Challenges and solutions for studying collective animal behaviour in the wild

Abstract

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.This article is part of the theme issue 'Collective movement ecology'.

Keywords: bio-logging; collective behaviour; collective motion; reality mining; remote sensing.

Figure 1.

Technology is changing our view of collective behaviour, offering a variety of different perspectives on animal movement and interactions. High-resolution satellite imaging, fixed-wing or multicopter photography allows imaging groups of animals as they move across the landscape or migrate great distances. Stationary or semi-stationary imaging techniques allow high-definition tracking of large groups, potentially in three dimensions, using standard cameras, imaging sonar or infrared cameras. Bio-logging tags that sample location, behaviour, activity, or interactions with conspecifics provide a continuous stream of data from tagged individuals, even in otherwise inaccessible locations or when moving across large distances.

Figure 2.

Still frame from a video sequence showing movement tracks of individual fish filmed from a stationary camera array in shallow water [16].

Figure 3.

Remotely sensed imagery affords a unique opportunity to empirically study the ecology of collective motion in large animal systems. For example, satellite (a) and aerial (b,c) imagery of wildebeest herds (top row) reveals aggregation patterns that are structurally similar to those previously described for smaller taxa (bottom row): (a) vacuole (fish), (b) cruise (insects), (c) wave front (slime mould). Remote sensing now enables hypotheses regarding the form and function of these repeated patterns to be experimentally tested under natural conditions and for a wider range of taxa than ever before. Images were reproduced with the following permissions. Top row: (a) Google Earth, © 2017 Digital Globe; (b) 'River crossing' by Colin J. Torney, Elaine Ferguson and Lacey Hughey; (c) 'Wave front' by Lacey Hughey. Bottom row: (a) iStock.com/Connah/, cropped from original; (b) ‘A column of Matabele ants streaming towards a termite mound' by Piotr Naskrecki © 2013, cropped from original; (c) ‘Physarum polycephalum (Physaridae)' by Norbert Hülsmann, used under CCBY-NC-SA-2.0 (https://creativecommons.org/licenses/by-nc-sa/2.0/), cropped and rotated from original.

Figure 4.

Still frame from a UAV video sequence demonstrating ability to automatically track unique individuals and species (e.g. zebra (15–19, 21, 22, 24, 25, 29, 31, 33) in red versus wildebeest (13, 20, 23, 28) in blue) across video frames (sensu [39,43]). Still frame was reproduced with permission from Colin J. Torney.

Figure 5.

Combining bio-logging with UAV imagery enables investigation of how the environment shapes collective movement in wild animal groups. Coloured lines show trajectories for the majority of baboons within a single troop (obtained using GPS collars), and background image shows 3D point cloud rendering of their habitat (obtained from UAV imagery). White lines show scale (each line extends 50 m). Adapted from [10].