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Improving Individual Identification of Wolves (Canis Lupus) Using the Fundamental Frequency and Amplitude of Their Howls: a New Survey Method

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Improving Individual Identification of Wolves (Canis Lupus) Using the Fundamental Frequency and Amplitude of Their Howls: a New Survey Method Improving Individual Identification of Wolves (Canis lupus) using the Fundamental Frequency and Amplitude of their Howls: A New Survey Method Holly Root-Gutteridge A thesis submitted in partial fulfilment of the requirements of Nottingham Trent University for the degree of Doctor of Philosophy September 2013 1 This works is the intellectual property of the author. You may copy up to 5% of this work for private study, or personal, non-commercial research. Any re-use of the information contained within this document should be fully referenced, quoting the author, title, university, degree level and pagination. Queries or requests for any other use, or if a more substantial copy is required, should be directed in the owner of the Intellectual Property Rights. 2 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 thesis, I investigate whether the inclusion of amplitude variables improves the accuracy of individual howl identification in captive Eastern grey wolves (Canis lupus lycaon). I 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, I 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 to 100% when using histogram-derived PCA values of F0 and amplitudes of the first harmonic. When this method was extended to wild Eastern wolf howls, a similar result was achieved of 100% for solo howls and 97.4% for chorus howls from 119 wolves using histogram derived PCA values. This was a new result for wild Eastern grey wolves. Individuality in howls was then tested in 10 other subspecies. The results showed that all wolf subspecies tested showed individuality in the F0 and amplitude changes of their howls and could be identified with 74.0% to 100% accuracy. Finally, the use of artificial neural networks (ANNs) to survey howls using novel data was assessed. The ANNs achieved higher accuracy than DFA, where DFA did not achieve 100%, and were capable of attributing novel howls to known wolves. Therefore howls could be used as a survey method in situ. 3 ‘Only a mountain has lived long enough to listen objectively to the howl of a wolf’ Aldo Leopold (1949) 4 Acknowledgements Many, many people contributed to the collection of howls for this PhD. With thanks to: Adria Sole Cantero; Alison Millard (Howletts & Port Lympne Zoos); Amanda Yeomans & the wolf keepers (Colchester Zoo); Anne Riddell (Wildwood Trust); Angelika Nelson (Borror Laboratory of Bioacoustics); Cheryl Tipp (British Library); Christine Anhalt; Claudia Capitani; Dries Kuijper (Polish Mammal Research Institute); Fiona Forrest (Tigress Productions); Jason Palmer (New Forest Wildlife Park); Jeff Wilson (BBC Natural History Unit); John & Mary Theberge; Julie Darby & wolf keepers (Longleat Safari Park); Karen Hallberg; Karl-Heinz Frommolt (Tierstimmen Archiv); Katie MacDonald (West Midlands Safari Park); Kayleigh Forsyth (Paradise Wildlife Park); Louise Peat (Cotswold Wildlife Park); Marcus Eldh (Wild Sweden); Monty Sloan and Joan Tilley (Wolf Park; Indiana); Tammy L. Bishop (Macaulay Sound Library); Toni Shelbourne and all the staff at UK Wolf Trust, particularly Pete; Vicente Palacios; Vladimir, Natasha & Nikita Bologov and Laetitia Becker (Lupus Laetus, Russia); Yorgos Iliopoulos (Callisto, Greece); Zena Tooze; and very special thanks to Vicky Allison-Hughes (UK Wolf Trust) for everything over the past four years. Also thanks to my supervisors Dr Richard Yarnell, Dr Louise Gentle, Dr Martin Bencsik and Dr Christopher Terrell-Nield; to Prof. Graham Ball for sharing his knowledge of Statistica and ANNs; and fellow students Manfred Chebli and Alexandra Bourit for writing the Matlab codes so brilliantly and making it so easy to use. More personal thanks must go to my family: Penny, John, Barbie, Mick, Alice, Julia & Dave Two, for all their support and my partner in howls and life, Dave Hale. 5 Table of Contents 1 Introduction ...................................................................................................................... 13 1.1 Answering the Call of the Wild: Using Acoustics in Wildlife Monitoring .............. 13 1.1.1 Introducing bioacoustics .................................................................................... 13 1.1.2 Animal communication ...................................................................................... 14 1.1.3 Bioacoustics – from first recognising birds to today ......................................... 15 1.1.4 Bioacoustics for surveying populations and species distributions ..................... 17 1.1.5 The Physics of and Physical Characteristics Expressed in Vocalisations ......... 19 1.1.6 Evolving vocalisations: speciation, geographic-associated signatures and environmental effects ....................................................................................................... 24 1.1.7 Social affiliation displayed in Vocalisations...................................................... 25 1.1.8 Individual identification from vocalisations ...................................................... 26 1.1.9 Advancing the technology and applications ...................................................... 33 1.2 A Brief Introduction to the Wolf: Biology, Ecology and Social Structure ............... 35 1.2.1 Brief Overview................................................................................................... 35 1.2.2 Grey Wolf Taxonomy – Debate and Dissent ..................................................... 35 1.2.3 Distribution ........................................................................................................ 38 1.2.4 Biology, Reproduction and Pack Life ................................................................ 41 1.2.5 Habitat ................................................................................................................ 44 1.2.6 Wolves as Predators ........................................................................................... 45 1.2.7 Conflict with Humans and Future Conservation ............................................... 48 1.2.8 Howling as a Tracking Method.......................................................................... 51 1.3 Rationale.................................................................................................................... 54 1.4 Aims .......................................................................................................................... 56 2 Improving individual identification in captive Eastern Grey Wolves (Canis lupus lycaon) using the time course of howl amplitudes................................................................... 58 2.1 Introduction ............................................................................................................... 58 2.2 Aims .......................................................................................................................... 63 2.3 Materials and methods .............................................................................................. 64 2.3.1 Source of wolf howls ......................................................................................... 64 2.3.2 Sound analysis ................................................................................................... 66 2.3.3 Using PCA for automatic identification of deviations defining individuality ... 76 2.4 Results ....................................................................................................................... 80 2.4.1 Choice of significant variables using ANOVA and stepwise DFA ................... 80 2.4.2 Benchmarking with Praat ................................................................................... 81 6 2.4.3 The application of bespoke code to extract howl features ................................. 83 2.5 Discussion ................................................................................................................. 85 2.6 Conclusion ................................................................................................................. 89 3 Identifying individual wild Eastern grey wolves (Canis lupus lycaon) using fundamental frequency and amplitude of howls ........................................................................................... 90 3.1 Introduction ............................................................................................................... 90 3.2 Aims .......................................................................................................................... 93 3.3 Materials and methods .............................................................................................. 94 3.3.1 Classification using DFA ................................................................................... 97 3.4 Results ....................................................................................................................
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