The International Whaling Commission’s Scientific Committee conducts regular Implementation Reviews (IRs) of the biology, threats and status of whale species subject to aboriginal subsistence whaling. The last IR of plans for hunting eastern North Pacific (ENP) gray whales by the Chukotka Natives of the Russian Federation and the Makah Tribe of the United States of America occurred in 2020. This paper presents a review of new scientific findings on gray whales to assess whether the current status of the stock(s) is within the parameter space tested in the 2020 IR. Updated information on gray whale stock structure hypotheses, abundance and calf productivity, health and strandings, human removals by hunting and non‐hunting sources, population growth rates, immigration into the Pacific Coast Feeding Group, parameterisation of the Makah hunt, and future episodic mortality events (EMEs) were reviewed for this assessment. For almost all factors, it appears that the current dynamics of the ENP gray whale population are within the parameter space evaluated in 2020 IR. The exception is that EMEs affecting whales in the ENP are occurring more frequently and at a greater magnitude than previously evaluated. However, preliminary evaluations suggest that the performances of the Gray Whale Strike Limit Algorithm (SLA) and Makah Management Plan are robust to recent and future EMEs of Northern Feeding Group gray whales and reductions of productivity of the Pacific Coast Feeding Group, at least under the initial parameterisations. We therefore conclude that there is no compelling need for a Special IR prior to the next scheduled IR in 2026, while noting that additional abundance data for 2022/23 and 2023/24 analysed after drafting this paper could strengthen or weaken the evidence for this conclusion.

Data from Bering-Chukchi-Beaufort Seas bowhead whales (Balaena mysticetus), harvested during 1973-2021 by aboriginal subsistence hunters, were used to estimate reproductive parameters: length at sexual maturity (LSM), age at sexual maturity (ASM), pregnancy rate (PR), and calving interval. Sexual maturity (N = 187 females) was determined from the presence/absence of corpora in the ovaries, or a fetus. Using sampling bias-corrected logistic regression, LSM was estimated at 13.5 m, 95% CI [13.0, 13.8]. There was a downward trend in LSM over time, statistically significant with one method but marginal with another. A growth model translated this estimate to an ASM estimate of 23.5 years, 95% CI [20.4, 26.7]. Pregnancy rate was determined from mature females (N = 125), and from a subset limited to certain autumn-caught whales (n = 37) to reduce bias. The PR was estimated at 0.46 globally, 95% CI [0.36, 0.55] and 0.38 for the autumn sample, 95% CI [0.20, 0.51]. Both estimated PRs are consistent with a 3-year calving interval, because the larger estimate includes two cohorts of pregnant whales harvested in spring, and bowhead whale gestation is longer than 12 months. These analyses represent the most conclusive empirical estimates of ASM, LSM, and PR for this bowhead whale stock from the largest available data sets to date.

The 2019 ice-based survey of Bering-Chukchi-Beaufort Seas bowhead whales was challenged by missed survey effort, unusual ice conditions, and frequent use of motor-powered skiffs by hunters. All three of these likely led to downward bias in the abundance estimate of Givens et al. (2021). Data were collected about boat excursions during the survey period. Indices of short-term whale abundance at the survey perch and short-term boat noise disturbance were computed from the available data, where ‘short term’ refers to a few hours. A generalized additive model (GAM) was fit to the results, predicting short-term whale abundance as a smooth function of the boat noise disturbance index, after controlling for long-term variation in the whale passage rate over the course of the season. The fitted GAM was then used to predict passage with and without the presence of boat noise. The ratio of the integrals of these two predicted passage curves provided a correction factor which can be applied post hoc to the abundance estimate of Givens et al. (2021). Variance of this correction factor was estimated using two approaches, and found to be small. A wide array of sensitivity analyses was conducted to examine the robustness of the result to potential changes in methodology, and the correction factor was found to be quite stable. The estimated correction factor would inflate the original abundance estimate by about 12%, yielding a corrected abundance of 14,025 (CV=0.228). We recommend that this replace the original abundance estimate.

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.

