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Explaining and Predicting Recruitment of Yellow Perch in North American Inland Lakes

机译：解释和预测北美内陆湖泊黄色鲈鱼的招聘

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摘要

Managing sustainable fisheries populations relies on an understanding of the interplay between recruitment, growth, and mortality. Recruitment is frequently noted as the most influential parameter of these three dynamic rate functions. The erratic recruitment dynamics of yellow perch (Perca flavescens) often confound fisheries scientists, managers, and regulators of inland lakes. Yellow perch populations provide many recreationally important fisheries directly or supports fisheries for other species. Additionally, recruitment patterns of yellow perch are expected to become more erratic under changing environmental conditions such as climate change. Traditional fisheries modeling approaches often fail to capture the dynamics and complexities of recruitment. In this paper, we describe the initial stages of building an SD model of yellow perch recruitment for inland lakes. We compare this approach to traditional fisheries recruitment modeling approaches and describe the next steps in model development and use. Initial model sensitivity testing shows promise in our model to date and is congruent with ecological information. We believe that our final SD model will benefit fisheries scientists, managers, and regulators in anticipating and potentially mitigating recruitment variation to provide sustainable recreational fisheries in inland lakes across the geographic range of yellow perch.
机译：管理可持续渔业群体依赖于对招聘，增长和死亡率之间的相互作用的理解。招聘经常指出是这三种动态率函数的最具影响力的参数。黄鲈鱼（Perca Flavescens）的不稳定招聘动态经常混淆渔业科学家，经理和内陆湖泊的监管机构。黄鲈种群直接提供许多娱乐重要渔业或支持其他物种的渔业。此外，预计黄鲈的招募模式在改变气候变化等环境条件下会变得更加不稳定。传统渔业建模方法往往无法捕捉招聘的动态和复杂性。在本文中，我们描述了对内陆湖泊的黄鲈鱼招聘SD模型构建SD模型的初始阶段。我们将这种方法与传统渔业招聘建模方法进行比较，并描述了模型开发和使用中的下一步。初始模型敏感性测试显示我们模型的承诺到目前为止，并与生态信息一致。我们认为，我们的最终SD模型将使渔业科学家，经理和监管机构预测，潜在地减轻招聘变异，以在陆地范围内为内陆湖泊提供可持续的娱乐渔业。

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