The maintenance of genetic diversity is essential for preserving adaptive potential in populations, yet it is increasingly threatened by landscape alteration. The field of landscape genetics offers a framework for assessing how patch-level landscape conditions, modeled at multiple scales, influence genetic diversity. We sought to assess how local environmental features and connectivity influence genetic diversity across 74 four-toed salamander (Hemidactylium scutatum) breeding wetlands in the southeastern United States. Using next-generation sequencing data and hierarchical Bayesian models, we examined genome-wide heterozygosity in relation to local landscape features and ecological connectivity. We also assessed the scale of effect of landscape features and tested for temporal lag effects. Genetic diversity was lower in wetlands with higher levels of historic deforestation and lower connectivity. An interaction between deforestation and connectivity indicated that deforestation had stronger negative effects in isolated wetlands but weaker effects in well-connected wetlands. Accounting for scale of effect and temporal lags was critical for detecting these relationships. Our analyses highlight the importance of assessing the spatial scale (scale of effect) and temporal lag of landscape features to detect key drivers of genetic diversity. In line with population genetic theory, our results indicate that the genetic consequences of habitat loss do not affect populations uniformly and are most severe in isolated populations where gene flow cannot buffer against loss of diversity. Altogether, we highlight the importance of considering the interaction of habitat loss and connectivity in conservation genetic management.

Land use/land cover change (LULCC) is rapidly altering the quality and quantity of the habitat matrix for many species, potentially reducing the connectivity of species across the landscape. Many measures of connectivity do not directly account for quantitative movement behavior, which can inform the relative influence of specific landscape features on overall connectivity. We evaluated the population genetics and landscape conductivity in two anuran species, exploring the utility of parameters derived from quantitative movement data and landscape features in models of landscape conductance and connectivity. We utilized a suite of population genetic tools to assess population genetic structure and gene flow between 21 localities of American toads in southwest Ohio, USA (Anaxyrus americanus) and Blanchard’s cricket frogs (Acris blanchardi) in an agriculturally dominated landscape. We used individual movement behavior data at habitat edges to select landscape variables and used major road and riparian networks to inform models of landscape conductance and functional connectivity. Parameters selected based on movement data were informative in landscape-scale models of connectivity; however, landscape features, especially river/riparian habitat had stronger influences on overall functional connectivity. We additionally found species-specific responses in functional connectivity across the same landscape. Movement behavior data scale up and can be utilized to inform models of connectivity across the landscape, though the inclusion of established landscape features should continue to be included in models of functional connectivity. Species-specific responses to landscape features can result in alternate patterns of connectivity across the same landscape, highlighting the need for individualized measures of connectivity in the context of rapid LULCC.

Conservation of threatened species can benefit from an evaluation of genes in the major histocompatibility complex (MHC), whose loci encode proteins that bind pathogens and are often under strong selection to maintain diversity in immune response to diseases. Despite this gene family's importance to disease resistance, little is known about these genes in reptiles including snakes. To address this issue, we assembled and annotated a highly contiguous genome assembly for the timber rattlesnake (Crotalus horridus), a pit viper which is threatened or endangered in parts of its range, and analyzed this new genome along with three other rattlesnake genomes to characterize snake MHC loci. We identified highly duplicated MHC Class I and Class IIβ genes in all species typified by a genomic architecture of discrete gene clusters localized on chromosome 2. The number of loci varied between species from 14 to 23 for MHC I and from 8 to 32 for MHC IIβ and was greater than previously identified in the few non-genome-based studies of reptile MHC to date. We present evidence of the gene family's complex evolutionary history, with extensive duplication and loss concurrent with speciation resulting in incomplete lineage sorting. The differences in gene number between species combined with a dynamic evolutionary history suggest that gene family expansion/contraction via rapid duplication/gene loss may represent an important mechanism for generating genetic diversity in rattlesnake MHC. Our work demonstrates the utility of whole-genome sequences for identifying functional genetic variation in the form of MHC genes relevant for conservation genomic studies in threatened snakes.

