A Statistical Framework for Analysis of Limit Reference Points

A Statistical Framework for Analysis of Limit Reference Points

L.J. Richards, J.T. Schnute, and N. Olsen

A Statistical Framework for Analysis of Limit Reference PointsThis is part of Fishery Stock Assessment Models
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Fisheries stock assessment typically involves an evaluation of historical stock status and a forecast of future status under one or more harvest policies. In this paper, we describe an integrated modeling framework both for estimation of historical stock status and for simulation of future status under a precautionary management regime. The paper extends our earlier work, where we used state-space models to conduct the estimation component of the analysis. Here we demonstrate how large uncertainties in historical biomass trends can be propagated into forecasts of future status. Following the precautionary approach, fisheries models must consider the probability that stock biomass will fall below preset limit (conservation) reference points within a specified time horizon. Because reference points are estimated from historical data, they are themselves highly uncertain. Our method uses estimates of the model parameters and covariance matrix to recreate plausible scenarios for the historical state dynamics and corresponding reference points. For each scenario, we then simulate future states under a fixed catch policy, allowing for process error in recruitment. The outcome for a given time horizon and harvest policy can be perceived as a bivariate scatterplot with forecasted biomass on one axis and the corresponding reference point on the other axis. Thus, our model framework provides an explicit method for evaluating the likelihood that future stock sizes will fall below the preset limit. We also suggest visualization tools for clearly portraying these risks to decision makers.

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