Modeling climate-driven range shifts in populations of two bird species limited by habitat independent of climate

Ranges of species around the world are expected to contract in response to climate change. Species distribution models (SDMs) are a powerful tool for predicting changes in habitat availability, but the variables selected to create SDMs influence their performance. In addition to climate, habitat characteristics and species traits can play a role in predicting species distribution. In this paper, we consider how variable selection influences the accuracy of SDMs when applied to isolated subpopulations of two widely distributed bird species: great gray owl (Strix nebulosa) and willow flycatcher (Empidonax traillii). In the Sierra Nevada of California, these species are restricted largely to discrete patches of meadow habitat within a forest matrix, providing the potential to identify specific locations to target conservation efforts. We contrast predictions made by SDMs that consider climatic variables alone with those that incorporate both climate and geophysical variables. Adding geophysical variables resulted in differing model predictions. For willow flycatchers, adding geophysical variables improved predictive performance. In the case of great gray owls, models with and without geophysical variables had nearly identical performance under historical conditions but differed starkly in their predictions. The full model (climatic and geophysical variables) predicted habitat availability to decrease moderately, whereas the climate-only model predicted nearly complete loss of favorable habitat by 2099. The climate-only model is consistent with expectations based on previous SDMs of birds across North America, but previous studies also assumed homogeneity in species traits and range-wide habitat requirements. The full model appears more consistent with recent trends in great gray owl numbers in the Sierra Nevada specifically, where the population has remained relatively stable over recent decades. Given contradictions in our model predictions, care should be taken when trying to apply similar SDMs to other systems.