DOI: 10.5061/dryad.rt535m8
title: Data from: Estimating interactions between individuals from concurrent animal movements

description: 1. Animal movements arise from complex interactions of individuals with
 their environment, including both conspecific and heterospecific
 individuals. Animals may be attracted to each other for mating, social
 foraging, or information gain, or may keep at a distance from others to
 avoid aggressive encounters related to, e.g., interference competition,
 territoriality, or predation. With modern tracking technology, more data
 sets are emerging that allow to investigate fine-scale interactions
 between free-ranging individuals from movement data, however, few methods
 exist to disentangle fine-scale behavioural responses of interacting
 individuals when these are highly individual-specific. 2. In a framework
 of step-selection functions, we related movements decisions of individuals
 to dynamic occurrence distributions of other individuals obtained through
 kriging of their movement paths. Using simulated data, we tested the
 method’s ability to identify various combinations of attraction,
 avoidance, and neutrality between individuals, including asymmetric (i.e.
 non-mutual) behaviours. Additionally, we analysed radio-telemetry data
 from concurrently tracked small rodents (bank vole, Myodes glareolus) to
 test whether our method could detect biologically plausible behaviours. 3.
 We found that our method was able to successfully detect and distinguish
 between fine-scale interactions (attraction, avoidance, neutrality), even
 when these were asymmetric between individuals. The method worked best
 when confounding factors were taken into account in the step-selection
 function. However, even when failing to do so (e.g. due to missing
 information), interactions could be reasonably identified. In bank voles,
 responses depended strongly on the sexes of the involved individuals and
 matched expectations. 4. Our approach can be combined with conventional
 uses of step-selection functions to tease apart the various drivers of
 movement, e.g. the influence of the physical and the social environment.
 In addition, the method is particularly useful in studying interactions
 when responses are highly individual-specific, i.e. vary between and
 towards different individuals, making our method suitable for both
 single-species and multi-species analyses (e.g. in the context of
 predation or competition).
description: Schlagel_et_al_2019_interaction_method_vole_dataBank vole (Myodes glareolus) movement data obtained from an automatic radio-telemetry (ART) system near Potsdam, Germany. Bank voles were caught in the field and released, in groups of 4 ("replicates"), into large outdoor enclosures (50 x 50 m). Data spans 3-5 days for individual replicates and has a temporal resolution of 6 minutes. Additional information available for each individual is its sex.Schlaegel_et_al_2019_interaction_method_R_codeZip archive comprising 3 files: (1) simulated movement data of two individuals that are attracted to each other, (2) R script for performing the analysis (estimating occurrences and performing step-selection analysis to estimate attraction-avoidance-neutrality) using package "amt", (3) documentation with output from the R script.