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Data from: Using spatially-nested hierarchical species distribution models to estimate current and future distributions of a cryptic species at a regional scale

Banks, Kaleb1; Edwards, Owen21; Zhang, Bo1; Reichert, Michael1

Published Apr 13, 2026 on Dryad. https://doi.org/10.5061/dryad.zw3r228nh

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Apr 13, 2026 version files 5.56 MB

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Abstract

Understanding a species’ conservation status requires evaluating its ecological relationships, contemporary distribution, and vulnerability to future environmental change. Species distribution models (SDMs) are widely used for these purposes, but regional-scale applications often suffer from extrapolation and niche truncation, reducing model transferability. Spatially-Nested Hierarchical SDMs (N-SDMs), which integrate data across multiple spatial scales, offer a promising solution but remain underutilized in regional conservation research. Crawfish frogs (Rana areolata) are a cryptic grassland species reliant on crayfish burrows that have experienced declines across their range and are data deficient in Oklahoma. This study combines comprehensive regional field surveys across Oklahoma with large-scale occurrence data from GBIF using an N-SDM framework to characterize the species’ current regional distribution, identify factors influencing habitat suitability, and forecast future range shifts under climate and land-use change. Additionally, we compared the performance of N-SDMs to regional-only and rangewide SDMs, and assessed how niche truncation and extrapolation influence model performance and transferability under future environmental conditions. We documented R. areolata at 303 survey locations and found no evidence of historical county-level extirpations, with our models suggesting large amounts of suitable habitat in eastern Oklahoma. Our rangewide SDM lacked the resolution and regional predictive performance necessary for regional conservation planning. While our regional-only SDM had higher predictive performance, it suffered from substantial extrapolation and niche truncation, leading to predictions of significant habitat loss under future conditions. In contrast, our N-SDMs had the highest regional predictive performance, and mitigated the effects of niche truncation and extrapolation, and projected no change or a slight increase in future habitat suitability. Our findings highlight the advantages of N-SDMs for improving model predictions and informing conservation assessments. Failure to account for niche truncation and extrapolation can lead to poor predictions and misguided conservation decisions. We advocate for the broader adoption of this approach in regional-scale studies to improve predictions of species responses to environmental change, and more effectively assess species status at a regional level.