Inferring fluctuating interaction probabilities in ecological networks across environmental change

Abstract

Knowledge of species' interactions unlocks our understanding of how ecological communities respond to climate change or habitat loss, explaining their resilience and robustness. Such knowledge requires inferring the presence, sign, and per capita strength of species interactions, as well as species' intrinsic growth rates. While various studies have attempted to infer these parameters in isolation, none has successfully inferred them simultaneously. Here, we solve this grand challenge using an integrative approach combining ecological mechanistic models and statistical inference to simultaneously infer these parameters across time, capturing environmental change. We validate our approach on synthetic data in constant and changing environments with both abrupt and gradual changes. Applying our method to a manipulated microcosm experiment demonstrates its ability to infer environmental change in empirical data. Finally, its application to a mesocosm experiment recovers the expectations from biological knowledge and unveils network rewiring due to unplanned environmental change. Our approach goes one step further in bridging the gap between mechanistic models and empirical ecology. It advances the understanding of ecological networks and their dynamics, thereby helping to validate existing hypotheses, spark new theories, and guide ecological management and conservation.