Detection Of Whales Research Articles

DeepWhaleNet: Climate Change-Aware FFT-Based Deep Neural Network for Passive Acoustic Monitoring

Climate change poses severe risks to the survival of many whale populations, whose habitats and migration patterns are affected by environmental changes. To detect these whales effectively, especially in deep-sea environments, we need to use AI-based techniques to handle the acoustic diversity and variability of different species. However, current methods for whale detection are based on pre- and post-processing steps that reduce their efficiency and generalizability. To address this issue, we present DeepWhaleNet, a novel deep-learning model that automates whale detection in Underwater Passive Acoustic Monitoring datasets. DeepWhaleNet simplifies the detection process by extracting relevant features from raw log-power spectrograms and helps protect these threatened species by supporting conservation efforts. Our model uses a larger short-time Fourier transform as input and a custom ResNet-18 architecture for classification, which enables it to separate whale sounds from noise and capture their temporal and spectral characteristics. We evaluate the performance of DeepWhaleNet and show that it surpasses state-of-the-art methods, achieving an 8.3% improvement in the F-1 score and 21% higher average precision of binary relevance than the baseline method. Moreover, our model demonstrates its versatility and suitability for species-specific retrieval problems through an ablation study on multi-label retrieval problems and a 99.1% recall for Blue Whales.

Evidence for seasonal migration by a cryptic top predator of the deep sea

BackgroundIn ecosystems influenced by strong seasonal variation in insolation, the fitness of diverse taxa depends on seasonal movements to track resources along latitudinal or elevational gradients. Deep pelagic ecosystems, where sunlight is extremely limited, represent Earth’s largest habitable space and yet ecosystem phenology and effective animal movement strategies in these systems are little understood. Sperm whales (Physeter macrocephalus) provide a valuable acoustic window into this world: the echolocation clicks they produce while foraging in the deep sea are the loudest known biological sounds on Earth and convey detailed information about their behavior.MethodsWe analyze seven years of continuous passive acoustic observations from the Central California Current System, using automated methods to identify both presence and demographic information from sperm whale echolocation clicks. By integrating empirical results with individual-level movement simulations, we test hypotheses about the movement strategies underlying sperm whales’ long-distance movements in the Northeast Pacific.ResultsWe detect foraging sperm whales of all demographic groups year-round in the Central California Current System, but also identify significant seasonality in frequency of presence. Among several previously hypothesized movement strategies for this population, empirical acoustic observations most closely match simulated results from a population undertaking a “seasonal resource-tracking migration”, in which individuals move to track moderate seasonal-latitudinal variation in resource availability.DiscussionOur findings provide evidence for seasonal movements in this cryptic top predator of the deep sea. We posit that these seasonal movements are likely driven by tracking of deep-sea resources, based on several lines of evidence: (1) seasonal-latitudinal patterns in foraging sperm whale detection across the Northeast Pacific; (2) lack of demographic variation in seasonality of presence; and (3) the match between simulations of seasonal resource-tracking migration and empirical results. We show that sperm whales likely track oceanographic seasonality in a manner similar to many surface ocean predators, but with dampened seasonal-latitudinal movement patterns. These findings shed light on the drivers of sperm whales’ long-distance movements and the shrouded phenology of the deep-sea ecosystems in which they forage.

Tracking Whale Calls in the Lower St. Lawrence Seaway at Land Seismometers

The Lower St. Lawrence Seaway (LSLS) is critical to Canada’s economy both as part of a major marine shipping corridor and a site of intensive fishing. Every year, fin whales and blue whales frequent the LSLS feeding ground. Understanding the mechanisms driving whale habitat usage is key for making informed decisions on shipping and fishing, reducing whale collision risks and mitigating noise pollution. We detect whales in the LSLS with land seismometers by using a method that relies on the intervals of the regularly repeating low frequency calls. The resulting catalogue contains 14,076 fin whale detections and 3,739 blue whale detections between February 2020 and January 2022. These detections follow the overall pattern of hydrophones, with most detections from fall to early winter in the Estuary and until mid-winter/spring in the Gulf. High detection rates in the Northwest Gulf throughout the winter months demonstrate that this region is potentially utilized year-round. This labelled catalogue may be suitable for developing a deep learning-based whale call detection algorithm. Making use of seismometers and deep learning can increase whale monitoring coverage within the LSLS and elsewhere.

Session 17. Oral Presentation for: Whale detection and microseismic monitoring via DAS using submarine telecommunications cables – a case study from the NWS, Western Australia

Session 17. Oral Presentation for: Whale detection and microseismic monitoring via DAS using submarine telecommunications cables – a case study from the NWS, Western Australia

Deep learning in marine bioacoustics: a benchmark for baleen whale detection

AbstractPassive acoustic monitoring (PAM) is commonly used to obtain year‐round continuous data on marine soundscapes harboring valuable information on species distributions or ecosystem dynamics. This continuously increasing amount of data requires highly efficient automated analysis techniques in order to exploit the full potential of the available data. Here, we propose a benchmark, which consists of a public dataset, a well‐defined task and evaluation procedure to develop and test automated analysis techniques. This benchmark focuses on the special case of detecting animal vocalizations in a real‐world dataset from the marine realm. We believe that such a benchmark is necessary to monitor the progress in the development of new detection algorithms in the field of marine bioacoustics. We ultimately use the proposed benchmark to test three detection approaches, namely ANIMAL‐SPOT, Koogu and a simple custom sequential convolutional neural network (CNN), and report performances. We report the performance of the three detection approaches in a blocked cross‐validation fashion with 11 site‐year blocks for a multi‐species detection scenario in a large marine passive acoustic dataset. Performance was measured with three simple metrics (i.e., true classification rate, noise misclassification rate and call misclassification rate) and one combined fitness metric, which allocates more weight to the minimization of false positives created by noise. Overall, ANIMAL‐SPOT performed the best with an average F metric of 0.83, followed by the custom CNN with an average fitness metric of 0.79 and finally Koogu with an average fitness metric of 0.59. The presented benchmark is an important step to advance in the automatic processing of the continuously growing amount of PAM data that are collected throughout the world's oceans. To ultimately achieve usability of developed algorithms, the focus of future work should be laid on the reduction of the false positives created by noise.

Cuvier's beaked whale (Ziphius cavirostris) detection through surface-sourced eDNA: A promising approach for monitoring deep-diving cetaceans

Among cetaceans, the Cuvier’s beaked whale is considered an extreme diver, thus not one of the easiest cetacean species to be studied due to its elusive behaviour and a preference for deep offshore waters. Although dedicated cetacean surveys have increased our knowledge on the distribution of Cuvier’s beaked whales in the Mediterranean Sea, knowledge gaps still remain where the study effort is non-existent. In this context, the use of new molecular methodologies capable of intercepting small traces of DNA left in the environment (eDNA) by marine organisms represents a valid approach to infer species’ occurrence. Moreover, the collection of water from the superficial layer is suitable for targeting marine mammals, as the constraints imposed by their nature implies periodic and frequent surfacing in order to breathe, releasing exhalants rich in their epithelial cells. Therefore, we designed and tested a taxon-specific primer set to infer Cuvier’s beaked whale presence, with the aims of 1) examining the effectiveness of the eDNA technique to detect the presence of a deep-diving cetacean in open waters, using the Cuvier’s beaked whale as case study; 2) providing data on the occurrence of this species within the Canyon of Caprera over a six-months study period and 3) assessing the species presence in adjacent waters in the central northern Mediterranean Sea based on the analysis of samples collected in a Citizen Science campaign. Results from this study demonstrated that superficial waters may retain biological traces of this cetacean despite the fact that it mostly inhabits deep waters. Specifically, this study provides evidence of the regular presence of Cuvier’s beaked whale in the Canyon of Caprera, with a preference for bathymetry in the range of 700–1000 m. Molecular traces’ distribution suggests a potential inshore movement of this species during Fall, which might be related to migration of its cephalopod prey or a shift in prey preferences, although this aspect requires further investigation. Overall, this study showed that the stronger positive signals were recorded in sampling stations located on surfaces above submarine canyon systems, demonstrating the importance of these areas as elective habitats for the Cuvier’s beaked whale, thus the pivotal priority to their conservation.

Southern Hemisphere humpback whales (Megaptera novaeangliae) (Artiodactyla: Balaenopteridae) singing activity at Caño Island Biological Reserve, Costa Rica before, during, and after COVID-19 lockdowns

Introduction: Boat traffic is recognized as a major contributor of underwater noise. Increasing presence of boats in coastal habitats is predicted to have important repercussions on the communication of marine mammals. In Costa Rica, the waters of the Caño Island Biological Reserve are an important breeding area for humpback whales from the Breeding-Stock G (BSG). Their predicted and abundant presence has fueled the development of whale watching activities as an important component of the local economy, and while the country has norms of conduct for this activity, whales often interact with multiple boats at the same time. The lockdowns associated with the COVID-19 pandemic provided a unique opportunity to study the potential impacts of noise associated with boat traffic on the singing activity of humpback whales.
 Objective: Determine whether noise levels and boat acoustic presence around Caño Island Biological Reserve changed during the COVID-19 lockdowns, and if it did, what is the impact on song detection of BSG humpback whales.
 Methods: Acoustic recordings were made using a bottom-mounted autonomous underwater recorder for 30 days in September 2019, 2020, and 2021, resulting in a total recording effort of 480 hours.
 Results: Our results show that broadband underwater noise levels (dBRMS) during pre-lockdown were significantly higher, particularly at frequencies below 1kHz, than during and post-lockdown. This is likely due to a decrease in the proportion of boat acoustic presence during the lockdown. Although the proportion of whale songs detected did not vary among years, whale songs were detected similarly throughout the day during the lockdown, compared to pre-and-post lockdown where the proportion of whale song presence decreased during hours when more boats were present.
 Conclusions: This study shows a clear change in underwater noise levels during the COVID-19 lockdown, likely due to a decrease in boat presence. The study also highlights the potential impact of noise associated with boat traffic on humpback whale singing activity. The results of this study can inform the Conservation Areas of Osa (ACOSA) in charge of managing Caño Island Biological Reserve, to develop and implement mitigation measures to regulate underwater anthropogenic noise associated with tour boats.

An automated approach for detection and classification of toothed whales in Hawai'i marine protected areas

Passive acoustic monitoring is an effective technique for studying cetacean presence within marine protected areas (MPAs). The Hawaiian archipelago is home to 18 species of resident toothed whales, but little is known regarding the spatio-temporal variability of false killer whales (Pseudorca crassidens—FKW) and short-finned pilot whales (Globicephala macrorhynchus) across Hawai‘i MPAs. Bottom-moored recorders were deployed inside and outside the Hawaiian Islands Humpback Whale National Marine Sanctuary and Papahānaumokuākea Marine National Monument through SanctSound and Pacific Islands Fisheries Science Center. High Frequency Acoustic Recording Packages (HARPs) and SoundTraps (200 kHz/48 kHz SR, respectively) deployed between 2018 and 2022, allowed leveraging of long-term datasets. Data used in this study include 27 deployments across 11 sites, encompassing >187 months of data. Here, we describe an automated approach for species classification which is challenging given the high spectral overlap in whistle features and acoustic masking during humpback whale season. PAMGuard detectors were used to detect whistles, clicks, and burst pulses. Features were extracted using PAMpal and species classified using BANTER models and confirmed from a Hawaiian Islands Ecosystem Assessment Survey annotated dataset. Acoustic monitoring provides vital information about the value of MPAs and a better understanding of endangered insular FKW habitat use.

