The attached spreadsheets are ranked climate change vulnerability estimates of amphibians in the north central United States. All species are listed as species of greatest conservation need in at least one of the 7 states in the north central region (Montana, Wyoming, Colorado, North Dakota, South Dakota, Nebraska, and Kansas). Vulnerability scores are developed from exposure data from evapotranspiration deficit projections across 2 climate change scenarios, along with sensitivity and adaptive capacity traits from literature values and range maps. We used readily-available data from climate projections, range maps, and life history traits to score vulnerability in these data-deficient species, where more detailed life history information may not be available.

Climate change is a primary threat to biodiversity, but for most species, we still lack information required to assess their potential vulnerability to changes. Climate change vulnerability assessment (CCVA) is a widely-used technique to rank relative vulnerability to climate change based on species distributions, habitat associations, environmental tolerances, and life-history traits. For species that we expect are vulnerable to climate change yet are data deficient, like many amphibians, we often lack information required to construct traditional CCVAs. We extended the CCVA framework by constructing models based on life history theory, using empirical evidence of traits and distributions that reflected sensitivity of data-deficient species to environmental perturbation. These csv data files were assembled to perform climate change vulnerability assessments of the 31 amphibian species, both across the north central region and within individual US states. We incorporated information from species' life history traits and other characteristics along with climate projections of evapotranspiration deficit change, to score relative vulnerability of the 31 amphibians. Associated R code is for scoring relative vulnerability, where overall score is a product of exposure to climate change times sensitivity to that change, minus adaptive capacity of each species. All species are listed as Species of Greatest Conservation Need in at least one of 7 states in the North Central United States: Montana, Wyoming, Colorado, North Dakota, South Dakota, Nebraska, and Kansas.

Climate change is a primary threat to biodiversity, but for most species, we still lack information required to assess their potential vulnerability to changes. Climate change vulnerability assessment (CCVA) is a widely-used technique to rank relative vulnerability to climate change based on species distributions, habitat associations, environmental tolerances, and life-history traits. For species that we expect are vulnerable to climate change yet are data deficient, like many amphibians, we often lack information required to construct traditional CCVAs. We extended the CCVA framework by constructing models based on life history theory, using empirical evidence of traits and distributions that reflected sensitivity of data-deficient species to environmental perturbation. These csv data files were assembled to perform climate change vulnerability assessments of the 31 amphibian species, both across the north central region and within individual US states. We incorporated information from species' life history traits and other characteristics along with climate projections of evapotranspiration deficit change, to score relative vulnerability of the 31 amphibians. Associated R code is for scoring relative vulnerability, where overall score is a product of exposure to climate change times sensitivity to that change, minus adaptive capacity of each species. All species are listed as Species of Greatest Conservation Need in at least one of 7 states in the North Central United States: Montana, Wyoming, Colorado, North Dakota, South Dakota, Nebraska, and Kansas.

This dataset details individual species and natural habitat vulnerability rankings, including contextual study-specific information. This data was collected from original publications found through a literature search. Information is cumulative to include climate change vulnerability assessment (CCVA) results summarized in Staudinger et al. (2015) and published as of December 2023.

This dataset details individual species and natural habitat vulnerability rankings, including contextual study-specific information. This data was collected from original publications found through a literature search. Information is cumulative to include climate change vulnerability assessment (CCVA) results summarized in Staudinger et al. (2015) and published as of December 2023.

This dataset is a partial literature review of life history traits and habitat characteristics which may be important to climate change vulnerability in amphibians. We describe through empirical studies and texts of life history theory, which traits and characteristics we used to construct a climate change vulnerability assessment, and the justification for using those traits in our analysis. We do not claim this review includes all relevant literature germain to the topic of climate change vulnerability.

This dataset is a partial literature review of life history traits and habitat characteristics which may be important to climate change vulnerability in amphibians. We describe through empirical studies and texts of life history theory, which traits and characteristics we used to construct a climate change vulnerability assessment, and the justification for using those traits in our analysis. We do not claim this review includes all relevant literature germain to the topic of climate change vulnerability.

