Testing For Independence of Observations in Animal Movements

TLDR

Many animal movement studies assume successive positions are independent, yet no statistical test exists for this assumption with bivariate data. The authors develop a bivariate test based on empirically derived critical values of the ratio of mean squared successive distance to mean squared distance from the activity center, and provide a method to identify the time lag at which autocorrelation vanishes, illustrated with cotton‑rat location data. The test enables efficient sampling design, uncovers behavioral patterns such as home‑range shifts and route following, and offers a tool to assess how internal circadian rhythms influence spatial use.

Abstract

Many analyses of animals movements assume that an animals's position at time t + 1 is independent of its position at time t, but no statistical procedure exists to test this assumption with bivariate data. Using empirically derived critical values for the ratio of mean squared distance between successive observations to mean squared distance from the center of activity, we demonstrate a bivariate test of the independence assumption first proposed by Schoener. For cases in which the null hypothesis of independence is rejected, we present a procedure for determining the time interval at which autocorrelation becomes negligible. To illustrate implementation of the test, locational data obtained from a ratio—tagged adult female cotton rat (Sigmodon hispidus) were used. The test can be used to design an efficient sampling schedule for movement studies, and it is also useful in revealing behavioral phenomena such as home range shifting and any tendency of animals to follow prescribed routes in their daily activities. Further, the test may provide a means of examining how an animal's use of space is affected by its internal clock.