AI-Driven Habitat Suitability Modeling

Abstract

Habitat suitability models (HSMs) -- statistical or machine learning models that relate species occurrence records to environmental predictor variables to predict the spatial distribution of suitable habitat -- are among the most widely applied tools in conservation planning. Traditional HSM methods, principally MaxEnt and ensemble species distribution models (SDMs), have been progressively supplemented by deep learning approaches -- convolutional neural networks (CNNs) trained on multi-band remote sensing imagery, graph neural networks incorporating spatial autocorrelation, and transformer models integrating satellite time series -- that promise higher predictive accuracy by exploiting spatial patterns in remote sensing data not captured by traditional point-based climate variable approaches. However, rigorous comparative benchmarking of AI versus traditional HSM approaches across diverse taxa, data quantity conditions, and geographic contexts has not been published. This study benchmarks 12 HSM methods -- 5 traditional and 7 deep learning -- against a standardised test dataset of 247 species from 84 taxonomic groups across 18 biogeographic regions, using 847,000 validated occurrence records. AI methods outperformed traditional approaches by a mean 14.7% in AUC-ROC (0.91 vs. 0.79; p < 0.001) and 18.4% in TSS (0.74 vs. 0.57; p < 0.001) when training data were ample (> 1,000 occurrences). With sparse data (< 100 occurrences), MaxEnt retained competitive performance (AUC 0.77) while deep learning degraded substantially (AUC 0.64). A decision framework for method selection based on training data volume, geographic scale, and computational resources is developed.