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A new approach to identifying behaviour in animals using tiger sharks, Galeocerdo cuvier, as a test species / WALLIS JAMES

Swansea University Author: WALLIS JAMES

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Abstract

There are many advantages in determining animal behaviour for conservation initiatives seeking to protect species impacted by the changing planet. However, for many years, direct observation of elusive or dangerous animals in challenging habitats precluded the acquisition of representative, non-bias...

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Published: Swansea 2022
Institution: Swansea University
Degree level: Master of Research
Degree name: MRes
Supervisor: Wilson, Rory P.
URI: https://cronfa.swan.ac.uk/Record/cronfa61285
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spelling v2 61285 2022-09-20 A new approach to identifying behaviour in animals using tiger sharks, Galeocerdo cuvier, as a test species 47a688bdbeed1c2f09b1e32b50971db5 WALLIS JAMES WALLIS JAMES true false 2022-09-20 There are many advantages in determining animal behaviour for conservation initiatives seeking to protect species impacted by the changing planet. However, for many years, direct observation of elusive or dangerous animals in challenging habitats precluded the acquisition of representative, non-biased behavioural data. Recently though, animal-attached tag technology incorporating accelerometers, magnetometers and pressure sensors, has greatly advanced our abilities to document the behaviour of a numerous vertebrate species (e.g. birds, reptiles, fish, mammals), even when they cannot be observed. For this, supervised and unsupervised machine learning are often used to categorise behaviours by identifying patterns within the biotelemetry data. However, supervised machine learning requires training data, which is not always available, and both methods are driven by machine-based software with no explicitly defined parameters associated with behaviours which can be cross-checked. This work aimed to use a proper physics-based understanding of triaxial accelerometer-, magnetometer- and pressure data taken from electronic tags deployed on tiger sharks, Galeocerdo cuvier, to interpret patterns and group them into behaviours. As part of this, multi-modality in frequency distributions of parameters was investigated, on the premise that different behaviours can result in different frequency distributions in particular metrics. Following examination, algorithms using defined numerical limits were created to isolate distinct behaviours and these used to detect the extent of identified patterns within entire data sets and across individuals. A total of 12,338 minutes of tag data was processed, from which 10 behaviours were identified. Seven of these were successfully described using numerical metric limits from recorded and/or derived data including; ‘descent’, ‘ascent’, ‘burst power’, etc. However, frequency distributions showed a continuum rather than multiple distinct modes, indicating that this approach is likely to be more complex than thought. The use of physical principles seems a promising method for interpreting accelerometer, magnetometer and pressure data to identify behaviours that occur in study animals that cannot be directly observed. Although these algorithms are specific to tiger sharks in this work, this method is likely to be applicable to other species in aerial, aquatic or terrestrial habitats and could inform a broad range of conservations initiatives in the future. E-Thesis Swansea Tiger shark, accelerometer tag, behaviour, quantitative, acceleration data, VeDBA 12 9 2022 2022-09-12 ORCiD identifier: https://orcid.org/0000-0002-8541-3976 COLLEGE NANME COLLEGE CODE Swansea University Wilson, Rory P. Master of Research MRes 2022-09-20T15:36:40.8938834 2022-09-20T13:30:01.9578250 College of Science Biosciences WALLIS JAMES 1 61285__25162__f97597fc44d94e7b8b391791e47e02b6.pdf James_Wallis_MRes_Thesis_Final_Redacted_Signature.pdf 2022-09-20T15:32:56.2755743 Output 3943554 application/pdf E-Thesis – open access true Copyright: The author, Wallis M. James, 2022. true eng
title A new approach to identifying behaviour in animals using tiger sharks, Galeocerdo cuvier, as a test species
spellingShingle A new approach to identifying behaviour in animals using tiger sharks, Galeocerdo cuvier, as a test species
WALLIS JAMES
title_short A new approach to identifying behaviour in animals using tiger sharks, Galeocerdo cuvier, as a test species
title_full A new approach to identifying behaviour in animals using tiger sharks, Galeocerdo cuvier, as a test species
title_fullStr A new approach to identifying behaviour in animals using tiger sharks, Galeocerdo cuvier, as a test species
title_full_unstemmed A new approach to identifying behaviour in animals using tiger sharks, Galeocerdo cuvier, as a test species
title_sort A new approach to identifying behaviour in animals using tiger sharks, Galeocerdo cuvier, as a test species
author_id_str_mv 47a688bdbeed1c2f09b1e32b50971db5
author_id_fullname_str_mv 47a688bdbeed1c2f09b1e32b50971db5_***_WALLIS JAMES
author WALLIS JAMES
author2 WALLIS JAMES
format E-Thesis
publishDate 2022
institution Swansea University
college_str College of Science
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hierarchy_top_id collegeofscience
hierarchy_top_title College of Science
hierarchy_parent_id collegeofscience
hierarchy_parent_title College of Science
department_str Biosciences{{{_:::_}}}College of Science{{{_:::_}}}Biosciences
document_store_str 1
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description There are many advantages in determining animal behaviour for conservation initiatives seeking to protect species impacted by the changing planet. However, for many years, direct observation of elusive or dangerous animals in challenging habitats precluded the acquisition of representative, non-biased behavioural data. Recently though, animal-attached tag technology incorporating accelerometers, magnetometers and pressure sensors, has greatly advanced our abilities to document the behaviour of a numerous vertebrate species (e.g. birds, reptiles, fish, mammals), even when they cannot be observed. For this, supervised and unsupervised machine learning are often used to categorise behaviours by identifying patterns within the biotelemetry data. However, supervised machine learning requires training data, which is not always available, and both methods are driven by machine-based software with no explicitly defined parameters associated with behaviours which can be cross-checked. This work aimed to use a proper physics-based understanding of triaxial accelerometer-, magnetometer- and pressure data taken from electronic tags deployed on tiger sharks, Galeocerdo cuvier, to interpret patterns and group them into behaviours. As part of this, multi-modality in frequency distributions of parameters was investigated, on the premise that different behaviours can result in different frequency distributions in particular metrics. Following examination, algorithms using defined numerical limits were created to isolate distinct behaviours and these used to detect the extent of identified patterns within entire data sets and across individuals. A total of 12,338 minutes of tag data was processed, from which 10 behaviours were identified. Seven of these were successfully described using numerical metric limits from recorded and/or derived data including; ‘descent’, ‘ascent’, ‘burst power’, etc. However, frequency distributions showed a continuum rather than multiple distinct modes, indicating that this approach is likely to be more complex than thought. The use of physical principles seems a promising method for interpreting accelerometer, magnetometer and pressure data to identify behaviours that occur in study animals that cannot be directly observed. Although these algorithms are specific to tiger sharks in this work, this method is likely to be applicable to other species in aerial, aquatic or terrestrial habitats and could inform a broad range of conservations initiatives in the future.
published_date 2022-09-12T15:36:39Z
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