We investigate spatially explicit models and ensemble modeling techniques for estimating animal abundance from line-transect survey data. Spatially explicit models are expected to be statistically more efficient, resulting in more precise abundance estimates, than design-based abundance estimators that rely heavily on assumptions about survey design and realization. Ensemble modeling reduces error by averaging among models, and allows for model selection uncertainty to propagate to the abundance estimator. We develop density surface models using Matérn covariance functions and spline-based smooths for a case study, belugas (Delphinapterus leucas) from the Eastern Bering Sea (EBS) stock. EBS belugas are upper trophic level predators in a rapidly changing ecosystem and are a vital nutritional and cultural resource for Alaska Natives. Effective management of this stock requires regular monitoring to derive accurate and unbiased estimates of abundance. Since 1992, aerial line-transect surveys have been the primary means of surveying and estimating abundance of EBS belugas in the region. We compare EBS beluga abundance estimates for 2017 and 2022 that were derived using post-stratified, design-based abundance estimators with analogous estimates the we derive using spatially explicit and ensemble modeling methods. The estimated precision in the abundance estimates from the individual density surface models (DSMs) and the ensemble average of DSMs is higher than for the design-based estimator in both survey years. The design-based models estimated that there were 12,269 belugas in 2017 (coefficient of variation (CV) = 0.118) and 19,811 belugas within a larger study area in 2022 (CV = 0.343). The ensemble spatial models estimate that there were 11,654 belugas in 2017 (CV = 0.118) and 13,313 belugas in 2022 (CV = 0.216). Among the individual spatially explicit models, abundance estimates range from 11,242 to 11,963 (CV = 0.111 to 0.114) in 2017 and 12,023 to 15,593 (CV = 0.172 to 0.198) in 2022. Because spatial models identify spatial patterns in beluga density at finer resolutions than design-based models, we argue that ensembles of spatially explicit density models provide a reasonable path forward for estimating EBS beluga abundance and distribution in a way that is useful to management and conservation efforts.

The offshore wind energy (OSW) industry is pivotal for renewable energy transition and climate resiliency. However, OSW activities may negatively affect aerofauna, contributing to CE from human activities and natural processes. Cumulative effects assessments (CEA) are vital for informed planning and management of OSW activities during regional assessment, site selection, and site evaluation. To reduce impacts on aerofauna, OSW developments should be sited in areas that avoid or minimize cumulative effects (CE). This study presents a cohesive and flexible framework for assessing the CE from OSW activities, other human activities, and natural processes on aerofauna to support decision-making during the initial OSW planning phases. The framework uses a species-based approach, applicable to various receptors, and adapts to available information on ecology, socioeconomics, and pressures. The analytical strategy uses a CE metric to indicate the presence or magnitude of effects from all pressures on receptors. Spatially explicit optimization methods identify OSW site configurations that minimize a CE metric. The framework accommodates alternative pressure scenarios that include foreseeable future human activities and natural processes and can explore the sensitivity of the result to uncertain parameters. Given sufficient spatial information on receptor density, pressure magnitude, and cause-effect pathways, the spatial optimization algorithm can find solutions that minimize population-level impacts from CE. If this ideal standard cannot be achieved due to information gaps, alternative metrics may be used to inform the immediate decision-making process. The CEA framework standardizes CE metrics and criteria across assessments, enabling comparability and consistency in decision-making. Although developed for Atlantic Canada, the framework is globally applicable. ### Competing Interest Statement The authors have declared no competing interest.

