Susannah J. Buchan

Reliable differentiation of similar acoustic signals remains a major challenge for passive acoustic monitoring (PAM) of sympatric marine mammal species. Blue ( Balaenoptera musculus ) and fin whales ( Balaenoptera physalus ) produce frequency-modulated (FM) downsweeping calls with overlapping frequency ranges and durations, making species attribution difficult. This study combines long-term PAM datasets with animal-borne tag recordings to investigate how these FM calls can be reliably distinguished by species. Tag recordings with confirmed species identity showed substantial overlap in most acoustic features, but the parameter Duration 90% offered partial separation: with most fin whale FM calls < 0.6 s, and blue whale FM calls > 0.7 s. Applied to high-quality calls from moored data with signal-to-noise ratios above 12 dB, this approach may provide a conservative, rule-based means of classification. A more robust approach was achieved by combining deep learning feature extraction with non-linear dimensionality reduction. Integrating confirmed ground-truth tag recordings as a reference space enabled previously unseen FM calls from long-term PAM recordings to be attributed by acoustic similarity to known calls while maintaining an explicit measure of uncertainty by leaving acoustically ambiguous calls unassigned. Together, these approaches provide a practical framework for more reliable call attribution, enhancing long-term PAM-based monitoring of blue and fin whales.

Aim: The urgency for remote, reliable and scalable biodiversity monitoring amidst mounting human pressures on ecosystems has sparked worldwide interest in Passive Acoustic Monitoring (PAM), which can track life underwater and on land. However, we lack a unified methodology to report this sampling effort and a comprehensive overview of PAM coverage to gauge its potential as a global research and monitoring tool. To address this gap, we created the Worldwide Soundscapes project, a collaborative network and growing database comprising metadata from 416 datasets across all realms (terrestrial, marine, freshwater and subterranean).Location: Worldwide, 12,343 sites, all ecosystem types.Time Period: 1991 to present.Major Taxa Studied: All soniferous taxa.Methods: We synthesise sampling coverage across spatial, temporal and ecological scales using metadata describing sampling locations, deployment schedules, focal taxa and audio recording parameters. We explore global trends in biological, anthropogenic and geophysical sounds based on 168 selected recordings from 12 ecosystems across all realms.Results: Terrestrial sampling is spatially denser (46 sites per million square kilometre—Mkm²) than aquatic sampling (0.3 and 1.8 sites/Mkm² in oceans and fresh water) with only two subterranean datasets. Although diel and lunar cycles are well sampled across realms, only marine datasets (55%) comprehensively sample all seasons. Across the 12 ecosystems selected for exploring global acoustic trends, biological sounds showed contrasting diel patterns across ecosystems, declined with distance from the Equator, and were negatively correlated with anthropogenic sounds.Main Conclusions: PAM can inform macroecological studies as well as global conservation and phenology syntheses, but representation can be improved by expanding terrestrial taxonomic scope, sampling coverage in the high seas and subterranean ecosystems, and spatio‐temporal replication in freshwater habitats. Overall, this worldwide PAM network holds promise to support cross‐realm biodiversity research and monitoring efforts.

This study investigated the detection range (DR) of blue whale (Balaenoptera musculus sp.) vocalizations (SEP2 and D-calls) in Chilean Patagonia using data collected from ocean glider-based hydrophones. DRs were determined by calculating the figure of merit of each vocalization. SEP2 consistently exhibited a greater DR compared to D-calls across the study area. Glider depth and bathymetry emerged as the most influential factors affecting DR. Taking DR and the factors that influence it into account enables a more robust interpretation of acoustic studies on the spatial distribution of cetaceans.

