Publication Date:
2022-05-25
Description:
Author Posting. © The Author(s), 2017. This is the author's version of the work. It is posted here under a nonexclusive, irrevocable, paid-up, worldwide license granted to WHOI. It is made available for personal use, not for redistribution. The definitive version was published in Philosophical Transactions of the Royal Society of London.Series B, Biological Sciences 373 (2018): 20170005, doi:10.1098/rstb.2017.0005.
Description:
Mobile animal groups provide some of the most compelling examples of self-organization in the natural world. While field observations of songbird flocks wheeling in the sky or anchovy schools fleeing from predators have inspired considerable interest in the mechanics of collective motion, the challenge of simultaneously monitoring multiple animals in the field has historically limited our capacity to study collective behaviour of wild animal groups with precision. However, recent technological advancements now present exciting opportunities to overcome many of these limitations. Here we review existing methods used to collect data on the movements and interactions of multiple animals in a natural setting. We then survey emerging technologies that are poised to revolutionize the study of collective animal behaviour by extending the spatial and temporal scales of inquiry, increasing data volume and quality, and expediting the post-processing of raw data.
Description:
This work was supported by the following: NSF grant IOS-1545888; NSF Graduate Research Fellowship (L.F.H.; 1650114); James S. McDonnell Foundation fellowship (A.M.H.); Max Planck Institute for Ornithology (A.S.-P.), the Human Frontier Science Program (A.S.-P.; LT000492/017); Gips-Schüle Foundation (A.S.-P.); Office of Naval Research (F.H.J.; N00014-1410410); Carlsberg Foundation (F.H.J.; CF15-0915); AIAS-COFUND fellowship from Aarhus Institute of Advanced Studies (F.H.J.).
Keywords:
Collective behaviour
;
Collective motion
;
Remote sensing
;
Bio-logging
;
Reality mining
Repository Name:
Woods Hole Open Access Server
Type:
Preprint
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