Microbial carbon use efficiency promotes global soil carbon storage.

Soils store more carbon than other terrestrial ecosystems1,2. How soil organic carbon (SOC) forms and persists remains uncertain1,3, which makes it challenging to understand how it will respond to climatic change3,4. It has been suggested that soil microorganisms play an important role in SOC formation, preservation and loss5-7. Although microorganisms affect the accumulation and loss of soil organic matter through many pathways4,6,8-11, microbial carbon use efficiency (CUE) is an integrative metric that can capture the balance of these processes12,13. Although CUE has the potential to act as a predictor of variation in SOC storage, the role of CUE in SOC persistence remains unresolved7,14,15. Here we examine the relationship between CUE and the preservation of SOC, and interactions with climate, vegetation and edaphic properties, using a combination of global-scale datasets, a microbial-process explicit model, data assimilation, deep learning and meta-analysis. We find that CUE is at least four times as important as other evaluated factors, such as carbon input, decomposition or vertical transport, in determining SOC storage and its spatial variation across the globe. In addition, CUE shows a positive correlation with SOC content. Our findings point to microbial CUE as a major determinant of global SOC storage. Understanding the microbial processes underlying CUE and their environmental dependence may help the prediction of SOC feedback to a changing climate.

Convergence in simulating global soil organic carbon by structurally different models after data assimilation.

Current biogeochemical models produce carbon-climate feedback projections with large uncertainties, often attributed to their structural differences when simulating soil organic carbon (SOC) dynamics worldwide. However, choices of model parameter values that quantify the strength and represent properties of different soil carbon cycle processes could also contribute to model simulation uncertainties. Here, we demonstrate the critical role of using common observational data in reducing model uncertainty in estimates of global SOC storage. Two structurally different models featuring distinctive carbon pools, decomposition kinetics, and carbon transfer pathways simulate opposite global SOC distributions with their customary parameter values yet converge to similar results after being informed by the same global SOC database using a data assimilation approach. The converged spatial SOC simulations result from similar simulations in key model components such as carbon transfer efficiency, baseline decomposition rate, and environmental effects on carbon fluxes by these two models after data assimilation. Moreover, data assimilation results suggest equally effective simulations of SOC using models following either first-order or Michaelis-Menten kinetics at the global scale. Nevertheless, a wider range of data with high-quality control and assurance are needed to further constrain SOC dynamics simulations and reduce unconstrained parameters. New sets of data, such as microbial genomics-function relationships, may also suggest novel structures to account for in future model development. Overall, our results highlight the importance of observational data in informing model development and constraining model predictions.

[Development situation and trends of Ginseng Radix et Rhizoma industry in China].

Ginseng Radix et Rhizoma is a unique traditional Chinese herbal medicine in China, with a long medicinal history, unique healthcare effects, and a profound cultural value. The development of the Ginseng Radix et Rhizoma industry has practical and symbolic significance for the traditional Chinese medicine(TCM) industry. Under the new situation, China's Ginseng Radix et Rhizoma industry has faced new development opportunities and also internal and external challenges. It is urgent to deeply analyze the practical problems and explore the solutions. This article systematically reviews the current situation of China's Ginseng Radix et Rhizoma industry from the industrial chain and analyzes the current problems and development trends of this industry, aiming to provide reference and a decision-making basis for the high-quality development of this industry.

[Identification and expression pattern analysis of Trihelix transcription factor family genes of ginseng].

The function of the Trihelix transcription factor is that it plays an important role in many abiotic stresses, especially in the signaling pathway of low temperature, drought, flood, saline, abscisic acid, methyl jasmonate, and other abiotic stresses. However, there are few studies on the Trihelix gene family of ginseng. In this study, 41 Trihelix gene family members were identified and screened from the ginseng genome database, and their physicochemical properties, cis-acting elements, subcellular localization, chromosomal assignment, and abiotic stress-induced expression patterns were analyzed by bioinformatics methods. The results showed that 85% of Trihelix family members of ginseng were located in the nucleus, and the main secondary structure of Trihelix protein was random coil and α helix. In the promoter region of Trihelix, cis-acting regulatory elements related to various abiotic stresses such as low temperature, hormone response, and growth and development were identified. Through the collinearity analysis of interspecific Trihelix transcription factors of model plants Arabidopsis thaliana and ginseng, 19 collinear gene pairs were found between A. thaliana and ginseng, and no collinear gene pairs existed on chromosomes 3, 6, and 12 only. qRT-PCR analysis showed that the expression of GWHGBEIJ010320.1 was significantly up-regulated under low temperature stress, a significant response to low temperature stress. This study lays a foundation for further research on the role of the Trihelix transcription factor of ginseng in abiotic stress, as well as the growth and development of ginseng.

