Whale Calls Research Articles

Quantifying northern bottlenose and sperm whale acoustic behavioural responses to anthropogenic noise in Baffin Bay, Canada

Marine mammals rely on their auditory system for a myriad of life functions (e.g., navigating, foraging, socializing) and consequently, are vulnerable to loud human activities (e.g., vessel traffic, fishing, military activities). These activities can impede communication, cause behavioural disturbances, and can even cause injuries. As the Arctic warms and sea ice coverage decreases, more opportunities for human activities are arising. How noise impacts the acoustic behaviour of Arctic marine mammals is unclear. In October 2022 and 2023 controlled noise exposure experiments were conducted using military sonar (source level of 176.4 dB re 1 μPa) on northern bottlenose and sperm whales in Baffin Bay while they were foraging around vessels actively fishing. Hydrophone suction-cup biologgers (DTAGs; n = 5, ∼72 cumulative hours) were used to capture vessel and sonar noises, and whale vocalizations before, during, and after the noise exposure periods. Using a click detector, focal whale clicks were identified and quantitatively compared to received noise levels. This information will then be used to determine noise thresholds for acoustical behavioural responses. These results will support risk-mitigation strategies for the Department of National Defence Canada and Fisheries and Oceans Canada, as well as address Inuit concerns about the effects of military sonar on marine mammals.

Automated detection of fin whale calls recorded with distributed acoustic sensing

As the global biodiversity crisis intensifies, wildlife monitoring has become a necessity. From an oceanographic perspective, the tracking of baleen whales allows studies of their habitat use and can support call density estimates, which are indicative of ocean health. Distributed Acoustic Sensing (DAS) is a promising observational technique that measures strain rates along a spare optical fiber with a spatial resolution of tens of meters to distances of ∼100 km. Over 4-days in November 2021, a public domain DAS dataset was collected on the submarine cables of the Ocean Observatories Initiative Regional Cabled Array off the coast of central Oregon. The experiment recorded the acoustic signals from tens of thousands of fin whale calls as well as blue whale calls, ship signals and T-phases. We will describe preliminary efforts to develop automated methods to detect and localize fin whale calls using small representative examples of the DAS data. Our longer-term goal is to extend this approach to the full data set to understand the sensitivity of DAS to fin whale calls as function of cable geometry and seafloor characteristics and to study the distribution of calling fin whales in coastal waters off central Oregon. [Work supported by ONR.]

Directional soundscapes in the Norwegian Seas observed with a coherent hydrophone array

Directional soundscaping is an efficient approach for examining marine ecosystems since it allows the study of living organisms, their behavior, and temporo-spatial interactions with other natural and man-made objects underwater, useful for ecosystem monitoring during offshore energy activities, maritime surveillance, and defense. A large aperture coherent hydrophone array was employed for remote sensing in the Norwegian and Barents Seas in Spring 2014. Extensive analysis have been conducted in post-processing of recorded hydrophone array data for automatic detection, bearing estimation, and classification of signals by different sound producers, such as whale calls, ship radiated noise, and fish sounds. Directional soundscaping through passive ocean acoustic waveguide remote sensing (POAWRS) provides bearing-time trajectories of signal detections that can be applied for geographical mapping. The received marine mammal sounds include vocalizations from baleen whales such as humpback, fin, and minke, and toothed whales, such as beluga, pilot, and sperm. We provide an insight to the vocalizations and behaviorial patterns of these whales in the Lofoten and Northern Finmark regions. The relative contributions of distinct sound sources, including whales, fish, and ships, to the directional soundscapes are quantified. We also examine their temporo-spatial dependences via geographic mapping of acoustic signals from distinct sound producers.

Application of a seismic network to baleen whale call detection and localization in the Panama basin-a Bryde's whale example.

