Interpreting ML in Ecology: A RAG-Based Approach to Explainability in Species Distribution Modeling

Species Distribution Modeling (SDM) relies increasingly on Machine Learning (ML), but many models remain opaque, limiting their usability for non-experts. While SHAP and LIME improve interpretability, they still require technical expertise. This study proposes an agentic Retrieval-Augmented Generation (RAG) framework, integrating ML models (Logistic Regression, Random Forests, MLP), XAI techniques (SHAP, LIME), and a LLM-powered explanation system to enhance explainability. Using GoAmazon 2014/15 environmental data and GBIF species occurrences, we evaluated explanations based on completeness, and context-awareness, achieving a significant improvement in this study case. Results indicate that LLMs combined with XAI can significantly enhance explainability in SDM.