Whale Calls Research Articles

Most “flight” baleen whale species are acoustically cryptic to killer whales, unlike “fight” species

AbstractKiller whales regularly attack and kill baleen whales, especially their calves. In response, slow‐moving maneuverable “fight” species defend their calves in winter aggregations in predictable shallow coastal locations; while fast‐swimming “flight” species flee on contact and typically disperse in winter across deep open waters. Male singing also differs between fight and flight groups, with fight species producing more intricate songs to win group competitions, while flight species produce loud, monotonous songs to attract distant females. Fight species rely more on group defense and have less need to hide acoustically, but loud‐singing flight species might draw in predatory killer whales. Since killer whales cannot detect sounds <100 Hz and can detect only loud tones below 1500 Hz, singing at low frequencies would result in acoustic crypsis. A review of baleen whale calling frequencies and source levels reveals that although all species are capable of acoustic crypsis, most fight populations produce calls >1500 Hz (24 of 27, 89%), unlike flight populations (12 of 49, 24%). Furthermore, flight species with higher frequency calls produced lower source level calls, greatly reducing detection distances. Thus, flight species may call at low frequencies not only for long‐distance communication but also to avoid detection by killer whales.

Study of Non-Linearities in Humpback Whale Song Units

Study of Non-Linearities in Humpback Whale Song Units

Potential vocalizations of Bryde's whales in False Bay, South Africa

Potential vocalizations of Bryde's whales in False Bay, South Africa

Automatic detection and classification of beluga whale calls in the St. Lawrence estuary.

The endangered beluga whale (Delphinapterus leucas) of the St. Lawrence Estuary (SLEB) faces threats from a variety of anthropogenic factors. Since belugas are a highly social and vocal species, passive acoustic monitoring has the potential to deliver, in a non-invasive and continuous way, real-time information on SLEB spatiotemporal habitat use, which is crucial for their monitoring and conservation. In this study, we introduce an automatic pipeline to analyze continuous passive acoustic data and provide standard and accurate estimations of SLEB acoustic presence and vocal activity. An object detector extracted vocalizations of beluga whales from an acoustic recording of beluga vocal activity. Then, two deep learning classifiers discriminated between high-frequency call types (40-120 kHz) and the presence of low-frequency components (0-20 kHz), respectively. Different algorithms were tested for each step and their main combinations were compared in time and performance. We focused our work on a high residency area, Baie Sainte-Marguerite (BSM), used for socialization and feeding by SLEB. Overall, this project showed that accurate continuous analysis of SLEB vocal activity at BSM could provide valuable information to estimate habitat use, link beluga behavior and acoustic activity within and between herds, and quantify beluga presence and abundance.

Killer whale vocalizations and presence in the Beaufort Sea

Killer whale vocalizations and presence in the Beaufort Sea

A multi-year analysis of acoustic occurrence and habitat use of blue and fin whales in eastern and central Fram Strait.

Climate change-induced habitat alterations in the Arctic Ocean are expected to affect spatial and temporal occurrence patterns of seasonally migrating baleen whale species, leading to poleward range shifts and prolonged stays in Arctic waters. The aim of this study was to investigate occurrence patterns of blue (Balaenoptera musculus musculus) and fin (B. physalus) whales in Fram Strait, a summering habitat and historic whaling ground for both species. Passive acoustic monitoring data were collected between 2012 and 2021 at eight different mooring positions in eastern and central Fram Strait. These data were initially analyzed for the acoustic presence of blue and fin whales. Afterwards, the environmental impact on acoustic occurrences and the potential temporal expansion of acoustic presence periods were investigated. Blue whale acoustic presence showed a clear seasonality, with most calls being detected from July to October. Additionally, sporadic blue whale calls were detected in winter in three years on one or a few consecutive days. Fin whale acoustic presence varied between years, with peak presence from mid-summer through autumn, moderate presence in winter, and sporadic presence in spring. Random forest models suggested that zooplankton mass content, sea surface temperature and day of the year influenced blue whale acoustic presence, while fin whale acoustic presence was impacted by day of the year. For both species, no temporal trend in the onset and offset of acoustic presence periods was found. Our findings highlight eastern Fram Strait as key marine mammal habitat, probably serving as a feeding ground. Occasional (blue whales) and regular (fin whales) acoustic presence during the winter months further supports the hypothesis of complex migration behaviors in both species.