Since 1958, the lampricide 3-trifluoromethyl-4-nitrophenol (TFM) has been periodically used in nursery streams of the Great Lakes Region to control the invasive Sea Lamprey (Petromyzon marinus). Some treatments of streams containing Common Mudpuppies (Necturus maculosus), a species of large-bodied fully aquatic salamander found throughout the Great Lakes Region, have resulted in large amounts of mudpuppy mortality. However, the long-term consequences of mudpuppy mortality are not well understood. Following a TFM application in 1986, the mudpuppy population experienced an estimated 29% decline in the Grand River, Ohio. Since then, the Grand River has continued to be treated with TFM every three to five years. Mudpuppy population estimates suggest a slow to moderate decline over the last 32 years. In 2021 and 2022, we returned to the Grand River to estimate mudpuppy population size, survival, growth rates, and population demographics using mark-recapture methods. Following our first year of mark-recapture surveys, the US Fish and Wildlife Service treated the Grand River with TFM in April 2022. Despite observed mudpuppy mortality in the Grand River following TFM treatment, our population and survival estimates did not indicate decline following the 2022 TFM treatment. We also conducted a mark-recapture study at Alum Creek, a river that has never been treated with TFM. While differences in habitat and survey methods preclude direct comparison of the sites, we found little difference in population size or survival between sites. However, individual growth rates suggest that Grand River mudpuppies may reach sexual maturity at a younger age and attain a smaller adult size, patterns consistent with an exploited population. Smaller adults could have reduced fecundity, be more susceptible to predation, and have lower competitive ability, which may have consequences for long-term population persistence. Our study highlights the need for future studies to compare treated and untreated tributaries in the Great Lakes to address lingering concerns and long-term effects of TFM treatments.

Ixodes scapularis (the blacklegged tick) is a prominent disease vector that has rapidly expanded across eastern North America in recent decades due to land use and climate change. Predictive modeling is popular for ecological inference and disease management, but few models have considered the multiscale tick and host relationships that drive blacklegged tick expansion. Predict the probability of occurrence and relative abundance of the blacklegged tick in Ohio, USA at an informative resolution for disease surveillance (800-m2) following CDC guidelines. Determine drivers of tick expansion at the tick and host level through a multiscale analysis of climate, habitat, and host density variables. We modeled blacklegged tick occurrence and abundance using Integrated Nested Laplace Approximation to analyze the relationships of climate, habitat, and host density with field-collected data from 161 sites. We analyzed habitat variables at several spatial scales (800–5000-m2) expecting they would be most influential at scales relevant to white-tailed deer home range size. Occurrence and abundance decreased in drier, hotter areas with higher precipitation variability, and increased in heavily forested areas at the spatial scale relevant to deer home range size (1500-m2). Only the abundance model included host density variables, indicating that host densities influence abundance but not occurrence. Our spatial projections show a high probability of occurrence and relative abundance throughout Ohio. Multiscale frameworks are vital for understanding the continuously changing distributions and abundances of arthropod disease vectors that rely on hosts for range expansion.

Rock outcrop specialists include many at‐risk species whose conservation requires effectively identifying and mapping outcrops. Canopy cover often obscures rock outcrops, making the mapping and detecting of these features difficult. Improvements in the availability of high‐resolution light detection and ranging data (LiDAR) provide opportunities to map outcrops, which has previously been conducted using a slope‐based method; however, this method exhibits varying success across different topographies and struggles to locate especially small outcrops. We measured the accuracy of a new method, focal range statistics, and compared it to the slope‐based method to detect and map outcrops in southern Ohio, USA, using a 0.75‐m digital elevation model (DEM) derived from LiDAR. This method exhibited low error and was notably better at detecting small outcrops compared to the slope‐based method. Focal range statistics also mapped 6 times more total outcrop area (243,027 m 2 ) than the slope‐based method, and field reconnaissance revealed 17 new localities of green salamander ( Aneides aeneus ), an outcrop specialist and state‐listed species. Applying focal range statistics to more widely available 3‐m DEM data detected outcrops with similar accuracy but resulted in mapping 4 times more area of rock outcrops. We recommend additional field testing with 3‐m DEM data to assess the accuracy of the topographic data sources with lower resolutions. Our study indicates that the use of focal range statistics can be a valuable tool for the conservation and management of outcrop specialists and offers notable advantages when used in regions with subtle topographic relief and small outcrops.