Significant effects of precipitation frequency on soil respiration and its components-A global synthesis.

Global warming intensifies the hydrological cycle, which results in changes in precipitation regime (frequency and amount), and will likely have significant impacts on soil respiration (Rs ). Although the responses of Rs to changes in precipitation amount have been extensively studied, there is little consensus on how Rs will be affected by changes in precipitation frequency (PF) across the globe. Here, we synthesized the field observations from 296 published papers to quantify the effects of PF on Rs and its components using meta-analysis. Our results indicated that the effects of PF on Rs decreased with an increase in background mean annual precipitation. When the data were grouped by climate conditions, increased PF showed positive effects on Rs under the arid condition but not under the semi-humid or humid conditions, whereas decreased PF suppressed Rs across all the climate conditions. The positive effects of increased PF mainly resulted from the positive response of heterotrophic respiration under the arid condition while the negative effects of decreased PF were mainly attributed to the reductions in root biomass and respiration. Overall, our global synthesis provided for the first time a comprehensive analysis of the divergent effects of PF on Rs and its components across climate regions. This study also provided a framework for understanding and modeling responses of ecosystem carbon cycling to global precipitation change.

Machine learning for accelerating process-based computation of land biogeochemical cycles.

Global change ecology nowadays embraces ever-growing large observational datasets (big-data) and complex mathematical models that track hundreds of ecological processes (big-model). The rapid advancement of the big-data-big-model has reached its bottleneck: high computational requirements prevent further development of models that need to be integrated over long time-scales to simulate the distribution of ecosystems carbon and nutrient pools and fluxes. Here, we introduce a machine-learning acceleration (MLA) tool to tackle this grand challenge. We focus on the most resource-consuming step in terrestrial biosphere models (TBMs): the equilibration of biogeochemical cycles (spin-up), a prerequisite that can take up to 98% of the computational time. Through three members of the ORCHIDEE TBM family part of the IPSL Earth System Model, including versions that describe the complex interactions between nitrogen, phosphorus and carbon that do not have any analytical solution for the spin-up, we show that an unoptimized MLA reduced the computation demand by 77%-80% for global studies via interpolating the equilibrated state of biogeochemical variables for a subset of model pixels. Despite small biases in the MLA-derived equilibrium, the resulting impact on the predicted regional carbon balance over recent decades is minor. We expect a one-order of magnitude lower computation demand by optimizing the choices of machine learning algorithms, their settings, and balancing the trade-off between quality of MLA predictions and need for TBM simulations for training data generation and bias reduction. Our tool is agnostic to gridded models (beyond TBMs), compatible with existing spin-up acceleration procedures, and opens the door to a wide variety of future applications, with complex non-linear models benefit most from the computational efficiency.

Compensatory water effects link yearly global land CO2 sink changes to temperature.

Large interannual variations in the measured growth rate of atmospheric carbon dioxide (CO2) originate primarily from fluctuations in carbon uptake by land ecosystems. It remains uncertain, however, to what extent temperature and water availability control the carbon balance of land ecosystems across spatial and temporal scales. Here we use empirical models based on eddy covariance data and process-based models to investigate the effect of changes in temperature and water availability on gross primary productivity (GPP), terrestrial ecosystem respiration (TER) and net ecosystem exchange (NEE) at local and global scales. We find that water availability is the dominant driver of the local interannual variability in GPP and TER. To a lesser extent this is true also for NEE at the local scale, but when integrated globally, temporal NEE variability is mostly driven by temperature fluctuations. We suggest that this apparent paradox can be explained by two compensatory water effects. Temporal water-driven GPP and TER variations compensate locally, dampening water-driven NEE variability. Spatial water availability anomalies also compensate, leaving a dominant temperature signal in the year-to-year fluctuations of the land carbon sink. These findings help to reconcile seemingly contradictory reports regarding the importance of temperature and water in controlling the interannual variability of the terrestrial carbon balance. Our study indicates that spatial climate covariation drives the global carbon cycle response.

Nonlinear interactions of land carbon cycle feedbacks in Earth System Models.

