Data from: Estimating encounter location distributions from animal tracking data

1. Ecologists have long been interested in linking individual
behavior with higher-level processes. For motile species, this
'upscaling' is governed by how well any given movement strategy
maximizes encounters with positive factors, and minimizes encounters with
negative factors. Despite the importance of encounter events for a broad
range of ecological processes, encounter theory has not kept pace with
developments in animal tracking or movement modeling. Furthermore,
existing work has focused primarily on the relationship between animal
movement and encounter rates while the relationship between
individual movement and the spatial locations of encounter events
in the environment has remained conspicuously understudied. 2. Here, we
bridge this gap by introducing a method for describing the long-term
encounter location probabilities for movement within home ranges, termed
the conditional distribution of encounters (CDE). We then derive this
distribution, as well as confidence intervals, implement its statistical
estimator into open source software, and demonstrate the broad ecological
relevance of this distribution. 3. We first use simulated data to show how
our estimator provides asymptotically consistent estimates. We then
demonstrate the general utility of this method for three simulation-based
scenarios that occur routinely in biological systems: i) a population of
individuals with home ranges that overlap with neighbors; ii) a pair of
individuals with a hard territorial border between their home ranges; and
iii) a predator with a large home range that encompassed the home ranges
of multiple prey individuals. Using GPS data from white-faced capuchins
(Cebus capucinus) tracked on Barro Colorado Island, Panama, and sleepy
lizards (Tiliqua rugosa) tracked in Bundey, South Australia, we then show
how the CDE can be used to estimate the locations of territorial borders,
identify key resources, quantify the potential for competitive or
predatory interactions, and/or identify any changes in behaviour that
directly result from location-specific encounter probability. 4. The CDE
enables researchers to better understand the dynamics of populations of
interacting individuals. Notably, the general estimation framework
developed in this work builds straightforwardly off of home range
estimation and requires no specialised data collection protocols. This
method is now openly available via the ctmm R package.