Climate change is substantially impacting earth’s biodiversity, with a massive number of affected species that are difficult to study comprehensively. An “indicator species” approach that generalizes species-specific climate change impacts to broader groups (e.g., ensembles) could theoretically help overcome this challenge and streamline climate-smart conservation planning. We assessed the viability of this approach using four specialist amphibians (Ascaphus montanus, Dicamptodon copei, Plethodon idahoensis, and Plethodon vandykei), which we expected would have similar climate-related trajectories given their shared dependence on a narrow range of groundwater-driven habitats. Using boosted regression trees, we constructed species distribution models (SDMs) for each species and (if appropriate) major intraspecific lineage, then projected changes in environmental suitability under two climate change scenarios (SSP370 and SSP585) and timeframes (mid-century and late-century). Contrary to our expectation, future suitability projections varied widely among species, with small-to-moderate projected gains in suitability for A. montanus, relatively small changes with ambiguous directionality for D. copei, large gains in multiple regions for P. idahoensis, and major losses-in-place for P. vandykei. In addition, lineage-specific SDMs that assumed different niches for coastal and Cascades P. vandykei populations projected climate vulnerability for only the latter, highlighting a need for better genetic and ecological data. Given our collective findings, attempts to generalize climate change projections for purported “indicator species” to larger groups can be misleading, even within narrowly-defined and highly specialized ensembles. Moreover, we found a strong link between recent historical SDM outputs and species-tailored variables (e.g., seep-related variables), but many of these variables lacked future projections under climate change and were thus not directly usable to forecast climate change responses. Lastly, our findings also highlight research and conservation needs for our study species under climate change, such as identifying taxonomic scales of niche variation and protecting in-situ climatic refugia.

Climate change is substantially impacting earth’s biodiversity, with a massive number of affected species that are difficult to study comprehensively. An “indicator species” approach that generalizes species-specific climate change impacts to broader groups (e.g., ensembles) could theoretically help overcome this challenge and streamline climate-smart conservation planning. We assessed the viability of this approach using four specialist amphibians (Ascaphus montanus, Dicamptodon copei, Plethodon idahoensis, and Plethodon vandykei), which we expected would have similar climate-related trajectories given their shared dependence on a narrow range of groundwater-driven habitats. Using boosted regression trees, we constructed species distribution models (SDMs) for each species and (if appropriate) major intraspecific lineage, then projected changes in environmental suitability under two climate change scenarios (SSP370 and SSP585) and timeframes (mid-century and late-century). Contrary to our expectation, future suitability projections varied widely among species, with small-to-moderate projected gains in suitability for A. montanus, relatively small changes with ambiguous directionality for D. copei, large gains in multiple regions for P. idahoensis, and major losses-in-place for P. vandykei. In addition, lineage-specific SDMs that assumed different niches for coastal and Cascades P. vandykei populations projected climate vulnerability for only the latter, highlighting a need for better genetic and ecological data. Given our collective findings, attempts to generalize climate change projections for purported “indicator species” to larger groups can be misleading, even within narrowly-defined and highly specialized ensembles. Moreover, we found a strong link between recent historical SDM outputs and species-tailored variables (e.g., seep-related variables), but many of these variables lacked future projections under climate change and were thus not directly usable to forecast climate change responses. Lastly, our findings also highlight research and conservation needs for our study species under climate change, such as identifying taxonomic scales of niche variation and protecting in-situ climatic refugia.

Climate change is substantially impacting earth’s biodiversity, with a massive number of affected species that are difficult to study comprehensively. An “indicator species” approach that generalizes species-specific climate change impacts to broader groups (e.g., ensembles) could theoretically help overcome this challenge and streamline climate-smart conservation planning. We assessed the viability of this approach using four specialist amphibians (Ascaphus montanus, Dicamptodon copei, Plethodon idahoensis, and Plethodon vandykei), which we expected would have similar climate-related trajectories given their shared dependence on a narrow range of groundwater-driven habitats. Using boosted regression trees, we constructed species distribution models (SDMs) for each species and (if appropriate) major intraspecific lineage, then projected changes in environmental suitability under two climate change scenarios (SSP370 and SSP585) and timeframes (mid-century and late-century). Contrary to our expectation, future suitability projections varied widely among species, with small-to-moderate projected gains in suitability for A. montanus, relatively small changes with ambiguous directionality for D. copei, large gains in multiple regions for P. idahoensis, and major losses-in-place for P. vandykei. In addition, lineage-specific SDMs that assumed different niches for coastal and Cascades P. vandykei populations projected climate vulnerability for only the latter, highlighting a need for better genetic and ecological data. Given our collective findings, attempts to generalize climate change projections for purported “indicator species” to larger groups can be misleading, even within narrowly-defined and highly specialized ensembles. Moreover, we found a strong link between recent historical SDM outputs and species-tailored variables (e.g., seep-related variables), but many of these variables lacked future projections under climate change and were thus not directly usable to forecast climate change responses. Lastly, our findings also highlight research and conservation needs for our study species under climate change, such as identifying taxonomic scales of niche variation and protecting in-situ climatic refugia.

