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Finding turning-points in ultra-high-resolution animal movement data

Jonathan R. Potts, Luca Borger Orcid Logo, D. Michael Scantlebury, Nigel C. Bennett, Abdulaziz Alagaili, Rory Wilson Orcid Logo

Methods in Ecology and Evolution, Volume: 9, Issue: 10, Pages: 2091 - 2101

Swansea University Authors: Luca Borger Orcid Logo, Rory Wilson Orcid Logo

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DOI (Published version): 10.1111/2041-210X.13056

Abstract

Recent advances in biologging have resulted in animal location data at unprecedentedly high temporal resolutions, sometimes many times per second. However, many current methods for analysing animal movement (e.g. step selection analysis or state‐space modelling) were developed with lower‐resolution...

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Published in: Methods in Ecology and Evolution
ISSN: 2041210X
Published: Wiley 2018
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URI: https://cronfa.swan.ac.uk/Record/cronfa48296
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However, many current methods for analysing animal movement (e.g. step selection analysis or state&#x2010;space modelling) were developed with lower&#x2010;resolution data in mind. To make such methods usable with high&#x2010;resolution data, we require techniques to identify features within the trajectory where movement deviates from a straight line. We propose that the intricacies of movement paths, and particularly turns, reflect decisions made by animals so that turn points are particularly relevant to behavioural ecologists. As such, we introduce a fast, accurate algorithm for inferring turning&#x2010;points in high&#x2010;resolution data. For analysing big data, speed and scalability are vitally important. We test our algorithm on simulated data, where varying amounts of noise were added to paths of straight&#x2010;line segments interspersed with turns. We also demonstrate our algorithm on data of free&#x2010;ranging oryx Oryx leucoryx. We compare our algorithm to existing statistical techniques for break&#x2010;point inference. The algorithm scales linearly and can analyse several hundred&#x2010;thousand data points in a few seconds on a mid&#x2010;range desktop computer. It identified turnpoints in simulated data with complete accuracy when the noise in the headings had a standard deviation of &#xB1;8&#x2218;, well within the tolerance of many modern biologgers. It has comparable accuracy to the existing algorithms tested, and is up to three orders of magnitude faster. Our algorithm, freely available in R and Python, serves as an initial step in processing ultra high&#x2010;resolution animal movement data, resulting in a rarefied path that can be used as an input into many existing step&#x2010;and&#x2010;turn methods of analysis. The resulting path consists of points where the animal makes a clear turn, and thereby provides valuable data on decisions underlying movement patterns. 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spelling 2021-07-16T14:38:21.0559077 v2 48296 2019-01-21 Finding turning-points in ultra-high-resolution animal movement data 8416d0ffc3cccdad6e6d67a455e7c4a2 0000-0001-8763-5997 Luca Borger Luca Borger true false 017bc6dd155098860945dc6249c4e9bc 0000-0003-3177-0177 Rory Wilson Rory Wilson true false 2019-01-21 SBI Recent advances in biologging have resulted in animal location data at unprecedentedly high temporal resolutions, sometimes many times per second. However, many current methods for analysing animal movement (e.g. step selection analysis or state‐space modelling) were developed with lower‐resolution data in mind. To make such methods usable with high‐resolution data, we require techniques to identify features within the trajectory where movement deviates from a straight line. We propose that the intricacies of movement paths, and particularly turns, reflect decisions made by animals so that turn points are particularly relevant to behavioural ecologists. As such, we introduce a fast, accurate algorithm for inferring turning‐points in high‐resolution data. For analysing big data, speed and scalability are vitally important. We test our algorithm on simulated data, where varying amounts of noise were added to paths of straight‐line segments interspersed with turns. We also demonstrate our algorithm on data of free‐ranging oryx Oryx leucoryx. We compare our algorithm to existing statistical techniques for break‐point inference. The algorithm scales linearly and can analyse several hundred‐thousand data points in a few seconds on a mid‐range desktop computer. It identified turnpoints in simulated data with complete accuracy when the noise in the headings had a standard deviation of ±8∘, well within the tolerance of many modern biologgers. It has comparable accuracy to the existing algorithms tested, and is up to three orders of magnitude