Climate warming accelerates carbon release from foliar litter-A global synthesis.

With over one-third of terrestrial net primary productivity transferring to the litter layer annually, the carbon release from litter serves as a crucial valve in atmospheric carbon dioxide concentrations. However, few quantitative global projections of litter carbon release rate in response to climate change exist. Here, we combined a global foliar litter carbon release dataset (8973 samples) to generate spatially explicitly estimates of the response of their residence time (τ) to climate change. Results show a global mean litter carbon release rate ( k $$ k $$ ) of 0.69 year-1 (ranging from 0.09-5.6 year-1). Under future climate scenarios, global mean τ is projected to decrease by a mean of 2.7% (SSP 1-2.6) and 5.9% (SSP 5-8.5) during 2071-2100 period. Locally, the alleviation of temperature and moisture restrictions corresponded to obvious decreases in τ in cold and arid regions, respectively. In contract, τ in tropical humid broadleaf forests increased by 4.6% under SSP 5-8.5. Our findings highlight the vegetation type as a powerful proxy for explaining global patterns in foliar litter carbon release rates and the role of climate conditions in predicting responses of carbon release to climate change. Our observation-based estimates could refine carbon cycle parameterization, improving projections of carbon cycle-climate feedbacks.

Global responses of wetland methane emissions to extreme temperature and precipitation.

As global warming continues, events of extreme heat or heavy precipitation will become more frequent, while events of extreme cold will become less so. How wetlands around the globe will react to these extreme events is unclear yet critical, because they are among the greatest natural sources of methane(CH4). Here we use seven indices of extreme climate and the rate of methane emission from global wetlands(WME) during 2000-2019 simulated by 12 published models as input data. Our analyses suggest that extreme cold (particularly extreme low temperatures) inhibits WME, whereas extreme heat (particularly extreme high temperatures) accelerates WME. Our results also suggest that daily precipitation >10 mm accelerates WME, while much higher daily precipitation levels can slow WME. The correlation of extreme high temperature and precipitation with rate of WME became stronger during the study period, while the correlation between extreme low temperature and WME rate became weaker.

Drought-induced increase in tree mortality and corresponding decrease in the carbon sink capacity of Canada's boreal forests from 1970 to 2020.

Canada's boreal forests, which occupy approximately 30% of boreal forests worldwide, play an important role in the global carbon budget. However, there is little quantitative information available regarding the spatiotemporal changes in the drought-induced tree mortality of Canada's boreal forests overall and their associated impacts on biomass carbon dynamics. Here, we develop spatiotemporally explicit estimates of drought-induced tree mortality and corresponding biomass carbon sink capacity changes in Canada's boreal forests from 1970 to 2020. We show that the average annual tree mortality rate is approximately 2.7%. Approximately 43% of Canada's boreal forests have experienced significantly increasing tree mortality trends (71% of which are located in the western region of the country), and these trends have accelerated since 2002. This increase in tree mortality has resulted in significant biomass carbon losses at an approximate rate of 1.51 ± 0.29 MgC ha-1  year-1 (95% confidence interval) with an approximate total loss of 0.46 ± 0.09 PgC year-1 (95% confidence interval). Under the drought condition increases predicted for this century, the capacity of Canada's boreal forests to act as a carbon sink will be further reduced, potentially leading to a significant positive climate feedback effect.

Nitrogen addition promotes terrestrial plants to allocate more biomass to aboveground organs: A global meta-analysis.

A significant increase in reactive nitrogen (N) added to terrestrial ecosystems through agricultural fertilization or atmospheric deposition is considered to be one of the most widespread drivers of global change. Modifying biomass allocation is one primary strategy for maximizing plant growth rate, survival, and adaptability to various biotic and abiotic stresses. However, there is much uncertainty as to whether and how plant biomass allocation strategies change in response to increased N inputs in terrestrial ecosystems. Here, we synthesized 3516 paired observations of plant biomass and their components related to N additions across terrestrial ecosystems worldwide. Our meta-analysis reveals that N addition (ranging from 1.08 to 113.81 g m-2 year-1 ) increased terrestrial plant biomass by 55.6% on average. N addition has increased plant stem mass fraction, shoot mass fraction, and leaf mass fraction by 13.8%, 12.9%, and 13.4%, respectively, but with an associated decrease in plant reproductive mass (including flower and fruit biomass) fraction by 3.4%. We further documented a reduction in plant root-shoot ratio and root mass fraction by 27% (21.8%-32.1%) and 14.7% (11.6%-17.8%), respectively, in response to N addition. Meta-regression results showed that N addition effects on plant biomass were positively correlated with mean annual temperature, soil available phosphorus, soil total potassium, specific leaf area, and leaf area per plant. Nevertheless, they were negatively correlated with soil total N, leaf carbon/N ratio, leaf carbon and N content per leaf area, as well as the amount and duration of N addition. In summary, our meta-analysis suggests that N addition may alter terrestrial plant biomass allocation strategies, leading to more biomass being allocated to aboveground organs than belowground organs and growth versus reproductive trade-offs. At the global scale, leaf functional traits may dictate how plant species change their biomass allocation pattern in response to N addition.

C:N:P stoichiometry of plants, soils, and microorganisms: Response to altered precipitation.

Precipitation changes modify C, N, and P cycles, which regulate the functions and structure of terrestrial ecosystems. Although altered precipitation affects above- and belowground C:N:P stoichiometry, considerable uncertainties remain regarding plant-microbial nutrient allocation strategies under increased (IPPT) and decreased (DPPT) precipitation. We meta-analyzed 827 observations from 235 field studies to investigate the effects of IPPT and DPPT on the C:N:P stoichiometry of plants, soils, and microorganisms. DPPT reduced leaf C:N ratio, but increased the leaf and root N:P ratios reflecting stronger decrease of P compared with N mobility in soil under drought. IPPT increased microbial biomass C (+13%), N (+15%), P (26%), and the C:N ratio, whereas DPPT decreased microbial biomass N (-12%) and the N:P ratio. The C:N and N:P ratios of plant leaves were more sensitive to medium DPPT than to IPPT because drought increased plant N content, particularly in humid areas. The responses of plant and soil C:N:P stoichiometry to altered precipitation did not fit the double asymmetry model with a positive asymmetry under IPPT and a negative asymmetry under extreme DPPT. Soil microorganisms were more sensitive to IPPT than to DPPT, but they were more sensitive to extreme DPPT than extreme IPPT, consistent with the double asymmetry model. Soil microorganisms maintained stoichiometric homeostasis, whereas N:P ratios of plants follow that of the soils under altered precipitation. In conclusion, specific N allocation strategies of plants and microbial communities as well as N and P availability in soil critically mediate C:N:P stoichiometry by altered precipitation that need to be considered by prediction of ecosystem functions and C cycling under future climate change scenarios.

Observational constraints reduce model spread but not uncertainty in global wetland methane emission estimates.

The recent rise in atmospheric methane (CH4 ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH4 source, estimates of global wetland CH4 emissions vary widely among approaches taken by bottom-up (BU) process-based biogeochemical models and top-down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi-model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH4 emission estimates and model performance. We find that using better-performing models identified by observational constraints reduces the spread of wetland CH4 emission estimates by 62% and 39% for BU- and TD-based approaches, respectively. However, global BU and TD CH4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH4 year-1 ) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter-site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH4 models to move beyond static benchmarking and focus on evaluating site-specific and ecosystem-specific variabilities inferred from observations.

Satellite monitoring reveals short-term cumulative and time-lag effect of drought and heat on autumn photosynthetic phenology in subtropical vegetation.

Comparing with the effect of the average climate change on vegetation phenology, the impacts of extreme climate events remain unclear, especially considering their characteristic cumulative and time-lag effects. Using solar-induced chlorophyll fluorescence (SIF) satellite records, we investigated the cumulative and time-lag effects of drought and heat events on photosynthesis, particularly for the end date of autumn photosynthesis (EOP), in subtropical vegetation in China. Our results showed a negative effect of drought on the delay of EOP, with the cumulative effect on 30.12% (maximum continuous dry days, CDD), 34.82% (dry days, DRD), and 26.14% (dry period, DSDI) of the study area and the general time-lag effect on 50.73% (maximum continuous dry days), 56.61% (dry days), and 47.55% (dry period) of the study area. The cumulative and lagged time were 1-3 months and 2-3 months, respectively. In contrast, the cumulative effect of heat on EOP was observed in 16.27% (warm nights, TN90P), 23.66% (moderate heat days, TX50P), and 19.19% (heavy heat days, TX90P) of the study area, with cumulative time of 1-3 months. The lagged time was 3-4 months, detected in 31.02% (warm nights), 45.86% (moderate heat days), and 36.52% (heavy heat days) of the study area. At the vegetation community level, drought and heat had relatively rapid impacts on EOP in the deciduous broadleaved forest, whereas evergreen forests and bushes responded to heat slowly and took a longer time. Our results revealed that drought and heat have short-term cumulative and time-lag effects on the EOP of subtropical vegetation in China, with varying effects among different vegetation types. These findings provide new insights into the effect of drought and heat on subtropical vegetation and confirm the need to consider these effects in the development of prediction models of autumn phenology for subtropical vegetation.

Modern cities modelled as "super-cells" rather than multicellular organisms: Implications for industry, goods and services.

The structure and "metabolism" (movement and conversion of goods and energy) of urban areas has caused cities to be identified as "super-organisms", placed between ecosystems and the biosphere, in the hierarchy of living systems. Yet most such analogies are weak, and render the super-organism model ineffective for sustainable development of cities. Via a cluster analysis of 15 shared traits of the hierarchical living system, we found that industrialized cities are more similar to eukaryotic cells than to multicellular organisms; enclosed systems, such as factories and greenhouses, paralleling organelles in eukaryotic cells. We further developed a "super-cell" industrialized city model: a "eukarcity" with citynucleus (urban area) as a regulating centre, and organaras (enclosed systems, which provide the majority of goods and services) as the functional components, and cityplasm (natural ecosystems and farmlands) as the matrix. This model may improve the vitality and sustainability of cities through planning and management.

Pervasive decreases in living vegetation carbon turnover time across forest climate zones.

Forests play a major role in the global carbon cycle. Previous studies on the capacity of forests to sequester atmospheric CO2 have mostly focused on carbon uptake, but the roles of carbon turnover time and its spatiotemporal changes remain poorly understood. Here, we used long-term inventory data (1955 to 2018) from 695 mature forest plots to quantify temporal trends in living vegetation carbon turnover time across tropical, temperate, and cold climate zones, and compared plot data to 8 Earth system models (ESMs). Long-term plots consistently showed decreases in living vegetation carbon turnover time, likely driven by increased tree mortality across all major climate zones. Changes in living vegetation carbon turnover time were negatively correlated with CO2 enrichment in both forest plot data and ESM simulations. However, plot-based correlations between living vegetation carbon turnover time and climate drivers such as precipitation and temperature diverged from those of ESM simulations. Our analyses suggest that forest carbon sinks are likely to be constrained by a decrease in living vegetation carbon turnover time, and accurate projections of forest carbon sink dynamics will require an improved representation of tree mortality processes and their sensitivity to climate in ESMs.

Warming-induced global soil carbon loss attenuated by downward carbon movement.

The fate of soil organic carbon (SOC) under warming is poorly understood, particularly across large extents and in the whole-soil profile. Using a data-model integration approach applied across the globe, we find that downward movement of SOC along the soil profile reduces SOC loss under warming. We predict that global SOC stocks (down to 2 m) will decline by 4% (~80 Pg) on average when SOC reaches the steady state under 2°C warming, assuming no changes in net primary productivity (NPP). To compensate such decline (i.e. maintain current SOC stocks), a 3% increase of NPP is required. Without the downward SOC movement, global SOC declines by 15%, while a 20% increase in NPP is needed to compensate that loss. This vital role of downward SOC movement in controlling whole-soil profile SOC dynamics in response to warming is due to the protection afforded to downward-moving SOC by depth, indicated by much longer residence times of SOC in deeper layers. Additionally, we find that this protection could not be counteracted by promoted decomposition due to the priming of downward-moving new SOC from upper layers on native old SOC in deeper layers. This study provides the first estimation of whole-soil SOC changes under warming and additional NPP required to compensate such changes across the globe, and reveals the vital role of downward movement of SOC in reducing SOC loss under global warming.

Soil GHG fluxes are altered by N deposition: New data indicate lower N stimulation of the N2 O flux and greater stimulation of the calculated C pools.

The effects of nitrogen (N) deposition on soil organic carbon (C) and greenhouse gas (GHG) emissions in terrestrial ecosystems are the main drivers affecting GHG budgets under global climate change. Although many studies have been conducted on this topic, we still have little understanding of how N deposition affects soil C pools and GHG budgets at the global scale. We synthesized a comprehensive dataset of 275 sites from multiple terrestrial ecosystems around the world and quantified the responses of the global soil C pool and GHG fluxes induced by N enrichment. The results showed that the soil organic C concentration and the soil CO2 , CH4 and N2 O emissions increased by an average of 3.7%, 0.3%, 24.3% and 91.3% under N enrichment, respectively, and that the soil CH4 uptake decreased by 6.0%. Furthermore, the percentage increase in N2 O emissions (91.3%) was two times lower than that (215%) reported by Liu and Greaver (Ecology Letters, 2009, 12:1103-1117). There was also greater stimulation of soil C pools (15.70 kg C ha-1  year-1 per kg N ha-1  year-1 ) than previously reported under N deposition globally. The global N deposition results showed that croplands were the largest GHG sources (calculated as CO2 equivalents), followed by wetlands. However, forests and grasslands were two important GHG sinks. Globally, N deposition increased the terrestrial soil C sink by 6.34 Pg CO2 /year. It also increased net soil GHG emissions by 10.20 Pg CO2 -Geq (CO2 equivalents)/year. Therefore, N deposition not only increased the size of the soil C pool but also increased global GHG emissions, as calculated by the global warming potential approach.

Global vegetation biomass production efficiency constrained by models and observations.

Plants use only a fraction of their photosynthetically derived carbon for biomass production (BP). The biomass production efficiency (BPE), defined as the ratio of BP to photosynthesis, and its variation across and within vegetation types is poorly understood, which hinders our capacity to accurately estimate carbon turnover times and carbon sinks. Here, we present a new global estimation of BPE obtained by combining field measurements from 113 sites with 14 carbon cycle models. Our best estimate of global BPE is 0.41 ± 0.05, excluding cropland. The largest BPE is found in boreal forests (0.48 ± 0.06) and the lowest in tropical forests (0.40 ± 0.04). Carbon cycle models overestimate BPE, although models with carbon-nitrogen interactions tend to be more realistic. Using observation-based estimates of global photosynthesis, we quantify the global BP of non-cropland ecosystems of 41 ± 6 Pg C/year. This flux is less than net primary production as it does not contain carbon allocated to symbionts, used for exudates or volatile carbon compound emissions to the atmosphere. Our study reveals a positive bias of 24 ± 11% in the model-estimated BP (10 of 14 models). When correcting models for this bias while leaving modeled carbon turnover times unchanged, we found that the global ecosystem carbon storage change during the last century is decreased by 67% (or 58 Pg C).

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.

Quantifying leaf-trait covariation and its controls across climates and biomes.

Plant functional ecology requires the quantification of trait variation and its controls. Field measurements on 483 species at 48 sites across China were used to analyse variation in leaf traits, and assess their predictability. Principal components analysis (PCA) was used to characterize trait variation, redundancy analysis (RDA) to reveal climate effects, and RDA with variance partitioning to estimate separate and overlapping effects of site, climate, life-form and family membership. Four orthogonal dimensions of total trait variation were identified: leaf area (LA), internal-to-ambient CO2 ratio (χ), leaf economics spectrum traits (specific leaf area (SLA) versus leaf dry matter content (LDMC) and nitrogen per area (Narea )), and photosynthetic capacities (Vcmax , Jmax at 25°C). LA and χ covaried with moisture index. Site, climate, life form and family together explained 70% of trait variance. Families accounted for 17%, and climate and families together 29%. LDMC and SLA showed the largest family effects. Independent life-form effects were small. Climate influences trait variation in part by selection for different life forms and families. Trait values derived from climate data via RDA showed substantial predictive power for trait values in the available global data sets. Systematic trait data collection across all climates and biomes is still necessary.

Global soil nitrous oxide emissions since the preindustrial era estimated by an ensemble of terrestrial biosphere models: Magnitude, attribution, and uncertainty.

Our understanding and quantification of global soil nitrous oxide (N2 O) emissions and the underlying processes remain largely uncertain. Here, we assessed the effects of multiple anthropogenic and natural factors, including nitrogen fertilizer (N) application, atmospheric N deposition, manure N application, land cover change, climate change, and rising atmospheric CO2 concentration, on global soil N2 O emissions for the period 1861-2016 using a standard simulation protocol with seven process-based terrestrial biosphere models. Results suggest global soil N2 O emissions have increased from 6.3 ± 1.1 Tg N2 O-N/year in the preindustrial period (the 1860s) to 10.0 ± 2.0 Tg N2 O-N/year in the recent decade (2007-2016). Cropland soil emissions increased from 0.3 Tg N2 O-N/year to 3.3 Tg N2 O-N/year over the same period, accounting for 82% of the total increase. Regionally, China, South Asia, and Southeast Asia underwent rapid increases in cropland N2 O emissions since the 1970s. However, US cropland N2 O emissions had been relatively flat in magnitude since the 1980s, and EU cropland N2 O emissions appear to have decreased by 14%. Soil N2 O emissions from predominantly natural ecosystems accounted for 67% of the global soil emissions in the recent decade but showed only a relatively small increase of 0.7 ± 0.5 Tg N2 O-N/year (11%) since the 1860s. In the recent decade, N fertilizer application, N deposition, manure N application, and climate change contributed 54%, 26%, 15%, and 24%, respectively, to the total increase. Rising atmospheric CO2 concentration reduced soil N2 O emissions by 10% through the enhanced plant N uptake, while land cover change played a minor role. Our estimation here does not account for indirect emissions from soils and the directed emissions from excreta of grazing livestock. To address uncertainties in estimating regional and global soil N2 O emissions, this study recommends several critical strategies for improving the process-based simulations.

Modeling and estimating aboveground biomass of Dacrydium pierrei in China using machine learning with climate change.

Accurate estimations of the aboveground biomass (AGB) of rare and endangered species are particularly important for protecting forest ecosystems and endangered species and for providing useful information to analyze the influence of past and future climate change on forest AGB. We investigated the feasibility of using three developed and two widely used models, including a generalized regression neural network (GRNN), a group method of data handling (GMDH), an adaptive neuro-fuzzy inference system (ANFIS), an artificial neural network (ANN) and a support vector machine (SVM), to estimate the AGB of Dacrydium pierrei (D. pierrei) in natural forests of China. The results showed that these models could explain the changes in the AGB of the D. pierrei using a limited amount of meteorological data. The GRNN and ANN models are superior to the other models for estimating the AGB of D. pierrei. The GMDH model consistently produced comparatively poor estimates of the AGB. Three climate scenarios, including the representative concentration pathway (RCP) 2.6, RCP 4.5, and RCP 8.5, were compared with the climate situation of 2013-2017. Under these scenarios, the AGB of D. pierrei females with the same diameter at breast height (DBH) would increase by 13.0 ±â€¯31.4% (mean ±â€¯standard deviation), 16.6 ±â€¯30.7%, and 18.5 ±â€¯30.9% during 2041-2060 and 15.6 ±â€¯32.1%, 21.2 ±â€¯33.2%, and 24.8 ±â€¯32.7% during 2061-2080; the AGB of males would increase by 16.3 ±â€¯32.3%, 21.7 ±â€¯32.5%, and 22.9 ±â€¯32.6% during 2041-2060 and 22.3 ±â€¯30.8%, 27.2 ±â€¯31.8%, and 30.1 ±â€¯34.4% during 2061-2080. The R2 values of all models range from 0.82 to 0.95. In conclusion, this study suggests that these advanced models are recommended to estimate the AGB of forests, and the AGB of forests would increase in 2041-2080 under future climate scenarios.

Modelling the impacts of climate and land use changes on soil water erosion: Model applications, limitations and future challenges.

The world is experiencing serious soil losses. Soil erosion has become an important environmental problem in certain regions and is strongly affected by climate and land use changes. By selecting and reviewing 13 extensively used soil water erosion models (SWEMs) from the published literature, we summarize the current model-based knowledge on how climate factors (e.g., rainfall, freeze-thaw cycles, rainstorms, temperature and atmospheric CO2 concentrations) and land use change impact soil erosion worldwide. This study also provides a critical review of the application of these 13 SWEMs. By comparing model structures, features, prediction accuracies, and erosion processes, we recommend the most suitable SWEMs for different regions of the globe (Asia, Europe, Africa and the America) based on the evaluations of 13 SWEMs. Future soil erosion could be simulated using the RUSLE, LISEM, WEPP v2010.1, SWAT, EPIC, KINEROS and AGNPS models in Asia; the RUSLE, WEPP v2010.1, SWAT, EPIC, WATEM-SEDEM, MEFIDIS, AGNPS and AnnAGNPS models in Europe; the RUSLE, LISEM, SWAT, and AGNPS models in Africa; and the WEPP v2010.1, SWAT, EPIC, KINEROS, AGNPS and AnnAGNPS models in America. Finally, the limitations and challenges of the 13 SWEMs are highlighted.

