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Finding turning-points in ultra-high-resolution animal movement data / Jonathan R. Potts; Luca Börger; D. Michael Scantlebury; Nigel C. Bennett; Abdulaziz Alagaili; Rory P. Wilson

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

Swansea University Author: Borger, Luca

  • Accepted Manuscript under embargo until: 1st October 2019

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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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. 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spelling 2019-03-11T11:17:28Z v2 48296 2019-01-21 Finding turning-points in ultra-high-resolution animal movement data Luca Borger Luca Borger true 0000-0001-8763-5997 false 8416d0ffc3cccdad6e6d67a455e7c4a2 be657dee57d87983ff5a094becb4144d KrNKIcwalNUkyxqMTsbkl9yvqZQRJmUl2lxhnzSZE7o= 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 of Science Biosciences CSCI SBI Swansea Lab for Animal Movement None 2019-03-11T11:17:28Z 2019-01-21T00:53:05Z College of Science Biosciences Jonathan R. Potts 1 Luca Börger 2 D. Michael Scantlebury 3 Nigel C. Bennett 4 Abdulaziz Alagaili 5 Rory P. Wilson 6 Under embargo Under embargo 2019-01-21T00:54:48Z Output 823612 application/pdf AM true Updated Copyright 11/03/2019 2019-10-01T00:00:00 true eng
title Finding turning-points in ultra-high-resolution animal movement data
spellingShingle Finding turning-points in ultra-high-resolution animal movement data
Borger, Luca
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
author_id_fullname_str_mv 8416d0ffc3cccdad6e6d67a455e7c4a2_***_Borger, Luca
author Borger, Luca
author2 Jonathan R. Potts
Luca Börger
D. Michael Scantlebury
Nigel C. Bennett
Abdulaziz Alagaili
Rory P. 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 College of Science
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hierarchy_top_title College of Science
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hierarchy_parent_title College of Science
department_str Biosciences{{{_:::_}}}College of Science{{{_:::_}}}Biosciences
url https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13056
document_store_str 0
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researchgroup_str Swansea Lab for Animal Movement
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 2018-10-01T04:52:30Z
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