Spatially varying coefficients can improve parsimony and descriptive power for species distribution models

Species distribution models (SDMs) are widely used to relate species occurrence and density to local environmental conditions, and often include a spatially correlated variable to account for spatial patterns in residuals. Ecologists have extended SDMs to include spatially varying coefficients (SVCs), where the response to a given covariate varies smoothly over space and time. However, SVCs see relatively little use perhaps because they remain less known relative to other SDM techniques. We therefore review ecological contexts where SVCs can improve the interpretability and descriptive power from SDMs, including local responses to regional indices that represent ecological teleconnections; density‐dependent habitat selection; spatially varying detectability; and context‐dependent covariate responses that represent interactions with unmeasured covariates. We then illustrate three additional examples in detail using the vector autoregressive spatio‐temporal (VAST) model. First, a spatially varying decadal trends model identifies decadal trends for arrowtooth flounder Atheresthes stomias density in the Bering Sea from 1982 to 2019. Second, a trait‐based joint SDM highlights the role of body size and temperature in spatial community assembly in the Gulf of Alaska. Third, an age‐structured SDM for walleye pollock Gadus chalcogrammus in the Bering Sea contrasts cohorts with broad spatial distributions (1996 and 2009) and those that are more spatially constrained (2002 and 2015). We conclude that SVCs extend SDMs to address a wide variety of ecological contexts and can be used to better understand a range of ecological processes, e.g. density dependence, community assembly and population dynamics.