Acoustic richness and composition changes of humpback whale (Megaptera novaeangliae) songs on breeding grounds off the coast of Ecuador

AbstractSong evolution and revolution events recorded through time are typical cultural features for humpback whale (Megaptera novaeangliae) populations worldwide. However, it is unknown if song dynamics are equivalent among subpopulations or breeding stocks of the South Pacific Ocean. To contribute to the understanding of the temporal song dynamics of humpback whale song off the coast of Ecuador, we analyzed acoustic richness and repertoire composition based on unit, phrase, and song composition over consecutive breeding seasons from 2012 to 2019. We observed incomplete song revolutions (all themes were replaced except one) and a nonsignificant variation in the acoustic richness during the study period. Our results show that over the years, humpback whale singers incorporate acoustic material from different subpopulations such as Central South Pacific (French Polynesia) and western South Atlantic (Brazil) into their song repertoire. The temporal acoustic contact of different subpopulations migrating through the eastern South Pacific may be reflected in their particular acoustic richness and temporal evolution. Off the coast of Ecuador, humpback whale song patterns point towards an alternative scenario of song evolution, indicating how cultural transmission among subpopulations can alter the song patterns in specific breeding grounds due lack of geographical barriers surrounding the southeastern Pacific region.

Reinforcement learning-based framework for whale rendezvous via autonomous sensing robots.

Rendezvous with sperm whales for biological observations is made challenging by their prolonged dive patterns. Here, we propose an algorithmic framework that codevelops multiagent reinforcement learning-based routing (autonomy module) and synthetic aperture radar-based very high frequency (VHF) signal-based bearing estimation (sensing module) for maximizing rendezvous opportunities of autonomous robots with sperm whales. The sensing module is compatible with low-energy VHF tags commonly used for tracking wildlife. The autonomy module leverages in situ noisy bearing measurements of whale vocalizations, VHF tags, and whale dive behaviors to enable time-critical rendezvous of a robot team with multiple whales in simulation. We conducted experiments at sea in the native habitat of sperm whales using an "engineered whale"-a speedboat equipped with a VHF-emitting tag, emulating five distinct whale tracks, with different whale motions. The sensing module shows a median bearing error of 10.55° to the tag. Using bearing measurements to the engineered whale from an acoustic sensor and our sensing module, our autonomy module gives an aggregate rendezvous success rate of 81.31% for a 500-meter rendezvous distance using three robots in postprocessing. A second class of fielded experiments that used acoustic-only bearing measurements to three untagged sperm whales showed an aggregate rendezvous success rate of 68.68% for a 1000-meter rendezvous distance using two robots in postprocessing. We further validated these algorithms with several ablation studies using a sperm whale visual encounter dataset collected by marine biologists.

DeepWhaleNet: Climate Change-Aware FFT-Based Deep Neural Network for Passive Acoustic Monitoring

Climate change poses severe risks to the survival of many whale populations, whose habitats and migration patterns are affected by environmental changes. To detect these whales effectively, especially in deep-sea environments, we need to use AI-based techniques to handle the acoustic diversity and variability of different species. However, current methods for whale detection are based on pre- and post-processing steps that reduce their efficiency and generalizability. To address this issue, we present DeepWhaleNet, a novel deep-learning model that automates whale detection in Underwater Passive Acoustic Monitoring datasets. DeepWhaleNet simplifies the detection process by extracting relevant features from raw log-power spectrograms and helps protect these threatened species by supporting conservation efforts. Our model uses a larger short-time Fourier transform as input and a custom ResNet-18 architecture for classification, which enables it to separate whale sounds from noise and capture their temporal and spectral characteristics. We evaluate the performance of DeepWhaleNet and show that it surpasses state-of-the-art methods, achieving an 8.3% improvement in the F-1 score and 21% higher average precision of binary relevance than the baseline method. Moreover, our model demonstrates its versatility and suitability for species-specific retrieval problems through an ablation study on multi-label retrieval problems and a 99.1% recall for Blue Whales.

Continuous active sonar may not fix noise pollution of killer whale habitats

Although quieter, continuous active sonar still interferes too much with killer whale communication.