Carbon cycle feedbacks were often quantified through the carbon-concentration and carbon-climate feedbacks with the assumption of no significant interaction between the two feedbacks in most previous studies. Here we calculated the strength of the interactions between the two responses using simulations of models participated in the phase 6 of the Coupled Model Intercomparison Project (CMIP6). We found that the nonlinear interaction contributed 11% of the land-atmosphere carbon exchange on average with large intermodel variation (from -20% to +162%). This nonlinear interaction is largely driven by the pattern of net primary production (NPP), with shifts in heterotrophic respiration that dampen the overall positive interactions from NPP. Photosynthetic rate per unit leaf area alone cannot adequately explain a wide variation of interactions in global NPP simulated by CMIP6 models. Plant respiration and processes that regulate leaf area are also important contributors to the interactions. Dominant factors that induce carbon-concentration and carbon-climate interactions are highly variable among models. One of those dominant factors is nutrient limitation. Using additional simulations of ACCESS-ESM1.5 that include both nitrogen and phosphorus limitation, we found that the estimated interactions by ACCESS-ESM1.5 with or without nutrient limitations covered the large intermodel variations among the CMIP6 models. It remains largely unknown how nutrient limitation complicates ecosystem's responses to simultaneously CO2 fertilization and warming at the global scale. Our modeling results point to a potential important role of nutrients, especially phosphorus on the nonlinear interactions. Yet, more studies are needed on ecosystem responses to concurrent changes in nutrient availability, atmospheric CO2 concentration, and warming.

Mycorrhizal fungi alleviate acidification-induced phosphorus limitation: Evidence from a decade-long field experiment of simulated acid deposition in a tropical forest in south China.

South China has been experiencing very high rate of acid deposition and severe soil acidification in recent decades, which has been proposed to exacerbate the regional ecosystem phosphorus (P) limitation. We conducted a 10-year field experiment of simulated acid deposition to examine how acidification impacts seasonal changes of different soil P fractions in a tropical forest with highly acidic soils in south China. As expected, acid addition significantly increased occluded P pool but reduced the other more labile P pools in the dry season. In the wet season, however, acid addition did not change microbial P, soluble P and labile organic P pools. Acid addition significantly increased exchangeable Al3+ and Fe3+ and the activation of Fe oxides in both seasons. Different from the decline of microbial abundance in the dry season, acid addition increased ectomycorrhizal fungi and its ratio to arbuscular mycorrhiza fungi in the wet season, which significantly stimulated phosphomonoesterase activities and likely promoted the dissolution of occluded P. Our results suggest that, even in already highly acidic soils, the acidification-induced P limitation could be alleviated by stimulating ectomycorrhizal fungi and phosphomonoesterase activities. The differential responses and microbial controls of seasonal soil P transformation revealed here should be implemented into ecosystem biogeochemical model for predicting plant productivity under future acid deposition scenarios.

Tropical tall forests are more sensitive and vulnerable to drought than short forests.

Our limited understanding of the impacts of drought on tropical forests significantly impedes our ability in accurately predicting the impacts of climate change on this biome. Here, we investigated the impact of drought on the dynamics of forest canopies with different heights using time-series records of remotely sensed Ku-band vegetation optical depth (Ku-VOD), a proxy of top-canopy foliar mass and water content, and separated the signal of Ku-VOD changes into drought-induced reductions and subsequent non-drought gains. Both drought-induced reductions and non-drought increases in Ku-VOD varied significantly with canopy height. Taller tropical forests experienced greater relative Ku-VOD reductions during drought and larger non-drought increases than shorter forests, but the net effect of drought was more negative in the taller forests. Meta-analysis of in situ hydraulic traits supports the hypothesis that taller tropical forests are more vulnerable to drought stress due to smaller xylem-transport safety margins. Additionally, Ku-VOD of taller forests showed larger reductions due to increased atmospheric dryness, as assessed by vapor pressure deficit, and showed larger gains in response to enhanced water supply than shorter forests. Including the height-dependent variation of hydraulic transport in ecosystem models will improve the simulated response of tropical forests to drought.

Bridge to the future: Important lessons from 20 years of ecosystem observations made by the OzFlux network.