The stochastic population dynamics model used by Aboriginal Whaling Management Procedure developers is revised to correct weaknesses related to uncertainty parameterisation and replacement yield estimation. Two variants of this model, along with the standard deterministic version, are used to assess the Bering-Chukchi-Beaufort Seas stock of bowhead whales. The variants differ with respect to the magnitude and complexity of the stochastic variation they introduce into natural mortality and birth/calf survival processes. An allowable catch statistic, E (Q0), is defined for appropriate use with stochastic model assessments. Using the same assessment methods, likelihood and priors as IWC (1999a), 5th percentiles of E (Q0) were found to be 117, 106 and 91 for the deterministic, simpler stochastic and extreme stochastic models, respectively. Bayes factor results show that there is no evidence suggesting that either stochastic model should be favoured over any simpler alternative, and the deterministic model yielded the best fit overall. The E (Q0) estimates confirm and strengthen past IWC Scientific Committee inference that under current bowhead subsistence hunting levels the stock should continue to increase towards stabilisation above its maximum sustainable yield level.

Accurate determination of the ages of individual whales is key to developing effective conservation strategies for the bowhead whale (Balaena mysticetus). Previous attempts to develop reliable methods of age determination for this species have included using body length and baleen length measurements, baleen carbon cycling analysis, assessments of corpora accumulation, and aspartic acid racemisation (AAR; conversion of L to D enantiomers) measurements. Each of these methods has its limitations. The primary objective of this study was to improve the AAR analysis technique for determining age in bowhead whales in order to obtain consistent, reproducible results for D/L ratios for estimated ages. Using a modified AAR method, lenses from 68 bowhead whales were analysed and ages estimated. A comparison of the results to previous ageing by corpora counting or baleen carbon cycling methods for 11 of the whales showed smaller standard errors for the AAR analyses. The modified AAR methods applied in this study increase the precision of D/L measurements and provide improved bowhead whale AAR results.

This paper provides a complete description of a Strike Limit Algorithm (SLA) considered by the International Whaling Commission (IWC) for the management of hunting of the Bering-Chukchi-Beaufort Seas stock of bowhead whales by native Alaskans to meet their cultural and subsistence needs. The algorithm applies a statistical estimation and optimisation strategy to extract the best features of selected SLAs to form a Bayes rule estimator. It focuses on safely satisfying moderate subsistence need, while favouring stock protection by setting strike limits below what would be required to fully satisfy need in the final portion of this century if need were more than doubled.

The standard null model of panmixia used to test for population subdivision is based on a set of assumptions that can be violated given recent events likely to result in non-equilibrial genetic composition coupled with the complex life histories of many species. Bowhead whales (Balaena mysticetus) represent such a species. Bowhead whales also have a well-documented history of severe commercial harvest in the recent past which would be expected to leave a population out of genetic equilibrium. They also have a very long life span, overlapping generations, and age and sex-structured migrations. In addition, samples come from whales killed in a hunt known to be non-random with respect to size at different whaling villages. Sampling of such a population could lead to erroneous conclusions regarding population structure, which could have real consequences for aboriginal whaling. To better interpret the results of standard population genetic analyses, an individual-based model of bowhead whale population dynamics and genetics was created using the R package rmetasim. The model re-created as closely as possible all aspects of the demography, genetics, and whaling history of bowhead whales. Simulated datasets were generated by sampling from the simulated population in a way that matched the age, sex and geographic distribution of empirically collected samples. The empirical bowhead datasets were compared to null distributions generated from the simulated datasets for a variety of genetic analyses. The analysis indicates that the empirical genetic data sampled from the Bering-ChukchiBeaufort (BCB) stock of bowhead whales are more consistent with the model of a population with the same whaling history and demographic composition as BCB whales than they are with a single, randomly-mating population in genetic equilibrium under a standard Wright-Fisher model. Additionally, it was demonstrated that by failing to account for the unique features of the population dynamics of the species, standard tests of genetic differentiation based on panmixia may produce misleading results. The approach outlined will likely prove useful for evaluating population structure in other species likely to be out of equilibrium.

Bowhead whales generally migrate into high Arctic waters in the summer months and move to lower latitudes in the wintertime. During the 1800s and early 1900s commercial whaling greatly reduced the numbers of bowhead whales in waters adjacent to the North Atlantic Ocean. In recent decades their numbers have been increasing. Thirteen sightings of a bowhead whale were recorded in four areas of the Gulf of Maine in 2012, 2014, and 2017 between latitudes 44°43’N and 41°36’N, far south of the normal range (54°N) for this species. Photographs obtained during these sightings were compared by experienced photo analysts and, based on scarring patterns, the sightings were confirmed to be of the same individual. The bowhead whale was observed alone, in addition to interacting in a social group and engaged in coordinated feeding with other mysticetes at times. The feeding and social behaviour of the bowhead whale was typical for the species but well south of its normal Arctic waters range and in the absence of conspecifics.