faster. Our algorithm, freely available in R and Python, serves as an initial step in processing ultra high‐resolution animal movement data, resulting in a rarefied path that can be used as an input into many existing step‐and‐turn methods of analysis. The resulting path consists of points where the animal makes a clear turn, and thereby provides valuable data on decisions underlying movement patterns. As such, it provides an important breakthrough required as a starting point for analysing subsecond resolution data. Journal Article Methods in Ecology and Evolution 9 10 2091 2101 Wiley 2041210X animal movement, biologging, turn angle, big data 1 10 2018 2018-10-01 10.1111/2041-210X.13056 https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13056 COLLEGE NANME Biosciences COLLEGE CODE SBI Swansea University 2021-07-16T14:38:21.0559077 2019-01-21T00:53:05.4760888 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Biosciences Jonathan R. Potts 1 Luca Borger 0000-0001-8763-5997 2 D. Michael Scantlebury 3 Nigel C. Bennett 4 Abdulaziz Alagaili 5 Rory Wilson 0000-0003-3177-0177 6 0048296-21012019005448.pdf cp_pottsetal_acceptedMS.pdf 2019-01-21T00:54:48.0330000 Output 823612 application/pdf Accepted Manuscript true 2019-10-01T00:00:00.0000000 true eng
title Finding turning-points in ultra-high-resolution animal movement data
spellingShingle Finding turning-points in ultra-high-resolution animal movement data
Luca Borger
Rory Wilson
title_short Finding turning-points in ultra-high-resolution animal movement data
title_full Finding turning-points in ultra-high-resolution animal movement data
title_fullStr Finding turning-points in ultra-high-resolution animal movement data
title_full_unstemmed Finding turning-points in ultra-high-resolution animal movement data
title_sort Finding turning-points in ultra-high-resolution animal movement data
author_id_str_mv 8416d0ffc3cccdad6e6d67a455e7c4a2
017bc6dd155098860945dc6249c4e9bc
author_id_fullname_str_mv 8416d0ffc3cccdad6e6d67a455e7c4a2_***_Luca Borger
017bc6dd155098860945dc6249c4e9bc_***_Rory Wilson
author Luca Borger
Rory Wilson
author2 Jonathan R. Potts
Luca Borger
D. Michael Scantlebury
Nigel C. Bennett
Abdulaziz Alagaili
Rory Wilson
format Journal article
container_title Methods in Ecology and Evolution
container_volume 9
container_issue 10
container_start_page 2091
publishDate 2018
institution Swansea University
issn 2041210X
doi_str_mv 10.1111/2041-210X.13056
publisher Wiley
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Biosciences{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Biosciences
url https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13056
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description Recent advances in biologging have resulted in animal location data at unprecedentedly high temporal resolutions, sometimes many times per second. However, many current methods for analysing animal movement (e.g. step selection analysis or state‐space modelling) were developed with lower‐resolution data in mind. To make such methods usable with high‐resolution data, we require techniques to identify features within the trajectory where movement deviates from a straight line. We propose that the intricacies of movement paths, and particularly turns, reflect decisions made by animals so that turn points are particularly relevant to behavioural ecologists. As such, we introduce a fast, accurate algorithm for inferring turning‐points in high‐resolution data. For analysing big data, speed and scalability are vitally important. We test our algorithm on simulated data, where varying amounts of noise were added to paths of straight‐line segments interspersed with turns. We also demonstrate our algorithm on data of free‐ranging oryx Oryx leucoryx. We compare our algorithm to existing statistical techniques for break‐point inference. The algorithm scales linearly and can analyse several hundred‐thousand data points in a few seconds on a mid‐range desktop computer. It identified turnpoints in simulated data with complete accuracy when the noise in the headings had a standard deviation of ±8∘, well within the tolerance of many modern biologgers. It has comparable accuracy to the existing algorithms tested, and is up to three orders of magnitude faster. Our algorithm, freely available in R and Python, serves as an initial step in processing ultra high‐resolution animal movement data, resulting in a rarefied path that can be used as an input into many existing step‐and‐turn methods of analysis. The resulting path consists of points where the animal makes a clear turn, and thereby provides valuable data on decisions underlying movement patterns. As such, it provides an important breakthrough required as a starting point for analysing subsecond resolution data.
published_date 2018-10-01T03:58:41Z
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score 11.037166

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