Marine passive acoustic monitoring can be used to study biological, geophysical, and anthropogenic phenomena in the ocean. The wide range of characteristics from geophysical, biological, and anthropogenic sounds sources makes the simultaneous automatic detection and classification of these sounds a significant challenge. Here, we propose a single Hidden Markov Model-based system with a Deep Neural Network (HMM-DNN) for the detection and classification of low-frequency biological (baleen whales), geophysical (earthquakes), and anthropogenic (air guns) sounds. Acoustic data were obtained from the Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization station off Juan Fernandez, Chile (station HA03) and annotated by an analyst (498 h of audio data containing 30,873 events from 19 different classes), and then divided into training (60%), testing (20%), and tuning (20%) subsets. Each audio frame was represented as an observation vector obtained through a filterbank-based spectral feature extraction procedure. The HMM-DNN training procedure was carried out discriminatively by setting HMM states as targets. A model with Gaussian Mixtures Models and HMM (HMM-GMM) was trained to obtain an initial set of HMM target states. Feature transformation based on Linear Discriminant Analysis and Maximum Likelihood Linear Transform was also incorporated. The HMM-DNN system displayed good capacity for correctly detecting and classifying events, with high event-level accuracy (84.46%), high weighted average sensitivity (84.46%), and high weighted average precision (89.54%). Event-level accuracy increased with higher event signal-to-noise ratios. Event-level metrics per class also showed that our HMM-DNN system generalized well for most classes but performances were best for classes that either had a high number of training exemplars (e.g., generally above 50) and/or were for classes of signals that had low variability in spectral features, duration, and energy levels. Fin whale and Antarctic blue whale song and air guns performed particularly well.

Northern Chilean Patagonia is a mega-estuarine system where oceanic waters mix with freshwater inputs in the coastal fjords, channels and gulfs. The aim of this study was to examine the distribution of blue and sei whales with respect to oceanographic conditions of the study area from the estuarine inner sea to the outer ocean. Ocean gliders were used, mounted with a hydrophone to determine acoustic presence of whales (Southeast Pacific and Antarctic blue whale song calls, and blue whales D-calls; sei whale downsweeps and upsweeps), and a temperature and salinity instrument. Four glider deployments were carried out in April 2018 and April-June 2019 navigating a total of 2817 kilometers during 2110 hours. To examine interannual variation, the average percentage of day with presence of calls was compared between years using the adjusted p-values for one-way ANOVA and descriptive statistics. To examine spatial variation between the hourly acoustic presence of blue whales and sei whales and temperature and salinity conditions, Generalized Linear Models (GLMs) were used. Salinities were higher in 2019 compared to 2018. Southeast Pacific blue whales produced song calls throughout the study area in both years, across estuarine and oceanic areas, but percentage of day with presence was higher in 2019 vs 2018. Percentage of day with presence of D-calls was similar between years, but higher in oceanic areas during both study periods. In contrast, the spatial pattern of sei whale acoustic presence was ambiguous and interannual variability was high, suggesting that sei whales preferred estuarine areas in 2018 and oceanic areas in 2019. We discuss possible explanations for observed acoustic presence in relation to foraging behavior and prey distribution.

The Southeast Pacific (SEP) or Chilean blue whale population is largely distributed between Northern Chilean Patagonia and the Eastern Tropical Pacific. Off South Georgia (SG), the majority of blue whales caught were Antarctic blue whales, but recent genetic and acoustic records indicate the possible presence of SEP individuals. To assess the presence of SEP blue whales off SG, we carried out new analyses of acoustic data previously reported as containing pygmy blue whale song and analyzed the length frequencies from 20th century catch data. The month of acoustic data reported to contain the presumed pygmy blue whale song was examined (August 2006), and 13 days were found to have songs which visually and quantitatively matched SEP2 songs from Northern Chilean Patagonia. The fundamental frequency of SEP2 song off SG, however, was not in line with the predicted frequency shift trend of the SEP2 songs in the SEP. A mixture analysis of lengths in historic whaling catches indicated that 3.3% of the catches from SG could be SEP blue whales, although this declined to 0.6%, 95% CI [0.0, 2.6] when fit to nonrounded lengths. Our results suggest that any SEP blue whales off SG are likely rare vagrants, at the edge of an endangered population.