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. 2022 Mar 2;12(5):631.
doi: 10.3390/ani12050631.

Bioacoustic Detection of Wolves: Identifying Subspecies and Individuals by Howls

Hanne Lyngholm Larsen  1 , Cino Pertoldi  1 , Niels Madsen  1 , Ettore Randi  1 , Astrid Vik Stronen  1   2 , Holly Root-Gutteridge  3   4 , Sussie Pagh  1
Affiliations

Bioacoustic Detection of Wolves: Identifying Subspecies and Individuals by Howls

Hanne Lyngholm Larsen et al. Animals (Basel). .

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.

Keywords: Canis lupus; acoustic variables; bioacoustics; discriminant analysis; fundamental frequency; habitats directive; monitoring.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Example of spectrograms of single howls from (A). Northwestern wolves; (B). Arctic wolves; (C). Captive Eurasian wolves; and (D). Eurasian wolves with noise. The y-axis shows the frequency in kHz and the x-axis shows the time in seconds. Colors indicate the amplitude of the howls in dB (decibel). The lowest band in the spectrograms are the fundamental frequency (f0) and the other two are the second and third harmonic, which are visible in (A–C). The amplitude is in negative dB as 0 is referring to the maximum sound [51]. Graphs were made in the program RStudio [52] using the package Seewave [53].
Figure 2
Figure 2
Linear discriminant (LD) analysis plot for identification of the subspecies Arctic, Eurasian, and Northwestern wolves with a 78% correct classification.
Figure 3
Figure 3
Linear discriminant (LD) analysis plot for individual identification of howls from Arctic wolves with a 95% correct classification.
Figure 4
Figure 4
Linear discriminant (LD) analysis plot for individual identification of howls from Eurasian wolves with a 92% correct classification.
See this image and copyright information in PMC

References

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