Spatial and temporal variations in global soil respiration and their relationships with climate and land cover

Nitrogen addition promotes soil carbon accumulation globally

Soil is the largest carbon (C) reservoir in terrestrial ecosystems and plays a crucial role in regulating the global C cycle and climate change. Increasing nitrogen (N) deposition has been widely considered as a critical factor affecting soil organic carbon (SOC) storage, but its effect on SOC components with different stability remains unclear. Here, we analyzed extensive empirical data from 304 sites worldwide to investigate how SOC and its components respond to N addition. Our analysis showed that N addition led to a significant increase in bulk SOC (6.7%), with greater increases in croplands (10.6%) and forests (6.0%) compared to grasslands (2.1%). Regarding SOC components, N addition promoted the accumulation of plant-derived C (9.7%-28.5%) over microbial-derived C (0.2%), as well as labile (5.7%) over recalcitrant components (-1.2%), resulting in a shift towards increased accumulation of plant-derived labile C. Consistently, N addition led to a greater increase in particulate organic C (11.9%) than mineral-associated organic C (3.6%), suggesting that N addition promotes C accumulation across all pools, with more increase in unstable than stable pools. The responses of SOC and its components were best predicted by the N addition rate and net primary productivity. Overall, our findings suggest that N enrichment could promote the accumulation of plant-derived and non-mineral associated C and a subsequent decrease in the overall stability of soil C pool, which underscores the importance of considering the effects of N enrichment on SOC components for a better understanding of C dynamics in soils.

Portraying on-road CO2 concentrations using street view panoramas and ensemble learning

A significant reduction in carbon dioxide (CO2) emissions caused by transportation is essential for attaining sustainable urban development. Carbon concentrations from road traffic in urban areas exhibit complex spatial patterns due to the impact of street configurations, mobile sources, and human activities. However, a comprehensive understanding of these patterns, which involve complex interactions, is still lacking due to the human perspective of road interface characteristics has not been taken into account. In this study, a mobile travel platform was constructed to collect both on-road navigation Street View Panoramas (OSVPs) and the corresponding CO2 concentrations. >100 thousand sample pairs that matched "street view-CO2 concentration" were obtained, covering 675.8 km of roads in Shenzhen, China. In addition, four ensemble learning (EL) models were utilized to establish nonlinear connections between the semantic and object features of streetscapes and CO2 concentrations. After performing EL fusion modeling, the predictive R2 in the test set exceeded 90 %, and the mean absolute error (MAE) was <3.2 ppm. The model was applied to Baidu Street View Panoramas (BSVPs) in Shenzhen to generate a map of average on-road CO2 with a 100 m resolution, and the Local Indicator of Spatial Association (LISA) was then used to identify high CO2 intensity spatial clusters. Additionally, the Light Gradient Boost-SHapley Additive exPlanation (LGB-SHAP) analysis revealed that vertically planted trees can reduce CO2 emissions from on-road sources. Moreover, the factors that affect on-road CO2 exhibit interaction and threshold effects. Street View Panoramas (SVPs) and Artificial Intelligence (AI) were adopted here to enhance the spatial measurement of on-road CO2 concentrations and the understanding of driving factors. Our approach facilitates the assessment and design of low-emission transportation in urban areas, which is critical for promoting sustainable traffic development.

Anthropogenic shrub encroachment has accelerated the degradation of desert steppe soil over the past four decades

Anthropogenic and natural shrub encroachment have similar ecological consequences on native grassland ecosystems. In fact, there is an accelerating trend toward anthropogenic shrub encroachment, as opposed to the century-long process of natural shrub encroachment. However, the soil quality during the transition of anthropogenic shrub encroachment into grasslands remains insufficiently understood. Here, we used a soil quality assessment method that utilized three datasets and two scoring methods to evaluate changes in soil quality during the anthropogenic transition from temperate desert grassland to shrubland. Our findings demonstrated that the soil quality index decreased with increasing shrub cover, from 0.49 in the desert grassland to 0.31 in the shrubland. Our final results revealed a gradual and significant decline of 36.73 % in soil quality during the transition from desert grassland to shrubland. Reduced soil moisture levels, nutrient availability, and microbial activity characterized this decline. Nearly four decades of anthropogenic shrub encroachment have exacerbated soil drought conditions while leading to a decrease in perennial herbaceous plants and an increase in bare ground cover; these factors can explain the observed decline in soil quality. These findings emphasize the importance of considering soil moisture availability and potential thresholds when implementing revegetation strategies in arid and semiarid regions.

