Abstract
In recent years, there have been significant advances in the technology used to collect data on the movement and activity patterns of humans and animals. GPS units, which form the primary source of location data, have become cheaper, more accurate, lighter and less power‐hungry, and their accuracy has been further improved with the addition of inertial measurement units. The consequence is a glut of geospatial time series data, recorded at rates that range from one position fix every several hours (to maximize system lifetime) to ten fixes per second (in high dynamic situations). Since data of this quality and volume have only recently become available, the analytical methods to extract behavioral information from raw position data are at an early stage of development. An instance of this lies in the analysis of animal movement patterns. When investigating solitary animals, the timing and location of instances of avoidance and association are important behavioral markers. In this paper, a novel analytical method to detect avoidance and association between individuals is proposed; unlike existing methods, assumptions about the shape of the territories or the nature of individual movement are not needed. Simulations demonstrate that false positives (type I error) are rare (1%–3%), which means that the test rarely suggests that there is an association if there is none.
Highlights
Methods for collecting data on the movement of animals have ad‐ vanced dramatically over the last two decades, with GPS and inertial measurement units becoming smaller, lighter, more energy efficient, and more accurate than ever before
When analysing the interaction between solitary animals, the quantification of association or avoidance be‐ tween territorial conspecifics would advance our understanding of animal ecology and, in the long term, the impact of changing envi‐ ronments
The results demonstrate that false positives are rare, which means that the test rarely suggests that there is an association if there is none
Summary
Methods for collecting data on the movement of animals have ad‐ vanced dramatically over the last two decades, with GPS and inertial measurement units becoming smaller, lighter, more energy efficient, and more accurate than ever before. When analysing the interaction between solitary animals, the quantification of association or avoidance be‐ tween territorial conspecifics would advance our understanding of animal ecology and, in the long term, the impact of changing envi‐ ronments Existing forms of such tests are predicated on assump‐ tions about the shape of each individual's territory (Dunn, 1979; Macdonald, Ball, & Hough, 1980) or, more recently, model the an‐ imal's movements as a random walk (Fortin et al, 2005; Latombe, Parrott, Basille, & Fortin, 2014; Potts, Mokross, Stouffer, & Lewis, 2014; Vanak et al, 2013). Elbroch, Quigley, and Caragiulo (2014) suggested a generalized linear model to test for predictive power of various factors on the number of spatial associations observed These factors included the number of elk in the study area and the mean genetic relatedness between interacting individuals. A further benefit is that there is no need to guess in advance which range of separation distances might constitute an interaction; rather, one just applies the test of interaction over mul‐ tiple different distance ranges
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