Abstract Modeling energy content and gain of individuals is of increasing importance in ecosystem modeling, especially in aquaculture and fisheries. Traditional models for estimating the content and gain are either imprecise or expensive, in part because of intensive data requirements. Here we show how routine biometric data (length and wet weight or condition index) can be used to estimate total energy content of fish. Starting with theoretical partitioning between structure and reserves, we create a model to relate energy to the Fulton's condition index. We then use data from cultured sea bream (Sparus aurata L.) to show that the model based on structure should be used to calculate energy content from biometric data. Validation using independent data shows remarkable ability of the model to predict energy content (R2>0.99), while comparison with previously used models demonstrates marked differences in predictions when fish condition is variable. Unlike traditional methods, our model predicts different energy content and gain (or loss) for small fat and large thin fish of equal weight, and can therefore give considerable additional value to biometric data commonly collected in aquaculture, fisheries, and related citizen science programs.

中文翻译：

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从长度和湿重估计鱼的能量含量和增益

摘要 对个体的能量含量和增益建模在生态系统建模中越来越重要，尤其是在水产养殖和渔业中。用于估计内容和增益的传统模型要么不精确，要么成本高昂，部分原因是对数据的要求很高。在这里，我们展示了如何使用常规生物特征数据（长度和湿重或状况指数）来估计鱼的总能量含量。从结构和储量之间的理论划分开始，我们创建了一个模型来将能量与富尔顿条件指数相关联。然后，我们使用养殖鲷鱼 (Sparus aurata L.) 的数据表明，应该使用基于结构的模型来计算生物特征数据的能量含量。使用独立数据的验证表明该模型具有显着的预测能量含量的能力 (R2>0.99)，而与以前使用的模型相比，当鱼类状况变化时，预测结果存在显着差异。与传统方法不同，我们的模型预测相同重量的小肥鱼和大瘦鱼的不同能量含量和增加（或损失），因此可以为水产养殖、渔业和相关公民科学计划中通常收集的生物识别数据提供可观的附加价值。