Artificial intelligence for geoscience: Progress, challenges, and perspectives

This paper explores the evolution of geoscientific inquiry, tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence (AI) and data collection techniques. Traditional models, which are grounded in physical and numerical frameworks, provide robust explanations by explicitly reconstructing underlying physical processes. However, their limitations in comprehensively capturing Earth's complexities and uncertainties pose challenges in optimization and real-world applicability. In contrast, contemporary data-driven models, particularly those utilizing machine learning (ML) and deep learning (DL), leverage extensive geoscience data to glean insights without requiring exhaustive theoretical knowledge. ML techniques have shown promise in addressing Earth science-related questions. Nevertheless, challenges such as data scarcity, computational demands, data privacy concerns, and the "black-box" nature of AI models hinder their seamless integration into geoscience. The integration of physics-based and data-driven methodologies into hybrid models presents an alternative paradigm. These models, which incorporate domain knowledge to guide AI methodologies, demonstrate enhanced efficiency and performance with reduced training data requirements. This review provides a comprehensive overview of geoscientific research paradigms, emphasizing untapped opportunities at the intersection of advanced AI techniques and geoscience. It examines major methodologies, showcases advances in large-scale models, and discusses the challenges and prospects that will shape the future landscape of AI in geoscience. The paper outlines a dynamic field ripe with possibilities, poised to unlock new understandings of Earth's complexities and further advance geoscience exploration.

Spatial heterogeneity of soil respiration after prescribed burning in Pinus koraiensis forest in China

Soil respiration (RS) is crucial for releasing carbon dioxide (CO2) from terrestrial ecosystems to atmosphere. Prescribed burning (a common forest management tool), along with its important by-product pyrogenic carbon (PyC), can influence the carbon cycle of forest soil. However, few studies explore RS and PyC spatial correlation after prescribed burning. In this study, we investigated the spatial pattern of RS and its influencing factors by conducting prescribed burnings in a temperate artificial Pinus koraiensis forest. RS was measured 1 day (1 d) pre-prescribed burning, 1 d, 1 year (1 yr) and two years (2 yr) after prescribed burning. Significant decrease in RS were observed 1-2 yr After burning (reductions of 65.2% and 41.7% respectively). The spatial autocorrelation range of RS decreased pre-burning (2.72m), then increased post-burning (1 d: 2.44m; 1 yr: 40.14m; 2 yr: 9.8m), indicating a more homogeneous distribution of patch reduction. Pyrogenic carbon (PyC) in the soil gradually decreased in the short term after burning with reductions of 19%, 52%, and 49% (1d., 1 yr And 2 yr After the fire, respectively). However, PyC and RS exhibited a strong spatial positive correlation from 1 d.- 1 yr post-burning. The spatial regression model of dissolved organic carbon (DOC) on RS demonstrated significant positive spatial correlation in all measurements (pre- and post-burning). Microbial carbon to soil nitrogen ratio (MCN) notably influenced RS pre-burning and 1-2 yr post-burning. RS also showed significant spatial correlation in cross-variance with NH4+-N and NO3--N post-burning. The renewal of the PyC positively influenced RS, subsequently affecting its spatial distribution in 1d.- 1yr. Introducing PyC into RS studies helps enhances understanding of prescribed fire effects on forest soil carbon (C) pools, and provides valuable information regarding regional or ecosystem C cycling, facilitating a more accurate prediction of post-burning changes in forest soil C pools.

Global soil respiration estimation based on ecological big data and machine learning model

Soil respiration (Rs) represents the greatest carbon dioxide flux from terrestrial ecosystems to the atmosphere. However, its environmental drivers are not fully understood, and there are still significant uncertainties in soil respiration model estimates. This study aimed to estimate the spatial distribution pattern and driving mechanism of global soil respiration by constructing a machine learning model method based on ecological big data. First, we constructed ecological big data containing five categories of 27-dimensional environmental factors. We then used four typical machine learning methods to develop the performance of machine learning models under four training strategies and explored the relationship between soil respiration and environmental factors. Finally, we used the RF machine learning algorithm to estimate the global Rs spatial distribution pattern in 2021, driven by multiple dimensions of environmental factors, and derived the annual soil respiration values. The results showed that RF performed better under the four training strategies, with a coefficient of determination R2 = 0.78216, root mean squared error (RMSE) = 285.8964 gCm-2y-1, and mean absolute error (MAE) = 180.4186 gCm-2y-1, which was more suitable for the estimation of large-scale soil respiration. In terms of the importance of environmental factors, unlike previous studies, we found that the influence of geographical location was greater than that of MAP. Another new finding was that enhanced vegetation index 2 (EVI2) had a higher contribution to soil respiration estimates than the enhanced vegetation index (EVI) and normalized vegetation index (NDVI). Our results confirm the potential of utilizing ecological big data for spatially large-scale Rs estimations. Ecological big data and machine learning algorithms can be considered to improve the spatial distribution patterns and driver analysis of Rs.

Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem

Coastal terrestrial-aquatic interfaces (TAIs) are crucial contributors to global biogeochemical cycles and carbon exchange. The soil carbon dioxide (CO2) efflux in these transition zones is however poorly understood due to the high spatiotemporal dynamics of TAIs, as various sub-ecosystems in this region are compressed and expanded by complex influences of tides, changes in river levels, climate, and land use. We focus on the Chesapeake Bay region to (i) investigate the spatial heterogeneity of the coastal ecosystem and identify spatial zones with similar environmental characteristics based on the spatial data layers, including vegetation phenology, climate, landcover, diversity, topography, soil property, and relative tidal elevation; (ii) understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. Specifically, we employed hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, followed by the determination of main driving factors using Random Forest regression and SHapley Additive exPlanations. Maximum and minimum temperature are the main drivers common to all sub-ecosystems, while each region also has additional unique major drivers that differentiate them from one another. Precipitation exerts an influence on vegetated lands, while soil pH value holds importance specifically in forested lands. In croplands characterized by high clay content and low sand content, the significant role is attributed to bulk density. Wetlands demonstrate the importance of both elevation and sand content, with clay content being more relevant in non-inundated wetlands than in inundated wetlands. The topographic wetness index significantly contributes to the mixed vegetation areas, including shrub, grass, pasture, and forest. Additionally, our research reveals that dense vegetation land covers and urban/developed areas exhibit distinct soil property drivers. Overall, our research demonstrates an efficient method of employing various open-source remote sensing and GIS datasets to comprehend the spatial variability and soil respiration mechanisms in coastal TAI. There is no one-size-fits-all approach to modeling carbon fluxes released by soil respiration in coastal TAIs, and our study highlights the importance of further research and monitoring practices to improve our understanding of carbon dynamics and promote the sustainable management of coastal TAIs.

Interactions between nitrogen and phosphorus in modulating soil respiration: A meta-analysis

BACKGROUND

Economic and social development worldwide increases the input of nutrients, especially nitrogen (N) and phosphorus (P), to soils. These nutrients affect soil respiration (Rs) in terrestrial ecosystems. They may act independently or have interactive effects on Rs. The effect of N and P on Rs and its components (autotrophic respiration [Ra] and heterotrophic respiration [Rh]), however, either individually or together, is poorly understood. We performed a meta-analysis of 130 studies to examine the effects of different fertilization treatments on Rs and its components across terrestrial ecosystems.

RESULTS

Our results showed that (1) The impact of fertilizer addition on Rs varies among different fertilizer types. N addition reduced Rs and Rh significantly but did not affect Ra; P addition had no significant effect on Rs, Rh, and Ra; NP addition increased Rs significantly but did not affect Rh and Ra. (2) Ecosystem type, duration of fertilization, fertilization rate, and fertilizer form influenced the response of Rs and its components to fertilizer application. (3) Based on our study, the annual average temperature may be a driving factor of Rs response to fertilizer addition, while soil total nitrogen may be an important predictor of Rs response to fertilizer addition.

CONCLUSION

Overall, our study highlights the complex and multifaceted nature of the response of soil Rs and its components to fertilizer application, underscoring the importance of considering multiple factors when predicting and modeling future Rs and its feedback to global change.

Data-driven modeling on the global annual soil nitrous oxide emissions: Spatial pattern and attributes

Previous assessments generated divergent estimates of global terrestrial soil nitrous oxide (N2O) emission and its spatial distributions, which did not match the observed data well. The objectives of this study were to generate a global map of terrestrial soil N2O emissions based on field observations (n = 5549) and quantify the contribution of different variables for predicting the global variation of N2O emissions. We provided spatially explicit maps of annual soil N2O emission rates across forest, grassland and cropland using the random forest approach. The global mean soil N2O emission rate in our data-driven model was 0.059 ± 0.006 g N m-2 year-1, which was lower than the estimates from previous model ensembles. Soil N2O emissions were higher in the northern than southern hemisphere. The average annual soil N2O emission rate of cropland (0.094 ± 0.009 g N m-2 year-1) was higher than that of forest (0.039 ± 0.004 g N m-2 year-1) and grassland (0.045 ± 0.007 g N m-2 year-1). In addition, we found that soil nitrogen substrates dominated the changes in soil N2O emissions and the relative importance of nitrate, ammonium, and fertilizer in predicting soil N2O emissions was greater than that of mean annual temperature and precipitation. Our data-driven model results implied that previous process-based model may overestimate the global soil N2O emission rates due to limited validation data and incomplete assumptions on related-mechanisms. This study highlights the importance of global field observations in N2O emission estimation, which can provide an independent dataset to constrain previous process-based models for better prediction.

