Why did the animal turn? Time‐varying step selection analysis for inference between observed turning‐points in high frequency data

Munden, Rhys; Borger, Luca; Wilson, Rory; Redcliffe, James; Brown, Rowan; Garel, Mathieu; Potts, Jonathan R.

doi:10.1111/2041-210x.13574

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Why did the animal turn? Time‐varying step selection analysis for inference between observed turning‐points in high frequency data

Rhys Munden, Luca Borger

, Rory Wilson

, James Redcliffe, Rowan Brown

, Mathieu Garel, Jonathan R. Potts

Methods in Ecology and Evolution, Volume: 12, Issue: 5, Pages: 921 - 932

Swansea University Authors: Luca Borger , Rory Wilson , James Redcliffe, Rowan Brown

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© 2021 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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DOI (Published version): 10.1111/2041-210x.13574

Abstract

Step selection analysis (SSA) is a fundamental technique for uncovering the drivers of animal movement decisions. Its typical use has been to view an animal as ‘selecting’ each measured location, given its current (and possibly previous) locations. Although an animal is unlikely to make decisions pr...

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Published in:	Methods in Ecology and Evolution
ISSN:	2041-210X 2041-210X
Published:	Wiley 2021
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa57055

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Time‐varying step selection analysis for inference between observed turning‐points in high frequency data</title><swanseaauthors><author><sid>8416d0ffc3cccdad6e6d67a455e7c4a2</sid><ORCID>0000-0001-8763-5997</ORCID><firstname>Luca</firstname><surname>Borger</surname><name>Luca Borger</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>017bc6dd155098860945dc6249c4e9bc</sid><ORCID>0000-0003-3177-0177</ORCID><firstname>Rory</firstname><surname>Wilson</surname><name>Rory Wilson</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>4046e46611e52bf1ee798d17411df8e9</sid><firstname>James</firstname><surname>Redcliffe</surname><name>James Redcliffe</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>d7db8d42c476dfa69c15ce06d29bd863</sid><ORCID>0000-0003-3628-2524</ORCID><firstname>Rowan</firstname><surname>Brown</surname><name>Rowan Brown</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2021-06-08</date><deptcode>BGPS</deptcode><abstract>Step selection analysis (SSA) is a fundamental technique for uncovering the drivers of animal movement decisions. Its typical use has been to view an animal as ‘selecting’ each measured location, given its current (and possibly previous) locations. Although an animal is unlikely to make decisions precisely at the times its locations are measured, if data are gathered at a relatively low frequency (every few minutes or hours) this is often the best that can be done. Nowadays, though, tracking data are increasingly gathered at very high frequencies, often ≥1 Hz, so it may be possible to exploit these data to perform more behaviourally-meaningful step selection analysis.Here, we present a technique to do this. We first use an existing algorithm to determine the turning-points in an animal's movement path. We define a ‘step’ to be a straight-line movement between successive turning-points. We then construct a generalised version of integrated SSA (iSSA), called time-varying iSSA (tiSSA), which deals with the fact that turning-points are usually irregularly spaced in time. We demonstrate the efficacy of tiSSA by application to data on both simulated animals and free-ranging goats Capra aegagrus hircus, comparing our results to those of regular iSSA with locations that are separated by a constant time-interval.Using (regular) iSSA with constant time-steps can give results that are misleading compared to using tiSSA with the actual turns made by the animals. Furthermore, tiSSA can be used to infer covariates that are dependent on the time between turns, which is not possible with regular iSSA. As an example, we show that our study animals tend to spend less time between successive turns when the ground is rockier and/or the temperature is hotter.By constructing a step selection technique that works between observed turning-points of animals, we enable step selection to be used on high-frequency movement data, which are becoming increasingly prevalent in modern biologging studies. 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spelling	2022-10-26T12:25:51.4275089 v2 57055 2021-06-08 Why did the animal turn? Time‐varying step selection analysis for inference between observed turning‐points in high frequency data 8416d0ffc3cccdad6e6d67a455e7c4a2 0000-0001-8763-5997 Luca Borger Luca Borger true false 017bc6dd155098860945dc6249c4e9bc 0000-0003-3177-0177 Rory Wilson Rory Wilson true false 4046e46611e52bf1ee798d17411df8e9 James Redcliffe James Redcliffe true false d7db8d42c476dfa69c15ce06d29bd863 0000-0003-3628-2524 Rowan Brown Rowan Brown true false 2021-06-08 BGPS Step selection analysis (SSA) is a fundamental technique for uncovering the drivers of animal movement decisions. Its typical use has been to view an animal as ‘selecting’ each measured location, given its current (and possibly previous) locations. Although an animal is unlikely to make decisions precisely at the times its locations are measured, if data are gathered at a relatively low frequency (every few minutes or hours) this is often the best that can be done. Nowadays, though, tracking data are increasingly gathered at very high frequencies, often ≥1 Hz, so it may be possible to exploit these data to perform more behaviourally-meaningful step selection analysis.Here, we present a technique to do this. We first use an existing algorithm to determine the turning-points in an animal's movement path. We define a ‘step’ to be a straight-line movement between successive turning-points. We then construct a generalised version of integrated SSA (iSSA), called time-varying iSSA (tiSSA), which deals with the fact that turning-points are usually irregularly spaced in time. We demonstrate the efficacy of tiSSA by application to data