Metrics for describing dyadic movement: a review

Abstract

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.Electronic supplementary materialThe online version of this article (10.1186/s40462-018-0144-2) contains supplementary material, which is available to authorized users.

A relative-motion method for parsing spatio-temporal behaviour of dyads using GPS relocation data

“…In particular, the index of proximity in space and the coefficient of association, example of point-based measures, look at the proportion of close simultaneous locations over the total number, while the correlation index and the dynamic interaction index, example of path-based measures, respectively evaluate the correlation and cohesiveness of movement segments that connect consecutive locations. Joo et al (2018), following Long et al (2014), assess the adequacy of 12 metrics introduced in previous works to assess specific aspects of joint-movement behaviour (two individuals move together for the total or a partial portion of their paths), focusing on proximity and coordination (synchrony) in direction and speed. The comparison is performed by building different scenarios with different levels of proximity and coordination to assess the ability of the metrics to capture various features.…”

“…Most studies of dyadic interactions involve making observations of individuals at close quarters (Whitehead and Dufault, 1999); for example, agonistic interactions in social insects Smith, 1986, Breed, 2003), grooming networks in primates (Voelkl et al, 2011), and dominance behavior in elephants (Archie et al, 2006, Wittemyer andGetz, 2007). An exception though are a new class of methods that use global positioning system (GPS) telemetry data to assess the joint movement of individuals that may be some distance apart and not simultaneously directly observable to a visual recorder (human or camera) (Joo et al, 2018). Of course, the assumption is that individuals not in visual contact with one another may still have auditory (Hulse, 2002, Erbe et al, 2016, olfactory (Shorey, 2013), or even low frequency vibratory cues (McComb et al, 2003, O'Connell-Rodwell, 2007 regarding the location of other individuals within a radius and direction salient to the perceptual modality involved (with wind direction playing a critical role in olfactory communication).…”

Modelling and inference for the movement of interacting animals

“…To compare our approach with other methods, we have examined our results from both the simulation and full baboon analyses alongside those obtained when applying dyadic metrics (Joo, Etienne, Bez, & Mahévas, 2018; Long et al., 2014) to the same data. Much like our approach, dyadic metrics utilise movement data from multiple animals to investigate any interdependence and in turn better understand their collective behaviours.…”

“…To compare our approach with other methods, we have examined our results from both the simulation and full baboon analyses alongside those obtained when applying dyadic metrics (Joo, Etienne, Bez, & Mahévas, 2018;Long et al, 2014) to the same data.…”

“…The metrics we have chosen to implement are proximity and dynamic interaction using the wildlifeDI R package from Long et al (2014). Joo et al (2018) indicate each metric offers an informative view into a specific element of interaction. Proximity evaluates whether two animals are within a distance threshold defined by the user; dynamic interaction, when split into its displacement and direction components, offers insight into whether two animals are moving at a similar speed or in a similar orientation respectively.…”