Modelling group dynamic animal movement
Group dynamic movement is a fundamental aspect of many species' movements. The need to adequately model individuals' interactions with other group members has been recognised, particularly in order to differentiate the role of social forces in individual movement from environmental factors...
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ftdatacite:10.48550/arxiv.1308.5850 2023-05-15T18:04:22+02:00 Modelling group dynamic animal movement Langrock, Roland Hopcraft, J. Grant C. Blackwell, Paul G. Goodall, Victoria King, Ruth Niu, Mu Patterson, Toby A. Pedersen, Martin W. Skarin, Anna Schick, Robert S. 2013 https://dx.doi.org/10.48550/arxiv.1308.5850 https://arxiv.org/abs/1308.5850 unknown arXiv https://dx.doi.org/10.1111/2041-210x.12155 arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Quantitative Methods q-bio.QM Applications stat.AP FOS Biological sciences FOS Computer and information sciences article-journal Article ScholarlyArticle Text 2013 ftdatacite https://doi.org/10.48550/arxiv.1308.5850 https://doi.org/10.1111/2041-210x.12155 2022-04-01T13:12:47Z Group dynamic movement is a fundamental aspect of many species' movements. The need to adequately model individuals' interactions with other group members has been recognised, 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. 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 multi-state random walks. 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. 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. Text Rangifer tarandus DataCite Metadata Store (German National Library of Science and Technology) |
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Quantitative Methods q-bio.QM Applications stat.AP FOS Biological sciences FOS Computer and information sciences |
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Quantitative Methods q-bio.QM Applications stat.AP FOS Biological sciences FOS Computer and information sciences Langrock, Roland Hopcraft, J. Grant C. Blackwell, Paul G. Goodall, Victoria King, Ruth Niu, Mu Patterson, Toby A. Pedersen, Martin W. Skarin, Anna Schick, Robert S. Modelling group dynamic animal movement |
topic_facet |
Quantitative Methods q-bio.QM Applications stat.AP FOS Biological sciences FOS Computer and information sciences |
description |
Group dynamic movement is a fundamental aspect of many species' movements. The need to adequately model individuals' interactions with other group members has been recognised, 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. 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 multi-state random walks. 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. 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. |
format |
Text |
author |
Langrock, Roland Hopcraft, J. Grant C. Blackwell, Paul G. Goodall, Victoria King, Ruth Niu, Mu Patterson, Toby A. Pedersen, Martin W. Skarin, Anna Schick, Robert S. |
author_facet |
Langrock, Roland Hopcraft, J. Grant C. Blackwell, Paul G. Goodall, Victoria King, Ruth Niu, Mu Patterson, Toby A. Pedersen, Martin W. Skarin, Anna Schick, Robert S. |
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 |
publisher |
arXiv |
publishDate |
2013 |
url |
https://dx.doi.org/10.48550/arxiv.1308.5850 https://arxiv.org/abs/1308.5850 |
genre |
Rangifer tarandus |
genre_facet |
Rangifer tarandus |
op_relation |
https://dx.doi.org/10.1111/2041-210x.12155 |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.1308.5850 https://doi.org/10.1111/2041-210x.12155 |
_version_ |
1766175722751131648 |