Abstract Commercial whaling severely depleted all four of the stocks of bowhead whales but ceased by the early 1900s. Subsistence hunting of bowheads continued at a low level on the Bering-Chukchi-Beaufort (BCB) and East Canada-West Greenland (ECWG) stocks and the stocks began to recover. Currently, the harvest of bowheads on these stocks is managed by the International Whaling Commission (IWC) so that Inuit communities are able to meet nutritional, cultural, and other needs and the whale populations are able to continue to grow. The IWC uses Strike Limit Algorithms, thoroughly tested in a large number of different scenarios, to set quotas. In 2018, the IWC quotas were approved to “automatically renew,” if the quota requests are unchanged and the science continues to support a sustainable harvest. The other two stocks [East Greenland-Svalbard-Barents Sea (EGSB) and Okhotsk Sea (OKH)] are not hunted. The Arctic is rapidly changing and there are concerns about how bowheads may respond. To date, the BCB and ECWG stocks are growing and seem to be resilient to the changes. It is unclear whether that will continue. Reductions in sea ice have also created opportunities for industrial activities including oil and gas, commercial shipping, commercial fishing, and tourism. Scientific research on the changing ecosystem is also increasing. All these activities have the potential to impact bowheads or the availability of the whales to the communities that depend on them. In Alaska, the Alaska Eskimo Whaling Commission (AEWC) has worked with oil and gas industry to develop a Conflict Avoidance Agreement. Time-area closures and other provisions allow hunters to harvest whales and the industry to explore and develop oil and gas resources. Monitoring of population health and individual animal health is needed to ensure the harvest continues to be sustainable and to assess and mitigate, as necessary and feasible, the potential impacts of climate change and increasing human activity.

An ice-based visual survey of the Bering-Chukchi-Beaufort Seas stock of bowhead whales (Balaena mysticetus) was conducted in spring 2019 near Utqiaġvik (formerly Barrow), Alaska. A Horvitz-Thompson-type estimator is used to estimate population abundance from the resulting data, correcting for detection probabilities, whale availability within visual range, and whale passage during periods of missed effort. Analytical methods mirror those used by Givens et al. (2016) for the 2011 survey as much as possible; however, unlike 2011, no simultaneous acoustic monitoring was conducted in 2019, so the availability correction factor had to be estimated from past years. The estimated abundance was 12,505 with 95% confidence interval of (7,994, 19,560) and a CV of 0.228. This estimated abundance is markedly lower than the 2011 estimate of 16,820, but the 2019 confidence interval wholly encompasses the 2011 interval. We do not interpret this finding as evidence of a decline for many reasons including: highly unusual ice conditions, an unusual migration route that was sometimes too distant from observers to detect whales, failure to conduct watch because of closed leads during the early weeks of the migration when numerous whales likely passed, an unusually short perch, and hunters’ heavy use of powered skiffs near the observation perch which likely disturbed the whales during the survey. Furthermore, bowhead health assessment information for 2019 suggests that harvested bowheads did not exhibit obvious reductions in health condition, and aerial surveys in summer 2019 indicated high calf production (Stimmelmayr et al. 2020). Despite the challenges of the 2019 survey, the estimate is adequate for use with the International Whaling Commission’s management procedure and complies with the survey requirements of the Aboriginal Whaling Scheme.

