Humpback whale song occurrence reflects ecosystem variability in feeding and migratory habitat of the northeast Pacific

Ryan, John P.; Cline, Danelle E.; Joseph, John E.; Margolina, Tetyana; Santora, Jarrod A.; Kudela, Raphael M.; Chávez, Francisco P.; Pennington, J. Timothy; Wahl, Christopher; Michisaki, Reiko; Benoit‐Bird, Kelly J.; Forney, Karin A.; Stimpert, Alison K.; DeVogelaere, Andrew; Black, Nancy A.; Fischer, Mark H.

doi:10.1371/journal.pone.0222456

Cited by 26 publications

(18 citation statements)

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“…If we proceed with the assumption that the increase in acoustic presence during winter is due to song production, then this pattern in the fall could represent a transition period; non-song vocalizations are produced while engaged in other behaviors, such as foraging, until song starts being produced regularly throughout the winter. In other regions, song production is highest at night (Au et al 2000;Huang et al 2016;Espanol-Jimenez and van der Schaar 2018;Kowarski et al 2018;Ryan et al 2019). While this diel pattern is hypothesized to relate to foraging or mating behaviors that are most efficient during daylight hours (Helweg and Herman 1994;Au et al 2000;Ryan et al 2019), humpback whales show a high degree of behavioral plasticity (Stimpert et al 2012) and so a closer examination of the call types recorded during these periods would be required to better understand how this changing diel pattern in the GoA may relate to the patterns observed in other regions.…”

Section: Diel Patternsmentioning

confidence: 99%

“…In other regions, song production is highest at night (Au et al 2000;Huang et al 2016;Espanol-Jimenez and van der Schaar 2018;Kowarski et al 2018;Ryan et al 2019). While this diel pattern is hypothesized to relate to foraging or mating behaviors that are most efficient during daylight hours (Helweg and Herman 1994;Au et al 2000;Ryan et al 2019), humpback whales show a high degree of behavioral plasticity (Stimpert et al 2012) and so a closer examination of the call types recorded during these periods would be required to better understand how this changing diel pattern in the GoA may relate to the patterns observed in other regions. It is also important to note that the presence and strength of diel patterns in humpback whale calling can be impacted by variations in ocean noise (Helble et al 2013;Fournet et al 2018a) and the use of call metrics that account for detection probability (Helble 2013).…”

Section: Diel Patternsmentioning

confidence: 99%

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Cetacean occurrence in the Gulf of Alaska from long-term passive acoustic monitoring

et al. 2021

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The Gulf of Alaska is an important habitat for a diverse array of marine mammals, many of which were severely depleted by historical whaling. To study current cetacean distributions in this region, passive acoustic monitoring was used to detect species-specific call types between 2011 and 2015 at five locations spanning the continental shelf, slope, and offshore seamounts. Spatial and temporal detection patterns were examined for nine species to compare differences in behavior and habitat use. Mysticetes showed seasonal increases in calling that indicated possible behavioral shifts between feeding and breeding in blue (Balaenoptera musculus), fin (B. physalus), and humpback (Megaptera novaeangliae) whales, and matched known migration timing of gray whales (Eschrichtius robustus). Interannual changes in blue and fin whale calling may relate to the marine heat wave that began in 2013 and lasted through the end of the monitoring period. Odontocete detections revealed unique spatial distributions, with killer whales (Orcinus orca) most common on the continental shelf and sperm whales (Physeter macrocephalus) most common on the continental slope, where detections occurred year-round. Beaked whales showed both spatial and temporal separation: Baird’s beaked whale (Berardius bairdii) detections were highest at Quinn Seamount in the spring, Cuvier’s (Ziphius cavirostris) at Pratt Seamount in winter, and Stejneger’s (Mesoplodon stejnegeri) on the continental slope in the fall. The year-round presence of many species highlights the ecological importance of the Gulf of Alaska and the spatiotemporal information reported here should inform future conservation efforts.

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Section: Diel Patternsmentioning

confidence: 99%

Section: Diel Patternsmentioning

confidence: 99%

Cetacean occurrence in the Gulf of Alaska from long-term passive acoustic monitoring

et al. 2021

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“…For example, 2015 and 2016 were the years with earliest mean date of behavioral transition in the present study, coincident with a prolonged marine heatwave that both reduced upwelling (Figure 3B) and impacted the abundance, distribution, and community structure of both lower trophic levels and predators in the CCLME (Brodeur et al, 2019;Ryan et al, 2019). The acoustically-inferred timing of blue whales' transition to migration reveals the response of a krillspecialist predator to this marine heatwave, providing information on the availability of forage for a range of krill-dependent predators.…”

Section: Ecosystem Sentinels and Adaptability To Global Changementioning

confidence: 66%

Acoustic signature reveals blue whales tune life history transitions to oceanographic conditions

Oestreich¹,

Abrahms²,

McKenna³

et al. 2021

Preprint

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1.Matching the timing of life history transitions with ecosystem phenology is critical for the survival of many species, especially those undertaking long-distance migrations. As a result, whether and how migratory populations adjust timing of life history transitions in response to environmental variability are important questions in ecology and conservation. Yet the flexibility and drivers of life history transitions remain largely untested for migratory marine populations, which contend with the unique spatiotemporal dynamics and sensory conditions found in marine ecosystems. 2.Here, using an acoustic signature of blue whales’ regional population-level transition from foraging to breeding migration, we document significant interannual flexibility in the timing of this life history transition (spanning roughly four months) over a continuous six-year study period. 3.We further show that timing of this transition follows the oceanographic phenology of blue whales’ foraging habitat, with a later transition from foraging to breeding migration occurring in years with an earlier onset, later peak, and greater accumulation of biological productivity. 4.These results indicate that blue whales use flexible cues, likely including individual sensing of foraging conditions and long-distance vocal signals from conspecifics, to match timing of this population-level life history transition with interannual oceanographic variability in their vast and dynamic foraging habitat. The use of flexible cues in timing a major life history transition may be key to the persistence of this endangered population facing the pressures of rapid environmental change. 5.Further, these findings extend theoretical understanding of the flexibility and drivers of population-level migration beyond insights derived primarily from group-living and terrestrial migrants, illuminating the drivers and flexibility of a life history transition in a relatively solitary marine migrant.

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“…The complex nature of humpback song and its temporal population-level variability makes designing a generalized automated detector that can identify song across years, breeding sites, and different recording equipment and conditions extremely difficult. For this reason, evaluation of long term trends in humpback whale song presence is often conducted using coarse time scale manual assessment (e.g., Munger et al, 2012), power spectral density computation combined with manual annotation (e.g., Au et al, 2000;Ryan et al, 2019), frequency contour algorithms (e.g., Magnúsdóttir et al, 2014), or spectrogram cross-correlation (e.g., Vu et al, 2012). However, each method has drawbacks, including significant user input, high false positive rates, or low resolution.…”

Section: Introductionmentioning

confidence: 99%

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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Humpback whale song occurrence reflects ecosystem variability in feeding and migratory habitat of the northeast Pacific

Cited by 26 publications

References 65 publications

Cetacean occurrence in the Gulf of Alaska from long-term passive acoustic monitoring

Cetacean occurrence in the Gulf of Alaska from long-term passive acoustic monitoring

Acoustic signature reveals blue whales tune life history transitions to oceanographic conditions

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

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