Fisheries scientists use biological models to determine sustainable fishing rates and forecast future dynamics. These models require both life‐history parameters (mortality, maturity, growth) and stock‐recruit parameters (juvenile production). However, there has been little research to simultaneously predict life‐history and stock‐recruit parameters. I develop the first data‐integrated life‐history model, which extends a simple model of evolutionary dynamics to field measurements of life‐history parameters as well as historical records of spawning output and subsequent recruitment. This evolutionary model predicts recruitment productivity (steepness) and variability (variance and autocorrelation in recruitment deviations) as well as mortality, maturity, growth, and size, and uses these to predict intrinsic growth rate (r) for all described fishes. The model confirms previous analysis showing little correlation between steepness and either natural mortality or asymptotic maximum size (). However, it does reveal taxonomic patterns, where family Sebastidae has lower steepness () and Salmonidae has elevated steepness () relative to the prediction for bony fishes (class Actinopterygii, ). Similarly, genus Sebastes has growth rate (0.09) approaching that of several shark families (Lamniformes: 0.02; Carcharhiniformes: 0.02). A cross‐validation experiment confirms that the model is accurate, explains a substantial portion of variance (32%–67%), but generates standard errors that are somewhat too small. Predictive intervals are tighter for species than for higher‐level organizations (e.g. families), and predictions (including intervals) are available for all fishes worldwide in R package FishLife. I conclude by outlining how multivariate predictions of life‐history and stock‐recruit parameters could be useful for stock assessment, decision theory, ensemble modelling and strategic management.

中文翻译：

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使用数据集成的生命历史模型预测全球所有鱼类的募集密度依赖性和内在增长率

渔业科学家使用生物模型来确定可持续的捕捞率并预测未来的动态。这些模型既需要生命历史参数（死亡率，成熟度，增长），又需要股票招募参数（青少年生产）。但是，很少有研究可以同时预测寿命历史和库存补充参数。我开发了第一个数据集成的生命历史模型，该模型将进化动力学的简单模型扩展到了生命历史参数的现场测量以及产卵结果和后续募集的历史记录。这种演化模型预测招募生产率（陡度）和变异（方差和招聘偏差自相关）以及死亡率，成熟，成长，和大小，并使用这些预测的内在增长率（[R）用于所有描述的鱼类。该模型证实了先前的分析，该分析表明陡度与自然死亡率或渐近最大大小（）之间几乎没有相关性。但是，它确实揭示了分类学模式，相对于对骨鱼的预测（类放线纲纲），Sebastidae家族的陡度较低（），而Salmonidae的陡度较高（）。同样，塞巴斯蒂斯（Sebastes）属的增长率（0.09）接近几个鲨鱼科（鲨鱼形：0.02；鲨鱼形：0.02）。交叉验证实验确认该模型是准确的，解释了很大一部分方差（32％–67％），但生成的标准误差有些过小。与上级组织（例如，家庭）相比，物种的预测间隔更小，并且全球范围内所有R包FishLife鱼类的预测（包括间隔）都可用。最后，我概述了寿命历史和库存补充参数的多变量预测如何对库存评估，决策理论，整体建模和战略管理有用。