Using perceptions of data accuracy and empirical weighting of information: assessment of a recreational fish population

Recreational fisheries management is often compromised by limited information of variable quality from several sources. We develop a form of catch-age analysis to combine uncertain information from creel surveys, age composition, and mark-recapture estimates of abundance. Four systems are used in we...

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Bibliographic Details
Published in:Canadian Journal of Fisheries and Aquatic Sciences
Main Authors: Merritt, Margaret F, Quinn II, Terrance J
Format: Article in Journal/Newspaper
Language:English
Published: Canadian Science Publishing 2000
Subjects:
Online Access:http://dx.doi.org/10.1139/f00-065
http://www.nrcresearchpress.com/doi/pdf/10.1139/f00-065
Description
Summary:Recreational fisheries management is often compromised by limited information of variable quality from several sources. We develop a form of catch-age analysis to combine uncertain information from creel surveys, age composition, and mark-recapture estimates of abundance. Four systems are used in weighting annual observations: equal, inverse of squared coefficients of variation (CV -2 ), perceptions of accuracy, and a combination of the latter two. The model is applied to a humpback whitefish (Coregonus pidschian) population in Alaska and evaluated for model fit, parameter uncertainty, conservative forecasts of exploitable abundance, and biological plausibility. The probability of forecasted stock abundance occurring below a threshold level defined by an agency management plan is evaluated for various recruitment and exploitation scenarios. The perception model is judged to be best with the use of the analytic hierarchy process, a decision-making technique. By incorporating perceptions into fisheries decision-making, beliefs in the accuracy of uncertain information are made explicit. In a conservative context, fishery management decisions should include reducing risk to the stock in the setting of harvest policy and in the selection of the assessment model.