Passive acoustic monitoring for marine mammals during Navy explosives training events off the coast of Virginia Beach, Virginia

AimSpecies distribution models (SDMs) are essential tools in ecology and conservation. However, the scarcity of visual sightings of marine mammals in remote polar areas hinders the effective application of SDMs there. Passive acoustic monitoring (PAM) data provide year‐round information and overcome foul weather limitations faced by visual surveys. However, the use of PAM data in SDMs has been sparse so far. Here, we use PAM‐based SDMs to investigate the spatiotemporal distribution of the critically endangered Antarctic blue whale in the Weddell Sea.LocationThe Weddell Sea.MethodsWe used presence‐only dynamic SDMs employing visual sightings and PAM detections in independent models. We compared the two independent models with a third combined model that integrated both visual and PAM data, aiming at leveraging the advantages of each data type: the extensive spatial extent of visual data and the broader temporal/environmental range of PAM data.ResultsVisual and PAM data prove complementary, as indicated by a low spatial overlap between daily predictions and the low predictability of each model at detections of other data types. Combined data models reproduced suitable habitats as given by both independent models. Visual data models indicate areas close to the sea ice edge (SIE) and with low‐to‐moderate sea ice concentrations (SIC) as suitable, while PAM data models identified suitable habitats at a broader range of distances to SIE and relatively higher SIC.Main ConclusionsThe results demonstrate the potential of PAM data to predict year‐round marine mammal habitat suitability at large spatial scales. We provide reasons for discrepancies between SDMs based on either data type and give methodological recommendations on using PAM data in SDMs. Combining visual and PAM data in future SDMs is promising for studying vocalized animals, particularly when using recent advances in integrated distribution modelling methods.

Integration of passive acoustic monitoring data into OBIS-SEAMAP, a global biogeographic database, to advance spatially-explicit ecological assessments

Concern about the potential effect of increased oceanic noise on marine mammals has led to the consideration of a variety of at-sea marine mammal monitoring methods. A recent marine mammal survey utilized both visual observation and passive acoustic monitoring. Visual observations were conducted using 7×50 binoculars during the day and generation III night vision devices (NVDs) at night. Acoustic data were collected with a towed hydrophone array and analyzed with a customized, PC-based acoustic workstation. Seventy-seven visual sightings were made. Forty sightings were made at night and 37 were made in daylight. The effective range of the NVDs was estimated through observations of a target at known distances; 50% detection rating was achieved at 130 meters. There were 98 acoustic detections. Of the 77 visual sightings, 42 were also detected acoustically. Six sightings were made without comparable acoustic detection, and 19 sightings were made when there was no acoustic monitoring. Conversely, 56 acoustic detections were made without visual sightings. These data suggest that, for species that commonly vocalize, a combined acoustic/visual survey will increase detection probabilities. Furthermore, night vision devices have the potential to be an effective observation tool for marine mammals.

Concern about the potential effect of increased oceanic noise on marine mammals has led to the consideration of a variety of at-sea marine mammal monitoring methods. A recent marine mammal survey utilized both visual observation and passive acoustic monitoring. Visual observations were conducted using 7×50 binoculars during the day and generation III night vision devices (NVDs) at night. Acoustic data were collected with a towed hydrophone array and analyzed with a customized, PC-based acoustic workstation. Seventy-seven visual sightings were made. Forty sightings were made at night and 37 were made in daylight. The effective range of the NVDs was estimated through observations of a target at known distances; 50% detection rating was achieved at 130 m. There were 98 acoustic detections. Of the 77 visual sightings, 42 were also detected acoustically. Six sightings were made without comparable acoustic detection, and 19 sightings were made when there was no acoustic monitoring. Conversely, 56 acoustic detections were made without visual sightings. These data suggest that, for species that commonly vocalize, a combined acoustic/visual survey will increase detection probabilities. Furthermore, night vision devices have the potential to be an effective observation tool for marine mammals. [Work sponsored by ONR.]