Carbon budget of different forests in China estimated by an individual-based model and remote sensing

Forests play a key role in the regional or global carbon cycle. Determining the forest carbon budget is of great significance for estimating regional carbon budgets and formulating forest management policies to cope with climate change. However, the carbon budget of Chinese different forests and their relative contributions are not completely clear so far. We evaluated the carbon budget of different forests from 1981 to 2020 in China through combining model with remote sensing observation. In addition, we also determined the relative contribution of carbon budget of each forest type to all forests in China. Eight forest types were studied: evergreen coniferous forest (ECF), deciduous coniferous forest (DCF), coniferous and broad-leaved mixed forest (CBF), deciduous broad-leaved forest (DBF), evergreen broad-leaved forest (EBF), evergreen deciduous broad-leaved mixed forest (EDBF), seasonal rain forest (SRF), and rain forest (RF). The results indicated that the Chinese forests were mainly carbon sink from 1981 to 2020, particularly the annual average carbon budget of forest from 2011 to 2020 was 0.191 PgC·a-1. Spatially, the forests' carbon budget demonstrated obvious regional differences, gradually decreasing from Southeast China to Northwest China. The relative contributions of carbon budget in different forests to all forests in China were different. During 2011-2020, the ECF forests contributed the most carbon budget (34.45%), followed by DBF forests (25.89%), EBF forests (24.82%), EDBF forests (13.10%), RF forests (2.23%), SRF forests (3.14%) and CBF forests (1.14%). However, the DCF forests were found mainly as carbon source. These results contribute to our understanding of regional carbon budget of forests.

Spatial and temporal variations of vegetation cover and its influencing factors in Shandong Province based on GEE

Economic development has rapidly progressed since the implementation of reform and opening up policies, posing significant challenges to sustainable development, especially to vegetation, which plays a crucial role in maintaining ecosystem service functions and promoting green low-carbon transformations. In this study, we estimated the fractional vegetation cover (FVC) in Shandong Province from 2000 to 2020 using the Google Earth Engine (GEE) platform. The spatial and temporal changes in FVC were analyzed using gravity center migration analysis, trend analysis, and geographic detector, and the vegetation changes of different land use types were analyzed to reveal the internal driving mechanism of FVC changes. Our results indicate that vegetation cover in Shandong Province was in good condition during the period 2000 to 2020. The high vegetation cover classes dominated, and overall changes were relatively small, with the center of gravity of vegetation cover generally shifting towards the southwest. Land use type, soil type, population density, and GDP factors had the most significant impact on vegetation cover change in Shandong Province. The interaction of these factors enhanced the effect on vegetation cover change, with land use type and soil type having the highest degree of influence. The observational results of this study can provide data support for the policy makers to formulate new ecological restoration strategies, and the findings would help facilitate the sustainability management of regional ecosystem and natural resource planning.

Spatially explicit estimate of nitrogen effects on soil respiration across the globe

Soil respiration (Rs), as the second largest flux of carbon dioxide (CO2 ) between terrestrial ecosystems and the atmosphere, is vulnerable to global nitrogen (N) enrichment. However, the global distribution of the N effects on Rs remains uncertain. Here, we compiled a new database containing 1282 observations of Rs and its heterotrophic component (Rh) in field N manipulative experiments from 317 published papers. Using this up-to-date database, we first performed a formal meta-analysis to explore the responses of Rs and Rh to N addition, and then presented a global spatially explicit quantification of the N effects using a Random Forest model. Our results showed that experimental N addition significantly increased Rs but had a minimal impact on Rh, not supporting the prevailing view that N enrichment inhibits soil microbial respiration. For the major biomes, the magnitude of N input was the main determinant of the spatial variation in Rs response, while the most important predictors for Rh response were biome specific. Based on the key predictors, global mapping visually demonstrated a positive N effect in the regions with higher anthropogenic N inputs (i.e., atmospheric N deposition and agricultural fertilization). Overall, our analysis not only provides novel insight into the N effects on soil CO2 fluxes, but also presents a spatially explicit assessment of the N effects at the global scale, which are pivotal for understanding ecosystem carbon dynamics in future scenarios with more frequent anthropogenic activities.

