Artificial Intelligence Integration of Traditional Knowledge for Wildlife Management

Abstract

The management of wildlife is increasingly challenged by habitat fragmentation, changing species distributions due to climate, and growing human–wildlife conflicts, but traditional monitoring is limited in terms of the number of sensors available and the length of the observation period. Indigenous and local communities possess extensive and multigenerational traditional ecological knowledge (TEK) about animal migration; feeding grounds; breeding seasons; and conflict areas, but this knowledge is largely oral and not used in formal decision making. Traditional Knowledge Integration Framework (AI-TKIF) which brings together community narratives, participatory maps, and seasonal calendars alongside camera-trap images and acoustic recordings, GPS telemetry, satellite imagery, and historical wildlife reports. Species, locations, seasons and behaviours are extracted from transcribed narratives by multilingual natural language processing (NLP); computer vision and geospatial learning analyse sensor data; and a wildlife knowledge graph can combine both streams using a community-agreement-based, expert-validated, observation-frequency-based and scientifically consistent confidence scores. An explainable decision-support module generates migration-corridor maps, habitat-priority zones, seasonal alerts and conflict-risk areas, which are analysed and discussed with the knowledge holders before being used operationally. The framework is based on free, prior and informed consent, community-controlled access and location masking. A prototype for human–elephant conflict in a forest–agriculture landscape reveals that the complete framework is equally effective for predicting the risk of conflict and reducing false alerts compared to scientific-data-only, traditional-knowledge-only, statistical-integration and unvalidated AI-fusion baselines, and it is robust against low sensor-density. The results indicate that culturally responsible AI can enhance participatory, adaptive, and community-inclusive wildlife conservation efforts