Further evidence of humpback whale presence in deep tropical ocean during the breeding season: confirmation and extension of acoustic detections between Hawaii and Mexico

During winter humpback whales converge on tropical breeding grounds characterized by shallow, warm seas. In the eastern half of the North Pacific two such breeding grounds are located along the shorelines and shallows of Mexico, and in mid-ocean around Hawaii, separated by 4,500-6,000 km of deep ocean basin. A 2018 acoustic survey by an autonomous Wave Glider from Hawaii eastward towards Mexico, at breeding ground latitudes (circa 20°N) and during peak breeding season, discovered singing whales between these locations near continuously out to mid-ocean - the first evidence of this latitudinal, tropical deep-water distribution. We report the results from a 2021 study which replicated the first half of the 2018 route and extended the survey to Isla Clarión, the westernmost breeding ground of Mexico. The portion of the 2021 survey replicating the earlier course resulted in markedly similar, near-continual detection of humpback whales from Hawaii out 2,161 km (over 1,000 nm) to mid-ocean. Detections occurred on 29 of the first 37 days eastbound from South Point Hawaii (vs. 30 of 35 days in 2018), with up to 3,000 calls a day, including multiple simultaneous singers. The 2021 extension (non-replicative portion) from mid-ocean eastward produced intermittent detections to Isla Clarión. The results, combined with recent reports of photo-identified individuals which traveled between Mexico and Hawaii in one winter season, indicate a recurring tropical offshore presence between these traditional breeding grounds - its extent and purpose to be determined.

Fin whale pulse detection with deep neural networks

In this paper, we present an application of Deep Neural Networks to the detection of Fin Whales (Balaenoptera physalus) pulses, also known as calls, from very long acoustic recordings. For the purpose of detection, acoustic signals are converted to images using a Fourier transform operation. Therefore, acoustic pulses become specific shapes. The detection of pulses and their seasonal distribution is used by biologists to estimate the presence of animals and understand their behavior, and they have important ecological value. However, the variations in shape and the presence of background noise make detection difficult. The use of automated instruments for this task is crucial to processing the huge amount of data that comes from hundreds of hours of recordings in a fast and effective way. In this work, different network architectures are compared to assess their effectiveness in the task on a subset of recordings. Additionally, these are compared with other Machine Learning methods from the literature. The best performing network architecture is then applied to the whole set, consisting of three months of recordings, showing an accuracy of 81.37% and a precision of 75%, comparable to the results obtained with other methods.

Gray whale detection in satellite imagery using deep learning

AbstractThe combination of very high resolution (VHR) satellite remote sensing imagery and deep learning via convolutional neural networks provides opportunities to improve global whale population surveys through increasing efficiency and spatial coverage. Many whale species are recovering from commercial whaling and face multiple anthropogenic threats. Regular, accurate population surveys are therefore of high importance for conservation efforts. In this study, a state‐of‐the‐art object detection model (YOLOv5) was trained to detect gray whales (Eschrichtius robustus) in VHR satellite images, using training data derived from satellite images spanning different sea states in a key breeding habitat, as well as aerial imagery collected by unoccupied aircraft systems. Varying combinations of aerial and satellite imagery were incorporated into the training set. Mean average precision, whale precision, and recall ranged from 0.823 to 0.922, 0.800 to 0.939, and 0.843 to 0.889, respectively, across eight experiments. The results imply that including aerial imagery in the training data did not substantially impact model performance, and therefore, expansion of representative satellite datasets should be prioritized. The accuracy of the results on real‐world data, along with short training times, indicates the potential of using this method to automate whale detection for population surveys.

La Niña conditions influence interannual call detections of pygmy blue whales in the eastern Indian Ocean

