climwin: An R Toolbox for Climate Window Analysis
climwin: An R Toolbox for Climate Window Analysis
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DOI：
10.1371/journal.pone.0167980
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发表时间：
2016-12-14
期刊：
影响因子：
3.7
通讯作者：
van de Pol, Martijn
中科院分区：
文献类型：
--
作者：
Bailey, Liam D.;van de Pol, Martijn
在研究气候变化的影响时，有一种倾向是从一小部分任意时间段或气候窗口中选择气候数据（例如，春季气温）。然而，这些任意的窗口可能不包括气候敏感性最强的时期，并可能导致错误的生物学解释。因此，有必要考虑更广泛的气候窗口，以更好地预测未来气候变化的影响。我们介绍了R包climwin，它提供了许多方法来测试不同的气候窗口对选定的响应变量的影响，并比较这些窗口，以确定潜在的气候信号。climwin为每个可能的气候窗口提取相关数据，并利用这些数据拟合一个统计模型，模型的结构由用户选择。然后使用信息标准方法对模型进行比较。这允许用户确定每个窗口解释响应变量的变化的程度，并比较窗口之间的模型支持。climwin还包含检测I型和II型错误的方法，这通常是这种探索性分析的问题。本文介绍了climwin软件包背后的统计框架和技术细节，并通过一些工作示例演示了该方法的适用性。
When studying the impacts of climate change, there is a tendency to select climate data from a small set of arbitrary time periods or climate windows (e.g., spring temperature). However, these arbitrary windows may not encompass the strongest periods of climatic sensitivity and may lead to erroneous biological interpretations. Therefore, there is a need to consider a wider range of climate windows to better predict the impacts of future climate change. We introduce the R package climwin that provides a number of methods to test the effect of different climate windows on a chosen response variable and compare these windows to identify potential climate signals. climwin extracts the relevant data for each possible climate window and uses this data to fit a statistical model, the structure of which is chosen by the user. Models are then compared using an information criteria approach. This allows users to determine how well each window explains variation in the response variable and compare model support between windows. climwin also contains methods to detect type I and II errors, which are often a problem with this type of exploratory analysis. This article presents the statistical framework and technical details behind the climwin package and demonstrates the applicability of the method with a number of worked examples.