Baleen whales use sounds of various characteristics for different tasks and interactions. This study focuses on recordings from the Costa Rica Rift, in the Eastern Tropical Pacific Ocean, made by 25 ocean-bottom seismographs and a vertical array of 12 hydrophones between January and February 2015. The whale calls observed are of two kinds: more commonly, repetitive 4-5 s-long signals separated into two frequency bands centered at ∼20 and ∼36 Hz; less commonly, a series of ∼0.5 to 1.0 s-long, lower amplitude signals with frequencies between 80 and 160 Hz. These characteristics are similar to calls attributed to Bryde's whales which are occasionally sighted in this region. In this study, the repetitive calls are detected using both the short-term average/long-term average approach and a network empirical subspace detector. In total, 188 and 1891 calls are obtained for each method, demonstrating the value of the subspace detector for highly similar signals. These signals are first localized using a non-linear grid search algorithm and then further relocalized using the double-difference technique. The high-resolution localizations reveal the presence of at least seven whales during the recording period, often crossing the instrument network from southwest to northeast.

Humpback whale song vocalization behavior and temporo-spatial distributions in the Norwegian and Barents Sea observed with a coherent hydrophone array

The vocalization behavior of humpback whales in the Norwegian and Barents Seas is examined based on recordings of a large-aperture, densely-populated coherent hydrophone array system. The passive ocean acoustic waveguide remote sensing (POAWRS) technique is employed to provide detection, bearing-time estimation, time-frequency characterization and classification of the humpback whale vocalizations. The song vocalizations, composed of highly structured and repeatable set of phrases, were detected throughout the diel cycle between February 18 to March 8, 2014. The beamformed spectrograms of the detected humpback vocalizations are classified as song sequences based on inter-pulse intervals and time-frequency characteristics, verified by visual inspection. The song structure is compared for humpback whale vocalizations recorded at three distinct regions off the Norwegian coast, Alesund, Lofoten and Northern Finmark. Multiple bearing-time trajectories for humpback songs were simultaneously observed indicating multiple singers present at each measurement site. Humpback whale received call rates and temporo-spatial distributions are compared across the three measurement sites. Geographic mapping of humpback whale calls from their bearing-time trajectories is accomplished via the moving array triangulation technique.

Development of a machine learning detector for North Atlantic humpback whale song.

The study of humpback whale song using passive acoustic monitoring devices requires bioacousticians to manually review hours of audio recordings to annotate the signals. To vastly reduce the time of manual annotation through automation, a machine learning model was developed. Convolutional neural networks have made major advances in the previous decade, leading to a wide range of applications, including the detection of frequency modulated vocalizations by cetaceans. A large dataset of over 60 000 audio segments of 4 s length is collected from the North Atlantic and used to fine-tune an existing model for humpback whale song detection in the North Pacific (see Allen, Harvey, Harrell, Jansen, Merkens, Wall, Cattiau, and Oleson (2021). Front. Mar. Sci. 8, 607321). Furthermore, different data augmentation techniques (time-shift, noise augmentation, and masking) are used to artificially increase the variability within the training set. Retraining and augmentation yield F-score values of 0.88 on context window basis and 0.89 on hourly basis with false positive rates of 0.05 on context window basis and 0.01 on hourly basis. If necessary, usage and retraining of the existing model is made convenient by a framework (AcoDet, acoustic detector) built during this project. Combining the tools provided by this framework could save researchers hours of manual annotation time and, thus, accelerate their research.

Acoustic diversity, bearing estimation, and localization of sound sources in shallow water and deep ocean off the U.S. Northeast coast using a coherent hydrophone array

An in-house developed 160-element large-aperture hydrophone array from Northeastern University was utilized for ocean monitoring in both the shallow waters of the Great South Channel (GSC) and the deep-water area off the continental slope south of Rhode Island in September 2021. Utilizing passive ocean acoustic waveguide remote sensing (POAWRS) technology, the system enabled real-time, broad-area surveillance of the oceanic environment. A novel multistage clustering was introduced for annotating the vast volume of underwater acoustic data, drawing inspiration from the human cognitive process of categorizing sounds. Initially, sounds are separated into broad groups, akin to a first human listening pass. Subsequent stages of clustering refine these categories, utilizing different sets of features to achieve a granularity of annotation that closely mirrors expert human annotation processes. It was found that the acoustic landscape of the GSC was primarily composed of diverse marine mammal sounds, such as fin whale 20 Hz pulses, humpback whale songs, minky whale buzz sequences, sperm whale and dolphin echolocation clicks, and unidentified whale calls. Conversely, the deep-water area off the continental slope featured predominantly fish sounds, chorus-like sounds, and dolphin vocalizations. Detailed analyses of the most prevalent vocalizations are presented, including their bearing-time trajectories and localizations.

