Ecosystem physio-phenology revealed using circular statistics

Abstract

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 (DOYGPPmax), 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 DOYGPPmax 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 DOYGPPmax 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 DOYGPPmax across sites. Woody savannas are an exception, where the most important factor is precipitation. Although the sensitivity of the DOYGPPmax 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., DOYGPPmax in the Southern Hemisphere).