A hidden Markov model approach to indicate Bryde's whale acoustics

dc.contributor.authorPutland, Rosalyn L
dc.contributor.authorRanjard, Louis
dc.contributor.authorConstantine, Rochelle
dc.contributor.authorRadford, Craig A
dc.date.accessioned2019-09-05T01:44:17Z
dc.date.issued2018
dc.date.updated2019-04-14T08:27:20Z
dc.description.abstractIncreasing sound in the ocean from human activity potentially threatens marine animals that use sound to communicate, detect prey, avoid predators and function within their ecosystem. The detection and classification of sound produced by marine animals, such as whales and fish, is an important component in noise mitigation strategies, while also providing valuable insights into their ecology. Traditionally, visual surveys are conducted to assess how these animals utilize a specific area, often underestimating the number of individuals as they don’t spend much time at the surface. Long-term passive acoustic monitoring efforts have become more prevalent to monitor such animals. The large datasets collected can be impractical to manually process, necessitating the development of automated detection methods, which often produce mixed results owing to the broad frequency range and variable duration of many biological sounds. Here we describe a novel approach for automated detection of underwater biophonic sounds employing hidden Markov models (HMM). Acoustic data was collected at a single listening station in Hauraki Gulf, from October 2014 to April 2016. HMM detection models were developed for Bryde’s whales (Balaenoptera edeni) that were used as a model organism because they are notoriously hard to study with traditional visual surveys and produce a characteristic call. Bryde’s whale calls also directly overlap the sounds of anthropogenic activity, in particular the sound of vessels transiting to the busiest port in New Zealand; therefore monitoring whale calls is of utmost importance when confronting increasing sound in the ocean. Vocalizations were detected with a sensitivity of 77% and false positive rate of 23%. Bryde’s whale vocalizations were detected on 11% of all recordings. Overall, there were significantly more detections during summer (n = 1716) than winter (n = 447), and significantly more during the day (n = 1991) compared to night (n = 1264). This study shows the feasibility of using HMMs on long-term acoustic datasets. The method has the potential to be used for a wide range of soniferous animals who, like the Bryde’s whale, also produce unique sounds. The detection method would be particularly useful for mitigation and management strategies of species that are difficult to detect using traditional visual methods.en_AU
dc.description.sponsorshipThis research was funded by a Rutherford Discovery Fellowship from the Royal Society of New Zealand (RDF-UOA1302) to CAR, including a PhD scholarship to RLP.en_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.issn1470-160Xen_AU
dc.identifier.urihttp://hdl.handle.net/1885/165696
dc.language.isoen_AUen_AU
dc.publisherElsevieren_AU
dc.rights© 2017 Elsevier Ltd.en_AU
dc.sourceEcological Indicatorsen_AU
dc.titleA hidden Markov model approach to indicate Bryde's whale acousticsen_AU
dc.typeJournal articleen_AU
local.bibliographicCitation.lastpage487en_AU
local.bibliographicCitation.startpage479en_AU
local.contributor.affiliationPutland, Rosalyn L, University of Aucklanden_AU
local.contributor.affiliationRanjard, Louis, College of Science, ANUen_AU
local.contributor.affiliationConstantine , Rochelle, University of Aucklanden_AU
local.contributor.affiliationRadford, Craig A, University of Aucklanden_AU
local.contributor.authoremailu1013186@anu.edu.auen_AU
local.contributor.authoruidRanjard, Louis, u1013186en_AU
local.description.embargo2037-12-31
local.description.notesImported from ARIESen_AU
local.identifier.absfor060412 - Quantitative Genetics (incl. Disease and Trait Mapping Genetics)en_AU
local.identifier.absseo970106 - Expanding Knowledge in the Biological Sciencesen_AU
local.identifier.ariespublicationu4485658xPUB2507en_AU
local.identifier.citationvolume84en_AU
local.identifier.doi10.1016/j.ecolind.2017.09.025en_AU
local.identifier.scopusID2-s2.0-85029592161
local.identifier.thomsonID000425828200049
local.identifier.uidSubmittedByu4485658en_AU
local.publisher.urlhttps://www.elsevier.com/en-auen_AU
local.type.statusPublished Versionen_AU

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