Search

The recently named Rice's whale in the Gulf of Mexico is one of the most endangered whales in the world, and improved knowledge of spatiotemporal occurrence patterns is needed to support their recovery and conservation. Passive acoustic monitoring methods for determining spatiotemporal occurrence patterns require identifying the species' call repertoire. Rice's whale call repertoire remains unvalidated though several potential call types have been identified. This study uses sonobuoys and passive acoustic tagging to validate the source of potential call types and to characterize Rice's whale calls. During concurrent visual and acoustic surveys, acoustic-directed approaches were conducted to obtain visual verifications of sources of localized sounds. Of 28 acoustic-directed approaches, 79% led to sightings of balaenopterid whales, of which 10 could be positively identified to species as Rice's whales. Long-moan calls, downsweep sequences, and tonal-sequences are attributed to Rice's whales based on these matches, while anthropogenic sources are ruled out. A potential new call type, the low-frequency downsweep sequence, is characterized from tagged Rice's whale recordings. The validation and characterization of the Rice's whale call repertoire provides foundational information needed to use passive acoustic monitoring for better understanding and conservation of these critically endangered whales.

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.

Six baleen whale species are found in the temperate western North Atlantic Ocean, with limited information existing on the distribution and movement patterns for most. There is mounting evidence of distributional shifts in many species, including marine mammals, likely because of climate‐driven changes in ocean temperature and circulation. Previous acoustic studies examined the occurrence of minke (Balaenoptera acutorostrata) and North Atlantic right whales (NARW; Eubalaena glacialis). This study assesses the acoustic presence of humpback (Megaptera novaeangliae), sei (B. borealis), fin (B. physalus), and blue whales (B. musculus) over a decade, based on daily detections of their vocalizations. Data collected from 2004 to 2014 on 281 bottom‐mounted recorders, totaling 35,033 days, were processed using automated detection software and screened for each species' presence. A published study on NARW acoustics revealed significant changes in occurrence patterns between the periods of 2004–2010 and 2011–2014; therefore, these same time periods were examined here. All four species were present from the Southeast United States to Greenland; humpback whales were also present in the Caribbean. All species occurred throughout all regions in the winter, suggesting that baleen whales are widely distributed during these months. Each of the species showed significant changes in acoustic occurrence after 2010. Similar to NARWs, sei whales had higher acoustic occurrence in mid‐Atlantic regions after 2010. Fin, blue, and sei whales were more frequently detected in the northern latitudes of the study area after 2010. Despite this general northward shift, all four species were detected less on the Scotian Shelf area after 2010, matching documented shifts in prey availability in this region. A decade of acoustic observations have shown important distributional changes over the range of baleen whales, mirroring known climatic shifts and identifying new habitats that will require further protection from anthropogenic threats like fixed fishing gear, shipping, and noise pollution., Limited information exists on the distribution and movement patterns for most baleen whale species in the western North Atlantic Ocean. Previously, we conducted a broad‐scale passive acoustic monitoring study to analyze North Atlantic right whale seasonal distribution and shifts therein, based on identified changes in occurrence patterns starting in 2010. This study uses similar acoustic datasets and protocols to understand the seasonal distribution of humpback, sei, fin, and blue whales within the western North Atlantic, and to determine whether any of these species exhibited similar shifts in distribution patterns across time.

The National Oceanic and Atmospheric Administration (NOAA)/National Park Service (NPS) Ocean Noise Reference Station (NRS) Network is an array of currently twelve calibrated autonomous passive acoustic recorders. The first NRS was deployed in June 2014, and eleven additional stations were added to the network during the following two years. The twelve stations record data that can be used to quantify baseline levels and multi-year trends in ocean ambient sound across the continental United States, Alaska, Hawaii, and island territories within and near to the United States Exclusive Economic Zone (U.S. EEZ). The network provides multi-year, continuous observations of low-frequency underwater sound between 10 Hz and 2000 Hz to capture anthropogenic, biological, and geophysical contributions to the marine soundscape at each location. Comparisons over time and among recording sites will provide information on the presence of calling animals and the prevalence of abiotic and anthropogenic activities that contribute to each soundscape. Implementation of the NRS Network advances broad-scale passive acoustic sensing capabilities within NOAA and the NPS and is an important tool for monitoring protected areas and marine species and assessing potential environmental impacts of anthropogenic noise sources. This analysis focuses on the first year of recordings and captures the wide variability of low-frequency sound levels among and within individual NRS sites over time. Continued data collection will provide information on long-term, low-frequency sound level trends within or near the U.S. EEZ and will be used to explore the value of using soundscape analysis to inform management and mitigation strategies.

Given new distribution patterns of the endangered North Atlantic right whale (NARW; Eubalaena glacialis) population in recent years, an improved understanding of spatio-temporal movements are imperative for the conservation of this species. While so far visual data have provided most information on NARW movements, passive acoustic monitoring (PAM) was used in this study in order to better capture year-round NARW presence. This project used PAM data from 2004 to 2014 collected by 19 organizations throughout the western North Atlantic Ocean. Overall, data from 324 recorders (35,600 days) were processed and analyzed using a classification and detection system. Results highlight almost year-round habitat use of the western North Atlantic Ocean, with a decrease in detections in waters off Cape Hatteras, North Carolina in summer and fall. Data collected post 2010 showed an increased NARW presence in the mid-Atlantic region and a simultaneous decrease in the northern Gulf of Maine. In addition, NARWs were widely distributed across most regions throughout winter months. This study demonstrates that a large-scale analysis of PAM data provides significant value to understanding and tracking shifts in large whale movements over long time scales.

