Tracking whales on the Scotian Shelf using passive acoustic monitoring on ocean gliders

Abstract

Expanded marine shipping and industrial activity has increased the risk of harmful effects on marine mammals. Quantitative estimates of marine mammal time and space distributions are essential for developing mitigation strategies designed to reduce the risks. Seasonal distributions of key marine mammals can be estimated by deploying passive acoustic monitoring (PAM) hydrophone systems and using the acoustic data to monitor, detect and identify species presence, often in near real-time. Most contemporary PAM deployments in the ocean are stationary and archive the acoustic data for post-recovery analyses after some extended period and are thus not ideal for addressing risk dynamics in near real-time. Substantive expansions of fixed PAM arrays over large ocean expanses can be economically and on-time limiting. Mobile autonomous vehicles now offer the economy of collecting the necessary acoustic and oceanographic data over extended periods and across large swaths of the ocean. They can operate with a high degree of spatial sampling flexibility in near real-time that cannot be easily achieved using fixed PAM arrays. The Whale Habitat and Listening Experiment (WHaLE), funded by the Marine Environmental Observation Prediction And Response Network (MEOPAR) at Dalhousie University, and using Ocean Tracking Network (OTN) autonomous vehicles, is searching for whale habitats and monitoring the distributional patterns of the endangered North Atlantic right whale and other at-risk baleen whales across the shelf waters of Atlantic Canada. This is being achieved through fixed PAM array deployments involving several research partners, as well as the deployment of profiling and surface gliders (autonomous vehicles) equipped with PAM systems capable of detecting and identifying baleen whales that produce sounds in the 10 - 2000 Hz frequency range. When fitted with onboard, automated detection and identification algorithms, the gliders can become powerful tools for near real-time monitoring of the at-risk whales and thus risk mitigation.

Publication
In OCEANS 2016 MTS/IEEE Monterey
Date
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