Bioacoustic Detection of Wolves: Identifying Subspecies and Individuals by Howls

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 a...

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Bibliographic Details
Published in:Animals
Main Authors: Hanne Lyngholm Larsen, Cino Pertoldi, Niels Madsen, Ettore Randi, Astrid Vik Stronen, Holly Root-Gutteridge, Sussie Pagh
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2022
Subjects:
bioacoustics
Canis lupus
discriminant analysis
habitats directive
monitoring
fundamental frequency
Veterinary medicine
SF600-1100
Zoology
QL1-991
Arctic
Online Access:https://doi.org/10.3390/ani12050631
https://doaj.org/article/76bb453f012d4a7691628c51ca94aa78
Description
Summary: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.

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