Functional data analysis to describe and classify southern resident killer whale calls

The Southern Resident Killer Whale (SRKW) is an endangered population of whales found in the northeast Pacific. They have a vocal dialect unique from other killer whales, having a repertoire of distinct stereotyped calls. A framework for distinguishing SRKW call types using the frequency traces of the amplitude ridges from their spectrograms (termed frequency ridges) is proposed. The first step is the extraction of these ridges of SRKW calls using an Sequential Monte Carlo approach. Next, they are converted into functional data using B-spline functions. They are analyzed with a functional principal component (FPC) analysis to characterise the intrinsic variability of frequency ridges within a call type. The FPCs are able to capture the general patterns in the frequency ridges of the different SRKW call types. The FPCs are also used as the basis for call classification. Using a cross-validation procedure to assess the robustness of the classification, this framework proves to be successful for classification with some call types having an F1-score ≥80%, but other calls less well discriminated. On balance, this approach showed reasonable performance given the small sample size available, and provides a useful contribution towards the development of a universal tool for call classification.

Auditory masking of tonal and conspecific signals by continuous active sonar, amplitude modulated noise, and Gaussian noise in killer whales (Orcinus orca).

Continuous active sonar is thought to mitigate severe acoustic impacts due to its lower sound pressure level compared to pulsed active sonar typically used by world navies. However, due to its almost continuous duty cycle, continuous active sonar could have a higher potential for auditory masking. Here, we evaluate the auditory masking potential of several noise types including a recording of continuous active sonar, amplitude modulated noise, and Gaussian noise, on signal detection in two killer whales. Signals were either a 1.5 kHz pure tone or a recording of a broadband burst-pulse killer whale call. For the 1.5 kHz tone, all noise types resulted in statistically significant masking, however, there was a release from masking of approximately 13 dB for the amplitude-modulated noise. When the killer whale call was the signal, the whales employed an off-frequency listening strategy where the whales were able to detect frequency components of the signal that did not directly overlap with the noise. However, this strategy was less useful for the continuous active sonar noise due to its broadband harmonic structure. Continuous active sonar has spectral features that considerably overlap with those of killer whale calls, making this type of noise an effective auditory masker.

Diverse baleen whale acoustic occurrence around two sub-Antarctic islands: A tale of residents and visitors

Knowledge on the occurrence and behaviour of baleen whales around sub-Antarctic regions is limited, and usually based on short, seasonal sighting research from shore or research vessels and whaling records, neither of which provide accurate and comprehensive year-round perspectives of these animals’ ecology. We investigated the seasonal acoustic occurrence and diel vocalizing pattern of baleen whales around the sub-Antarctic Prince Edward Islands (PEIs) using passive acoustic monitoring data from mid-2021 to mid-2023, detecting six distinct baleen whale songs from Antarctic blue whales, Madagascan pygmy blue whales, fin whales, Antarctic minke whales, humpback whales, and sei whales. Antarctic blue and fin whales were detected year-round whereas the other species’ songs were detected seasonally, including a new Antarctic minke whale bio-duck song sub-type described here for the first time. Antarctic minke and sei whales were more vocally active at night-time whereas the other species had no clear diel vocalizing patterns. Random forest models identified month and/or sea surface temperature as the most important predictors of all baleen whale acoustic occurrence. These novel results highlight the PEIs as a useful habitat for baleen whales given the number of species that inhabit or transit through this region.

Beyond counting calls: estimating detection probability for Antarctic blue whales reveals biological trends in seasonal calling

We explore the utility of estimating the density of calls of baleen whales for better understanding acoustic trends over time. We consider as a case study stereotyped ‘song’ calls of Antarctic blue whales (Balaenoptera musculus intermedia) on their Antarctic feeding grounds over the course of a year-long, continuous recording from 2014. The recording was made in the Southern Ocean from a deep-water autonomous hydrophone moored near the seafloor in the Eastern Indian sector of the Antarctic. We estimated call density seasonally via a Monte-Carlo simulation based on the passive sonar equation, and compared our estimates to seasonal estimates of detection rate, which are commonly reported in acoustic studies of Antarctic blue whales. The resulting seasonal call densities at our Antarctic site were strongly influenced by seasonally varying noise levels, which in turn yielded seasonal differences in detection range. Incorporating the seasonal estimates of detection area into our analysis revealed a pattern of call densities in accord with historic (non-acoustic) knowledge of Antarctic blue whale seasonal distribution and migrations, a pattern that differed from seasonal detection rates. Furthermore, our methods for estimating call densities produced results that were more statistically robust for comparison across sites and time and more meaningful for interpretation of biological trends compared to detection rates alone. These advantages came at the cost of a more complex analysis that accounts for the large variability in detection range of different sounds that occur in Antarctic waters, and also accounts for the performance and biases introduced by automated algorithms to detect sounds. Despite the additional analytical complexities, broader usage of call densities, instead of detection rates, has the potential to yield a standardized, statistically robust, biologically informative, global investigation of acoustic trends in baleen whale sounds recorded on single hydrophones, especially in the remote and difficult to access Antarctic.

