结合状态空间和机械多鱼种模型量化多鱼种捕捞死亡率、渔获量和生物量的不确定性和动态变化
Quantifying uncertainty and dynamical changes in multi-species fishing mortality rates, catches and biomass by combining state-space and mechanistic multi-species models
摘要：
在海洋管理中，鱼类种群往往采用单一物种模式逐个种群管理。这些模型中的许多都是基于统计技术，擅长评估当前状态和做出短期预测;然而，由于它们不模拟股票之间的相互作用，它们缺乏对较长时间尺度的预测能力。此外，还有机械多物种模型，代表关键的生物过程，并考虑种群之间的相互作用，如捕食和资源竞争。由于这些模型的复杂性，它们很难拟合数据，因此许多机械多物种模型依赖于单物种模型，或者当它们不存在时，对于诸如年度捕捞死亡率之类的参数进行特别假设。在本文中，我们表明，通过采取状态空间的方法，许多不确定的参数可以动态处理，使我们能够适应，可量化的不确定性，机械多物种模型直接的数据。我们证明了这一点，通过拟合不确定的参数，包括每年的捕捞死亡率，一个基于大小的多物种模型的凯尔特海，物种与和没有单一物种的股票评估。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。建立内部一致的、具有可量化不确定性的多物种机制模型，将提高其管理的可信度和实用性。这可能导致它们被用于证实单一物种模型;直接在咨询过程中对未来进行预测;或用于提供管理数据有限的种群的新方法。
英文摘要：
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 mechanistic 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 mechanistic multi-species models depend upon single-species models where they exist, or ad hoc assumptions when they don't, 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, mechanistic 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 mechanistic 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
