Modelling group dynamic animal movement
1). Group dynamics are a fundamental aspect of many species' movements. The need to adequately model individuals' interactions with other group members has been recognized, particularly in order to differentiate the role of social forces in individual movement from environmental factors. H...
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Online Access: | https://hdl.handle.net/10023/4555 https://doi.org/10.1111/2041-210X.12155 |
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ftstandrewserep:oai:research-repository.st-andrews.ac.uk:10023/4555 2024-09-30T14:41:42+00:00 Modelling group dynamic animal movement Langrock, Roland Hopcraft, Grant Blackwell, Paul Goodall, Victoria King, Ruth Niu, Mu Patterson, Toby Pedersen, Martin Skarin, Anna Schick, Robert Schilling EPSRC University of St Andrews.Statistics University of St Andrews.School of Mathematics and Statistics University of St Andrews.Scottish Oceans Institute University of St Andrews.Centre for Research into Ecological & Environmental Modelling 2014-04-03T14:01:01Z 1286639 application/pdf https://hdl.handle.net/10023/4555 https://doi.org/10.1111/2041-210X.12155 eng eng Methods in Ecology and Evolution 43921402 9f6085ff-0270-4eea-9f6c-621a13690d2d 84893961321 000331402000011 Langrock , R , Hopcraft , G , Blackwell , P , Goodall , V , King , R , Niu , M , Patterson , T , Pedersen , M , Skarin , A & Schick , R S 2014 , ' Modelling group dynamic animal movement ' , Methods in Ecology and Evolution , vol. 5 , no. 2 , pp. 190-199 . https://doi.org/10.1111/2041-210X.12155 2041-210X https://hdl.handle.net/10023/4555 doi:10.1111/2041-210X.12155 EP/F069766/1 EP/I000917/1 © 2013 The Authors. Methods in Ecology and Evolution © 2013 British Ecological Society. Behavioural state Hidden Markov model Maximum likelihood Random walk Journal article 2014 ftstandrewserep https://doi.org/10.1111/2041-210X.12155 2024-09-03T23:50:27Z 1). Group dynamics are a fundamental aspect of many species' movements. The need to adequately model individuals' interactions with other group members has been recognized, particularly in order to differentiate the role of social forces in individual movement from environmental factors. However, to date, practical statistical methods, which can include group dynamics in animal movement models, have been lacking. 2). We consider a flexible modelling framework that distinguishes a group-level model, describing the movement of the group's centre, and an individual-level model, such that each individual makes its movement decisions relative to the group centroid. The basic idea is framed within the flexible class of hidden Markov models, extending previous work on modelling animal movement by means of multistate random walks. 3). While in simulation experiments parameter estimators exhibit some bias in non-ideal scenarios, we show that generally the estimation of models of this type is both feasible and ecologically informative. 4). We illustrate the approach using real movement data from 11 reindeer (Rangifer tarandus). Results indicate a directional bias towards a group centroid for reindeer in an encamped state. Though the attraction to the group centroid is relatively weak, our model successfully captures group-influenced movement dynamics. Specifically, as compared to a regular mixture of correlated random walks, the group dynamic model more accurately predicts the non-diffusive behaviour of a cohesive mobile group. 5). As technology continues to develop, it will become easier and less expensive to tag multiple individuals within a group in order to follow their movements. Our work provides a first inferential framework for understanding the relative influences of individual versus group-level movement decisions. This framework can be extended to include covariates corresponding to environmental influences or body condition. As such, this framework allows for a broader understanding of the many internal and ... Article in Journal/Newspaper Rangifer tarandus University of St Andrews: Digital Research Repository Methods in Ecology and Evolution 5 2 190 199 |
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Open Polar |
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University of St Andrews: Digital Research Repository |
op_collection_id |
ftstandrewserep |
language |
English |
topic |
Behavioural state Hidden Markov model Maximum likelihood Random walk |
spellingShingle |
