Bioacoustic Detection of Wolves Using AI (BirdNET, Cry-Wolf and BioLingual).

ABSTRACT: Rising numbers of wolf (Canis lupus) populations make traditional, resource-intensive methods of wolf monitoring increasingly challenging and often insufficient. This study explores how wolf howls can be used as a new monitoring tool for wolves by applying Artificial Intelligence (AI) methods to detect and classify wolf howls from acoustic recordings, thereby improving the effectiveness of wolf population monitoring. Three AI approaches are evaluated: BirdNET, Yellowstone's Cry-Wolf project system, and BioLingual. Data were collected using Song Meter SM4 (SM4) audio recorders in a known wolf territory in Klelund Dyrehave, Denmark, and manually validated to establish a ground truth of 260 wolf howls. Results demonstrate that while AI solutions currently do not achieve the complete pr

SUBMITTER: Jacobsen JH 

PROVIDER: S-EPMC12838021 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature