The thermal environment strongly structures species ranges through its influence on physiology and resource distributions and is expected to give rise to species richness patterns over broad spatial scales. Understanding whether and how temperature drives species richness patterns is thus particularly important in the context of global change and for effective conservation planning. Thermal Infrared (TIR) data acquired by the Landsat 8 satellite now facilitate direct mapping of thermal conditions at high spatial resolutions (30 m), overcoming previous limitations associated with coarse interpolated weather station data that poorly capture fine-scale thermal patterns and thermal proxies that inadequately reflect true thermal conditions. TIR data thus offer the unique opportunity to understand how properties of the thermal environment influence species richness patterns. We used TIR data from 2013-2018 to derive metrics of relative temperature and intra-seasonal spatial temperature variability and assessed their performance in predicting bird richness patterns across the conterminous United States during winter, a period of marked thermal stress for birds. We used data for resident birds with stable annual distributions from the North American Breeding Bird Survey, the most extensive survey of birds in the US.
Results/Conclusions
We expected relative temperature and spatial temperature variability should have strong positive associations with winter bird richness because colder temperatures heighten thermal stress for birds and spatial temperature variability can indicate more microsite choices and create thermal refugia, potentially supporting more species. We further expected that both the strength of the effects and the relative importance of these variables would be greater for species with greater climate sensitivities, such as small-bodied species and species more threatened by climate change. Consistent with our predictions, relative temperature and spatial temperature variability strongly positively influenced winter bird richness patterns, with statistical models explaining 37.3% of the variance in resident bird richness. Spatial temperature variability was the strongest predictor in our models of small-bodied and climate-threatened species, whereas relative temperature was the strongest predictor of large-bodied and climate-stable species. Our results demonstrate the dominant role that the thermal environment plays in governing winter bird richness patterns and highlight the previously underappreciated role of intra-seasonal spatial temperature variability in supporting high species richness during winter. In light of our findings, our approach illustrates the exciting potential for TIR data to guide conservation planning in an era of global change.