Discussion

Managing the wellbeing of ecosystems requires identifying when and where human activities are impacting species’ occurrence, movement, and behaviour. PAM is a useful approach for the detection of both large- and small-scale changes in urban and wild environments, as it allows for continuous and prolonged ecosystem monitoring. Challenges in employing PAM as a standard monitoring tool arise after data collection, when researchers and practitioners need to quickly extract useful information from large acoustic datasets, to understand when and where management actions are needed to preserve the well-being of ecosystems. The relatively new field of ecoacoustics provides the theoretical background for linking specific characteristics of the acoustic environment to biodiversity and ecosystem health. However, identifying a common analytical approach has been an obstacle to the broad application of ecoacoustics theory so far, and most studies employing ecoacoustics indices are not suited for replicability and comparison.
We addressed these problems by linking marine ecoacoustics assessment to the realms of machine learning and dimensionality reduction. We applied a deep-learning approach to characterize the biological and anthropogenic components of marine acoustic environments, and we illustrated how acoustic features derived from a pre-trained Convolutional Neural Network capture both the coarse and fine-grained structure of large PAM datasets. These methods can be applied to a broad range of marine and terrestrial systems.
Our analyses revealed several applications for inferring population- and location-specific information from acoustic datasets. When datasets are already labelled and focused on a specific taxon, such as the WMD, we found that acoustic features were particularly suited for the discrimination of marine mammal vocalizations. Understanding the evolution of vocal diversity and the role of vocalizations in the ecology of a species is one of the key objectives of bioacoustics research (Luís et al., 2021). Full acoustic repertoires are not available for most species, as building comprehensive lists of vocalizations requires considerable research effort. Here we show how a general acoustic classification model (VGGish) used as a feature extractor allows us to detect differences and similarities among marine mammal species, without requiring prior knowledge on the species’ vocal repertoires. Our results for orcas are of particular interest, as they provide insights on the vocal similarities and differences between distinct populations of the same species. A large number of orca call samples labelled as EN Pacific were classified as WN Atlantic whales using the methodology in this study. Orcas show both genetic divergence and differences in call frequency that are more pronounced for sympatric ecotypes than whales found in different ocean basins (Filatova et al., 2015). Although we cannot consider the artefactual conflation of EN Pacific orcas with NW Atlantic orcas in the WMD as definitive evidence of convergence in vocal behaviour, we suggest that this aspect should be further investigated, perhaps using more recent recordings of these different orca populations.
More than 60 different ecoacoustic indices are being employed as descriptors of terrestrial soundscapes (Bradfer-Lawrence et al., 2019), making the search for indices that are successfully measuring biodiversity across widely variable environments very challenging (Minello et al., 2021). So far, ecoacoustic indices have been applied to marine environments with little success (Bohnenstiehl et al., 2018). Due to higher sound propagation efficiency, marine acoustic environments can receive acoustic energy from many sources with some that are hundreds of kilometres distant, making them more complex to study than terrestrial environments. Accordingly, the biases shown by acoustic indices measuring terrestrial species diversity (Eldridge et al., 2018; Fairbrass et al., 2017; Heath et al., 2021) are amplified when transferred to the study of marine environments (Bohnenstiehl et al., 2018; Dimoff et al., 2021; Minello et al., 2021).
Machine learned acoustic features are a promising alternative to the use of ecoacoustics indices for monitoring terrestrial biodiversity (Heath et al., 2021; Sethi et al., 2020). In this study, we show how this approach can also be extended to the study of marine soundscapes. The derived acoustic features were successful in discriminating between two different marine environments that differed in type and intensity of anthropic activity: recordings collected in Burin were dominated by distant seismic airgun pulses in the low frequency range, and the Red Island hydrophone recordings were characterized by frequent ship noise. Both sites yielded recordings of humpback whale vocalizations, and our results show that machine-learned acoustic features can be employed for detecting marine mammal sounds across different acoustic contexts. Machine-learned acoustic features respond to multiple marine sound sources, and can be employed successfully for investigating both the biological and anthropic components of marine soundscapes.
Reducing acoustic features to two UMAP dimensions, however, resulted in poorly performing classifiers for three sets of labels: airgun noise presence, ship presence, and humpback whale presence. In all three cases, repeating the analysis on a larger set of 128 features improved model performance at the cost of increased processing time. The best models used as little as two features, and as many as 64, whereas classifiers based on the full 128 features were selected as best models for all iterations of the humpback whale classifier (Appendix S1). This indicates that the number of acoustic features could be significantly reduced in some instances, thus reducing processing time and virtual memory requirements. The poor performance observed in the UMAP ship presence classifiers could be partly due to the approach adopted for labelling presences and to the fact that ship noise was almost ubiquitous in the Red Island recordings. Most samples collected at the Red Island deployment location were more than 3 dB higher than the full dataset median, but only a fraction of such samples contributed to the broadband SPL (Appendix S2.2), indicating that ship presence may have been underestimated. As an alternative, using records of vessel positions obtained from the Automatic Identification System (AIS) as an indicator of ship presence may improve model performance, at the cost of underestimating the presence of small vessels, which are rarely equipped with AIS.
