Fluctuations in Hawaii’s humpback whale Megaptera novaeangliae population inferred from male song chorusing off Maui

Kügler, Anke; Lammers, Marc O.; Zang, Eden; Kaplan, Maxwell B.; Mooney, T. Aran

doi:10.3354/esr01080

Cited by 27 publications

(42 citation statements)

References 55 publications

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“…Starting in 2014, scientists in Hawaii and Alaska noticed a significant decrease in humpback whale sightings in several areas of known high abundance, coinciding with the Pacific decadal oscillation, Northeast Pacific heatwave, and an El Niño event in 2016 (Gabriele et al, 2017;Cartwright et al, 2019). An acoustic study from Maui showed a decline in daily song levels starting in the 2015/2016 season and persisting through 2018/2019 when levels increased again at some sites (Kügler et al, 2020). Our data show a similar decline in humpback song presence at Kona, beginning in the 2015/2016 season and persisting until the end of the data in 2019, with a concurrent reduction in season length.…”

Section: Long Term Temporal Humpback Trendssupporting

confidence: 64%

A Convolutional Neural Network for Automated Detection of Humpback Whale Song in a Diverse, Long-Term Passive Acoustic Dataset

et al. 2021

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Passive acoustic monitoring is a well-established tool for researching the occurrence, movements, and ecology of a wide variety of marine mammal species. Advances in hardware and data collection have exponentially increased the volumes of passive acoustic data collected, such that discoveries are now limited by the time required to analyze rather than collect the data. In order to address this limitation, we trained a deep convolutional neural network (CNN) to identify humpback whale song in over 187,000 h of acoustic data collected at 13 different monitoring sites in the North Pacific over a 14-year period. The model successfully detected 75 s audio segments containing humpback song with an average precision of 0.97 and average area under the receiver operating characteristic curve (AUC-ROC) of 0.992. The model output was used to analyze spatial and temporal patterns of humpback song, corroborating known seasonal patterns in the Hawaiian and Mariana Islands, including occurrence at remote monitoring sites beyond well-studied aggregations, as well as novel discovery of humpback whale song at Kingman Reef, at 5∘ North latitude. This study demonstrates the ability of a CNN trained on a small dataset to generalize well to a highly variable signal type across a diverse range of recording and noise conditions. We demonstrate the utility of active learning approaches for creating high-quality models in specialized domains where annotations are rare. These results validate the feasibility of applying deep learning models to identify highly variable signals across broad spatial and temporal scales, enabling new discoveries through combining large datasets with cutting edge tools.

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Section: Long Term Temporal Humpback Trendssupporting

confidence: 64%

A Convolutional Neural Network for Automated Detection of Humpback Whale Song in a Diverse, Long-Term Passive Acoustic Dataset

et al. 2021

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“…Our analysis revealed only a small correlation between automated humpback whale detections and soundscape metrics, which have been used by other researchers to monitor and quantify humpback whale presence at other locations in the Hawaiian Islands (e.g., Au et al, 2000;Kügler et al, 2020). There were likely other sound sources in our data set contributing to the 125 Hz octave levels, adding to the signals of the whales in this band.…”

Section: The Contribution Of Biological Signalsmentioning

confidence: 60%

Characterizing the Long-Term, Wide-Band and Deep-Water Soundscape Off Hawai’i

Merkens¹,

Baumann‐Pickering

Ziegenhorn

et al. 2021

Front. Mar. Sci.

