Data-Driven Discovery of Mechanistic Ecosystem Models with LLMs

Abstract

Ecosystem models are essential for ecosystem management, but their development traditionally requires significant time and expertise, creating bottlenecks in addressing urgent environmental challenges. We present LEMMA (LLM Enabled Mechanistic Modelling for ecosystem Assessment), a framework that programmatically generates and iteratively refines mechanistic ecosystem models by combining large language models (LLMs) for equation synthesis and parameter search, evolutionary algorithms for structural optimization, and Template Model Builder (TMB) for efficient parameter estimation. We critically review LEMMAs ability to recover known ecological relationships through two complementary marine case studies: (1) a nutrient-phytoplankton-zooplankton model, and (2) a Crown-of-Thorns starfish (COTS) model. In the first case, our best models displayed almost perfect recovery of known ecological dynamics while maintaining strong predictive performance across multivariate time-series. In the second case, best LEMMA generated models approached human expert models in terms of their ability to successfully capture COTS outbreak dynamics and demonstrated strong out-of-sample predictive power. LEMMA produces interpretable models with meaningful parameters that capture real biological processes, facilitating scientific insight and potentially accelerating management applications. By dramatically accelerating model development while offering ecological interpretability, LEMMA offers a powerful new tool for addressing urgent ecological challenges in a changing world.