A Multimodal Bayesian AI Framework for Multispecies Ecological Monitoring with Indigenous Knowledge Integration

Mahule Roy, Subhas Roy

Abstract:

Contemporary conservation biology faces unprecedented challenges in monitoring biodiversity across scales while respecting
cultural sovereignty and ecological complexity. We present an integrated AI framework that synergistically combines multimodal sensor networks with deeply embedded Indigenous Ecological Knowledge (IEK) to enable real-time, multispecies monitoring across heterogeneous ecosystems. Through rigorous evaluation across 12 benchmark datasets encompassing 3,800+ species and 4.2 million samples, our framework achieves state-of-the-art performance with 94.3% macro F1-score for species identification and 89.7% for behavioral classification. Critically, the Bayesian integration of IEK as structured priors improves rare species detection by 32.7% and enhances ecological plausibility by 41.2% compared to conventional data-driven approaches. Systematic ablation studies reveal the essential nature of multimodal fusion, with combined acoustic-visual-environmental models outperforming unimodal baselines by 23.8-45.6%. The architecture maintains practical deployability, with optimized edge models achieving 15.3 FPS while preserving 91.2% of cloud-level accuracy. This work establishes a new paradigm for ethically grounded, ecologically intelligent conservation technologies that bridge artificial and ancestral intelligence.