Evaluating the Landscape of AI in Ecology: A Systematic Review of Machine Learning for Species Distribution Models

The application of Machine Learning (ML) to Species Distribution Modeling (SDM)—a critical task for biodiversity monitoring and wildlife management—has expanded significantly. To consolidate the state-of-the-art, this paper presents a systematic literature review of 201 articles published between 2017 and 2024. Our analysis reveals a clear and increasing trend in the adoption of AI and ML for SDM, which is predominantly framed as a classification problem. We identify Random Forests (RF) as the dominant technique, appearing in 52.2% of reviewed studies, followed by Maximum Entropy (MaxEnt) and Boosting-based models. These models consistently demonstrate high predictive performance, with the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) being the primary evaluation metric. The review also reveals a significant taxonomic bias, with the Animal kingdom (62.7% of studies) and the Chordate phylum being the most common subjects. This work provides a definitive benchmark of current methods, performance standards, and application trends, offering valuable guidance for researchers and practitioners in ecological informatics.