Characterizing the acoustic environment of Placentia Bay: estimating the contribution of vessels to noise levels experienced by marine mammals in a coastal marine environment

Cominelli, Simone (2024) Characterizing the acoustic environment of Placentia Bay: estimating the contribution of vessels to noise levels experienced by marine mammals in a coastal marine environment. Doctoral (PhD) thesis, Memorial University of Newfoundland.

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Abstract

Rising anthropogenic noise pollution in the ocean is threatening marine environments and species. Exposure to underwater noise can elicit both behavioural and physical responses in marine species, including physiological changes, altered vocal behaviours, and reduced survival and reproductive success. These consequences, in concert with the effects of other anthropogenic stressors, have the potential to affect the health, distribution and abundance of species. As they rely on acoustic signals for foraging, socialization, and reproduction, marine mammals are among the species that can be most severely impacted by underwater anthropogenic noise. Vessels are the main and most widespread source of anthropogenic noise in the ocean, and routes collecting large volumes of traffic often overlap with the key habitats of several protected marine mammal species. In order to protect such species from further decline, there is a growing need for studies investigating how the presence of vessel noise alters underwater acoustic environments in areas important to marine mammals. This need is paired with the challenge of developing approaches for analyzing large passive acoustic monitoring (PAM) datasets with the goal of establishing links between audio recordings and environmental processes. In this dissertation, I focus on characterizing the underwater acoustic environment of a coastal marine area, Placentia Bay (Newfoundland, Canada), with the overarching goal of assessing if anthropogenic noise from vessels in the region is reaching levels that could have negative impacts on marine mammal species. First, I address the analytical challenge of establishing a relationship between audio recordings and environmental processes by applying unsupervised machine learning techniques to the analysis of underwater PAM datasets. Second, I investigate changes in underwater ambient noise levels at two PAM stations in Placentia Bay over five months (June-October, 2019), and assess the contribution of vessels and wind to ambient noise levels that might be experienced by fin whales in the area. Finally, I provide a spatial and temporal assessment of vessel traffic, showing how the distribution of vessel noise sources in Placentia Bay has changed over a five-year period (2019-2023), with a focus on areas important to baleen and toothed whales based on previous systematic surveys and opportunistic sightings data. Together, the three studies demonstrate how unsupervised machine learning can support the analysis and interpretation of large marine PAM datasets, and provide an initial evaluation of how the presence of vessel noise in Placentia Bay exposes marine mammals to increased noise levels, sometimes exceeding the theoretical threshold for the onset of behavioural disturbance. Furthermore, growing vessel traffic accompanied by changes in the distribution of noise sources indicate that both baleen and toothed whales have experienced increasing exposure to vessel noise in Placentia Bay between 2019 and 2023. These results support the conservation of protected cetacean species by indicating a need for the introduction of noise management measures in Placentia Bay, and by informing the development of a national strategy addressing the impacts of underwater anthropogenic noise. Future research is required to better understand how noise interacts with other marine anthropogenic stressors and how these combined effects translate into impacts on marine species and communities.

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