Modelling animal movement using the Argos satellite telemetry location error ellipse

摘要

The Argos satellite telemetry system is popular for studying the movement and space use of marine animals. The life histories of marine mammals, in particular, result in a relatively large proportion of inaccurate locations, thus making analysis methods that do not account for location measurement error inappropriate for these data. Using a new Kalman filtering algorithm, Argos now provides locations and estimated error ellipses associated with each satellite fix, but to our knowledge, the location error ellipse has yet to be incorporated into analyses of animal movement or space use. We first present an observation model utilizing the Argos error ellipse and then demonstrate how this observation model can be combined with a simple three-dimensional movement model in a state-space formulation to infer activity budgets and movement characteristics from location and dive data of two species of seal, the bearded seal (Erignathus barbatus) and the Hawaiian monk seal (Monachus schauinslandi). These example data sets are of variable quality and represent species that differ in both space use and latitudinal range relative to the polar orbits of Argos satellites. We also compare the results from our error ellipse model with those from an approximate (isotropic) error circle model. We found the error circle to be a crude approximation of the actual anisotropic error ellipse for the higher quality bearded seal data, but inferences from the lower quality Hawaiian monk seal data were more robust to the choice of observation model. In both examples, we found the theoretical bivariate normal distribution corresponding to the error ellipse often failed to adequately explain the most extreme location outliers. In practice, we suspect the inferential consequences of using traditional isotropic location quality classes or other crude approximations in lieu of the error ellipse will be largely case-dependent. We support the Argos recommendation that practitioners wishing to more properly account for location measurement error utilize the error ellipse in analyses. However, the continued presence of outliers using the new algorithm suggests practitioners should consider using a fat-tailed distribution derived from the error ellipse (e.g. bivariate t-distribution) or filtering extreme outliers during data pre-processing.