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...
Published in: | Philosophical Transactions of the Royal Society B: Biological Sciences |
---|---|
Main Authors: | , , , , , , , |
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 |
id |
ftpubmed:oai:pubmedcentral.nih.gov:5882977 |
---|---|
record_format |
openpolar |
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 |
container_volume |
373 |
container_issue |
1746 |
container_start_page |
20170007 |
_version_ |
1766388643166945280 |