Year: 2018
Type: dissertation
Abstract: Understanding the behaviours of free-ranging animals over biologically meaningful time scales (e.g.diel, tidal, lunar, seasonal, annual) gives important insights into their ecology.Bio-logging tools allow the remote study of elusive or inaccessible animals by recording high resolution multi-channel movement data, however archival device recording duration is limited to relatively short temporal-scales by memory and battery capacity.Machine learning (ML) is becoming common for automatic classification of behaviours from large data sets.This thesis develops a framework for the programming of bio-loggers for the classification of shark behaviour through the optimisation of sampling frequency (Chapter 2) and the choice of movement sensor (Chapter 3).The effects of sampling frequency on behavioural classification were assessed using data published in a previous study collected from accelerometer equipped juvenile lemon sharks (Negaprion brevirostris) during captive trials in Bimini, Bahamas.The impacts of different combinations of movement sensors (accelerometer, magnetometer and gyroscope) were assessed using data collected from sub adult sicklefin lemon sharks (Negaprion acutidens).Sharks were equipped with multi-sensor devices recording acceleration, angular rotation and angular velocity during captive trials at St Joseph Atoll, Seychelles.Catalogues of discrete classes of behaviours (ethograms) were developed by observing sharks during captive trials.Behaviours (swim, rest, burst, chafe, headshake) were classified using a random forest ML algorithm with predictor variables extracted from the ground-truthed data.A range of sampling frequencies (30, 15, 10, 5, 3 and 1 Hz) and combinations of movement sensors were tested.For each dataset, a confusion matrix was determined from model predictions for calculation and comparison of evaluation metrics.Classifier performance was best Most importantly thank you to my inspirational supervisor, Adrian Gleiss.I consider myself privileged to have had the opportunity to study under your continually supportive guidance and for being considered to undertake such an exciting and novel project.I am so grateful and wholeheartedly look forward to the prospect of future collaboration and research.Thank you to all of my colleagues and friends at the lab.Lauran Brewster, for your help with data and support when I could not see the forest for the trees.Karissa Lear, for your early support and hints with IGOR
Author: Jenna L. Hounslow
Language: English
Subfield: Nature and Landscape Conservation
Field: Environmental Science
Domain: Physical Sciences
SDG: Life below water
Open Access status: green
OpenAlex ID: W2910665929