Quantifying how vegetation phenology responds to climate variability is a key prerequisite to predicting how ecosystem dynamics will shift with climate change. So far, many studies have focused on responses of classical phenological events (e.g., budburst or flowering) to climatic variability for individual species. Comparatively little is known on the dynamics of physio-phenological events such as the timing of maximum gross primary production (DOY_GPPmax), i.e., quantities that are relevant for understanding terrestrial carbon cycle responses to climate variability and change. In this study, we aim to understand how DOY_GPPmax depends on climate drivers across 52 eddy covariance (EC) sites in the FLUXNET network for different regions of the world. Most phenological studies rely on linear methods that cannot be generalized across both hemispheres and therefore do not allow for deriving general rules that can be applied for future predictions. One solution could be a new class of circular–linear (here called circular) regression approaches. Circular regression allows circular variables (in our case phenological events) to be related to linear predictor variables as climate conditions. As a proof of concept, we compare the performance of linear and circular regression to recover original coefficients of a predefined circular model for artificial data. We then quantify the sensitivity of DOY_GPPmax across FLUXNET sites to air temperature, shortwave incoming radiation, precipitation, and vapor pressure deficit. Finally, we evaluate the predictive power of the circular regression model for different vegetation types. Our results show that the joint effects of radiation, temperature, and vapor pressure deficit are the most relevant controlling factor of DOY_GPPmax across sites. Woody savannas are an exception, where the most important factor is precipitation. Although the sensitivity of the DOY_GPPmax to climate drivers is site-specific, it is possible to generalize the circular regression models across specific vegetation types. From a methodological point of view, our results reveal that circular regression is a robust alternative to conventional phenological analytic frameworks. The analysis of phenological events at the global scale can benefit from the use of circular statistics. Such an approach yields substantially more robust results for analyzing phenological dynamics in regions characterized by two growing seasons per year or when the phenological event under scrutiny occurs between 2 years (i.e., DOY_GPPmax in the Southern Hemisphere).

中文翻译：

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利用循环统计揭示生态系统生理物候

量化植被物候对气候变化的响应方式是预测生态系统动态将如何随着气候变化而变化的关键前提。迄今为止，许多研究都集中在经典物候事件（例如，芽芽或开花）对单个物种的气候变化的响应上。在诸如最大总初级生产时间（DOY _GPPmax）之类的生理物候事件动力学方面，人们所知_甚少，即与了解陆地碳循环对气候变化和变化的响应有关的数量。在这项研究中，我们旨在了解DOY _GPPmax如何 取决于FLUXNET网络中遍布世界不同地区的52个涡动协方差（EC）站点的气候驱动因素。大多数物候学研究依赖于无法在两个半球上推广的线性方法，因此不允许推导可用于未来预测的一般规则。一种解决方案可能是一类新的循环–线性（这里称为循环）回归方法。循环回归使循环变量（在我们的情况下为物候事件）与线性预测变量（如气候条件）相关。作为概念证明，我们比较了线性回归和圆回归的性能，以恢复用于人工数据的预定义圆模型的原始系数。然后我们量化DOY _GPPmax的灵敏度穿过FLUXNET站点的温度，短波入射辐射，降水和蒸气压不足。最后，我们评估了圆形回归模型对不同植被类型的预测能力。我们的结果表明，辐射，温度和蒸气压不足的联合效应是跨站点DOY _GPPmax的最相关控制因素。伍迪大草原是个例外，其中最重要的因素是降水。尽管DOY _GPPmax的敏感性 由于气候驱动因素是针对特定地点的，因此有可能对特定植被类型的循环回归模型进行概括。从方法论的角度来看，我们的结果表明，循环回归是常规物候分析框架的可靠替代。通过使用循环统计，可以在全球范围内对物候事件进行分析。这样的方法在分析以每年两个生长季节为特征的区域中的物候动态时，或在受到审查的物候事件在2年之间发生时（即，南半球的DOY _GPPmax），可产生更为可靠的结果。