A critical examination of indices of dynamic interaction for wildlife telemetry studies

Abstract

Summary 1.Wildlife scientists continue to be interested in studying ways to quantify how the movements of animals are interdependent -dynamic interaction. While a number of applied studies of dynamic interaction exist, little is known about the comparative effectiveness and applicability of available methods used for quantifying interactions between animals. 2. We highlight the formulation, implementation and interpretation of a suite of eight currently available indices of dynamic interaction. Point-and path-based approaches are contrasted to demonstrate differences between methods and underlying assumptions on telemetry data. 3. Correlated and biased correlated random walks were simulated at a range of sampling resolutions to generate scenarios with dynamic interaction present and absent. We evaluate the effectiveness of each index at identifying different types of interactive behaviour at each sampling resolution. Each index is then applied to an empirical telemetry data set of three whitetailed deer (Odocoileus virginianus) dyads. 4. Results from the simulated data show that three indices of dynamic interaction reliant on statistical testing procedures are susceptible to Type I error, which increases at fine sampling resolutions. In the white-tailed deer examples, a recently developed index for quantifying local-level cohesive movement behaviour (the di index) provides revealing information on the presence of infrequent and varying interactions in space and time. 5. Point-based approaches implemented with finely sampled telemetry data overestimate the presence of interactions (Type I errors). Indices producing only a single global statistic (7 of the 8 indices) are unable to quantify infrequent and varying interactions through time. The quantification of infrequent and variable interactive behaviour has important implications for the spread of disease and the prevalence of social behaviour in wildlife. Guidelines are presented to inform researchers wishing to study dynamic interaction patterns in their own telemetry data sets. Finally, we make our code openly available, in the statistical software R, for computing each index of dynamic interaction presented herein.

Estimating interactions between individuals from concurrent animal movements

“…When considering dyadic interactions, we found many asymmetric responses, mostly one‐sided behaviours in which an individual is attracted to or avoids another, neutrally behaving one, which would remain undetected in classical dyadic analyses (e.g. Long et al, ). In light of our simulation analysis of mixed behaviours, the relatively large number of non‐significant interaction coefficients, especially for males, might indicate that those individuals switched their behaviours during the observation period.…”

Familiarity Breeds Contempt: Combining Proximity Loggers and GPS Reveals Female White-Tailed Deer (Odocoileus Virginianus) Avoiding Close Contact With Neighbors

“…To measure how strongly correlated were the movements of the two deer comprising each dyad, we used the dynamic interaction metric of Long et al (2014). This metric generalizes the vector correlation coefficient of Shirabe (2006) to allow examination of short time periods and does not involve physical proximity.…”

Terrestrial animal tracking as an eye on life and planet

“…Attraction and avoidance can be inferred from concurrent movements of neighboring animals (88,89), providing important insight into the behavioral processes underlying the territorial dynamics of individuals (90,91) and groups (88,92). For example, simultaneous tracking has revealed that the outcome of competitive interactions between neighboring primate social groups depends more strongly on the location of the encounter than the relative size of the groups and has documented the costs of losing territorial interactions (93,94,95).…”