwood forests, hemlock forests, and high elevation spruce-fir forests (Jenkins 2007 ). Prior to being declared a United States National Park in 1934, commercial timber harvest occurred in the GSMNP (nearly 80% of the GSMNP had been logged by 1930; Tuttle & White 2016 ) and numerous small settlements were spread across the area. The red-cheeked salamander ( Plethodon jordani ) is endemic to high elevation areas of the GSMNP between 678 and 2025 m (Hocking et al. 2021 ). At higher elevations, red-cheeked salamanders are exceptionally abundant; for example, Merchant ( 1972 ) estimated densities of nearly one million red-cheeked salamanders per km 2 . Like all woodland salamanders (genus Plethodon ), red-cheeked salamanders are fully terrestrial, direct developing, and sensitive to water loss. Therefore, they rely on cool, moist microhabitats for survival (Heatwole 1962 ; Feder 1983 ; Peterman & Semlitsch 2014 ). Population Sampling We collected 0.5 cm tail tissue samples from red-cheeked salamanders at 23 locations along US Route 441 and Kuwohi Road in the GSMNP (Fig. 1 ), preserved them in 95% ethanol, and stored them at − 20°C until DNA extraction. At each location, samples were collected within 50 m of one another and always on the same side of a road, trail, or stream that could potentially act as a barrier to movement and gene flow (Marsh et al. 2005 ; Marsh et al. 2008 ). The minimum distance between sample locations and the elevation of sample locations ranged from 222–2,137m and 669–2018 m, respectively. All samples were collected during nocturnal surveys in July 2012. Laboratory Methods We extracted DNA using the Wizard SV 96 Genomic DNA Purification System (Promega, Madison, WI, USA), following the manufacturer’s protocols. PCR amplification targeted 11 tetra- and pentanucleotide microsatellite loci (Spatola et al. 2013 ) with fluorescently labeled primers arranged into two multiplex reactions (Supplement 1). Products were sized on an ABI 3730xl DNA Analyzer (Applied Biosystems, Foster City, CA, USA) with Liz 600 size standard at the University of Missouri DNA Core Facility, and alleles were scored using GENEMARKER (v.1.97; Softgenetics, State College, PA, USA). Population Genetic Analyses We tested for deviations from Hardy-Weinberg equilibrium (HWE) and linkage disequilibrium among loci using Genepop 4.2 (Raymond & Rousset 1995 ; Rousset 2008 ), with 250 batches of 2,500 iterations following a 2,500-iteration burnin. Potential null alleles were identified using Micro-Checker (Van Oosterhout et al. 2004 ). We calculated rarefied allelic richness with HP-RARE as a measure of genetic diversity (Kalinowski 2005 ). We also calculated expected heterozygosity (H E ) for each sampling site using the hierfstat package in R as another measure of genetic diversity (Goudet 2005 ). However, A R and H E were highly correlated in our dataset (r = 0.96), and we thus chose to use H E for all downstream analyses. We calculated estimates of F ST between each sampling site using the hierstat package. We then assessed isolation by distance (IBD) within our study area by comparing the genetic differentiation (F ST ) and geographic distance between each sampling site using a mantel test in the “ecodist” package in R (Goslee & Urban 2007 ). Models of Genetic Diversity To assess the impact of disturbance history and elevation on red-cheeked salamander genetic diversity, we fit Bayesian regression models using the brms package in R (Bürkner 2017 ). To evaluate historic disturbance, we used a categorical spatial dataset provided by the GSMNP that was separated into the following 5 categories: heavy logging, light logging, settlement, selective cut, and unaffected. For our analyses we simplified this layer by considering heavy logging, light logging, settlement as “disturbed” and selective cut and unaffected as “undisturbed.” We then fit 4 candidate models including: only the effect of disturbance, only the effect of elevation, the effect of disturbance and elevation, and the effect of disturbance and elevation with an interaction term. We also included a Gaussian process term in each model to account for potential spatial autocorrelation in our genetic data (Rasmussen 2003 ). All variables were scaled and centered prior to analyses. We used weak, regularizing priors and ran four chains with 20,000 iterations (4000 warmup) for each model (McElreath 2018 ). We assessed model convergence by visually inspecting trace plots and Gelman–Rubin statistics (Rhat < 1.05). We compared model performance using leave one out cross validation (LOO), expected log pointwise predictive density (ELPD; Vehtari et al. 2017 ), and Bayesian R 2 calculated using the “bayes_R2” function in brms. To assess the proper scale of effect of disturbance in our candidate models, we used a multi-scale focal site study design (see: Jackson & Fahrig 2015 ; Martin 2018 ) in which we calculated percent disturbance within 30 concentric buffers (100–3000m) around each data point. We then evaluated the performance of models based on each buffer distance using LOO and ELPD to determine the appropriate scale to model disturbance in our analyses. Effect of Historic Disturbance on Modern Forest Characteristics We also examined how historic timber harvest has shaped modern forest vegetation structure and soil conditions. These habitat features are known to influence plethodontid salamanders (e.g., Harper & Guynn Jr 1999 ; Maerz et al. 2009 ; Wade et al. 2021 ) and may help explain how past timber harvest indirectly influences current salamander population dynamics (Cosentino & Brubaker 2018 ). Specifically, Hocking et al. ( 2021 ) found that both understory density and leaf litter depth were positively associated with red-cheeked salamander abundance in the GSMNP. Therefore, we obtained a raster dataset of forest understory density from the GSMNP and used field-derived measurements of leaf litter depth at each of our sampling sites (see: Hocking et al. 2021 ). We then fit simple Bayesian regression models assessing the relationship between historic disturbance and elevation and understory density (within our optimized buffer distance) and leaf litter depth at sampling sites. Results Population Genetic Analyses We sampled a total of 549 individual red-cheeked salamanders across 23 sampling sites with a range of 7–38 samples per site (mean = 23.87; Table 1 ). There were 6–93 alleles in each of the 11 microsatellite loci (mean = 36.4 ± 28.8) across all samples. Missingness was 0.76% across the entire data set and ranged from 0–3.21% for individual loci. Most loci were in Hardy-Weinberg equilibrium across all populations with no consistent deviations for any locus. There was no evidence of linkage disequilibrium. The average H E across sampling sites was 0.788 and varied between 0.658 and 0.835. The average A R across our sampling sites was 6.64 and varied between 4.45 and 7.39. The average F ST between our sampling sites was 0.032 and ranged between 0 and 0.113. Our test for IBD revealed a significant, positive association (Mantel r = 0.69; p < 0.01) between genetic differentiation and geographic distance within the GSMNP (Fig. 2 ). Table 1 Sample size, elevation, expected heterozygosity (H E ) and allelic richness (A R ) for each red-cheeked salamander ( Plethodon jordani ) sampling site in the GSMNP. Population ID Sample Size Elevation (m) Expected Heterozygosity (H E ) Rarefied Allelic Richness (A R ) 61s 21 669 0.716 5.15 3s 7 713 0.658 4.45 45s 24 763 0.807 6.71 5s 21 765 0.806 6.75 46s 24 807 0.826 7.24 7s 15 834 0.749 6.35 8Bs 27 871 0.76 6.24 9s 26 985 0.769 6.45 10s 25 1008 0.793 6.86 49s 23 1048 0.819 7.02 50s 24 1050 0.817 6.9 11s 27 1057 0.786 6.6 13s 31 1132 0.765 6.19 51s 27 1176 0.816 7.13 36s 38 1257 0.778 6.7 33s 23 1321 0.821 6.87 15s 21 1375 0.758 6.37 16s 25 1416 0.762 6.4 19s 23 1488 0.819 7.3 21Bs 25 1575 0.815 7.39 23s 25 1716 0.825 7.1 25As 25 1850 0.835 7.31 28s 22 2019 0.823 7.24 Effects of Disturbance and Elevation on Genetic Diversity Based on our assessment of scale of effect, historic disturbance was best modeled at a scale of 2600 m (Figs. S1–S2) and we used this amount of disturbance within 2600 m of sampling sites for all analyses. We found that historic disturbance and elevation were moderately correlated (r = − 0.49), but not enough to preclude us from including them together in the same model. The top performing model in our analyses included historic