Behavioural state Hidden Markov model Maximum likelihood Random walk Langrock, Roland Hopcraft, Grant Blackwell, Paul Goodall, Victoria King, Ruth Niu, Mu Patterson, Toby Pedersen, Martin Skarin, Anna Schick, Robert Schilling Modelling group dynamic animal movement |
topic_facet |
Behavioural state Hidden Markov model Maximum likelihood Random walk |
description |
1). Group dynamics are a fundamental aspect of many species' movements. The need to adequately model individuals' interactions with other group members has been recognized, particularly in order to differentiate the role of social forces in individual movement from environmental factors. However, to date, practical statistical methods, which can include group dynamics in animal movement models, have been lacking. 2). We consider a flexible modelling framework that distinguishes a group-level model, describing the movement of the group's centre, and an individual-level model, such that each individual makes its movement decisions relative to the group centroid. The basic idea is framed within the flexible class of hidden Markov models, extending previous work on modelling animal movement by means of multistate random walks. 3). While in simulation experiments parameter estimators exhibit some bias in non-ideal scenarios, we show that generally the estimation of models of this type is both feasible and ecologically informative. 4). We illustrate the approach using real movement data from 11 reindeer (Rangifer tarandus). Results indicate a directional bias towards a group centroid for reindeer in an encamped state. Though the attraction to the group centroid is relatively weak, our model successfully captures group-influenced movement dynamics. Specifically, as compared to a regular mixture of correlated random walks, the group dynamic model more accurately predicts the non-diffusive behaviour of a cohesive mobile group. 5). As technology continues to develop, it will become easier and less expensive to tag multiple individuals within a group in order to follow their movements. Our work provides a first inferential framework for understanding the relative influences of individual versus group-level movement decisions. This framework can be extended to include covariates corresponding to environmental influences or body condition. As such, this framework allows for a broader understanding of the many internal and ... |
author2 |
EPSRC University of St Andrews.Statistics University of St Andrews.School of Mathematics and Statistics University of St Andrews.Scottish Oceans Institute University of St Andrews.Centre for Research into Ecological & Environmental Modelling |
format |
Article in Journal/Newspaper |
author |
Langrock, Roland Hopcraft, Grant Blackwell, Paul Goodall, Victoria King, Ruth Niu, Mu Patterson, Toby Pedersen, Martin Skarin, Anna Schick, Robert Schilling |
author_facet |
Langrock, Roland Hopcraft, Grant Blackwell, Paul Goodall, Victoria King, Ruth Niu, Mu Patterson, Toby Pedersen, Martin Skarin, Anna Schick, Robert Schilling |
author_sort |
Langrock, Roland |
title |
Modelling group dynamic animal movement |
title_short |
Modelling group dynamic animal movement |
title_full |
Modelling group dynamic animal movement |
title_fullStr |
Modelling group dynamic animal movement |
title_full_unstemmed |
Modelling group dynamic animal movement |
title_sort |
modelling group dynamic animal movement |
publishDate |
2014 |
url |
https://hdl.handle.net/10023/4555 https://doi.org/10.1111/2041-210X.12155 |
genre |
Rangifer tarandus |
genre_facet |
Rangifer tarandus |
op_relation |
Methods in Ecology and Evolution 43921402 9f6085ff-0270-4eea-9f6c-621a13690d2d 84893961321 000331402000011 Langrock , R , Hopcraft , G , Blackwell , P , Goodall , V , King , R , Niu , M , Patterson , T , Pedersen , M , Skarin , A & Schick , R S 2014 , ' Modelling group dynamic animal movement ' , Methods in Ecology and Evolution , vol. 5 , no. 2 , pp. 190-199 . https://doi.org/10.1111/2041-210X.12155 2041-210X https://hdl.handle.net/10023/4555 doi:10.1111/2041-210X.12155 EP/F069766/1 EP/I000917/1 |
op_rights |
© 2013 The Authors. Methods in Ecology and Evolution © 2013 British Ecological Society. |
op_doi |
https://doi.org/10.1111/2041-210X.12155 |
container_title |
Methods in Ecology and Evolution |
container_volume |
5 |
container_issue |
2 |
container_start_page |
190 |
op_container_end_page |
199 |
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1811644164781113344 |