Unsupervised discrimination of male Tawny owls ( Strix aluco ) individual calls using robust measurements of the acoustic signal

ABSTRACTVocal individuality has been widely documented in the Tawny owl (Strix aluco); however, all statistical tools employed thus far to discriminate individual vocalisations have relied on prior knowledge regarding number and identity of individuals. In this study, we tested the effectiveness of four unsupervised clustering algorithms in distinguishing among eight Tawny owl males, solely based on acoustic characteristics of their vocalisations. We also employed both traditional bound-based and robust measurements of acoustic signal to compare their efficacy. We finally evaluated the applicability of this method in identifying the number and distribution of the remaining males recorded in our study area. Three of the four unsupervised techniques had a high rate of success in discriminating among vocalisations of the eight males. In all cases, the best results were obtained using robust measurements. However, when extending the analysis to the remaining unknown males recorded, the highest rate of misclassification errors made results more difficult to interpret. Our study provided a useful tool to discriminate male Tawny owls when only their call recordings are available. Furthermore, this method could be extended to other nocturnal and vociferous species, representing one of the few existing approaches for unsupervised classification of individuals based on acoustic features.KEYWORDS: Hierarchical clusterindividual discriminationrobust signal measurementsunsupervised clusteringunsupervised classificationvocal individuality AcknowledgementsThe authors would like to thank the University of Pisa Working group (Tavolo Tecnico) for the Monte Pisano fires, which is dedicated to support the local community to study the effects of fires and to promote restoration of burnt areas. Moreover, we would like to thank Regione Toscana, the University of Pisa and the Department of Biology for funding the PhD scholarship for Tomassini Orlando following the recommendation provided by the Tavolo Tecnico. In particular, we would like to thank Gianni Bedini and Giulio Petroni for the financial support. Our thanks go to Elisabetta Palagi for providing us with the software Raven Pro. We extend our appreciation to Marta Berti, Giulia Cerritelli and Pietro Valente and for their help with data collection in the field. Additionally, we are grateful to Lorenzo Vanni and Luca Puglisi for their advice on data collection. Lastly, we are indebted to John Kastelic for his valuable assistance in refining the written content.Author contributionsDaniele Roccazzello, together with Orlando Tomassini, conducted the fieldwork, acoustical and statistical analyses, and wrote the manuscript. Elena Bernardini contributed to the fieldwork and acoustical analysis. Marco Dragonetti directed the acoustic analysis and edited the manuscript. Alessandro Massolo suggested statistical analyses and edited the manuscript. Dimitri Giunchi supervised and supported the overall study and also edited the manuscript. All the authors approved the final version of the manuscript.Disclosure statementNo potential conflict of interest was reported by the author(s).Supplementary materialSupplemental data for this article can be accessed online at https://doi.org/10.1080/09524622.2023.2270486.Additional informationFundingThis work was supported by Regione Toscana.