disturbance, elevation, and an interaction term as predictors of genetic diversity (Table 2 ). Elevation alone performed relatively poorly and when added to a model that included disturbance, this model performed worse than disturbance alone and added very little explanatory power (Table 2 ). Based on our top performing model, the relationship between historic disturbance and modern genetic diversity is negative (β = − 0.449, CI = [–0.863, − 0.003], probability of direction [POD] = 0.976) and the relationship between elevation and genetic diversity is potentially positive, but with a high degree of uncertainty (β = 0.227, CI = [–0.183, 0.627], POD = 0.875). A positive interaction term (β = 0.357, CI = [–0.128, 0.816], POD = 0.931) implies that this negative effect of disturbance may be buffered at high elevation (Fig. 3 ). Table 2 Performance of each candidate model based on difference in ELPD from the top model, the standard error (SE) of that difference, and Bayesian R 2 (SE). Model ELPD Difference SE of Difference Bayesian R 2 Disturbance*Elevation 0 0 0.91 (0.13) Disturbance –3.0 1.5 0.89 (0.16) Disturbance + Elevation –3.6 1.6 0.89 (0.15) Elevation –6.8 2.4 0.85 (0.18) Null (Gaussian process only) –11.5 3.5 0.74 (0.20) Effect of Historic Disturbance on Modern Forest Characteristics We found that historic disturbance had a negative association (β = − 0.82, CI = [–1.09, − 0.55]) with modern estimates of understory density surrounding our sampling sites (Fig. S3) and explained a large amount of the variation in understory density (R 2 = 0.65). On the other hand, elevation explained very little variation in understory density (R 2 = 0.11) and performed poorly based on ELPD (difference = − 11.3, SE of difference = 3.1). This implies that modern variation in understory density may be largely driven by historic disturbance in the GSMNP. We did not find evidence that historic disturbance (β = − 0.03, CI = [–0.49, 0.43]) or elevation (β = − 0.08, CI = [–0.54, 0.38]) were associated with our field-derived estimates of leaf litter depth in the GSMNP. Discussion In this study we assessed spatial patterns of genetic diversity of the high elevation endemic red-cheeked salamander in the GSMNP. We found evidence for spatial variation in genetic diversity in this species across the GSMNP and an effect of isolation by distance. We also found that red-cheeked salamanders in areas that had historic timber harvest and settlement tended to have lower levels of genetic diversity (H E ), but that this effect may be buffered against at high elevations. Additionally, we found that historic disturbance was negatively associated with modern levels of understory density, an important factor in predicting red-cheeked salamander abundance. Altogether, our results provide evidence that historic anthropogenic activities like timber harvest have long-term impacts on forests and the genetic diversity of wildlife populations that inhabit them. Effect of Disturbance and Elevation on Genetic Diversity Although large-scale timber harvest within the GSMNP has not occurred since the park’s dedication in 1940 (Pyle 1988 ), we find evidence that these historic land use practices are still relevant for understanding modern patterns of genetic diversity. Red-cheeked salamander sampling sites that were located in or near areas of historic timber harvest/settlement tended to have lower levels of genetic diversity. This pattern may be explained by both the primary impacts of historic disturbance and legacy impacts on forest characteristics. In areas that were heavily harvested, red-cheeked salamanders may have been entirely extirpated or their populations sizes may have been significantly reduced (e.g., Ash 1988 ; Grialou et al. 2000 ; Knapp et al. 2003 ). If these declines caused by primary timber harvest occurred, they may have resulted in genetic bottlenecks or founder effects that are still detectable in modern populations (McCauley 1991 ; Landergott et al. 2001 ; Bouzat 2010 ). Timber harvest may also alter red-cheeked salamander genetic diversity over time by changing forest characteristics. As disturbed forests recover, they may have less desirable microhabitat conditions for plethodontid salamanders (hotter and dryer), leading to lower survival rates and depressed carrying capacity in historically disturbed areas (Cosentino & Brubaker 2018 ; Gade & Peterman 2019 ). Additionally, forest disturbance may directly influence arthropod diversity and abundance, and reduced prey availability may further depress salamander population size in historically disturbed areas (Jeffries et al. 2006 ; Summerville et al. 2009 ; Cox et al. 2022 ). Small populations lose genetic diversity more rapidly through genetic drift (Conner & Hartl 2004 ), and this reduced diversity can persist for decades after a disturbance. On the other hand, we found limited evidence that elevation was associated with red-cheeked salamander genetic diversity, despite an established positive relationship with abundance (Hocking et al. 2021 ). However, we did find evidence for a positive interaction effect between elevation and disturbance. In other words, populations occurring at high elevations may be somewhat insulated from the negative effects of historic timber harvest (Fig. 3 ). High elevation areas of the GSMNP are particularly cool and wet which limits the ability of microclimatic variation to alter plethodontid salamander distribution and abundance (Dodd 2004 ; Gade & Peterman 2019 ; Hocking et al. 2021 ). Thus, red-cheeked salamanders may have persisted in these areas despite forest alterations. Favorable microhabitat conditions between disturbed sites and adjacent areas may also lead to a higher rate of successful dispersal and gene flow (Peterman et al. 2014 ; Mulder et al. 2019 ; Wade et al. 2025 ), and this increased gene flow may compensate for losses in genetic diversity that originally occurred during the timber harvest process (Gómez-Fernández et al. 2016 ; Jangjoo et al. 2016 ). Effect of Disturbance on Modern Forest Characteristics We found that forest understory density was lower in areas where historic timber harvest and settlement occurred (Fig. S2 ). Large-scale timber harvest can influence forest understory dynamics for many years post-harvest (Ramovs & Roberts 2003 ; Halpern et al. 2012 ; Munteanu et al. 2015 ) and new growth forest that eventually succeeds timber-harvested areas may have lower abundance and diversity of understory vegetation due to higher overstory density and subsequent heavy shading (Knapp et al. 2013 ). Previous studies have indicated that salamander abundance is positively associated with higher understory density in forests (Pough et al. 1987 ; Riedel et al. 2008 ; Hocking et al. 2021 ), meaning that reduced understory density may represent a mechanism for a modern depression of population size and genetic diversity. A high density of understory plants can help create the vital cool, moist soil conditions that are essential for maintaining plethodontid salamander populations, particularly in summer when red-cheeked salamanders are most active (Clinton 2003 ; Stickley & Fraterrigo 2021 ). Forest understory also plays an important role in terrestrial salamander biology, as climbing behavior is a major part of salamander thermoregulation, moisture retention, and foraging activities (Pough et al. 1987 ; Homyack et al. 2011 ; McEntire & Maerz 2019 ). Conclusions and Conservation Implications We found that despite decades of ecological recovery, organisms inhabiting historically altered landscapes like the GSMNP may still suffer from reduced genetic diversity. The genetic diversity of populations in altered landscapes may eventually return to pre-disturbance levels (especially with sufficient gene flow), but this process is slow, and it may take numerous generations before genetic diversity in recovered landscapes is higher than that it is in currently degraded landscapes (Wei et al. 2023 ; Mualim et al. 2024 ). This genetic recovery lag is particularly concerning for high elevation endemic organisms like the red-cheeked salamander. As the pace of anthropogenic climate change increases, these organisms must adapt to changing conditions as continued upward migration into more habitable zones is often not an option (Dirnböck et al. 2011 ). Therefore, high-elevation endemics in disturbed areas may be disproportionately impacted by modern depression of genetic diversity as