Beyond the Howl: An Acoustic Framework for Wolf Monitoring and Pack-Composition Inference

Effective wolf monitoring is more critical than ever for supporting robust population estimates, identifying breeding packs, and anticipating and mitigating attacks on livestock. This study evaluates bioacoustic monitoring to estimate wolf population size and detect packs and pups based on howling activity, using recordings collected from free-ranging wolves in four different study areas in Denmark. It assesses whether howl structure can be used to identify wild individuals and discriminate between current-year pups (aged between four and nine months) and adults. At wild Location 1, we identified two free-ranging individuals from 40 adult howls by quantifying fundamental frequency features and applying linear discriminant and multivariate variance analyses; the individual classification accuracy was 92%. We examined howls recorded from late August 2021 to February 2022 using maximum fundamental frequency. Across months, mixture modelling and principal component analysis consistently resolved two groups in the wild data, and multivariate tests indicated significant separation each month (p < 0.001), consistent with a pup–adult contrast and the expected autumnal decline in pup frequencies as they mature. A focused analysis restricted to adult-range howls also resolved two groups with very strong multivariate separation (p < 0.001), but this pattern is considered exploratory and does not constitute confirmed sex discrimination. Overall, passive bioacoustics is an effective, non-invasive approach for wide-area monitoring, individual-level acoustic identification, and detecting the presence of pups within wolf packs; however, inference about pack composition or sex based solely on acoustic clustering requires further validation.