AI-Driven Knowledge Synthesis for Food WebParameterisation

Abstract

We introduce a proof-of-concept framework, Synthesising Parameters for Ecosystem modelling with LLMs (SPELL), that automates species grouping and diet matrix generation to accelerate food web construction for ecosystem models. SPELL retrieves species lists, classifies them into functional groups, and synthesizes trophic interactions by integrating global biodiversity databases (e.g., FishBase, GLOBI), species interaction repositories, and optionally curated local knowledge using Large Language Models (LLMs). We validate the approach through a marine case study across four Australian regions, achieving high reproducibility in species grouping (>99.7%) and moderate consistency in trophic interactions (51-59%). Comparison with an expert-derived food web for the Great Australian Bight indicates strong but incomplete ecological accuracy: 92.6% of group assignments were at least partially correct and 82% of trophic links were identified. Specialized groups such as benthic organisms, parasites, and taxa with variable feeding strategies remain challenging. These findings highlight the importance of expert review for fine-scale accuracy and suggest SPELL is a generalizable tool for rapid prototyping of trophic structures in marine and potentially non-marine ecosystems. HighlightsO_LILLM-based framework automates species grouping and diet matrix creation with >99.7% consistency C_LIO_LI51-59% of trophic interactions show high stability (stability score > 0.7) across iterations C_LIO_LIIn expert comparison, SPELL achieved 81.6% agreement and 80% of diet differences < 0.2 C_LIO_LILLM-driven synthesis integrates global databases with unstructured local knowledge C_LIO_LIReduces ecosystem model development time from months to hours C_LI