Contrasting Effects of Hydrothermal Drivers on Gross Primary Productivity and Ecosystem Respiration

The balance between gross primary productivity (GPP) and ecosystem respiration (ER) defines an ecosystem’s carbon sink-source status. Under global warming, hydrothermal conditions critically shape carbon fluxes, yet their differential impacts on GPP and ER remain insufficiently understood, especially across biomes. Elucidating these differences is essential for reducing uncertainties in terrestrial carbon cycle projections under ongoing climate change. Here, based on flux observations from global terrestrial sites with a focus on forest ecosystems, we selected mean annual temperature (MAT), latent heat flux (LH), vapor pressure deficit (VPD), soil water content (SWC), and annual precipitation as representative indicators of hydrothermal conditions, and employed mixed-effects models to examine how these key environmental drivers influence GPP and ER. After analyzing the fixed effects, LH and MAT promoted GPP more strongly than ER (slope = 0.5 > 0.253, slope = 0.595 > 0.392, respectively), whereas VPD suppressed GPP more than ER (slope = −0.658 < −0.499), yet accounted for a greater proportion of variance in ER than in GPP (R2 = 0.14 > 0.07). Although SWC had a significant (p < 0.001) positive effect on GPP, the effect size was minimal, and its impact on ER was insignificant. R2 decomposition showed that marginal R2 values were similar for the GPP and ER models (0.43 and 0.44), whereas the GPP model exhibited a substantially higher conditional R2 (0.82 vs. 0.63), indicating that MAT exerted a stronger influence on GPP than on ER across ecosystem types. The combined analysis of fixed and random effects indicated that MAT affected GPP more variably than ER across ecosystem types, with the strongest responses in mixed forests and savannas, intermediate responses in evergreen needleleaf forests, and the weakest responses in evergreen broadleaf forests. Overall, this study advances our understanding of how environmental factors differently influence GPP and ER, and incorporating these differences can improve predictions of forest carbon fluxes and climate-carbon feedbacks.