Place-based approaches to marine conservation identify areas that are crucial to the success of populations, species, communities, or ecosystems, and that may be candidates for special management actions. In the United States, the National Oceanic and Atmospheric Administration defined Biologically Important Areas (BIAs) for cetaceans (whales, dolphins, and porpoises) as areas and periods that individual populations or species are known to preferentially use for certain activities or where small resident populations occur. The activities considered to be biologically important are feeding, migrating, and activities associated with reproduction. We present an approach using spatial optimization to refine the BIA delineation process to be more objective and reproducible for conservation planners and decision makers who wish to use various spatial criteria to address conservation or management objectives. We present a case study concerning feeding bowhead whales ( Balaena mysticetus ) and bowhead whale calves in the western Beaufort Sea to illustrate the mechanics and benefits of our optimization model. In the case study, we incorporate spatial information about whales’ relative density and optimally delineate BIAs under different thresholds for minimum patch (cluster) size and total area encompassed within the BIA network. Results from our case study showed three consistent patterns related to minimum cluster size (contiguity) and maximum area threshold for both BIA types and all months: (1) cells with the highest whale density were selected when contiguity or maximum area thresholds were small; (2) for a given area threshold, the number of whales inside BIAs was inversely proportional to cluster size; and (3) the number of whales inside BIAs initially increased rapidly as the area threshold increased, but eventually approached an asymptote. Additionally, information on temporal variability in a BIA may influence the development of conservation, management, monitoring, or mitigation methods. To provide additional insight into the ecological characteristics of the BIAs selected during the optimization step, we quantified inter-annual variability in whale occurrence and density within individual BIAs using statistical techniques. The bowhead whale BIAs and associated information that we present can be incorporated with other relevant information (e.g., objectives, stressors, costs, acceptable risk, legal constraints) into conservation and management decision-making processes.

The Bowhead Whale Aerial Survey Project (BWASP) has been conducted annually since 1979 in the Alaskan Beaufort Sea to monitor the distribution and relative abundance of the Bering-Chukchi-Beaufort (BCB) stock of bowhead whales (Balaena mysticetus) during their autumn migration. BWASP was created to specifically address broad-scale research and management questions related to bowhead whale ecology, with particular interest in the potential effects of oil and natural gas exploration, development and production activities on the BCB bowhead whales. With elevated concerns about climate change, increasing oil and gas activities and the forecasted increase in vessel traffic, it is expected that interest in the BWASP dataset will also increase in order to evaluate effects of these anthropogenic activities on BCB bowhead whales and indigenous whaling. The following analysis quantified the spatial characteristics of the BWASP survey design and provided guidelines for the types of investigations that the BWASP data can potentially address. Sampling lags (transect spacing) in the BWASP survey design of approximately 20km along the east/west axis of the study area limit the spatial scale of phenomena that can be detected using data from a single BWASP survey. Therefore, BWASP data are relatively uninformative for studying variability in distribution or relative abundance along the east/west axis over short time scales (one survey) and within small areas measuring less than approximately 20km. In addition, computer simulations showed spatial heterogeneity in the long-term survey coverage probability (the probability that a given location will be included in a survey having an assumed effective search width under the BWASP survey design). Pooled transects created from simulated surveys resulted in a repeating diamond pattern in which coverage probability was low. Analyses incorporating data from many BWASP surveys should account for this spatial heterogeneity, via either the survey coverage probabilities or quantification of survey effort; otherwise estimates of variables such as relative density, density, or habitat use may be biased. The BWASP surveys have increased understanding of the broad-scale patterns of bowhead distribution, relative abundance and behaviour. The utility of this dataset in informing other questions will depend upon the scale of the ecological phenomena under investigation and the analytical scales used to address the questions.

We delineated and scored Biologically Important Areas (BIAs) in the Arctic region. The Arctic region extends from the Bering Strait to the Chukchi Sea, Beaufort Sea, Amundsen Gulf, and Viscount Melville Sound. This NOAA-led effort uses structured elicitation principles to build upon the first version of NOAA BIAs (BIA I) for cetaceans. In addition to narratives, maps, and metadata tables, BIA II products incorporated a scoring and labeling system to improve their utility and interpretability. BIAs are compilations of the best available science and have no inherent regulatory authority. They have been used by NOAA, other federal agencies, and the public to support marine spatial planning and marine mammal impact assessments, and to inform the development of conservation measures for cetaceans. Supporting evidence for Arctic BIA II came from data derived from aerial-, land-, and vessel-based surveys; satellite telemetry; passive acoustic monitoring; Indigenous knowledge; photo-identification; aboriginal subsistence harvests, including catch and sighting locations and stomach contents; and prey studies. BIAs were identified for bowhead ( Balaena mysticetus ), gray ( Eschrichtius robustus ), humpback ( Megaptera novaeangliae ), fin ( Balaenoptera physalus ), and beluga ( Delphinapterus leucas ) whales. In total, 44 BIAs were delineated and scored for the Arctic, including 12 reproduction, 24 feeding, and 8 migration BIAs. BIAs were identified in all months except January-March. Fifteen candidate areas did not have sufficient information to delineate as BIAs and were added to a watch list for future consideration in the BIA process. Some BIAs were transboundary between the Arctic region and the Aleutian Islands-Bering Sea region. Several BIAs were transnational, extending into territorial waters of Russia (in the Chukchi Sea) and Canada (in the Beaufort Sea), and a few BIAs were delineated in international waters.

A strong negative relationship has been observed between calf production of eastern North Pacific gray whales Eschrichtius robustus and Pacific Arctic sea ice area with the exception of 2 potentially anomalous periods: 2013-2014 and 2017-2019. Sea ice may play a role in reproductive variability by blocking gray whale access to foraging hotspot habitats. We tested this ‘sea ice exclusion’ hypothesis using complementary aerial survey and moored hydrophone data sets that together provide broad spatial and temporal coverage. A negative relationship (generalized additive model, p < 0.001) was found between gray whale aerial counts and ice concentration in the northeast Chukchi Sea (2008-2019), with a nonlinear increase in negative slope above 45-55% concentration. A comparison of distribution patterns between years further indicated the absence of sightings within northern Bering and northeastern Chukchi foraging hotspots during years with delayed ice break-up versus years with an early-to-average ice retreat. Passive acoustic recordings likewise showed the near-absence of detections during periods of dense ice cover and indicated a strongly positive relationship between ice break-up dates and the timing of acoustic detection onset. Together, these analyses point to the important role of sea ice in gray whale distribution, habitat use, and phenology. However, a relatively consistent 10-15 d lag observed between ice break-up and acoustic onset dates suggested that the mechanism(s) underpinning gray whale interactions with sea ice may be more complex than simply ‘sea ice exclusion.’ Here, we propose ‘prey quality timing’ as a potential alternative hypothesis that warrants further investigation.

Estimating the abundance and density of beaked whales is more difficult than for most other cetacean species. Consequently few estimates appear in the published literature. Field identification is problematic, especially for the smaller species, and visual detection rates decrease dramatically with Beaufort sea state; prior experience is very important to an observer’s ability to detect beaked whales. Passive acoustics may hold future promise for detecting beaked whales from their vocalisations, especially for the larger species. Most published estimates of abundance or density are based on visual line-transect studies that found narrower effective strip widths and lower trackline detection probabilities for beaked whales than for most other cetaceans. Published density estimates range from 0.4-44 whales per 1,000km2 for small beaked whales and up to 68 whales per 1,000km2 for large beaked whales. Mark-recapture methods based on photo-identification have been used to estimate abundance in a few cases in limited geographical areas. Focused research is needed to improve beaked whale abundance and density estimates worldwide.

In social animals, group composition can cause variations in individual needs that can influence responses to habitat trade-offs, such as predator exposure or foraging opportunities. The Eastern Beaufort Sea beluga whales (Delphinapterus leucas (Pallas, 1776)) form different group types and cover multiple habitat types in summer. This study compares the habitat preference of three beluga social group types: (1) individual belugas, (2) groups of adults, and (3) groups with at least one calf. Observations were collected during aerial surveys in July and August 2019. For each month, beluga distribution was analyzed with hierarchical generalized additive models, as a function of group type and four covariates: sea surface temperature, bathymetry, slope, and distance to the coastline. Group type, water temperature, and bathymetric features best explained beluga distribution. In July, groups of adults preferred the continental shelf, whereas individual belugas and groups with calves preferred the continental slope. In August, groups of adults and groups with calves were found in Amundsen Gulf at similar depths. For both months, individual belugas associated more with deeper and colder areas. The preferences often corresponded to previously published distributions of the beluga's main prey species, suggesting that foraging opportunities and size-related energy requirements strongly influence habitat use.

Temporally dynamic environmental variables and fixed geographic variables were used to construct generalised additive models to predict Cuvier’s ( Ziphius cavirostris) and Mesoplodon beaked whale encounter rates (number of groups per unit survey effort) and group sizes in the eastern tropical Pacific Ocean. The beaked whale sightings and environmental data were collected simultaneously during the Southwest Fisheries Science Center’s cetacean line-transect surveys conducted during the summers and autumns of 1986-90 and 1993. Predictions from the encounter rate and group size models were combined with previously published estimates of line-transect sighting param eters to describe patterns in beaked whale population density (number of individuals per unit area) throughout the study area. Results p rovide evidence that the previously proposed definition of beaked whale habitat may be too narrow and that beaked whales may be found from the continental slope to the abyssal plain, in waters ranging from well-mixed to highly stratified. Areas with the highest predicte d population densities were the Gulf of California, the equatorial cold tongue and coastal waters, including the west coast of the Baja Peni nsula and the Costa Rica Dome. Offshore waters in the northern and southern subtropical gyres had the lowest predicted Mesoplodon densities, but density predictions were high for Cuvier’s beaked whales in the waters southeast of the Hawaiian Islands. For both encounter rate and group size models, there was no geographic pattern evident in the residuals as measured by the ratio of pooled predicted to pooled ob served values within geographic strata.

Building on earlier work identifying Biologically Important Areas (BIAs) for cetaceans in U.S. waters (BIA I), we describe the methodology and structured expert elicitation principles used in the “BIA II” effort to update existing BIAs, identify and delineate new BIAs, and score BIAs for 25 cetacean species, stocks, or populations in seven U.S. regions. BIAs represent areas and times in which cetaceans are known to concentrate for activities related to reproduction, feeding, and migration, as well as known ranges of small and resident populations. In this BIA II effort, regional cetacean experts identified the full extent of any BIAs in or adjacent to U.S. waters, based on scientific research, Indigenous knowledge, local knowledge, and community science. The new BIA scoring and labeling system improves the utility and interpretability of the BIAs by designating an overall Importance Score that considers both (1) the intensity and characteristics underlying an area’s identification as a BIA; and (2) the quantity, quality, and type of information, and associated uncertainties upon which the BIA delineation and scoring depend. Each BIA is also scored for boundary uncertainty and spatiotemporal variability (dynamic, ephemeral, or static). BIAs are region-, species-, and time-specific, and may be hierarchically structured where detailed information is available to support different scores across a BIA. BIAs are compilations of the best available science and have no inherent regulatory authority. BIAs may be used by international, federal, state, local, or Tribal entities and the public to support planning and marine mammal impact assessments, and to inform the development of conservation and mitigation measures, where appropriate under existing authorities. Information provided online for each BIA includes: (1) a BIA map; (2) BIA scores and label; (3) a metadata table detailing the data, assumptions, and logic used to delineate, score, and label the BIA; and (4) a list of references used in the assessment. Regional manuscripts present maps and scores for the BIAs, by region, and narratives summarizing the rationale and information upon which several representative BIAs are based. We conclude with a comparison of BIA II to similar international efforts and recommendations for improving future BIA assessments.

We delineated and scored Biologically Important Areas (BIAs) for cetaceans in the Aleutian Islands and Bering Sea region. BIAs represent areas and times in which cetaceans are known to concentrate for activities related to reproduction, feeding, and migration, and also the known ranges of small and resident populations. This effort, the second led by the National Oceanic and Atmospheric Administration (NOAA), uses structured elicitation principles to build upon the first version of NOAA’s BIAs (BIA I) for cetaceans. Supporting evidence for BIA II came from aerial-, land-, and vessel-based surveys; satellite-tagging data; passive acoustic monitoring; Indigenous knowledge; photo-identification data; whaling data, including stomach and fecal contents; prey studies; and genetics. In addition to narratives, maps, and metadata tables, the BIA II products incorporate a scoring and labeling system, which will improve their utility and interpretability. BIAs are compilations of the best available science and have no inherent regulatory authority. They have been used by NOAA, other federal agencies, and the public to support planning and marine mammal impact assessments, and to inform the development of conservation measures for cetaceans. In the Aleutian Islands and Bering Sea region, a total of 19 BIAs were identified, delineated, and scored for seven species, including bowhead, North Pacific right, gray, humpback, fin, and sperm whales, and belugas. These include one hierarchical BIA for belugas that consists of one localized “child” BIA within an overarching “parent” BIA. There were 15 feeding, 3 migratory, and 1 small and resident population BIAs; no reproductive BIAs were identified. In some instances, information existed about a species’ use of a particular area and time, but the information was insufficient to confidently delineate the candidate BIA; in those cases, the candidate BIA was added to a watch list. A total of 22 watch list areas were identified and delineated for 10 species, including all species mentioned above and minke whales, harbor porpoises, and Dall’s porpoises. There were 15 feeding, 4 migratory, 2 reproductive, and 1 small and resident population watch list areas. Some BIAs and watch list areas were transboundary between the Aleutian Islands and Bering Sea region and the Arctic region.