In 2020, the Australian and New Zealand flux research and monitoring network, OzFlux, celebrated its 20th anniversary by reflecting on the lessons learned through two decades of ecosystem studies on global change biology. OzFlux is a network not only for ecosystem researchers, but also for those 'next users' of the knowledge, information and data that such networks provide. Here, we focus on eight lessons across topics of climate change and variability, disturbance and resilience, drought and heat stress and synergies with remote sensing and modelling. In distilling the key lessons learned, we also identify where further research is needed to fill knowledge gaps and improve the utility and relevance of the outputs from OzFlux. Extreme climate variability across Australia and New Zealand (droughts and flooding rains) provides a natural laboratory for a global understanding of ecosystems in this time of accelerating climate change. As evidence of worsening global fire risk emerges, the natural ability of these ecosystems to recover from disturbances, such as fire and cyclones, provides lessons on adaptation and resilience to disturbance. Drought and heatwaves are common occurrences across large parts of the region and can tip an ecosystem's carbon budget from a net CO2 sink to a net CO2 source. Despite such responses to stress, ecosystems at OzFlux sites show their resilience to climate variability by rapidly pivoting back to a strong carbon sink upon the return of favourable conditions. Located in under-represented areas, OzFlux data have the potential for reducing uncertainties in global remote sensing products, and these data provide several opportunities to develop new theories and improve our ecosystem models. The accumulated impacts of these lessons over the last 20 years highlights the value of long-term flux observations for natural and managed systems. A future vision for OzFlux includes ongoing and newly developed synergies with ecophysiologists, ecologists, geologists, remote sensors and modellers.

Toward a Global Model for Soil Inorganic Phosphorus Dynamics: Dependence of Exchange Kinetics and Soil Bioavailability on Soil Physicochemical Properties.

The representation of phosphorus (P) cycling in global land models remains quite simplistic, particularly on soil inorganic phosphorus. For example, sorption and desorption remain unresolved and their dependence on soil physical and chemical properties is ignored. Empirical parameter values are usually based on expert knowledge or data from few sites with debatable global representativeness in most global land models. To overcome these issues, we compiled from data of inorganic soil P fractions and calculated the fraction of added P remaining in soil solution over time of 147 soil samples to optimize three parameters in a model of soil inorganic P dynamics. The calibrated model performed well (r 2 > 0.7 for 122 soil samples). Model parameters vary by several orders of magnitude, and correlate with soil P fractions of different inorganic pools, soil organic carbon and oxalate extractable metal oxide concentrations among the soil samples. The modeled bioavailability of soil P depends on, not only, the desorption rates of labile and sorbed pool, inorganic phosphorus fractions, the slope of P sorbed against solution P concentration, but also on the ability of biological uptake to deplete solution P concentration and the time scale. The model together with the empirical relationships of model parameters on soil properties can be used to quantify bioavailability of soil inorganic P on various timescale especially when coupled within global land models.

Multiresidue screening of pesticides in Panax Ginseng C. A. Meyer by ultra-high-performance liquid chromatography with quadrupole time-of-flight mass spectrometry.

In this study, an efficient screening method based on a modified quick, easy, cheap, effective, rugged, and safe extraction method combined with ultra-high-performance liquid chromatography coupled to tandem quadrupole time-of-flight mass spectrometry was established for the determination of 90 pesticides residues in Panax Ginseng. The accuracy of the method was then verified by analyzing the false positive rate and the screening detection limit in Ginseng. The results revealed that the screening detection limit of 33 of 90 pesticide residues were 0.01 mg·kg-1 , 22 species were 0.05 mg·kg-1 , 11 species were 0.10 mg·kg-1 , 8 species were 0.20 mg·kg-1 , and another 16 species were greater than 0.20 mg·kg-1 . A total of 73 pesticides were ultimately suitable to be practically applied for rapid analysis of pesticide residues in Ginseng. Finally, the established method was used to analyze the pesticide residues in 35 Ginseng samples available on the market. And the residual of dimethomorph, azoxystrobin, tebuconazole, and pyraclostrobin was relatively severe in Ginseng samples. This work expanded the range of pesticides detected and provided a rapid, effective method for pesticides screening in Ginseng.

Platycodin D suppresses cisplatin-induced cytotoxicity by suppressing ROS-mediated oxidative damage, apoptosis, and inflammation in HEK-293 cells.

