No abstract

Summary Despite widespread anthropogenic nutrient enrichment, it remains unclear how nutrient enrichment influences plant–arbuscular mycorrhizal fungi (AMF) symbiosis and ecosystem multifunctionality at the global scale. Here, we conducted a meta‐analysis to examine the worldwide effects of nutrient enrichment on AMF and plant diversity and ecosystem multifunctionality using data of field experiments from 136 papers. Our analyses showed that nutrient addition simultaneously decreased AMF diversity and abundance belowground and plant diversity aboveground at the global scale. The decreases in AMF diversity and abundance associated with nutrient addition were more pronounced with increasing experimental duration, mean annual temperature (MAT) and mean annual precipitation (MAP). Nutrient addition‐induced changes in soil pH and available phosphorus (P) predominantly regulated the responses of AMF diversity and abundance. Furthermore, AMF diversity correlated with ecosystem multifunctionality under nutrient addition worldwide. Our findings identify the negative effects of nutrient enrichment on AMF and plant diversity and suggest that AMF diversity is closely linked with ecosystem function. This study offers an important advancement in our understanding of plant–AMF interactions and their likely responses to ongoing global change.

Background An increasing number of ecological processes have been incorporated into Earth system models. However, model evaluations usually lag behind the fast development of models, leading to a pervasive simulation uncertainty in key ecological processes, especially the terrestrial carbon (C) cycle. Traceability analysis provides a theoretical basis for tracking and quantifying the structural uncertainty of simulated C storage in models. Thus, a new tool of model evaluation based on the traceability analysis is urgently needed to efficiently diagnose the sources of inter-model variations on the terrestrial C cycle in Earth system models. Methods A new cloud-based model evaluation platform, i.e., the online traceability analysis system for model evaluation (TraceME v1.0), was established. The TraceME was applied to analyze the uncertainties of seven models from the Coupled Model Intercomparison Project (CMIP6). Results The TraceME can effectively diagnose the key sources of different land C dynamics among CMIIP6 models. For example, the analyses based on TraceME showed that the estimation of global land C storage varied about 2.4 folds across the seven CMIP6 models. Among all models, IPSL-CM6A-LR simulated the lowest land C storage, which mainly resulted from its shortest baseline C residence time. Over the historical period of 1850–2014, gross primary productivity and baseline C residence time were the major uncertainty contributors to the inter-model variation in ecosystem C storage in most land grid cells. Conclusion TraceME can facilitate model evaluation by identifying sources of model uncertainty and provides a new tool for the next generation of model evaluation.

Climate warming and extreme hydrological events are threatening the sustainability of wetlands across the globe. However, whether climate warming will amplify or diminish the impact of extreme flooding on wetland ecosystems is unknown. Here, we show that climate warming significantly reduced wetland resistance and resilience to a severe flooding event via a 6-year warming experiment. We first found that warming rapidly altered plant community structure by increasing the dominance of low-canopy species. Then, we showed that warming reduced the resistance and resilience of vegetation productivity to a 72-cm flooding event. Last, we detected slower postflooding carbon processes, such as gross ecosystem productivity, soil respiration, and soil methane emission, under the warming treatment. Our results demonstrate how severe flooding can destabilize wetland vegetation structure and ecosystem function under climate warming. These findings indicate an enhanced footprint of extreme hydrological events in wetland ecosystems in a warmer climate.

<p><strong>Abstract.</strong> Multiple lines of evidence have demonstrated the persistence of global land carbon (C) sink during the past several decades. However, both annual net ecosystem productivity (NEP) and its inter-annual variation (IAV<sub>NEP</sub>) keep varying over space. Thus, identifying local indicators for the spatially varying NEP and IAV<sub>NEP</sub> is critical for locating the major and sustainable C sinks on the land. Here, based on a machine-learning-derived database, we first showed that the variations of NEP and IAV<sub>NEP</sub> are spatially asynchronous. Then, based on daily NEP observations from eddy covariance sites, we found robust logarithmic correlation between annual NEP and ratio of total CO<sub>2</sub> exchanges during net uptake (<i>U</i>) and release (<i>R</i>) periods (i.e., <i>U/R</i>). The cross-site variation of mean annual NEP can be linearly indicated by ln(<i>U/R</i>), while the spatial distribution of IAV<sub>NEP</sub> was well indicated by the slope (i.e., <i>β</i>) of the demonstrated logarithmic correlation. Among biomes, for example, forests and croplands had the largest <i>U/R</i> ratio (1.06 ± 0.83) and <i>β</i> (473 ± 112 g C m<sup>−2</sup> yr<sup>−1</sup>), indicating the highest NEP and IAV<sub>NEP</sub> in forests and croplands, respectively. We further showed that the spatial variations of NEP and IAV<sub>NEP</sub> were both underestimated by the machine-learning-based and process-based global models. Overall, this study underscores the asynchronously changes in the strength and stability of land C sinks over space, and provides two simple local indicators for their intricate spatial variations. These indicators could be helpful for locating the persistent terrestrial C sinks and provides valuable constraints for improving the simulation of land-atmospheric C exchanges.</p>