This dataset contains the components of and resultant Rarity and Climate Sensitivity (RCS) values for 90 species of frogs and toads native to the conterminous United States. The RCS metric uses point occurrences to describe area of occurrence, quantifies the variation of climate conditions within that area, and combines and scales the two for a multispecies assessment of intrinsic sensitivity to climate change. The RCS metric was calculated at two geographic extents, the conterminous US and the North American continent. We also used two spatial grains: 1 km buffered occurrence points and small watersheds (Hydrologic Unit Code (HUC) 12 watershed boundaries within and HydroBASIN level 12 watershed boundaries outside the conterminous US). Point occurrences from the Global Biodiversity Information Facility (GBIF) and HerpMapper were used to calculate area of occurrence at each spatial extent and grain size. Climate specificity was calculated by extracting five bioclimatic variables (annual mean temperature, maximum temperature of the warmest month, minimum temperature of the minimum month, annual precipitation, and precipitation seasonality) from the area of occurrence and calculating the area-weighted standard deviation of each climate variable for each species. Rarity, as described by area of occurrence, and climate sensitivity is scaled and combined to form the RCS index. Because we evaluated the relationships among intrinsic sensitivity, taxonomy, and conservation status, species taxonomic family, genera, International Union for the Conservation of Nature Red List status, Endangered Species Act status, and Species of Greatest Conservation Need status is included in this dataset. Each row contains the calculated RCS index and its components for two spatial grain sizes for a unique species and spatial extent combination (90 species x 2 spatial extents = 180 rows). First posted - June 27, 2022 (available from author) Revised - October 20, 2022 (version 2.0)

This dataset contains the components of and resultant Rarity and Climate Sensitivity (RCS) values for 90 species of frogs and toads native to the conterminous United States. The RCS metric uses point occurrences to describe area of occurrence, quantifies the variation of climate conditions within that area, and combines and scales the two for a multispecies assessment of intrinsic sensitivity to climate change. The RCS metric was calculated at two geographic extents, the conterminous US and the North American continent. We also used two spatial grains: 1 km buffered occurrence points and small watersheds (Hydrologic Unit Code (HUC) 12 watershed boundaries within and HydroBASIN level 12 watershed boundaries outside the conterminous US). Point occurrences from the Global Biodiversity Information Facility (GBIF) and HerpMapper were used to calculate area of occurrence at each spatial extent and grain size. Climate specificity was calculated by extracting five bioclimatic variables (annual mean temperature, maximum temperature of the warmest month, minimum temperature of the minimum month, annual precipitation, and precipitation seasonality) from the area of occurrence and calculating the area-weighted standard deviation of each climate variable for each species. Rarity, as described by area of occurrence, and climate sensitivity is scaled and combined to form the RCS index. Because we evaluated the relationships among intrinsic sensitivity, taxonomy, and conservation status, species taxonomic family, genera, International Union for the Conservation of Nature Red List status, Endangered Species Act status, and Species of Greatest Conservation Need status is included in this dataset. Each row contains the calculated RCS index and its components for two spatial grain sizes for a unique species and spatial extent combination (90 species x 2 spatial extents = 180 rows). First posted - June 27, 2022 (available from author) Revised - October 20, 2022 (version 2.0)

USGS’s Southeast Climate Adaptation Science Center and the University of Tennessee, Knoxville are undertaking research to support southeastern states and partners engaged in the process of preparing revisions of State Wildlife Action Plans (SWAPs). This effort focuses specifically on integration of climate change into SWAPs. This data product provides a collation of estimates of species vulnerability to climate change that have been made using correlative niche models. Other common names synonymous with “correlative niche models” include: species distribution models, ecological niche models, climate envelope models, and MaxEnt models (where MaxEnt is the most popular software package used to generate these models). These models identify shared climate conditions found at sites where a species is known to occur today. The models can be used with future climate projections to predict where climate conditions that a species needs may be found in the future. Resulting predictions allow estimates of whether the climate conditions a species needs are likely to remain stable, become more prevalent, or more scarce in the future. If suitable climate conditions for a species become very scarce in the future, then that species would be considered more vulnerable to climate change than would a species for which climate conditions are predicted to remain suitable over a large geographic area.