The method of Cooke (1996) and Punt and Butterworth (1999) for computing abundance estimates for bowhead whales of the BeringChukchi-Beaufort Seas stock is reviewed. These abundance estimates are computed from estimates N4 of the number of whales that passed within the 4km visual range of the observation ‘perch’ from which the whales are counted, the estimated proportions P4 of the whales that passed within this range and the estimated standard errors (SE) of N4 and P4. Errors discovered while assembling the data used in developing previous estimates were corrected, and new estimated detection probabilities, N4 and P4 values and SEs were computed using the corrected data. The method of Cooke (1996) and Punt and Butterworth (1999) was then applied. The resulting 2001 abundance estimate was 10,545 (95% confidence interval 8,200 to 13,500), extremely close to the 2001 N4/P4 abundance estimate of 10,470 (95% confidence interval 8,100 to 13,500) (George et al., 2004). The estimated rate of increase of this population from 1978 to 2001 was 3.4% per year (95% confidence interval 1.7% to 5%).

As a part of a review of bowhead whale (Balaena mysticetus) stocks, a study was conducted to evaluate how much mixing occurs in the whales’spring migration, a period which immediately follows the mating season. This study has used aerial photography of bowhead whales during their spring migration near Point Barrow, which has resulted in 5,800 images, primarily from 1984 through 1994. These photographs included 40 different whales seen in at least two years, and of these, two were seen in three different years, making for a pair-wise sample size of 42 matches between years. Differences between dates of initial sightings and subsequent sightings (i.e. resightings) ranged from -31 to +23 days comparing month and day only, irrespective of year. These resightings were well dispersed across most of the bowhead spring migration; 98% of the photographs were taken across 45 days from 19 April through 2 June. Models for predicting resighting date from initial sighting date, whale length, presence of a calf, year of initial sighting and year of subsequent sightings were considered, and the best model was chosen using Akaike’s Information Criterion (AIC). The best model included most predictors but did not include initial sighting date. Thus, all of the available evidence indicates that individual mature bowheads do not have a consistent migration timing past Barrow; instead, in subsequent years they may appear on almost any date within the normal migratory period. This wide mixing and near-random distribution of resighting dates throughout the spring migration is indicative of a single stock of whales that have a somewhat plastic schedule.

Gross morphology and morphometry of the bowhead whale ovary, including ovulatory corpora, were investigated in 50 whales from the Chukchi and Beaufort seas off the coast of Alaska. Using the presence of ovarian corpora to define sexual maturity, 23 sexually immature whales (7.6-14.2 m total body length) and 27 sexually mature whales (14.2-17.7 m total body length) were identified. Ovary pair weights ranged from 0.38 to 2.45 kg and 2.92 to 12.02 kg for sexually immature and sexually mature whales, respectively. In sexually mature whales, corpora lutea (CLs) and/or large corpora albicantia (CAs) projected beyond ovary surfaces. CAs became increasingly less interruptive of the surface contour as they regressed, while remaining identifiable within transverse sections of the ovarian cortex. CLs formed large globular bodies, often with a central lumen, featuring golden parenchymas enfolded within radiating fibrous cords. CAs, sometimes vesicular, featured a dense fibrous core with outward fibrous projections through the former luteal tissue. CLs (never more than one per ovary pair) ranged from 6.7 to 15.0 cm in diameter in 13 whales. Fetuses were confirmed in nine of the 13 whales, with the associated CLs ranging from 8.3 to 15.0 cm in diameter. CLs from four whales where a fetus was not detected ranged from 6.7 to 10.6 cm in diameter. CA totals ranged from 0 to 22 for any single ovary, and from 1 to 41 for an ovary pair. CAs measured from 0.3 to 6.3 cm in diameter, and smaller corpora were more numerous, suggesting an accumulating record of ovulation. Neither the left nor the right ovary dominated in the production of corpora. Anat Rec, 299:769-797, 2016. © 2016 Wiley Periodicals, Inc.

We used baleen lengths and age estimates from 175 whales and body lengths and age estimates from 205 whales to test which of several single- and multi-stage growth models best characterized age-specific baleen and body lengths for bowhead whales (Balaena mysticetus L., 1758) with the goal of determining which would be best for predicting whale age based on baleen or body length. Previous age estimates were compiled from several techniques, each of which is valid over a relatively limited set of physical characteristics. The best fitting single-stage growth model was a variation of the von Bertalanffy growth model for both baleen and body length data. Based on Bayesian information criterion, the two- and three-stage versions of the von Bertalanffy model fit the data better than did the single-stage models for both baleen and body length. The best baleen length models can be used to estimate expected ages for bowhead whales with up to 300-325 cm baleen, depending on sex, which correspond to age estimates approaching 60 years. The best body length models can be used to estimate expected ages for male bowhead whales up to 14 m, and female bowheads up to 15.5 m or ages up to approximately 40 years.

