Meeting Abstract
Species distribution models typically use correlative approaches that characterize the species-environment relationship using occurrence or abundance data for a single species (i.e., the Grinnellian niche). However, species’ distributions are determined by both abiotic conditions and interactions with other species in the community (i.e., the Eltonian niche). Therefore, climate change is expected to impact species through direct effects on their physiology and indirect effects propagated through their resources, predators, competitors and mutualists. Furthermore, the species that comprise a community may not exhibit the same response to abiotic conditions, and as a result, the strength of species interactions could change across a species’ range or with climate change. Here, we develop a multi-species dynamic distribution model that estimates the species-environment relationship simultaneously for a subset of strongly interacting species in a community. Our approach also incorporates a competition module commonly used in community ecology that models the dynamic feedbacks that arise from intraspecific density-dependence and interspecific interactions. A main innovation of this study is that our models capture the way the intra- and interspecific interaction coefficients in the community module vary with abiotic conditions. We use a hierarchical, multivariate Bayesian approach and illustrate the model with a spatially-explicit time series of data on the abundance of interacting species in the rocky intertidal zone. This work bridges the disciplines of biogeography and community ecology to develop tools to better predict the higher-order, indirect effects of climate change on ecological communities.