Global warming accelerates soil heterotrophic respiration

Carbon efflux from soils is the largest terrestrial carbon source to the atmosphere, yet it is still one of the most uncertain fluxes in the Earth's carbon budget. A dominant component of this flux is heterotrophic respiration, influenced by several environmental factors, most notably soil temperature and moisture. Here, we develop a mechanistic model from micro to global scale to explore how changes in soil water content and temperature affect soil heterotrophic respiration. Simulations, laboratory measurements, and field observations validate the new approach. Estimates from the model show that heterotrophic respiration has been increasing since the 1980s at a rate of about 2% per decade globally. Using future projections of surface temperature and soil moisture, the model predicts a global increase of about 40% in heterotrophic respiration by the end of the century under the worst-case emission scenario, where the Arctic region is expected to experience a more than two-fold increase, driven primarily by declining soil moisture rather than temperature increase.

Microbial carbon use efficiency promotes global soil carbon storage

Soils store more carbon than other terrestrial ecosystems1,2. How soil organic carbon (SOC) forms and persists remains uncertain1,3, which makes it challenging to understand how it will respond to climatic change3,4. It has been suggested that soil microorganisms play an important role in SOC formation, preservation and loss5-7. Although microorganisms affect the accumulation and loss of soil organic matter through many pathways4,6,8-11, microbial carbon use efficiency (CUE) is an integrative metric that can capture the balance of these processes12,13. Although CUE has the potential to act as a predictor of variation in SOC storage, the role of CUE in SOC persistence remains unresolved7,14,15. Here we examine the relationship between CUE and the preservation of SOC, and interactions with climate, vegetation and edaphic properties, using a combination of global-scale datasets, a microbial-process explicit model, data assimilation, deep learning and meta-analysis. We find that CUE is at least four times as important as other evaluated factors, such as carbon input, decomposition or vertical transport, in determining SOC storage and its spatial variation across the globe. In addition, CUE shows a positive correlation with SOC content. Our findings point to microbial CUE as a major determinant of global SOC storage. Understanding the microbial processes underlying CUE and their environmental dependence may help the prediction of SOC feedback to a changing climate.

Mapping global nitrogen deposition impacts on soil respiration

Soil respiration (R_s) is a key indicator of belowground biological activities of terrestrial ecosystems. Despite ongoing atmospheric nitrogen (N) deposition due to anthropogenic activities, it remains uncertain how R_s responds to globally varied atmospheric N deposition. Based on a meta-analysis of 340 simulated experimental nitrogen addition studies, we aimed to identify the key factors altering the responses of R_s to N deposition and extrapolate these results to the global mapping of R_s changes under N deposition. We found the overall experimental N addition effect on R_s was insignificant, but the responses of R_s significantly shifted from positive to negative with increasing accumulated N addition amount and lower soil pH, and the negative responses to increasing N amounts were significantly intensified in acid soils. Also, the response of heterotrophic respiration to N addition significantly increased with a lower N amount, and both responses of heterotrophic and autotrophic respiration were significantly more negative in soils with lower pH. Our mapping efforts showed that global R_s overall increased by 2.8 % in response to the accumulated N deposition from 2000 to 2020. Regions with combined characteristics of high accumulated N deposition amounts and low soil pH, including Eastern U.S., Europe, and Eastern Asia, were hotspots of R_s declines under current and future atmospheric N deposition. Our findings challenge the long-held notion that N deposition has universal negative impacts on R_s, and suggest the spatial heterogeneity in the impacts of N deposition on belowground activities and carbon release across the globe.

Multiple factors co-limit short-term in situ soil carbon dioxide emissions

Soil respiration is a major source of atmospheric CO2. If it increases with warming, it will counteract efforts to minimize climate change. To improve understanding of environmental controls over soil CO2 emission, we applied generalized linear modeling to a large dataset of in situ measurements of short-term soil respiration rate, with associated environmental attributes, which was gathered over multiple years from four locations that varied in climate, soil type, and vegetation. Soil respiration includes many CO2-producing processes: we theorized that different environmental factors could limit each process distinctly, thereby diminishing overall CO2 emissions. A baseline model that included soil temperature, soil volumetric water content, and their interaction was effective in estimating soil respiration at all four locations (p < 0.0001). Model fits, based on model log likelihoods, improved continuously as additional covariates were added, including mean daily air temperature, enhanced vegetation index (EVI), and quadratic terms for soil temperature and water content, and their interactions. The addition of land cover and its direct interactions with environmental variables further improved model fits. Significant interactions between covariates were observed at each location and at every stage of analysis, but the interaction terms varied among sites and models, and did not consistently maintain importance in more complex models. A main-effects model was therefore tested, which included soil temperature and water content, their quadratic effects, EVI, and air temperature, but no interactions. In that case all six covariates were significant (p < 0.0001) when applied across sites. We infer that local-scale soil-CO2 emissions are commonly co-limited by EVI and air temperature, in addition to soil temperature and water content. Importantly, the quadratic soil temperature and moisture terms were significantly negative: estimated soil-CO2 emissions declined when soil temperature exceeded 22.5°C, and as soil moisture differed from the optimum of 0.27 m3 m-3.

