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Recent studies show coordinated relationships between plant leaf traits and their capacity to predict ecosystem functions. However, how leaf traits will change within species and whether interspecific trait relationships will shift under future environmental changes both remain unclear. Here, we examine the bivariate correlations between leaf economic traits of 515 species in 210 experiments which mimic climate warming, drought, elevated CO2, and nitrogen deposition. We find divergent directions of changes in trait-pairs between species, and the directions mostly do not follow the interspecific trait relationships. However, the slopes in the logarithmic transformed interspecific trait relationships hold stable under environmental changes, while only their elevations vary. The elevation changes of trait relationship are mainly driven by asymmetrically interspecific responses contrary to the direction of the leaf economic spectrum. These findings suggest robust interspecific trait relationships under global changes, and call for linking within-species responses to interspecific coordination of plant traits.

Global and regional projections of climate change by Earth system models are limited by their uncertain estimates of terrestrial ecosystem productivity. At the middle to low latitudes, the East Asian monsoon region has higher productivity than forests in Europe‐Africa and North America, but its estimate by current generation of terrestrial biosphere models (TBMs) has seldom been systematically evaluated. Here, we developed a traceability framework to evaluate the simulated gross primary productivity (GPP) by 15 TBMs in the East Asian monsoon region. The framework links GPP to net primary productivity, biomass, leaf area and back to GPP via incorporating multiple vegetation functional properties of carbon‐use efficiency (CUE), vegetation C turnover time (τveg), leaf C fraction (Fleaf), specific leaf area (SLA), and leaf area index (LAI)‐level photosynthesis (PLAI), respectively. We then applied a relative importance algorithm to attribute intermodel variation at each node. The results showed that large intermodel variation in GPP over 1901–2010 were mainly propagated from their different representation of vegetation functional properties. For example, SLA explained 77% of the intermodel difference in leaf area, which contributed 90% to the simulated GPP differences. In addition, the models simulated higher CUE (18.1 ± 21.3%), τveg (18.2 ± 26.9%), and SLA (27.4±36.5%) than observations, leading to the overestimation of simulated GPP across the East Asian monsoon region. These results suggest the large uncertainty of current TBMs in simulating GPP is largely propagated from their poor representation of the vegetation functional properties and call for a better understanding of the covariations between plant functional properties in terrestrial ecosystems.

With the development of economy, most of Chinese cities are at the stage of rapid urbanization in recent years, which has caused many environmental problems, especially the serious deterioration of water quality. Therefore, the research of the relationship between urbanization and water quality has important theoretical and practical significance, and it is also the main restriction factor in the urbanization advancement. In this work, we investigated the impact of urbanization on the water quality of the nearby river. We established a comprehensive environmental assessment framework by combining urbanization and water quality, and one model was designed to examine the impact of urbanization on the water quality in Jinan from 2001 to 2010 with factor component analysis. The assessment of urbanization level was accomplished using a comprehensive index system, which was based on four aspects: demographic urbanization, economic urbanization, land urbanization, and social urbanization. In addition, synthetic pollution index method was utilized to assess the water pollution of Xiaoqing River in the study area. Through the analysis of regression curves, we conclude that (1) when the urbanization level is below 25 %, the relationship is low and irregular; (2) if the urbanization level varies between 25 and 40 %, there will be an irreversible degradation of stream water quality; (3) there is a positive correlation between urbanization and pollution levels of urban river after the adjustment period; and (4) land and demographic aspects have the highest independent contribution. This study is a useful reference for policymakers in terms of economic and environmental management.

The spatial and temporal variations in terrestrial carbon storage play a pivotal role in regulating future climate change. However, Earth system models (ESMs), which have coupled the terrestrial biosphere and atmosphere, show great uncertainty in simulating the global land carbon storage. Here, based on multiple global datasets and a traceability analysis, we diagnosed the uncertainty source of terrestrial carbon storage in 22 ESMs that participated in phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). The modeled global terrestrial carbon storage has converged among ESMs from CMIP5 (1936.9 ± 739.3 PgC) to CMIP6 (1774.4 ± 439.0 PgC) but is persistently lower than the observation-based estimates (2285 ± 669 PgC). By further decomposing terrestrial carbon storage into net primary production (NPP) and ecosystem carbon residence time (τE), we found that the decreased intermodel spread in land carbon storage primarily resulted from more accurate simulations on NPP among ESMs from CMIP5 to CMIP6. The persistent underestimation of land carbon storage was caused by the biased τE. In CMIP5 and CMIP6, the modeled τE was far shorter than the observation-based estimates. The potential reasons for the biased τE could be the lack of or incomplete representation of nutrient limitation, vertical soil biogeochemistry, and the permafrost carbon cycle. Moreover, the modeled τE became the key driver for the intermodel spread in global land carbon storage in CMIP6. Overall, our study indicates that CMIP6 models have greatly improved the terrestrial carbon cycle, with a decreased model spread in global terrestrial carbon storage and less uncertain productivity. However, more efforts are needed to understand and reduce the persistent data–model disagreement on carbon storage and residence time in the terrestrial biosphere.

Summary Plant plastic responses are critical to the adaptation and survival of species under climate change, but whether they are constrained by evolutionary history (phylogeny) is largely unclear. Plant leaf traits are key in determining plants’ performance in different environments, and if these traits and their variation are phylogenetically dependent, predictions could be made to identify species vulnerable to climate change. We compiled data on three leaf traits (photosynthetic rate, specific leaf area, and leaf nitrogen content) and their variation under four environmental change scenarios (warming, drought, elevated CO2, or nitrogen addition) for 434 species, from 210 manipulation experiments. We found phylogenetic signal in the three traits but not in their variation under the four scenarios. This indicates that closely related species show similar traits but that their plastic responses could not be predicted from species relatedness under environmental change. Meanwhile, phylogeny weakened the slopes but did not change the directions of conventional pairwise trait relationships, suggesting that co‐evolved leaf trait pairs have consistent responses under contrasting environmental conditions. Phylogeny can identify lineages rich in species showing similar traits and predict their relationships under climate change, but the degree of plant phenotypic variation does not vary consistently across evolutionary clades.

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