Brydeís whales Balaenoptera edeni are the only resident baleen whale species in the Gulf of Mexico (GoM), where they are extremely rare, have a restricted distribution, and represent a unique evolutionary lineage. The reasons for the restricted distribution and small population size are unknown, but high levels of industrial activity in the GoM may be a major factor. We evaluated the geospatial overlap of GoM Brydeís whales with 2 industries known to impact baleen whale species: commercial shipping and commercial fisheries. We further evaluated the potential for impacts by examining the first dive behavior data collected from a kinematic tag attached to a GoM Brydeís whale for 3 d. Vessel traffic and fishery effort are low in GoM Brydeís whale habitat compared to the rest of the northern GoM, but several shipping lanes transit through the habitat, and the reef fish bottom longline fishery exerts considerable effort within the habitat. The tagged whale exhibited diel diving behavior with diurnal deep dives and foraging lunges at or near the sea floor, and shallow nocturnal diving, with 88% of its nighttime spent near the surface within the draught depths of most large commercial vessels. Given the location of commercial shipping traffic in GoM Brydeís whale habitat, ship strikes may pose a threat to this population if the whales commonly spend time near the surface, especially at night. Also, if bottom or near-bottom feeding is a normal feeding strategy for these whales, there is potential for entanglement in bottom longline gear. Managing these threats may improve population recovery.

Fish chorusing is a major component of the marine acoustic environment, and much of this chorusing activity happens at night. These nocturnal sounds are commonly associated with reproductive behavior. For many co-occurring taxa, increases in vocal activity may intensify acoustic competition within a constrained signaling environment; for nocturnal species, there is a limited time window for these critical behaviors, and competition to be heard by conspecifics likely increases. Using passive acoustic recording units deployed in the nearshore waters off Georgia and eastern Florida, we evaluated the nocturnal acoustic habits of fishes and examined how the sounds from nocturnal fish chorusing contribute to the overall soundscape. We examined long-term spectrograms for spatial and temporal patterns of fish calling. Black drum [Pogonias cromis (Linnaeus, 1766)] and toadfish (Opsanus sp.) dominated the nocturnal acoustic scene, but calls of other identified [e.g., red drum, Sciaenops ocellatus (Linnaeus, 1766); silver perch, Bairdiella chrysoura (Lacepede, 1802)] and unidentified calling species also occur. We examined the acoustic indices of entropy, acoustic diversity, and acoustic complexity to compare nocturnal and diurnal fish calling activity across the region. When sustained fish chorusing activity increases, entropy and acoustic diversity decrease, but acoustic complexity increases. With the acoustic differences in composition of nocturnally- and diurnally-active species groups, there is a different nocturnal soundscape than during the day. Passive acoustic surveys represent an exciting approach to understand the nocturnal reproductive activity of coastal fishes.

The AN/SSQ-53 Directional Frequency Analysis and Recording (DIFAR) sonobuoy is an expendable device that can derive acoustic particle velocity along two orthogonal horizontal axes, along with acoustic pressure. This information enables computation of azimuths of low-frequency acoustic sources from a single compact sensor. The standard approach for estimating azimuth from these sensors is by conventional beamforming (i.e., adding weighted time series), but the resulting “cardioid” beampattern is imprecise, computationally expensive, and vulnerable to directional noise contamination for weak signals. Demonstrated here is an alternative multiplicative processing scheme that computes the “active intensity” of an acoustic signal to obtain the dominant directionality of a noise field as a function of time and frequency. This information is conveniently displayed as an “azigram,” which is analogous to a spectrogram, but uses color to indicate azimuth instead of intensity. Data from several locations demonstrate this approach, which can be computed without demultiplexing the raw signal. Azigrams have been used to help diagnose sonobuoy issues, improve detectability, and estimate bearings of low signal-to-noise ratio signals. Azigrams may also enhance the detection and potential classification of signals embedded in directional noise fields.

Pygmy sperm whales (Kogia breviceps) and dwarf sperm whales (Kogia sima) are deep diving cetaceans that commonly strand along the coast of the southeast US, but that are difficult to study visually at sea because of their elusive behavior. Conventional visual surveys are thought to significantly underestimate the presence of Kogia and they have proven difficult to approach for tracking and tagging. An approach is presented for density estimation of signals presumed to be from Kogia spp. based on passive acoustic monitoring data collected at sites in the Gulf of Mexico (GOM) from the period following the Deepwater Horizon oil spill (2010-2013). Both species of Kogia are known to inhabit the GOM, although it is not possible to acoustically separate the two based on available knowledge of their echolocation clicks. An increasing interannual density trend is suggested for animals near the primary zone of impact of the oil spill, and to the southeast of the spill. Densities were estimated based on both counting individual echolocation clicks and counting the presence of groups of animals during one-min time windows. Densities derived from acoustic monitoring at three sites are all substantially higher (4-16 animals/1000 km 2 ) than those that have been derived for Kogia from line transect visual surveys in the same region (0.5 animals/1000 km 2 ). The most likely explanation for the observed discrepancy is that the visual surveys are underestimating Kogia spp. density, due to the assumption of perfect detectability on the survey trackline. We present an alternative approach for density estimation, one that derives echolocation and behavioral parameters based on comparison of modeled and observed sound received levels at sites of varying depth.

