Ecology. 2022 Apr 11:e3718. doi: 10.1002/ecy.3718. Online ahead of print.
ABSTRACT
Monitoring technologies now provide real-time animal location information which opens the possibility of forecasting systems to fuse these data with movement models to predict future trajectories. State space modelling approaches are well established for retrospective location estimation and behavioural inference through state and parameter estimation. Here, we use a state space model within a comprehensive data assimilative framework for probabilistic animal movement forecasting. Real-time location information is combined with stochastic movement model predictions to provide forecasts of future animal locations and trajectories, as well as estimation of key behavioural parameters. Implementation uses ensemble-based sequential Monte Carlo methods (a particle filter). We first apply the framework to an idealized case using a non-dimensional animal movement model based on a continuous-time random walk process. A set of numerical forecasting experiments demonstrate the workflow and key features, such as the online estimation of behavioural parameters using state augmentation, the use of potential functions for habitat preference, as well as the role of observation error and sampling frequency on forecast skill. For a realistic demonstration, we adapt the framework to short-term forecasting of the endangered Southern Resident Killer Whale (SRKW) in the Salish Sea using visual sighting information wherein the potential function reflects historical habitat utilization of SRKW. We successfully estimate whale locations up to 2.5 hours in advance with a moderate prediction error ($< 5$ km), providing reasonable lead-in time to mitigate vessel-whale interactions. It is argued that this forecasting framework can be used to synthesize diverse data types, improve animal movement models and behavioural understanding, and has the potential to become an important new direction for movement ecology.
PMID:35405019 | DOI:10.1002/ecy.3718