Recoupling Industrial Dairy Feedlots and Industrial Farmlands Mitigates the Environmental Impacts of Milk Production in China.

Dairy production is becoming more industrialized globally, especially in developing countries. The large amount of animal wastes from industrial feedlots cannot be fully used on nearby farmlands, leading to severe environmental problems. Using China as a case study, we found that most dairy feedlots employ a semicoupled mode that only recycles solid manure to farmlands, and only a few dairy feedlots employ a fully coupled mode that recycles both solid and liquid animal manure. To produce 1 ton of milk, the fully coupled mode could reduce greenhouse gas (including carbon dioxide, methane, and nitrous oxide in this paper) emissions by 24%, ammonia emissions by 14%, and N discharge into water by 29%, compared with the semicoupled systems. Coupling feedlots with constructed wetlands can further result in greater mitigation of N leaching into groundwater. However, the fully coupled system has not been widely used due to the low benefit to farmers and the institutional barrier that the feedlot owners have no right to use adjacent farmlands. Since a fully coupled system improves net ecosystem services that favor the public, a policy that supports removing the economic and institutional barriers is necessary. Our approach provides a template for mitigating environmental impacts from livestock production without sacrificing milk production.

Effects of elevated sulfate concentration on the mobility of arsenic in the sediment-water interface.

The adsorption/desorption of arsenic (As) at the sediment-water interface in lakes is the key to understanding whether As can enter the ecosystem and participate in material circulation. In this study, the concentrations of As(III), total arsenic [As(T)], sulfide, iron (Fe), and dissolved organic carbon (DOC) in overlying water were observed after the initial sulfate (SO42-) concentrations were increased by four gradients in the presence and absence of microbial systems. The results indicate that increased SO42- concentrations in overlying water triggered As desorption from sediments. Approximately 10% of the desorbed As was desorbed directly as arsenite or arsenate by competitive adsorption sites on the iron salt surface; 21% was due to the reduction of iron (hydr)oxides; and 69% was due to microbial activity, as compared with a system with no microbial activity. The intensity of microbial activity was controlled by the SO42- and DOC concentrations in the overlying water. In anaerobic systems, which had SO42- and DOC concentrations higher than 47 and 7 mg/L, respectively, microbial activity was promoted by SO42- and DOC; As(III) was desorbed under these indoor simulation conditions. When either the SO42- or DOC concentration was lower than its respective threshold of 47 or 7 mg/L, or when either of these indices was below its concentration limit, it was difficult for microorganisms to use SO42- and DOC to enhance their own activities. Therefore, conditions were insufficient for As desorption. The migration of As in lake sediments was dominated by microbial activity, which was co-limited by SO42- and DOC. The concentrations of SO42- and DOC in the overlying water are thus important for the prevention and control of As pollution in lakes. We recommend controlling SO42- and DOC concentrations as a method for controlling As inner-source pollution in lake water.

Influence of multiple global change drivers on terrestrial carbon storage: additive effects are common.

The interactive effects of multiple global change drivers on terrestrial carbon (C) storage remain poorly understood. Here, we synthesise data from 633 published studies to show how the interactive effects of multiple drivers are generally additive (i.e. not differing from the sum of their individual effects) rather than synergistic or antagonistic. We further show that (1) elevated CO2 , warming, N addition, P addition and increased rainfall, all exerted positive individual effects on plant C pools at both single-plant and plant-community levels; (2) plant C pool responses to individual or combined effects of multiple drivers are seldom scale-dependent (i.e. not differing from single-plant to plant-community levels) and (3) soil and microbial biomass C pools are significantly less sensitive than plant C pools to individual or combined effects. We provide a quantitative basis for integrating additive effects of multiple global change drivers into future assessments of the C storage ability of terrestrial ecosystems.