Oceans across the globe are warming rapidly and marine ecosystems are changing as a result. However, there is a lack of information regarding how blue whales are responding to these changing environments, especially in the Southern Hemisphere. This is because long term data are needed to determine whether blue whales respond to variability in environmental conditions. Using over 16 years of passive acoustic data recorded at Cape Leeuwin, we investigated whether oceanic environmental drivers are correlated with the migration patterns of eastern Indian Ocean (EIO) pygmy blue whales off Western Australia. To determine which environmental variables may influence migration patterns, we modelled the number of acoustic call detections of EIO pygmy blue whale calls with broad and fine scale environmental variables. We found a positive correlation between total annual whale call detections and El Niño Southern Oscillation (ENSO) cycles and the Indian Ocean Dipole (IOD), with more whale calls detected during La Niña years. We also found that monthly whale call detections correlated with sea surface height around the hydrophone and chlorophyll-a concentration at a prominent blue whale feeding aggregation area (Bonney Upwelling) where whales feed during the summer before migrating up the west Australian coast. At the interannual scale, ENSO had a stronger relationship with call detections than IOD. During La Niña years, up to ten times more EIO pygmy blue whale calls were detected than in neutral or El Niño years. This is likely linked to changes in productivity in the feeding areas of the Great Australian Bight and Indian Ocean. We propose that in lower productivity years whales either skipped migration or altered their habitat use and moved further offshore from the hydrophones and therefore were not detected. The frequency and intensity of ENSO events are predicted to increase with climate change, which is likely to impact the productivity of the areas used by blue whales. These changes in productivity may affect the physical condition and reproductive success of individual whales. A reduction in reproductive success could have a significant impact on blue whale recovery from historical whaling and their ability to adapt to a changing environment.

Deep learning based whale detection from satellite imagery

In spite of their fascinating and vulnerable status, the number of whales in the world's oceans remain debatable because of their living environment. As per statics, with the efforts made towards their conservation, there have been signs of recovery in the population of whales but still, many remote areas remain unsurveyed and thus the number of whales remain ambiguous. To maintain the balance of ecology it is needed to preserve the whales to balance the ecosystem and a key component of that will be to have a count of whales.To date, whale detection has been carried out using ship/aerial surveys, acoustic surveys, etc. but with the development in the field of Very High Resolution (VHR) satellite imagery, satellite images along with deep learning is also being effectively used for the detection of whales.Most of the previous work carried out for the detection of whales via satellite imagery is based upon the manual detection of whales by observers or scientists and that suffers from a high risk of false and inappropriate detections. There is also limited availability of satellite data in this field and there is a lack of usage of Artificial Intelligence for the automatic detection of whales.In this work we propose, a novel deep learning-based automatic whale detection approach from VHR satellite feed. We created a dataset of satellite images having evident chances of whales or probable whales by combining images from two different sources, used Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) to improve the quality of the images and maintain the natural texture of the images, augmented the captured satellite images to train the model, labelled the dataset as whale and probable whale. Finally, a whale dataset of 6000 whale satellite images is created which is the largest dataset available in this field. A deep learning-based whale detection approach is proposed to detect the existence of whales in the satellite image. The proposed approach performed better in terms of precision, recall, and mAP (Mean Average Precision). The precision achieved by the proposed system is 0.95, recall is 0.85, and mAP is 0.74 which is better as compared to the state-of-the-art work in this field.

Fully corrected estimates of common minke whale abundance in West Greenland in 2007

A visual aerial line transect survey for common minke whales (Balaenoptera acutorostrata) was conducted off West Greenland in August and September 2007. A total of 8,670km of survey effort covered 11 strata in sea states <5 with a total stratum area of 213,807km2 . The 27 sightings of common minke whales were all within a strip width of 300m and the average time from first detection to when the sighting passed abeam was 1.7 sec. Due to the uniform and narrow distribution of the detections, strip census methods were used to analyse the survey. Two methods were deployed to correct the strip census estimates for whales missed by the observers and whales that were submerged during the passage of the plane. Method 1 included all detections of common minke whales (n = 27) and correction for an instantaneous availability that included submergence of whales. Using data from sea states <3 (n = 22) the ‘at surface’ abundance of common minke whales was 1,866 (CV = 0.30) whales. A correction for whales missed by the observers with a simple mark-recapture estimator resulted in a corrected abundance of 1,904 (CV = 0.30) whales. Adjusting for the availability bias resulted in a fully corrected estimate of 16,609 (95% CI 7,172–38,461) common minke whales. Method 2 used only detections of common minke whales that were observed to break the surface (n = 19). Applying this method to effort data at sea state <3 (n = 14) resulted in an ‘at surface’ abundance of 1,174 (CV = 0.39) whales. A correction for whales missed by the observers increased the abundance to 1,198 (0.39) whales. Adjusting for the availability bias resulted in a fully corrected estimate of 22,952 (95% CI 7,815–67,403) common minke whales.

Factors that influence aerial line transect detection of BeringChukchi-Beaufort Seas bowhead whales

This paper presents a rich, complex dataset including 25 years of aerial line transect surveys for bowhead whales in the Bering, Chukchi and Beaufort Seas, for which a distance detection function was estimated. The analysis was limited to the autumn migratory period and to the portions of the Beaufort and Chukchi Seas occupied by bowhead whales during this period. The primary purpose of the work was to improve the understanding of what factors significantly affect detection. Comprehensive model selection efforts based on the AIC identified useful predictors. Results showed that Beaufort Sea state, ocean depth, inter-sighting waiting distance and year were among the factors affecting detections. For example, increased depth and long wait distances between sightings were both associated with narrower effective strip widths. Some of the results can be interpreted as evidence for a relationship between detection probabilities and whale behaviour. The complexity of the overall dataset required substantial data organisation and offered many alternative analysis approaches, but the results were fairly consistent across such choices. Notwithstanding successful estimation of the detection function, the data present substantial challenges to standard abundance estimation using line transect methods.

Southern Hemisphere Breeding Stock D humpback whale population estimates from North West Cape, Western Australia

Estimates of the abundance of Breeding Stock D humpback whales (Megaptera novaeangliae) are key to the conservation and management of what is thought to be one of the largest populations of the species. Five years (2000, 2001, 2006, 2007 and 2008) of aerial surveys carried out over an eight-year period at North West Cape (Western Australia) using line transect methodology allowed trends in whale numbers to be investigated, and provided a base for comparison with estimates made approximately 400km south at Shark Bay (Western Australia). A total of 3,127 whale detections were made during 74 surveys of the 7,043km2 study area west of NWC. Pod abundance for each flight was computed using a HorvitzThompson like estimator and converted to an absolute measure of abundance after corrections were made for estimated mean cluster size, unsurveyed time, swimming speed and animal availability. Resulting estimates from the migration model of best fit with the most credible assumptions were 7,276 (CI = 4,993–10,167) for 2000, 12,280 (CI = 6,830–49,434) for 2001, 18,692 (CI = 12,980–24,477) for 2006, 20,044 (CI = 13,815–31,646) for 2007, and 26,100 (CI = 20,152–33,272) for 2008. Based on these data, the trend model with the greatest r2 was exponential with an annual increase rate of 13% (CI = 5.6%–18.1%). While this value is above the species’ estimated maximum plausible growth rate of 11.8%, it is reasonably close to previous reports of between 10–12%. The coefficient of variation, however, was too large for a reliable trend estimate. Perception bias was also not accounted for in these calculations. Based on a crude appraisal which yielded an estimated p(0) of 0.783 (from independent observer effort, CV = 0.973), the 2008 humpback population size may be as large as 33,300. In conclusion, the work here provides evidence of an increasing Breeding Stock D population, but further surveys are necessary to confirm whether the population is indeed increasing at its maximum rate.

Comparison of visual and acoustic surveys for the detection and dynamic management of North Atlantic right whales (Eubalaena glacialis) in Canada

AbstractThe goal of this study was to develop a simulation to quantitatively compare acoustic and visual surveys and use it to inform current and future North Atlantic right whale (Eubalaena glacialis) risk mitigation. We expanded upon an established whale movement model, incorporating realistic right whale cues for visual and acoustic detection within dynamic management zones in the Gulf of Saint Lawrence, Canada. Survey transits by acoustic (Slocum gliders) and visual (aircraft, vessels, and Remotely Piloted Aircraft Systems) platforms were simulated using representative platform movements and detection functions. We used a Monte Carlo approach to estimate the probability of detecting a cue, in each zone, as a function of survey platform, number of right whales, and survey transits. Acoustic gliders detected right whale presence in every scenario. Single transits of a management zone by visual surveys were only able to reliably (>0.5 probability) detect right whales when more than 20 whales were present. Twenty or more transits were required to reliably detect a single right whale. Our results serve as a tool to be used by decision‐makers to inform optimal right whale monitoring strategies that consider the relative strengths of the various platforms.

On the availability bias in narwhal abundance estimates

Abundance estimation of narwhals is usually done with either visual or photographic aerial surveys. The basic estimation for both methods is detection of whales at the surface, and to obtain fully corrected abundance estimates, the at-surface detections need to be corrected for the proportion of whales that, at any given time, is available to be detected at the surface. The surfacing time or ‘availability correction factor’ is obtained from whales instrumented with dive recorders, that either relay concatenated information on the proportion of time spent at different depth intervals to satellites, or from recovered instruments that collect complete dive profiles, measured at high frequency. Concatenated data binned in depth histograms from Satellite-Linked-Time-Depth-Recorders (SLTDR) falls in two categories, where those that correct the zero depth values with information from the saltwater switch provide larger and apparently more accurate surfacing times, than those collected from instruments that does not correct the zero depth readings. The erroneous detection of near-surface pressure values is likely due to slow response of pressure transducers made from temperature sensitive materials. The high frequency sampling from AcousondeTM recorders documents erroneous surface detections, and adjustments of the dive profiles are needed to obtain realistic near-surface values. Any reconstruction of dive profiles and near-surface values apparently involves some level of corrections and it is recommended, for development of availability correction factors for aerial surveys, that data from zero-adjusted SLTDRs or TDR instruments are used. The mean estimate of surface time from 7 SLTDRs was 29% (CV=0.05). One SLTDR, with steel pressure transducer and zero-adjustments, that was retrieved from the whale, provided a particular long-record (83 days) of reliable high-resolution data. The surface time for this sample was 31%, when calculated as the sum of all depth readings at or above 2 m. The mean of 144 hourly depth readings during 06:00-18:00, and including dives above 3m, was 27.36% (CV=0.8) for 12 days overlapping with the usual timing of aerial surveys. Accurate estimation of smaller depth bins (e.g. 0-1 m) should, even with high resolution instruments, be used with caution when estimating availability bias.

Enhancing UAV images to improve the observation of submerged whales using a water column correction method

Enhancing <scp>UAV</scp> images to improve the observation of submerged whales using a water column correction method

Beluga (D. leucas), harbor porpoise (P. phocoena), and killer whale (O. orca) acoustic presence in kotzebue sound, alaska: Silence speaks volumes

Prior to 1984, belugas (Delphinapterus leucas) were seen in large numbers during spring and summer in Kotzebue Sound, Alaska, and provided an important subsistence resource to coastal residents. Sightings and harvest declined sharply beginning in 1984: the average annual harvest dropped from 84/yr (1977–1983) to 16/yr (1984–2021). To examine the current seasonal and spatial occurrence of belugas in Kotzebue Sound, passive acoustic moorings were deployed in summer 2013 and year-round in 2014–2016. Three moorings were deployed off Cape Krusenstern, northwestern Kotzebue Sound, to monitor cetaceans traveling nearshore. A mooring was also deployed near Chamisso Island, southeastern Kotzebue Sound. We used automatic detectors to process the recordings for echolocation and tonal signals, and all detections were manually validated. Belugas, harbor porpoises (Phocoena), and transient killer whales (Orcinus orca) were detected in both areas, primarily from June to November. Detections extended into early winter for belugas, and sporadic detections were confirmed for porpoises from January to March. Belugas were detected on a total of 20 days, killer whales on 96 days, and porpoises on 179 days. All beluga detections were echolocation signals; the absence of social signals likely reflects an anti-predator response to transient killer whales and possibly to subsistence hunters. Killer whale detections were composed of echolocation signals, limited to very short click trains, double clicks, and single clicks, a known cryptic acoustic behavior used when targeting prey. Killer whales also emitted high frequency whistles (17–51 kHz) providing the first evidence of these types of signals for transients. Our results suggest transient killer whales in predation mode scouting harbor porpoise and beluga habitat, concurrent with belugas in silent anti-predation mode. This anti-predation acoustic behavior by belugas was also evident when killer whales were not present, conveying a continued perception of predation risk for this habitat. The combined natural and anthropogenic predation pressure in Kotzebue Sound could be playing an important role in the continued low occurrence of belugas.