We examined the effects of temperature and salt concentration on growth of the freshwater oomycete Saprolegnia parasitica that has recently (since 2013) been found to infect an important subsistence fish (in Iñupiaq, Aanaakłiq; broad whitefish, Coregonus nasus) on the Colville River in Nuiqsut, Alaska. Using two confirmed isolates (one from the Colville River and another from a southern British Columbia aquaculture facility), we tested the following hypotheses: (1) the isolate from Alaska will grow at a greater rate than the isolate from British Columbia at lower temperatures, (2) the isolate from British Columbia will grow at a greater rate at higher temperatures than the Alaska isolate, and (3) increasing salinity will reduce the growth rate of both isolates similarly at all temperatures. In addition, we used local observations—subsistence fishers and observations associated with scientific monitoring—to assist in interpreting the potential implications of our experimental results in the context of these environmental observations. In the habitat relevant to this study, water temperature ranges between <0°C and 18°C, and salinity ranges between 0 and 30 parts per thousand due to a seasonal (and occasional west wind-driven) saltwater intrusions. No statistically significant differences were detected in growth rate or salt tolerance between the two isolates at the temperatures and salinities tested; high temperature (24°C) and low salt concentration are associated with the highest growth rate for both isolates. From our lab study, one might conclude that the peak host colonization would occur during the seasonal period of warmest water temperature; however, the observations by local fishers and biologists show this not to be the case. We conclude that, at this time, we do not have evidence that peak warm water is the primary cause of an increased incidence of infection by this freshwater mold. Although indirect and lag analysis of temperature and timing of infection were not part of this study, we note that there is a greater role of complex interactions among biotic and abiotic factors (including temperature) that may predispose some individuals in the population to become infected during spawning season.

Aerial line transect surveys were conducted during 19 July – 20 August in each of the years 2012 – 17, with onshore – offshore transects covering a study area of approximately 110 000 km2, from 140˚ W to 157˚ W longitude and from shore to 72˚ N latitude. These data were used to estimate abundance of the eastern Chukchi Sea (ECS) stock of beluga whales. The data were stratified based on bathymetry to reflect strong large-scale gradients in beluga density. A half-normal key function was used to model detection from a dataset of 999 sightings of 2465 belugas. The detection function was found to depend significantly on sky condition and ice coverage. For the years 2012 through 2017, respectively, the estimated numbers of ECS belugas in the study area during the study period were 7355 (CV = 0.17), 6813 (CV = 0.18), 16 598 (CV = 0.21), 6456 (CV = 0.21), 6965 (CV = 0.23) and 13 305 (CV = 0.27). There is no statistically significant trend. These estimates do not correct for belugas outside the study region. Indeed, diverse data indicate that belugas venture far outside the study region and their distribution varies interannually due to prey availability and other factors. Recently reviewed tagging data suggest that correcting for whales outside the study area would approximately double our abundance estimates. These results provide no indication that the stock has substantially declined during these six years due to the impact of subsistence hunting, industrial activity or climate change, although interannual variation and estimated CVs are both large, thereby potentially masking small-scale impacts.

The Bering-Chukchi-Beaufort Seas (BCBS) bowhead whale (Balaena mysticetus) has been considered at low-risk for entanglement injuries and ship strikes because their range is mainly north of commercial fisheries; nevertheless, changes to their arctic habitat, including a longer open water period and declining sea ice, have resulted in increasing commercial activity and concern about fisheries interactions. We examined interyear matches (between 1985 and 2011) from a photo identification project and identified whales that had acquired entanglement injuries. We estimated the probability of a bowhead acquiring an entanglement injury using two statistical methods: interval censored survival analysis and a simple binomial model. Both methods give similar results, suggesting a 2.2% (95% CI: 1.1%-3.3%) annual probability of acquiring a scar. We also include an entanglement scar frequency analysis of aerial photographs from the 2011 spring and fall surveys near Point Barrow, Alaska, which suggest 12.4% of live bowheads show evidence of entanglement scarring. Entanglement rates for the BCBS bowhead stock are lower than many other large whale stocks, and abundance has increased over the past 35 yr; however, our findings indicate that fishing gear entanglement is a more serious concern for the BCBS bowhead whale population than previously thought.

New photo-identification data were collected from a 2011 aerial survey of Bering-ChukchiBeaufort Seas bowhead whales. We scored and matched these images to existing images from 1985, 1986, 2003, 2004, and 2005. Other interyear comparisons between this set of years were also conducted to generate a complete matching matrix for the 6 years. These data were used to estimate bowhead adult survival rate and population abundance using Huggins models embedded in a Robust Design capture-recapture analysis. Our estimated survival rate was 0.996 with approximate lower confidence bound 0.976, which is consistent with previous estimates and with research showing that bowhead lifetimes can be very long. Estimated 2011 abundance was 27,133 (CV=0.217, 95% CI 17,809 to 41,337). Although much less precise than the 2011 ice-based abundance estimate (16,820 with CV=0.052, 95% CI 15,176 to 18,643) of Givens et al. (2016), the 2011 photo-id estimate adds to the evidence that the stock is abundant, increasing from previous years, and unlikely to be harmed by limited subsistence hunting.

