Development and application of a machine learning algorithm for classification of elasmobranch behaviour from accelerometry data.

ABSTRACT: Discerning behaviours of free-ranging animals allows for quantification of their activity budget, providing important insight into ecology. Over recent years, accelerometers have been used to unveil the cryptic lives of animals. The increased ability of accelerometers to store large quantities of high resolution data has prompted a need for automated behavioural classification. We assessed the performance of several machine learning (ML) classifiers to discern five behaviours performed by accelerometer-equipped juvenile lemon sharks (Negaprion brevirostris) at Bimini, Bahamas (25°44'N, 79°16'W). The sharks were observed to exhibit chafing, burst swimming, headshaking, resting and swimming in a semi-captive environment and these observations were used to ground-truth data for ML trai

SUBMITTER: Brewster LR 

PROVIDER: S-EPMC5842499 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature