Direct and indirect effects of climatic variations on the interannual variability in net ecosystem exchange across terrestrial ecosystems

Climatic variables not only directly affect the interannual variability (IAV) in net ecosystem exchange of CO2 (NEE) but also indirectly drive it by changing the physiological parameters. Identifying these direct and indirect paths can reveal the underlying mechanisms of carbon (C) dynamics. In this study, we applied a path analysis using flux data from 65 sites to quantify the direct and indirect climatic effects on IAV in NEE and to evaluate the potential relationships among the climatic variables and physiological parameters that represent physiology and phenology of ecosystems. We found that the maximum photosynthetic rate was the most important factor for the IAV in gross primary productivity (GPP), which was mainly induced by the variation in vapour pressure deficit. For ecosystem respiration (RE), the most important drivers were GPP and the reference respiratory rate. The biome type regulated the direct and indirect paths, with distinctive differences between forests and non-forests, evergreen needleleaf forests and deciduous broadleaf forests, and between grasslands and croplands. Different paths were also found among wet, moist and dry ecosystems. However, the climatic variables can only partly explain the IAV in physiological parameters, suggesting that the latter may also result from other biotic and disturbance factors. In addition, the climatic variables related to NEE were not necessarily the same as those related to GPP and RE, indicating the emerging difficulty encountered when studying the IAV in NEE. Overall, our results highlight the contribution of certain physiological parameters to the IAV in C fluxes and the importance of biome type and multi-year water conditions, which should receive more attention in future experimental and modelling research

Relative importance of climatic variables, soil properties and plant traits to spatial variability in net CO2 exchange across global forests and grasslands

Compared to the well-known drivers of spatial variability in gross primary productivity (GPP), the relative importance of climatic variables, soil properties and plant traits to the spatial variability in net ecosystem exchange of CO2 between terrestrial ecosystem and atmosphere (NEE) is poorly understood. We used principal component regression to analyze data from 147 eddy flux sites to disentangle effects of climatic variables, soil properties and plant traits on the spatial variation in annual NEE and its components (GPP and ecosystem respiration (RE)) across global forests and grasslands. Our results showed that the largest unique contribution (proportion of variance only explained by one class of variables) to NEE variance came from climatic variables for forests (24%-30%) and soil properties for grasslands (41%-54%). Specifically, mean annual precipitation and potential evapotranspiration were the most important climatic variables driving forest NEE, whereas available soil water capacity, clay content and cation exchange capacity mainly influenced grassland NEE. Plant traits showed a small unique contribution to NEE in both forests and grasslands. However, leaf phosphorus content strongly interacted with soil total nitrogen density and clay content, and these combined factors represented a major contribution for grassland NEE. For GPP and RE, the majority of spatial variance was attributed to the common contribution of climate, soil and plant traits (50% - 62%, proportion of variance explained by more than one class of variables), rather than their unique contributions. Interestingly, those factors with only minor influences on GPP and RE variability (e.g., soil properties) have significant contributions to the spatial variability in NEE. Such emerging factors and the interactions between climatic variables, soil properties and plant traits are not well represented in current terrestrial biosphere models, which should be considered in future model improvement to accurately predict the spatial pattern of carbon cycling across forests and grasslands globally.Peer reviewe

Quantifying uncertainties from additional nitrogen data and processes in a terrestrial ecosystem model with Bayesian probabilistic inversion

Abstract Substantial efforts have recently been made toward integrating more processes to improve ecosystem model performances. However, model uncertainties caused by new processes and/or data sets remain largely unclear. In this study, we explore uncertainties resulting from additional nitrogen (N) data and processes in a terrestrial ecosystem (TECO) model framework using a data assimilation system. Three assimilation experiments were conducted with TECO‐C‐C (carbon (C)‐only model), TECO‐CN‐C (TECO‐CN coupled model with only C measurements as assimilating data), and TECO‐CN‐CN (TECO‐CN model with both C and N measurements). Our results showed that additional N data had greater effects on ecosystem C storage (+68% and +55%) compared with added N processes (+32% and −45%) at the end of the experimental period (2009) and the long‐term prediction (2100), respectively. The uncertainties mainly resulted from woody biomass (relative information contributions are +50.4% and +36.6%) and slow soil organic matter pool (+30.6% and −37.7%) at the end of the experimental period and the long‐term prediction, respectively. During the experimental period, the additional N processes affected C dynamics mainly through process‐induced disequilibrium in the initial value of C pools. However, in the long‐term prediction period, the N data and processes jointly influenced the simulated C dynamics by adjusting the posterior probability density functions of key parameters. These results suggest that additional measurements of slow processes are pivotal to improving model predictions. Quantifying the uncertainty of the additional N data and processes can help us explore the terrestrial C‐N coupling in ecosystem models and highlight critical observational needs for future studies

Grazing intensity significantly affects belowground carbon and nitrogen cycling in grassland ecosystems: a meta-analysis

Livestock grazing activities potentially alter ecosystem carbon (C) and nitrogen (N) cycles in grassland ecosystems. Despite the fact that numerous individual studies and a few meta-analyses had been conducted, how grazing, especially its intensity, affects belowground C and N cycling in grasslands remains unclear. In this study, we performed a comprehensive meta-analysis of 115 published studies to examine the responses of 19 variables associated with belowground C and N cycling to livestock grazing in global grasslands. Our results showed that, on average, grazing significantly decreased belowground C and N pools in grassland ecosystems, with the largest decreases in microbial biomass C and N (21.62 and 24.40%, respectively). In contrast, belowground fluxes, including soil respiration, soil net N mineralization and soil N nitrification increased by 4.25%, 34.67 and 25.87%, respectively in grazed grasslands compared to ungrazed ones. More importantly, grazing intensity significantly affected the magnitude (even direction) of changes in the majority of the assessed belowground C and N pools and fluxes, and C:N ratio as well as soil moisture. Specifically，light grazing contributed to soil C and N sequestration whereas moderate and heavy grazing significantly increased C and N losses. In addition, soil depth, livestock type and climatic conditions influenced the responses of selected variables to livestock grazing to some degree. Our findings highlight the importance of the effects of grazing intensity on belowground C and N cycling, which may need to be incorporated into regional and global models for predicting effects of human disturbance on global grasslands and assessing the climate- biosphere feedbacks

Grazing intensity significantly affects belowground carbon and nitrogen cycling in grassland ecosystems: A meta-analysis

Hosseini Bai, S ORCiD: 0000-0001-8646-6423Livestock grazing activities potentially alter ecosystem carbon (C) and nitrogen (N) cycles in grassland ecosystems. Despite the fact that numerous individual studies and a few meta-analyses had been conducted, how grazing, especially its intensity, affects belowground C and N cycling in grasslands remains unclear. In this study, we performed a comprehensive meta-analysis of 115 published studies to examine the responses of 19 variables associated with belowground C and N cycling to livestock grazing in global grasslands. Our results showed that, on average, grazing significantly decreased belowground C and N pools in grassland ecosystems, with the largest decreases in microbial biomass C and N (21.62% and 24.40%, respectively). In contrast, belowground fluxes, including soil respiration, soil net N mineralization and soil N nitrification increased by 4.25%, 34.67% and 25.87%, respectively, in grazed grasslands compared to ungrazed ones. More importantly, grazing intensity significantly affected the magnitude (even direction) of changes in the majority of the assessed belowground C and N pools and fluxes, and C : N ratio as well as soil moisture. Specifically,light grazing contributed to soil C and N sequestration whereas moderate and heavy grazing significantly increased C and N losses. In addition, soil depth, livestock type and climatic conditions influenced the responses of selected variables to livestock grazing to some degree. Our findings highlight the importance of the effects of grazing intensity on belowground C and N cycling, which may need to be incorporated into regional and global models for predicting effects of human disturbance on global grasslands and assessing the climate-biosphere feedbacks. © 2016 John Wiley & Sons Lt

Effects of tree mycorrhizal type on soil respiration and carbon stock via fine root biomass and litter dynamic in tropical plantations

12 páginas.- 7 figuras.- 1 tabla.- referencias.- Supplementary Material Supplementary material is available at Journal of Plant Ecology onlineTropical forests are among the most productive and vulnerable ecosystems in the planet. Several global forestation programs are aiming to plant millions of trees in tropical regions in the future decade. Mycorrhizal associations are known to largely influence forest soil carbon (C) stocks. However, to date, little is known on whether and how different tree mycorrhizal types affect soil respiration (Rs) and C stocks in tropical forests. In this study, we used a three-decade tropical common garden experiment, with three arbuscular mycorrhizal (AM) and three ectomycorrhizal (EM) monocultures, to investigate the impacts of tree mycorrhizal type on Rs and soil C stocks. Associating biotic (e.g. root biomass, litter dynamic, soil microbes) and abiotic factors (e.g. microclimate) were also measured. Our results showed that AM stands supported significantly higher Rs and soil C stock, litter turnover rate and fine root biomass than EM stands. Further statistical analysis displayed that tree mycorrhizal type was the most important factor in regulating Rs and soil C stock compared with other biotic or abiotic factors. Moreover, we found that mycorrhizal type directly and indirectly affected Rs and soil C stocks via fine root biomass and litter dynamic, i.e. litter production, litter standing crop and litter turnover rate. Our findings highlight important effects of tree mycorrhizal type on forest C cycle, suggesting that planting AM tree species could contribute to promotion of soil C stock in tropical ecosystems.This research was financially supported by the National Natural Science Foundation of China (31930072, 31770559, 31600387, 31370489) and the Postdoctoral Innovation Talents Program of China (BX20200133). M.D-B. is supported by a Ramón y Cajal grant from the Spanish Ministry of Science and Innovation (RYC2018-025483-I)Peer reviewe

Plant evolutionary history mainly explains the variance in biomass responses to climate warming at a global scale

Hosseini Bai, S ORCiD: 0000-0001-8646-6423Evolutionary history shapes the interspecific relatedness and intraspecific variation, which has a profound influence on plant functional traits and productivity. However, it is far from clear how the phylogenetic relatedness among species and intraspecific variation could contribute to the observed variance in plant biomass responses to climate warming. We compiled a dataset with 284 species from warming experiments to explore the relative importance of phylogenetic, intraspecific, experimental and ecological factors to warming effects on plant biomass, using phylogenetic eigenvector regression and variance decomposition. Our results showed that phylogenetic relatedness could account for about half the total variance in biomass responses to warming, which were correlated with leaf economic traits at the family but not species levels. The intraspecific variation contributed to approximately one‐third of the variance, while the experimental design and ecological characteristics only explained 7–17%. These results suggest that intrinsic factors (evolutionary history) play more important roles than extrinsic factors (experimental treatment and environment) in determining the responses of plant biomass to warming at the global scale. This highlights the urgent need for land surface models to include evolutionary aspects in predicting ecosystem functions under climate change

Differential magnitude of rhizosphere effects on soil aggregation at three stages of subtropical secondary forest successions

Hosseini Bai, S ORCiD: 0000-0001-8646-6423Background and aims: Roots and their rhizosphere considerably influence soil structure by regulating soil aggregate formation and stabilization. This study aimed to examine the rhizosphere effects on soil aggregation and explore potential mechanisms along secondary forest successions. Methods: Effects of roots and their rhizosphere on soil aggregation in two subtropical secondary forest successions were examined by separating soils into rhizosphere and bulk soils. Soil aggregate mean weight diameter (MWD), soil organic carbon (SOC), soil nutrients, and fine-root traits were simultaneously measured. Results: Soil aggregate MWD increased significantly in the bulk soils along secondary forest successions, but did not differ in the rhizosphere soils. Rhizosphere effects on soil aggregate MWD (i.e., root-induced differences between the rhizosphere and bulk soils) were thus significantly higher at the early-successional stage of subtropical forest with low soil fertility than those at the late stages with high fertility. Rhizosphere significantly increased SOC and soil total nitrogen (TN) throughout the entire secondary forest successions, which was nonlinearly correlated with soil aggregate MWD. Principal components regression analysis showed that SOC was the primary abiotic factor and positively correlated with soil aggregate MWD. As for biotic factors, fine-root length density and N concentration were two important root traits having significant effects on soil aggregate stability. An improved conceptual framework was developed to advance our understanding of soil aggregation and rhizosphere effects, highlighting the roles of soil fertility (i.e., SOC and available nutrients), root traits, and forest age in driving soil aggregation. Conclusions: Impacts of root-derived organic compounds inputs to rhizosphere on soil aggregation were stronger at the early-successional stage of subtropical forest than those at the late stages. This succession-specific pattern in rhizosphere effects largely resulted from the nonlinear relationships between soil aggregate MWD and SOC concentration with a plateau at high SOC. Incorporating the SOC-dependent rhizosphere effects on biogeochemical cycle into Earth system models might improve the prediction of forest soil C dynamics. © 2019, Springer Nature Switzerland AG