Using a sequence deep learning model to increase the acoustic context of a killer whale detector

Although Killer whales (Orcinus orca) produce many stereotypical vocalizations, their sounds can be difficult to identify in isolation. Experts often rely on acoustic context to accurately identify these animals acoustically. Automated detectors and classifiers, on the other hand, frequently rely on short clips that capture individual vocalizations, not leveraging information regarding previous sounds. We developed deep learning models that used 1-minute inputs containing from 0 to 50 calls, with the average clip having 18. We tested three artificial neural network architectures that used recurrent layers to take the sequence of acoustic events into account. As a baseline, we used a convolutional neural network that only took 3-s clips at a time, without considering sequences of events. Here, we present preliminary evaluations on a dataset containing 360 min with Southern Resident killer whale activity in the Salish Sea, and an equal amount of data without killer whale sounds. The best model used a combination of temporal convolutional layers and gated recurrent units to achieve a recall of 0.95 at the maximum precision of 0.98. The models will be applied to near real-time monitoring efforts and will be open-sourced in the future.

Linear discriminant analysis based hidden Markov model for detection of Mysticetes’ vocalisations

Mysticetes’ produce distinctive vocalisations which are used for echolocation, communication, and other marine functions. These cryptic vocalisations are studied by marine scientist to determine the behavioural patterns and movement of this suborder of cetaceans within their ecosystem. In practice, these vocalisations are gathered using passive acoustic monitoring over days, weeks, months, and even years. Therefore, it is complex to study these sounds using traditional visual inspection techniques because the gathered datasets are huge. Machine learning (ML) tools such as Gaussian mixture models (GMMs), support vector machines (SVMs), and hidden Markov models (HMMs) have been adopted in recent times to proffer analytic solutions to automatically detect and study these cryptic vocalisations. Notwithstanding, the feature extraction techniques employed play a vital role in determining the performance of these ML tools. In most cases, the performance of the feature extraction technique is directly proportional to the performance of the ML tools. Thus, the method of linear discriminant analysis (LDA) is introduced in this article as a feature extraction technique that can be adapted with the HMMs (LDA-HMM) to seamlessly detect the vocalisations of Mysticetes. The performance of the proposed LDA-HMM detector is compared with other recent detectors for Mysticetes’ vocalisations in the literature using two different species: Humpback whale songs and Bryde’s whale pulses. Experimental results show that the developed LDA-HMM detector is a performance-efficient alternative in comparison to the recent detection techniques studied in this article. Besides, the LDA-HMM detector offers less computational time complexity; as such, it is more suitable for real-time applications.

Passive acoustic monitoring and visual sighting survey of cetacean occurrence patterns in the Kingdom of Saudi Arabia

AbstractThe available data on occurrence patterns of cetaceans in the Red Sea area of the Kingdom of Saudi Arabia is very limited. The dearth of information is of pressing conservation concern as the coastline is undergoing rapid development as part of the Kingdom's effort to diversify its national economy. To understand how these developments will impact cetaceans in the region, the first large‐scale acoustic and visual survey in the Kingdom's part of the Gulf of Aqaba and the northeastern Red Sea was undertaken in 2020. The results of the acoustic surveys reveal 3.6 encounters per 100 km of track line of odontocete species with variable distribution across the study area. No baleen whale vocalizations were detected. Through visual surveys, five odontocete species were identified within the study area: Tursiops truncatus, T. aduncus, Stenella attenuata, S. longirostris, and two opportunistic sightings of a single Grampus griseus.

