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The container shipping line Maersk undertook a Radical Retrofit to improve the energy efficiency of twelve sister container ships. Noise reduction, identified as a potential added benefit of the retrofitting effort, was investigated in this study. A passive acoustic recording dataset from the Santa Barbara Channel off Southern California was used to compile over 100 opportunistic vessel transits of the twelve G-Class container ships, pre- and post-retrofit. Post-retrofit, the G-Class vessels’ capacity was increased from ~9,000 twenty-foot equivalent units (TEUs) to ~11,000 TEUs, which required a draft increase of the vessel by 1.5 m on average. The increased vessel draft resulted in higher radiated noise levels (<2 dB) in the mid- and high-frequency bands. Accounting for the Lloyd’s mirror (dipole source) effect, the monopole source levels of the post-retrofit ships were found to be significantly lower (>5 dB) than the pre-retrofit ships in the low-frequency band and the reduction was greatest at low speed. Although multiple design changes occurred during retrofitting, the reduction in the low-frequency band most likely results from a reduction in cavitation due to changes in propeller and bow design.

Passive acoustic monitoring has become an important data collection method, yielding massive datasets replete with biological, environmental and anthropogenic information. Automated signal detectors and classifiers are needed to identify events within these datasets, such as the presence of species-specific sounds or anthropogenic noise. These automated methods, however, are rarely a complete substitute for expert analyst review. The ability to visualize and annotate acoustic events efficiently can enhance scientific insights from large, previously intractable datasets. A MATLAB-based graphical user interface, called DetEdit, was developed to accelerate the editing and annotating of automated detections from extensive acoustic datasets. This tool is highly-configurable and multipurpose, with uses ranging from annotation and classification of individual signals or signal-clusters and evaluation of signal properties, to identification of false detections and false positive rate estimation. DetEdit allows users to step through acoustic events, displaying a range of signal features, including time series of received levels, long-term spectral averages, time intervals between detections, and scatter plots of peak frequency, RMS, and peak-to-peak received levels. Additionally, it displays either individual, or averaged sound pressure waveforms, and power spectra within each acoustic event. These views simultaneously provide analysts with signal-level detail and encounter-level context. DetEdit creates datasets of signal labels for further analyses, such as training classifiers and quantifying occurrence, abundances, or trends. Although designed for evaluating underwater-recorded odontocete echolocation click detections, DetEdit can be adapted to almost any stereotyped impulsive signal. Our software package complements available tools for the bioacoustic community and is provided open source at https://github.com/MarineBioAcousticsRC/DetEdit.

Low-frequency ocean ambient noise is dominated by noise from commercial ships, yet understanding how individual ships contribute deserves further investigation. This study develops and evaluates statistical models of container ship noise in relation to design characteristics, operational conditions and oceanographic settings. Five-hundred ship passages and nineteen covariates were used to build generalized additive models. Opportunistic acoustic measurements of ships transiting offshore California were collected using seafloor acoustic recorders. A 5–10 dB range in broadband source level was found for ships depending on the transit conditions. For a ship recorded multiple times traveling at different speeds, cumulative noise was lowest at 8 knots, 65% reduction in operational speed. Models with highest predictive power, in order of selection, included ship speed, size and time of year. Uncertainty in source depth and propagation affected model fit. These results provide insight on the conditions that produce higher levels of underwater noise from container ships.

Sperm whales exhibit sexual dimorphism and sex-specific latitudinal segregation. Females and their young form social groups and are usually found in temperate and tropical latitudes, while males forage at higher latitudes. Historical whaling data and rare sightings of social groups in high latitude regions of the North Pacific, such as the Gulf of Alaska (GOA) and Bering Sea/Aleutian Islands (BSAI), suggest a more complex distribution than previously understood. Sperm whales are the most sighted and recorded cetacean in marine mammal surveys in these regions but capturing their demographic composition and habitat use has proven challenging. This study detects sperm whale presence using passive acoustic data from seven sites in the GOA and BSAI from 2010 to 2019. Differences in click characteristics between males and females (i.e., inter-click and inter-pulse interval) was used as a proxy for animal size/sex to derive time series of animal detections. Generalized additive models with generalized estimation equations demonstrate how spatiotemporal patterns differ between the sexes. Social groups were present at all recording sites with the largest relative proportion at two seamount sites in the GOA and an island site in the BSAI. We found that the seasonal patterns of presence varied for the sexes and between the sites. Male presence was highest in the summer and lowest in the winter, conversely, social group peak presence was in the winter for the BSAI and in the spring for the GOA region, with the lowest presence in the summer months. This study demonstrates that social groups are not restricted to lower latitudes and capture their present-day habitat use in the North Pacific. It highlights that sperm whale distribution is more complex than accounted for in management protocol and underscores the need for improved understanding of sperm whale demographic composition to better understand the impacts of increasing anthropogenic threats, particularly climate change.

