Improving individual identification in captive Eastern grey wolves (Canis lupus lycaon) using the time course of howl amplitudes

Abstract

Many bioacoustic studies have been able to identify individual mammals from variations in the fundamental frequency (F0) of their vocalizations. Other characteristics of vocalization which encode individuality, such as amplitude, are less frequently used because of problems with background noise and recording fidelity over distance. In this paper, we investigate whether the inclusion of amplitude variables improves the accuracy of individual howl identification in captive Eastern grey wolves (Canis lupus lycaon). We also explore whether the use of a bespoke code to extract the howl features, combined with histogram-derived principal component analysis (PCA) values, can improve current individual wolf howl identification accuracies. From a total of 89 solo howls from six captive individuals, where distances between wolf and observer were short, we achieved 95.5% (+9.0% improvement) individual identification accuracy of captive wolves using discriminant function analysis (DFA) to classify simple scalar variables of F0 and normalized amplitudes. Moreover, this accuracy was increased by 100% when using histogram-derived PCA values of F0 and amplitudes of the first harmonic. We suggest that individual identification accuracy can be improved by including amplitude changes for species where F0 has only been included so far. Using DFA on PCA values of both F0 and amplitude could optimize vocal identification in a range of mammal bioacoustic studies.