Fin whale song characteristics and potential subpopulation identity in the New York Bight

Fin whale (Balaenoptera physalus) song can follow a highly consistent pattern, and regional differences in song patterns can be a valuable indicator of subpopulation identity and distribution. In the Northwest Atlantic, endangered fin whales are currently managed as a single stock despite previous identification of different regional song patterns, which indicates potential subpopulation structuring and vulnerability to anthropogenic disturbance if not managed accordingly. Here we document fin whale song in the New York Bight (NYB) from 2017 to 2020 using passive acoustic data to identify monthly and yearly trends in song patterns and to explore potential subpopulation structuring. The predominant song pattern observed was highly consistent with the pattern documented almost a decade prior in the NYB, with short inter-note intervals (INI) from fall–winter and long-INIs in the spring. However, in one song year the majority of songs were composed of long-INIs. This change in song pattern could be due to a shift in fin whale behavior or possibly multiple fin whale subpopulations using the NYB. Fin whales in the NYB may be particularly vulnerable to disturbance given the increasing anthropogenic pressures in this region, and further research into subpopulation structuring is needed to ensure adequate management of these endangered whales.

Antarctic Minke Whale (Balaenoptera bonaerensis) Bio-Duck Call Detection in the Río de la Plata, Uruguay

Passive acoustic monitoring (PAM) has become a popular method for studying both marine mammal behavior and distribution, especially among cetaceans. The Antarctic minke whale (Balaenoptera bonaerensis) occurs in the Southern Hemisphere, and our knowledge concerning its vocal behavior is very limited. Along the coast of Uruguay, minke whales constitute one of the species with the most stranding data collected. Until now, sounds of Antarctic minke whales had never been recorded off the Uruguayan coast. Two different bio-duck call types were recorded along the Atlantic Ocean and the Río de la Plata River coasts by employing PAM stations off the coast of Uruguay in five different positions. One call was recorded off the Atlantic coast, and the four other calls were recorded along the Río de la Plata River, including the classic bio-duck call. In addition to the aforementioned bio-duck call, evidence of a novel call, the bio-duck pulse train, was found. This call was produced in an environment very different from the oceanic environment. The bio-duck pulse train has never been described in previous works, making the findings of the present work all the more novel.

Wavelet-based feature extraction with hidden Markov model classification of Antarctic blue whale sounds

An approach for analyzing blue whale vocalizations in continuous acoustic recordings using wavelet transform (WT) algorithm as a feature extraction method is proposed in this study. The WT-based feature extraction technique offers a unique perspective on the frequency-time properties of whale vocalizations, enabling a comprehensive understanding of the complexity of non-stationary signals. The WT-based features are seamlessly adopted with a Hidden Markov Model (HMM) for efficient and accurate classification. Through extensive theoretical evaluation and comparison with conventional methods such as Principal Component Analysis (PCA) and Dynamic Mode Decomposition (DMD), the proposed approach demonstrates superior performance in accurately detecting and classifying blue whale calls from background noise. Furthermore, when benchmarked against state-of-the-art machine learning techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM), and Spectrogram Correlation Detector (SCD), the proposed WT-HMM detector exhibits the highest average accuracy, affirming its applicability for real-time scenarios. This study presents a valuable contribution to the field of marine bioacoustics, offering an effective solution for robust blue whale vocalization analysis.

Fin whale song evolution in the North Atlantic.

Animal songs can change within and between populations as the result of different evolutionary processes. When these processes include cultural transmission, the social learning of information or behaviours from conspecifics, songs can undergo rapid evolutions because cultural novelties can emerge more frequently than genetic mutations. Understanding these song variations over large temporal and spatial scales can provide insights into the patterns, drivers and limits of song evolution that can ultimately inform on the species' capacity to adapt to rapidly changing acoustic environments. Here, we analysed changes in fin whale (Balaenoptera physalus) songs recorded over two decades across the central and eastern North Atlantic Ocean. We document a rapid replacement of song INIs (inter-note intervals) over just four singing seasons, that co-occurred with hybrid songs (with both INIs), and a clear geographic gradient in the occurrence of different song INIs during the transition period. We also found gradual changes in INIs and note frequencies over more than a decade with fin whales adopting song changes. These results provide evidence of vocal learning in fin whales and reveal patterns of song evolution that raise questions on the limits of song variation in this species.