Globally invariant metabolism but density-diversity mismatch in springtails

Soil life supports the functioning and biodiversity of terrestrial ecosystems. Springtails (Collembola) are among the most abundant soil arthropods regulating soil fertility and flow of energy through above- and belowground food webs. However, the global distribution of springtail diversity and density, and how these relate to energy fluxes remains unknown. Here, using a global dataset representing 2470 sites, we estimate the total soil springtail biomass at 27.5 megatons carbon, which is threefold higher than wild terrestrial vertebrates, and record peak densities up to 2 million individuals per square meter in the tundra. Despite a 20-fold biomass difference between the tundra and the tropics, springtail energy use (community metabolism) remains similar across the latitudinal gradient, owing to the changes in temperature with latitude. Neither springtail density nor community metabolism is predicted by local species richness, which is high in the tropics, but comparably high in some temperate forests and even tundra. Changes in springtail activity may emerge from latitudinal gradients in temperature, predation and resource limitation in soil communities. Contrasting relationships of biomass, diversity and activity of springtail communities with temperature suggest that climate warming will alter fundamental soil biodiversity metrics in different directions, potentially restructuring terrestrial food webs and affecting soil functioning.

Spatial and Temporal Pattern of Net Ecosystem Productivity in China and Its Response to Climate Change in the Past 40 Years

Net ecosystem productivity (NEP), which is considered an important indicator to measure the carbon source/sink size of ecosystems on a regional scale, has been widely studied in recent years. Since China's terrestrial NEP plays an important role in the global carbon cycle, it is of great significance to systematically examine its spatiotemporal pattern and driving factors. Based on China's terrestrial NEP products estimated by a data-driven model from 1981 to 2018, the spatial and temporal pattern of China's terrestrial NEP was analyzed, as well as its response to climate change. The results demonstrate that the NEP in China has shown a pattern of high value in the west and low value in the east over the past 40 years. NEP in China from 1981 to 2018 showed a significantly increasing trend, and the NEP change trend was quite different in two sub-periods (i.e., 1981-1999 and 2000-2018). The temporal and spatial changes of China's terrestrial NEP in the past 40 years were affected by both temperature and precipitation. However, the area affected by precipitation was larger. Our results provide a valuable reference for the carbon sequestration capacity of China's terrestrial ecosystem.

Fertilization treatments affect soil CO2 emission through regulating soil bacterial community composition in the semiarid Loess Plateau

A growing body of literature have emphasized the effects of fertilization regimes on soil respiration and microbial community in the semiarid region, however, fertilization treatment effects on the soil CO2 emission, soil bacterial community, and their relationships from long-term experiments is lacking. In the present study, we investigated the effects of long-term fertilization regimes on soil bacterial community and thereafter on soil CO2 emission. A 9-year field experiment was conducted with five treatments, including no fertilizer (NA) and four fertilization treatments (inorganic fertilizer (CF), inorganic plus organic fertilizer (SC), organic fertilizer (SM), and maize straw (MS)) with equal N input as N 200 kg hm-2. The results indicated that CO2 emission was significantly increased under fertilization treatments compared to NA treatment. The bacterial abundance was higher under MS treatment than under NA treatment, while the Chao1 richness showed opposite trend. MS treatment significantly change soil bacterial community composition compared to NA treatment, the phyla (Alphaproteobacteria and Gammaproteobacteria) and potential keystone taxa (Nitrosomonadaceae and Beijerinckiaceae) were higher, while the Acidobacteriota was lower under MS treatment than under NA treatment. CO2 emission was positively correlated with the abundance of Alphaproteobacteria, Gammaproteobacteria, and keystone taxa, negatively correlated with these of Acidobacteriota. Random forest modeling and structural equation modeling determined soil organic carbon, total nitrogen, and the composition and network module III of the bacterial community are the main factors contribute to CO2 emission. In conclusion, our results suggest that the increased CO2 emission was affected by the varied of soil bacterial community composition derived from fertilization treatments, which was related to Alphaproteobacteria, Gammaproteobacteria, Acidobacteriota, and potential keystone taxa (Nitrosomonadaceae and Beijerinckiaceae), and highlight that the ecological importance of the bacterial community in mediating carbon cycling in the semiarid Loess Plateau.

