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Abstract Biodiversity underpins critical ecological processes, yet its relationship with phosphorus (P) remains poorly understood. Understanding the distinct responses of plant and soil microbial diversity to P availability changes is particularly crucial on a global scale. Integrating meta-analysis and natural gradient approaches, this study evaluates these responses globally. Specifically, we conducted a meta-analysis using 393 observations from 128 field P addition experiments and supplemented this with a natural gradient analysis of forest tree diversity and vascular plant diversity. Our meta-analysis results showed that P additions reduced plant species richness by 8.5% and Shannon index by 1.3% in global grasslands, while exerting minimal effects on soil bacterial and fungal diversity across major terrestrial ecosystems. Natural gradient analysis further demonstrated significant correlations between both forest tree richness and vascular plant richness with soil total P concentrations. Notably, partial correlation analyses showed negative correlations when controlling for gross primary productivity and edaphic variables, but positive correlations when controlling for climatic variables. These complementary approaches collectively suggest that plant diversity exhibits greater sensitivity to altered soil P availability than soil microbial diversity. Consequently, elucidating the differential responses of above- and below-ground biodiversity to nutrient supply changes provides a scientific foundation for sound management of terrestrial ecosystem functions and processes. 

Abstract Land use change (LUC) alters the global carbon (C) stock, but our estimation of the alteration remains uncertain and is a major impediment to predicting the global C cycle. The uncertainty is partly due to the limited number and geographical bias of observations, and limited exploration of its predictors. Here we generated a comprehensive global database of 5,980 observations from 790 articles. The number of sites evaluated is at least seven times larger than in previous meta‐analyses. Our constrained estimates of different LUC's effects on soil organic C (SOC) and their variations across global climates reveal underestimation/overestimation in previous estimates. Converting forests and grasslands to croplands reduced SOC by 24.5% ± 1.53% (−11.03 ± 1.06 Mg ha⁻¹) and 22.7% ± 1.22% (−8.09 ± 0.67 Mg ha⁻¹), while 28.0% ± 1.56% (4.46 ± 0.42 Mg ha⁻¹) and 33.5% ± 1.68% (5.8 ± 0.38 Mg ha⁻¹) increases, respectively, were obtained in the reverse processes. Converting forests to grasslands decreased SOC by 2.1% ± 1.22% (−1.13 ± 0.44 Mg ha⁻¹), while the reverse process increased SOC by 18.6% ± 1.73% (3.31 ± 0.51 Mg ha⁻¹). Modeled relative importance of 10 drivers of LUC's impact on SOC revealed that higher initial SOC (iSOC) does not solely determine SOC loss in SOC‐negative LUC scenarios as previously proposed. Across four decades, reconverting croplands to forests and grasslands recovered only 49.5% (6.1 ± 0.51 Mg ha⁻¹) and 75.3% (7.0 ± 0.38 Mg ha⁻¹) of the iSOC, respectively, indicating the need for protecting C‐rich ecosystems. Our global data set advances information on LUC's effect on SOC and can be valuable to constrain Earth system models to reliably estimate global SOC stocks and plan climate change mitigation strategies. 

Abstract Microbes are the drivers of soil phosphorus (P) cycling in terrestrial ecosystems; however, the role of soil microbes in mediating P cycling in P‐rich soils during primary succession remains uncertain. This study examined the impacts of bacterial community structure (diversity and composition) and its functional potential (absolute abundances of P‐cycling functional genes) on soil P cycling along a 130‐year glacial chronosequence on the eastern Tibetan Plateau. Bacterial community structure was a better predictor of soil P fractions than P‐cycling genes along the chronosequence. After glacier retreat, the solubilization of inorganic P and the mineralization of organic P were significantly enhanced by increased bacterial diversity, changed interspecific interactions, and abundant species involved in soil P mineralization, thereby increasing P availability. Although 84% of P‐cycling genes were associated with organic P mineralization, these genes were more closely associated with soil organic carbon than with organic P. Bacterial carbon demand probably determined soil P turnover, indicating the dominant role of organic matter decomposition processes in P‐rich alpine soils. Moreover, the significant decrease in the complexity of the bacterial co‐occurrence network and the taxa‐gene‐P network at the later stage indicates a declining dominance of the bacterial community in driving soil P cycling with succession. Our results reveal that bacteria with a complex community structure have a prominent potential for biogeochemical P cycling in P‐rich soils during the early stages of primary succession. 

