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Initial Analysis of Alternative Sample Designs for the Deployment of Observers in Alaska.

机译：阿拉斯加观测员部署替代样本设计的初步分析。

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Changes in regulation enacted in 2013 have enabled the Alaska Fisheries Science Center’s Fishery Monitoring and Analysis Division (FMA) and Alaska Regional Office’s Sustainable Fisheries Division to work collaboratively on an Annual Deployment Plan (ADP). Each ADP documents how the National Marine Fisheries Service (NMFS) plans to deploy observers into fishing activities for the coming year under the limits of available funding. Draft ADPs are presented to the North Pacific Fishery Management Council (Council) during September - October and are finalized in December. The sampling design for observer deployment has two elements: how the population is subdivided (i.e., stratification schemes) and how available samples are allocated (i.e., allocation strategies). Here the relative performance of 12 alternative sampling designs for the deployment of observers into at-sea operations are compared in support of the 2016 draft ADP. These designs were defined by combinations of six potential stratifications and whether allocations were proportional to effort (defined by trips) or to reduce the weighted variance of total groundfish retained and discarded ('optimal') allocation. Performance metrics included a gap analyses as well as single-stage estimates of precision and accuracy. These three metrics were combined into a single score for design comparison. Gap analysis scores were used in a 'hurdle-model' approach for final design evaluations. The four designs with above-average gap analyses and final scores were proposed for consideration in the draft 2016 ADP; two of these stratified by gear (Trawl, Hook and Line, and Pot). Preliminary anticipated coverage rates were generated for the four candidate designs. For the draft 2016 ADP, the NMFS proposed that the sample design for the observer deployment be defined in units of trips stratified by three gear types with sample sizes allocated according to a blended optimal allocation strategy. At their October 2016 meeting, the Council supported this design for the final 2016 ADP.

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