In marine management, fish stocks are often managed on a stock-by-stock basis using single-species models. Many of these models are based upon statistical techniques and are good at assessing the current state and making short-term predictions; however, as they do not model interactions between stocks, they lack predictive power on longer timescales. Additionally, there are size-based multi-species models that represent key biological processes and consider interactions between stocks such as predation and competition for resources. Due to the complexity of these models, they are difficult to fit to data, and so many size-based multi-species models depend upon single-species models where they exist, or ad hoc assumptions when they do not, for parameters such as annual fishing mortality. In this paper, we demonstrate that by taking a state-space approach, many of the uncertain parameters can be treated dynamically, allowing us to fit, with quantifiable uncertainty, size-based multi-species models directly to data. We demonstrate this by fitting uncertain parameters, including annual fishing mortality, of a size-based multi-species model of the Celtic Sea, for species with and without single-species stock assessments. Consequently, errors in the single-species models no longer propagate through the multi-species model and underlying assumptions are more transparent. Building size-based multi-species models that are internally consistent, with quantifiable uncertainty, will improve their credibility and utility for management. This may lead to their uptake by being either used to corroborate single-species models; directly in the advice process to make predictions into the future; or used to provide a new way of managing data-limited stocks.

中文翻译：

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通过结合状态空间和基于大小的多物种模型量化多物种捕捞死亡率、渔获量和生物量的不确定性和动态变化

在海洋管理中，鱼类种群通常使用单一物种模型逐个种群进行管理。其中许多模型基于统计技术，擅长评估当前状态并做出短期预测；然而，由于它们不对股票之间的相互作用进行建模，因此它们缺乏对较长时间尺度的预测能力。此外，还有基于大小的多物种模型，代表关键的生物过程并考虑种群之间的相互作用，例如捕食和资源竞争。由于这些模型的复杂性，它们难以拟合数据，因此许多基于大小的多物种模型依赖于存在的单物种模型，或者在不存在时的临时假设，例如年度参数捕捞死亡率。在本文中，我们证明，通过采用状态空间方法，可以动态处理许多不确定参数，使我们能够以可量化的不确定性将基于大小的多物种模型直接拟合到数据中。我们通过拟合不确定参数（包括年度捕捞死亡率）来证明这一点，该模型基于凯尔特海的基于大小的多物种模型，适用于有和没有单一物种种群评估的物种。因此，单物种模型中的错误不再通过多物种模型传播，并且基本假设更加透明。构建内部一致且具有可量化不确定性的基于大小的多物种模型，将提高它们的可信度和管理效用。这可能会导致它们被用于证实单物种模型；直接在建议过程中对未来进行预测；或用于提供管理数据有限库存的新方法。