Climate change is substantially impacting earth’s biodiversity, with a massive number of affected species that are difficult to study comprehensively. An “indicator species” approach that generalizes species-specific climate change impacts to broader groups (e.g., ensembles) could theoretically help overcome this challenge and streamline climate-smart conservation planning. We assessed the viability of this approach using four specialist amphibians (Ascaphus montanus, Dicamptodon copei, Plethodon idahoensis, and Plethodon vandykei), which we expected would have similar climate-related trajectories given their shared dependence on a narrow range of groundwater-driven habitats. Using boosted regression trees, we constructed species distribution models (SDMs) for each species and (if appropriate) major intraspecific lineage, then projected changes in environmental suitability under two climate change scenarios (SSP370 and SSP585) and timeframes (mid-century and late-century). Contrary to our expectation, future suitability projections varied widely among species, with small-to-moderate projected gains in suitability for A. montanus, relatively small changes with ambiguous directionality for D. copei, large gains in multiple regions for P. idahoensis, and major losses-in-place for P. vandykei. In addition, lineage-specific SDMs that assumed different niches for coastal and Cascades P. vandykei populations projected climate vulnerability for only the latter, highlighting a need for better genetic and ecological data. Given our collective findings, attempts to generalize climate change projections for purported “indicator species” to larger groups can be misleading, even within narrowly-defined and highly specialized ensembles. Moreover, we found a strong link between recent historical SDM outputs and species-tailored variables (e.g., seep-related variables), but many of these variables lacked future projections under climate change and were thus not directly usable to forecast climate change responses. Lastly, our findings also highlight research and conservation needs for our study species under climate change, such as identifying taxonomic scales of niche variation and protecting in-situ climatic refugia.

Climate change is substantially impacting earth’s biodiversity, with a massive number of affected species that are difficult to study comprehensively. An “indicator species” approach that generalizes species-specific climate change impacts to broader groups (e.g., ensembles) could theoretically help overcome this challenge and streamline climate-smart conservation planning. We assessed the viability of this approach using four specialist amphibians (Ascaphus montanus, Dicamptodon copei, Plethodon idahoensis, and Plethodon vandykei), which we expected would have similar climate-related trajectories given their shared dependence on a narrow range of groundwater-driven habitats. Using boosted regression trees, we constructed species distribution models (SDMs) for each species and (if appropriate) major intraspecific lineage, then projected changes in environmental suitability under two climate change scenarios (SSP370 and SSP585) and timeframes (mid-century and late-century). Contrary to our expectation, future suitability projections varied widely among species, with small-to-moderate projected gains in suitability for A. montanus, relatively small changes with ambiguous directionality for D. copei, large gains in multiple regions for P. idahoensis, and major losses-in-place for P. vandykei. In addition, lineage-specific SDMs that assumed different niches for coastal and Cascades P. vandykei populations projected climate vulnerability for only the latter, highlighting a need for better genetic and ecological data. Given our collective findings, attempts to generalize climate change projections for purported “indicator species” to larger groups can be misleading, even within narrowly-defined and highly specialized ensembles. Moreover, we found a strong link between recent historical SDM outputs and species-tailored variables (e.g., seep-related variables), but many of these variables lacked future projections under climate change and were thus not directly usable to forecast climate change responses. Lastly, our findings also highlight research and conservation needs for our study species under climate change, such as identifying taxonomic scales of niche variation and protecting in-situ climatic refugia.

Environmental and demographic information used in population projections. This dataset is associated with the following publication: Awkerman, J., and C. Greenberg. Projected Climate and Hydroregime Variability Constrain Ephemeral Wetland-Dependent Amphibian Populations in Simulations of Southern Toads. Ecologies. MDPI, Basel, SWITZERLAND, 3(2): 235-248, (2022).

Environmental and demographic information used in population projections. This dataset is associated with the following publication: Awkerman, J., and C. Greenberg. Projected Climate and Hydroregime Variability Constrain Ephemeral Wetland-Dependent Amphibian Populations in Simulations of Southern Toads. Ecologies. MDPI, Basel, SWITZERLAND, 3(2): 235-248, (2022).

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

These data include field-collected observations of the occurrence of adult and larval amphibians at 174 sites in 14 watersheds at Yosemite National Park from 2007 through 2021. Also included in the data are potential variables affecting site occurrence, probability of reproduction, and probability of detection of amphibians, including static site-specific variables like site size and elevation, and dynamic variables including surveyors, when surveys occurred, and site- and year-specific weather variables. These data were used to fit the models in the accompanying publication and can be used with the associated code to replicate the results found in the publication.