Supplementary material from "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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ftdatacite:10.6084/m9.figshare.c.3999492.v1 2023-05-15T15:53:32+02:00 Supplementary material from "Disentangling social interactions and environmental drivers in multi-individual wildlife tracking data" Calabrese, Justin M. Fleming, Chris H. Fagan, William F. Rimmler, Martin Kaczensky, Petra Bewick, Sharon Leimgruber, Peter Mueller, Thomas 2018 https://dx.doi.org/10.6084/m9.figshare.c.3999492.v1 https://figshare.com/collections/Supplementary_material_from_Disentangling_social_interactions_and_environmental_drivers_in_multi-individual_wildlife_tracking_data_/3999492/1 unknown Figshare https://dx.doi.org/10.1098/rstb.2017.0007 https://dx.doi.org/10.6084/m9.figshare.c.3999492 CC BY 4.0 https://creativecommons.org/licenses/by/4.0 CC-BY Ecology FOS Biological sciences 60801 Animal Behaviour Collection article 2018 ftdatacite https://doi.org/10.6084/m9.figshare.c.3999492.v1 https://doi.org/10.1098/rstb.2017.0007 https://doi.org/10.6084/m9.figshare.c.3999492 2021-11-05T12:55:41Z 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 in ecology: from emerging technologies to conservation and management'. Article in Journal/Newspaper caribou DataCite Metadata Store (German National Library of Science and Technology) |
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Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
Ecology FOS Biological sciences 60801 Animal Behaviour |
spellingShingle |
Ecology FOS Biological sciences 60801 Animal Behaviour Calabrese, Justin M. Fleming, Chris H. Fagan, William F. Rimmler, Martin Kaczensky, Petra Bewick, Sharon Leimgruber, Peter Mueller, Thomas Supplementary material from "Disentangling social interactions and environmental drivers in multi-individual wildlife tracking data" |
topic_facet |
Ecology FOS Biological sciences 60801 Animal Behaviour |
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 in ecology: from emerging technologies to conservation and management'. |
format |
Article in Journal/Newspaper |
author |
Calabrese, Justin M. Fleming, Chris H. Fagan, William F. Rimmler, Martin Kaczensky, Petra Bewick, Sharon Leimgruber, Peter Mueller, Thomas |
author_facet |
Calabrese, Justin M. Fleming, Chris H. Fagan, William F. Rimmler, Martin Kaczensky, Petra Bewick, Sharon Leimgruber, Peter Mueller, Thomas |
author_sort |
Calabrese, Justin M. |
title |
Supplementary material from "Disentangling social interactions and environmental drivers in multi-individual wildlife tracking data" |
title_short |
Supplementary material from "Disentangling social interactions and environmental drivers in multi-individual wildlife tracking data" |
title_full |
Supplementary material from "Disentangling social interactions and environmental drivers in multi-individual wildlife tracking data" |
title_fullStr |
Supplementary material from "Disentangling social interactions and environmental drivers in multi-individual wildlife tracking data" |
title_full_unstemmed |
Supplementary material from "Disentangling social interactions and environmental drivers in multi-individual wildlife tracking data" |
title_sort |
supplementary material from "disentangling social interactions and environmental drivers in multi-individual wildlife tracking data" |
publisher |
Figshare |
publishDate |
2018 |
url |
https://dx.doi.org/10.6084/m9.figshare.c.3999492.v1 https://figshare.com/collections/Supplementary_material_from_Disentangling_social_interactions_and_environmental_drivers_in_multi-individual_wildlife_tracking_data_/3999492/1 |
genre |
caribou |
genre_facet |
caribou |
op_relation |
https://dx.doi.org/10.1098/rstb.2017.0007 https://dx.doi.org/10.6084/m9.figshare.c.3999492 |
op_rights |
CC BY 4.0 https://creativecommons.org/licenses/by/4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.6084/m9.figshare.c.3999492.v1 https://doi.org/10.1098/rstb.2017.0007 https://doi.org/10.6084/m9.figshare.c.3999492 |
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1766388639778996224 |