they face rapid climatic changes to their environment. In addition to other established drivers in the spatial distribution of genetic diversity such as the distance to range edge (i.e., the central-marginal hypothesis; Eckert et al. 2008 ; Micheletti & Storfer 2015 ; Ursenbacher et al. 2015 ) or latitudinal patterns (Johansson et al. 2005 ; Howes & Lougheed 2008 ; Adams & Hadly 2013 ), we can use historic disturbance data to help predict the spatial patterns of genetic diversity for organisms inhabiting landscapes with a matrix of historic land use intensity. Our results indicate that populations inhabiting historically undisturbed regions should be protected as they may serve as a source of future genetic variation. This transfer of genetic diversity may happen through natural migration or human-mediated genetic rescue (Whiteley et al. 2015 ). Additionally, the enhancement of ecological connectivity between historically disturbed and unspoiled landscapes through direct conservation management actions such as the establishment of corridors (e.g., Mech & Hallett 2001 ; Christie & Knowles 2015 ; Stickley et al. 2025 ) or creation of new habitat cores that serve as stepping stones (e.g., Karstens et al. 2022 ; Wang et al. 2023 ; Carter et al. 2025 ) can be especially powerful as increased rates of gene flow may help to offset historic depression of population size and genetic diversity (Slatkin 1985 ; Navascués & Emerson 2007 ; Jangjoo et al. 2016 ). Further assessment of the impacts of historic anthropogenic disturbance of different ages, in different landscapes, and with varying taxa will help establish well-documented patterns that can be used to directly inform conservation management strategies. Declarations Funding Funding for this project was provided by the National Geographic Society Waitt Grant Program (grant W203-11). Bryce S. Wade was also supported by a National Science Foundation Graduate Research Fellowship during this work. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contribution Data collection and laboratory work were performed by William E. Peterman and John A. Crawford. Analyses were conducted by Bryce S. Wade. The first draft of the manuscript was written by Bryce S. Wade and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgement We thank P. Super and the GSMNP for their support of this work. We thank D.J. Hocking and J.R. Milanovich for their assistance in the field and L.R. Pauley for their assistance in the lab. We also thank C.D. Cousins, L. Dixson, B.M. Fitzpatrick, J.M. Fleming, J.A. Fordyce, and B. Oye for their helpful review of this manuscript. All research was conducted in accordance with animal care protocols of the National Park Service animal care and use committee (permit #SER_GRSM_Crawford_Salamander_2012), and all scientific activities were conducted under permits from the National Park Service (permit GRSM-2012-SCI-2244), North Carolina Wildlife Resources Commission (permit 12-SC00602), and Tennessee Wildlife Resources Agency (permit 3680). Data Availability The datasets generated during and/or analyzed during the current study are available from the corresponding authors on reasonable request. References Adams RI, Hadly EA (2013) Genetic diversity within vertebrate species is greater at lower latitudes. Evolutionary Ecology, 27, 133-143. Alexander LC, Hawthorne DJ, Palmer MA, Lamp WO (2011) Loss of genetic diversity in the North American mayfly Ephemerella invaria associated with deforestation of headwater streams. Freshwater Biology, 56, 1456-1467. 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12:42:45","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":155089,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7623606/v1/9fc1507a9f948f7b313d948c.png"},{"id":93331788,"identity":"c6120adb-2ff2-4eb5-aa57-fd1b604d09a5","added_by":"auto","created_at":"2025-10-12 12:34:45","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":190286,"visible":true,"origin":"","legend":"","description":"","filename":"ad375f00b8ea47b393ef4669cd549fee1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7623606/v1/ef30cd78fdd670f3e1e0c2a7.xml"},{"id":93331786,"identity":"8febdfe1-0867-4fc9-b3cb-60980b593f08","added_by":"auto","created_at":"2025-10-12 12:34:45","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":193532,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7623606/v1/3743c9823d9441e4f02e2d2f.html"},{"id":93331772,"identity":"2728ab6f-c675-4400-88b8-1cecc6570f41","added_by":"auto","created_at":"2025-10-12 12:34:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":555470,"visible":true,"origin":"","legend":"\u003cp\u003eA map of red-cheeked salamander (\u003cem\u003ePlethodon jordani\u003c/em\u003e) sampling localities (red points) along US Route 441 and Kuwohi Road (a; solid black lines) in the Great Smoky Mountains National Park (GSMNP). The bottom left inset (b) shows the GSMNP’s location within the southeastern United States and the photo in the bottom right corner (c) depicts an adult red-cheeked salamander from the GSMNP (Photograph by Bryce S. Wade).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7623606/v1/593aaeb19782b6eef95f8e58.png"},{"id":93331773,"identity":"53b8c966-c7a4-449d-9aaa-1c522e6de32f","added_by":"auto","created_at":"2025-10-12 12:34:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":137183,"visible":true,"origin":"","legend":"\u003cp\u003eAn isolation-by-distance plot for red-cheeked salamanders (\u003cem\u003ePlethodon jordani\u003c/em\u003e) in the Great Smoky Mountains National Park. Each point corresponds to pairwise geographic and genetic differentiation (F\u003csub\u003eST\u003c/sub\u003e/1 − F\u003csub\u003eST\u003c/sub\u003e) between two sites and the line represents a line of best fit for the data\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7623606/v1/1b341323f346599bea0d55bd.png"},{"id":93332240,"identity":"b527081e-94a2-4b89-927a-ea99eef48e2b","added_by":"auto","created_at":"2025-10-12 12:42:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":343706,"visible":true,"origin":"","legend":"\u003cp\u003eThe predicted association between percent historic disturbance (within 2600 m of sampling sites) on modern estimates of red-cheeked salamander (\u003cem\u003ePlethodon jordani\u003c/em\u003e) expected heterozygosity (H\u003csub\u003eE\u003c/sub\u003e) in the Great Smoky Mountains National Park (GSMNP) at 763 m (10\u003csup\u003eth\u003c/sup\u003e percentile of elevation; a), 1056 m (50\u003csup\u003eth\u003c/sup\u003e percentile of elevation; b), and 1689 m (90\u003csup\u003eth\u003c/sup\u003e percentile of elevation; c) based on our top-performing model (Disturbance*Elevation). The blue line represents the posterior mean regression line a 95% credible interval. (d) A map of red-cheeked salamander sampling sites in the GSMNP colored by their H\u003csub\u003eE\u003c/sub\u003e. Red areas represent the extent of historic timber harvest and settlement\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7623606/v1/2e1634986582b2b8d6137909.png"},{"id":99172403,"identity":"7e8bd271-64a6-414b-a392-ad9fff7a5aec","added_by":"auto","created_at":"2025-12-29 16:08:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2049932,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7623606/v1/fa3e8c1f-7add-420b-b67f-f70d5b69cd2e.pdf"},{"id":93331774,"identity":"91bd2b9e-59d9-4a6a-83a1-1da9a62781fd","added_by":"auto","created_at":"2025-10-12 12:34:44","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":16602,"visible":true,"origin":"","legend":"","description":"","filename":"pcrsupplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-7623606/v1/e8787a9ac00ca0f518716e37.docx"},{"id":93331789,"identity":"5ba8259a-7de5-4ff9-b7f4-c7722763656f","added_by":"auto","created_at":"2025-10-12 12:34:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15609984,"visible":true,"origin":"","legend":"","description":"","filename":"jordanicongensupplementalfigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-7623606/v1/0ece7dd64861ca9682d6386a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Legacy of historical disturbance shapes modern genetic diversity in a high-elevation salamander","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHabitat loss and fragmentation caused by anthropogenic activities are the primary factors driving modern biodiversity loss (Brooks et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Caro