Tympanic bullae and baleen plates from bowhead whales of the Western Arctic population were examined. Growth layer groups (GLGs) in the involucrum of the tympanic bone were used to estimate age of the whales, and compared to stable isotope signatures along transects of baleen plates and the involucrum. The involucrum of the tympanic bone consists of three regions that form in utero, during nursing in the first year, and during the first decades of life, respectively. Life history events, such as annual migration, are recorded in the bowhead tympanic bulla. It is likely that bone growth in the bowhead tympanic occurs during periods of high food intake, while slow or arrested growth occurs during periods of low food intake. Comparisons between numbers of GLGs in the tympanic, number of isotopic oscillations in a baleen plate, length of the baleen plate, and total whale length show correlation coefficients as high as 0.97. The tympanic GLG method is particularly useful for estimating the age of whales up to 20 yr old.

In facial recognition applications, the upper tail of the distribution of non-match scores is of interest because existing algorithms classify a pair of images as a match if their score exceeds some high quantile of the non-match distribution. We develop a general model for the non-match distribution above u tau, the (1-tau) th quantile, borrowing ideas from extreme value theory. We call this model the GPD tau, as it can be viewed as a reparameterized generalized Pareto distribution (GPD). This novel model treats tau as fixed and allows us to estimate ut in addition to parameters describing the tail. Inference for both ut and the GPD tau scale and shape parameters is performed via M-estimation, where our objective function is a combination of the quantile regression loss function and GPD tau density. By parameterizing u tau and the GPD tau parameters in terms of available covariates, we gain understanding of these covariates' influence on the tail of the distribution of non-match scores. A simulation study shows that our method is able to estimate both the set of parameters describing the covariates' influence and high quantiles of the non-match distribution. We apply our method to a data set of non-match scores and find that covariates such as gender, use of glasses, and age difference have a strong influence on the tail of the non-match distribution.

While face recognition algorithms perform under many different unconstrained conditions, predicting this performance is not possible when a new location is introduced. Analyzing the impostor distribution of the videos of the Point-and-Shoot Challenge (PaSC) as well as its relationship to the genuine match distribution, we present a method for predicting the performance of an algorithm using only unlabeled data for a new location.

The evolution of baleen constituted a major evolutionary change that made it possible for baleen whales to reach enormous body sizes while filter feeding on tiny organisms and migrating over tremendous distances. Bowhead whales (Balaena mysticetus) live in the Arctic where the annual cycle of increasing and decreasing ice cover affects their habitat, prey, and migration. During the nursing period, bowheads grow rapidly; but between weaning and approximately year 5, bowhead whales display sustained baleen and head growth while limiting growth in the rest of their bodies. During this period, they withdraw resources from the skeleton, in particular the ribs, which may lose 40% of bone mass. Such dramatic changes in bones of immature mammals are rare, although fossil cetaceans between 40 and 50 million years ago show an array of rib specializations that include bone loss and are usually interpreted as related to buoyancy control.

A Horvitz–Thompson‐type estimator is introduced to estimate total abundance of the Bering–Chukchi–Beaufort Seas population of bowhead whales using combined visual and acoustic location data. The estimator divides sightings counts by three correction factors that are themselves estimated from various portions of the data. The first correction models how detection probabilities depend on covariates like offshore distance and visibility. The second correction adjusts for availability using the acoustic location data to estimate a time‐varying smooth function of the probability that animals pass within visual range of the observation stations. The third correction accounts for whales passing during periods when one or both sighting stations were temporarily closed down. We derive an asymptotically unbiased estimator of abundance incorporating all these components and a corresponding variance estimate. Correcting the count of 4011 observed whales yields a 2011 abundance estimate of 16,820 with a 95% confidence interval of (15,176, 18,643) and an estimated annual rate of population increase of 3.7% (2.9%, 4.6%). These results are indicative of very low conservation risk for this population under the current low levels of aboriginal hunting permitted by the International Whaling Commission. Although few other capture–recapture surveys will confront exactly the same set of challenges addressed here, many studies face one or more issues that could be resolved by adapting portions of our approach or relevant underlying concepts thereof. Moreover, the generic estimator we derive represents an improved way to handle random correction factors rather than assuming fixed values. Copyright © 2016 John Wiley & Sons, Ltd.