  Species distribution models (SDMs) are increasingly applied across
macroscales. Such models typically assume that a single set of regression
coefficients can adequately describe species-environment relationships and/or
population trends. However, such relationships often show nonlinear and/or
spatially-varying patterns that arise from complex interactions with abiotic
and biotic processes that operate at different scales. Spatially-varying
coefficient (SVC) models can readily account for variability in the effects of
environmental covariates. Yet, their use in ecology is relatively scarce due to
gaps in understanding the inferential benefits that SVC models can provide
compared to simpler frameworks. Here we demonstrate the inferential benefits of
SVC SDMs, with a particular focus on how this approach can be used to generate
and test ecological hypotheses regarding the drivers of spatial variability in
population trends and species-environment relationships. We illustrate the
inferential benefits of SVC SDMs with simulations and two case studies: one
that assesses spatially-varying trends of 51 forest bird species in the eastern
US over two decades and a second that evaluates spatial variability in the
effects of five decades of land cover change on Grasshopper Sparrow occurrence
across the continental US. We found strong support for SVC SDMs compared to
simpler alternatives in both empirical case studies. These applications display
the utility of SVC SDMs to help reveal the environmental factors that drive
species distributions across both local and broad scales. We conclude by
discussing the potential applications of SVC SDMs in ecology and conservation.
