Speed and distance travelled are two important metrics that can be calculated from animal movement data. They allow researchers to make inferences about how species expend energy and, therefore, how they can meet their nutritional and reproductive requirements across heterogenous landscapes.
Speed and distance are traditionally estimated by calculating straight line distances between discrete GPS location estimates. Yet, animals rarely move in straight lines between these points, especially when data are collected infrequently. As a result, straight line distances often result in underestimation of speed and distance travelled. On the other hand, not accounting for GPS (and Argos) error causes overestimation of speed and distance travelled when the data are collected frequently.
To address these issues, Smithsonian scientists have devised a way to simulate continuous trajectories that connect points and are consistent with the data’s autocorrelation structure—a statistic that contains the signature of the unknown movement path (Fig. 1). These simulations can then be used to quantify our uncertainty in how far the animal travelled, among other quantities.
Autocorrelation in animal tracking data is an expression of the fact that locations obtained more closely in time also tend to be geographically closer. The precise relationship between distance and time contains a wealth of information regarding the animal’s movement behavior and can be leveraged to make more accurate predictions about the animal’s true movement path.