A real-time data assimilative forecasting system for animal tracking.

ABSTRACT: Monitoring technologies now provide real-time animal location information, which opens up the possibility of developing forecasting systems to fuse these data with movement models to predict future trajectories. State-space modeling approaches are well established for retrospective location estimation and behavioral 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 estimates of key behavioral parameters. Implementation uses ensemble-based sequential Monte Carlo methods (a particle filter). We first app

SUBMITTER: Randon M 

PROVIDER: S-EPMC9541799 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature