Humpback Whales Research Articles

Location Tag Adapted from Fitness Tracker Watch, Tested on Humpback Whales (Megaptera novaeangliae)

Location Tag Adapted from Fitness Tracker Watch, Tested on Humpback Whales (Megaptera novaeangliae)

Using acoustic monitoring to reveal nearly year‐round presence of humpback whales (Megaptera novaeangliae) in the waters of southern Iceland

AbstractIcelandic waters are a crucial feeding ground for North Atlantic humpback whales, yet their occurrence in these subarctic waters remains underexplored. This study examined seasonal and diel patterns in humpback whale occurrence off the Vestmannaeyjar archipelago, southern Iceland, from June 2018 to May 2019, using automated acoustic detections from bottom‐moored hydrophones. Vocalizations were manually categorized into social calls, song fragments, or songs. Further classification identified seven social sound types and 25 unique song units. Humpback whales were detected on 126 out of 329 recording days and were present in nearly all months except April and May. Social calls were most common in summer and fall, while song fragments and songs were prevalent in winter. No diel singing pattern was observed. These findings suggest southern Iceland serves as a vital habitat for humpback whales, acting as a feeding ground in summer and a migration stopover or overwintering site in winter. The study underscores the potential of acoustic monitoring to uncover important habitats year‐round, especially when field observations are scarce, and emphasizes the need for ongoing monitoring of habitat use. Continued acoustic monitoring could provide further insights into the whales' behavioral patterns and preferences, essential for their conservation.

Humpback whales are excellent toolmakers to gather food

Humpback whales are excellent toolmakers to gather food

Numerical simulation of cavitation flow around a wing with new tubercles design

The study investigates the benefits of adding non-uniform leading-edge tubercles to hydrofoils, drawing inspiration from the complex shaping of humpback whale flippers. The focus is on managing cavitation, a phenomenon that can affect hydrofoil performance. While previous research has mainly looked at uniform sinusoidal tubercles and their positive impact on flow dynamics and cavitation control, this study introduces a new perspective by examining the effects of non-uniform tubercles on hydrofoil performance. Advanced computational fluid dynamics (CFD) simulations using ANSYS Fluent 2024 were used to assess how these non-uniform tubercles affect the lift, drag, and cavitation characteristics of the NACA 634-021 hydrofoil. The simulations incorporated the Schnerr-Sauer cavitation model and the SST k-ω turbulence model to accurately capture the flow dynamics. The results show that non-uniform tubercles improve cavitation control by disrupting the flow in a way that delays the onset and reduces the severity of cavitation. The modified hydrofoils (non-uniform tubercles) display improved hydrodynamic performance compared to baseline designs, with significant reductions in drag and increased lift at higher angles of attack. This study provides valuable insights into the potential of mimicking natural designs to enhance flow stability and address cavitation issues, offering a significant contribution to advanced hydrofoil design in scenarios where controlling cavitation is essential. The broader implications of this research underscore the potential of mimicking natural designs to transform hydrofoil engineering and enhance flow stability in various applications.

Novel Megaptera novaeangliae (Humpback whale) haplotype chromosome-level reference genome

The sequencing of a kidney sample (KW2013002) from a stranded Megaptera novaeangliae (Humpback whale) calf is the first chromosome-level reference genome for this species1. The calf, a 457 cm and 2,500 lbs male, was found stranded in Hawai’i Kai, HI, in 2013 and was marked as abandoned/orphaned. In 2023, 1 g of kidney was sequenced with PacBio long-read DNA sequencing, chromatin conformation capture (Hi-C), RNA sequencing, and mitochondrial sequencing to comprehensively characterize the genome and transcriptome of M. novaeangliae. Data validation includes a synteny analysis, mitochondrial annotation, and a comparison of BUSCO scores (scaffold v. reference genome and Balaenoptera musculus (Blue whale) v. M. novaeangliae). BUSCO analysis was performed on an M. novaeangliae scaffold-level assembly to determine genomic completeness of the reference genome, with a scaffold BUSCO score of 91.2% versus a score of 95.4%. Synteny analysis was performed using the B. musculus genome as comparison to determine chromosome-level coverage and structure. Further, a time-based phylogenetic tree was constructed using the sequenced data and publicly available genomes.

Characterizing humpback whale behavior along the North-Norwegian coast

BackgroundStudying movement patterns of individual animals over time can give insight into how they interact with the environment and optimize foraging strategies. Humpback whales (Megaptera novaeangliae) undertake long seasonal migrations between feeding areas in polar regions and breeding grounds in tropical areas. During the last decade, several individuals have had up to a 3-month stop-over period around specific fjord-areas in Northern Norway to feed on Norwegian spring-spawning (NSS-) herring (Culpea harengus L.). Their behavioral patterns during this period are not well understood, including why some whales seemingly leave the fjords and then later return within the same season.MethodsTo investigate whale behavior during this seasonal stopover, we classified humpback whale tracks into five distinct movement modes; ranging, encamped, nomadic, roundtrip and semi-roundtrip. A behavioral change point analysis (BCPA) was used to select homogeneous segments based on persistence velocity. Then, net squared displacement (NSD) over time was modeled to differentiate movement modes. This study also manually identified longer roundtrips away from the fjords that lasted several days and examined movement modes within these.ResultsInside the fjord systems, encamped mode was most prevalent in December–January, suggesting the whales were mainly foraging on overwintering NSS-herring in this area. During the same winter seasons, half of the whales left the fjords and then returned. We hypothesize that these trips serve as ‘searching trips’ during which the whales seek better feeding opportunities outside the fjords. If better foraging conditions are not found, they return to the fjords to continue their feeding. The overall most common mode was ranging (54%), particularly seen during the start of their southwards migration and in areas outside the fjord systems, indicating that the whales mainly moved over larger distances in the offshore habitat.ConclusionsThis study serves as a baseline for future studies investigating both the searching trip theory and humpback whale behavior in general, and confirms that this method could be useful to analyze local scale movement patterns of satellite tagged whales.

An Improved Whale Optimization Algorithm with Adaptive Fitness‐Distance Balance

AbstractWhale optimization algorithm (WOA) is a new bio‐meta‐heuristic algorithm presented to simulate the predatory humpback whales' behavior in the ocean. In previous studies, WOA has been observed to exhibit lower accuracy and slower convergence rates. In this paper, we propose an improved the WOA by innovatively incorporating an adaptive fitness‐distance balance strategy, namely AFWOA. AFWOA can continuously and efficiently identify the maximum potential candidate solutions from the population within the search process, thus improving the accuracy rate and convergence speed of the algorithm. Through various experiments in IEEE CEC2017 and an ill‐conditional problem, AFWOA is proven to be more competitive than the original WOA, several other state‐of‐the‐art WOA variants and other four classic meta‐heuristic algorithms. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Legal perspectives on unintended and unsanctioned humpback whale entanglement in the South Atlantic trawl fishery

Whale interactions with fishing gear pose a major anthropogenic threat, yet entanglements in South Atlantic trawl fisheries remain underreported, with no published records. This lack of data extends to reports of whales found stranded with amputated tails, despite documented cases of survival even without a fluke. This study presents a novel case: a video recorded in May 2020 captured a shrimp trawler in Santa Catarina, Brazil, entangled with a humpback whale (Megaptera novaeangliae). Vocalizing and bleeding, the whale had recently lost its fluke and remained caught in the vessel's gear. Following the video's public outcry, authorities identified and apprehended the responsible vessel, conducting a thorough investigation. Although initial sanctions were issued, and evidence confirmed illegal fishing practices and neglect towards the whale, subsequent legal proceedings deemed the act unintentional and acquitted the involved parties. This report seeks to not only document this unprecedented case but also to analyze why such infractions against cetacean often receive minimal legal consequences. Examining this case within the context of broader policy and enforcement frameworks, we discuss potential explanations for the perceived low legal severity of such incidents and argue for the need for enhanced legal accountability to effectively protect marine mammals in the South Atlantic.

3D audio recording of complex underwater sound fields

In this study, we developed a method to capture underwater sound fields and reproduce them for human listeners, providing an immersive 3D auditory experience. We recorded the sound field with an array of four hydrophones arranged in a tetrahedral structure. The side length of the tetrahedron (around 70 cm) was a compromise between capturing accurately the low frequency pressure gradients and spatial aliasing effects at higher frequencies. Subsequently, we transformed the signals into the domain of spherical harmonics facilitating a representation as first-order Ambisonics that is independent of the recording. For playback on devices such as headphones or loudspeaker systems, we used established decoding routines that enhance the immersive audio experience with techniques such as parametric decoding and diffuse sound field equalization. During an expedition to northern Norway, we recorded the underwater soundscape in deep fjords with vocalizing killer whales and singing humpback whales. The resulting binaural renderings for headphones of these sound scenes are astonishing and sparked interest among scientist, artists and general public. This research also helps to enhance our understanding and perception of these intelligent marine mammals and their unique acoustic underwater world, ultimately aiding in better protecting them from noise pollution.

Male humpback whales switch to singing in the presence of seismic air guns

Ocean noise produced by seismic exploration has been implicated in causing changes in baleen whale hearing, physiology, feeding, breeding, and migratory behaviours. Here, we observed changes in the mating tactics of humpback whales (Megaptera novaeangliae) during a one-hour exposure to nearby seismic air guns. Males employ a conditional mating strategy where they switch between singing and non-singing tactics. Singing is presumably an advertisement signal, while non-singing behaviours include seeking out and joining with females as well as forming competitive groups. During periods of active air guns, the number of male singing whales increased, and singers were more likely to be observed joining females. Conversely, non-singing males were less likely to engage in joining interactions suggesting that active air guns caused a switch in male breeding tactics. Though we cannot translate these effects into changes in breeding success, this indicates that seismic exploration has the potential to alter breeding behaviours in baleen whales.

Similarity learning networks uniquely identify individuals of four marine and terrestrial species

AbstractEstimating the size of animal populations plays an important role in evidence‐based conservation and management. Some methods for estimating population size rely on animals being individually identifiable. Traditionally, this has been done by marking physically captured animals, but increasingly, animals with distinctive natural markings are surveyed noninvasively using cameras. Animal reidentification from photographs is usually done manually, which is expensive, laborious, and requires considerable skill. An alternative is to develop computer vision methods that can support or replace the manual identification task. We developed an automated approach using deep learning to identify whether a pair of photographs is of the same individual or not. The core of the approach is a similarity learning network that uses paired convolutional neural networks with a triplet loss function to summarize image pairs and decide whether they are from the same individual. Prior to the main matching step, two additional convolutional neural networks perform image segmentation, cropping the animal object within the image, and orientation prediction, deciding which side of the animal was photographed. We applied the approach to four species, with images of the same individual often spanning several years: systematic surveys of bottlenose dolphins (Tursiops truncatus, 2008–2019) and harbor seals (Phoca vitulina, 2015–2019), a citizen science dataset of western leopard toads (Sclerophrys pantherina, unknown dates), and a publicly available repository of humpback whale images (Megaptera novaeangliae, unknown dates). For these species, our best‐performing models were able to identify whether a pair of images were from the same individual or different individuals in 95.8%, 94.6%, 88.2%, and 83.8% of the cases, respectively. We found that triplet loss functions outperformed binary cross‐entropy loss functions and that data augmentation and additional manual curation of training data provided small but consistent improvements in performance. These results demonstrate the potential of deep learning to replace or, more likely, support and facilitate manual individual identification efforts.

Optimization Design of Bionic Leading-edge Protuberances on Horizontal Axis Wind Turbine Blades

Abstract The complex operating environments of horizontal axis wind turbines has made the phenomenon of blade flow separation more obvious. As a passive flow control method, the bionic humpback whale leading-edge protuberances have been shown to effectively enhance the performance of the wind turbine. This study employs a multi-objective genetic algorithm and Computational Fluid Dynamics method to analyse the impact of leading-edge protuberances on the aerodynamic performance and flow characteristics of the blades. The results indicate a significant improvement in blade performance after optimization in the wind turbine’s output power. The streamwise vortices generated by the bionic protuberances not only reduce the suction surface pressure, but also suppress the spanwise flow over the blade surface. The length of the flow separation Region is reduced from 0.410 to 0.368 radius. In the process of wind speed change, the tangential forces on the bionic blade exhibit a wave-like variation. The bionic protuberance alters the pressure coefficient of the position. Compared with the original blade, the pressure coefficient of the trough sections on both sides of the bionic blade protuberance is improved. In this study, the bionic protuberance applied to the leading-edge of horizontal-axis wind turbine blades is proposed for the first time, and holds practical value for guiding practical applications.

Hybrid Bee Colony Algorithm with Whale Algorithm

In this research paper, two hybrid algorithms of Meta-Heuristic algorithms, both of which are inspired by nature, are presented: the Bee- settlement Amendment algorithm (BCO) and the Whale Amendment Algorithm (WOA). The Bee settlement Amendment algorithm is an amendment algorithm founded on swarm intelligence modeling attitude. It is one of the techniques of synthetic information that focuses on studying the grouping conduct of decentralized systems, which are represented by groups of modest elements that react locally simultaneously and with the surrounding perimeter. One of the methods that distinguishes it is the method of exploration. As for the Whale Amendment Algorithm, whom represents the friendly conduct of the hump-back whale. It is based on the fancy net fishing design. One of the advantages of this algorithm depends on the poise between screening and utilization and avoidance of falling into local solutions. A hybridization process was carried out between the two algorithms, and the new algorithm was named (ABCWOA), and the whole algorithm was used to 16 rising -measurement Amendment assignment with diverse frequencies between (100, 200, 500, and 1000). The outcomes of the proposition algorithm showed access to optimality solutions by achieving the minimum value (f_min). For most assignment, the outcomes of this algorithm were disparity with the search algorithms.

Notes on the occurrence of ectoparasites on cetaceans off the northeastern coast of Brazil

ABSTRACT This study investigated the presence of ectoparasites in stranded cetaceans along the coast of Sergipe, Brazil. The parasitological material, dates and informations about the hosts was obtained from a biological collection. Were analysed four Megaptera novaeangliae, one of Sotalia guianensis, and one of Peponocephala electra. A total of 264 ectoparasites were collected from these carcasses – 263 amphipods of the species Cyamus boopis and Syncyamus ilheusensis, and a single cymothoid isopod, identified as Livoneca redmanii. C. boopis showed a average intensity of 77.0 parasites per host, and a prevalence of 100% in the analysed humpback whale carcasses, representing the first record of this louse species on humpback whales in the region. S. ilheusensis appears to have a rare occurrence and specificity to the host P. electra, being its third report in the scientific literature. Additionally, the discovery of the isopod L. redmanii on Sotalia guianensis is unprecedented, being the first report of an isopod parasitizing this cetacean and expanding our knowledge about the host flexibility of this ectoparasite. The attachment of the isopod to the dolphin's skin was likely favored by the absence of mucus, as found in fish. These findings provide new insights into the ecology of these ectoparasites.

Analyzing dynamic stall on tubercle mounted VAWT blades: A simplistic experimental approach using an oscillating rig

Leading-edge tubercles, inspired by the flippers of humpback whales, are widely adopted passive flow control devices to enhance the aerodynamic performance of various lifting surfaces. This experimental study investigates the implementation of sinusoidal and triangular tubercles on H-type Vertical Axis Wind Turbine blades to analyze their effects on dynamic stall characteristics. Experimental tests were conducted using a specially designed oscillating rig to replicate blade motion at different reduced frequencies. The results reveal that tubercle blades exhibit a lower stall angle and maximum normal force compared to the baseline configuration. Moreover, the dynamic stall characteristics of tubercle blades are notably smoother, leading to reduced hysteresis losses. A variation in the tubercle amplitude-wavelength ratio further decreases hysteresis, albeit at the cost of reduced normal force generation. At the highest tested reduced frequency of 0.065, tubercles reduce hysteresis by up to 38%. Despite the reduction in normal force, tubercles effectively mitigate the effects of dynamic stall vortices, resulting in smoother stall behavior. The observed reduction in hysteresis can contribute to enhancing the turbine’s lifespan and increasing power production efficiency. This experimental approach provides a cost-effective alternative to more expensive methods for studying dynamic stall characteristics.

Overwintering humpback whales adapt foraging strategies to shallow water environments at the mouth of the Chesapeake Bay, Virginia, USA

AbstractSome humpback whales from the Northwestern Atlantic population forgo migration to the Caribbean, spending winter months feeding along the U.S. mid‐Atlantic coast. We studied the foraging behavior of these whales at the mouth of the Chesapeake Bay, Virginia during winter from 2017 to 2022. While shipping channels here reach depths of up to 30 m, most of the area is 11–15 m deep. This shallow‐water environment poses physical constraints on classical humpback whale feeding modes. We deployed 20 digital acoustic tags (DTAGs) on humpback whales and identified foraging lunges from accelerometer data, detecting 788 lunges from 10 animals. Tagged whales averaged a single lunge per dive, lunging primarily in a horizontal orientation, with limited maneuvering compared to other study sites. Our results indicate that some elements of humpback whale foraging behavior are conserved across environments, but that the shallow depths in our study area constrain how animals are able to feed. The relatively high lunge rates we observed suggest this area is an important foraging ground. However, foraging in shipping channels increases the risk of ship strikes, which frequently occur in this area.

Diverse baleen whale acoustic occurrence around two sub-Antarctic islands: A tale of residents and visitors

Knowledge on the occurrence and behaviour of baleen whales around sub-Antarctic regions is limited, and usually based on short, seasonal sighting research from shore or research vessels and whaling records, neither of which provide accurate and comprehensive year-round perspectives of these animals’ ecology. We investigated the seasonal acoustic occurrence and diel vocalizing pattern of baleen whales around the sub-Antarctic Prince Edward Islands (PEIs) using passive acoustic monitoring data from mid-2021 to mid-2023, detecting six distinct baleen whale songs from Antarctic blue whales, Madagascan pygmy blue whales, fin whales, Antarctic minke whales, humpback whales, and sei whales. Antarctic blue and fin whales were detected year-round whereas the other species’ songs were detected seasonally, including a new Antarctic minke whale bio-duck song sub-type described here for the first time. Antarctic minke and sei whales were more vocally active at night-time whereas the other species had no clear diel vocalizing patterns. Random forest models identified month and/or sea surface temperature as the most important predictors of all baleen whale acoustic occurrence. These novel results highlight the PEIs as a useful habitat for baleen whales given the number of species that inhabit or transit through this region.

From Breeding to Feeding Grounds: Tracking the Survival of a White Humpback Whale (Megaptera novaeangliae)

From Breeding to Feeding Grounds: Tracking the Survival of a White Humpback Whale (Megaptera novaeangliae)

Exploring cavitation dynamics and noise reduction in hydraulic machinery with bionic leading edge impeller

Cavitation and induced noise are common issues in the operation of mixed-flow pumps, affecting their stability. Studying these phenomena is of both academic and engineering interest. Previous research has mainly focused on modifying the impeller blade profile or optimizing the pump structure to reduce cavitation and noise. In this paper, we apply a bionic design to the mixed-flow pump impeller by mimicking the natural shape of a humpback whale's pectoral fin on the leading edge of the blade. We compare the cavitation suppression and noise reduction effects of this design with those of a conventional mixed-flow pump. Unsteady flow calculations for the original pump at different flow rates revealed that at its rated speed, increasing flow rates led to lower pressure in the low-pressure region on the suction surface of the impeller, expanding the cavitation-prone area. By applying the bionic design, we studied the unsteady cavitation flow and its induced noise. Results showed that the impeller and guide vane experienced larger pressure pulsations at the rotation frequency and its multiples. The bionic modification increased the cavitation initiation speed and reduced the cavity volume within the runner. With an NPSH (net positive suction head) of 6.76, the void volume in the bionic pump was only 79% of that in the original pump, and the head increased by 16%. Additionally, the bionic design reduced the maximum far-field sound pressure level by 12.5%, achieving significant noise reduction. This study demonstrates that the bionic design effectively controls dynamic flow separation, reduces flow-induced noise, and enhances the mixed-flow pump's performance. These findings have important theoretical and practical implications for optimizing mixed-flow pump design, improving energy efficiency, and reducing noise.

Automated brain tumor diagnostics: Empowering neuro-oncology with deep learning-based MRI image analysis.

Brain tumors, characterized by the uncontrolled growth of abnormal cells, pose a significant threat to human health. Early detection is crucial for successful treatment and improved patient outcomes. Magnetic Resonance Imaging (MRI) is the primary diagnostic tool for brain tumors, providing detailed visualizations of the brain's intricate structures. However, the complexity and variability of tumor shapes and locations often challenge physicians in achieving accurate tumor segmentation on MRI images. Precise tumor segmentation is essential for effective treatment planning and prognosis. To address this challenge, we propose a novel hybrid deep learning technique, Convolutional Neural Network and ResNeXt101 (ConvNet-ResNeXt101), for automated tumor segmentation and classification. Our approach commences with data acquisition from the BRATS 2020 dataset, a benchmark collection of MRI images with corresponding tumor segmentations. Next, we employ batch normalization to smooth and enhance the collected data, followed by feature extraction using the AlexNet model. This involves extracting features based on tumor shape, position, shape, and surface characteristics. To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). AWO mimics the hunting behavior of humpback whales to iteratively search for the optimal feature subset. With the selected features, we perform image segmentation using the ConvNet-ResNeXt101 model. This deep learning architecture combines the strengths of ConvNet and ResNeXt101, a type of ConvNet with aggregated residual connections. Finally, we apply the same ConvNet-ResNeXt101 model for tumor classification, categorizing the segmented tumor into distinct types. Our experiments demonstrate the superior performance of our proposed ConvNet-ResNeXt101 model compared to existing approaches, achieving an accuracy of 99.27% for the tumor core class with a minimum learning elapsed time of 0.53 s.