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Many animals use sound for communication, navigation, and foraging, particularly in deep water or at night when light is limited, so describing the soundscape is essential for understanding, protecting, and managing these species and their environments. The nearshore deep-water acoustic environment off the coast of Kona, Hawai’i, is not well documented but is expected to be strongly influenced by anthropogenic activities such as fishing, tourism, and other vessel activity. To characterize the deep-water soundscape in this area we used High-frequency Acoustic Recording Packages (HARPs) to record acoustic data year-round at a 200 or 320 kHz sampling rate. We analyzed data spanning more than 10 years (2007-2018) by producing measurements of frequency-specific energy and using a suite of detectors and classifiers for general and specific sound sources. This provided a time series for sounds coming from biological, anthropogenic and physical sources. The soundscape in this location is dominated by signals generated by humans and odontocete cetaceans (mostly delphinids), generally alternating on a diel cycle. During daylight hours the dominant sound sources are vessels and echosounders, with strong signals ranging from 10 Hz to 80 kHz and above, while during the night the clicks from odontocetes dominate the soundscape in mid-to-high frequencies, generally between 10 and 90 kHz. Winter-resident humpback whales are present seasonally and produce calls in lower frequencies (200-2,000 Hz). Overall, seasonal variability is relatively subtle, which is unsurprising given the tropical latitude and deep-water environment. These results, and particularly the inclusion of sounds from frequencies above 2 kHz, represent the first long-term analysis of a marine soundscape in the North Pacific, and the first assessment of the intense, daily presence of manmade noise at this site. The decadal time series allows us to characterize the dynamic nature of this location, and to begin to identify changes in the soundscape over time. This type of analysis facilitates protection of natural resources and effective management of human activities in an ecologically important area.

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“…Here sound from biological sources is predicted to contribute the most to the soundscape. For example, the peaks in SPL at 315 and 630 Hz decidecade bands from February to March at the Hawaiian Islands site likely reflect the nearly continuous vocalizations from humpback whales who arrive during this time to breed (Au et al, 2000;Lammers et al, 2011;Kügler et al, 2020). The American Samoa and Caribbean Sea site soundscapes were likely influenced by fish and invertebrates in addition to humpback whales (Kaplan et al, 2015;Haver et al, 2018Haver et al, , 2019Lillis and Mooney, 2018).…”

Section: Discussionmentioning

confidence: 99%

The Next Wave of Passive Acoustic Data Management: How Centralized Access Can Enhance Science

et al. 2021

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Passive acoustic data collection has grown exponentially over the past decade resulting in petabytes of data that document our ocean soundscapes. This effort has resulted in two big data challenges: (1) the curation, management, and global dissemination of passive acoustic datasets and (2) efficiently extracting critical information and comparing it to other datasets in the context of ecosystem-based research and management. To address the former, the NOAA National Centers for Environmental Information recently established an archive for passive acoustic data. This fast-growing archive currently contains over 100 TB of passive acoustic audio files mainly collected from stationary recorders throughout waters in the United States. These datasets are documented with standards-based metadata and are freely available to the public. To begin to address the latter, through standardized processing and centralized stewardship and access, we provide a previously unattainable comparison of first order sound level-patterns from archived data collected across three distinctly separate long-term passive acoustic monitoring (PAM) efforts conducted at regional and national scales: NOAA/National Park Service Ocean Noise Reference Station Network, the Atlantic Deepwater Ecosystem Observatory Network, and the Sanctuary Soundscape Monitoring Project. Nine sites were selected from these projects covering the Alaskan Arctic, Northeast and Central Pacific, Gulf of Mexico, Caribbean Sea, and Mid and Northwest Atlantic. Sites could generally be categorized into those strongly influenced by anthropogenic noise (e.g., vessel traffic) and those that were not. Higher sound levels, specifically for lower frequencies (<125 Hz), and proximity to densely populated coastal zones were common characteristics of sites influenced by anthropogenic noise. Conversely, sites with lower overall sound levels and away from dense populations resulted in soundscape patterns influenced by biological sources. Seasonal variability in sound levels across selected decidecade bands was apparent for most sites and often represented changes in the presence or behavior of sound-producing species. This first order examination of levels across projects highlights the utility of these initial metrics to identify patterns that can then be examined in more detail. Finally, to help the PAM community collectively and collaboratively move forward, we propose the next frontier for scalable data stewardship, access, and processing flow.

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Fluctuations in Hawaii’s humpback whale Megaptera novaeangliae population inferred from male song chorusing off Maui

Cited by 27 publications

References 55 publications

A Convolutional Neural Network for Automated Detection of Humpback Whale Song in a Diverse, Long-Term Passive Acoustic Dataset

A Convolutional Neural Network for Automated Detection of Humpback Whale Song in a Diverse, Long-Term Passive Acoustic Dataset

Characterizing the Long-Term, Wide-Band and Deep-Water Soundscape Off Hawai’i

The Next Wave of Passive Acoustic Data Management: How Centralized Access Can Enhance Science

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