et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Human actions such as timber harvest have caused large scale declines in wildlife populations and left remaining populations isolated (e.g., Duguay et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Karraker \u0026amp; Welsh Jr \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; McIntyre et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). As populations become small and isolated, they are prone to rapid loss of genetic diversity through the natural process of genetic drift (Conner \u0026amp; Hartl \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Reduced levels of genetic diversity can further exacerbate biodiversity declines as affected populations have increased rates of inbreeding depression that leads to lower individual fitness and a higher probability of population extirpation (Booy et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Reed \u0026amp; Frankham \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Vandewoestijne et al. \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Low standing genetic variation also means that populations may not have the adaptive capacity to respond to future environmental change (Jump et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Markert et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Therefore, assessing spatial patterns of genetic diversity and understanding how anthropogenic activities influence these patterns are primary goals in conservation genetics (Escudero et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Allendorf et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePopulation genetic analyses have consistently documented the negative effects of anthropogenic disturbance on genetic diversity of a variety of taxa (for thorough reviews see: Banks et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Almeida-Rocha et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The loss of forest cover due to timber harvest or other anthropogenic activities is a primary cause of the loss of population-level genetic diversity (Almeida‐Rocha et al. 2020). A major component of this loss occurs through the reduction of gene flow between habitat patches (e.g., Crawford et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; G\u0026oacute;mez-Fern\u0026aacute;ndez et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Jangjoo et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and the importance of forest cover for maintaining connectivity and gene flow between isolated habitat patches has been well documented in the field of landscape genetics for a variety of taxa (e.g., Munshi‐South 2012; Khimoun et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wade et al. \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTimber harvest can also have both immediate and long-term consequences for the genetic diversity of wildlife populations. Timber harvest may negatively impact genetic diversity directly through large mortality events (and subsequent genetic bottlenecks) in tree species (Andr\u0026eacute; et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Soares et al. \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Roque et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and the animals that depend on them (Andersen et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Alexander et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Gonz\u0026aacute;lez-Fern\u0026aacute;ndez et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, timber harvesting may reduce the size of forest patches and alter key biotic and abiotic conditions, including plant community composition, soil moisture, leaf litter depth, understory density, and canopy cover (Matlack \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Hermy \u0026amp; Verheyen \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Knapp et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These changes may in turn lead to lower carrying capacities of habitat patches and altered survival rates of organisms occupying these modified patches (e.g., Nupp \u0026amp; Swihart \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Peterman et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Thornton et al. \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), leading to a more rapid loss of genetic diversity (Frankham \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Studies assessing the impact of forest loss on genetic diversity and gene flow have often found evidence for a time lag between when the disturbance occurred and when the effect can be detected using genetic analyses (e.g., Richter et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Reisch et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Antunes et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In other words, modern patterns of genetic diversity across the landscape may be best explained by historic disturbance patterns. Therefore, when attempting to ascertain the effect of timber harvest on genetic diversity, it is important to utilize historic disturbance data rather than estimates of modern forest cover that may be more readily available (e.g., from the National Land Cover Database).\u003c/p\u003e\u003cp\u003eAmphibians are the most threatened vertebrate group on earth and are particularly sensitive to disturbances like loss of forest cover (Beebee \u0026amp; Griffiths \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Previous work has shown that forest loss poses a risk to amphibian abundance (e.g., Pough et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Petranka et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Semlitsch et al. \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and species diversity (e.g., Ernst et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Konopik et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ribeiro Jr et al. \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), but considerably less work has been devoted toward understanding how deforestation influences genetic diversity (but see: Curtis \u0026amp; Taylor \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Richter et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Haugen et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Among amphibians, plethodontid, or lungless, salamanders are particularly sensitive to land use changes as activities like timber harvesting can lead to drier microhabitats that are not conducive to cutaneous respiration. There has been debate concerning the extent to which timber harvest affects plethodontid salamander populations and the time to recovery (Petranka et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Ash \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Petranka \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). However, there is strong evidence salamander abundance is influenced by the disturbance history of the forests they inhabit (Herbeck \u0026amp; Larsen \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Hicks \u0026amp; Pearson \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Cosentino \u0026amp; Brubaker \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), with detectable population impacts persisting\u0026thinsp;\u0026gt;\u0026thinsp;100 years post-harvest (Connette \u0026amp; Semlitsch \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). What is not well-understood is how the genetic diversity of plethodontid salamander populations is affected by timber harvesting.