Cisplatin, a proven effective chemotherapeutic agent, has been used clinically to treat malignant solid tumors, whereas its clinical use is limited by serious side effect including nephrotoxicity. Platycodin D (PD), the major and marked saponin isolated from Platycodon grandiflorum, possesses many pharmacological effects. In this study, we evaluated its protective effect against cisplatin-induced human embryonic kidney 293 (HEK-293) cells injury and elucidated the related mechanisms. Our results showed that PD (0.25, 0.5, and 1 µM) can dose-dependently alleviate oxidative stress by decreasing malondialdehyde and reactive oxygen species, while increasing the levels of glutathione, superoxide dismutase, and catalase. Moreover, the elevation of apoptosis including Bax, Bad, cleaved caspase-3,-9, and decreased protein levels of Bcl-2, Bcl-XL induced by cisplatin were reversed after PD treatment. Importantly, PD pretreatment can also regulate PI3K/Akt and ERK/JNK/p38 signaling pathways. Furthermore, PD was found to reduce NF-κB-mediated inflammatory relative proteins. Our finding indicated that PD exerted significant effects on cisplatin induced oxidative stress, apoptosis and inflammatory, which will provide evidence for the development of PD to attenuate cisplatin-induced nephrotoxicity.

The p53/p21/p16 and PI3K/Akt signaling pathways are involved in the ameliorative effects of maltol on D-galactose-induced liver and kidney aging and injury.

Successive evidence has established that maltol, a flavor-enhancing agent, could provide resistance to oxidative stress-induced tissue injury in various animal models though its benefits for aging-induced liver and kidney injuries are still undetermined. In the present work, for demonstrating maltol's ameliorative effect and probable mechanism against aging-induced liver and kidney injuries, D-galactose (D-Gal)-induced animal in vivo and HEK293 cells in vitro models were established and results demonstrated that long-term D-Gal treatment increases the accumulation of advanced glycation end products (AGEs) in liver and kidney tissues, mitigates cell viability, and arrests the cycle. Interestingly, 4-weeks maltol treatment at 50 and 100 mg/kg activated aging-associated proteins including p53, p21, and p16 followed by inhibiting malondialdehyde (MDA)'s over-production and increasing the levels of antioxidant enzymes. Therefore, decreases in cytochrome P450 E1 (CYP2E1) and 4-hydroxydecene (4-HNE)'s immunofluorescence expression levels are confirmed. Furthermore, maltol improved oxidative stress injury by activating the phosphatidylinositol-3-kinase (PI3K)/protein kinase B (Akt) signaling pathway. In conclusion, the purpose of the present study was to estimate the mechanistic insights into maltol's role as an antioxidant in liver and kidney cell senescence and injury, which will reflect potential of therapeutic strategy for antiaging and aging-related disease treatment.

Saponins derived from the stems and leaves of Panax ginseng attenuate scrotal heat-induced spermatogenic damage via inhibiting the MAPK mediated oxidative stress and apoptosis in mice.

Heat stress (HS) reaction is a stress response caused by adverse conditions. Currently, the incidence of reproductive malignancies particularly in males has been constantly increasing. This work investigated the effects of saponins derived from the stems and leaves of Panax ginseng (GSLS) on testicular injury induced by scrotal hyperthermia in mice. GSLS (150, 300 mg/kg) were administered intragastrically to mice for 14 days, then exposed to a single scrotal heat treatment at 43°C for 18 min on seventh day. HS induced a significant loss of multinucleate giant cells, desquamation of germ cells in destructive seminiferous tubules. Moreover, HS reduced the serum testosterone, testicular tissue superoxide dismutase activity and glutathione (GSH) content, while significantly enhanced the production of malondialdehyde (p < .05). GSLS exhibited the protective potential against HS-induced injury not only by modulating Bcl-2 family and caspase protease family, but also by suppressing the protein levels of heme oxygenase-1 (HO-1), heat shock protein 70 (HSP70), hypoxia inducible factor-1α (HIF-1α) and activation of Mitogen-activated protein kinase (MAPK) signaling pathways (p < .05). In conclusion, we clearly demonstrated that GSLS exhibited a significant protective effect against HS-induced testicular dysfunction, mainly the inhibition of oxidative stress associated apoptosis partly via regulation of the MAPK signaling pathway.

The response of soil respiration to precipitation change is asymmetric and differs between grasslands and forests.

