Behavior Of Whales Research Articles

Baleen stable isotopes reveal climate-driven behavioural shifts in North Atlantic fin whales

Climate variability impacts the structure and functioning of marine ecosystems and can trigger behavioural responses in organisms. We investigated whether such variability modulates diet and migration in the North Atlantic fin whale (Balaenoptera physalus). To reconstruct the dietary and migratory behaviours over time, we conducted stable isotope analysis of nitrogen (δ15N) and carbon (δ13C) along baleen plates from 29 fin whales sampled off southwestern (SW) Iceland in summer. We estimated a baleen growth rate of 16.1 ± 2.5 cm per year from the stable isotope oscillations observed along the baleens. We also assigned a deposition date for each baleen segment, thus obtaining isotopic sequential time series. We then assessed the potential association of these time series with the main climate patterns of the North Atlantic basin. Baleen δ15N and δ13C values are associated with the North Atlantic Oscillation (NAO) and the Atlantic Multidecadal Oscillation (AMO). During high AMO and low NAO periods, which tend to decrease krill abundance, there is an increase in both the mean and standard deviation of baleen δ15N values, suggesting that fin whales shift to higher trophic resources and expand their dietary niche. Additionally, high AMO periods, which relate to positive temperature anomalies, lead to a decrease in baleen δ13C values, suggesting that fin whales adjust their migratory routes and destinations towards higher latitudes. Significant variation in isotopic niche width between years also reflected these dietary and migratory behavioural shifts. This highlights the plasticity of the North Atlantic fin whale behaviour, a trait likely to strengthen the resilience of the species within the current context of rapid and intense climate variability.

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.

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.

Revolutionizing crowd safety: A breakthrough hybrid whale-bat chaotic algorithm (WOABCM) for optimized evacuation simulations

The Whale-Bat Chaotic Algorithm (WOABCM) is a revolutionary tool for optimizing pathfinding navigation in crowd evacuation scenarios. Conventional evacuation plans rely on static routes, causing traffic jams and decreased safety. WOABCM uses the erratic behavior of whales and bats to optimize escape routes in real time, making it particularly useful in large crowd evacuations. The algorithm can adapt to different scenarios, ensuring its continued efficacy across real-world conditions. Iteratively iterating through the evacuation environment, WOABCM iteratively finds the most effective ways for agents to reach exits. In this experiment, the results show notable improvements in evacuation times, with WOABCM determining and directing agents through the shortest and least crowded paths in a room setting and assigning agents to exits in more complicated scenarios. The algorithm's flexibility also allows it to swiftly recompute pathways for agents, avoiding obstructions or congestion points. This groundbreaking approach to crowd evacuation simulation demonstrates how chaos theory-inspired algorithms can be used to solve practical problems

Movement and diving behavior of satellite-tagged male sperm whales in the Gulf of Alaska

Male sperm whales (Physeter macrocephalus) are known to interact with and depredate from commercial longline fishing vessels targeting sablefish (Anoplopoma fimbria) in the Gulf of Alaska (GOA). This study aims to better understand their movement patterns and diving behavior in this region, and in relation to depredation behavior. Between 2007 and 2016 a total of 33 satellite tags were deployed on sperm whales interacting with fishing vessels in the eastern GOA. A subset of these tags also collected dive characteristics. We used state space models to interpolate hourly positions from tags and estimate behavioral state from 29 usable tag records, 14 of which had associated dive information. Whales exhibited slower horizontal movement (1.4 km/hr) within GOA waters compared to south of the GOA (5.5 km/hr), indicating tagged whales sped up when they left the region. Behavioral states indicated primarily foraging behavior (82% of locations) in the GOA and primarily transiting behavior (74% of locations) when whales left the GOA. Dive data showed average ( ± Standard Deviation) maximum dive depths of 396 m ( ± 166), and dive durations of 32 min (± 9). Generalized additive models indicated that dives were significantly deeper and longer during the daytime than dawn, dusk, or nighttime, and dives were significantly deeper and shorter during quarter moons, when tidal currents are weakest. Maximum dive depth decreased in areas of higher sablefish CPUE, suggesting a potential link between the sablefish fishery and depredation behavior. As seafloor depth increased, up to 800 m, dives became deeper, indicating that whales were likely targeting both bathypelagic and mesopelagic prey. This highlights the importance of the GOA continental slope as a foraging ground for male sperm whales. This enhanced understanding of sperm whale foraging ecology informs management and conservation efforts in high latitude foraging grounds.

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.

Whale Optimization Technique Based Economic Load Dispatch

This study concentrates on optimizing the Economic Load Dispatch (ELD) for three major Nigerian power systems: Sapele, Jebba, and Egbin. These systems, each comprising varying numbers of generating units and facing fluctuating load demands ranging from 300 MW to 1000 MW, necessitate efficient resource allocation to minimize operational expenses. Employing the innovative Whale Optimization Algorithm (WOA), inspired by the cooperative behaviour of humpback whales, this research tackles the intricate non-linear characteristics of the ELD problem. The primary goal is to determine the ideal power generation timetable that reduces total generation costs while fulfilling power demand constraints. Through mathematical modelling, the power systems and their economic aspects are represented. The proposed WOA-based approach is implemented and juxtaposed against optimization methods to gauge its efficacy in achieving cost-effective load dispatch. In addition to the fast convergence characteristics of the optimization technique, the study reveals minimum optimal generation costs of 150,567 Naira/Hr, 189,352 Naira/Hr, and 244,075 Naira/Hr for the Sapele, Jebba, and Egbin power systems, respectively, under various load conditions. Conversely, maximum optimal generation costs reach 480,431 Naira/Hr, 590,871 Naira/Hr, and 750,453 Naira/Hr for the same systems, demonstrating the algorithm's adaptability to diverse load scenarios.

Simulations of IDCR and SOWER Antarctic minke whale abundance surveys

The International Whaling Commission (IWC) conducted abundance line‐transect surveys of Antarctic minke whales (Balaenoptera bonaerensis) during the International Decade of Cetacean Research (IDCR) between 1978/79 and 2003/04. These surveys continued throughout 2004/05–2009/10 under the Southern Ocean Whale and Ecosystem Research (SOWER) programme. Several methods used to analyse these data resulted in very different abundance estimates. In order to evaluate the accuracy of these methods, the IWC Scientific Committee recommended using simulated line‐transect data. The software package TRANSIM was therefore modified to generate simulated line‐transect data that emulated an IDRC/SOWER survey in a study area populated with whales that followed the distribution, density and behaviour of Antarctic minke whales. Simulations incorporated factors that could potentially bias resulting abundance estimates, and in order to investigate the effects of these factors on the resulting abundance estimates, simulated datasets were generated for different scenarios, each of which incorporated combinations of factors that were chosen using the mirror‐image fold‐over partial factorial method that is designed to identify factors related to biased estimates. This paper describes the programme used to generate these datasets and reports on the robustness of the statistical methods applied to analyse them. In addition, a brief discussion is included on how this information was used when the actual line‐transect data were analysed. The statistical methods investigated were the hazard probability method (OK), integrated hazard probability method (IM), spatial point independence method (SPL) and standard method. After developers analysed the simulated data, they used this information to improve their analytical methods. The results reported here focus on the performance of the most advanced versions of the OK and SPL methods compared with the standard method as of 2009. In general, SPL estimates of group density were negatively biased, OK estimates were positively biased, standard estimates were the most negatively biased and IM estimates were the least biased. Density estimates from SPL were less biased than estimates from OK in the complex scenarios that contained spatial gradients and correlations between density, location and weather conditions. The OK method was less robust when: (1) closing‐mode data collection procedures were simulated; (2) whale groups were diving in a non‐synchronous fashion; and (3) there were complex spatial patterns. In almost every case, the density estimates from the OK method were greater than the SPL methods, on average by 10–15%. These observations assisted the development of more robust analysis methods and highlighted which factors should be addressed when analysing the actual data.

Failure-aware resource provisioning for hybrid computation offloading in cloud-assisted edge computing using gravity reference approach

This paper tackles the challenges of computation offloading in the cloud–edge paradigm. Although many solutions exist for enhancing the server’s computational and communication efficiency, they mainly focus on reducing latency and often neglect the impact of overlapping multi-request processing on scheduling reliability. Additionally, these approaches do not account for the preemptive characteristics of applications running in the VMs that lead to higher energy consumption. We propose a novel hybrid integer multi-objective dynamic decision-making approach enhanced with the gravity reference point method. This method determines the proportion of computations executed on cloud servers versus those handled locally on edge servers. Our hybrid approach leverages the gravitational potential reference point and crowding degrees to improve the characteristics of whale populations, addressing the limitations of the traditional whale algorithm, which depends on individual whales’ varying foraging behaviors influenced by a random probability number. By evaluating the crowding level around the prey, the foraging behavior of individual whales is adjusted to enhance the algorithm’s convergence speed and optimization accuracy, thereby increasing its reliability. The results show that our hybrid computation offloading model significantly improves time latency by 76.45%, energy efficiency by 63.12%, reliability by 82%, quality of service by 83.78%, distributor throughput by 87.31%, asset availability by 73.05%, and guarantee ratio by 89.72% compared to traditional offloading methods.

Synchronization of bowhead whales

Inferring animal behavior from irregular tracking data is a challenging area of research. It is particularly difficult to determine if whales, who intermittently explore different depths while staying in the same acoustic medium, synchronize their days with their prey and each other over kilometer-scale distances. Here, we aim to better understand the diving behavior of bowhead whales () in Disko Bay, West Greenland, using the largest high-frequency dive-depth dataset to date (144 days at 1 Hz from 12 different whales) and nonlinear dynamics, whereby we consider the whales to be chaotic (aperiodic) oscillators. We find that foraging whales dive deeper during the daytime in spring, with this diving behavior being in apparent synchrony with their vertically migrating prey. Furthermore, we demonstrate that bowhead whales can synchronize their behavior with each other for up to a week while staying within a range of up to ∼100km. This discovery agrees with the acoustic herd theory of long-range signaling in baleen whales. On the other hand, the synchrony might emerge when animals experience similar ecological conditions, which are, however, difficult to name because targeted depths and locations were separated for hundreds of meters and tens of kilometers, respectively. In this paper, we identify a framework for studying the sociality and behavior of such chaotically moving, unrestrained marine animals and call for more simultaneous tagging campaigns. Published by the American Physical Society 2024

Exploring the Performance of Whale Optimization Algorithm on Rosenbrock's Function

Optimization of complex and nonlinear functions is essential across various domains, from engineering and finance to artificial intelligence and machine learning. Rosenbrock's function stands as a fundamental benchmark for evaluating optimization algorithms due to its highly nonlinear and multimodal nature. Among the multitude of optimization algorithms, the Whale Optimization Algorithm (WOA) has garnered attention for its inspiration from the social behavior of humpback whales. However, its performance on Rosenbrock's function remains relatively unexplored. This paper aims to investigate the effectiveness of the WOA specifically on Rosenbrock's function through rigorous experimentation and analysis. By evaluating convergence speed, solution accuracy, and robustness, this study sheds light on WOA's behavior when confronted with the challenges posed by Rosenbrock's function. Comparative analysis with other optimization algorithms further elucidates WOA's adaptability and scalability. The findings contribute valuable insights for selecting suitable optimization algorithms in real-world applications and advance understanding of optimization algorithms' behavior in challenging landscapes.

The Bonney Coast upwelling: How physical processes shape the feeding behaviour of blue whales

This study employs a fully coupled physical-biological model to explore the oceanic dynamics and phytoplankton production in one of Australia’s most prominent coastal upwelling systems, the Bonney Coast Upwelling, that has barely been studied before. The study focusses on how physical processes provide two different food sources for blue whales (Balaenoptera musculus), namely, krill (treated as nonbuoyant particles) and zooplankton, both feeding on phytoplankton. While plankton grows in the euphotic zone in response to nutrient enrichment on time scales of weeks, krill can only be transported into the region via ambient currents. Findings of this study suggest that phytoplankton blooms appear slowly in the main upwelling plume on timescales of 4-8 weeks. Dynamical influences from incoming coastal Kelvin waves significantly weaken or strengthen this classical upwelling plume and its plankton productivity. On the other hand, the upwelling-favourable wind induces a continuous coastal current that also extends eastward past the Bonney Coast. This current operates to transport and distribute krill (that cannot swim horizontally) westward along the shelf, which explains the apparent conundrum why blue whales also feed on the upstream side of the upwelling plume. The author postulates that the variability of both plankton production and the intensity of the upwelling flow (passing krill swarms along the shelf) control the feeding locations of blue whales and other baleen whales on Australia’s southern shelves.

Investigating the effects of underwater noise from two vessels on the behaviour of short-finned pilot whales

Multiple whale-watching vessels may operate around cetaceans at any one time, and targeted animals may experience underwater noise effects. We hypothesised that the cumulative noise of two vessels with low source levels (SLs) will elicit lower behavioural disturbance in short-finned pilot whales (Globicephala macrorhynchus) compared to a single vessel with a higher SL. We measured the behaviour of whales during 26 controls (stationary vessel >300 m) and 44 treatments off Tenerife (Canary Islands, Spain). Treatments consisted of vessel approaches mimicking whale-watch scenarios (distance ∼60 m, speed 1.5 kn). Approaches with two simultaneous vessels, with maximum cumulative mid and low-frequency (0.2–110 kHz) weighted source levels (SLsMF-LF) 137–143 dB, did not affect mother-calf pairs’ resting, nursing, diving, respiration rate or inter-breath interval. However, a louder single vessel approach with twin petrol engines at SLsMF-LF 139–151 dB significantly decreased the proportion of time resting for the mother. The results suggest that if a single or two vessels are present, if the cumulative SL is < 143 dB, the behavioural disturbance on the whales will be negligible. By examining noise effects from multiple vessels on the behaviour of pilot whales, the importance of incorporating a noise threshold into whale-watching guidelines was emphasised.

Meta‐heuristic optimization techniques for scheduling in multiple‐input multiple‐output multiple‐access channel system

SummaryIn a highly dense multiple‐input multiple‐output (MIMO) communication system, proper resource allocation is exceedingly challenging. The simultaneous selection of antennas at the base‐station (BS) and scheduling of users in an uplink multiple access channel (MAC) system add up to the signal processing load in the MIMO wireless system. The method chosen for combining the user scheduling and antenna selection must have a very low computational complexity. However, the exhaustive search algorithm (ESA), which achieves the best sum‐rate capacity in a MIMO‐MAC system, has a very high level of complexity. In this paper, we use a metaheuristic‐based optimization algorithms to optimize the sum‐rate capacity in a MAC system with significantly less computational complexity. First, we use the beluga whale optimization (BWO) metaheuristic method to solve the optimization problem. The BWO is a swarm‐based algorithm that draws inspiration from beluga whale behavior. An improved BWO (iBWO) is also presented and is utilized for the same optimization challenge. The proposed iBWO scheme improves the search capability of the algorithm and thereby ensures better exploration and exploitation capabilities of the beluga whales. We compare the performance of these algorithms with a standard differential evolution (DE) algorithm. The simulation results demonstrate that the proposed iBWO is a very competitive meta‐heuristic algorithm for solving optimization problems. The proposed iBWO algorithm achieves near optimal throughput in a MIMO‐MAC system. Also, the computational complexity related to the suggested optimization technique in a MIMO‐MAC system is found to be very less compared with the complexity related to ESA.

Antarctic sonobuoy surveys for blue whales from 2006-2021 reveal contemporary distribution, changes over time, and paths to further our understanding of their distribution and biology

Seven passive acoustic surveys for marine mammal sounds were conducted by deploying sonobuoys along ship tracks during Antarctic voyages spanning years 2006-2021. These surveys included nearly 330° of longitude throughout Antarctic (south of 60°S) and sub-Antarctic (between 50-60°S) latitudes. Here, we summarise the presence of calls from critically endangered Antarctic blue whales (Balaenoptera musculus intermedia) detected on all seven of these surveys. We describe and compare the spatial distribution of detections of three different types of Antarctic blue whale calls: unit-A, Z-calls, and D-calls. Three sets of voyages partially overlapped spatially but in different years, providing three regions (Indian Sector, Dumont d’Urville Sea, Ross Sea) to investigate differences over time for these three different call types. The proportion of sonobuoys with calls present was significantly higher in the more recent years for seven of the 15 combinations of years, regions, and call type. The proportion of sonobuoys with calls present was significantly lower only for one of the 15 combinations (unit A in the Ross Sea between 2015 vs 2017), and not significantly different for the remaining seven pairwise comparisons. We discuss possible explanations for these observations including: differences in probability of detection, whale behaviour, whale distribution, and abundance. These explanations are not mutually exclusive and cannot yet be resolved without application of complex analytical methods and collection of additional data. Lastly, we discuss future work that could help clarify the contributions of each of these potential drivers of acoustic detection. We propose continued acoustic data collection, application of new analytical methods, and collection of other synergistic data from Antarctic blue whales on their feeding grounds as a basis for future work on this species. This could provide a cost effective and holistic means of monitoring their status after the effects of 20th century industrial whaling, as well as their responses to natural and anthropogenic changes to their main prey, Antarctic krill, and a changing climate.

Antecedent description and depiction of the recently described cetacean behaviour of trap/tread-water feeding inferred from a nineteenth-century sighting of a ‘sea monster’ in the Gulf of Suez, Egypt

In 2017 and 2018, two groups of biologists published papers in which they independently described what was referred to as a ‘novel’ or ‘new’ feeding behaviour for cetaceans. Called ‘trap’ or ‘tread-water’ feeding, the behaviour was of interest as it was the first time that passive or stationary in contrast to lunge, and vertical as opposed to horizontal position, feeding had been observed by whales. A subsequent historical ecology paper suggested that the recently described behaviour had in fact been previously observed and documented by illustrators and writers in Classical Antiquity and the Middle Ages. Because yesterday’s ‘sea monsters’ are today’s megafauna, recounted sightings of the former can provide early insight into whale behaviour. One such example is an 19th century sighting of a ‘sea monster’ in the Gulf of Suez, whose description and illustration are nearly identical to modern scientific reporting of whales engaged in trap/tread-water feeding. Such concordance is further evidence in support of a historical precedence with respect to observing and documenting this behaviour.

Smart infrastructure design: Machine learning solutions for securing modern cities

In the realm of securing smart cities against emerging cyber threats, this research encompasses three distinct yet interconnected initiatives. First, a pioneering data architecture design leveraging CycleGAN is proposed to counteract False Data Injection Attacks (FDIA). By cyclically transforming data distributions, CycleGAN ensures the integrity and reliability of smart city information, fortifying against malicious manipulations. Second, an innovative Internet of Things (IoT) concept is introduced, aiming to enhance real-time monitoring and context-aware threat detection within Intrusion Detection Systems (IDS). Harnessing the wealth of data generated by IoT devices, this concept provides a comprehensive understanding of network activities, fostering adaptive responses to emerging threats. Lastly, the research delves into the development of a novel IDS hyperparameter adjusting system. This system integrates the strengths of Biogeography-Based Optimization (BBO) and Whale Optimization Algorithm (WOA) to fine-tune IDS configuration parameters. Drawing inspiration from biogeographical principles and collaborative whale behavior, the hybrid optimization system balances exploration and exploitation, adapting the IDS to diverse network environments. Together, these initiatives represent a holistic approach to fortifying smart cities through cutting-edge data architecture, IoT-driven threat detection, and optimized IDS configurations, contributing to the resilience and cybersecurity of modern urban landscapes.

Foraging behaviour and ecology of transient killer whales within a deep submarine canyon system.

Transient killer whales have been documented hunting marine mammals across a variety of habitats. However, relatively little has been reported about their predatory behaviours near deep submarine canyons and oceanic environments. We used a long-term database of sightings and encounters with these predators in and around the Monterey Submarine Canyon, California to describe foraging behaviour, diet, seasonal occurrence, and habitat use patterns. Transient killer whales belonging to the outer coast subpopulation were observed within the study area 261 times from 2006-2021. Occurrences, behaviours, and group sizes all varied seasonally, with more encounters occurring in the spring as grey whales migrated northward from their breeding and calving lagoons in Mexico (March-May). Groups of killer whales foraged exclusively in open water, with individuals within the groups following the contours of the submarine canyon as they searched for prey. Focal follows revealed that killer whales spent 51% of their time searching for prey (26% of their time along the shelf-break and upper slope of the canyon, and 25% in open water). The remainder of their time was spent pursuing prey (10%), feeding (23%), travelling (9%), socializing (6%), and resting (1%). Prey species during 87 observed predation events included California sea lions, grey whale calves, northern elephant seals, minke whales, common dolphins, Pacific white-sided dolphins, Dall's porpoise, harbour porpoise, harbour seals, and sea birds. The calculated kill rates (based on 270 hours of observing 50 predation events) were 0.26 California sea lions per killer whale over 24 hours, 0.11 grey whale calves, and 0.15 for all remaining prey species combined. These behavioural observations provide insights into predator-prey interactions among apex predators over submarine canyons and deep pelagic environments.

Environmental, behavioral, and design-related factors affect accuracy and precision of beluga abundance estimates from aerial surveys

Abundance estimation of wildlife populations is frequently derived from systematic survey data. Accuracy and precision of estimates, however, depend on the number of replicate surveys, and on adjustments made for animals unavailable to (availability bias), or available but undetected (perception bias) by observers. This study offers a comprehensive analysis of the relative influence of methodological, environmental and behavioral factors on availability bias estimates from photographic and visual aerial surveys of a small cetacean with a highly clumped distribution, the beluga (Delphinapterus leucas). It also estimates the effect of the number of surveys on accuracy and precision of abundance estimates, using 28 replicate visual surveys flown within a 16—29 day window depending on survey year. Availability bias was estimated using detailed dive data from 27 beluga from the St. Lawrence Estuary, Canada, and applied to systematic visual and photographic aerial surveys of this population, flown using various survey platforms. Dive and surface interval durations varied among individuals, and averaged (weighted) 176.6 s (weighted s.e. = 12.6 s) and 51.6 s (weighted s.e. = 4.5 s), respectively. Dive time and instantaneous availability, but not surface time, were affected by local turbidity, seafloor depth, whale behavior (i.e., whether beluga were likely in transit or not), and latent processes that were habitat-specific. Overall, adjustments of availability for these effects remained minor compared to effects from survey design (photographic or visual) and type of platform, and observer search patterns. For instance, mean availability varied from 0.33—0.38 among photographic surveys depending on sightings distribution across the study area, but exceeded 0.40 for all visual surveys. Availability also varied considerably depending on whether observers searched within 0-90° (0.42—0.60) or 170° (0.70—0.80). Simulation-based power analysis indicates a large benefit associated with conducting more than 1 or 2 survey reps, but a declining benefit of conducting &amp;gt; 5—10 survey reps. An increase in sample size from 2, to 5, and 10 reps decreased the CV from 30, to 19 and 13%, respectively, and increased the probability of the abundance estimate being within 15% of true abundance from 0.42, to 0.59 and 0.69 in species like beluga.

Deep Learning Enabled Whale Optimization Algorithm for Accurate Prediction of RA Disease

Whale Optimization Algorithm (WOA) is an optimization technique and based on food foraging behavior of whales. It has been applied in many domain including processing of images, framework controls, and ML (machine learning). WOA assists in choosing the right parameters required for Deep Neural Networks. This work uses DNN to examine metacarpophalangeal (MCP) rheumatoid joint discomforts in patients from diagnostic medical images including X-rays or Magnetic Resource images. The use of WOA enhances resultant outcomes of DNN as it searched for optimal solutions within search spaces, instead of getting trapped in local minima found by gradient descent. The combination of WOA and DNN for grading MCP rheumatoid arthritis can provide an efficient and accurate solution for medical practitioners and researchers