Acoustic features have been shown to overcome many of the limitations of ecoacoustics indices; for example, acoustic features outperform common ecoacoustic indices in discriminating different environmental characteristics (Sethi et al., 2020). Furthermore, acoustic features are resilient to audio file compression and reduction of Nyquist frequency, and provide results that are independent from type of recorders deployed and choices relative to the temporal fragmentation of acoustic datasets (Heath et al., 2021; Sethi et al., 2020). Here, we show that acoustic features and UMAP dimensions allow for the comprehensive exploration of marine PAM datasets. Features can be used to train classification models focusing on biological and anthropogenic sound sources and allow for fine-grain comparison of marine mammal vocalizations.
Two limitations persist. VGGish, the CNN used to extract the acoustic features, is pre-trained on audio files with a sampling rate of 16 kHz, resulting in a Nyquist frequency of 8 kHz. This is sufficient to capture low frequency vocalizations but reduces its ability to discriminate high-frequency sounds. Nonetheless, we were able to correctly classify both high- and low-frequency vocalizations in the WMD examples, including Phocoenidae sounds, a family that includes species that can produce sounds up to 150 kHz. A second limitation is that acoustic features are not a plug and play product, as establishing links between features and relevant ecological variables requires additional analyses, while ecoacoustic indices are designed as measures of specific environmental characteristics.
By presenting a set of examples focused on marine mammals, we have demonstrated the benefits and challenges of implementing acoustic features as descriptors of marine acoustic environments. Our future research will extend feature extraction and testing to full PAM datasets spanning several years and inclusive of multiple hydrophone deployment locations. Other aspects warranting further investigation are how acoustic features perform when the objective is discriminating vocalizations of individuals belonging to the same species or population, as well as their performance in identifying samples with multiple active sound sources.
Acoustic features are abstract representations of PAM recordings which preserve the original structure and underlying relationships between the original samples, and, at the same time, are a broadly applicable set of metrices that can be used to answer ecoacoustics, ecology, and conservation questions. As such, they can help us understand how natural systems interact with, and respond to, anthropogenic pressures across multiple environments. Lastly, the universal nature of acoustic features analysis could help bridge the gap between terrestrial and marine soundscape research. This approach could deepen our understanding of natural systems by enabling multi-system environmental assessments, allowing researchers to investigate and monitor, for example, how stressor-induced changes in one system may manifest in another. And these benefits accrue from an approach that is more objective than manual analyses and requires far less human effort.
ACKNOWLEDGEMENTS
This project was funded by the Species at Risk, Oceans Protection Plan, and Marine Ecosystem Quality programmes of the Department of Fisheries and Oceans Canada, Newfoundland and Labrador Region, by Memorial University of Newfoundland and Labrador, and by the Ph.D. program in Evolutionary Biology and Ecology (University of Parma, agreement with University of Ferrara and University of Firenze). Simone Cominelli was also supported by the TD Bank Bursary for Environmental Studies. Nicolo’ Bellin was also supported by the ‘COMP-HUB’ Initiative, funded by the ‘Departments of Excellence’ Project of the Italian Ministry for Education, University and Research (MIUR). We would like to express our gratitude to the curators of the Watkins Marine Mammal Sound Database as we believe that open access databases are incredibly relevant to the development of global monitoring of natural systems. We thank all the graduate students of the Northern EDGE Lab for their support, and their effort in creating a welcoming and inclusive work environment. Lastly, we would like to thank Madelyn Swackhamer, Sean Comeau, Lee Sheppard, Greg Furey, and Andrew Murphy from DFO’s Marine Mammal Section for collecting the data used in this study, providing detailed information about the hydrophone deployments, and for their help and support with accessing DFO’s PAM databases.
CONFLICT OF INTEREST STATEMENT
The authors declare that there are no conflicts of interest.
AUTHORS’ CONTRIBUTIONS
Simone Cominelli and Nicolo’ Bellin developed the concepts and methodology described here, acquired the necessary databases, ran the analysis, and prepared the first draft of the manuscript. Dr. Carissa Brown and Dr. Valeria Rossi supervised the two main authors (Simone Cominelli and Nicolo’ Bellin) throughout the preparation of the manuscript, and provided space and equipment for conducting the research. Dr. Jack Lawson provided access to DFO’s PAM database, provided input during the development of the methodology, and reviewed analysis results. All authors contributed critically to the drafts and final submission, and gave approval for publication.
DATA AVAILABILITY
Scripts to reproduce the images and analysis described here, as well as sample wav files, and tables containing all acoustic features and their labels are available for download as Jupiter Notebooks through Dryad:
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