Revealing Complex Ecological Dynamics via Symbolic Regression

Complex ecosystems, from food webs to our gut microbiota, are essential to human life. Understanding the dynamics of those ecosystems can help us better maintain or control them. Yet, reverse-engineering complex ecosystems (i.e., extracting their dynamic models) directly from measured temporal data has not been very successful so far. Here we propose to close this gap via symbolic regression. We validate our method using both synthetic and real data. We firstly show this method allows reverse engineering two-species ecosystems, inferring both the structure and the parameters of ordinary differential equation models that reveal the mechanisms behind the system dynamics. We find that as the size of the ecosystem increases or the complexity of the inter-species interactions grow, using a dictionary of known functional responses (either previously reported or reverse-engineered from small ecosystems using symbolic regression) opens the door to correctly reverse-engineer large ecosystems.