Understanding the suitable habitat of endangered species is crucial for agencies such as the Bureau of Land Management to plan management and conservation. However, few species distribution models are directly validated, potentially limiting their application. In preparation for a Species Status Assessment of clay‐loving wild buckwheat (Eriogonum pelinophilum), an endangered subshrub found in southwest Colorado, we ran a series of species distribution models to estimate the species' potential occupied habitat and validated these models in the field. A 1‐meter resolution digital elevation model derived from LiDAR and a high‐resolution geology mapping helped identify biologically relevant characteristics of the species' habitat. We employed a weighted ensemble model based on two Random Forest and one Boosted Regression Tree model, and the discrimination performance of the ensemble model was high (AUC-PR = 0.793). We then conducted a systematic field survey of model habitat suitability predictions, during which we discovered 55 new subpopulations of the species and demonstrated that new species observations were strongly associated with model predictions (p < .0001, Cliff's delta = 0.575). We then further refined our original models by incorporating the additional species occurrences collected in the field survey, a new explanatory variable, and a more diverse set of models. These iterative changes to the model marginally improved performance (AUC‐PR = 0.825). Direct validation of species distribution models is extremely rare, and our field survey provides strong validation of our model results. This helps increase confidence in utilizing predictions in planning. The final model predictions greatly improve the Bureau of Land Management's understanding of the species' habitat and increase our ability to consider potential habitat in planning land use activities such as road development and travel management.

This dataset includes LiDAR data collected in Delta and Montrose counties in Colorado, United States. A digital elevation model (DEM) from this LiDAR collection is included here. Slope, eastness, and northness derived from the DEM are also included. This dataset was available at 1-m resolution.

A dataset of geologic mapping was included. Only one geologic stratum, the Smoky Hill member of the Mancos Shale formation (formation 'kms'), was relevant for this analysis. We selected only this stratum, then calculated a buffer of distance from it. We transformed this into four classes: 0 to 1 meters from the formation, 1 to 10 meters from it, 10 to 100 meters away, and farther than 100 meters. This buffer was then rasterized at 1-m resolution.

30-year normal precipitation data (1991-2020) from PRISM are included too (PRISM Climate Group. 2016. PRISM climate data. Oregon State University). Originally available at 800-m resolution, this was resampled to 1-m resolution by nearest neighbor interpolation.

A clear Landsat scene from the study area was utilized to calculate the soil color index. This was calculated using the Red and Green bands as follows: (Red - Green) / (Red + Green). Originally available at 30-m resolution, this was resampled to 1-m resolution by nearest neighbor interpolation.

Points of species presence used in the analysis are provided, but have had a 1-km buffer applied to them because the species of interest is an endangered species in an accessible area with recreation and other land uses that may threaten the species.