Macroecological models for predicting species distributions usually only include abiotic environmental conditions as explanatory variables, despite knowledge from community ecology that all species are linked to other species through biotic interactions. This disconnect is largely due to the different spatial scales considered by the two sub-disciplines: macroecologists study patterns at large extents and coarse resolutions, while community ecologists focus on small extents and fine resolutions. A general framework for including biotic interactions and the networks they form in macroecological models would help bridge this divide, as it would allow for rigorous testing of the role that biotic interactions play in determining species ranges. Here we present an approach that combines species distribution models with Bayesian networks, which enables the direct and indirect effects of biotic interactions to be modelled as propagating conditional dependencies among species' presences.
We demonstrate our approach using a species pool of grassland plants in the western United States for which community-level interactions are well defined from 18 years of research in a California study system where all species co-occur. Our species pool contains 52 plants and the most abundant shared consumer of these species at the study site, a generalist grasshopper. This record of community-level understanding provides an opportunity to test interactions derived from statistical associations of species occurrences at macro scales for community-level ecological realism. Of 52 derived interactions, 15 match known interspecific processes and 32 reflect shared habitat suitability expectations. We find that including biotic interactions in models with our approach improves predictions of species occurrence for 11 of 14 focal species in this example system. Finally, we use future climate scenarios to show how species' predicted distributions in 2050 are refined when biotic interactions are included in models. Our new approach will be important for improving estimates of species distributions and their dynamics under environmental change, and will have great practical value across a range of applications, from predicting range shifts under climate change to anticipating the spread of invasive species and zoonotic diseases.