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., Author summary Health of marine mammal populations is often considered an indicator of overall marine ecosystem health and resilience, particularly in highly-impacted regions such as the Gulf of Mexico. Marine mammal populations are difficult to monitor given the many challenges of observing animals at sea (e.g. weather, limited daylight, ocean conditions, and expense). An increasingly common approach is the use of underwater acoustic sensors capable of recording marine mammal calls at remote locations for months at a time. Acoustic sensors generate large datasets in which dolphin echolocation clicks are commonly present. Dolphins are the most diverse family of marine mammals, and distinguishing click characteristics have only been described for a small subset of species. We developed a workflow to automatically identify distinct dolphin click types within large datasets without prior knowledge of their distinguishing features. Our algorithm then recognizes these click types in novel recording data across a range of monitoring locations. Known species-specific click types emerge from the data using this approach, as well as new click types potentially associated with additional species. This technique is a key step toward determining species identification for passive acoustic monitoring of offshore populations of dolphins and other toothed whales under a big data paradigm.

Discrimination of bioacoustic signals to the species or population level is critical for using passive acoustic monitoring to study cetacean ecology. Risso's dolphins off southern California have distinctive peaks and notches in their echolocation clicks, but it was unknown whether Risso's dolphins from other geographic areas have similarly distinctive click spectra and whether populations are acoustically distinct. This study investigates using clicks for species and population identification by characterizing the spectral structure of Risso's dolphin echolocation clicks recorded over wide-ranging geographic regions including the U.S. waters of the North Atlantic Ocean, Gulf of Mexico, and North Pacific Ocean; and international waters of the Eastern Tropical Pacific. All recordings with Risso's dolphin clicks exhibited the spectral peak and notch pattern described off southern California, indicating the presence of peak banding patterns is useful for species discrimination. Geographic regions were a significant explanatory factor for variability in the frequencies of click spectral peaks, with relatively higher frequency peaks and notches found off Hawaii compared to California waters and off the southeast U.S. compared to the Gulf of Mexico. In the North Atlantic Ocean, a latitudinal cline in frequencies was evident. Potential causes of acoustic variation within and among acoustic encounters are evaluated.

DayNight 6560 1 6560 122.26 0* DrumPA 36092.1 1 36092.1 672.66 0* DeployIdx(2009,2010) 844.3 1 844.3 15.74 0.0001* SiteIdx(J,S) 56615 1 56615 1055.16 0* DayNight×DrumPA 8738.5 1 8738.5 162.86 0* DayNight×DeployIdx(2009,2010) 960.8 1 960.8 17.91 0* DayNight×SiteIdx(J,S) 250.8 1 250.8 4.67 0.0307 DrumPA×DeployIdx(2009,2010) 5269.9 1 5269.9 98.22 0* DrumPA×SiteIdx(J,S) 3904.1 1 3904.1 72.76 0* DeployIdx(2009,2010)×SiteIdx(J,S) 4096.9 1 4096.9 76.35 0* Error 403328.9 7517 53.7 Total 671732.4 7527

We successfully developed an extension of the OBIS-SEAMAP database, a global biogeographic database specializing in marine mammals, seabirds and sea turtles, to integrate passive acoustic monitoring (PAM) data with other commonly collected data types (i.e. line-transect visual sightings, animal telemetry, and photo-identification). As part of this effort, we made significant improvements in mapping and visualization tools for PAM data, including spatially and temporally interactive summary statistics, diel plots, temporal effort representation, and the unique rendering of PAM data to distinguish them from other data types. In this paper, we summarize technical challenges we overcame, report the methodologies and implementation of the integration, and conduct case studies using visual sightings and PAM data from bowhead whales and Risso's dolphins to demonstrate how the integrated database facilitates in-depth ecological assessments that form the foundation for spatially-explicit conservation efforts.

A typical wide-bandwidth passive acoustic seafloor sensor can record tens of millions of impulsive signals produced by biological, anthropogenic, and physical sources each year. Sources include echolocating toothed whales, snapping shrimp, ship propeller cavitation, echosounders, and weather. The volume and variety of detections make manual classification by human analysts unmanageable without in-depth knowledge of the overall acoustic context of each monitoring location. We describe an interactive machine learning toolkit in development for efficiently detecting and classifying short, highly variable impulsive signals in large passive acoustic datasets. Modules include a configurable event detector, an unsupervised clustering module for identifying dominant signal categories, a deep learning unit for learning and applying event classes, and a graphical user interface for viewing, correcting and evaluating detectors and classifiers. These tools are discussed and illustrated in the context of efforts to extract distinctive features of toothed whale clicks from towed array recordings collected by NOAA SEFSC and to use them to classify detections in unlabeled seafloor sensor recordings. The goal of the toolkit is to facilitate classification of individual signals to species in very large PAM datasets across sensor types and monitoring locations, and to improve quantitative assessment of these sources.A typical wide-bandwidth passive acoustic seafloor sensor can record tens of millions of impulsive signals produced by biological, anthropogenic, and physical sources each year. Sources include echolocating toothed whales, snapping shrimp, ship propeller cavitation, echosounders, and weather. The volume and variety of detections make manual classification by human analysts unmanageable without in-depth knowledge of the overall acoustic context of each monitoring location. We describe an interactive machine learning toolkit in development for efficiently detecting and classifying short, highly variable impulsive signals in large passive acoustic datasets. Modules include a configurable event detector, an unsupervised clustering module for identifying dominant signal categories, a deep learning unit for learning and applying event classes, and a graphical user interface for viewing, correcting and evaluating detectors and classifiers. These tools are discussed and illustrated in the context of efforts to ex...