\u003c/p\u003e\u003cp\u003eGlobal environmental change, such as shifting temperature and moisture regimes, severely threatens high-elevation endemic species as their potential for range shifts is limited by the elevation of mountains within their range (Dirnb\u0026ouml;ck et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Consequently, a key conservation priority is to maintain their adaptive potential by protecting and enhancing standing genetic variation (Ofori et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Foden et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hyman et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Here, we assessed spatial patterns of genetic diversity in a narrow-range endemic lungless salamander, the red-cheeked salamander (\u003cem\u003ePlethodon jordani\u003c/em\u003e), within the Great Smoky Mountains National Park (GSMNP). Specifically, we utilized historic disturbance data to ask: (1) how are modern patterns of genetic diversity shaped by historic disturbance and (2) how might inferences change along an elevational gradient? We predicted that populations of red-cheeked salamanders in areas affected by historic timber harvesting would have lower genetic diversity, but these effects would be less pronounced at higher elevations where microhabitat conditions may be insulated from the effects of deforestation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy site and focal organism\u003c/h2\u003e\u003cp\u003eOur study took place in the GSMNP in Tennessee and North Carolina, USA. The GSMNP, ranging from 267\u0026ndash;2025 m in elevation, encompasses many different forest types including cove hardwood forests, hemlock forests, and high elevation spruce-fir forests (Jenkins \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Prior to being declared a United States National Park in 1934, commercial timber harvest occurred in the GSMNP (nearly 80% of the GSMNP had been logged by 1930; Tuttle \u0026amp; White \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and numerous small settlements were spread across the area. The red-cheeked salamander (\u003cem\u003ePlethodon jordani\u003c/em\u003e) is endemic to high elevation areas of the GSMNP between 678 and 2025 m (Hocking et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). At higher elevations, red-cheeked salamanders are exceptionally abundant; for example, Merchant (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e1972\u003c/span\u003e) estimated densities of nearly one million red-cheeked salamanders per km\u003csup\u003e2\u003c/sup\u003e. Like all woodland salamanders (genus \u003cem\u003ePlethodon\u003c/em\u003e), red-cheeked salamanders are fully terrestrial, direct developing, and sensitive to water loss. Therefore, they rely on cool, moist microhabitats for survival (Heatwole \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1962\u003c/span\u003e; Feder \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Peterman \u0026amp; Semlitsch \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePopulation Sampling\u003c/h3\u003e\n\u003cp\u003eWe collected 0.5 cm tail tissue samples from red-cheeked salamanders at 23 locations along US Route 441 and Kuwohi Road in the GSMNP (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), preserved them in 95% ethanol, and stored them at \u0026minus;\u0026thinsp;20\u0026deg;C until DNA extraction. At each location, samples were collected within 50 m of one another and always on the same side of a road, trail, or stream that could potentially act as a barrier to movement and gene flow (Marsh et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Marsh et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The minimum distance between sample locations and the elevation of sample locations ranged from 222\u0026ndash;2,137m and 669\u0026ndash;2018 m, respectively. All samples were collected during nocturnal surveys in July 2012.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eLaboratory Methods\u003c/h3\u003e\n\u003cp\u003eWe extracted DNA using the Wizard SV 96 Genomic DNA Purification System (Promega, Madison, WI, USA), following the manufacturer\u0026rsquo;s protocols. PCR amplification targeted 11 tetra- and pentanucleotide microsatellite loci (Spatola et al. \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) with fluorescently labeled primers arranged into two multiplex reactions (Supplement 1). Products were sized on an ABI 3730xl DNA Analyzer (Applied Biosystems, Foster City, CA, USA) with Liz 600 size standard at the University of Missouri DNA Core Facility, and alleles were scored using GENEMARKER (v.1.97; Softgenetics, State College, PA, USA).\u003c/p\u003e\n\u003ch3\u003ePopulation Genetic Analyses\u003c/h3\u003e\n\u003cp\u003eWe tested for deviations from Hardy-Weinberg equilibrium (HWE) and linkage disequilibrium among loci using Genepop 4.2 (Raymond \u0026amp; Rousset \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Rousset \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), with 250 batches of 2,500 iterations following a 2,500-iteration burnin. Potential null alleles were identified using Micro-Checker (Van Oosterhout et al. \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). We calculated rarefied allelic richness with HP-RARE as a measure of genetic diversity (Kalinowski \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). We also calculated expected heterozygosity (H\u003csub\u003eE\u003c/sub\u003e) for each sampling site using the hierfstat package in R as another measure of genetic diversity (Goudet \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). However, A\u003csub\u003eR\u003c/sub\u003e and H\u003csub\u003eE\u003c/sub\u003e were highly correlated in our dataset (r\u0026thinsp;=\u0026thinsp;0.96), and we thus chose to use H\u003csub\u003eE\u003c/sub\u003e for all downstream analyses. We calculated estimates of F\u003csub\u003eST\u003c/sub\u003e between each sampling site using the hierstat package. We then assessed isolation by distance (IBD) within our study area by comparing the genetic differentiation (F\u003csub\u003eST\u003c/sub\u003e) and geographic distance between each sampling site using a mantel test in the \u0026ldquo;ecodist\u0026rdquo; package in R (Goslee \u0026amp; Urban \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eModels of Genetic Diversity\u003c/h3\u003e\n\u003cp\u003eTo assess the impact of disturbance history and elevation on red-cheeked salamander genetic diversity, we fit Bayesian regression models using the brms package in R (B\u0026uuml;rkner \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). To evaluate historic disturbance, we used a categorical spatial dataset provided by the GSMNP that was separated into the following 5 categories: heavy logging, light logging, settlement, selective cut, and unaffected. For our analyses we simplified this layer by considering heavy logging, light logging, settlement as \u0026ldquo;disturbed\u0026rdquo; and selective cut and unaffected as \u0026ldquo;undisturbed.\u0026rdquo; We then fit 4 candidate models including: only the effect of disturbance, only the effect of elevation, the effect of disturbance and elevation, and the effect of disturbance and elevation with an interaction term. We also included a Gaussian process term in each model to account for potential spatial autocorrelation in our genetic data (Rasmussen \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAll variables were scaled and centered prior to analyses. We used weak, regularizing priors and ran four chains with 20,000 iterations (4000 warmup) for each model (McElreath \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). We assessed model convergence by visually inspecting trace plots and Gelman\u0026ndash;Rubin statistics (Rhat\u0026thinsp;\u0026lt;\u0026thinsp;1.05). We compared model performance using leave one out cross validation (LOO), expected log pointwise predictive density (ELPD; Vehtari et al. \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and Bayesian R\u003csup\u003e2\u003c/sup\u003e calculated using the \u0026ldquo;bayes_R2\u0026rdquo; function in brms. To assess the proper scale of effect of disturbance in our candidate models, we used a multi-scale focal site study design (see: Jackson \u0026amp; Fahrig \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Martin \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) in which we calculated percent disturbance within 30 concentric buffers (100\u0026ndash;3000m) around each data point. We then evaluated the performance of models based on each buffer distance using LOO and ELPD to determine the appropriate scale to model disturbance in our analyses.