Diel spatio-temporal patterns of humpback whale singing on a high-density breeding ground.

Humpback whale song chorusing dominates the marine soundscape in Hawai'i during winter months, yet little is known about spatio-temporal habitat use patterns of singers. We analysed passive acoustic monitoring data from five sites off Maui and found that ambient noise levels associated with song chorusing decreased during daytime hours nearshore but increased offshore. To resolve whether these changes reflect a diel offshore-onshore movement or a temporal difference in singing activity, data from 71 concurrently conducted land-based theodolite surveys were analysed. Non-calf pods (n = 3082), presumably including the majority of singers, were found further offshore with increasing time of the day. Separately, we acoustically localized 217 nearshore singers using vector-sensors. During the day, distances to shore and minimum distances among singers increased, and singers switched more between being stationary and singing while travelling. Together, these findings suggest that the observed diel trends in humpback whale chorusing off Maui represent a pattern of active onshore-offshore movement of singers. We hypothesize that this may result from singers attempting to reduce intraspecific acoustic masking when densities are high nearshore and avoidance of a loud, non-humpback, biological evening chorus offshore, creating a dynamic of movement of singers aimed at increasing the efficiency of their acoustic display.

Killer whale acoustic patterns respond to prey abundance and environmental variability around the Prince Edward Islands, Southern Ocean.

Killer whales are apex predators with temporally and spatially varying distributions throughout the world's oceans. Their ecology and behaviour are poorly understood in most regions due to limited research, often because of logistical challenges. Here, we used a passive acoustic monitoring device to investigate the seasonal acoustic occurrence and diel vocalizing behaviour of killer whales around the remote sub-Antarctic Prince Edward Islands (PEIs), Southern Ocean. Killer whales showed diel vocalizing patterns that varied seasonally in relation to their prey abundance and social activities. Killer whale calls were intermittently detected year-round with a high number of hours containing calls in October to December, and a secondary peak in February to May, corresponding to seal prey abundance. Random forest modelling identified wind speed as the primary predictor of the occurrence of killer whale calls (with a negative correlation) while sea surface height, chlorophyll-a and sea surface temperature were moderately important. We provide the first acoustic evidence that killer whale occurrence around the PEIs might coincide with variability in environmental conditions and prey abundance. Our results provide the first indication of diel vocalizing pattern of killer whales in the Southern Ocean. This knowledge is important for understanding killer whale ecology and adaptation to the changing oceans.

Tracking pygmy blue whales in the Perth Canyon using passive acoustic observatories

Eastern Indian Ocean pygmy blue (EIOPB) whales were localised and tracked through the Perth Canyon area utilising a passive acoustic tracking array. Vocalising animals were tracked using the Type II unit of the EIOPB whale song across recordings for an average of 109.3 min (95% CI ± 27 min), suggesting that animals move slowly through the array area. Comparisons of the distribution of animals between sample years revealed non-uniform distribution of animals suggesting that environmental variables may drive pygmy blue whale spatial distribution within foraging areas. Analysis of the movement patterns of acoustically tracked animals found that animals tracked between January and early March generally exhibited directional movement while singing animals tracked between March and May exhibited directional and milling movement patterns, indicating that they may be foraging, and thus that animals may sing between feeding bouts. Comparisons of visual and acoustic localisations found animals appeared to utilise similar areas around the passive acoustic receiver array on days where survey effort overlapped, though acoustic survey methods returned consistently higher numbers of detections. Further, acoustic and visual detections of animals within the same day were not within the same location highlighting the need for holistic studies of species habitat utilisation. The inclusion of in-situ environmental data collection in the design of future acoustic tracking studies would be valuable to understand whether there is a link between environmental factors at the local scale and the localised movement of whales around the listening area.

