Kinematics and energetics of foraging behavior in Rice's whales of the Gulf of Mexico.

Rorqual foraging behavior varies with species, prey type and foraging conditions, and can be a determining factor for their fitness. Little is known about the foraging ecology of Rice's whales (Balaenoptera ricei), an endangered species with a population of fewer than 100 individuals. Suction cup tags were attached to two Rice's whales to collect information on their diving kinematics and foraging behavior. The tagged whales primarily exhibited lunge-feeding near the sea bottom and to a lesser extent in the water-column and at the sea surface. During 6-10 min foraging dives, the whales typically circled their prey before executing one or two feeding lunges. Longer duration dives and dives with more feeding-lunges were followed by an increase in their breathing rate. The median lunge rate of one lunge per dive of both animals was much lower than expected based on comparative research on other lunge-feeding baleen whales, and may be associated with foraging on fish instead of krill or may be an indication of different foraging conditions. Both animals spent extended periods of the night near the sea surface, increasing the risk for ship strike. Furthermore, their circling before lunging may increase the risk for entanglement in bottom-longline fishing gear. Overall, these data show that Rice's whale foraging behavior differs from other lunge feeding rorqual species and may be a significant factor in shaping our understanding of their foraging ecology. Efforts to mitigate threats to Rice's whales will benefit from improved understanding of patterns in their habitat use and fine-scale ecology.

Critically endangered Rice's whales (Balaenoptera ricei) selectively feed on high-quality prey in the Gulf of Mexico.

Determining the drivers of prey selection in marine predators is critical when investigating ecosystem structure and function. The newly recognized Rice's whale (Balaenoptera ricei) is one of the most critically endangered large whales in the world and endemic to the industrialized Gulf of Mexico. Here, we investigated the drivers of resource selection by Rice's whales in relation to prey availability and energy density. Bayesian stable isotope (δ13C, δ15N) mixing models suggest that Rice's whales feed primarily on a schooling fish, Ariomma bondi (66.8% relative contribution). Prey selection using the Chesson's index revealed that active prey selection was found to be positive for three out of the four potential prey identified in the mixing model. A low degree of overlap between prey availability and diet inferred from the mixing model (Pianka Index: 0.333) suggests that prey abundance is not the primary driver of prey selection. Energy density data suggest that prey selection may be primarily driven by the energy content. Results from this study indicate that Rice's whales are selective predators consuming schooling prey with the highest energy content. Environmental changes in the region have the potential to influence prey species that would make them less available to Rice's whales.

A New Acoustical Autonomous Method for Identifying Endangered Whale Calls: A Case Study of Blue Whale and Fin Whale.

In this paper, we study to improve acoustical methods to identify endangered whale calls with emphasis on the blue whale (Balaenoptera musculus) and fin whale (Balaenoptera physalus). A promising method using wavelet scattering transform and deep learning is proposed here to detect/classify the whale calls quite precisely in the increasingly noisy ocean with a small dataset. The performances shown in terms of classification accuracy (>97%) demonstrate the efficiency of the proposed method which outperforms the relevant state-of-the-art methods. In this way, passive acoustic technology can be enhanced to monitor endangered whale calls. Efficient tracking of their numbers, migration paths and habitat become vital to whale conservation by lowering the number of preventable injuries and deaths while making progress in their recovery.

An unknown nocturnal call type in the Mariana Archipelago.

In spring/summer of 2018 and 2021, the Pacific Islands Fisheries Science Center Cetacean Research Program deployed drifting acoustic recorders in the U.S. Exclusive Economic Zones surrounding the Mariana Archipelago. Manual assessments revealed a low-frequency (median 473-554 Hz), short-duration (median 0.596 s), stereotypic tonal nocturnal call throughout the Mariana Archipelago. Based on time of year, spatiotemporal patterns, clear division among calls (i.e., no chorusing), comparisons with known vocalizations of whales, turtles, and fish, and presence of Bryde's whale calls, and because the call has not been detected elsewhere, we hypothesize this 500-Hz pulsed call is produced by Bryde's whales (Balaenoptera brydei).

Occurrence of emerging brominated flame retardants and organophosphate esters in marine wildlife from the Norwegian Arctic.

To understand the exposure and potential sources of emerging brominated flame retardants (EBFR) and organophosphate esters (OPEs) in marine wildlife from the Norwegian Arctic, we investigated concentrations of EBFRs in 157 tissue samples from nine species of marine vertebrates and OPEs in 34 samples from three whale species. The samples, collected from a wide range of species with contrasting areal use and diets, included blubber of blue whales, fin whales, humpback whales, white whales, killer whales, walruses and ringed seals and adipose tissue and plasma from polar bears, as well as adipose tissue from glaucous gulls. Tris(2-ethylhexyl) phosphate (TEHP) and tris(2-chloroisopropyl) phosphate (TCIPP) ranged from <0.61 to 164 and < 0.8-41 ng/g lipid weight, respectively, in blue whales and fin whales. All other EBRFs and OPEs were below the detection limit or detected only at low concentration. In addition to the baseline information on the occurrence of EBFRs and OPEs in marine wildlife from the Arctic, we provide an in-depth discussion regarding potential sources of the detected compounds. This information is important for future monitoring and management of EBFRs and OPEs.

Oceanic giants dance to atmospheric rhythms: Ephemeral wind-driven resource tracking by blue whales.

Trophic transfer of energy through marine food webs is strongly influenced by prey aggregation and its exploitation by predators. Rapid aggregation of some marine fish and crustacean forage species during wind-driven coastal upwelling has recently been discovered, motivating the hypothesis that predators of these forage species track the upwelling circulation in which prey aggregation occurs. We examine this hypothesis in the central California Current Ecosystem using integrative observations of upwelling dynamics, forage species' aggregation, and blue whale movement. Directional origins of blue whale calls repeatedly tracked upwelling plume circulation when wind-driven upwelling intensified and aggregation of forage species was heightened. Our findings illustrate a resource tracking strategy by which blue whales may maximize energy gain amid ephemeral foraging opportunities. These findings have implications for the ecology and conservation of diverse predators that are sustained by forage populations whose behaviour is responsive to episodic environmental dynamics.

Blue whales increase feeding rates at fine-scale ocean features.

Marine predators face the challenge of reliably finding prey that is patchily distributed in space and time. Predators make movement decisions at multiple spatial and temporal scales, yet we have a limited understanding of how habitat selection at multiple scales translates into foraging performance. In the ocean, there is mounting evidence that submesoscale (i.e. less than 100 km) processes drive the formation of dense prey patches that should hypothetically provide feeding hot spots and increase predator foraging success. Here, we integrated environmental remote-sensing with high-resolution animal-borne biologging data to evaluate submesoscale surface current features in relation to the habitat selection and foraging performance of blue whales in the California Current System. Our study revealed a consistent functional relationship in which blue whales disproportionately foraged within dynamic aggregative submesoscale features at both the regional and feeding site scales across seasons, regions and years. Moreover, we found that blue whale feeding rates increased in areas with stronger aggregative features, suggesting that these features indicate areas of higher prey density. The use of fine-scale, dynamic features by foraging blue whales underscores the need to take these features into account when designating critical habitat and may help inform strategies to mitigate the impacts of human activities for the species.

Abundance and distribution patterns of cetaceans and their overlap with vessel traffic in the Humboldt Current Ecosystem, Chile.

The Humboldt Current Ecosystem (HCE) is one of the most productive marine ecosystems, sustaining one of the largest fishing industries in the world. Although several species of cetaceans are known to inhabit these productive waters, quantitative assessments of their abundance and distribution patterns are scarce and patchy. Here, we present the first abundance and distribution estimates for fin whale (Balaenoptera physalus), southeast Pacific blue whales (Balaenoptera musculus), sperm whale (Physeter macrocephalus), dusky dolphin (Lagenorhynchus obscurus), and common dolphin (Delphinus spp.) in the entire Chilean portion of the HCE. Line transect surveys were conducted during 2016-2021 between 18° S and 41° S and up to ~ 200 km offshore, and data were analyzed using distance sampling methods. Group counts were modelled as a function of environmental variables using single step Bayesian Binomial N-mixture model (BNMM), which allows full uncertainty propagation between model components. By using spatially explicit predictions of cetacean densities and observed vessel densities in the HCE, we provide quantitative assessments on the relative probability of cetaceans encountering vessels (RPCEV). Dusky dolphin and fin whale showed the largest distribution overlap with industrial and artisanal fishery fleets. Our results highlight areas where effort should be prioritized to address the extant but unquantified negative interactions between vessels and cetaceans in Chilean HCE.

Mathematical models of long term evolution of blue whale song types' frequencies.

The linear decrease in the frequency of blue whale songs around the world is, to date, an unexplained phenomenon. We show it can be reproduced by a mathematical model considering two antagonistic behavioral trends: first, a bias towards conformity in the song, and second, a tendency to try and sing lower than the other whales. We check the robustness of our model by considering some more complex premises. First, different hierarchical relations between the singers are explored, adapting methods used in the flocking motion studies. Then a population-dependant simulation shows that even considering the gradual addition of new whales, the evolution is still globally linear. Finally, we show that intra-annual variations surging from different causes can be naturally incorporated into the model. We then conclude that, unlike other explanations, a cultural hypothesis seems compatible with the observed linearity of the blue whales's songs frequency shift.