Delphinid echolocation clicks are often recorded in large, high-quality datasets collected by moored autonomous passive acoustic sensors, but the particular species present during these acoustic encounters is generally unknown. We combined two acoustic data types with visual observations to obtain species identity for some of these clicks. We assigned species identity to acoustic encounters in eight years of recordings at five western North Atlantic mooring sites using sighting data from contemporaneous shipboard and aerial visual surveys. We also assigned species identity to acoustic encounters in towed acoustic array data collected concurrent with visual surveys during four ship-based cetacean surveys in the western North Atlantic and the Gulf of Mexico. 114 mooring encounters with seven species and 181 towed array encounters with nine species were labeled this way. We identified recurring click types during all encounters using an unsupervised clustering algorithm to groups clicks based on pairwise spectral distances. Labeled click types thus identified in the moored data were compared to labeled click types identified in the towed array data to determine their consistency and utility for delphinid species discrimination in moored passive acoustic data. Results for bottlenose dolphin, Atlantic spotted dolphin, Risso’s dolphin, and pilot whales will be presented.

Recent expansion in the capabilities of passive acoustic monitoring of sound-producing animals is providing expansive data sets in many locations. These long-termdata sets will allowthe investigation of questions related to the ecology of sound-producing animals on time scales ranging fromdiel and seasonal to inter-annual and decadal. Analyses of these data often span multiple analysts from various research groups over several years of effort and, as a consequence, have begun to generate large amounts of scattered acoustic metadata. It has therefore become imperative to standardize the types of metadata being generated. A critical aspect of being able to learn from such large and varied acoustic data sets is providing consistent and transparent access that can enable the integration of various analysis efforts. This is juxtaposed with the need to include new information for specific research questions that evolve over time. Hence, a method is proposed for organizing acoustic metadata that addresses many of the problems associated with the retention of metadata from large passive acoustic data sets. A structure was developed for organizing acoustic metadata in a consistent manner, specifying required and optional terms to describe acoustic information derived from a recording. A client-server database was created to implement this data representation as a networked data service that can be accessed from several programming languages. Support for data import froma wide variety of sources such as spreadsheets and databases is provided. The implementation was extended to access Internet-available data products, permitting access to a variety of environmental information types (e.g. sea surface temperature, sunrise/sunset, etc.) fromawide range of sources as if they were part of the data service. This metadata service is in use at several institutions and has been used to track and analyze millions of acoustic detections from marine mammals, fish, elephants, and anthropogenic sound sources. Publisher PDF

Many odontocetes produce frequency modulated tonal calls known as whistles. The ability to automatically determine time × frequency tracks corresponding to these vocalizations has numerous applications including species description, identification, and density estimation. This work develops and compares two algorithms on a common corpus of nearly one hour of data collected in the Southern California Bight and at Palmyra Atoll. The corpus contains over 3000 whistles from bottlenose dolphins, long- and short-beaked common dolphins, spinner dolphins, and melon-headed whales that have been annotated by a human, and released to the Moby Sound archive. Both algorithms use a common signal processing front end to determine time × frequency peaks from a spectrogram. In the first method, a particle filter performs Bayesian filtering, estimating the contour from the noisy spectral peaks. The second method uses an adaptive polynomial prediction to connect peaks into a graph, merging graphs when they cross. Whistle contours are extracted from graphs using information from both sides of crossings. The particle filter was able to retrieve 71.5% (recall) of the human annotated tonals with 60.8% of the detections being valid (precision). The graph algorithm's recall rate was 80.0% with a precision of 76.9%.

Habitat characterization allows prediction of dolphin distributions in response to oceanographic processes and can be used to understand and predict effects of anthropogenic disturbances. Many habitat models focus on contemporary dolphin occurrence and environmental predictor data, but time-lagged oceanographic data may increase a model's predictive power due to ecological successional processes. Using hourly occurrence of Risso's dolphin Grampus griseus clicks and 2 types of Pacific white-sided dolphin Lagenorhynchus obliquidens clicks in autonomous passive acoustic recordings, we investigate the importance of time-lagged predictor variables with general- ized additive models. These models relate dolphin acoustic activity from recordings at 6 sites in the Southern California Bight between August 2005 and December 2007 to oceanographic variables including sea surface temperature (SST), SST coefficient of variation (CV), sea surface chlorophyll concentration (chl), chl CV, upwelling indices, and solar and lunar temporal indices. The most consis- tently selected variables among the trial models evaluated during cross-validation were SST (100% of models) and SST CV (80%) for Risso's dolphin clicks; solar indices (100%) and SST and SST CV (60% each) for Pacific white-sided type A (PWS A) clicks; and SST CV (100%), solar indices (100%) and SST (80%) for Pacific white-sided type B (PWS B) clicks. Best predictive models for Risso's dolphins and PWS A clicks included time-lagged variables, suggesting the importance of ecological succession between abiotic variables and dolphin occurrence, while best models of PWS B clicks were for current conditions, suggesting association with prey-aggregating features such as fronts and eddies.

