WildAlert: A Real-Time, AI-Driven Early Warning System for Wildlife Health and Ecological Threat Detection

Abstract

Emerging infections and environmental disruptions increasingly threaten wildlife and ecosystem health. Free-ranging wildlife often serve as early indicators of ecological instability, making timely detection of morbidity and mortality events critical for early warning. Yet, existing systems lack the analytical capacity for real-time outbreak detection. We present WildAlert, an AI-driven early warning system that integrates fine-tuned BERT-based natural language processing models with unsupervised anomaly detection framework to identify unusual wildlife health events using real-time pre-diagnostic clinical data from wildlife rehabilitation organizations. The NLP module achieved high accuracy across clinical classifications and circumstances of admissions, enabling a pre-diagnostic syndromic surveillance framework. Retrospective validation demonstrated that WildAlert anomalies frequently coincided with or preceded confirmed morbidity events, including highly pathogenic avian influenza (HPAI), harmful algal bloom-associated toxicosis, cold-stunning in sea turtles, mass stranding events, West Nile virus, and mycoplasmosis. WildAlert establishes the worlds largest standardized, near real-time wildlife health surveillance system, transforming wildlife rehabilitation clinical records into actionable intelligence capable of detecting anomalies across taxa and regions, often before other surveillance methods. WildAlert provides a transferable analytical framework and scalable One Health model linking biodiversity monitoring, zoonotic disease preparedness, and ecosystem-linked environmental threats, with implications for conservation, public health and environmental hazard response.