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eEffect of Historic Disturbance on Modern Forest Characteristics\u003c/h2\u003e\u003cp\u003eWe also examined how historic timber harvest has shaped modern forest vegetation structure and soil conditions. These habitat features are known to influence plethodontid salamanders (e.g., Harper \u0026amp; Guynn Jr \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Maerz et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Wade et al. \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and may help explain how past timber harvest indirectly influences current salamander population dynamics (Cosentino \u0026amp; Brubaker \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Specifically, Hocking et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that both understory density and leaf litter depth were positively associated with red-cheeked salamander abundance in the GSMNP. Therefore, we obtained a raster dataset of forest understory density from the GSMNP and used field-derived measurements of leaf litter depth at each of our sampling sites (see: Hocking et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We then fit simple Bayesian regression models assessing the relationship between historic disturbance and elevation and understory density (within our optimized buffer distance) and leaf litter depth at sampling sites.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003ePopulation Genetic Analyses\u003c/h2\u003e\u003cp\u003eWe sampled a total of 549 individual red-cheeked salamanders across 23 sampling sites with a range of 7\u0026ndash;38 samples per site (mean\u0026thinsp;=\u0026thinsp;23.87; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). There were 6\u0026ndash;93 alleles in each of the 11 microsatellite loci (mean\u0026thinsp;=\u0026thinsp;36.4\u0026thinsp;\u0026plusmn;\u0026thinsp;28.8) across all samples. Missingness was 0.76% across the entire data set and ranged from 0\u0026ndash;3.21% for individual loci. Most loci were in Hardy-Weinberg equilibrium across all populations with no consistent deviations for any locus. There was no evidence of linkage disequilibrium. The average H\u003csub\u003eE\u003c/sub\u003e across sampling sites was 0.788 and varied between 0.658 and 0.835. The average A\u003csub\u003eR\u003c/sub\u003e across our sampling sites was 6.64 and varied between 4.45 and 7.39. The average F\u003csub\u003eST\u003c/sub\u003e between our sampling sites was 0.032 and ranged between 0 and 0.113. Our test for IBD revealed a significant, positive association (Mantel r\u0026thinsp;=\u0026thinsp;0.69; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) between genetic differentiation and geographic distance within the GSMNP (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSample size, elevation, expected heterozygosity (H\u003csub\u003eE\u003c/sub\u003e) and allelic richness (A\u003csub\u003eR\u003c/sub\u003e) for each red-cheeked salamander (\u003cem\u003ePlethodon jordani\u003c/em\u003e) sampling site in the GSMNP.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSample Size\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eElevation (m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExpected Heterozygosity (H\u003csub\u003eE\u003c/sub\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRarefied Allelic Richness (A\u003csub\u003eR\u003c/sub\u003e)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e61s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e669\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.716\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e713\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.658\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.806\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e46s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.826\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e834\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.749\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8Bs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e871\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e985\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e49s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e51s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e36s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e33s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.758\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.762\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e19s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e21Bs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1575\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e23s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1716\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25As\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e28s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.823\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eEffects of Disturbance and Elevation on Genetic Diversity\u003c/h2\u003e\u003cp\u003eBased on our assessment of scale of effect, historic disturbance was best modeled at a scale of 2600 m (Figs. S1\u0026ndash;S2) and we used this amount of disturbance within 2600 m of sampling sites for all analyses. We found that historic disturbance and elevation were moderately correlated (r = \u0026minus;\u0026thinsp;0.49), but not enough to preclude us from including them together in the same model. The top performing model in our analyses included historic disturbance, elevation, and an interaction term as predictors of genetic diversity (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Elevation alone performed relatively poorly and when added to a model that included disturbance, this model performed worse than disturbance alone and added very little explanatory power (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Based on our top performing model, the relationship between historic disturbance and modern genetic diversity is negative (β = \u0026minus;\u0026thinsp;0.449, CI = [\u0026ndash;0.863, \u0026minus;\u0026thinsp;0.003], probability of direction [POD]\u0026thinsp;=\u0026thinsp;0.976) and the relationship between elevation and genetic diversity is potentially positive, but with a high degree of uncertainty (β\u0026thinsp;=\u0026thinsp;0.227, CI = [\u0026ndash;0.183, 0.627], POD\u0026thinsp;=\u0026thinsp;0.875). A positive interaction term (β\u0026thinsp;=\u0026thinsp;0.357, CI = [\u0026ndash;0.128, 0.816], POD\u0026thinsp;=\u0026thinsp;0.931) implies that this negative effect of disturbance may be buffered at high elevation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePerformance of each candidate model based on difference in ELPD from the top model, the standard error (SE) of that difference, and Bayesian R\u003csup\u003e2\u003c/sup\u003e (SE).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eELPD Difference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE of Difference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBayesian R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisturbance*Elevation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.91 (0.13)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisturbance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.89 (0.16)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisturbance\u0026thinsp;+\u0026thinsp;Elevation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.89 (0.15)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElevation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.85 (0.18)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNull (Gaussian process only)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;11.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.74 (0.20)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eEffect of Historic Disturbance on Modern Forest Characteristics\u003c/h2\u003e\u003cp\u003eWe found that historic disturbance had a negative association (β = \u0026minus;\u0026thinsp;0.82, CI = [\u0026ndash;1.09, \u0026minus;\u0026thinsp;0.55]) with modern estimates of understory density surrounding our sampling sites (Fig. S3) and explained a large amount of the variation in understory density (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.65). On the other hand, elevation explained very little variation in understory density (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.11) and performed poorly based on ELPD (difference = \u0026minus;\u0026thinsp;11.3, SE of difference\u0026thinsp;=\u0026thinsp;3.1). This implies that modern variation in understory density may be largely driven by historic disturbance in the GSMNP. We did not find evidence that historic disturbance (β = \u0026minus;\u0026thinsp;0.03, CI = [\u0026ndash;0.49, 0.43]) or elevation (β = \u0026minus;\u0026thinsp;0.08, CI = [\u0026ndash;0.54, 0.38]) were associated with our field-derived estimates of leaf litter depth in the GSMNP.