Studies of the impacts of climate change on Arctic marine ecosystems have largely centered on endemic species and ecosystems, and the people who rely on them. Fewer studies have focused on the northward expansion of upper trophic level (UTL) subarctic species. We provide an overview of changes in the temporal and spatial distributions of subarctic fish, birds, and cetaceans, with a focus on the Pacific Arctic Region. Increasing water temperatures throughout the Arctic have increased “thermal habitat” for subarctic fish species, resulting in northward shifts of species including walleye pollock and pink salmon. Ecosystem changes are altering the community composition and species richness of seabirds in the Arctic, as water temperatures change the available prey field, which dictates the presence of planktivorous versus piscivorous seabird species. Finally, subarctic whales, among them killer and humpback whales, are arriving earlier, staying later, and moving consistently farther north, as evidenced by aerial survey and acoustic detections. Increasing ice-free habitat and changes in water mass distributions in the Arctic are altering the underlying prey structure, drawing UTL species northwards by increasing their spatial and temporal habitat. A large-scale shuffling of subarctic and Arctic communities is reorganizing high-latitude marine ecosystems.

Information on factors contributing to the morbidity and mortality of the Bering–Chukchi–Beaufort seas stock of bowhead whales (Balaena mysticetus) is fundamental to its successful management and recovery. The Alaska Arctic coastline is remote and expansive, making monitoring for and gross examination of carcasses difficult. However, sighting data and imagery collected during aerial surveys in the eastern Chukchi (EC) and western Beaufort (WB) seas from 2009 through 2019 provide information on bowhead whale mortality. We present bowhead whale carcass data from the 2019 aerial surveys that add to the long series of consistent information on floating and beach-cast bowhead whale carcasses. The 2019 carcass data suggest an increased occurrence of probable killer whale (Orcinus orca) predation on bowhead whales in the WB. Eleven bowhead whale carcasses were photo-documented from July to October 2019 in the EC and WB study areas. Of the 11 carcasses documented, 7 had injuries consistent with probable killer whale predation—2 in the EC and 5 in the WB. Probable cause of death could not be assigned to four carcasses. No carcasses were associated with aboriginal subsistence hunting. Despite similar annual survey effort from 2009 to 2019, several compelling deviations in carcass numbers and locations were observed in 2019. Compared to 2009–2018, 2019 had the highest yearly number of documented carcasses and the most categorized as probable killer whale predation. Carcass locations exhibited a striking shift from the EC to the WB. Lastly, more carcasses were categorized as probable killer whale predation in the WB during 2019 than in 2009–2018 combined.

Bearded seals are pan-Arctic ice-obligate phocids; for the threatened Beringia population, the majority of the population feeds in the summer in the Chukchi Sea, then migrates south to overwinter in the northern Bering Sea. Contemporary information on the impact of rapidly changing climatic conditions on bearded seal distribution is essential for effective management. To monitor for marine mammals, passive acoustic recorders were deployed throughout the eastern Chukchi and northern Bering seas (64 degrees N to 72 degrees N), sampling at a rate of 16 kHz on a duty cycle of either 80 or 85 min every 5 h. Data from year-long deployments at nine sites over four years (2012-2016) were manually analyzed, totaling 13,275 days (similar to 75,000 h). Bearded seal calling activity was present at every site in every year. Calling activity increased from September through February and reached sustained and saturated levels from March through June, at which point calling ceased abruptly regardless of ice cover. The timing of calling and its abrupt cessation correspond with the known breeding season of bearded seals. However, the timing of the cessation of calling occurred earlier each year, corresponding with an earlier sea ice retreat. The sustained calling detected overwinter at all locations suggests that this is more than just a few animals that are remaining in the Chukchi Sea. Preceding this main pulse was a smaller peak in calling that progressed southward, corresponding with the fall migration of bearded seals to the Bering Sea. These results increase our knowledge on the year-round spatio-temporal distribution and migration patterns of this pagophilic species, and the relationship between calling activity and sea ice concentration.