Tracking Whale Calls in the Lower St. Lawrence Seaway at Land Seismometers

The Lower St. Lawrence Seaway (LSLS) is critical to Canada’s economy both as part of a major marine shipping corridor and a site of intensive fishing. Every year, fin whales and blue whales frequent the LSLS feeding ground. Understanding the mechanisms driving whale habitat usage is key for making informed decisions on shipping and fishing, reducing whale collision risks and mitigating noise pollution. We detect whales in the LSLS with land seismometers by using a method that relies on the intervals of the regularly repeating low frequency calls. The resulting catalogue contains 14,076 fin whale detections and 3,739 blue whale detections between February 2020 and January 2022. These detections follow the overall pattern of hydrophones, with most detections from fall to early winter in the Estuary and until mid-winter/spring in the Gulf. High detection rates in the Northwest Gulf throughout the winter months demonstrate that this region is potentially utilized year-round. This labelled catalogue may be suitable for developing a deep learning-based whale call detection algorithm. Making use of seismometers and deep learning can increase whale monitoring coverage within the LSLS and elsewhere.

BASSA: New software tool reveals hidden details in visualisation of low-frequency animal sounds.

The study of animal sounds in biology and ecology relies heavily upon time-frequency (TF) visualisation, most commonly using the short-time Fourier transform (STFT) spectrogram. This method, however, has inherent bias towards either temporal or spectral details that can lead to misinterpretation of complex animal sounds. An ideal TF visualisation should accurately convey the structure of the sound in terms of both frequency and time, however, the STFT often cannot meet this requirement. We evaluate the accuracy of four TF visualisation methods (superlet transform [SLT], continuous wavelet transform [CWT] and two STFTs) using a synthetic test signal. We then apply these methods to visualise sounds of the Chagos blue whale, Asian elephant, southern cassowary, eastern whipbird, mulloway fish and the American crocodile. We show that the SLT visualises the test signal with 18.48%-28.08% less error than the other methods. A comparison between our visualisations of animal sounds and their literature descriptions indicates that the STFT's bias may have caused misinterpretations in describing pygmy blue whale songs and elephant rumbles. We suggest that use of the SLT to visualise low-frequency animal sounds may prevent such misinterpretations. Finally, we employ the SLT to develop 'BASSA', an open-source, GUI software application that offers a no-code, user-friendly tool for analysing short-duration recordings of low-frequency animal sounds for the Windows platform. The SLT visualises low-frequency animal sounds with improved accuracy, in a user-friendly format, minimising the risk of misinterpretation while requiring less technical expertise than the STFT. Using this method could propel advances in acoustics-driven studies of animal communication, vocal production methods, phonation and species identification.

Variation in glider-detected North Atlantic right, blue, and fin whale calls in proximity to high-traffic shipping lanes

Variation in glider-detected North Atlantic right, blue, and fin whale calls in proximity to high-traffic shipping lanes

The 2019 Marine Heatwave at Ocean Station Papa: A Multi‐Disciplinary Assessment of Ocean Conditions and Impacts on Marine Ecosystems