Abrupt change in humpback whale song from Brazil suggests cultural revolutions may occur in the South Atlantic

Abrupt change in humpback whale song from Brazil suggests cultural revolutions may occur in the South Atlantic

Southern Hemisphere humpback whales (Megaptera novaeangliae) (Artiodactyla: Balaenopteridae) singing activity at Caño Island Biological Reserve, Costa Rica before, during, and after COVID-19 lockdowns

Introduction: Boat traffic is recognized as a major contributor of underwater noise. Increasing presence of boats in coastal habitats is predicted to have important repercussions on the communication of marine mammals. In Costa Rica, the waters of the Caño Island Biological Reserve are an important breeding area for humpback whales from the Breeding-Stock G (BSG). Their predicted and abundant presence has fueled the development of whale watching activities as an important component of the local economy, and while the country has norms of conduct for this activity, whales often interact with multiple boats at the same time. The lockdowns associated with the COVID-19 pandemic provided a unique opportunity to study the potential impacts of noise associated with boat traffic on the singing activity of humpback whales.
 Objective: Determine whether noise levels and boat acoustic presence around Caño Island Biological Reserve changed during the COVID-19 lockdowns, and if it did, what is the impact on song detection of BSG humpback whales.
 Methods: Acoustic recordings were made using a bottom-mounted autonomous underwater recorder for 30 days in September 2019, 2020, and 2021, resulting in a total recording effort of 480 hours.
 Results: Our results show that broadband underwater noise levels (dBRMS) during pre-lockdown were significantly higher, particularly at frequencies below 1kHz, than during and post-lockdown. This is likely due to a decrease in the proportion of boat acoustic presence during the lockdown. Although the proportion of whale songs detected did not vary among years, whale songs were detected similarly throughout the day during the lockdown, compared to pre-and-post lockdown where the proportion of whale song presence decreased during hours when more boats were present.
 Conclusions: This study shows a clear change in underwater noise levels during the COVID-19 lockdown, likely due to a decrease in boat presence. The study also highlights the potential impact of noise associated with boat traffic on humpback whale singing activity. The results of this study can inform the Conservation Areas of Osa (ACOSA) in charge of managing Caño Island Biological Reserve, to develop and implement mitigation measures to regulate underwater anthropogenic noise associated with tour boats.

First song description of the humpback whales, Megaptera novaeangliae (Balaenopteridae: Artiodactyla), breeding off Nicaragua

Introduction: Humpback whales belonging to the Central American (CA) Distinct Population Segment breed off the Pacific coast of Nicaragua. Knowledge on this endangered population and its conservation status is limited.
 Objective: The aim of this study is to provide the first description of the CA humpback whale song off Nicaragua, which helps further understanding on the population’s dynamics.
 Methods: Acoustic recordings of songs were obtained during dedicated boat-based surveys at two locations on the Pacific coast of Nicaragua in 2018. Recordings were made from the boat using a portable system for a total of 23 hours and 56 minutes over 32 days from January to April 2018. A total of nine recordings of high enough quality for the song analysis were identified during this period from three different days at Padre Ramos (PR) (northern site) and four different days at San Juan del Sur (southern site). Song structure was described using standard humpback whale song elements, i.e. themes, phrases, and units.
 Results: A total of seven themes, seven phrases, and 19 unit types were identified. Three of the themes were common and frequently repeated in a song cycle while the others were less common in the repertoire and were recorded only during the middle of the season. Song theme order was variable, both within and across song sessions.
 Conclusions: This study provides the first song description of humpback whales along the Pacific coast of Nicaragua. Comparison of these songs with other datasets from the CA population and other breeding areas in the Northern Hemisphere could help understand the migratory patterns of these animals and the level of connectivity among populations since song can be socially learnt. Future data collection of humpback whale song recordings in Nicaragua is necessary to gain further understanding on the song structure variation within this population and the mechanisms of song transmission and dynamics across populations in the region.