These data include environmental covariates used to develop species distribution models for Gopherus agassizii and Gopherus morafkai, along with PCA-reduced environmental covariates used to explore local species-environment relationships within a subregion of the ectone between the two species. We also provide the genotype association used to test the mapped clusters of multiscale geographically weighted regression coefficients against models of (i) a geographically-based taxonomic designation these two sister species, and (ii) an environmental ecoregion designation. These data support the following publication: Inman et al. 2019. Local niche differences predict genotype associations in sister taxa of desert tortoise. Diversity and Distributions. https://doi.org/10.1111/ddi.12927

This dataset provides spatial predictions of clustering and the genotype association index for the Mojave genotype in local species-environment relationships of Desert Tortoises (Gopherus agassizi and Gopherus morafkaii) for individuals in the subregion encompassing the genetic sampling locations used by Edwards et al. (2015). This region offered an opportunity to explore habitat selection across the ecotone between the Mojave and Sonoran deserts and the secondary contact zone between G. agassizii and G. morafkai, and is referred to as the focal study area. The raster layers contained here accompany the manuscript Inman et al. 2019 and were used to identify multivariate clusters and map them back to geographic space. Inman et al. 2019. Local niche differences predict genotype associations in sister taxa of desert tortoise. Diversity and Distributions. https://doi.org/10.1111/ddi.12927

This dataset provides the environmental explanatory variables used to explore spatial patterns in species-environment relationships in Gopherus agassizii and Gopherus morafkai across the subregion encompassing the genetic sampling locations used by Edwards et al. (2015). This region offered an opportunity to explore habitat selection across the ecotone between the Mojave and Sonoran deserts and the secondary contact zone between G. agassizii and G. morafkai, and is referred to as the focal study area. The raster layers contained here accompany the manuscript Inman et al. 2019 and were used to identify multivariate clusters and map them back to geographic space. Inman et al. 2019. Local niche differences predict genotype associations in sister taxa of desert tortoise. Diversity and Distributions. xxx.xxx

This dataset provides spatial predictions of clustering and the genotype association index for the Mojave genotype in local species-environment relationships of Desert Tortoises (Gopherus agassizi and Gopherus morafkaii) for individuals in the subregion encompassing the genetic sampling locations used by Edwards et al. (2015). This region offered an opportunity to explore habitat selection across the ecotone between the Mojave and Sonoran deserts and the secondary contact zone between G. agassizii and G. morafkai, and is referred to as the focal study area. The raster layers contained here accompany the manuscript Inman et al. 2019 and were used to identify multivariate clusters and map them back to geographic space. Inman et al. 2019. Local niche differences predict genotype associations in sister taxa of desert tortoise. Diversity and Distributions. https://doi.org/10.1111/ddi.12927

This dataset provides the environmental explanatory variables used to explore spatial patterns in species-environment relationships in Gopherus agassizii and Gopherus morafkai across the subregion encompassing the genetic sampling locations used by Edwards et al. (2015). This region offered an opportunity to explore habitat selection across the ecotone between the Mojave and Sonoran deserts and the secondary contact zone between G. agassizii and G. morafkai, and is referred to as the focal study area. The raster layers contained here accompany the manuscript Inman et al. 2019 and were used to identify multivariate clusters and map them back to geographic space. Inman et al. 2019. Local niche differences predict genotype associations in sister taxa of desert tortoise. Diversity and Distributions. xxx.xxx

This dataset provides spatial predictions of the pooled-SDM residuals from a multiscale geographically weighted regression model (MGWR) and the resulting local R2 values for individuals in the subregion encompassing the genetic sampling locations used by Edwards et al. (2015). This region offered an opportunity to explore habitat selection across the ecotone between the Mojave and Sonoran deserts and the secondary contact zone between G. agassizii and G. morafkai, and is referred to as the focal study area. The raster layers contained here accompany the manuscript Inman et al. 2019 and were used to identify multivariate clusters and map them back to geographic space. Inman et al. 2019. Local niche differences predict genotype associations in sister taxa of desert tortoise. Diversity and Distributions. https://doi.org/10.1111/ddi.12927

This dataset provides spatial predictions of the pooled-SDM residuals from a multiscale geographically weighted regression model (MGWR) and the resulting local R2 values for individuals in the subregion encompassing the genetic sampling locations used by Edwards et al. (2015). This region offered an opportunity to explore habitat selection across the ecotone between the Mojave and Sonoran deserts and the secondary contact zone between G. agassizii and G. morafkai, and is referred to as the focal study area. The raster layers contained here accompany the manuscript Inman et al. 2019 and were used to identify multivariate clusters and map them back to geographic space. Inman et al. 2019. Local niche differences predict genotype associations in sister taxa of desert tortoise. Diversity and Distributions. https://doi.org/10.1111/ddi.12927

This dataset provides spatial predictions of habitat suitability for Gopherus agassizii (Agassiz’s desert tortoise), Gopherus morafkai (Morafka’s desert tortoise) and a pooled-species model under current conditions (1950 – 2000 yr). The raster layers contained here accompany the manuscript Inman et al. 2019 and were used to evaluate subtle ecological niche differences between Gopherus agassizii and Gopherus morafkai, and identify local species-environment relationships. Spatial predictions of habitat suitability were created using MaxEnt version 3.4.0 (Phillips et al., 2006), a widely-used software for SDM in presence-background frameworks. Detailed methods are provided in Inman et al. 2019. Inman et al. 2019. Local niche differences predict genotype associations in sister taxa of desert tortoise. Diversity and Distributions. https://doi.org/10.1111/ddi.12927

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).

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).