AbstractIn the past decade, two large marine heatwaves (MHWs) formed in the northeast Pacific near Ocean Station Papa (OSP), one of the oldest oceanic time series stations. Physical, biogeochemical, and biological parameters observed at OSP from 2013 to 2020 are used to assess ocean response and potential impacts on marine life from the 2019 northeast Pacific MHW. The 2019 MHW reached peak surface and subsurface temperature anomalies in the summertime and had both coastal, impacting fisheries, and offshore consequences that could potentially affect multiple trophic levels in the Gulf of Alaska. In the Gulf of Alaska, the 2019 MHW was preceded by calm and stratified upper ocean conditions, which preconditioned the enhanced surface warming in late spring and early summer. The MHW coincided with lower dissolved inorganic carbon and higher pH of surface waters relative to the 2013–2020 period. A spike in the summertime chlorophyll followed by a decrease in surface macronutrients suggests increased productivity in the well‐lit stratified upper ocean during summer 2019. More blue whale calls were recorded at OSP in 2019 compared to the prior year. This study shows how the utility of long‐term, continuous oceanographic data sets and analysis with an interdisciplinary lens is necessary to understand the potential impact of MHWs on marine ecosystems.

From citizen science to AI models: Advancing cetacean vocalization automatic detection through multi-annotator campaigns

Continuous underwater Passive Acoustic Monitoring (PAM) has emerged as a strong tool for cetacean research. To handle the vast volume of collected data, it is essential to employ automated detection and classification methods. The recent advancement of deep learning, involving model training and testing, requires a large amount of labeled data. These labels are derived through the manual annotation of audio files often reliant on human experts. Based on an annotation campaign focusing on blue whale calls in the Indian Ocean involving 19 novice annotators and one expert in bioacoustics, this study explores the integration of novice annotators in marine bioacoustics research, through citizen science programs, which could drastically increase the size of labeled datasets and enhance the performance of detection and classification models. The analysis reveals distinctive annotation profiles influenced by the complexity of vocalizations and the annotators' strategies, ranging from conservative to permissive. To address the challenges of annotation discrepancies, Convolutional Neural Networks (CNNs) are trained on annotations from both novices and the expert. The results show variations in model performance. Our work highlights the importance of annotation guidelines encouraging a more conservative approach to improve overall annotation quality. In an effort to optimize the potential of multi-annotation and mitigate the presence of noisy labels, two annotation aggregation methods (majority voting and soft labeling) are proposed and tested. The results demonstrate that both methods, particularly when a sufficient number of annotators are involved, significantly improve model performance and reduce variability: the standard deviation of the area under PR and ROC curves fall under 0.02 for both vocalizations with 13 aggregated annotators, while it was at 0.17 and 0.21 for the Blue Whale Dcalls and 0.05 and 0.04 for the SEIO PBW vocalizations with all annotators separately. Moreover, these aggregation methods enable the training of models using non-expert annotations that achieve performance of models trained with expert annotations. These findings suggest that crowdsourced annotations from novice annotators can be a viable alternative to expert annotations.

A decade of change and stability for fin whale song in the North Atlantic

Fin whale song is a ubiquitous low-frequency pulsing that has been recorded in every ocean basin. In recent years, the complexity of fin whale song has been realized. In the North Atlantic, this song is made up of two low-frequency note types (A and B notes) and one higher-frequency note type (upsweeps). The A and B notes are produced with both singlet and doublet inter-note intervals. These song patterns are interwoven throughout an individual’s song and have been observed to shift over time, both gradually and suddenly. Like many baleen whales, the population size of fin whales is poorly defined. Passive acoustic monitoring has been suggested as a way to estimate abundance if a cue rate or calling rate can be determined. In this study, we used recordings from 119 fin whale tracks from 2013–2023 generated from passive acoustic recorders in the North Atlantic to define fin whale song patterns in this ocean basin, identify changes in these song patterns over time, and calculate cue rates. The fin whales recorded sang in 4 distinct patterns. Inter-note intervals were 25 s for A-A singlets, 13 s for B-B singlets, 12/20 s for B-B doublets, and 10/11 s for A-B doublets. B-B doublet inter-note intervals significantly increased from May 2019–April 2023 and A-B inter-note intervals showed an intra-annual increasing trend that reset each summer. Fin whale song in the North Atlantic also often includes higher frequency upsweep notes immediately prior to B notes. While A and B note peak frequency was steady over these years, upsweep peak frequency significantly decreased, continuing a similar trend that has been observed over 30 years. Despite these changes, the dominant song pattern remained consistent and cue rate for actively singing whales was relatively stable, which is in contrast to patterns observed in the North Pacific. Many of these nuances in fin whale singing behavior are newly described herein, and these findings are vital for monitoring baseline behavior, behavioral trends over time, and ultimately estimating abundance of a poorly understood species.