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study we assessed spatial patterns of genetic diversity of the high elevation endemic red-cheeked salamander in the GSMNP. We found evidence for spatial variation in genetic diversity in this species across the GSMNP and an effect of isolation by distance. We also found that red-cheeked salamanders in areas that had historic timber harvest and settlement tended to have lower levels of genetic diversity (H\u003csub\u003eE\u003c/sub\u003e), but that this effect may be buffered against at high elevations. Additionally, we found that historic disturbance was negatively associated with modern levels of understory density, an important factor in predicting red-cheeked salamander abundance. Altogether, our results provide evidence that historic anthropogenic activities like timber harvest have long-term impacts on forests and the genetic diversity of wildlife populations that inhabit them.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eEffect of Disturbance and Elevation on Genetic Diversity\u003c/h2\u003e\u003cp\u003eAlthough large-scale timber harvest within the GSMNP has not occurred since the park’s dedication in 1940 (Pyle \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), we find evidence that these historic land use practices are still relevant for understanding modern patterns of genetic diversity. Red-cheeked salamander sampling sites that were located in or near areas of historic timber harvest/settlement tended to have lower levels of genetic diversity. This pattern may be explained by both the primary impacts of historic disturbance and legacy impacts on forest characteristics. In areas that were heavily harvested, red-cheeked salamanders may have been entirely extirpated or their populations sizes may have been significantly reduced (e.g., Ash \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Grialou et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Knapp et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). If these declines caused by primary timber harvest occurred, they may have resulted in genetic bottlenecks or founder effects that are still detectable in modern populations (McCauley \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Landergott et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Bouzat \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Timber harvest may also alter red-cheeked salamander genetic diversity over time by changing forest characteristics. As disturbed forests recover, they may have less desirable microhabitat conditions for plethodontid salamanders (hotter and dryer), leading to lower survival rates and depressed carrying capacity in historically disturbed areas (Cosentino \u0026amp; Brubaker \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gade \u0026amp; Peterman \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, forest disturbance may directly influence arthropod diversity and abundance, and reduced prey availability may further depress salamander population size in historically disturbed areas (Jeffries et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Summerville et al. \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Cox et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Small populations lose genetic diversity more rapidly through genetic drift (Conner \u0026amp; Hartl \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), and this reduced diversity can persist for decades after a disturbance.\u003c/p\u003e\u003cp\u003eOn the other hand, we found limited evidence that elevation was associated with red-cheeked salamander genetic diversity, despite an established positive relationship with abundance (Hocking et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, we did find evidence for a positive interaction effect between elevation and disturbance. In other words, populations occurring at high elevations may be somewhat insulated from the negative effects of historic timber harvest (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). High elevation areas of the GSMNP are particularly cool and wet which limits the ability of microclimatic variation to alter plethodontid salamander distribution and abundance (Dodd \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Gade \u0026amp; Peterman \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hocking et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, red-cheeked salamanders may have persisted in these areas despite forest alterations. Favorable microhabitat conditions between disturbed sites and adjacent areas may also lead to a higher rate of successful dispersal and gene flow (Peterman et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mulder et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wade et al. \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and this increased gene flow may compensate for losses in genetic diversity that originally occurred during the timber harvest process (Gómez-Fernández et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Jangjoo et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eEffect of Disturbance on Modern Forest Characteristics\u003c/h2\u003e\u003cp\u003eWe found that forest understory density was lower in areas where historic timber harvest and settlement occurred (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Large-scale timber harvest can influence forest understory dynamics for many years post-harvest (Ramovs \u0026amp; Roberts \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Halpern et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Munteanu et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and new growth forest that eventually succeeds timber-harvested areas may have lower abundance and diversity of understory vegetation due to higher overstory density and subsequent heavy shading (Knapp et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Previous studies have indicated that salamander abundance is positively associated with higher understory density in forests (Pough et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Riedel et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Hocking et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), meaning that reduced understory density may represent a mechanism for a modern depression of population size and genetic diversity. A high density of understory plants can help create the vital cool, moist soil conditions that are essential for maintaining plethodontid salamander populations, particularly in summer when red-cheeked salamanders are most active (Clinton \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Stickley \u0026amp; Fraterrigo \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Forest understory also plays an important role in terrestrial salamander biology, as climbing behavior is a major part of salamander thermoregulation, moisture retention, and foraging activities (Pough et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Homyack et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; McEntire \u0026amp; Maerz \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions and Conservation Implications","content":"\u003cp\u003eWe found that despite decades of ecological recovery, organisms inhabiting historically altered landscapes like the GSMNP may still suffer from reduced genetic diversity. The genetic diversity of populations in altered landscapes may eventually return to pre-disturbance levels (especially with sufficient gene flow), but this process is slow, and it may take numerous generations before genetic diversity in recovered landscapes is higher than that it is in currently degraded landscapes (Wei et al. \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mualim et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This genetic recovery lag is particularly concerning for high elevation endemic organisms like the red-cheeked salamander. As the pace of anthropogenic climate change increases, these organisms must adapt to changing conditions as continued upward migration into more habitable zones is often not an option (Dirnböck et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Therefore, high-elevation endemics in disturbed areas may be disproportionately impacted by modern depression of genetic diversity as they face rapid climatic changes to their environment.