Biotic interactions drive multitrophic species community assembly. Yet, explicitly incorporating this process in species distribution models (SDMs) is particularly challenging, even when biotic interactions are known. Here, we propose a framework that combines knowledge of trophic interactions with Bayesian structural equation models to model each species as a function of its prey or predators and environmental conditions. We tested and validated our framework on realistic simulated communities spanning different theoretical models and ecological setups. We showed that our framework improves the inference of both species’ potential and realized niches compared to single SDMs (mean performances increased by 8% and 6% respectively), especially for species with strong biotic control, thus increasing model predictive performance. Our framework can easily integrate various SDM extensions (e.g., occupancy models) and algorithms, and stands out as a novel solution for modeling multitrophic community distributions when trophic interactions are known or assumed.
