Identifying individual wild Eastern grey wolves ( Canis lupus lycaon ) using fundamental frequency and amplitude of howls

• Main Authors: Root-Gutteridge, Holly, Bencsik, Martin, Chebli, Manfred, Gentle, Louise K., Terrell-Nield, Christopher, Bourit, Alexandra, Yarnell, Richard W.
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Taylor & Francis 2013
• Subjects: Genetics; FOS Biological sciences; Neuroscience; Ecology; Science Policy; Sociology; FOS Sociology; Canis lupus
• Online Access: https://dx.doi.org/10.6084/m9.figshare.825628.v2; https://tandf.figshare.com/articles/media/Identifying_individual_wild_Eastern_grey_wolves_i_Canis_lupus_lycaon_i_using_fundamental_frequency_and_amplitude_of_howls/825628/2

The use of amplitudes to identify individuals has historically been ignored by bioacoustic researchers due to problems of attenuation. However, recent studies have shown that amplitudes encode identity in a variety of mammal species. Previously, individuality has been demonstrated in both fundamental frequency ( F 0 ) and amplitude changes of captive Eastern wolf ( Canis lupus lycaon ) howls with 100% accuracy where attenuation of amplitude due to distance was controlled in a captive environment. In this study, we aim to determine whether both fundamental frequency and amplitude data collected from vocalizations of wild wolves recorded over unknown distances, in variable conditions and with different recording equipment, can still encode identity. We used a bespoke code, developed in Matlab, to extract simple scalar variables from 67 high-quality solo howls from 10 wild individuals and 112 chorus howls from another 109 individuals, including lower quality howls with wind or water noise. Principal component analysis (PCA) was carried out on the fundamental frequency and normalized amplitude of harmonic 1, yielding histogram-derived PCA values on which discriminant function analysis was applied. An accuracy of 100% was achieved when assigning solo howls to individuals, and for the chorus howls a best accuracy of 97.4% was achieved. We suggest that individual recognition using our new extraction and analysis methods involving fundamental frequency and amplitudes together can identify wild wolves with high accuracy, and that this method should be applied to surveys of individuals in capture–mark–recapture and presence–absence studies of canid species.