With projected decreases in biodiversity looming due to changing environmental conditions, it is important for conservation managers to have accurate predictions of species' distributions. Species distribution models (SDM) and mechanistic models that account for biophysical factors are important tools for predicting potential distributions for many species. Incorporating microclimate data into SDMs and mechanistic models has become an important step for developing biologically relevant models for organisms reliant on microclimatic regimes. However, there remains a need to compare and integrate the predictions from microclimate-derived SDMs and mechanistic models at fine spatio-temporal scales to improve predictive accuracy and quantify model uncertainty. We developed correlative SDMs and mechanistic models of potential resistance to surface activity for two salamanders using fine-resolution (3 m) microclimate data. Models were produced for the Great Smoky Mountains National Park, USA during the 2010, 2030, and 2050 time periods. We determined the spatio-temporal agreement between SDMs and mechanistic models to assess model uncertainty at varying spatial resolutions. We also modeled and assessed spatio-temporal variability within potential activity corridors. We found that agreement between fine-resolution microclimate SDMs and mechanistic models was generally poor and varied temporally, but model agreement increased and converged at varying coarser spatial resolutions. Furthermore, potential activity corridors spatio-temporally varied and demonstrated increased habitat fragmentation under future projections. The findings from this study highlight a contradiction in which we may need to model species distributions with microclimate data at finer, more biologically meaningful resolutions, but model agreement between correlative and mechanistic approaches may be weakened at these fine scales. Researchers may therefore need to strike a balance between increasing spatial resolution and study extent when integrating model approaches. Further quantifying model uncertainty and identifying alternative methods for integrating SDMs and mechanistic models will be an important step towards accurately predicting species distributions under changing environmental conditions.

Understanding how three-dimensional (3D) habitat structure drives biodiversity patterns is key to predicting how habitat alteration and loss will affect species and community-level patterns in the future. To date, few studies have contrasted the effects of 3D habitat composition with those of 3D habitat configuration on biodiversity, with existing investigations often limited to measures of taxonomic diversity (i.e., species richness). Here, we examined the influence of Light Detecting and Ranging (LiDAR)-derived 3D habitat structure-both its composition and configuration-on multiple facets of bird diversity. Specifically, we used data from the National Ecological Observatory Network (NEON) to test the associations between 11 measures of 3D habitat structure and avian species richness, functional and trait diversity, and phylogenetic diversity. We found that 3D habitat structure was the most consistent predictor of avian functional and trait diversity, with little to no effect on species richness or phylogenetic diversity. Functional diversity and individual trait characteristics were strongly associated with both 3D habitat composition and configuration, but the magnitude and the direction of the effects varied across the canopy, subcanopy, midstory, and understory vertical strata. Our findings suggest that 3D habitat structure influences avian diversity through its effects on traits. By examining the effects of multiple aspects of habitat structure on multiple facets of avian diversity, we provide a broader framework for future investigations on habitat structure.

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.

ContextAnalyses in landscape ecology often seek to understand how the landscape surrounding field survey locations relates to ecological responses measured at those sites. A central challenge in these studies is defining the spatial scale at which landscape variables matter. While the limitations of standard approaches to estimating this scale are well known, practical alternatives remain limited and often difficult to apply.ObjectivesUsing simulation and the newly developed R package `mulitScaleR`, this paper describes the performance of scale optimization in relation to data type, sample size, effect size, sample independence, raster surface correlation, spatial autocorrelation, and habitat aggregation. I demonstrate how `multiScaleR` is a significant and accessible advancement for estimating scales of effect.Methods and resultsThe package builds upon existing methods that apply kernel weighting functions to landscape variables but is more general and versatile than existing methods. Functions have been optimized for computational speed and efficiency, including parallelization, use of sparse matrices, and C + + , facilitating efficient analyses of large data sets. Maximum likelihood-based regression frameworks commonly used in landscape ecology, including models from `unmarked`, `spaMM`, and `glmmTMB` can be seamlessly integrated with `multiScaleR`. The package provides a complete workflow for fitting models, conducting model selection, and spatially projecting models. Two critical insights emerge from simulations and analyses with `multiScaleR`: (1) scales of effect can be estimated with high accuracy and precision alongside regression parameters, but (2) achieving reliable estimates requires large sample sizes.Conclusions`multiScaleR` is a purpose-built R package to estimate scales of effect of landscape variables in regression analyses. The accessibility and flexibility of this package make it a powerful new resource in the toolbox of spatial ecologists.