An accelerometer-derived ballistocardiogram method for detecting heart rate in free-ranging marine mammals.

Physio-logging methods, which use animal-borne devices to record physiological variables, are entering a new era driven by advances in sensor development. However, existing datasets collected with traditional bio-loggers, such as accelerometers, still contain untapped eco-physiological information. Here, we present a computational method for extracting heart rate from high-resolution accelerometer data using a ballistocardiogram. We validated our method with simultaneous accelerometer-electrocardiogram tag deployments in a controlled setting on a killer whale (Orcinus orca) and demonstrate the predictions correspond with previously observed cardiovascular patterns in a blue whale (Balaenoptera musculus), including the magnitude of apneic bradycardia and increase in heart rate prior to and during ascent. Our ballistocardiogram method may be applied to mine heart rates from previously collected accelerometery data and expand our understanding of comparative cardiovascular physiology.

Unreported catches, impact of whaling and current status of blue whales in the South European Atlantic Shelf.

The North Atlantic blue whale was depleted by modern whaling and it is still considered to be highly endangered. Despite its protection in 1954, catches continued in the South European Atlantic Shelf (SEAS) region and immediately adjacent waters until 1979. We compiled catches and investigate abundance trends in the region using original data from whaling (1921-1985) and scientific surveys around the last years of exploitation (1981-1987). The struck and lost rate was estimated at 3.2% for sperm whales and 2.3% for baleen whales. The compiled records include 60 catches, with an additional 1-2 blue whales likely struck and lost. From these, 29 individuals had been correctly reported as blue whales but 31 were mislabelled as fin whales. After correcting for loss rates, the number of blue whales killed in the region was estimated at 61 in 55 years (1.12 individuals/year). The data from the 1950s shows some oversized fin whales but it is unclear whether they are due to an incorrect reporting of species or to incorrect measurements, so it cannot be discarded that the actual number of blue whales caught was slightly higher than estimated. Mean body length of reported blue whales was lower than in higher latitudes of the North Atlantic, probably reflecting geographical stratification with higher proportion of immatures in the SEAS. The ratio between catches or sightings of blue whales and those of fin whales was 5.9% in the southern part of the SEAS previous to exploitation, it declined to 0.02-0.18% in the 1920s, and increased thereafter up to 1.6% in the 1980-1990s. Taking as reference the population size of fin whales in the SEAS, that of blue whales at the end of the 1980s can be guessed to be at ca337-497 individuals. Considering accepted population estimates in other areas as well as the observed rates of increase, current abundance is thought to be over a thousand whales in the SEAs and at in the order of 4000-5000 individuals for the whole eastern North Atlantic basin.

Update on frequency decline of Northeast Pacific blue whale (Balaenoptera musculus) calls.

Worldwide, the frequency (pitch) of blue whale (Balaenoptera musculus) calls has been decreasing since first recorded in the 1960s. This frequency decline occurs over annual and inter-annual timescales and has recently been documented in other baleen whale species, yet it remains unexplained. In the Northeast Pacific, blue whales produce two calls, or units, that, when regularly repeated, are referred to as song: A and B calls. In this population, frequency decline has thus far only been examined in B calls. In this work, passive acoustic data collected in the Southern California Bight from 2006 to 2019 were examined to determine if A calls are also declining in frequency and whether the call pulse rate was similarly impacted. Additionally, frequency measurements were made for B calls to determine whether the rate of frequency decline is the same as was calculated when this phenomenon was first reported in 2009. We found that A calls decreased at a rate of 0.32 Hz yr-1 during this period and that B calls were still decreasing, albeit at a slower rate (0.27 Hz yr-1) than reported previously. The A call pulse rate also declined over the course of the study, at a rate of 0.006 pulses/s yr-1. With this updated information, we consider the various theories that have been proposed to explain frequency decline in blue whales. We conclude that no current theory adequately accounts for all aspects of this phenomenon and consider the role that individual perception of song frequency may play. To understand the cause behind call frequency decline, future studies might want to explore the function of these songs and the mechanism for their synchronization. The ubiquitous nature of the frequency shift phenomenon may indicate a consistent level of vocal plasticity and fine auditory processing abilities across baleen whale species.

Extraction of Energy Characteristics of Blue Whale Vocalizations Based on Empirical Mode Decomposition.

