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Using a qualitative model to explore the impacts of ecosystem and anthropogenic drivers upon declining marine survival in Pacific salmon

机译：使用定性模型探索生态系统和人为驱动因素对太平洋鲑鱼海洋生存下降的影响

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Coho salmon (Oncorhynchus kisutch), Chinook salmon (Oncorhynchus tshawytscha) and steelhead (Oncorhynchus mykiss) in Puget Sound and the Strait of Georgia have exhibited declines in marine survival over the last 40 years. While the cause of these declines is unknown, multiple factors, acting cumulatively or synergistically, have likely contributed. To evaluate the potential contribution of a broad suite of drivers on salmon survival, we used qualitative network modelling (QNM). QNM is a conceptually based tool that uses networks with specified relationships between the variables. In a simulation framework, linkages are weighted and then the models are subjected to user-specified perturbations. Our network had 33 variables, including: environmental and oceanographic drivers (e.g., temperature and precipitation), primary production variables, food web components from zooplankton to predators and anthropogenic impacts (e.g., habitat loss and hatcheries). We included salmon traits (survival, abundance, residence time, fitness and size) as response variables. We invoked perturbations to each node and to suites of drivers and evaluated the responses of these variables. The model showed that anthropogenic impacts resulted in the strongest negative responses in salmon survival and abundance. Additionally, feedbacks through the food web were strong, beginning with primary production, suggesting that several food web variables may be important in mediating effects on salmon survival within the system. With this model, we were able to compare the relative influence of multiple drivers on salmon survival.
机译：在过去40年中，普吉特海湾和格鲁吉亚海峡的银大麻哈鱼（Oncorhynchus kisutch），奇努克鲑鱼（Oncorhynchus tshawytscha）和硬皮海鱼（Oncorhynchus mykiss）的海洋存活率下降。尽管这些下降的原因尚不清楚，但可能有多种因素共同起作用或起协同作用。为了评估各种驱动因素对鲑鱼生存的潜在贡献，我们使用了定性网络建模（QNM）。 QNM是基于概念的工具，它使用在变量之间具有指定关系的网络。在模拟框架中，对链接进行加权，然后对模型进行用户指定的扰动。我们的网络有33个变量，包括：环境和海洋驱动因素（例如温度和降水），主要生产变量，从浮游动物到捕食者的食物网成分以及人为影响（例如栖息地丧失和孵化场）。我们将鲑鱼性状（生存率，丰度，停留时间，适应性和体型）作为响应变量。我们对每个节点和驱动程序套件调用扰动，并评估这些变量的响应。该模型表明，人为影响导致鲑鱼生存和丰度最强的负面反应。此外，从初级生产开始，通过食物网的反馈就很强，这表明几个食物网变量在调解系统中鲑鱼存活的影响方面可能很重要。使用该模型，我们能够比较多种驱动因素对鲑鱼存活率的相对影响。

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