Methane (CH4) is the second most important atmospheric greenhouse gas (GHG) and forest soils are a significant sink for atmospheric CH4. Uptake of CH4 by global forest soils is affected by nitrogen (N) deposition; clarifying the effect of N deposition helps to reduce uncertainties of the global CH4 budget. However, it remains an unsolved puzzle why N input stimulates soil CH4 flux (RCH4) in some forests while suppressing it in others. Combining previous findings and data from N addition experiments conducted in global forests, we proposed and tested a “stimulating-suppressing-weakening effect” (“three stages”) hypothesis on the changing responses of RCH4 to N input. Specifically, we calculated the response factors (f) of RCH4 to N input for N-limited and N-saturated forests across biomes; the significant changes in f values supported our hypothesis. We also estimated the global forest soil CH4 uptake budget to be approximately 11.2 Tg yr–1. CH4 uptake hotspots were located predominantly in temperate forests. Furthermore, we quantified that current level of N deposition reduced global forest soil CH4 uptake by ~3%. This suppression effect was more pronounced in temperate forests than in tropical or boreal forests, likely due to differences in N status. The proposed “three stages” hypothesis in this study generalizes the diverse effects of N input on RCH4, which could help improve experimental design. Additionally, our findings imply that by regulating N pollution and reducing N deposition, soil CH4 uptake can be significantly increased in the N-saturated forests in tropical and temperate biomes.

Accurately predicting gross primary productivity (GPP) is crucial for understanding carbon cycling; however, many studies have predominantly investigated GPP using environmental metrics, overlooking the pivotal role of functional traits as intermediaries between environment and GPP and the predictive potential of GPP. Therefore, this study proposes and employed a three-dimensional ”engine” framework to predict GPP and tested it by leveraging functional traits from 2040 plant communities on the Tibetan Plateau, incorporating environmental factors and the length of the plant growing season. Our results challenged the conventional emphasis that the environment plays a predominant role in predicting GPP dynamics, showing that while the environment exerts a minor direct effect, density traits of leaf and length of plant growing season significantly contributed to GPP predictions. With a prediction accuracy close to 0.90, this study underscores the feasibility of the three-dimensional engine framework in GPP prediction However, incorporating nitrogen-to-phosphorus ratio to the framework diminished the model’s predictive accuracy. Within the stoichiometric dimension alone, the prediction accuracy significantly increased with the number of input traits, indicating a substantial potential for enhancing predictive capability. Our research facilitates the dynamic, continuous, and relatively accurate monitoring of GPP, contributing to a better understanding of carbon cycle dynamics and supporting informed ecosystem planning and management.