This study extracts the energy characteristic distributions of the intrinsic mode functions (IMFs) and residue functions (RF) for a blue whale sound signal, with empirical mode decomposition (EMD) as the basic theoretical framework. A high-resolution marginal frequency characteristics extraction method, based on EMD with energy density intensity (EDI) parameters for blue B call vocalizations, was proposed. The extraction algorithm included six steps: EMD, energy analysis, marginal frequency (MF) analysis with EDI parameters, feature extraction (FE), classification, and Hilbert spectrum (HS) analysis. The blue whale sound sources were obtained from the website of the Scripps Whale Acoustics Lab of the University of California, San Diego, USA. The source is a type of B call with a time duration of 46.65 s, from which 59 analysis samples with a time duration of 180 ms were taken. The average energy distribution ratios of the IMF1, IMF2, IMF3, IMF4, and RF are 49.06%, 20.58%, 13.51%, 10.94% and 3.84%, respectively. New classification criteria and EDI parameters were proposed to extract the blue whale B call vocalization (BWBCV) characteristics. The analysis results show that the main frequency bands of the signal are distributed at 41-43 Hz in the MF of IMF1 for Class I BWBCV and 11-13 Hz in the MF of IMF2 for Class II BWBCV, respectively.

A Preliminary Comparison on Faecal Microbiomes of Free-Ranging Large Baleen (Balaenoptera musculus, B. physalus, B. borealis) and Toothed (Physeter macrocephalus) Whales.

Large baleen and toothed whales play crucial ecological roles in oceans; nonetheless, very little is known about their intestinal microbiomes. Based on striking differences in natural history and thus in feeding behaviours, it can be expected that intestinal microbiomes of large baleen whales and toothed whales are different. To test this hypothesis, the phylogenetic composition of faecal microbiomes was investigated by a 16S rRNA gene amplicon sequence-based approach for Bacteria and Archaea. Faecal samples from free-ranging large whales collected off the Azores Archipelago (Portugal) were used, comprising 13 individual baleen whales (one sei, two blue and ten fin whales) and four sperm whales. The phylogenetic composition of the Bacteria faecal microbiomes of baleen and toothed whales showed no significant differences at the phylum level. However, significant differences were detected at the family and genus levels. Most abundant phyla were Firmicutes, Bacteroidetes, Proteobacteria, Tenericutes and Spirochaeta. Few highly abundant bacterial genera were identified as key taxa with a high contribution to differences among baleen and toothed whales microbiomes. Only few archaeal sequences were detected, primarily Methanomassiliicoccales representing potential methanogenic Archaea. This is the first study that directly compares the faecal bacterial and archaeal microbiomes of free-ranging baleen and toothed whales which represent the two parvorders of Cetacea which members are fully aquatic large mammals which were evolutionary split millions of years ago.

A method for tracking blue whales (Balaenoptera musculus) with a widely spaced network of ocean bottom seismometers.

Passive acoustic monitoring is an important tool for studying marine mammals. Ocean bottom seismometer networks provide data sets of opportunity for studying blue whales (Balaenoptera musculus) which vocalize extensively at seismic frequencies. We describe methods to localize calls and obtain tracks using the B call of northeast Pacific blue whale recorded by a large network of widely spaced ocean bottom seismometers off the coast of the Pacific Northwest. The first harmonic of the B call at ~15 Hz is detected using spectrogram cross-correlation. The seasonality of calls, inferred from a dataset of calls identified by an analyst, is used to estimate the probability that detections are true positives as a function of the strength of the detection. Because the spacing of seismometers reaches 70 km, faint detections with a significant probability of being false positives must be considered in multi-station localizations. Calls are located by maximizing a likelihood function which considers each strong detection in turn as the earliest arrival time and seeks to fit the times of detections that follow within a feasible time and distance window. An alternative procedure seeks solutions based on the detections that maximize their sum after weighting by detection strength and proximity. Both approaches lead to many spurious solutions that can mix detections from different B calls and include false detections including misidentified A calls. Tracks that are reliable can be obtained iteratively by assigning detections to localizations that are grouped in space and time, and requiring groups of at least 20 locations. Smooth paths are fit to tracks by including constraints that minimize changes in speed and direction while fitting the locations to their uncertainties or applying the double difference relocation method. The reliability of localizations for future experiments might be improved by increasing sampling rates and detecting harmonics of the B call.

Automatic detection and classification of baleen whale social calls using convolutional neural networks.

