Estimation of silver eel production, Anguilla anguilla L., is fundamental for the management of eel stocks. In the hydropower‐regulated rivers Shannon and Erne, Ireland, production is calculated using catch data from a conservation trap and transport programme. However, in both rivers gaps in silver eel catch datasets tend to occur, which can lead to biases in production estimates. Generalised additive models (GAMs) were used to model daily catch in these rivers based on a variety of environmental variables. Final models for the Shannon and Erne explained 83.7% and 78.8% of deviance in daily catch, respectively. A second model on the Erne included catch from a nearby fishing site in an attempt to increase explanatory power and explained 91.7% of deviance. Although model accuracy was increased, reliance on catch from another site may limit the applicability of the model. Model predictions were combined with estimates of fishing efficiency to predict production for the Shannon (36,210 kg; 0.85 kg/ha) and Erne (66,899–67,047 kg; 2.55–2.56 kg/ha). These values represented a 9.3% and 2.8%–3.0% increase on production estimated from incomplete catch records alone for the Shannon and Erne, respectively.

中文翻译：

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模拟两条水电管制河流中的银相欧洲鳗鱼（安圭拉鳗）的每日捕捞量

估计银鳗的产量，安圭拉（Anguilla anguilla L.），是鳗鱼存量管理的基础。在受水力调节的爱尔兰香农和厄恩河中，使用保护性陷阱和运输计划的捕获数据计算产量。但是，在两条河流中，银鳗捕获数据集之间往往会出现缺口，这可能导致产量估算出现偏差。基于各种环境变量，使用通用添加剂模型（GAM）对这些河流的日捕获量进行建模。Shannon和Erne的最终模型分别解释了每日捕获量的83.7％和78.8％的偏差。Erne上的第二个模型包括从附近一个渔场捕获的鱼，以提高解释力，并解释了91.7％的偏差。尽管提高了模型的准确性，但是依赖于其他站点的捕获可能会限制模型的适用性。将模式预测与捕捞效率估算结合起来，以预测香农（36210千克； 0.85千克/公顷）和欧恩（66899-67047千克； 2.55-2.56千克/公顷）的产量。仅根据香农和欧恩的渔获量不完全记录，这些值分别代表产量增加9.3％和2.8％–3.0％。