Elsen, PR; Farwell, LS; Pidgeon, AM; Radeloff, VC (2020). Landsat 8 TIRS-derived relative temperature and thermal heterogeneity predict winter bird species richness patterns across the conterminous United States. REMOTE SENSING OF ENVIRONMENT, 236, 111514.

The thermal environment limits species ranges through its influence on physiology and resource distributions and thus affects species richness patterns over broad spatial scales. Understanding how temperature drives species richness patterns is particularly important in the context of global change and for effective conservation planning. Landsat 8's Thermal Infrared Sensor (TIRS) allows direct mapping of temperature at moderate spatial resolutions (100 m, downscaled by the USGS to 30 m), overcoming limitations inherent in coarse interpolated weather station data that poorly capture fine-scale temperature patterns over broad areas. TIRS data thus offer the unique opportunity to understand how the thermal environment influences species richness patterns. Our aim was to develop and assess the ability of TIRS-based temperature metrics to predict patterns of winter bird richness across the conterminous United States during winter, a period of marked temperature stress for birds. We used TIRS data from 2013-2018 to derive metrics of relative temperature and intra-seasonal thermal heterogeneity. To quantify winter bird richness across the conterminous US, we tabulated the richness only for resident bird species, i.e., those species that do not move between the winter and breeding seasons, from the North American Breeding Bird Survey, the most extensive survey of birds in the US. We expected that relative temperature and thermal heterogeneity would have strong positive associations with winter bird richness because colder temperatures heighten temperature stress for birds, and thermal heterogeneity is a proxy for thermal niches and potential thermal refugia that can support 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 sensitivity, such as small-bodied species and climate-threatened species (i.e., those with large discrepancies between their current and future distributions following projected climate change). Consistent with our predictions, relative temperature and thermal heterogeneity strongly positively influenced winter bird richness patterns, with statistical models explaining 37.3% of the variance in resident bird richness. Thermal heterogeneity was the strongest predictor of small-bodied and climate-threatened species in our models, whereas relative temperature was the strongest predictor of large-bodied and climate-stable species. Our results demonstrate the important role that the thermal environment plays in governing winter bird richness patterns and highlight the previously underappreciated role that intra-seasonal thermal heterogeneity may have in supporting high winter bird species richness. Our findings thus illustrate the exciting potential for TIRS data to guide conservation planning in an era of global change.