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- W1594330921 abstract "In conservation ecology, abundance estimates are an important factor from which management decisions arebased. Methods to estimate abundance of cetaceans from visual detections are largely developed, whereasparallel methods based on passive acoustic detections are still in their infancy. To estimate the abundance ofcetacean species using acoustic detection data, it is first necessary to correctly identify the species that aredetected. The current automatic PAMGUARD Whistle Classifier used to automatically identify whistledetection of cetacean species is modified with the objective to facilitate the use of these detections toestimate 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 isunfeasible. However, through the examples of two case studies it is shown that large and high qualitydatasets with which to develop these automatic classifiers increase the probability of creating reliableclassifiers with low and precise misclassification probability.Given that misclassification is unavoidable, it is necessary to consider the effect of misclassified detectionson the number of observed acoustic calls detected and thus on abundance estimates, and to develop robustmethods to cope with these misclassifications. Through both heuristic and Bayesian approaches it isdemonstrated that if misclassification probabilities are known or estimated precisely, it is possible toestimate the true number of detected calls accurately and precisely. However, misclassification anduncertainty increase the variance of the estimates. If the true numbers of detections from different speciesare similar, then a small amount of misclassification between species and a small amount of uncertainty inthe probabilities of misclassification does not have a detrimental effect on the overall variance and bias ofthe estimate. However, if there is a difference in the encounter rate between species calls associated with alarge amount of uncertainty in the probabilities of misclassification, then the variance of the estimatesbecomes larger and the bias increases; this in return increases the variance and the bias of the finalabundance estimate. This study despite not bringing perfect results highlights for the first time theimportance of dealing with the problem of species misclassification for cetacean if acoustic detections are tobe used to estimate abundance of cetaceans." @default.
- W1594330921 created "2016-06-24" @default.
- W1594330921 creator A5066394218 @default.
- W1594330921 date "2013-11-29" @default.
- W1594330921 modified "2023-09-23" @default.
- W1594330921 title "Assessing and correcting for the effects of species misclassification during passive acoustic surveys of cetaceans" @default.
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