Passive acoustic monitoring has proven to be an indispensable tool for many aspects of baleen whale research. Manual detection of whale calls on these large data sets demands extensive manual labor. Automated whale call detectors offer a more efficient approach and have been developed for many species and call types. However, calls with a large level of variability such as fin whale (Balaenoptera physalus) 40 Hz call and blue whale (B. musculus) D call have been challenging to detect automatically and hence no practical automated detector exists for these two call types. Using a modular approach consisting of faster region-based convolutional neural network followed by a convolutional neural network, we have created automated detectors for 40 Hz calls and D calls. Both detectors were tested on recordings with high- and low density of calls and, when selecting for detections with high classification scores, they were shown to have precision ranging from 54% to 57% with recall ranging from 72% to 78% for 40 Hz and precision ranging from 62% to 64% with recall ranging from 70 to 73% for D calls. As these two call types are produced by both sexes, using them in long-term studies would remove sex-bias in estimates of temporal presence and movement patterns.

Detecting, classifying, and counting blue whale calls with Siamese neural networks.

The goal of this project is to use acoustic signatures to detect, classify, and count the calls of four acoustic populations of blue whales so that, ultimately, the conservation status of each population can be better assessed. We used manual annotations from 350 h of audio recordings from the underwater hydrophones in the Indian Ocean to build a deep learning model to detect, classify, and count the calls from four acoustic song types. The method we used was Siamese neural networks (SNN), a class of neural network architectures that are used to find the similarity of the inputs by comparing their feature vectors, finding that they outperformed the more widely used convolutional neural networks (CNN). Specifically, the SNN outperform a CNN with 2% accuracy improvement in population classification and 1.7%-6.4% accuracy improvement in call count estimation for each blue whale population. In addition, even though we treat the call count estimation problem as a classification task and encode the number of calls in each spectrogram as a categorical variable, SNN surprisingly learned the ordinal relationship among them. SNN are robust and are shown here to be an effective way to automatically mine large acoustic datasets for blue whale calls.

Source level and vocalizing depth estimation of two blue whale subspecies in the western Indian Ocean from single sensor observations.

The source level (SL) and vocalizing source depth (SD) of individuals from two blue whale (BW) subspecies, an Antarctic blue whale (Balaenoptera musculus intermedia; ABW) and a Madagascar pygmy blue whale (Balaenoptera musculus brevicauda; MPBW) are estimated from a single bottom-mounted hydrophone in the western Indian Ocean. Stereotyped units (male) are automatically detected and the range is estimated from the time delay between the direct and lowest-order multiply-reflected acoustic paths (multipath-ranging). Allowing for geometric spreading and the Lloyd's mirror effect (range-, depth-, and frequency-dependent) SL and SD are estimated by minimizing the SL variance over a series of units from the same individual over time (and hence also range). The average estimated SL of 188.5 ± 2.1 dB re 1µPa measured between [25-30] Hz for the ABW and 176.8 ± 1.8 dB re. 1µPa measured between [22-27] Hz for the MPBW agree with values published for other geographical areas. Units were vocalized at estimated depths of 25.0 ± 3.7 and 32.7 ± 5.7 m for the ABW Unit A and C and, ≃20 m for the MPBW. The measurements show that these BW calls series are stereotyped in frequency, amplitude, and depth.

Blubber endocrine profiles provide insights into reproductive biology in blue whales from the eastern North Pacific Ocean.

The goal of the present study was to complement existing data of testosterone and progesterone in blue whale (Balaenoptera musculus) blubber from the eastern North Pacific Ocean to evaluate effects of seasonality and location on these hormones and to better assess reproductive status of individuals. Physiological parameters regarding reproduction are fundamental for describing population dynamics, and hormones can be a valid tool to estimate those for wildlife populations. In this study, blubber tissue was validated for testosterone and progesterone assays. Hormone concentrations were measured in 69 (35 males and 34 females) blubber samples from live (n = 66) and stranded (n = 3) animals collected between 2002 and 2016 from a known winter reproductive ground in the Gulf of California (GoC) and summer feeding areas along the United States West Coast (USWC), specifically off the states of California and Oregon. Results were combined with sighting histories as a tool to determine reproductive status of individual whales. Testosterone concentrations in adult male blue whales were significantly higher (p < 0.05) in blubber biopsies sampled off the USWC between the months of June and November compared to those sampled in the GoC between February and April. Elevated testosterone concentrations likely indicate physiological preparation for reproductive activity while the animals were present off the USWC. Progesterone concentrations were significantly elevated in pregnant females, confirming progesterone as an indicator of pregnancy in blue whales. Probabilities of being pregnant were estimated for adult females with unknown sighting histories based on progesterone concentrations. Testosterone in females was detected and measured only in pregnant whales suggesting its biosynthesis or metabolism is altered during gestation. These results provide updated and new information on the reproductive cycle of blue whales in the eastern North Pacific, posing new milestones to better estimate the timing of the mating season for this endangered population.