Abstract Large across‐model spread in simulating land carbon (C) dynamics has been ubiquitously demonstrated in model intercomparison projects (MIPs), and became a major impediment in advancing climate change prediction. Thus, it is imperative to identify underlying sources of the spread. Here, we used a novel matrix approach to analytically pin down the sources of across‐model spread in transient peatland C dynamics in response to a factorial combination of two atmospheric CO 2 levels and five temperature levels. We developed a matrix‐based MIP by converting the C cycle module of eight land models (i.e., TEM, CENTURY4, DALEC2, TECO, FBDC, CASA, CLM4.5 and ORCHIDEE) into eight matrix models. While the model average of ecosystem C storage was comparable to the measurement, the simulation differed largely among models, mainly due to inter‐model difference in baseline C residence time. Models generally overestimated net ecosystem production (NEP), with a large spread that was mainly attributed to inter‐model difference in environmental scalar. Based on the sources of spreads identified, we sequentially standardized model parameters to shrink simulated ecosystem C storage and NEP to almost none. Models generally captured the observed negative response of NEP to warming, but differed largely in the magnitude of response, due to differences in baseline C residence time and temperature sensitivity of decomposition. While there was a lack of response of NEP to elevated CO 2 (eCO 2 ) concentrations in the measurements, simulated NEP responded positively to eCO 2 concentrations in most models, due to the positive responses of simulated net primary production. Our study used one case study in Minnesota peatland to demonstrate that the sources of across‐model spreads in simulating transient C dynamics can be precisely traced to model structures and parameters, regardless of their complexity, given the protocol that all the matrix models were driven by the same gross primary production and environmental variables. 

Abstract Background Countries have long been making efforts by reducing greenhouse-gas emissions to mitigate climate change. In the agreements of the United Nations Framework Convention on Climate Change, involved countries have committed to reduction targets. However, carbon (C) sink and its involving processes by natural ecosystems remain difficult to quantify. Methods Using a transient traceability framework, we estimated country-level land C sink and its causing components by 2050 simulated by 12 Earth System Models involved in the Coupled Model Intercomparison Project Phase 5 (CMIP5) under RCP8.5. Results The top 20 countries with highest C sink have the potential to sequester 62 Pg C in total, among which, Russia, Canada, USA, China, and Brazil sequester the most. This C sink consists of four components: production-driven change, turnover-driven change, change in instantaneous C storage potential, and interaction between production-driven change and turnover-driven change. The four components account for 49.5%, 28.1%, 14.5%, and 7.9% of the land C sink, respectively. Conclusion The model-based estimates highlight that land C sink potentially offsets a substantial proportion of greenhouse-gas emissions, especially for countries where net primary production (NPP) likely increases substantially and inherent residence time elongates. 

Abstract Climate forecasts project change not only in the mean of climate variables but also in their variance. If these dual changes interact, then future ecological dynamics will be difficult to predict using current experimental approaches, which typically change the mean or impose a single extreme event, such as drought. We designed a new field experiment to factorially reduce mean precipitation and increase its interannual variability. Across 4 years, drier, more variable precipitation additively reduced aboveground primary productivity by 48%–69% and interactively reduced the dominant plant species, but had no effect on the plant species predicted to dominate in the future, which could lead to state transition. Drier, more variable precipitation also interactively reduced biodiversity more than either climate factor alone, with 37%–42% fewer plant species than under ambient conditions, a pattern that matched declining richness during the past 20 years of ongoing climate change. Drier, more variable precipitation restructured the composition and spatiotemporal variation of the plant community. Altered precipitation mean or variance affected 14% of plant species, with eight species sensitive to the mean × variance interaction. Results suggest that future forecasts of plant community structure may be inadequate if they fail to incorporate climate mean × variance interactions.