Abstract
Fishery stock assessment models connect ecosystem data to quantitative fishery management. Control rules that calculate annual catch limits and targets from stock assessment results are a common component of US Fishery Management Plans. Ideally, the outcome of such control rules are updated annually on the basis of stock assessment forecasts to track fluctuations in stock abundance. When the stock assessment - fishery management enterprise achieves this level of through- put, they truly are operational models, much as the complex physical models used to routinely update climate forecasts. In reality, many contemporary assessments are closer to an individual scientific investigation than to an operational model. As a result, the review of each stock assessment is extensive and the lag between data acquisition and quota adjustment may extend to several years. If the future stock assessment process is to move towards an operational status, there will need to be changes in three aspects of the process. First, key data streams will themselves need to be made more operational and corporate so that relevant data are immedi- ately available and trusted. Second, stock assessment models need to be made more capable of including diverse relevant data and comprehensively calculating levels of uncertainty, while also being more completely tested, documented, and standard- ized. The class of models called integrated analysis has these characteristics and is described here, with emphasis on the features of the Stock Synthesis model. Areas of future model development, especially to include more ecosystem and environ- mental factors, are explored. Third, increased throughput of assessment updates will require streamlining of the extensive review process now routinely required before stock assessment results can serve as the scientific basis for fishery manage- ment. Emphasizing review of broadly applicable assessment data and methods, rather than each final result, is a logical step in this streamlining, while maintaining public trust in the final results.
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