Examining Eastern North Pacific gray whale ( Eschrichtius robustus ) carcasses and tracking mortality and morbidity are essential for assessing the health of this stock. In the eastern Chukchi Sea, the expansive coastline relative to few coastal communities makes monitoring for and physical examination of gray whale carcasses difficult. The Aerial Surveys of Arctic Marine Mammals (ASAMM) project offers an unparalleled dataset of gray whale carcasses, documented and photographed from July to October 2009–2019, providing a unique opportunity to investigate imaged gray whale carcasses for possible cause of death. Surveys covered expanses of gray whale and killer whale ( Orcinus orca ) summer and autumn habitat. ASAMM documented a total of 59 gray whale carcasses, distributed across the eastern Chukchi Sea (67.5° N–72.0° N, 155.5° W–169.0° W). Carcass sighting rates ([CPUE] carcasses per 1000-km of effort) varied by month and year. The highest numbers of carcasses were observed in 2012 (13) and 2019 (8). August had the highest number of gray whale carcass sightings (22) and the highest carcass sighting rate (0.231 CPUE). Images were obtained for 56 gray whale carcasses. The majority (41) of imaged gray whale carcasses had injuries consistent with probable killer whale predation, and were photo-documented every year except 2010 (when no carcasses were seen) and 2011. Eight carcasses were suspect killer whale predation, and cause of death could not be determined for seven carcasses. These results will be valuable for evaluating mortality, concurrent with rapid oceanographic changes, and increases in anthropogenic activities.

Collisions between vehicles and wildlife is a global conservation concern, and vessel strikes are a leading cause of serious injury and mortality for baleen whales. Yet vessel strikes have rarely been studied in the Arctic. Vessel traffic is increasing throughout the Arctic as sea ice is declining, leading to increased overlap between vessels and whales. We examined hypothetical vessel strike risk for the Bering-Chukchi-Beaufort (BCB) and Eastern Canada-West Greenland (ECWG) populations of bowhead whales during the open-water shipping season. We used satellite telemetry and aerial survey data to calculate monthly relative density of both populations, and satellite vessel tracking data to calculate monthly vessel density and speed. We estimated vessel strike risk by multiplying whale density by vessel density corrected by vessel speed. For the BCB population, the highest relative risk was near Utqiaġvik and Prudhoe Bay, Alaska, USA, and near Tuktoyaktuk, Northwest Territories, Canada. For the ECWG population, the highest risk was in the Gulf of Boothia, Cumberland Sound, and near Isabella Bay, Nunavut, Canada. Strike risk was highest in August and September, corresponding with monthly trends in vessel traffic. This study provides important information for focussed monitoring and to minimize/mitigate the threat of vessel strikes to bowhead whales. Although vessel strike risk is presently lower for these populations than for other temperate large cetacean populations, bowhead whale behaviour and projected increases in traffic elevates their risk in the Arctic. Measures to mitigate vessel strike risk to bowhead whales will likely benefit other Arctic marine mammals like beluga and narwhal.

Successful reproduction is essential to a species existence. Here we summarize Bering–Chukchi–Beaufort (BCB) bowhead whale (Balaena mysticetus Linnaeus, 1758) calf distribution, ratio of calf to adult sightings, and encounter rate from data collected during line–transect aerial surveys conducted from July to October 2012–2019 in the western Beaufort Sea (140°W–157°W). During 223,000 on effort km, a total of 274 calves were seen: 100 in summer (July–August) and 174 in fall (September–October), compared with nearly 3,200 non-calves. Calves were widely distributed in the study area in August and September, with distribution in July largely east of 150°W and distribution in October west of 143°W. Calf ratios and encounter rates appear to follow a 3–4 year cycle. Most calves (240/274; 88%) were seen near an adult assumed to be the maternal female, but 9% (26/274) of all calves were observed unaccompanied at the surface and 3% (8/274) were observed with large whales at the surface but not close by. Of the total calves detected, 60% (165/274) were observed after circling was initiated, highlighting the importance of closely investigating all bowhead whale sightings if identification of calves is critical to project goals. Bowhead whale calf data from the eastern Beaufort Sea and Amundsen Gulf in August 2019 are also summarized.

Bowhead whales (Balaena mysticetus) in the western Beaufort Sea (west of 140°W, south of 72°N) exhibit considerable spatiotemporal variability in distribution, density, and behavior that can be largely explained by variability in feeding opportunities, both local and remote. Bowhead whale feeding opportunities are dynamic and ephemeral, dependent on interannual variability driven by biological and physical forces. These insights were made possible by multidisciplinary investigations centered around the Aerial Surveys of Arctic Marine Mammals (ASAMM, https://www.fisheries.noaa.gov/alaska/marine-mammal-protection/aerial-surveys-arctic-marine-mammals) time series, a long-term (1979–2019) dataset of line-transect surveys in the western Beaufort and eastern Chukchi Seas conducted during summer (July–August) and autumn (September–October). Seasonal patterns of bowhead whale distribution and density in the western Beaufort Sea during two time periods, prior to 2000 and since 2000, are evident in spatially explicit models of relative density that were created using ASAMM data. These models illustrate a general transition in bowhead distribution toward shallower waters as the open-water season (July–October) progresses. Notably, however, the location and number of high-density areas and the timing of the spatial transition have shifted between the two periods. The association between bowhead whales and sea ice in the western Beaufort Sea is rather enigmatic, as exemplified by the similarities in bowhead whale distribution in autumn 2019 (when sea ice retreat was extensive) with the distribution observed when sea ice extent was heavy in the 1980s and 1990s. Anthropogenic factors also affect bowhead whale distribution and density. The effects of anthropogenic and environmental factors on bowheads may be confounded, especially lacking sufficient understanding of the underlying environmental variability inherent in the ecosystem. Variability is a defining characteristic of the Arctic, but the parameters appear to be changing, sometimes in unexpected ways. Improving our understanding and capacity to predict arctic variability is fundamentally important to sound natural resource management. It follows that sound natural resource management is founded on continued monitoring of the ecosystem so that our understanding of the ecological linkages that shape animal distributions and densities tracks the changes occurring, and expected to continue to occur, in the Arctic.

Cook Inlet, Alaska, is home to an endangered and declining population of 279 belugas ( Delphinapterus leucas ). Recovery efforts highlight a paucity of basic ecological knowledge, impeding the correct assessment of threats and the development of recovery actions. In particular, information on diet and foraging habitat is very limited for this population. Passive acoustic monitoring has proven to be an efficient approach to monitor beluga distribution and seasonal occurrence. Identifying acoustic foraging behavior could help address the current gap in information on diet and foraging habitat. To address this conservation challenge, eight belugas from a comparative, healthy population in Bristol Bay, Alaska, were instrumented with a multi-sensor tag (DTAG), a satellite tag, and a stomach temperature transmitter in August 2014 and May 2016. DTAG deployments provided 129.6 hours of data including foraging and social behavioral states. A total of 68 echolocation click trains ending in terminal buzzes were identified during successful prey chasing and capture, as well as during social interactions. Of these, 37 click trains were successfully processed to measure inter-click intervals (ICI) and ICI trend in their buzzing section. Terminal buzzes with short ICI (minimum ICI <8.98 ms) and consistently decreasing ICI trend (ICI increment range <1.49 ms) were exclusively associated with feeding behavior. This dual metric was applied to acoustic data from one acoustic mooring within the Cook Inlet beluga critical habitat as an example of the application of detecting feeding in long-term passive acoustic monitoring data. This approach allowed description of the relationship between beluga presence, feeding occurrence, and the timing of spawning runs by different species of anadromous fish. Results reflected a clear preference for the Susitna River delta during eulachon ( Thaleichthys pacificus ), Chinook ( Oncorhynchus tshawytscha ), pink ( Oncorhynchus gorbuscha ), and coho ( Oncorhynchus kisutch ) salmon spawning run periods, with increased feeding occurrence at the peak of the Chinook and pink salmon runs.

Eight years of passive acoustic data (2007-2014) from the Beaufort Sea were used to estimate the mean cue rate (calling rate) of individual bowhead whales (Balaena mysticetus) during their fall migration along the North Slope of Alaska. Calls detected on directional acoustic recorders (DASARs) were triangulated to provide estimates of locations at times of call production, which were then translated into call densities (calls/h/km2). Various assumptions were used to convert call density into animal cue rates, including the time for whales to cross the arrays of acoustic recorders, the population size, the fraction of the migration corridor missed by the localizing array system, and the fraction of the seasonal migration missed because recorders were retrieved before the end of the migration. Taking these uncertainties into account in various combinations yielded up to 351 cue rate estimates, which summarize to a median of 1.3 calls/whale/h and an interquartile range of 0.5-5.4 calls/whale/h.