A field-validated ensemble species distribution model of Eriogonum pelinophilum, an endangered subshrub in Colorado, USA

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.