This study presents a system for classifying echolocation clicks of six species of odontocetes in the Southern California Bight: Visually confirmed bottlenose dolphins, short- and long-beaked common dolphins, Pacific white-sided dolphins, Risso’s dolphins, and presumed Cuvier’s beaked whales. Echolocation clicks are represented by cepstral feature vectors that are classified by Gaussian mixture models. A randomized cross-validation experiment is designed to provide conditions similar to those found in a field-deployed system. To prevent matched conditions from inappropriately lowering the error rate, echolocation clicks associated with a single sighting are never split across the training and test data. Sightings are randomly permuted before assignment to folds in the experiment. This allows different combinations of the training and test data to be used while keeping data from each sighting entirely in the training or test set. The system achieves a mean error rate of 22% across 100 randomized three-fold cross-validation experiments. Four of the six species had mean error rates lower than the overall mean, with the presumed Cuvier’s beaked whale clicks showing the best performance (

A comparison of temporal and geographical trends in different echolocation click types produced by Pacific white-sided dolphin Lagenorhynchus obliquidens can lead to insights into the significance of their usage by the dolphins. Using autonomous seafloor recording packages, the spa- tial, diel and seasonal patterns of Pacific white-sided dolphin echolocation click activity are described for 6 locations in the Southern California Bight. Click bouts of the 2 types of Pacific white-sided dol- phin echolocation clicks are identified based on their unique spectral characteristics in long-term spectral averages. Type A clicks were detected on 317 of 1959 recording days and were heard at all 6 sites, with the majority of detections occurring at San Clemente Island and Point Conception. Type B clicks were detected on 130 recording days and were only heard at the 2 southern inshore sites. Significant diel patterns were evident for both click types: Type A click bouts were detected during more hours and with higher click rates at night than during the day, while Type B click bouts exhib- ited the opposite behavior, with greater activity during the day. At the southern sites, both click types exhibited a fall-winter peak in seasonal occurrence. At Point Conception, where only Type A was detected, peak occurrence was during spring. The described spatial and seasonal patterns support the hypothesis that click types are population-specific, while diel patterns suggest differences in prey preferences.

Geographical and temporal trends in echolocation clicking activity can lead to insights into the foraging and migratory behaviors of pelagic dolphins. Using autonomous acoustic recording packages, the geographical, diel, and seasonal patterns of Risso's dolphin (Grampus griseus) echolocation click activity are described for six locations in the Southern California Bight between 2005 and 2007. Risso's dolphin echolocation click bouts are identified based on their unique spectral characteristics. Click bouts were identified on 739 of 1959 recording days at all 6 sites, with the majority occurring at nearshore sites. A significant diel pattern is evident in which both hourly occurrences of click bouts and click rates are higher at night than during the day. At all nearshore sites, Risso's dolphin clicks were identified year-round, with the highest daily occurrence at the southern end of Santa Catalina Island. Seasonal and interannual variabilities in occurrence were high across sites with peak occurrence in autumn of most years at most sites. These results suggest that Risso's dolphins forage at night and that the southern end of Santa Catalina Island represents an important habitat for Risso's dolphins throughout the year.

The spectral and temporal properties of echolocation clicks and the use of clicks for species classification are investigated for five species of free-ranging dolphins found offshore of southern California: short-beaked common (Delphinus delphis), long-beaked common (D. capensis), Risso's (Grampus griseus), Pacific white-sided (Lagenorhynchus obliquidens), and bottlenose (Tursiops truncatus) dolphins. Spectral properties are compared among the five species and unique spectral peak and notch patterns are described for two species. The spectral peak mean values from Pacific white-sided dolphin clicks are 22.2, 26.6, 33.7, and 37.3 kHz and from Risso's dolphins are 22.4, 25.5, 30.5, and 38.8 kHz. The spectral notch mean values from Pacific white-sided dolphin clicks are 19.0, 24.5, and 29.7 kHz and from Risso's dolphins are 19.6, 27.7, and 35.9 kHz. Analysis of variance analyses indicate that spectral peaks and notches within the frequency band 24-35 kHz are distinct between the two species and exhibit low variation within each species. Post hoc tests divide Pacific white-sided dolphin recordings into two distinct subsets containing different click types, which are hypothesized to represent the different populations that occur within the region. Bottlenose and common dolphin clicks do not show consistent patterns of spectral peaks or notches within the frequency band examined (1-100 kHz).

A method for the automatic classification of free-ranging delphinid vocalizations is presented. The vocalizations of short-beaked and long-beaked common (Delphinus delphis and Delphinus capensis), Pacific white-sided (Lagenorhynchus obliquidens), and bottlenose (Tursiops truncatus) dolphins were recorded in a pelagic environment of the Southern California Bight and the Gulf of California over a period of 4 years. Cepstral feature vectors are extracted from call data which contain simultaneous overlapping whistles, burst-pulses, and clicks from a single species. These features are grouped into multisecond segments. A portion of the data is used to train Gaussian mixture models of varying orders for each species. The remaining call data are used to test the performance of the models. Species are predicted based upon probabilistic measures of model similarity with test segment groups having durations between 1 and 25 s. For this data set, 256 mixture Gaussian mixture models and segments of at least 10 s of call data resulted in the best classification results. The classifier predicts the species of groups with 67%-75% accuracy depending upon the partitioning of the training and test data.

