Generalizing the first-difference correlated random walk for marine animal movement data

TL;DR

This paper introduces a generalized movement model for marine animals that accounts for irregular telemetry data, improving accuracy in path reconstruction and enabling efficient multi-animal analysis.

Contribution

It extends the first-difference correlated random walk model to irregular time steps with drift and coordinate-specific autocorrelation, enhancing movement path accuracy.

Findings

The model accurately estimates parameters from irregular data.

It reconstructs movement paths more precisely than regular time models.

Application to seal data demonstrates practical utility.

Abstract

Animal telemetry data are often analysed with discrete time movement models assuming rotation in the movement. These models are defined with equidistant distant time steps. However, telemetry data from marine animals are observed irregularly. To account for irregular data, a time-irregularised first-difference correlated random walk model with drift is introduced. The model generalizes the commonly used first-difference correlated random walk with regular time steps by allowing irregular time steps, including a drift term, and by allowing different autocorrelation in the two coordinates. The model is applied to data from a ringed seal collected through the Argos satellite system, and is compared to related movement models through simulations. Accounting for irregular data in the movement model results in accurate parameter estimates and reconstruction of movement paths. Measured by distance, the introduced model can provide more accurate movement paths than the regular time counterpart. Extracting accurate movement paths from uncertain telemetry data is important for evaluating space use patterns for marine animals, which in turn is crucial for management. Further, handling irregular data directly in the movement model allows efficient simultaneous analysis of several animals.