Preserving biodiversity is crucial for ecosystem function, necessitating its measurement and monitoring to comprehend human impacts on the environment. β-diversity, a key metric, can be used to measure changes in community diversity and assemblages over time. Multiple lines of evidence show that climate, land use land cover (LULC), and topography independently influence diversity; more recent work also shows that LULC can decouple climate effects, creating refugia. Spatial structures also affect the relationships between ecological processes and environmental factors. This study uses two discrete generations of breeding bird atlas data in Ohio, 24 years apart, to investigate the effects of environmental changes and spatial heterogeneity on statewide forest bird community diversity. Utilizing machine learning, we identified elevation variability, mean forest patch size, and total forest area as the most significant variables affecting temporal β diversity dissimilarity (referred to as temporal dissimilarity). The smallest changes in temporal dissimilarity occurred in landscapes with increased mean forest patch size, forest class area, and varied elevation. Furthermore, the results revealed a weak interaction between total forest area and elevation variability, with the largest temporal dissimilarity occurring in sites with the highest loss of total forest area and invariable elevation. We pinpointed regions in Ohio experiencing the greatest amount of composition changes in intensified agricultural and developed lands. A geographically weighted regression model indicated that the relationships between temporal dissimilarity and elevation variability, mean forest patch size, total forest area, and annual maximum temperature varied spatially within the state. Our study integrates ecological patterns with spatial patterns, showing that landscapes can interact with climate to mitigate regional climatic impacts and suggest effective conservation areas. Our findings also indicate that the relationships between ecological processes and the environment are not always spatially consistent.

Wildfire is an increasingly common disturbance in forested landscapes that can drastically alter local habitats. Under current climate change predictions, wildfires are likely to become more frequent and severe. In regions and ecosystems that have historically infrequent fire return intervals, there is little known about how organisms will respond to the more severe and frequent wildfires predicted under climate change. In the southern Appalachian Mountains, USA, fire has been suppressed and severe burns are historically uncommon. This region boasts immense biodiversity and is considered a biological hot spot for diversity in the salamander family Plethodontidae. These species rely upon cool, moist microclimates that may be impacted more by severe fire than by low-intensity wild or prescribed fire. In 2016, the Chimney Tops Two wildfire burned >6000 ha of Great Smoky Mountains National Park, USA, and left a mosaic of burn severity across the landscape. This presented an opportunity to examine how five plethodontid salamander species respond to and recover from a range of burn severity. Even though the landscape had been recovering for 5 yr at the time of study, populations of Plethodon jordani, Plethodon glutinosus spp., Desmognathus wrighti, Desmognathus imitator, and Eurycea wilderae within the burn boundary had lower abundance than those in unburned habitat. In addition, there was a trend of even lower abundances in more severely burned habitat. Evidence of recovery, as indicated by a relationship between population abundance and distance from the burn boundary, was only present for D. imitator. Finally, body size distributions were different between burned and unburned sites for three of the five species and individuals were larger, on average, in burned sites. This work provides insights into how terrestrial salamander populations may respond to the more severe and frequent wildfires predicted under climate change for the southern Appalachian Mountains region.

