Event Title
Streaming Media
Presentation Abstract
Marine ship traffic is a growing source of anthropogenic stress for at-risk cetaceans through physical and acoustic disturbances. Real-time whale locations and short-term forecasts of a few hours can mitigate these risks by providing lead time for commercial vessels to adjust their path and speed. Towards this end, we develop a real-time forecasting system that assimilates observations into a stochastic movement model to provide forecasts of future whale locations and trajectories. A state space model is used to combine the movement model with location observations. Real-time data ingestion and forecasting is implemented using a sequential data assimilation cycle based on a particle filter. We apply this framework to a visual sighting observation track of the endangered Southern Resident Killer Whales of the Salish Sea, with expectation that real-time acoustic data sources are also compatible. The stochastic continuous-time movement model is an Orstein-Uhlenbeck process that includes a deterministic drift term and a time-varying persistence parameter estimated through state augmentation. We implement the model and demonstrate its capability of estimating whale locations up to 2.5 hours in advance with a moderate prediction error (= 10 km). This statistical framework is promising for the management and conservation of other at-risk cetaceans and easily adaptable to other data types and regions.
Session Title
Vessel Traffic
Conference Track
SSE5: Southern Resident Killer Whales and Vessel Impacts
Conference Name
Salish Sea Ecosystem Conference (2022 : Online)
Document Type
Event
SSEC Identifier
SSE-traditionals-433
Start Date
26-4-2022 9:45 AM
End Date
26-4-2022 11:15 AM
Rights
Copying of this document in whole or in part is allowable only for scholarly purposes. It is understood, however, that any copying or publication of this document for commercial purposes, or for financial gain, shall not be allowed without the author's written permission.
Type
Text
Language
English
A real-time data assimilative forecasting system for Southern Resident killer whales in the Salish Sea
Marine ship traffic is a growing source of anthropogenic stress for at-risk cetaceans through physical and acoustic disturbances. Real-time whale locations and short-term forecasts of a few hours can mitigate these risks by providing lead time for commercial vessels to adjust their path and speed. Towards this end, we develop a real-time forecasting system that assimilates observations into a stochastic movement model to provide forecasts of future whale locations and trajectories. A state space model is used to combine the movement model with location observations. Real-time data ingestion and forecasting is implemented using a sequential data assimilation cycle based on a particle filter. We apply this framework to a visual sighting observation track of the endangered Southern Resident Killer Whales of the Salish Sea, with expectation that real-time acoustic data sources are also compatible. The stochastic continuous-time movement model is an Orstein-Uhlenbeck process that includes a deterministic drift term and a time-varying persistence parameter estimated through state augmentation. We implement the model and demonstrate its capability of estimating whale locations up to 2.5 hours in advance with a moderate prediction error (= 10 km). This statistical framework is promising for the management and conservation of other at-risk cetaceans and easily adaptable to other data types and regions.