Global biogeography and projection of soil antibiotic resistance genes

Although edaphic antibiotic resistance genes (ARGs) pose serious threats to human well-being, their spatially explicit patterns and responses to environmental constraints at the global scale are not well understood. This knowledge gap is hindering the global action plan on antibiotic resistance launched by the World Health Organization. Here, a global analysis of 1088 soil metagenomic samples detected 558 ARGs in soils, where ARG abundance in agricultural habitats was higher than that in nonagricultural habitats. Soil ARGs were mostly carried by clinical pathogens and gut microbes that mediated the control of climatic and anthropogenic factors to ARGs. We generated a global map of soil ARG abundance, where the identified microbial hosts, agricultural activities, and anthropogenic factors explained ARG hot spots in India, East Asia, Western Europe, and the United States. Our results highlight health threats from soil clinical pathogens carrying ARGs and determine regions prioritized to control soil antibiotic resistance worldwide.

Characterization and attribution of vegetation dynamics in the ecologically fragile South China Karst: Evidence from three decadal Landsat observations

Plant growth and its changes over space and time are effective indicators for signifying ecosystem health. However, large uncertainties remain in characterizing and attributing vegetation changes in the ecologically fragile South China Karst region, since most existing studies were conducted at a coarse spatial resolution or covered limited time spans. Considering the highly fragmented landscapes in the region, this hinders their capability in detecting fine information of vegetation dynamics taking place at local scales and comprehending the influence of climate change usually over relatively long temporal ranges. Here, we explored the spatiotemporal variations in vegetation greenness for the entire South China Karst region (1.9 million km²) at a resolution of 30m for the notably increased time span (1987-2018) using three decadal Landsat images and the cloud-based Google Earth Engine. Moreover, we spatially attributed the vegetation changes and quantified the relative contribution of driving factors. Our results revealed a widespread vegetation recovery in the South China Karst (74.80%) during the past three decades. Notably, the area of vegetation recovery tripled following the implementation of ecological engineering compared with the reference period (1987-1999). Meanwhile, the vegetation restoration trend was strongly sustainable beyond 2018 as demonstrated by the Hurst exponent. Furthermore, climate change contributed only one-fifth to vegetation restoration, whereas major vegetation recovery was highly attributable to afforestation projects, implying that anthropogenic influences accelerated vegetation greenness gains in karst areas since the start of the new millennium during which ecological engineering was continually established. Our study provides additional insights into ecological restoration and conservation in the highly heterogeneous karst landscapes and other similar ecologically fragile areas worldwide.

Functional Diversity and CO2 Emission Characteristics of Soil Bacteria during the Succession of Halophyte Vegetation in the Yellow River Delta

Carbon dioxide (CO₂) is the most important greenhouse gas in the atmosphere, which is mainly derived from microbial respiration in soil. Soil bacteria are an important part of the soil ecosystem and play an important role in the process of plant growth, mineralization, and decomposition of organic matter. In this paper, we discuss a laboratory incubation experiment that we conducted to investigate the CO₂ emissions and the underlying bacterial communities under the natural succession of halophyte vegetation in the Yellow River Delta by using high-throughput sequencing technology and PICRUSt functional prediction. The results showed that the bacterial abundance and diversity increased significantly along with the succession of halophyte vegetation. Metabolic function is the dominant function of soil bacteria in the study area. With the succession of halophyte vegetation, the rate of CO₂ emissions gradually increased, and were significantly higher in soil covered with vegetation than that of the bare land without vegetation coverage. These results helped to better understand the relationships of soil bacterial communities under the background of halophyte vegetation succession, which can help to make efficient strategies to mitigate CO₂ emissions and enhance carbon sequestration.

Land Cover Changing Pattern in Pre- and Post-Earthquake Affected Area from Remote Sensing Data: A Case of Lushan County, Sichuan Province

Extremely hard-hit areas face frequent secondary geological hazards and difficulties in vegetation recovery, and subsequent effects have a significant impact on land cover changes. At present, there is a lack of research on the dynamic restoration of, and changes in, the ecological environment before and after an earthquake, and especially a lack of quantitative assessment of the impact of earthquakes on land cover at the microscopic scale of spatial distribution of landscape indices. Taking the Lushan earthquake in Sichuan Province as an example, this paper obtained land cover data from the study area between 2012 and 2020, and analyzes the spatial distribution characteristics and influencing factors of land cover change frequency by using a comprehensive land cover degree index, land cover transfer matrix and landscape ecology index. The results show that the types of cropland, forest, built-up and bare land have changed significantly in the study area. During the earthquake recovery period, the comprehensive land cover index of the study area showed an increasing trend, and land cover has been continuously improved under the effect of artificial measures and natural restoration. After 2013, patch density (PD) and landscape shape index (LSI) values decreased and aggregation index (AI) values increased for the vast majority of landscape land classes, indicating a benign ecological development across the region in the post-earthquake period. The research results are not only helpful to establish scientific ecological environmental management in the earthquake-stricken areas, but also helpful to formulate medium- and long-term ecological environmental monitoring and ecological restoration plans based on land cover change patterns.