on both simulated animals and free-ranging goats Capra aegagrus hircus, comparing our results to those of regular iSSA with locations that are separated by a constant time-interval.Using (regular) iSSA with constant time-steps can give results that are misleading compared to using tiSSA with the actual turns made by the animals. Furthermore, tiSSA can be used to infer covariates that are dependent on the time between turns, which is not possible with regular iSSA. As an example, we show that our study animals tend to spend less time between successive turns when the ground is rockier and/or the temperature is hotter.By constructing a step selection technique that works between observed turning-points of animals, we enable step selection to be used on high-frequency movement data, which are becoming increasingly prevalent in modern biologging studies. Furthermore, since turning-points can be viewed as decisions, our method places step selection analysis on a more behaviourally-meaningful footing compared to previous techniques. Journal Article Methods in Ecology and Evolution 12 5 921 932 Wiley 2041-210X 2041-210X Ecological Modelling, Ecology, Evolution, Behavior and Systematics 4 5 2021 2021-05-04 10.1111/2041-210x.13574 COLLEGE NANME Biosciences Geography and Physics School COLLEGE CODE BGPS Swansea University External research funder(s) paid the OA fee (includes OA grants disbursed by the Library) Leverhulme Trust Identifier: FundRef 10.13039/501100000275 2022-10-26T12:25:51.4275089 2021-06-08T11:07:20.2214771 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Biosciences Rhys Munden 1 Luca Borger 0000-0001-8763-5997 2 Rory Wilson 0000-0003-3177-0177 3 James Redcliffe 4 Rowan Brown 0000-0003-3628-2524 5 Mathieu Garel 6 Jonathan R. Potts 7 57055__20079__de4aea874d4c4dc3b15528dca1cce6ac.pdf 57055.VOR.pdf 2021-06-08T11:53:25.1220241 Output 1386030 application/pdf Version of Record true © 2021 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. true eng http://creativecommons.org/licenses/by/4.0/
title	Why did the animal turn? Time‐varying step selection analysis for inference between observed turning‐points in high frequency data
spellingShingle	Why did the animal turn? Time‐varying step selection analysis for inference between observed turning‐points in high frequency data Luca Borger Rory Wilson James Redcliffe Rowan Brown
title_short	Why did the animal turn? Time‐varying step selection analysis for inference between observed turning‐points in high frequency data
title_full	Why did the animal turn? Time‐varying step selection analysis for inference between observed turning‐points in high frequency data
title_fullStr	Why did the animal turn? Time‐varying step selection analysis for inference between observed turning‐points in high frequency data
title_full_unstemmed	Why did the animal turn? Time‐varying step selection analysis for inference between observed turning‐points in high frequency data
title_sort	Why did the animal turn? Time‐varying step selection analysis for inference between observed turning‐points in high frequency data
author_id_str_mv	8416d0ffc3cccdad6e6d67a455e7c4a2 017bc6dd155098860945dc6249c4e9bc 4046e46611e52bf1ee798d17411df8e9 d7db8d42c476dfa69c15ce06d29bd863
author_id_fullname_str_mv	8416d0ffc3cccdad6e6d67a455e7c4a2_*_Luca Borger 017bc6dd155098860945dc6249c4e9bc__Rory Wilson 4046e46611e52bf1ee798d17411df8e9__James Redcliffe d7db8d42c476dfa69c15ce06d29bd863_*_Rowan Brown
author	Luca Borger Rory Wilson James Redcliffe Rowan Brown
author2	Rhys Munden Luca Borger Rory Wilson James Redcliffe Rowan Brown Mathieu Garel Jonathan R. Potts
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publisher	Wiley
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description	Step selection analysis (SSA) is a fundamental technique for uncovering the drivers of animal movement decisions. Its typical use has been to view an animal as ‘selecting’ each measured location, given its current (and possibly previous) locations. Although an animal is unlikely to make decisions precisely at the times its locations are measured, if data are gathered at a relatively low frequency (every few minutes or hours) this is often the best that can be done. Nowadays, though, tracking data are increasingly gathered at very high frequencies, often ≥1 Hz, so it may be possible to exploit these data to perform more behaviourally-meaningful step selection analysis.Here, we present a technique to do this. We first use an existing algorithm to determine the turning-points in an animal's movement path. We define a ‘step’ to be a straight-line movement between successive turning-points. We then construct a generalised version of integrated SSA (iSSA), called time-varying iSSA (tiSSA), which deals with the fact that turning-points are usually irregularly spaced in time. We demonstrate the efficacy of tiSSA by application to data on both simulated animals and free-ranging goats Capra aegagrus hircus, comparing our results to those of regular iSSA with locations that are separated by a constant time-interval.Using (regular) iSSA with constant time-steps can give results that are misleading compared to using tiSSA with the actual turns made by the animals. Furthermore, tiSSA can be used to infer covariates that are dependent on the time between turns, which is not possible with regular iSSA. As an example, we show that our study animals tend to spend less time between successive turns when the ground is rockier and/or the temperature is hotter.By constructing a step selection technique that works between observed turning-points of animals, we enable step selection to be used on high-frequency movement data, which are becoming increasingly prevalent in modern biologging studies. Furthermore, since turning-points can be viewed as decisions, our method places step selection analysis on a more behaviourally-meaningful footing compared to previous techniques.
published_date	2021-05-04T07:58:30Z
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Why did the animal turn? Time‐varying step selection analysis for inference between observed turning‐points in high frequency data

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