In the late 1990s, George E. Ioup began studying echolocation clicks as a means of understanding marine mammals in the Gulf of Mexico (GOM). He also led one of the few research programs focused on pelagic species in this chronically impacted region in the years preceding the Deepwater Horizon oil spill. Today, passive acoustic monitoring (PAM) is one of the primary tools used to study the nearly 20 pelagic odontocete species found in the GOM, including sperm whales, beaked whales, dolphins, and Kogia species. Since 2010, PAM devices have been deployed nearly continuously in the region, driven by an urgent need to understand the long-term effects of both acute and chronic anthropogenic impacts on GOM marine mammal populations. Recent advances fueled by robust, reliable PAM technologies include the development of multi-year timeseries documenting changes in species densities across continental shelf and slope habitats, differentiating GOM odontocete species based on echolocation click properties, and levera...

The ability to classify odontocetes to species and population from acoustic recordings leads to improvements in stock identification, abundance and density estimation, and habitat-based density modeling, which are crucial for conservation and management. Risso's dolphins off Southern California have distinctive peaks and valleys in their echolocation clicks, which allow researchers to easily distinguish them from other species in passive acoustic recordings. However, Risso's dolphin echolocation clicks from other geographic areas have not been described and it remains unknown whether they have similarly distinctive click spectra and whether stocks are acoustically distinct. We investigate the potential for using acoustics to identify populations by quantifying the acoustic structure of Risso's dolphin echolocation clicks recorded over wide-ranging geographic regions including the U.S. waters of the North Atlantic Ocean (north and south of Cape Hatteras), Gulf of Mexico, and North Pacific Ocean (Eastern Tropical Pacific and Southern California). Several distinctive peak and valley patterns are found and we evaluate these in light of variability within individuals, groups, and regions as these acoustic differences may indicate differences in age, sex, or stock composition, foraging behavior, or ambient noise environment of the dolphin schools.

Passive acoustic monitoring of blue whales (Balaenoptera musculus) has been used to gain insight into their presence and behavior. Many of their calls have been shown to be detectable through spectrogram correlation due to the low variation in these stereotyped calls. In this work, we describe rule based classifiers for tonal B and D calls using the pitch/frequency contour information obtained from a contour extractor. B calls can be detected by spectrogram correlation, but the D calls are highly variable and are therefore difficult to detect using spectrogram kernel methods. Experiments on four hours of evaluation data from different field seasons show that 91.3% of B calls and 85.8% of D calls were correctly retrieved. For both types of calls, less than 2% of the retrieved calls were false positives.

The sea is home to a myriad of marine animal species, many of which use sound as a primary means of communication, navigation and foraging. Of particular interest are the Blue whales (Balaenoptera musculus) of the cetacean family. Massive commercial whaling prior to 1960 brought the species close to extinction and its population still remains very low. Passive acoustic monitoring of baleen whales has recently been used to provide long-term information about their presence and behavior, and provides an attractive complement to traditional visual based monitoring. In this work we present a frequency domain based algorithm developed for extracting the frequency contours of the dominant harmonic in tonal calls of blue whales (B and D calls). The algorithm uses a two pass approach to contour extraction. In the first pass, partial candidate contours are formed, followed by a second pass which uses the partial information to construct complete contours. When evaluated on a one hour labeled recording, the algorithm had 90% recall and 76% precision.

This study uses remote imaging technology to quantify, compare, and contrast the cephalic anatomy between a neonate female and a young adult male Cuvier’s beaked whale. Primary results reveal details of anatomic geometry with implications for acoustic function and diving. Specifically, we describe the juxtaposition of the large pterygoid sinuses, a fibrous venous plexus, and a lipid-rich pathway that connects the acoustic environment to the bony ear complex. We surmise that the large pterygoid air sinuses are essential adaptations for maintaining acoustic isolation and auditory acuity of the ears at depth. In the adult male, an acoustic waveguide lined with pachyosteosclerotic bones is apparently part of a novel transmission pathway for outgoing biosonar signals. Substitution of dense tissue boundaries where we normally find air sacs in delphinoids appears to be a recurring theme in deep-diving beaked whales and sperm whales. The anatomic configuration of the adult male Ziphius forehead resembles an upside-down sperm whale nose and may be its functional equivalent, but the homologous relationships between forehead structures are equivocal. Anat Rec, 291:353–378, 2008. 2008 Wiley-Liss, Inc.

