Metrics for describing dyadic movement: a review

Type Article
Date 2018-12
Language English
Author(s) Joo RocioORCID1, 2, Etienne Marie-Pierre3, Bez Nicolas4, Mahevas StephanieORCID2
Affiliation(s) 1 : Univ Florida, Dept Wildlife Ecol & Conservat, Ft Lauderdale Res & Educ Ctr, 3205 Coll Ave, Davie, FL 33314 USA.
2 : Ecol & Modeles Halieut, IFREMER, BP 21105, F-44311 Nantes 03, France.
3 : Univ Rennes, CNRS, Agrocampus Ouest, IRMAR,UMR 6625, F-35000 Rennes, France.
4 : Univ Montpellier, CNRS, MARBEC, IRD,Ifremer, Sete, France.
Source Movement Ecology (2051-3933) (Bmc), 2018-12 , Vol. 6 , N. 26 , P. 17p.
DOI 10.1186/s40462-018-0144-2
WOS© Times Cited 18
Keyword(s) Collective behaviour, Dyadic movement, Indices, Movement ecology, Spatio-temporal dynamics, Trajectories

In movement ecology, the few works that have taken collective behaviour into account are data-driven and rely on simplistic theoretical assumptions, relying in metrics that may or may not be measuring what is intended. In the present paper, we focus on pairwise joint-movement behaviour, where individuals move together during at least a segment of their path. We investigate the adequacy of twelve metrics introduced in previous works for assessing joint movement by analysing their theoretical properties and confronting them with contrasting case scenarios. Two criteria are taken into account for review of those metrics: 1) practical use, and 2) dependence on parameters and underlying assumptions. When analysing the similarities between the metrics as defined, we show how some of them can be expressed using general mathematical forms. In addition, we evaluate the ability of each metric to assess specific aspects of joint-movement behaviour: proximity (closeness in space-time) and coordination (synchrony) in direction and speed. We found that some metrics are better suited to assess proximity and others are more sensitive to coordination. To help readers choose metrics, we elaborate a graphical representation of the metrics in the coordination and proximity space based on our results, and give a few examples of proximity and coordination focus in different movement studies.

Full Text
File Pages Size Access
Publisher's official version 17 2 MB Open access
Additional file 1 Graphical examples of two kernel functions for Proximity metrics. 1 88 KB Open access
Additional file 2 Cs1 requirements to take large negative values. 3 358 KB Open access
Additional file 3 Lixn: Table for computing probabilities. 1 118 KB Open access
Additional file 4 How to define the ellipse of the potential path area 1 61 KB Open access
Additional file 5 Metrics derived for each case scenario. 2 62 KB Open access
Additional file 6 Summary figures for proximity-speed and proximity-coordination scenarios. 2 58 KB Open access
Additional file 7 Computational cost of each metric. 2 90 KB Open access
Additional file 8 Principal component analysis of the metrics for the case scenarios. 3 189 KB Open access
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