Bioacoustic Detection of Wolves: Identifying Subspecies and Individuals by Howls

• Published in: Animals
• Main Authors: Larsen, Hanne Lyngholm, Pertoldi, Cino, Madsen, Niels, Randi, Ettore, Stronen, Astrid Vik, Root-Gutteridge, Holly, Pagh, Sussie
• Format: Text
• Language: English
• Published: MDPI 2022
• Subjects: Article; Arctic; Canis lupus
• Online Access: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8909475/; https://doi.org/10.3390/ani12050631

SIMPLE SUMMARY: This study evaluates the use of acoustic devices as a method to monitor wolves by analyzing different variables extracted from wolf howls. By analyzing the wolf howls, we focused on identifying individual wolves, subspecies. We analyzed 170 howls from 16 individuals from the three subspecies: Arctic wolves (Canis lupus arctos), Eurasian wolves (C.l. lupus), and Northwestern wolves (C.l. occidentalis). We assessed the potential for individual recognition and recognition of three subspecies: Arctic, Eurasian, and Northwestern wolves. ABSTRACT: Wolves (Canis lupus) are generally monitored by visual observations, camera traps, and DNA traces. In this study, we evaluated acoustic monitoring of wolf howls as a method for monitoring wolves, which may permit detection of wolves across longer distances than that permitted by camera traps. We analyzed acoustic data of wolves’ howls collected from both wild and captive ones. The analysis focused on individual and subspecies recognition. Furthermore, we aimed to determine the usefulness of acoustic monitoring in the field given the limited data for Eurasian wolves. We analyzed 170 howls from 16 individual wolves from 3 subspecies: Arctic (Canis lupus arctos), Eurasian (C. l. lupus), and Northwestern wolves (C. l. occidentalis). Variables from the fundamental frequency (f0) (lowest frequency band of a sound signal) were extracted and used in discriminant analysis, classification matrix, and pairwise post-hoc Hotelling test. The results indicated that Arctic and Eurasian wolves had subspecies identifiable calls, while Northwestern wolves did not, though this sample size was small. Identification on an individual level was successful for all subspecies. Individuals were correctly classified with 80%–100% accuracy, using discriminant function analysis. Our findings suggest acoustic monitoring could be a valuable and cost-effective tool that complements camera traps, by improving long-distance detection of wolves.