Disentangling social interactions and environmental drivers in multi-individual wildlife tracking data

While many animal species exhibit strong conspecific interactions, movement analyses of wildlife tracking datasets still largely focus on single individuals. Multi-individual wildlife tracking studies provide new opportunities to explore how individuals move relative to one another, but such dataset...

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Published in:Philosophical Transactions of the Royal Society B: Biological Sciences
Main Authors: Calabrese, Justin M., Fleming, Christen H., Fagan, William F., Rimmler, Martin, Kaczensky, Petra, Bewick, Sharon, Leimgruber, Peter, Mueller, Thomas
Format: Text
Language:English
Published: The Royal Society 2018
Subjects:
Online Access:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5882977/
http://www.ncbi.nlm.nih.gov/pubmed/29581392
https://doi.org/10.1098/rstb.2017.0007
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spelling ftpubmed:oai:pubmedcentral.nih.gov:5882977 2023-05-15T15:53:32+02:00 Disentangling social interactions and environmental drivers in multi-individual wildlife tracking data Calabrese, Justin M. Fleming, Christen H. Fagan, William F. Rimmler, Martin Kaczensky, Petra Bewick, Sharon Leimgruber, Peter Mueller, Thomas 2018-05-19 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5882977/ http://www.ncbi.nlm.nih.gov/pubmed/29581392 https://doi.org/10.1098/rstb.2017.0007 en eng The Royal Society http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5882977/ http://www.ncbi.nlm.nih.gov/pubmed/29581392 http://dx.doi.org/10.1098/rstb.2017.0007 © 2018 The Author(s) http://royalsocietypublishing.org/licence Published by the Royal Society. All rights reserved. Articles Text 2018 ftpubmed https://doi.org/10.1098/rstb.2017.0007 2019-05-26T00:08:27Z While many animal species exhibit strong conspecific interactions, movement analyses of wildlife tracking datasets still largely focus on single individuals. Multi-individual wildlife tracking studies provide new opportunities to explore how individuals move relative to one another, but such datasets are frequently too sparse for the detailed, acceleration-based analytical methods typically employed in collective motion studies. Here, we address the methodological gap between wildlife tracking data and collective motion by developing a general method for quantifying movement correlation from sparsely sampled data. Unlike most existing techniques for studying the non-independence of individual movements with wildlife tracking data, our approach is derived from an analytically tractable stochastic model of correlated movement. Our approach partitions correlation into a deterministic tendency to move in the same direction termed ‘drift correlation’ and a stochastic component called ‘diffusive correlation’. These components suggest the mechanisms that coordinate movements, with drift correlation indicating external influences, and diffusive correlation pointing to social interactions. We use two case studies to highlight the ability of our approach both to quantify correlated movements in tracking data and to suggest the mechanisms that generate the correlation. First, we use an abrupt change in movement correlation to pinpoint the onset of spring migration in barren-ground caribou. Second, we show how spatial proximity mediates intermittently correlated movements among khulans in the Gobi desert. We conclude by discussing the linkages of our approach to the theory of collective motion. This article is part of the theme issue 'Collective movement ecology'. Text caribou PubMed Central (PMC) Philosophical Transactions of the Royal Society B: Biological Sciences 373 1746 20170007
institution Open Polar
collection PubMed Central (PMC)
op_collection_id ftpubmed
language English
topic Articles
spellingShingle Articles
Calabrese, Justin M.
Fleming, Christen H.
Fagan, William F.
Rimmler, Martin
Kaczensky, Petra
Bewick, Sharon
Leimgruber, Peter
Mueller, Thomas
Disentangling social interactions and environmental drivers in multi-individual wildlife tracking data
topic_facet Articles
description While many animal species exhibit strong conspecific interactions, movement analyses of wildlife tracking datasets still largely focus on single individuals. Multi-individual wildlife tracking studies provide new opportunities to explore how individuals move relative to one another, but such datasets are frequently too sparse for the detailed, acceleration-based analytical methods typically employed in collective motion studies. Here, we address the methodological gap between wildlife tracking data and collective motion by developing a general method for quantifying movement correlation from sparsely sampled data. Unlike most existing techniques for studying the non-independence of individual movements with wildlife tracking data, our approach is derived from an analytically tractable stochastic model of correlated movement. Our approach partitions correlation into a deterministic tendency to move in the same direction termed ‘drift correlation’ and a stochastic component called ‘diffusive correlation’. These components suggest the mechanisms that coordinate movements, with drift correlation indicating external influences, and diffusive correlation pointing to social interactions. We use two case studies to highlight the ability of our approach both to quantify correlated movements in tracking data and to suggest the mechanisms that generate the correlation. First, we use an abrupt change in movement correlation to pinpoint the onset of spring migration in barren-ground caribou. Second, we show how spatial proximity mediates intermittently correlated movements among khulans in the Gobi desert. We conclude by discussing the linkages of our approach to the theory of collective motion. This article is part of the theme issue 'Collective movement ecology'.
format Text
author Calabrese, Justin M.
Fleming, Christen H.
Fagan, William F.
Rimmler, Martin
Kaczensky, Petra
Bewick, Sharon
Leimgruber, Peter
Mueller, Thomas
author_facet Calabrese, Justin M.
Fleming, Christen H.
Fagan, William F.
Rimmler, Martin
Kaczensky, Petra
Bewick, Sharon
Leimgruber, Peter
Mueller, Thomas
author_sort Calabrese, Justin M.
title Disentangling social interactions and environmental drivers in multi-individual wildlife tracking data
title_short Disentangling social interactions and environmental drivers in multi-individual wildlife tracking data
title_full Disentangling social interactions and environmental drivers in multi-individual wildlife tracking data
title_fullStr Disentangling social interactions and environmental drivers in multi-individual wildlife tracking data
title_full_unstemmed Disentangling social interactions and environmental drivers in multi-individual wildlife tracking data
title_sort disentangling social interactions and environmental drivers in multi-individual wildlife tracking data
publisher The Royal Society
publishDate 2018
url http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5882977/
http://www.ncbi.nlm.nih.gov/pubmed/29581392
https://doi.org/10.1098/rstb.2017.0007
genre caribou
genre_facet caribou
op_relation http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5882977/
http://www.ncbi.nlm.nih.gov/pubmed/29581392
http://dx.doi.org/10.1098/rstb.2017.0007
op_rights © 2018 The Author(s)
http://royalsocietypublishing.org/licence
Published by the Royal Society. All rights reserved.
op_doi https://doi.org/10.1098/rstb.2017.0007
container_title Philosophical Transactions of the Royal Society B: Biological Sciences
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container_issue 1746
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