A combination of machine learning and expert analyst review was used to detect odontocete echolocation clicks, identify dominant click types, and classify clicks in 32 years of acoustic data collected at 11 autonomous monitoring sites in the western North Atlantic between 2016 and 2019. Previously-described click types for eight known odontocete species or genera were identified in this data set: Blainville’s beaked whales ( Mesoplodon densirostris ), Cuvier’s beaked whales ( Ziphius cavirostris ), Gervais’ beaked whales ( Mesoplodon europaeus ), Sowerby’s beaked whales ( Mesoplodon bidens ), and True’s beaked whales ( Mesoplodon mirus ), Kogia spp ., Risso’s dolphin ( Grampus griseus ), and sperm whales ( Physeter macrocephalus ). Six novel delphinid echolocation click types were identified and named according to their median peak frequencies. Consideration of the spatiotemporal distribution of these unidentified click types, and comparison to historical sighting data, enabled assignment of the probable species identity to three of the six types, and group identity to a fourth type. UD36, UD26, and UD28 were attributed to Risso’s dolphin ( G . griseus ), short-finned pilot whale ( G . macrorhynchus ), and short-beaked common dolphin ( D . delphis ), respectively, based on similar regional distributions and seasonal presence patterns. UD19 was attributed to one or more species in the subfamily Globicephalinae based on spectral content and signal timing. UD47 and UD38 represent distinct types for which no clear spatiotemporal match was apparent. This approach leveraged the power of big acoustic and big visual data to add to the catalog of known species-specific acoustic signals and yield new inferences about odontocete spatiotemporal distribution patterns. The tools and call types described here can be used for efficient analysis of other existing and future passive acoustic data sets from this region.

Understanding abundance and trends of beaked whales in the heavily industrialized Gulf of America (formerly Gulf of Mexico), is critical for management but challenging with visual-based distance-sampling due to their elusive surface behavior. Acoustic-based distance-sampling methods rely on accurate modeling of detection probability as a function of distance from a recorder, requiring population-specific diving and acoustic behavior parameters, which is currently lacking for Gulf populations. To address this, we used passive acoustic tracking with two 4-channel High-Frequency Acoustic Recording Packages (HARPs) deployed off Louisiana (~1100 m depth) in 2021. Echolocation clicks detected on both recorders were localized in 3D to characterize acoustic and diving behavior. These data informed a Monte Carlo cue-based simulation to estimate the probability of detection by a near-seafloor single-sensor HARP. A trial-based approach also estimated detection probability as a function of range to a single-channel sensor deployed at the site. Results show species-specific differences. Goose-beaked whales ( Ziphius cavirostris ), were detected for longer periods during foraging dives (n = 24 dives, mean: 20.5 min; range: 7–42) compared with Blainville’s ( Mesoplodon densirostris , n = 2 dives, 13.6 min; 11–16) and Gervais’ ( Mesoplodon europaeus , n = 24 dives, 12.7 min; 7–19) beaked whales. Maximum dive depths also differed, with some goose-beaked whales foraging at or near the seafloor. Descent and ascent rates were similar within species but differed among them (1.34/1.40 m/s for goose-beaked and 1.15/1.19 m/s for Gervais’ beaked whales). Source level and broadband directivity index were estimated at 225 dB pp re 1 μPa-1m and 26 dB for goose-beaked whales, and 218 dB pp re 1 μPa-1m and 20 dB for Gervais’ beaked whales. Estimates were not possible for Blainville’s beaked whales due to limited data. In both the Monte Carlo simulation and trial-based approach, detection probability declined sharply with ranges, reflecting the highly directional beam of beaked whale echolocation clicks.