\u003c/p\u003e\u003cp\u003eIn addition to other established drivers in the spatial distribution of genetic diversity such as the distance to range edge (i.e., the central-marginal hypothesis; Eckert et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Micheletti \u0026amp; Storfer \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ursenbacher et al. \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) or latitudinal patterns (Johansson et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Howes \u0026amp; Lougheed \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Adams \u0026amp; Hadly \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), we can use historic disturbance data to help predict the spatial patterns of genetic diversity for organisms inhabiting landscapes with a matrix of historic land use intensity. Our results indicate that populations inhabiting historically undisturbed regions should be protected as they may serve as a source of future genetic variation. This transfer of genetic diversity may happen through natural migration or human-mediated genetic rescue (Whiteley et al. \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Additionally, the enhancement of ecological connectivity between historically disturbed and unspoiled landscapes through direct conservation management actions such as the establishment of corridors (e.g., Mech \u0026amp; Hallett \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Christie \u0026amp; Knowles \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Stickley et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) or creation of new habitat cores that serve as stepping stones (e.g., Karstens et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Carter et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) can be especially powerful as increased rates of gene flow may help to offset historic depression of population size and genetic diversity (Slatkin \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Navascués \u0026amp; Emerson \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Jangjoo et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Further assessment of the impacts of historic anthropogenic disturbance of different ages, in different landscapes, and with varying taxa will help establish well-documented patterns that can be used to directly inform conservation management strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\u003cp\u003eFunding for this project was provided by the National Geographic Society Waitt Grant Program (grant W203-11). Bryce S. Wade was also supported by a National Science Foundation Graduate Research Fellowship during this work.\u003c/p\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eData collection and laboratory work were performed by William E. Peterman and John A. Crawford. Analyses were conducted by Bryce S. Wade. The first draft of the manuscript was written by Bryce S. Wade and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank P. Super and the GSMNP for their support of this work. We thank D.J. Hocking and J.R. Milanovich for their assistance in the field and L.R. Pauley for their assistance in the lab. We also thank C.D. Cousins, L. Dixson, B.M. Fitzpatrick, J.M. Fleming, J.A. Fordyce, and B. Oye for their helpful review of this manuscript. All research was conducted in accordance with animal care protocols of the National Park Service animal care and use committee (permit #SER_GRSM_Crawford_Salamander_2012), and all scientific activities were conducted under permits from the National Park Service (permit GRSM-2012-SCI-2244), North Carolina Wildlife Resources Commission (permit 12-SC00602), and Tennessee Wildlife Resources Agency (permit 3680).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding authors on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdams RI, Hadly EA (2013) Genetic diversity within vertebrate species is greater at lower latitudes. 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Trends in ecology \u0026amp; evolution, 30, 42-49.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":true,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"conservation-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"coge","sideBox":"Learn more about [Conservation Genetics](https://www.springer.com/journal/10592)","snPcode":"10592","submissionUrl":"https://submission.nature.com/new-submission/10592/3","title":"Conservation Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"anthropogenic disturbance, elevation, heterozygosity, microsatellites, Plethodon jordani, timber harvest","lastPublishedDoi":"10.21203/rs.3.rs-7623606/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7623606/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMaintaining the genetic diversity of wildlife populations is important as reductions in genetic diversity can have negative consequences like reduced fitness caused by inbreeding depression or increased risk of extirpation. Anthropogenic disturbance can have long-lasting impacts on the genetic diversity of wildlife populations. Here, we evaluated how historic timber harvesting and settlement shape modern spatial patterns of genetic diversity in the narrow-range endemic red-cheeked salamander (\u003cem\u003ePlethodon jordani\u003c/em\u003e) in the Great Smoky Mountains National Park (GSMNP). Using microsatellite genotypes from 549 individuals across 23 sites, we quantified genetic diversity, tested for isolation by distance, and assessed the relationship between genetic diversity and historic disturbance and elevation. We found spatial variation in genetic diversity and statistically significant isolation by distance. Sampling sites located near historically harvested or settled areas exhibited reduced genetic diversity, but this negative effect was moderated at high elevations where salamander densities are higher and microhabitats are more favorable. Additionally, historic disturbance was associated with reduced modern understory density, a habitat feature that positively influences salamander abundance. Our findings demonstrate that land-use legacies continue to shape both forest structure and the genetic diversity of a narrowly distributed amphibian nearly a century after large-scale timber harvest ceased. These results highlight the importance of incorporating historic landscape change into conservation planning, especially for high-elevation endemics whose long-term persistence may depend on maintaining genetic diversity and adaptive capacity.\u003c/p\u003e","manuscriptTitle":"Legacy of historical disturbance shapes modern genetic diversity in a high-elevation salamander","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-12 12:34:40","doi":"10.21203/rs.3.rs-7623606/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-03T23:21:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-02T18:39:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177657138400322223806017778578501557557","date":"2025-10-15T16:55:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-30T16:01:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250213571358540022099565472985156844900","date":"2025-09-28T11:57:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-26T17:55:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-18T14:08:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-18T14:07:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Conservation Genetics","date":"2025-09-15T18:58:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"conservation-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"coge","sideBox":"Learn more about [Conservation Genetics](https://www.springer.com/journal/10592)","snPcode":"10592","submissionUrl":"https://submission.nature.com/new-submission/10592/3","title":"Conservation Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"521e67fd-9a48-4b68-ad8e-b95c9445ec78","owner":[],"postedDate":"October 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T16:03:52+00:00","versionOfRecord":{"articleIdentity":"rs-7623606","link":"https://doi.org/10.1007/s10592-025-01750-x","journal":{"identity":"conservation-genetics","isVorOnly":false,"title":"Conservation Genetics"},"publishedOn":"2025-12-27 15:58:36","publishedOnDateReadable":"December 27th, 2025"},"versionCreatedAt":"2025-10-12 12:34:40","video":"","vorDoi":"10.1007/s10592-025-01750-x","vorDoiUrl":"https://doi.org/10.1007/s10592-025-01750-x","workflowStages":[]},"version":"v1","identity":"rs-7623606","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7623606","identity":"rs-7623606","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}