Data from: Using time series analysis to characterize evolutionary and plastic responses to environmental change: a case study of a shift toward earlier migration date in sockeye salmon

Environmental change can shift the phenotype of an organism through either evolutionary or nongenetic processes. Despite abundant evidence of phenotypic change in response to recent climate change, we typically lack sufficient genetic data to identify the role of evolution. We present a method of using phenotypic data to characterize the hypothesized role of natural selection and environmentally driven phenotypic shifts (plasticity). We modeled historical selection and environmental predictors of interannual variation in mean population phenotype using a multivariate state-space model framework. Through model comparisons, we assessed the extent to which an estimated selection differential explained observed variation better than environmental factors alone. We applied the method to a 60-year trend toward earlier migration in Columbia River sockeye salmon Oncorhynchus nerka, producing estimates of annual selection differentials, average realized heritability, and relative cumulative effects of selection and plasticity. We found that an evolutionary response to thermal selection was capable of explaining up to two-thirds of the phenotypic trend. Adaptive plastic responses to June river flow explain most of the remainder. This method is applicable to other populations with time series data if selection differentials are available or can be reconstructed. This method thus augments our toolbox for predicting responses to environmental change.

环境变化可通过进化或非遗传途径改变生物体的表型。尽管已有大量研究证实，生物体表型会响应近期气候变化发生改变，但我们通常缺乏足够的遗传数据以明确进化在其中所扮演的角色。本研究提出一种利用表型数据，刻画自然选择与环境驱动的表型转变（表型可塑性）的假设性作用的方法。我们基于多变量状态空间模型框架，对历史选择过程以及种群平均表型年际变化的环境预测因子开展建模。通过模型比较，我们评估了相较于仅考虑环境因子，估算得到的选择差能够在多大程度上更好地解释观测到的表型变异。我们将该方法应用于哥伦比亚河红大麻哈鱼（Oncorhynchus nerka）洄游时间提前的60年趋势数据，估算了年选择差、平均实现遗传力，以及选择与表型可塑性的相对累积效应。研究结果显示，针对温度选择的进化响应可解释该表型趋势中至多三分之二的变异；而对6月河川径流量的适应性塑性响应则解释了剩余的大部分变异。若已有选择差数据或可重建选择差，该方法可推广应用于其他具备时间序列数据的种群，从而扩充我们预测生物对环境变化响应的工具库。