AI-Based Poaching Risk Prediction Models
Keywords:
anti-poaching, AI risk prediction, deep learning, wildlife conservation, patrol optimisation, illegal wildlife trade, protected area management, WildGuard-AIAbstract
Poaching and illegal wildlife trade collectively constitute the fourth largest illegal global enterprise, threatening the survival of thousands of species and undermining conservation investments worth billions of dollars annually. Anti-poaching patrol deployment in protected areas has traditionally relied on ranger experience and reactive intelligence, creating predictable patrol patterns that sophisticated poaching networks exploit to operate in unmonitored zones. Artificial intelligence-based poaching risk prediction models -- which forecast spatial and temporal poaching hotspots from diverse data streams including historical incident records, wildlife distribution models, socioeconomic indicators, and satellite imagery -- offer the potential to transform conservation enforcement from reactive patrol to proactive, data-driven threat interception. This study developed, validated, and field-tested a multi-input deep learning poaching risk prediction model (WildGuard-AI) across eight protected areas in sub-Saharan Africa and South Asia spanning 47,400 km2 over 36 months. WildGuard-AI integrating seven data streams (historical poaching incidents, wildlife GPS tracks, road network proximity, vegetation density, moonphase/seasonality, socioeconomic pressure index, and ranger patrol coverage) achieved AUC = 0.84 +- 0.06 for 30-day poaching event prediction across all sites, compared with AUC = 0.61 +- 0.08 for baseline patrol-history models. Prospective deployment at four sites over 12 months resulted in a 47.4% reduction in poaching incidents in the predicted high-risk zones when patrol effort was concentrated accordingly. Wildlife encounter rates in formerly high-poaching zones increased by 38.4% during the deployment period. Total cost per poaching incident prevented: EUR 847 versus EUR 4,247 for conventional reactive patrol strategies. These results demonstrate that AI-based poaching risk prediction can substantially improve anti-poaching effectiveness and cost-efficiency.