Tissue physical properties from a Cuvier's beaked whale (Ziphius cavirostris) neonate head are reported and compared with computed tomography (CT) X-ray imaging. Physical properties measured include longitudinal sound velocity, density, elastic modulus and hysteresis. Tissues were classified by type as follows: mandibular acoustic fat, mandibular blubber, forehead acoustic fat (melon), forehead blubber, muscle and connective tissue. Results show that each class of tissues has unique, co-varying physical properties. The mandibular acoustic fats had minimal values for sound speed (1350+/-10.6 m s(-1)) and mass density (890+/-23 kg m(-3)). These values increased through mandibular blubber (1376+/-13 m s(-1), 919+/-13 kg m(-3)), melon (1382+/-23 m s(-1), 937+/-17 kg m(-3)), forehead blubber (1401+/-7.8 m s(-1), 935+/-25 kg m(-3)) and muscle (1517+/-46.8 m s(-1), 993+/-58 kg m(-3)). Connective tissue had the greatest mean sound speed and density (1628+/-48.7 m s(-1), 1087+/-41 kg m(-3)). The melon formed a low-density, low-sound-speed core, supporting its function as a sound focusing organ. Hounsfield unit (HU) values from CT X-ray imaging are correlated with density and sound speed values, allowing HU values to be used to predict these physical properties. Blubber and connective tissues have a higher elastic modulus than acoustic fats and melon, suggesting more collagen structure in blubber and connective tissues. Blubber tissue elastic modulus is nonlinear with varying stress, becoming more incompressible as stress is increased. These data provide important physical properties required to construct models of the sound generation and reception mechanisms in Ziphius cavirostris heads, as well as models of their interaction with anthropogenic sound.

The detection of individual clicks produced by odontocetes is useful for many bioacoustic tasks such as the analysis of interclick interval, classification of odontocete species, etc. Automation of this task is required for the large data sets gathered by remote instruments that can collect terabytes of information over short time periods. A method is presented that locates individual clicks using the Teager energy operator, which is a time‐domain metric designed to measure fast variations in signal energy. The Teager energy of a high‐pass filtered signal is analyzed by a rule‐based classifier that determines the location of individual clicks. The click start can be determined with very high accuracy. Click ends are reasonably well characterized, but the ends of clicks are difficult to determine, even by human experts, due to the influence of external factors such as acoustic reflections. The proposed method is compared with existing techniques and is demonstrated on the calls of several species of free‐r...

A growing number of passive acoustic monitoring systems have resulted in a wealth of annotation information, or metadata, for recordings. These metadata are semi-structured. Some parameters are essentially mandatory (e.g., time of detection and what was detected) while others are highly dependent upon the question that a researcher is asking. Tethys is a metadata system for spatial-temporal acoustic data that provides structure where it is appropriate and flexibility where it is needed. Networked metadata are stored in an extended markup language (XML) database, and served to workstations over a network. The ability to export summary data to OBIS-SEAMAP is in development. The second purpose of Tethys is to serve as a scientific workbench. Interfaces are provided to networked databases, permitting the import of data from a wide variety of sources, such as lunar illumination or sea ice coverage. Interfaces currently exist for Matlab, Java, and Python. Writing data driven queries using a single interface ena...

Shallow water environments, such as the North Atlantic right whale calving grounds, pose a challenge to cetacean passive acoustic monitoring due to high variability in ambient noise and environmental conditions. In this region of high shipping traffic and increased ship-strike risk, passive acoustic monitoring may reduce right whale ship strikes. This study describes temporal variability in right whale call detections, ambient noise sources, and environmental conditions on the right whale calving grounds during 2009-2010 and 2010-2011. Right whale detections occurred between November 19 and March 11, on up to 25% of days per deployment with increased nocturnal call detections, and increased acoustic presence off Jacksonville, FL during 2010-2011. Shipping noise was most common off Jacksonville, detected in up to 74% of minutes, with a diurnal peak, while tidally-associated broadband impulses were detected in up to 43% of minutes off Savannah GA. Environmental conditions including SST, wind, waves, and tidal height varied on daily and semi-diurnal scales. While sightings were higher in 2009-2010, the fewer sightings in 2010-2011 were more narrowly distributed within the depth range of the acoustic instruments. Passive acoustic monitoring is effective for detecting right whales in this environment, especially at night when they cannot be seen.

High-Frequency Acoustic Recording Packages (HARPs) continuously monitored delphinids at five sites in the northeastern Gulf of Mexico during and after the Deepwater Horizon oil spill. Surface oil reached two sites, while the three unexposed sites functioned as “controls.” Presence of dolphin vocalizations (clicks, whistles, and burst pulses) was documented at exposed and unexposed sites over the course of a year following the oil spill. These sites are within the known habitat ranges of 11 species of delphinids. Broadband towed array recordings with visual identifications were used to determine species-specific vocalization characteristics, which were then compared with autonomously recorded vocalizations. Two species have distinctive vocalizations that match between towed array and autonomous recordings. At least four more unique vocalization patterns were detected autonomously, which may be species-specific. Both clicks and whistles were explored for identifying features. The data provide a comparative ...

The automated characterization of delphinid whistle contours can lead to insights (both potential and realized) into biological questions such as habitat use and behavior. Prior to the 1990s, most measurements of whistle contours were conducted manually by trained analysts, and even today the noisy signal environment offers significant challenges to fully automated classification systems. In this talk, we provide an overview of challenges and historical approaches to this problem. We present our recent research using graph search algorithms to characterize complex auditory scenes involving numerous animals vocalizing simultaneously. We end by discussing techniques that have the potential to further advance this area of research. [Work sponsored by ONR.]

