Catch estimates from recreational fisheries are an important component of many fishery management plans. Estimates of recreational catch (in weight) on the U.S. West Coast are often derived as the product of catch in numbers of fish and average fish weights. When estimates of average fish weight are imprecise (e.g., due to small sample sizes), the resulting estimates of catch in weight can fluctuate and unnecessarily trigger or delay management actions. This and other challenges associated with average weight estimation are currently addressed through replication of data based on deterministic algorithms (‘borrowing rules’). These methods differ among states and do not present a viable method for variance estimation. In this study, we describe a model-based framework for estimation of average fish weights, with an application to the recreational groundfish fishery off Washington, U.S.A. The model-based framework identifies important sources of variability in mean weight, quantifies uncertainty in estimates, pools information to better inform strata with small sample sizes, predicts average weight for unsampled strata, and does not require data replication. We examine the effect of sample size on model-based estimates, and recommend propagation of uncertainty in average catch into estimates of recreational catch in weight.

休闲渔业的产量估算是许多渔业管理计划的重要组成部分。美国西海岸休闲捕鱼量（以重量计）的估算通常是鱼类捕获量与平均鱼重的乘积。当平均鱼重的估计值不准确时（例如，由于样本量小），所得的重量捕捞量估计值可能会波动，从而不必要地触发或延迟管理行动。当前，通过基于确定性算法（“借阅规则”）的数据复制来解决与平均权重估计相关的这一挑战和其他挑战。这些方法因州而异，并且没有提出可行的方差估计方法。在这项研究中，我们描述了一种基于模型的框架，用于估算平均鱼体重，基于模型的框架可识别平均重量变异性的重要来源，量化估计值的不确定性，汇集信息以更好地告知小样本量的地层，预测未抽样地层的平均权重，以及不需要数据复制。我们研究了样本量对基于模型的估算的影响，并建议将平均渔获量的不确定性传播到体重休闲渔获量的估算中。