Passive acoustic monitoring is increasingly being used as a cost‐effective way to study wildlife populations, especially those that are difficult to census using conventional methods. Burrow‐nesting seabirds are among the most threatened birds globally, but they are also one of the most challenging taxa to census, making them prime candidates for research into such automated monitoring platforms. Passive acoustic monitoring has the potential to determine presence/absence or quantify burrow‐nesting populations, but its effectiveness remains unclear. We compared passive acoustic monitoring, tape‐playbacks andGPStracking data to investigate the ability of passive acoustic monitoring to capture unbiased estimates of within‐colony variation in nest density for the Manx ShearwaterPuffinus puffinus. Variation in acoustic activity across 12 study plots on an island colony was examined in relation to burrow density and environmental factors across 2 years. As predicted fewer calls were recorded when wind speed was high, and on moon‐lit nights, but there was no correlation between acoustic activity and the density of breeding birds within the plots as determined by tape‐playback surveys. Instead, acoustic indices correlated positively with spatial variation in the in‐colony flight activity of breeding individuals detected byGPS. Although passive acoustic monitoring has enormous potential in avian conservation, our results highlight the importance of understanding behaviour when using passive acoustic monitoring to estimate density and distribution.

ABSTRACTAutomated detection of acoustic signals is crucial for effective monitoring of sound‐producing animals and their habitats across ecologically relevant spatial and temporal scales. Recent advances in deep learning have made these approaches more accessible. However, few deep learning approaches can be implemented natively in the R programming environment; approaches that run natively in R may be more accessible for ecologists. The “torch for R” ecosystem has made deep learning with convolutional neural networks (CNNs) accessible for R users. Here, we evaluate a workflow for the automated detection and classification of acoustic signals from passive acoustic monitoring (PAM) data. Our specific goals include (1) present a method for automated detection of gibbon calls from PAM data using the “torch for R” ecosystem, (2) conduct a series of benchmarking experiments and compare the results of six CNN architectures; and (3) investigate how well the different architectures perform on data sets of the female calls from two different gibbon species: the northern gray gibbon (Hylobates funereus) and the southern yellow‐cheeked crested gibbon (Nomascus gabriellae). We found that the highest‐performing architecture depended on the species and test data set. We successfully deployed the top‐performing model for each gibbon species to investigate spatial variation in gibbon calling behavior across two grids of autonomous recording units in Danum Valley Conservation Area, Malaysia and Keo Seima Wildlife Sanctuary, Cambodia. The fields of deep learning and automated detection are rapidly evolving, and we provide the methods and data sets as benchmarks for future work.

Simple SummaryMarine mammal welfare research of professionally managed species has primarily focused on enrichment, habitat usage, and activity, as well as the impacts of human-oriented training sessions. However, the importance of sound in the welfare of marine mammals has rarely been mentioned. In this review, methods for acoustic welfare monitoring are discussed, including hearing tests, the incorporation of listening systems to monitor noise and communication, and cataloguing vocalizations in various health contexts. Examples from the US Navy Marine Mammal program are provided, as well as opportunities for facilities to initiate acoustic welfare monitoring. Suggested future directions of study, such as research examining the impact of sound on cognition, are also discussed.Research evaluating marine mammal welfare and opportunities for advancements in the care of species housed in a professional facility have rapidly increased in the past decade. While topics, such as comfortable housing, adequate social opportunities, stimulating enrichment, and a high standard of medical care, have continued to receive attention from managers and scientists, there is a lack of established acoustic consideration for monitoring the welfare of these animals. Marine mammals rely on sound production and reception for navigation and communication. Regulations governing anthropogenic sound production in our oceans have been put in place by many countries around the world, largely based on the results of research with managed and trained animals, due to the potential negative impacts that unrestricted noise can have on marine mammals. However, there has not been an established best practice for the acoustic welfare monitoring of marine mammals in professional care. By monitoring animal hearing and vocal behavior, a more holistic view of animal welfare can be achieved through the early detection of anthropogenic sound sources, the acoustic behavior of the animals, and even the features of the calls. In this review, the practice of monitoring cetacean acoustic welfare through behavioral hearing tests and auditory evoked potentials (AEPs), passive acoustic monitoring, such as the Welfare Acoustic Monitoring System (WAMS), as well as ideas for using advanced technologies for utilizing vocal biomarkers of health are introduced and reviewed as opportunities for integration into marine mammal welfare plans.

