| Summary: | Large whales face increased threats from vessel traffic. Current threat mitigation strategies are difficult in constricted geographies, such as fjords, and direct observations of whales during nighttime, which can inform approaches, remains challenging. However, technological advances in whale research are also facilitating new methods to understand vulnerability during day and night. Passive acoustic monitoring, and specifically localization, provides spatial and behavioural information on species in continuous space and time. The objective of this thesis research is to examine how acoustic localizations might inform behaviour investigations of fin whale (Balaenoptera physalus) vulnerability throughout diurnal cycles. I first examined how passive acoustic localization data and species distribution modeling can be used to inform behavioural context of different fin whale calls. I then investigated the degree to which whales’ vulnerability to different vessel traffic types (associated with different threats) varies among whale behaviours. This research focuses on two call types of fin whales sensed by a four-hydrophone array on a northern latitude foraging site within Gitga'at Territory in the Pacific Northwest. This site is characterized by a narrow channel at the entrance to a fjord system and serves as a particularly relevant point of study as it faces rapid expansion of large vessel traffic in the coming decade and its constricted geography limits traditional management solutions. I found selected habitat features varied among whale behaviours, which confirmed expected social and foraging partitioning of distinct whale calls. Results also suggested potential for nighttime foraging in this area. Social calling fin whales were more vulnerable to cruise ships during daytime, while foraging callers were more vulnerable to passenger, pleasure craft, tug/towing, and fishing vessels, especially at night. It is expected that nighttime foraging callers will likely be the most vulnerable to planned increases in large ...
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