Understanding what causes large year classes and predicting them has been called the holy grail of fisheries science, one of the last great unanswered questions. Recruitment prediction, or forecasting, is an important component for setting fishery catch limits. We propose a new approach, called the “poor-recruitment paradigm”, for predicting recruitment using environmental variables. This approach hypothesizes that it is easier to predict poor recruitment rather than good recruitment because an environmental variable affects recruitment only when its value is extreme (lethal); otherwise, the variable may be benign and not influence recruitment. Thus, good recruitment necessitates all environmental conditions not be harmful and for some to be especially favorable; poor recruitment, however, requires only one environmental variable to be extreme.

This idea was evaluated using recruitment and river discharge data for striped bass (Morone saxatilis) from seven major spawning tributaries of Chesapeake Bay. Low spring river discharge reliably resulted in poor recruitment of striped bass. Specifically, in all rivers, median recruitment and standard deviation of recruitment were lower when spring river discharge was low compared to when it was average or high; additionally, the proportion of years with poor recruitment was higher in years of low discharge than in years of average to high discharge. The consistent predictability of poor recruitment has the potential to improve stock projections, and therefore, has the potential to improve catch advice.

了解导致大年课程的原因并对其进行预测被称为渔业科学的圣杯，这是最后一个未解决的重大问题之一。招募预测或预测是设定渔业捕捞限制的重要组成部分。我们提出了一种新方法，称为“不良招聘范式”，用于使用环境变量预测招聘。这种方法假设更容易预测不良招聘而不是良好招聘，因为环境变量仅在其值极端（致命）时才会影响招聘；否则，变量可能是良性的，不会影响招聘。因此，良好的招聘要求所有环境条件都不是有害的，并且对某些环境条件特别有利；然而，糟糕的招聘只需要一个极端的环境变量。

这个想法是使用来自切萨皮克湾的七个主要产卵支流的条纹鲈鱼 ( Morone saxatilis ) 的补充和河流流量数据进行评估的。春季河流流量低可靠地导致条纹鲈鱼的招募不良。具体而言，在所有河流中，春季河流流量较低时的补充中位数和补充标准差均低于平均或较高时；此外，低出院年份中招募不良年份的比例高于平均出院至高出院年份。较差招募的持续可预测性有可能改善库存预测，因此有可能改善捕捞建议。