We quantify the existing Aboriginal Management Scheme for subsistence whaling, which can be implemented for the Bering-Chukchi-Beaufort Seas stock of bowhead whales using the Bayesian synthesis assessment method. The catch limit given by this implementation meets our quantification of aboriginal management objectives established by the Commission, and results from a direct implementation of the aboriginal management principles given in paragraph 13(a) of the Schedule. 1 INTRODUCTION This paper demonstrates how the International Whaling Commission's (IWC) current Aboriginal Management Scheme for subsistence whaling can be implemented for the BeringChukchi -Beaufort Seas stock of bowhead whales. Sub-paragraph 13(a) and footnote 1 of paragraph 13 of the Schedule (IWC, 1995b) constitute the existing Aboriginal Management Scheme. The Commission also has longstanding objectives for management of subsistence whaling. These objectives were accepted by the Commission at its 34th Annual Meeting...

Results from the approximate reweighting method used by the Scientific Committee for its 1994 assessment of the Bering-Chukchi-Beaufort Seas stock of bowhead whales are confirmed using the full Bayesian synthesis approach. Sensitivity trials are examined to investigate several areas of interest identified during this assessment. The results show that the full analysis gives results which are very close to those obtained by the Scientific Committee in 1994, and which are not very sensitive to changing the distributions of model parameters in ways which are reasonable for bowheads. The Scientific Committee obtained an estimated 5 th percentile for replacement yield of 104 whales; four independent runs of the full analysis give values of 104, 108, 104, and 115. The results show no reason to question the conclusions of the 1994 Scientific Committee assessment. In its 1994 bowhead assessment, the Scientific Committee agreed to use a stock abundance estimate based on data from 1988 because...

Results from the approximate reweighting method used by the Scientific Committee for its 1994 assessment of the Bering-Chukchi-Beaufort Seas stock of bowhead whales are confirmed using the full Bayesian synthesis approach. Sensitivity trials are examined to investigate several areas of interest identified during this assessment. The results show that the full analysis gives results which are very close to those obtained by the Scientific Committee in 1994, and these results are not very sensitive to changing the distributions of model parameters in ways which are reasonable for bowheads. The Scientific Committee obtained an estimated 5 th percentile for replacement yield of 104 whales; four independent runs of the full analysis give values of 104, 108, 104, and 115. The results show no reason to question the conclusions of the 1994 Scientific Committee assessment. 1 INTRODUCTION At its 1993 meeting, the Scientific Committee (SC) of the International Whaling Commission (IWC) recommen...

Human Development. The views and opinions expressed in this paper are those of the authors and do not necessarily represent those of the United Nations. This paper has not been formally edited and cleared by the United Nations. Current methods for reconstructing human population structures of the past are deterministic or do not formally account for measurement error. We propose a method for simultaneously estimating age-specific population counts, fertility rates, mortality rates and net international migration flows from fragmentary data, that incorporates measurement error. Inference is based on joint posterior probability distributions which yield fully probabilistic interval estimates. It is designed for the kind of data commonly collected in modern demographic surveys and censuses. Population dynamics over the period of reconstruction are modeled by embedding formal demographic accounting relationships in a Bayesian hierarchical model. Informative priors are specified for vital rates, migration rates, population counts at baseline, and the accuracies of their respective