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 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

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

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 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

We produced 13 hierarchically nested cluster levels that reflect the results from developing a hierarchical monitoring framework for greater sage-grouse across the western United States. Polygons (clusters) within each cluster level group a population of sage-grouse leks (sage-grouse breeding grounds) and each level increasingly groups lek clusters from previous levels. We developed the hierarchical clustering approach by identifying biologically relevant population units aimed to use a statistical and repeatable approach and include biologically relevant landscape and habitat characteristics. We desired a framework that was spatially hierarchical, discretized the landscape while capturing connectivity (habitat and movements), and supported management questions at different spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different population growth rates among smaller clusters. Equally so, the spatial structure and ecological organization driving scale-dependent systems in a fragmented landscape affects dispersal behavior, suggesting inclusion in population monitoring frameworks. Studies that compare conditions among spatially explicit hierarchical clusters may elucidate the cause of differing growth rates at local scales affected by changes in habitat quality compared to larger scaled processes affecting growth rates, such as regional climate/vegetation communities. Therefore, the use of multiple scales (hierarchical cluster levels) that group demographic data can provide information driving population changes at different spatial scales, thereby providing a tool for population monitoring and adaptive management.

We produced 13 hierarchically nested cluster levels that reflect the results from developing a hierarchical monitoring framework for greater sage-grouse across the western United States. Polygons (clusters) within each cluster level group a population of sage-grouse leks (sage-grouse breeding grounds) and each level increasingly groups lek clusters from previous levels. We developed the hierarchical clustering approach by identifying biologically relevant population units aimed to use a statistical and repeatable approach and include biologically relevant landscape and habitat characteristics. We desired a framework that was spatially hierarchical, discretized the landscape while capturing connectivity (habitat and movements), and supported management questions at different spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different population growth rates among smaller clusters. Equally so, the spatial structure and ecological organization driving scale-dependent systems in a fragmented landscape affects dispersal behavior, suggesting inclusion in population monitoring frameworks. Studies that compare conditions among spatially explicit hierarchical clusters may elucidate the cause of differing growth rates at local scales affected by changes in habitat quality compared to larger scaled processes affecting growth rates, such as regional climate/vegetation communities. Therefore, the use of multiple scales (hierarchical cluster levels) that group demographic data can provide information driving population changes at different spatial scales, thereby providing a tool for population monitoring and adaptive management.