Classifying area-restricted search (ARS) using a partial sum approach

Many animals perform two distinct alternating movement strategies when foraging: intensive searches with low speed and high turning to cover a small area in high detail and extensive searches with high speed and low turning to cover a large area in low detail. Observed movement paths will tend to exhibit differences in speed and correlation between these different search strategies. Identifying transitions between strategies can enable one to acquire information regarding both the distribution of resources and the underlying behavioural mechanisms performed by a foraging animal. Methods such as the moving average, first-passage time, residence time and fractal landscape methods have been used to identify behavioural states of various real and simulated foragers. We provide a review of these current methods and identify a set of common limitations associated with each procedure. We develop a new mathematical approach: the partial sum method, which is designed to avoid these limitations. A comprehensive test is undertaken to evaluate and compare the performance of the partial sum and the existing methods using a carefully constructed set of computer-generated movement paths. Each simulated track was designed to replicate the possible paths performed by an animal under different foraging conditions. Our results provide strong evidence that the partial sum method is better than existing analytical methods for identifying transitions between two different search strategies. © 2011 Springer Science+Business Media B.V.