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

Effective wolf monitoring is more critical than ever to support robust population estimates, identify breeding packs, and anticipate and mitigate attacks on livestock. This study evaluates bioacoustic monitoring to estimate wolf population size and detect packs and pups by howling activity, using recordings collected from free-ranging wolves in four different study areas in Denmark and from captive wolves housed in two zoos. It assesses whether howl structure can identify wild individuals and discriminate between current year-pups (aged between four and nine months) and adults. At one 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; individual classification accuracy was 92%. In captivity, the same workflow yielded 84% accuracy for three wolves at Location 5 and 86% for four wolves at Location 6, including perfect classification for one animal. 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), in line with female–male differences. Overall, passive bioacoustics is an effective, non-invasive approach for wide-area coverage and for inferring pack composition from acoustic evidence alone.