Delphinids produce large numbers of short duration, broadband echolocation clicks which may be useful for species classification in passive acoustic monitoring efforts. A challenge in echolocation click classification is to overcome the many sources of variability to recognize underlying patterns across many detections. An automated unsupervised network-based classification method was developed to simulate the approach a human analyst uses when categorizing click types: Clusters of similar clicks were identified by incorporating multiple click characteristics (spectral shape and inter-click interval distributions) to distinguish within-type from between-type variation, and identify distinct, persistent click types. Once click types were established, an algorithm for classifying novel detections using existing clusters was tested. The automated classification method was applied to a dataset of 52 million clicks detected across five monitoring sites over two years in the Gulf of Mexico (GOM). Seven distinct click types were identified, one of which is known to be associated with an acoustically identifiable delphinid (Risso's dolphin) and six of which are not yet identified. All types occurred at multiple monitoring locations, but the relative occurrence of types varied, particularly between continental shelf and slope locations. Automatically-identified click types from autonomous seafloor recorders without verifiable species identification were compared with clicks detected on sea-surface towed hydrophone arrays in the presence of visually identified delphinid species. These comparisons suggest potential species identities for the animals producing some echolocation click types. The network-based classification method presented here is effective for rapid, unsupervised delphinid click classification across large datasets in which the click types may not be known a priori.

Where’s Whaledo is a software toolkit that uses a combination of automated processes and user interfaces to greatly accelerate the process of reconstructing animal tracks from arrays of passive acoustic recording devices. Passive acoustic localization is a non-invasive yet powerful way to contribute to species conservation. By tracking animals through their acoustic signals, important information on diving patterns, movement behavior, habitat use, and feeding dynamics can be obtained. This method is useful for helping to understand habitat use, observe behavioral responses to noise, and develop potential mitigation strategies. Animal tracking using passive acoustic localization requires an acoustic array to detect signals of interest, associate detections on various receivers, and estimate the most likely source location by using the time difference of arrival (TDOA) of sounds on multiple receivers. Where’s Whaledo combines data from two small-aperture volumetric arrays and a variable number of individual receivers. In a case study conducted in the Tanner Basin off Southern California, we demonstrate the effectiveness of Where’s Whaledo in localizing groups of Ziphius cavirostris . We reconstruct the tracks of six individual animals vocalizing concurrently and identify Ziphius cavirostris tracks despite being obscured by a large pod of vocalizing dolphins.

Distribution models are needed to understand spatiotemporal patterns in cetacean occurrence and to mitigate anthropogenic impacts. Shipboard line-transect visual surveys are the standard method for estimating abundance and describing the distributions of cetacean populations. Ship-board surveys provide high spatial resolution but lack temporal resolution and seasonal coverage. Stationary passive acoustic monitoring (PAM) employs acoustic sensors to sample point locations nearly continuously, providing high temporal resolution in local habitats across days, seasons and years. To evaluate whether cross-platform data synthesis can improve distribution predictions, models were developed for Cuvier’s beaked whales, sperm whales, and Risso’s dolphins in the oceanic Gulf of Mexico using two different methods: generalized additive models and neural networks. Neural networks were able to learn unspecified interactions between drivers. Models that incorporated PAM datasets out-performed models trained on visual data alone, and joint models performed best in two out of three cases. The modeling results suggest that, when taken together, multiple species distribution models using a variety of data types may support conservation and management of Gulf of Mexico cetacean populations by improving the understanding of temporal and spatial species distribution trends.

Commercial shipping is the dominant source of low-frequency noise in the ocean. It has been shown that the noise radiated by an individual vessel depends upon the vessel’s speed. This study quantified the reduction in source levels (SLs) and sound exposure levels (SELs) for ships participating in two variations of a vessel speed reduction (VSR) program. SLs and SELs of individual ships participating in the program between 2014 and 2017 were statistically lower than non-participating ships ( p  < 0.001). In the 2018 fleet-based program, there were statistical differences between the SLs and SELs of fleets that participated with varying degrees of cooperation. Significant reductions in SL and SEL relied on cooperation of 25% or more in slowing vessel speed. This analysis highlights how slowing vessel speed to 10 knots or less is an effective method in reducing underwater noise emitted from commercial ships.