Increased knowledge about marine mammal seasonal distribution and species assemblage from the South Orkney Islands waters is needed for the development of management regulations of the commercial fishery for Antarctic krill (Euphausia superba) in this region. Passive acoustic monitoring (PAM) data were collected during the autumn and winter seasons in two consecutive years (2016, 2017), which represented highly contrasting environmental conditions due to the 2016 El Niño event. We explored differences in seasonal patterns in marine mammal acoustic presence between the two years in context of environmental cues and climate variability. Acoustic signals from five baleen whale species, two pinniped species and odontocete species were detected and separated into guilds. Although species diversity remained stable over time, the ice-avoiding and ice-affiliated species dominated before and after the onset of winter, respectively, and thus demonstrating a shift in guild composition related to season. Herein, we provide novel information about local marine mammal species diversity, community structure and residency times in a krill hotspot. Our study also demonstrates the utility of PAM data and its usefulness in providing new insights into the marine mammal habitat use and responses to environmental conditions, which are essential knowledge for the future development of a sustainable fishery management in a changing ecosystem.

Passive acoustic monitoring (PAM) data collection has been growing exponentially, resulting in petabytes of data that document ocean soundscapes, how they change over time, and what animals use these ecosystems at varying timescales. Efficiently extracting this critical information and comparing it to other datasets in the context of ecosystem-based management is a Big Data challenge that traditional desktop processing methods cannot address. The curation, management, and dissemination of PAM datasets is another challenge in need of collaborative progress. To meet these exigencies, a multi-agency funded Sound Cooperative (SoundCoop) project is building community-focused, national cyberinfrastructure capability for PAM data to promote improved, scalable and sustainable accessibility and applications for management and science. Driven by partnerships and framed by four case studies, the SoundCoop has established guidance on the standardized processing of sound level metrics using free software toolkits and begun developing core cyberinfrastructure components that future PAM projects can leverage. U.S. and international scientists contributed PAM data collected across 10 long-term monitoring projects to operationalize the production of hybrid-millidecade spectra across a diversity of labs/instruments. Collectively, the contributed data demonstrate the value of standardized processing that enables the creation of comparable results from disparate monitoring efforts.

Monitoring the presence of marine mammals in the vicinity of an anthropogenic activity using passive sonar can greatly improve detection rates by visual monitoring, and it is the only way to detect marine mammals at large distances during nighttime monitoring. Therefore, passive acoustic monitoring (PAM) is sometimes required by regulatory agencies as a mean to supplement visual monitoring during anthropogenic activities that may potentially adversely affect marine mammals. However, there are many critical aspects that need to be taken into consideration when prescribing PAM to support mitigation measures. These challenges include (1) training for shipboard observers to operate and maintain sophisticated PAM hardware and software; (2) proper design of PAM system that works well during industrial operations (such as seismic vessels); (3) reliable bearing and ranging of calling of animals, thus providing basis for mitigation measures; and (4) the affordability of PAM system to small businesses. This presentation provides a comprehensive analysis on the above aspects that are essential for marine mammal passive acoustic monitoring during anthropogenic activities, and highlights future needs to improve and expands PAM as a standard technique to support mitigation measures to reduce anthropogenic impacts.

Passive acoustic monitoring is a standard technique for studying bat ecology and behavior. However, an issue that has received little attention is how to appropriately analyze data within a long‐term acoustic monitoring dataset when the equipment has been replaced and updated. Equipment changes are often inevitable, especially for microphones, which need to be replaced regularly due to extended weather exposure and associated reductions in recording quality. We compared 2 ultrasonic microphone models (Wildlife Acoustics SMM‐U1 and SMM‐U2) by deploying them side‐by‐side with the same acoustic detector unit. We tested 9 or 10 microphones per model in field deployments lasting an average of 9 nights. We compared triggering frequency, species classification, detection rates, and echolocation call parameters (as indicators of signal quality) from both microphones. The vast majority (97%) of our 25,949 paired recordings were captured simultaneously by both microphones. Yet, the SMM‐U2 outperformed the SMM‐U1 in terms of proportion of files classifiable to the species level (70% versus 61%), rate of bat detections per night (1–6.5 more detections per night depending on species), and recording quality. Based on our results, we propose a correction factor to facilitate direct comparison of datasets collected with these 2 different microphones. Our study will assist bat researchers in selecting appropriate equipment and accounting for potential biases in long‐term acoustic monitoring programs.

