Modeling Community Dynamics Through Environmental Effects, Species Interactions and Movement

Abstract

Understanding how communities respond to environmental change is frustrated by the fact that both species interactions and movement affect biodiversity in unseen ways. To evaluate the contributions of species interactions on community growth, dynamic models that can capture nonlinear responses to the environment and the redistribution of species across a spatial range are required. We develop a time-series framework that models the effects of environment–species interactions as well as species–species interactions on population growth within a community. Novel aspects of our model include allowing for species redistribution across a spatial region, and addressing the issue of zero inflation. We adopt a hierarchical Bayesian approach, enabling probabilistic uncertainty quantification in the model parameters. To evaluate the impacts of interactions and movement on population growth, we apply our model using data from eBird, a global citizen science database. To do so, we also present a novel method of aggregating the spatially biased eBird data collected at point-level. Using an illustrative region in North Carolina, we model communities of six bird species. The results provide evidence of nonlinear responses to interactions with the environment and other species and demonstrate a pattern of strong intraspecific competition coupled with many weak interspecific species interactions.