Prototype-based Explainable Deep Learning for Sex Classification of Mountain Gazelles in the Wild

Standard deep learning tools in automated wildlife monitoring often function as opaque “black boxes,” hindering the trust required for ecological management. To address this challenge, this study presents an explainable visual AI framework that classifies the sex of endangered Mountain Gazelles from unconstrained camera trap imagery. The approach extends prototype-based deep learning by adapting visual exemplars across multiple spatial scales, capturing coherent anatomical features rather than rigid image patches. The model achieves 75% classification accuracy while transparently grounding predictions in recognized morphological traits, including male horn structures and female neck profiles. This multi-size architecture demonstrates that transparent artificial intelligence can deliver biologically meaningful demographic monitoring without forfeiting predictive performance.