Intensification of the Earth's hydrological cycle amplifies the interannual variability of precipitation, which will significantly impact the terrestrial carbon (C) cycle. However, it is still unknown whether previously observed relationship between soil respiration (Rs ) and precipitation remains applicable under extreme precipitation change. By analyzing the observations from a much larger dataset of field experiments (248 published papers including 151 grassland studies and 97 forest studies) across a wider range of precipitation manipulation than previous studies, we found that the relationship of Rs response with precipitation change was highly nonlinear or asymmetric, and differed significantly between grasslands and forests, between moderate and extreme precipitation changes. Response of Rs to precipitation change was negatively asymmetric (concave-down) in grasslands, and double-asymmetric in forests with a positive asymmetry (concave-up) under moderate precipitation changes and a negative asymmetry (concave-down) under extreme precipitation changes. In grasslands, the negative asymmetry in Rs response was attributed to the higher sensitivities of soil moisture, microbial and root activities to decreased precipitation (DPPT) than to increased precipitation (IPPT). In forests, the positive asymmetry was predominantly driven by the significant increase in microbial respiration under moderate IPPT, while the negative asymmetry was caused by the reductions in root biomass and respiration under extreme DPPT. The different asymmetric responses of Rs between grasslands and forests will greatly improve our ability to forecast the C cycle consequences of increased precipitation variability. Specifically, the negative asymmetry of Rs response under extreme precipitation change suggests that the soil C efflux will decrease across grasslands and forests under future precipitation regime with more wet and dry extremes.

Microbial dynamics and soil physicochemical properties explain large-scale variations in soil organic carbon.

First-order organic matter decomposition models are used within most Earth System Models (ESMs) to project future global carbon cycling; these models have been criticized for not accurately representing mechanisms of soil organic carbon (SOC) stabilization and SOC response to climate change. New soil biogeochemical models have been developed, but their evaluation is limited to observations from laboratory incubations or few field experiments. Given the global scope of ESMs, a comprehensive evaluation of such models is essential using in situ observations of a wide range of SOC stocks over large spatial scales before their introduction to ESMs. In this study, we collected a set of in situ observations of SOC, litterfall and soil properties from 206 sites covering different forest and soil types in Europe and China. These data were used to calibrate the model MIMICS (The MIcrobial-MIneral Carbon Stabilization model), which we compared to the widely used first-order model CENTURY. We show that, compared to CENTURY, MIMICS more accurately estimates forest SOC concentrations and the sensitivities of SOC to variation in soil temperature, clay content and litter input. The ratios of microbial biomass to total SOC predicted by MIMICS agree well with independent observations from globally distributed forest sites. By testing different hypotheses regarding (using alternative process representations) the physicochemical constraints on SOC deprotection and microbial turnover in MIMICS, the errors of simulated SOC concentrations across sites were further decreased. We show that MIMICS can resolve the dominant mechanisms of SOC decomposition and stabilization and that it can be a reliable tool for predictions of terrestrial SOC dynamics under future climate change. It also allows us to evaluate at large scale the rapidly evolving understanding of SOC formation and stabilization based on laboratory and limited filed observation.

Rainfall manipulation experiments as simulated by terrestrial biosphere models: Where do we stand?

Changes in rainfall amounts and patterns have been observed and are expected to continue in the near future with potentially significant ecological and societal consequences. Modelling vegetation responses to changes in rainfall is thus crucial to project water and carbon cycles in the future. In this study, we present the results of a new model-data intercomparison project, where we tested the ability of 10 terrestrial biosphere models to reproduce the observed sensitivity of ecosystem productivity to rainfall changes at 10 sites across the globe, in nine of which, rainfall exclusion and/or irrigation experiments had been performed. The key results are as follows: (a) Inter-model variation is generally large and model agreement varies with timescales. In severely water-limited sites, models only agree on the interannual variability of evapotranspiration and to a smaller extent on gross primary productivity. In more mesic sites, model agreement for both water and carbon fluxes is typically higher on fine (daily-monthly) timescales and reduces on longer (seasonal-annual) scales. (b) Models on average overestimate the relationship between ecosystem productivity and mean rainfall amounts across sites (in space) and have a low capacity in reproducing the temporal (interannual) sensitivity of vegetation productivity to annual rainfall at a given site, even though observation uncertainty is comparable to inter-model variability. (c) Most models reproduced the sign of the observed patterns in productivity changes in rainfall manipulation experiments but had a low capacity in reproducing the observed magnitude of productivity changes. Models better reproduced the observed productivity responses due to rainfall exclusion than addition. (d) All models attribute ecosystem productivity changes to the intensity of vegetation stress and peak leaf area, whereas the impact of the change in growing season length is negligible. The relative contribution of the peak leaf area and vegetation stress intensity was highly variable among models.