Reversal of the sensitivity of vegetation productivity to precipitation in global terrestrial biomes over the recent decade

Abstract

The sensitivity of vegetation productivity to precipitation (S_ppt) is crucial for grasping how vegetation responds to changing precipitation and forecasting future shifts in ecosystem function. However, comprehensive assessment of S_ppt globally is limited by specific technical defects or objective limitations, leading to a poor understanding of its spatial distribution and temporal variations. In this study, we examined the spatial patterns and temporal changes o.ff S_ppt across global terrestrial ecosystems from 2001 to 2021 using a change-based method and various satellite observations, including solar-induced fluorescence (SIF), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI). Additionally, we obtained various high-resolution global datasets and applied extreme gradient boosting (XGBoost) along with SHapley Additive Explanations (SHAP) to explain how key climatic, topographic, edaphic, and vegetation variables regulate S_ppt. Spatially, S_ppt exhibited positive values in most regions, particularly in arid areas, while lower values were found in mesic regions. Temporally, S_ppt shifted from a declining to an increasing trend in most regions over the past two decades, with the breakpoint occurring primarily between 2011 and 2015. This shift could be related to the fertilization effect of elevated CO₂, intensified drought caused by increased vapor pressure deficit, and atmospheric nitrogen deposition. In forest ecosystems, radiation, temperature, and soil nutrients were found to be critical in regulating S_ppt, whereas leaf functional traits demonstrated relatively greater importance in grasslands and shrublands. Negative regulatory relationships were shown to exist between land slope and forest age with S_ppt. Overall, this research contributes to a deeper understanding of the mechanisms that drive vegetation productivity in the context of changing precipitation patterns.