We compiled age estimates and baleen plate δ13C data from 86 bowhead whales ( Balaena mysticetus L., 1758). We used previous whale age estimates based on aspartic acid racemization (AAR) and corpora counts to extend the use of δ13C data for age determination from cycle counting to a modified exponential model using annual baleen growth increments. Our approach used the growth increment data from individual whales in a nonlinear mixed effects model to assess both population-level and whale-specific growth parameters. Although age estimates from baleen-based models become less precise as the whales age, and baleen growth and length near steady state, the growth increment model shows promise in estimating ages of bowhead whales 10–13.5 m long with baleen lengths <250 cm, where other techniques are less precise or the data are scarce. Ages estimated using the growth increment data from such whales ranged from 6.4 to 19.8 years.

Ninety-eight eye globes (from 84 individual bowhead whales) were collected and analyzed to estimate ages of the whales using the aspartic acid racemization aging technique. Racemization rate (kAsp) was based on data from earlier studies of humans and fin whales; the estimate used was 1.175 x 10 –3 yr . The D/L ratio at birth (D/L)0 was estimated using eyes from two term bowhead fetuses. The (D/L)0 value was 0.02708. Its variance, as well as the variance of the D/L ratios measured for whales older than age 0, was calculated via analysis of variance using multiple measurements from the same whale. Age estimates for each whale and standard errors (SE) of these age estimates were obtained using the delta method. It was found that five individuals exceeded 100 years of age; all of them were male. The SE increased with estimated age, but the age estimates had lower coefficients of variation for older animals. The SE of the age estimates could be reduced by improving the laboratory protocol for determining the D/L ratio. There are limited apparent gross and histologic changes that are indicative of aging in the >200 bowheads examined as well as a striking lack of evidence for senescence for the many gonads examined. This, along with the recovery of “traditional” whale hunting tools from five recently harvested whales, suggests that life spans in excess of 100 years may be possible.

Bowhead whales (Balaena mysticetus) are counted near Point Barrow, Alaska, during their spring migration. Missed whales have been accounted for in two different ways, the first based on data from two observation sites operating simultaneously and the second on locations of whales detected acoustically as well as visually. Estimates of the probability that observers at the primary observation site missed a whale ranged from 0.3 to nearly 1, depending on distance of the whale offshore from the observation site, visibility, number of observers, and estimation approach. Neither approach fully accounted for heterogeneity in detection probabilities. Errors in determining which detections belonged to the same whale could lead to bias. Other problems for combined acoustic and visual surveys include the failure of whales to vocalize in some situations and negative correlation between acoustic and visual detections that could cause positive bias in estimated probabilities of missing whales and estimated abundance.

Estimating the population size and rate of increase of bowhead whales, Balaena mysticetus, is important because bowheads were the first species of great whale for which commerical whaling stopped and so their status indicates the recovery prospects of other great whales, and also because this information is used by the International Whaling Commission (IWC) to set the aboriginal subsistence whaling quota for Alaskan Eskimos. We describe the 1993 visual and acoustic census off Point Barrow, Alaska, which provides the best data available for estimating these quantities. We outline the definitive version of two statistical methods for estimating the population: the generalized removal method and the Bayes empirical Bayes method. The two methods give results that are close. The estimate of bowhead population size most recently accepted by the IWC Scientific Committee, 8,200 with 95% estimation interval from 7,200 to 9,400, is based on the Bayes empirical Bayes posterior distribution presented here. The Scientific Committee also accepted our estimate of the annual rate of increase of the population from 1978 to 1993. This estimate, based on the generalized removal method population estimates, is 3.2% with a 95% confidence interval (1.4%, 5.1%). This shows that bowheads are increasing at a healthy rate, indicating that stocks of great whales that have been decimated by commercial hunting can recover after it ends, even in the presence of limited aboriginal subsistence whaling.

The survival rate of adult bowhead whales (Balaena mysticetus) is one of the few remaining life history parameters still needing empirical support before full assessment of the health of this stock can be completed. Members of the Scientiic Committee of the International Whaling Commission have requested an estimate of this survival rate. Accordingly, databases from aerial photographs of identiiable bowhead whales were used for mark-recapture estimation of the rate. The photographs were collected over the past 15 years by LGL Ltd., the National Marine Mammal Laboratory and Cascadia Research Collective. Data from the highest quality images collected 1981{91 were put through both a modiied Jolly-Seber analysis and a Sandland-Kirkwood-Clobert (SKC) analysis to calculate annual survival probabilities. Using a weighted arithmetic mean of annual survival probabilities, and deleting the rst estimate for each cohort, the modiied Jolly-Seber estimate is 0.986 (with a 95% conndence interval of 0.941 to 1.000). A whale survival probability of 0.97 was obtained from the SKC model by using time dependent capture probabilities and constant survival probability. Although these estimates are reasonable, they are tentative in that data from some years could not be used in the analyses, and an accurate selection of images that satissed the assumptions of a mark-recapture analysis was hampered by an image classiication scheme that did not diierentiate between photographic quality and whale identiiability. Following completion of a review of image quality, procedures developed in this project may be rerun for a more precise calculation of bowhead whale survival rates.