A graph search algorithm is presented to extract tonal contours from audio signals in a fully automated manner. The algorithm is capable of tracking multiple contours simultaneously. Search is done in the spectral domain, with contours being treated as paths through a phase‐magnitude space. A graph search formalism is combined with dynamic programming techniques to track candidate paths that may overlap or cross. Disambiguation of path crossings is accomplished by examining a combination of slope and the derivative of phase. The algorithm is general in nature and applicable to tonal calls from many animals with little modification. We target this work specifically toward delphinid whistles and demonstrate its efficacy on calls from bottlenose (Tursiops truncatus), short‐ and long‐beaked common (Delphinus delphis and D. capensis), and Risso’s (Grampus griseus) dolphins recorded in the Southern California Bight. Characterizing the complete contour will permit the development of call comparison techniques th...

Visual surveys along the U.S. west coast conducted in the 1980s‐90s suggested that Pacific white‐sided and Risso's dolphins likely undergo annual movement between California during winter‐spring, and Oregon‐Washington in the summer‐fall. Using high‐frequency autonomous acoustic recordings within the southern California, central California, and Washington regions of the California Current System, we evaluated the seasonal occurrence of these dolphin species through detection of their echolocation clicks. The clicks of Pacific white‐sided and Risso's dolphins are characterized by a unique combination of local frequency peaks for each species, providing a statistically robust means of identification from autonomous acoustic records. Based on these unique spectral characters, we extracted periods of clicking by each species from concurrently sampled coastal and shelf/slope locations off southern California and Washington, and a single offshore site off central California. The number of hours that each species...

Spectral characteristics of clicks are described for five species of delphinids in the Southern California Bight (long‐beaked common dolphins, Delphinus capensis, short‐beaked common dolphins, Delphinus delphis, Risso's dolphins, Grampus griseus, Pacific white‐sided dolphins, Lagenorhynchus obliquidens, and bottlenose dolphins, Tursiops truncatus). Recent technological advancements allow long‐term, broadband (100 kHz bandwidth), passive acoustic monitoring from autonomous sea‐floor mounted instruments. The ability to record higher frequencies allows study of a broader range of odontocete vocalizations including echolocation clicks. To determine whether delphinid species could be identified by their clicks, concurrent ship‐based visual and acoustic surveys were conducted. We find that clicks from Pacific white‐sided and Risso's dolphins contain spectral peaks and notches that are unique and consistent for each species. These spectral patterns are also apparent in long‐term autonomous acoustic recordings th...

A system is presented for the near‐real‐time classification of calls from captive Alaskan (Canis lupus occidentalis) and Mexican (Canis lupus baileyi) wolves as well as other ambient sounds such as bird calls and anthropomorphic noise. Signals are detected on a Viper network node at The California Wolf Center using a signal‐to‐noise ratio based call activity detector. These calls are transmitted over a regional high‐speed wireless network (HPWREN) to a remote processing facility located 50 miles from the collection site. Cepstral feature vectors are extracted from candidate calls at the remote facility and are classified using a hidden Markov model pattern recognition system trained with manually labeled data representing the wolf ethogram and other common noises in the soundscape. In an offline evaluation with known segmented data, the system achieves an 84% accuracy. [This work is supported by HPWREN, NSF Grant No. 0426879.]

Recent advances in the technology for long‐term underwater acoustic recording provide new data on the temporal patterns of marine mammal sounds. Autonomous acoustic recordings are now being made with broad frequency bandwidth up to 200‐kHz sampling rates. These data allow sound recording from most marine mammal species, including, for instance, the echolocation clicks of odontocetes. Large data storage capacity up to 1280 Gbytes allow these recordings to be conducted over long time periods for study of diel and seasonal calling patterns. Examples will be presented of temporal patterns from long‐term recordings collected in four regions: the Bering Sea, offshore southern California, the Gulf of California, and the Southern Ocean. These data provide new insight on marine mammal distribution, seasonality, and behavior.

The SCORE site, located near San Clemente Island, is known to be abundant in marine mammals, and is the site of frequent naval operations, making it an ideal site to study what impact man‐made activities may have upon these creatures. In August 2004, an experiment was performed to collect both visual and acoustic observations of odontocetes at the SCORE site. Odontocete vocalizations consist of clicks and whistles. The clicks are very wideband (often 100 kHz), so times of arrival can be measured with extremely high resolution, which makes clicks attractive waveforms for multipath ranging and depth estimation. Although the whistles are narrowband, their fundamental frequency often ranges over tens of kilohertz, so these too can be processed as if they were wideband. We will present results of processing excerpts from several weeks of data recorded on a high‐frequency horizontal line array deployed from the FLIP research vessel. Bearing tracks of individuals and groups from a number of different species will be correlated and compared with visual observations. In addition, we will discuss the possibilities for range and depth estimates derived from the multipath arrivals observed in the clicks and whistles.

In this work, a method for the automatic classification of odontocete species from their vocalizations is presented. We consider the vocalizations of three dolphin species which were recorded in a pelagic environment of the southern California Bight over a period of approximately three years. 57B naval sonobuoys sampling at 48 kHz were used to record the calls of common (Delphinus delphis), Risso’s (Grampus griseus) and Pacific white‐sided (Lagenorhynchus obliquidens) dolphins. In this study, approximately 3 h of calls were selected, with a disproportionate number of common dolphin calls appearing. An emphasis has been made on classifying calls across recording sessions, thus illustrating the system’s ability to perform classification in different acoustic environments with a high likelihood of different individuals performing the vocalizations. Cepstral feature vectors are extracted from manually endpointed call data and classified using a Gaussian mixture model system. The performance of the automatic c...