Department of Civil Engineering, University of Colorado Denver, Denver, CO, United States America.
PLoS One. 2022 Jun 9;17(6):e0269193. doi: 10.1371/journal.pone.0269193. eCollection 2022.
The migration timing of Pacific salmon in the Columbia River basin is subject to multiple influences related to climate, human water resource management, and lagged effects such as oceanic conditions. We apply an information theory-based approach to analyze drivers of adult Chinook salmon migration within the spring and fall spawning seasons and between years based on salmon counts at dams along the Columbia and Snake Rivers. Time-lagged mutual information and information decomposition measures, which characterize lagged and nonlinear dependencies as reductions in uncertainty, are used to detect interactions between salmon counts and lagged streamflows, air and water temperatures, precipitation, snowpack, climate indices and downstream salmon counts. At a daily timescale, these interdependencies reflect migration timing and show differences between fall and spring run salmon, while dependencies based on variables at an annual resolution reflect long-term predictability. We also highlight several types of joint dependencies where predictability of salmon counts depends on the knowledge of multiple lagged sources. This study illustrates how co-varying human and natural drivers could propagate to influence salmon migration timing or overall returns, and how nonlinear types of dependencies between variables enhance predictability of a target. This information-based framework is broadly applicable to assess driving factors in other types of complex water resources systems or species life cycles.
哥伦比亚河流域太平洋鲑鱼的洄游时间受到与气候、人类水资源管理以及海洋条件等滞后效应相关的多种因素的影响。我们应用基于信息理论的方法，根据哥伦比亚河和蛇河流域大坝的鲑鱼计数，分析春季和秋季产卵季节以及年份之间成鱼奇努克鲑鱼洄游的驱动因素。时间滞后互信息和信息分解度量用于检测鲑鱼计数与滞后径流量、空气和水温、降水、积雪、气候指数和下游鲑鱼计数之间的相互作用，这些度量特征是减少不确定性的滞后和非线性依赖性。在每日时间尺度上，这些相互关系反映了洄游时间，并显示了秋季和春季洄游鲑鱼之间的差异，而基于年度分辨率变量的依赖性反映了长期可预测性。我们还强调了几种联合依赖性，其中鲑鱼计数的可预测性取决于多个滞后源的知识。本研究说明了人类和自然驱动因素如何共同传播，从而影响鲑鱼的洄游时间或整体产量，以及变量之间的非线性依赖性如何增强目标的可预测性。这种基于信息的框架广泛适用于评估其他类型的复杂水资源系统或物种生命周期中的驱动因素。
