The exploratory analysis of autocorrelation in animal-movement studies

Studies of animal movements have been popularized for many large and shy species by the increasing use of radio telemetry methods (VHF and GPS technologies). Data are collected with high sampling frequency, and consist of successive observations of the position of an individual animal. The statistical analysis of such data poses several problems due to the lack of independence of successive observations. However, the statistical description of the temporal autocorrelation between successive steps is rarely performed by ecologists studying the patterns of animals movements. The aim of this paper is to warn ecologists against the consequences of failing to consider this aspect. We discuss the various issues related to analyzing autocorrelated data, and show how the exploratory analysis of autocorrelation can both reveal important biological insights and help to improve the accuracy of movement models. We suggest some tools that can be used to measure, test, and adjust for temporal autocorrelation. A short ecological illustration is presented