Many organizations collect large passive acoustic monitoring (PAM) data sets that need to be efficiently and reliably analyzed. To determine appropriate methods for effective analysis of big PAM data sets, we undertook a literature review of baleen whale PAM analysis methods. Methodologies from 166 studies (published between 2000–2019) were summarized, and a detailed review was performed on the 94 studies that recorded more than 1,000 hr of acoustic data (“big data”). Analysis techniques for extracting baleen whale information from PAM data sets varied depending on the research observed. A spectrum of methodologies was used and ranged from manual analysis of all acoustic data by human experts to completely automated techniques with no manual validation. Based on this assessment, recommendations are provided to encourage robust research methods that are comparable across studies and sectors, achievable across research groups, and consistent with previous work. These include using automated techniques when possible to increase efficiency and repeatability, supplementing automation with manual review to calculate automated detector performance, and increasing consistency in terminology and presentation of results. This work can be used to facilitate discussion for minimum standards and best practices to be implemented in the field of marine mammal PAM.

The effective analysis of Passive Acoustic Monitoring (PAM) data has the potential to determine spatial and temporal variations in ecosystem health and species presence if automated detection and classification algorithms are capable of discrimination between marine species and the presence of anthropogenic and environmental noise. Extracting more than a single sound source or call type will enrich our understanding of the interaction between biological, anthropogenic and geophonic soundscape components in the marine environment. Advances in extracting ecologically valuable cues from the marine environment, embedded within the soundscape, are limited by the time required for manual analyses and the accuracy of existing algorithms when applied to large PAM datasets. In this work, a deep learning model is trained for multi-class marine sound source detection using cloud computing to explore its utility for extracting sound sources for use in marine mammal conservation and ecosystem monitoring. A training set is developed comprising existing datasets amalgamated across geographic, temporal and spatial scales, collected across a range of acoustic platforms. Transfer learning is used to fine-tune an open-source state-of-the-art ‘small-scale’ convolutional neural network (CNN) to detect odontocete tonal and broadband call types and vessel noise (from 0 to 48 kHz). The developed CNN architecture uses a custom image input to exploit the differences in temporal and frequency characteristics between each sound source. Each sound source is identified with high accuracy across various test conditions, including variable signal-to-noise-ratio. We evaluate the effect of ambient noise on detector performance, outlining the importance of understanding the variability of the regional soundscape for which it will be deployed. Our work provides a computationally low-cost, efficient framework for mining big marine acoustic data, for information on temporal scales relevant to the management of marine protected areas and the conservation of vulnerable species.

Growing concern over anthropogenic underwater noise, highlighted by initiatives like the Marine Strategy Framework Directive (MSFD) and its Technical Group on Underwater Noise (TG Noise), emphasizes regions like the Western Black Sea, where increasing activities threaten marine habitats. This region is experiencing rapid growth in maritime traffic and resource exploitation, which is intensifying concerns over the noise impacts on its unique marine habitats. While machine learning offers promising solutions, a research gap persists in comprehensively evaluating diverse ML models within an integrated framework for complex underwater acoustic data, particularly concerning real-world data limitations like class imbalance. This paper addresses this by presenting a multi-faceted framework using passive acoustic monitoring (PAM) data from fixed locations (50–100 m depth). Acoustic data are processed using advanced signal processing (broadband Sound Pressure Level (SPL), Power Spectral Density (PSD)) for feature extraction (Mel-spectrograms for deep learning; PSD statistical moments for classical/unsupervised ML). The framework evaluates Convolutional Neural Networks (CNNs), Random Forest, and Support Vector Machines (SVMs) for noise event classification, alongside Gaussian Mixture Models (GMMs) for anomaly detection. Our results demonstrate that the CNN achieved the highest classification accuracy of 0.9359, significantly outperforming Random Forest (0.8494) and SVM (0.8397) on the test dataset. These findings emphasize the capability of deep learning in automatically extracting discriminative features, highlighting its potential for enhanced automated underwater acoustic monitoring.

Model-based unsupervised clustering for distinguishing Cuvier's and Gervais' beaked whales in acoustic data