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Abstract Litter decomposition is a key ecological process that determines carbon (C) and nutrient cycling in terrestrial ecosystems. The initial concentrations of C and nutrients in litter play a critical role in this process, yet the global patterns of litter initial concentrations of C, nitrogen (N) and phosphorus (P) are poorly understood. We employed machine learning with a global database to quantitatively assess the global patterns and drivers of leaf litter initial C, N and P concentrations, as well as their returning amounts (i.e. amounts returned to soils). The medians of litter C, N and P concentrations were 46.7, 1.1, and 0.1%, respectively, and the medians of litter C, N and P returning amounts were 1.436, 0.038 and 0.004 Mg ha −1  year −1 , respectively. Soil and climate emerged as the key predictors of leaf litter C, N and P concentrations. Predicted global maps showed that leaf litter N and P concentrations decreased with latitude, while C concentration exhibited an opposite pattern. Additionally, the returning amounts of leaf litter C, N and P all declined from the equator to the poles in both hemispheres. Synthesis : Our results provide a quantitative assessment of the global concentrations and returning amounts of leaf litter C, N and P, which showed new light on the role of leaf litter in global C and nutrients cycling.

Abstract Increased greenhouse gas emissions are causing unprecedented climate change, which is, in turn, altering emissions and removals (referring to the oxidation of atmospheric CH 4 by methanotrophs within the soil) of the atmospheric CH 4 in terrestrial ecosystems. In the global CH 4 budget, wetlands are the dominant natural source and upland soils are the primary biological sink. However, it is unclear whether and how the soil CH 4 exchanges across terrestrial ecosystems and the atmosphere will be affected by warming and changes in precipitation patterns. Here, we synthesize 762 observations of in situ soil CH 4 flux data based on the chamber method from the past three decades related to temperature and precipitation changes across major terrestrial ecosystems worldwide. Our meta‐analysis reveals that warming (average warming of +2°C) promotes upland soil CH 4 uptake and wetland soil CH 4 emission. Decreased precipitation (ranging from −100% to −7% of local mean annual precipitation) stimulates upland soil CH 4 uptake. Increased precipitation (ranging from +4% to +94% of local mean annual precipitation) accelerates the upland soil CH 4 emission. By 2100, under the shared socioeconomic pathway with a high radiative forcing level (SSP585), CH 4 emissions from global terrestrial ecosystems will increase by 22.8 ± 3.6 Tg CH 4  yr −1 as an additional CH 4 source, which may be mainly attributed to the increase in precipitation over 30°N latitudes. Our meta‐analysis strongly suggests that future climate change would weaken the natural buffering ability of terrestrial ecosystems on CH 4 fluxes and thus contributes to a positive feedback spiral. , Plain Language Summary This study is the first investigation to include scenarios of CH 4 sink–source transition due to climate change and provides the global estimate of soil CH 4 budgets in natural terrestrial ecosystems in the context of climate change. The enhanced effect of climate change on CH 4 emissions was mainly attributed to increased CH 4 emissions from natural upland ecosystems. Although an increased CH 4 uptake by forest and grassland soils caused by increased temperature and decreased precipitation can offset some part of additional CH 4 sources, the substantial increase of increased precipitation on CH 4 emissions makes these sinks insignificant. These findings highlight that future climate change would weaken the natural buffering ability of terrestrial ecosystems on CH 4 emissions and thus form a positive feedback spiral between methane emissions and climate change. , Key Points This study is the first CH 4 budget investigation to include CH 4 sink‐source transition due to climate change Climate change is estimated to add 22.8 ± 3.6 Tg CH 4  yr −1 emission by 2100 under the high socioeconomic pathway Climate change weakens the buffering capacity of upland soils to CH 4 emissions

Abstract Increasing evidence indicates that current dynamic global vegetation models (DGVMs) have suffered from insufficient realism and are difficult to improve, particularly because they are built on plant functional type (PFT) schemes. Therefore, new approaches, such as plant trait-based methods, are urgently needed to replace PFT schemes when predicting the distribution of vegetation and investigating vegetation sensitivity. As an important direction towards constructing next-generation DGVMs based on plant functional traits, we propose a novel approach for modelling vegetation distributions and analysing vegetation sensitivity through trait-climate relationships in China. The results demonstrated that a Gaussian mixture model (GMM) trained with a LMA-N mass -LAI data combination yielded an accuracy of 72.82% in simulating vegetation distribution, providing more detailed parameter information regarding community structures and ecosystem functions. The new approach also performed well in analyses of vegetation sensitivity to different climatic scenarios. Although the trait-climate relationship is not the only candidate useful for predicting vegetation distributions and analysing climatic sensitivity, it sheds new light on the development of next-generation trait-based DGVMs.

Tropical rainforest ecosystems are important when considering the global methane (CH4) budget and in climate change mitigation. However, there is a lack of direct and year-round observations of ecosystem-scale CH4 fluxes from tropical rainforest ecosystems. In this study, we examined the temporal variations in CH4 flux at the ecosystem scale and its annual budget and environmental controlling factors in a tropical rainforest of Hainan Island, China, using 3 years of continuous eddy covariance measurements from 2016 to 2018. Our results show that CH4 uptake generally occurred in this tropical rainforest, where strong CH4 uptake occurred in the daytime, and a weak CH4 uptake occurred at night with a mean daily CH4 flux of −4.5 nmol m−2 s−1. In this rainforest, the mean annual budget of CH4 for the 3 years was −1260 mg CH4 m−2 year−1. Furthermore, the daily averaged CH4 flux was not distinctly different between the dry season and wet season. Sixty-nine percent of the total variance in the daily CH4 flux could be explained by the artificial neural network (ANN) model, with a combination of air temperature (Tair), latent heat flux (LE), soil volumetric water content (VWC), atmospheric pressure (Pa), and soil temperature at −10 cm (Tsoil), although the linear correlation between the daily CH4 flux and any of these individual variables was relatively low. This indicates that CH4 uptake in tropical rainforests is controlled by multiple environmental factors and that their relationships are nonlinear. Our findings also suggest that tropical rainforests in China acted as a CH4 sink during 2016–2018, helping to counteract global warming.