[MARMAM] New publication - Forecasting system for Southern Resident Killer Whales

Marine Randon marine_randon at sfu.ca
Mon Jul 4 00:51:15 PDT 2022

Dear MARMAM community,

On behalf of my co-authors, I am pleased to announce the publication of our paper entitled "A real-time data assimilative forecasting system for animal tracking" in the journal Ecology.

Randon, M., Dowd, M., & Joy, R. (2022). A real‐time data assimilative forecasting system for animal tracking. Ecology, e3718.


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 apply the framework to an idealized case using a nondimensional animal movement model based on a continuous-time random walk process. A set of numerical forecasting experiments demonstrates the workflow and key features, such as the online estimation of behavioral parameters using state augmentation, the use of potential functions for habitat preference, and 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 h 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 and improve animal movement models and behavioral understanding and has the potential to lead to important advances in movement ecology.

The full text is available Open Access following this link: https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1002/ecy.3718

If you have questions, feel free to contact me at this address: mrandon at ifremer.fr

Best wishes,

Dr. Marine Randon

Postdoctoral fellow

Simon Fraser University

8888 University Drive

Burnaby, BC V5A 2S6

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