One key research goal of evolutionary biology is to understand the origin and maintenance of genetic variation. In the Cerrado, the South American savanna located primarily in the Central Brazilian Plateau, many hypotheses have been proposed to explain how landscape features (e.g., geographic distance, river barriers, topographic compartmentalization, and historical climatic fluctuations) have promoted genetic structure by mediating gene flow. Here, we asked whether these landscape features have influenced the genetic structure and differentiation in the lizard species Norops brasiliensis (Squamata: Dactyloidae). To achieve our goal, we used a genetic clustering analysis and estimate an effective migration surface to assess genetic structure in the focal species. Optimized isolation-by-resistance models and a simulation-based approach combined with machine learning (convolutional neural network; CNN) were then used to infer current and historical effects on population genetic structure through 12 unique landscape models. We recovered five geographically distributed populations that are separated by regions of lower-than-expected gene flow. The results of the CNN showed that geographic distance is the sole predictor of genetic variation in N. brasiliensis, and that slope, rivers, and historical climate had no discernible influence on gene flow. Our novel CNN approach was accurate (89.5%) in differentiating each landscape model. CNN and other machine learning approaches are still largely unexplored in landscape genetics studies, representing promising avenues for future research with increasingly accessible genomic datasets.

Amphibian populations, including those of lungless salamanders (Plethodontidae), are susceptible to population declines, especially considering current and predicted climate change. Like many organisms, plethodontid salamanders exhibit reproductive plasticity to maximize population growth in response to environmental conditions. A better understanding of the external causes of reproductive plasticity can provide more accurate population models through the explicit incorporation of such drivers. To this end, we sampled Southern Pygmy Salamanders (Desmognathus wrighti) along a 1345-m elevational gradient in Great Smoky Mountains National Park, USA, to determine the effects of body size and elevation on reproductive investment. Using Bayesian mixed effects models, we assessed whether body size, elevation, or a combination of these factors best explained the probability an individual was gravid, the number of ova in gravid females, and the number of testis lobes in mature males. We found that the interaction of body size and elevation best explained each reproductive measure. Larger females were generally more likely to be gravid, with annual reproduction in fully mature females at low to mid-elevations and roughly biennial reproduction at high elevations. We also found a positive relationship between body size and ova counts at low and mid-elevations, but despite a reduction in reproductive frequency at higher elevations, we found no effect of body size on ova count. In addition, the addition of testis lobes, which has commonly been used as a proxy of age in plethodontid salamanders, occurred at larger body sizes with increasing elevation. Our findings support previous work showing a relationship between body size and reproductive ecology in desmognathine salamanders while providing insight into the complexity of this relationship in the context of elevational clines. Reproductive ecology significantly influences population dynamics; thus, further refinement of these observed patterns and their underlying causes is necessary to advance plethodontid salamander population ecology.

Blacklegged ticks (Ixodes scapularis Say) pose an enormous public health risk in eastern North America as the vector responsible for transmitting 7 human pathogens, including those causing the most common vector-borne disease in the United States, Lyme disease. Species distribution modeling is an increasingly popular method for predicting the potential distribution and subsequent risk of blacklegged ticks, however, the development of such models thus far is highly variable and would benefit from the use of standardized protocols. To identify where standardized protocols would most benefit current distribution models, we completed the "Overview, Data, Model, Assessment, and Prediction" (ODMAP) distribution modeling protocol for 21 publications reporting 22 blacklegged tick distribution models. We calculated an average adherence of 73.4% (SD +/- 29%). Most prominently, we found that authors could better justify and connect their selection of variables and associated spatial scales to blacklegged tick ecology. In addition, the authors could provide clearer descriptions of model development, including checks for multicollinearity, spatial autocorrelation, and plausibility. Finally, authors could improve their reporting of variable effects to avoid undermining the models' utility in informing species-environment relationships. To enhance future model rigor and reproducibility, we recommend utilizing several resources including the ODMAP protocol, and suggest that journals make protocol compliance a publication prerequisite.

Patterns of species distributions and abundance are driven by a combination of species abiotic niches and biotic interactions between members of a community. Joint-species distribution models extend traditional modeling of occurrence or abundance to quantify species corelationships that may result from interspecific interactions. In the southern Appalachian Mountains, stream-associated salamander communities are thought to be structured by competitive and predatory interactions; therefore, spatial patterns in occurrence and abundance and temporal patterns in activity may reflect species associations. To evaluate the role of interspecific interactions in salamander community occurrence and abundance patterns, we conducted repeated point-count and leaf-bag surveys for adult and larval communities, respectively. We estimated adult and larval community occurrence and abundance by using the hierarchical modeling of species communities framework to identify the extent to which residual co-occurrence and correlation in abundance was present within these communities. For both adult and larval communities, we found no evidence that residual co-occurrence or correlation in abundance was present. Instead, results indicated that environmental covariates characterizing both plot- and landscape-level habitat conditions were responsible for observed patterns. In addition, we found no evidence for temporal niche partitioning, suggesting competition does not play a significant role in inter- or intraday activity patterns. Our results contrast with the accepted paradigm that extant biotic interactions drive community composition among stream-associated salamanders. Instead, our findings suggest that the effects of biotic interactions are not evident at the spatial or temporal scales associated with occurrence or abundance.

