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dc.contributor.advisorGillespie, Douglas
dc.contributor.advisorThomas, Len
dc.contributor.authorCaillat, Marjolaine
dc.coverage.spatial239en_US
dc.date.accessioned2013-11-15T14:33:39Z
dc.date.available2013-11-15T14:33:39Z
dc.date.issued2013-11-29
dc.identifieruk.bl.ethos.581845
dc.identifier.urihttp://hdl.handle.net/10023/4209
dc.description.abstractIn conservation ecology, abundance estimates are an important factor from which management decisions are based. Methods to estimate abundance of cetaceans from visual detections are largely developed, whereas parallel methods based on passive acoustic detections are still in their infancy. To estimate the abundance of cetacean species using acoustic detection data, it is first necessary to correctly identify the species that are detected. The current automatic PAMGUARD Whistle Classifier used to automatically identify whistle detection of cetacean species is modified with the objective to facilitate the use of these detections to estimate cetacean abundance. Given the variability of cetacean sounds within and between species, developing an automated species classifier with a 100% correct classification probability for any species is unfeasible. However, through the examples of two case studies it is shown that large and high quality datasets with which to develop these automatic classifiers increase the probability of creating reliable classifiers with low and precise misclassification probability. Given that misclassification is unavoidable, it is necessary to consider the effect of misclassified detections on the number of observed acoustic calls detected and thus on abundance estimates, and to develop robust methods to cope with these misclassifications. Through both heuristic and Bayesian approaches it is demonstrated that if misclassification probabilities are known or estimated precisely, it is possible to estimate the true number of detected calls accurately and precisely. However, misclassification and uncertainty increase the variance of the estimates. If the true numbers of detections from different species are similar, then a small amount of misclassification between species and a small amount of uncertainty in the probabilities of misclassification does not have a detrimental effect on the overall variance and bias of the estimate. However, if there is a difference in the encounter rate between species calls associated with a large amount of uncertainty in the probabilities of misclassification, then the variance of the estimates becomes larger and the bias increases; this in return increases the variance and the bias of the final abundance estimate. This study despite not bringing perfect results highlights for the first time the importance of dealing with the problem of species misclassification for cetacean if acoustic detections are to be used to estimate abundance of cetaceans.en_US
dc.language.isoenen_US
dc.publisherUniversity of St Andrews
dc.subjectClassificationen_US
dc.subjectMisclassificationen_US
dc.subjectCetaceansen_US
dc.subjectPassive acoustic monitoringen_US
dc.subject.lccQL737.C4C2
dc.subject.lcshCetacea populations--Estimates--Techniqueen_US
dc.subject.lcshCetacea--Classificationen_US
dc.subject.lcshCetacea--Monitoringen_US
dc.subject.lcshWhale soundsen_US
dc.subject.lcshDolphin soundsen_US
dc.titleAssessing and correcting for the effects of species misclassification during passive acoustic surveys of cetaceansen_US
dc.typeThesisen_US
dc.contributor.sponsorNatural Environment Research Council (NERC)en_US
dc.contributor.sponsorSMRU Marineen_US
dc.type.qualificationlevelDoctoralen_US
dc.type.qualificationnamePhD Doctor of Philosophyen_US
dc.publisher.institutionThe University of St Andrewsen_US
dc.publisher.departmentSchool of Mathematicsen_US


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