A neuroevolution-driven agent-based model of coral larvae settlement

Coral reefs face significant threats due to climate change and human activities, which require innovative approaches to study and protect these ecosystems. Successful settlement is the bottleneck linking a reef's free-swimming larvae to the benthic community, ultimately determining the next generation of corals and the long-term resilience of the entire ecosystem. This work introduces an agent-based model (ABM) driven by neuroevolution (NE) to simulate coral larval settlement behavior under various environmental conditions. Inspired by biological processes, the model combines sensory input with a neural network (NN) to guide larval actions, optimizing settlement success through evolutionary algorithms. The model replicates three experimental setups from previous studies, validating it against key metrics such as settlement success, vertical distribution, and orientation to environmental cues. The evolved controllers capture the characteristic cue responses of each experiment: crustose coralline algae (CCA)-driven settlement, the bimodal vertical distribution, and orientation towards reef sound, while also exposing the limits of the simplified environment, as its reduced hydrodynamics. Sensitivity analyses further indicate that these behaviors are driven by different mechanisms across the experiments, illustrating how the model yields interpretable, testable outcomes. This approach provides a basis for integrating adaptive behaviors into coral larval simulations, and future work will focus on refining the model to enhance biological realism and scalability.