One of the allures of landscape genetics is the ability to leverage pairwise genetic distance metrics to infer how landscape features promote or constrain gene flow (i.e. landscape resistance surfaces). Critically, properly parameterized landscape resistance surfaces are foundational to applied conservation and management decisions. As such, there has been considerable effort expended assessing methods and metrics to estimate landscape resistance from genetic data (Balkenhol et al., Ecography, 32, 2009, 818; Peterman et al., Landsc. Ecol., 34, 2019, 2197; Shirk et al., Mol. Ecol. Resour., 17, 2017, 1308; Shirk et al., Mol. Ecol. Resour., 18, 2018, 55). Nonetheless, a primary challenge to assessing the effects of landscapes on gene flow is in the estimation of landscape resistance values, and this problem becomes increasingly challenging as more landscape features or land cover classes are considered. It quickly becomes infeasible to adequately assess the potential parameter space through manual or systematic assignment of resistance values. The development of ResistanceGA (Peterman, Methods Ecol. Evol., 9, 2018, 1638) provided a framework for using genetic algorithms to optimize landscape resistance values and identify the best statistical relationship between pairwise effective distances and genetic distances. ResistanceGA has seen extensive use in both population- and individual-based landscape genetic analyses. However, there has been relatively limited assessment of ResistanceGA's ability to identify the landscape features affecting gene flow (but see Peterman et al., Landsc. Ecol., 34, 2019, 2197; Winiarski et al., Mol. Ecol. Resour., 20, 2020, 1583) or the sensitivity of ResistanceGA results to the choice of genetic distance metric used. In the current issue of Molecular Ecology Resources, Beninde et al. (2023) aim to address these knowledge gaps by examining the impact of individual-based genetic distance measures on landscape genetic inference.

In May 2023, herpetologists from six countries converged at the 8th Conference on the Biology of Plethodontid Salamanders in Hammond, Louisiana to share their latest cutting-edge research. The conference was hosted by the Department of Biological Sciences at Southeastern Louisiana University. Dr. Richard Bruce was the honoree. The presentations covered a diverse array of topics from gene expression to behavior to speciation, illustrating the value of plethodontid salamander biology to a range of disciplines. This special issue of Herpetologica includes 16 papers highlighting a range of research on plethodontids and encouraging new approaches to tackle old and new questions.

Characterizing the population density of species is a central interest in ecology. Eastern North America is the global hotspot for biodiversity of plethodontid salamanders, an inconspicuous component of terrestrial vertebrate communities, and among the most widespread is the eastern red-backed salamander, Plethodon cinereus . Previous work suggests population densities are high with significant geographic variation, but comparisons among locations are challenged by lack of standardization of methods and failure to accommodate imperfect detection. We present results from a large-scale research network that accounts for detection uncertainty using systematic survey protocols and robust statistical models. We analysed mark–recapture data from 18 study areas across much of the species range. Estimated salamander densities ranged from 1950 to 34 300 salamanders ha −1 , with a median of 9965 salamanders ha −1 . We compared these results to previous estimates for P. cinereus and other abundant terrestrial vertebrates. We demonstrate that overall the biomass of P. cinereus , a secondary consumer, is of similar or greater magnitude to widespread primary consumers such as white-tailed deer ( Odocoileus virginianus ) and Peromyscus mice, and two to three orders of magnitude greater than common secondary consumer species. Our results add empirical evidence that P. cinereus , and amphibians in general, are an outsized component of terrestrial vertebrate communities in temperate ecosystems.