Scripps Institution of Oceanography, University of California at San Diego, La Jolla, CA 92093, USA.
Proc Natl Acad Sci U S A. 2013 Apr 16;110(16):6430-5. doi: 10.1073/pnas.1215506110. Epub 2013 Mar 27.
For many marine species and habitats, climate change and overfishing present a double threat. To manage marine resources effectively, it is necessary to adapt management to changes in the physical environment. Simple relationships between environmental conditions and fish abundance have long been used in both fisheries and fishery management. In many cases, however, physical, biological, and human variables feed back on each other. For these systems, associations between variables can change as the system evolves in time. This can obscure relationships between population dynamics and environmental variability, undermining our ability to forecast changes in populations tied to physical processes. Here we present a methodology for identifying physical forcing variables based on nonlinear forecasting and show how the method provides a predictive understanding of the influence of physical forcing on Pacific sardine.
对于许多海洋物种和栖息地而言,气候变化和过度捕捞是双重威胁。为了有效地管理海洋资源,有必要使管理适应物理环境的变化。在渔业和渔业管理中,人们长期以来一直使用环境条件与鱼类丰度之间的简单关系。然而,在许多情况下,物理、生物和人为变量会相互反馈。对于这些系统,随着系统随时间的演变,变量之间的关联可能会发生变化。这可能会使种群动态与环境可变性之间的关系变得模糊,从而削弱我们预测与物理过程相关的种群变化的能力。在这里,我们提出了一种基于非线性预测的物理强迫变量识别方法,并展示了该方法如何提供对物理强迫对太平洋沙丁鱼影响的预测性理解。