Taking climate change into account: Non-stationarity in climate drivers of ecological response
Taking climate change into account: Non-stationarity in climate drivers of ecological response
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DOI：
10.1111/1365-2745.13572
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发表时间：
2021-01-13
影响因子：
5.5
通讯作者：
Suding, Katharine N.
中科院分区：
文献类型：
--
作者：
Bueno de Mesquita, Clifton P.;White, Caitlin T.;Suding, Katharine N.
全球气候系统的变化正在创造越来越多的非模拟气候条件，并期望气候驱动因素具有非平稳性。气候驱动因素之间的脱钩使对气候变化的生态响应的评估变得复杂，因为这些特征曾经可以被视为一系列条件，现在需要被视为具有潜在协同效应的潜在独立特征。生态学家通常在大型气候和环境数据集上使用排序技术（通常是主成分分析；PCA），将一系列变量减少到几个互不相关的轴，这些轴通常可以解释原始数据中很大一部分的变化。然而，随时间变化的变量之间的相关性的非平稳性可能会影响这种方法。本文利用美国科罗拉多州Niwot Ridge长期生态研究站点的37年气候数据集，介绍了移动窗口主成分分析和移动窗口相关分析的应用，以确定气候数据的非平稳性。气候变量之间以及输入变量与主成分分析轴之间的关系随时间变化；这模糊了PCA轴与生态响应（植物生物量）之间关系的解释，表明对环境变量的一次性PCA可能导致不适当的推断。当预测变量表现出非平稳性时，在分析气候-生态关系时必须小心。我们提出了一个概念性决策树，以帮助生态学家考虑何时使用PCA并提取轴分数，或使用替代方法将非平稳性纳入后续分析，包括单独测试变量以帮助解释，断点分析和平均PC分数。
Changes in the global climate system are creating increasingly non-analogue climate conditions with expectations of non-stationarity among climate drivers. Decoupling among climate drivers complicates the assessment of ecological response to the changing climate as characteristics that could be once treated as a suite of conditions now need to be treated as potentially independent with possible synergistic effects.Ecologists commonly use ordination techniques (often principal component analysis; PCA) on large climate and environmental datasets to reduce a range of variables to a few axes that are uncorrelated with each other and often explain large proportion of the variation in the original data. However, non-stationarity, with correlations among variables changing over time, can affect this approach. Here, we use a 37-year climate dataset from the Niwot Ridge Long Term Ecological Research site (Colorado, USA) to present the use of both moving window principal component analysis and moving window correlation analysis to determine non-stationarity in climate data.Relationships among climate variables and between input variables and PCA axes changed over time; this obscured interpretation of relationships between PCA axes and an ecological response (plant biomass), suggesting that one-time PCA for environmental variables may lead to inappropriate inferences.Synthesis. Care must be taken in analysing climate-ecological relationships when predictor variables exhibit non-stationarity. We present a conceptual decision-making tree to help ecologists consider when to use PCA and extract axis scores or use alternative approaches for incorporating non-stationarity into subsequent analysis, including testing variables individually to aid in interpretation, break point analyses and averaging PC scores.