Improving the sustainability of Northwest Atlantic fisheries via predictive modelling

野生商业鱼类种群对加拿大人极其重要，生态管理单位依靠对各种生态系统属性的准确预测来支持管理决策。对这些属性的预测构成了短期战术管理建议和长期管理规划中战略建议的基础。然而，复杂的种群动态往往产生于各种空间和时空过程，如物种之间的相互作用，与物理环境的相互作用，高内在增长率，密度依赖的动态，人为干预，和其他随机过程误差。这使得能够分析复杂系统的准确和自适应预测模型对于成功管理至关重要。地质统计建模是利用生态时间序列的空间性质的线性预测模型的主要工具。渔业资源评估认识到这种重要性，并已转向空间资源评估。预测分布模型利用时空模型，将环境协变量和鱼类运动，使更精细的预测。另一方面，人们发现非线性动力学在海洋渔业中很常见，考虑到所观察到的高维模式和开发系统中的非线性，自然系统和人类干预之间的相互作用可能会限制预测的潜力。非线性时间序列预测方法的最新发展为数据科学提供了另一种工具，以提高生态时间序列的预测能力，如经验动态模型。因此，它是至关重要的，以测试的预测能力的两个现代方法，使用丰富的生态时间序列存在的研究调查，商业捕捞量和气候监测在西北大西洋地区。具体而言，我将专注于非线性预测建模和地统计时空分布建模，以研究以下具体目标：1）西北大西洋主要商业鱼类的时间序列本质上是非线性的，可以使用非线性预测方法进行预测2）这些鱼类及其群落是否显示出可预测的分布变化，无论是在短期还是长期使用地质统计模型; 3）我们能否通过纳入这些模型的预测来改进各种空间过程，以进行种群评估;以及4）从预测建模中获得的知识如何有助于更好地进行鱼类种群管理，以平衡物种，生态系统和行业。

Wild commercial fish populations are extremely important to Canadians and ecological management units rely on accurate forecasting of various ecosystem attributes to support management decisions. The forecasts of those attributes form the basis for tactical management advice in the short term and strategic recommendations in long-term management planning. However, complex population dynamics often arise from various spatial and spatiotemporal processes such as interactions among species, interactions with the physical environment, high intrinsic growth rates, density-dependent dynamics, human intervention, and other stochastic process errors. This makes accurate and adaptive predictive models that are capable to analyze complex systems very critical for successful management. Geostatistical modelling is a major tool of linear predictive models that utilizes the spatial nature of ecological time series. Fisheries stock assessment recognizes such importance and has been moving towards spatial stock assessment. Predictive distribution modelling utilizes spatiotemporal models to incorporate environmental covariates and fish movement to make finer projections. On the other hand, it has been found nonlinear dynamics are common in marine fisheries, and the interaction between the natural system and human interventions can limit the potential for predictions given the observed patterns of high dimensionality and the nonlinearity in exploited systems. Recent developments in nonlinear time series forecasting method provide an alternative tool from data science to improve the predictive ability of ecological time series such as Empirical Dynamic models. Thus, it is crucial to test the predictive ability of the two modern methods using the rich ecological time-series exist from both research surveys, commercial catch, and climate monitoring in the Northwest Atlantic region. Specifically, I will focus on the nonlinear forecasting modelling and geostatistical spatiotemporal distribution modelling to investigate the following specific objectives: 1) are the time series of the major commercial fish in the Northwest Atlantic nonlinear in nature and can they be forecasted using nonlinear forecasting methods 2) do those fish species and their community display predictable distribution shifts in both the short term and long term using geostatistical models; 3) can we improve various spatial processes for stock assessment by incorporating predictions from those models; and 4) how the knowledge gained from predictive modelling contribute to better fish stock management to balance the species, the ecosystem, and the industry.