An Ocean Observing System for Large-Scale Monitoring and Mapping of Noise Throughout the Stellwagen Bank National Marine Sanctuary

Clark, Christopher W.; Ellison, William T.; Hatch, Leila T.; Merrick, Richard L.; Parijs, Sofie M. Van; Wiley, David N.

doi:10.21236/ada539192

Cited by 2 publications

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“…The acoustic detection range for the monitored species is much lower than the spatial scales at which we observed significant associations between aerial survey and near real‐time acoustic occurrence estimates. Right whales are estimated to have detection ranges of up to 9 km in shallow continental shelf waters (Clark, Brown, & Corkeron, ), and humpback whales, producing calls at similar frequencies and source levels as right whales (Au et al, ; Clark et al, ; Thompson, Cummings, & Ha, ), likely have a similar detection range. Sei whales produce lower frequency calls at louder source levels (Baumgartner et al, ; Newhall, Lin, Lynch, Baumgartner, & Gawarkiewicz, ), and Baumgartner et al () estimated an acoustic detection range 10–15 km.…”

Section: Discussionmentioning

confidence: 99%

Persistent near real‐time passive acoustic monitoring for baleen whales from a moored buoy: System description and evaluation

et al. 2019

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Managing interactions between human activities and marine mammals often relies on an understanding of the real‐time distribution or occurrence of animals. Visual surveys typically cannot provide persistent monitoring because of expense and weather limitations, and while passive acoustic recorders can monitor continuously, the data they collect are often not accessible until the recorder is recovered. We have developed a moored passive acoustic monitoring system that provides near real‐time occurrence estimates for humpback, sei, fin and North Atlantic right whales from a single site for a year, and makes those occurrence estimates available via a publicly accessible website, email and text messages, a smartphone/tablet app and the U.S. Coast Guard's maritime domain awareness software. We evaluated this system using a buoy deployed off the coast of Massachusetts during 2015–2016 and redeployed again during 2016–2017. Near real‐time estimates of whale occurrence were compared to simultaneously collected archived audio as well as whale sightings collected near the buoy by aerial surveys. False detection rates for right, humpback and sei whales were 0% and nearly 0% for fin whales, whereas missed detection rates at daily time scales were modest (12%–42%). Missed detections were significantly associated with low calling rates for all species. We observed strong associations between right whale visual sightings and near real‐time acoustic detections over a monitoring range 30–40 km and temporal scales of 24–48 hr, suggesting that silent animals were not especially problematic for estimating occurrence of right whales in the study area. There was no association between acoustic detections and visual sightings of humpback whales. The moored buoy has been used to reduce the risk of ship strikes for right whales in a U.S. Coast Guard gunnery range, and can be applied to other mitigation applications.

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Section: Discussionmentioning

confidence: 99%

Persistent near real‐time passive acoustic monitoring for baleen whales from a moored buoy: System description and evaluation

et al. 2019

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Deep neural networks for automated detection of marine mammal species

Shiu

Palmer

Roch

et al. 2020

Sci Rep

129

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Deep neural networks have advanced the field of detection and classification and allowed for effective identification of signals in challenging data sets. Numerous time-critical conservation needs may benefit from these methods. We developed and empirically studied a variety of deep neural networks to detect the vocalizations of endangered North Atlantic right whales (Eubalaena glacialis). We compared the performance of these deep architectures to that of traditional detection algorithms for the primary vocalization produced by this species, the upcall. We show that deep-learning architectures are capable of producing false-positive rates that are orders of magnitude lower than alternative algorithms while substantially increasing the ability to detect calls. We demonstrate that a deep neural network trained with recordings from a single geographic region recorded over a span of days is capable of generalizing well to data from multiple years and across the species' range, and that the low false positives make the output of the algorithm amenable to quality control for verification. The deep neural networks we developed are relatively easy to implement with existing software, and may provide new insights applicable to the conservation of endangered species.

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An Ocean Observing System for Large-Scale Monitoring and Mapping of Noise Throughout the Stellwagen Bank National Marine Sanctuary

Cited by 2 publications

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Persistent near real‐time passive acoustic monitoring for baleen whales from a moored buoy: System description and evaluation

Persistent near real‐time passive acoustic monitoring for baleen whales from a moored buoy: System description and evaluation

Deep neural networks for automated detection of marine mammal species

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