Learning Eco-Replicator Dynamics with Heterogeneous Intrinsic Growth: From Biofilm Experiments to Theory

Develop a data-driven framework to infer eco-replicator dynamics of the form growth = baseline (strategy-specific) + interaction payoffs + environmental feedback, using sparse identification with constraints respecting eco-evolutionary structure. Apply to Candida–Staphylococcus mixed biofilms, explicitly including farnesol as an environmental variable. This relaxes the common assumption of equal intrinsic growth rates, allowing data to disentangle baseline growth from payoff-mediated effects. It tests whether observed patterns in mixed-species biofilms can be explained by baseline growth heterogeneity and state-dependent payoffs. The method discovers effective payoff structures and environment couplings directly from data, bridging mechanistic evolutionary game theory with empirical inference. This offers a principled route to resolve deviations from classical predictions and improves the fidelity of evolutionary game models in systems with intrinsic growth differences.

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