Integrating visual and acoustic observations to build an “intelligent” killer whale movement forecast system

Abstract

Shipping traffic continues to grow in the Salish Sea with considerable marine industrial developments planned for the region. With this comes an increase in the risk of cetaceans being disturbed, harassed, and potentially colliding with commercially operating vessels. One key conservation concern is these waters coincide with designated Critical Habitat for the endangered population of Southern Resident killer whales (SRKW). Monitoring technologies such as hydrophones provide the promise of real-time animal detection and localisation. To use these continuous streams of real-time whale location data, we are developing algorithms that automate acoustic detection and classification of SRKW calls using deep learning models trained on extensive new and updated annotated datasets from the region. We are developing open-source models for pod-level classification, while also differentiating SRKW from other ecotypes and species. We are developing a forecasting system to merge these whale detections with opportunistic visual observations. This is based on sequential data assimilation methods using advanced animal movement models to estimate SRKW pod locations with probabilistic predictions of whale directional movement. These methods will be transformative for shipping by providing forecasting with a lead time of a few hours allowing vessels to adjust their speed or pathway to minimize whale-vessel interactions.