We quantify the existing Aboriginal Management Scheme for subsistence whaling,which can be implemented for the Bering-Chukchi-Beaufort Seas stock of bowheadwhales using the Bayesian synthesis assessment method. The catch limit given by thisimplementation meets our quantification of aboriginal management objectives establishedby the Commission, and results from a direct implementation of the aboriginalmanagement principles given in paragraph 13(a) of the Schedule.1 INTRODUCTIONThis paper ...

Purpose: We attempt to increase our understanding of human immunodeficiency virus (HIV) shedding in semen.Materials and Methods: We followed 16 seropositive men for up to 27 months by HIV cocultivation, with a subset evaluated using the polymerase chain reaction.Results: The proportion with at least 1 HIV positive semen culture increased from 3 of 16 subjects (19%) at visit 1 to 10 (63%) by visit 5. Overall, HIV was cultured from 25 of 114 specimens (22%). Shedding was intermittent for each of the 10 men with at least 1 positive culture and seminal shedding patterns were highly variable.Conclusions: By culture and polymerase chain reaction, HIV is shed intermittently in the semen. If cultures are performed often enough most seropositive men shed HIV in the semen.

We consider the problem of inference about a quantity of interest given different sources of information linked by a deterministic population dynamics model. Our approach consists of translating all the available information into a joint premodel distribution on all the model inputs and outputs and then restricting this to the submanifold defined by the model to obtain the joint postmodel distribution. Marginalizing this yields inference, conditional on the model, about quantities of interest, which can be functions of model inputs, model outputs. or both. Samples from the postmodel distribution are obtained by importance sampling and Rubin's SIR algorithm. The framework includes as a special case the situation where the pre-model information about the outputs consists of measurements with error; this reduces to standard Bayesian inference. The results are in the form of a sample from the postmodel distribution and so can be examined using the full range of exploratory data analysis techniques. Methods for comparing competing population dynamics models are developed. based on a generalization of the Bayes factor idea. A keg. quantity used by the International Whaling Commission (IWC) in making decisions about bowhead whales, Balaena mysticetus, is the replacement yield, RY. Information about the species is of three main types: recent census information. historical catch records. and evidence about birth and death rates. These are combined using a special case of the Leslie matrix population dynamics model. Our method yields full inference about RY and also sheds light on other. sometimes controversial. questions of scientific interest. These ideas are also applicable to many simulation models in other areas of science and policy making. Software to implement these methods is available from StatLib.

Raftery, Givens and Zeh (1994) have estimated replacement yield for bowheadwhales using a Bayesian synthesis framework which allows inference based on the structureof a population dynamics model (PDM) (an improved version of the Leslie matrixmodel of Breiwick, Eberhardt and Braham, 1984) and independent evidence and uncertaintyabout model inputs and outputs. Givens, Zeh, and Raftery (1993) reportresults on the same problem using the Bayesian synthesis approach with the PDM atthe core of...

A Bayesian synthesis approach has been proposed by Raftery, Givens, and Zeh (1992) for making inferences from a deterministic model with many inputs and outputs. The approach was applied to population dynamics models for bowhead whales. The approach consists of establishing a joint prior, or pre-model distribution, on the model inputs and outputs for which there exists evidence independent of the model. The restriction of this distribution to a subspace deened by the model mapping then constitutes a post-model distribution, from which inferences are drawn. We brieey review a methodology for implementing the Bayesian synthesis approach, and then consider in detail the potential uses of the results and the strengths and weaknesses of the approach compared to past methodologies.

The Bayes empirical Bayes method for estimating bowhead whale, Balaena mysticetus, population size is used to obtain estimates based on the 1988 combined visual and acoustic census off Point Barrow, Alaska. It is based on an explicit scientific model of the census process and of whale behavior, and yields a full posterior probability distribution of the number of whales given the data. It takes account of the imperfection of our scientific knowledge, of errors made by the tracking algorithm used, and of uncertainty about the control parameters for the tracking algorithm and about the parameters of the model.

AbstractThe number of bowhead whales, Balaena mysticetus, passing within viewing range of the ice‐based census at Point Barrow, Alaska, during spring migrations from 1978 to 1988 is estimated from the visual census data. The trend in the annual numbers yields an estimated rate of increase of 3.1% per year with a 95% confidence interval ranging from 0.1% to 6.2% for the Bering‐Chukchi‐Beaufort Seas bowhead stock during this period. Alternative treatments of the data suggest less precise or somewhat lower estimates, but all results indicate that the stock was increasing.