Natural resource managers worldwide grapple with protecting aquatic environments and species in the face of intensifying land use and climatic changes. The hierarchical landscape theory suggests that a species distribution is impacted by multiple series of environmental factors affecting the species at multiple spatial resolutions. For a species to inhabit an area, it must pass through these environmental filters similar to a set of sieves. Utilizing this theory as a modeling framework, we created Nested Ensemble Species Distribution Models (NESDMs). This approach assesses species distributions at multiple spatial scales, consecutively limiting the predicted habitat range as it passes through a series of static resolution models that increase in spatial resolution. We found land use/land cover (LULC), climate, and topographic variables were useful in predicting the distribution for all of our focal species (American eel, tessellated darter, and white perch) at each spatial resolutions tested (1000m, 100m, and 10m). The variables that influenced model accuracy changed at finer spatial resolutions and unsuitable areas were removed. Additionally, total area predicted as suitable for all three species assessed was greatly reduced from the area selected at the coarsest resolution (85-90% reduction) to the area selected at the finest resolution. In utilizing the NESDM approach, computation time and processing power were substantially reduced compared to modeling at the entire spatial extent of the study. Modeling accuracy of both individual distribution methods (e.g., Maxent, Random Forest, GLM, etc.) and ensembles of multiple methods showed adequate predictive accuracy based on our model assessment criteria. The demonstrated NESDM framework potentially increases the precision and utility of the final outputs for conservation management and strategic resource allocation. The data describes topographical, climatic, land use and land cover, flow accumulation, watershed influence. The data in this released was used in Kiser et al., (2024) Nested Ensemble Species Distribution Models: A Multi-Resolution Approach to Species Distribution Modeling study.