Spatiotemporal Changes and Driver Analysis of Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands

Ecosystem respiration (RE) plays a critical role in terrestrial carbon cycles, and quantification of RE is important for understanding the interaction between climate change and carbon dynamics. We used a multi-level attention network, Geoman, to identify the relative importance of environmental factors and to simulate spatiotemporal changes in RE in northern China’s grasslands during 2001–2015, based on 18 flux sites and multi-source spatial data. Results indicate that Geoman performed well (R2 = 0.87, RMSE = 0.39 g C m−2 d−1, MAE = 0.28 g C m−2 d−1), and that grassland type and soil texture are the two most important environmental variables for RE estimation. RE in alpine grasslands showed a decreasing gradient from southeast to northwest, and that of temperate grasslands showed a decreasing gradient from northeast to southwest. This can be explained by the enhanced vegetation index (EVI), and soil factors including soil organic carbon density and soil texture. RE in northern China’s grasslands showed a significant increase (1.81 g C m−2 yr−1) during 2001–2015. The increase rate of RE in alpine grassland (2.36 g C m−2 yr−1) was greater than that in temperate grassland (1.28 g C m−2 yr−1). Temperature and EVI contributed to the interannual change of RE in alpine grassland, and precipitation and EVI were the main contributors in temperate grassland. This study provides a key reference for the application of advanced deep learning models in carbon cycle simulation, to reduce uncertainties and improve understanding of the effects of biotic and climatic factors on spatiotemporal changes in RE.

Sedimentary organic carbon storage of thermokarst lakes and ponds across Tibetan permafrost region

Sedimentary soil organic carbon (SOC) stored in thermokarst lakes and ponds (hereafter referred to as thaw lakes) across high-latitude/altitude permafrost areas is of global significance due to increasing thaw lake numbers and their high C vulnerability under climate warming. However, to date, little is known about the SOC storage in these lakes, which limits our better understanding of the fate of these active carbon in a warming future. Here, by combining large-scale field observation data and published deep (e.g., 0-300 cm) permafrost SOC data with a random forest (RF) machine learning technique, we provided the first comprehensive estimation of thaw lake SOC stocks to 3 m depth on the Tibetan Plateau. This study demonstrated that combining multiple environmental factors with the RF model could effectively predict the spatial distributions of the thaw lake SOC density values (SOCDs). The model results revealed that the soil respiration, normalized difference vegetation index (NDVI), and mean annual precipitation (MAP) were the most influential factors for predicting thaw lake SOCDs. In total, the sedimentary SOC stocks in the thaw lakes were approximately 52.62 Tg in the top 3 m, with 53% of the SOC stored in the upper layers (0-100 cm). The SOCDs generally exhibited high values in eastern Tibetan Plateau, and low values in mid- and western Tibetan Plateau, which were similar to the patterns of the land cover types that affected the SOCDs. We further found that the SOCDs of thaw lakes were generally higher than those of their surrounding permafrost soils at different layer depths, which could be ascribed to the erosion of soil particles or leaching solution from the thawing permafrost soils to lakes and/or enhanced vegetation growth at the lake bottom. This research highlights the necessity of explicitly considering the thaw lake SOC stocks in Earth system models for more comprehensive future projections of the carbon dynamics on the plateau.

Time-Lag Effect of Climate Conditions on Vegetation Productivity in a Temperate Forest–Grassland Ecotone

Climate conditions can significantly alter the vegetation net primary productivity (NPP) in many of Earth’s ecosystems, although specifics of NPP–climate condition interactions, especially time-lag responses on seasonal scales, remain unclear in ecologically sensitive forest–grassland ecotones. Based on the Moderate-Resolution Imaging Spectroradiometer (MODIS) and meteorological datasets, we analyzed the relationship between NPP and precipitation, temperature, and drought during the growing season (April–August), considering the time-lag effect (0–5 months) at the seasonal scale in Hulunbuir, Inner Mongolia, China from 2000 to 2018. The results revealed a delayed NPP response to precipitation and drought throughout the growing season. In April, the precipitation in the 4 months before (i.e., the winter of the previous year) explained the variation in NPP. In August, the NPP in some areas was influenced by the preceding 1~2 months of drought. The time-lag effect varied with vegetation type and soil texture at different spatial patterns. Compared to grass and crop, broadleaf forest and meadow exhibited a longer legacy of precipitation during the growing season. The length of the time-lag effects of drought on NPP increased with increasing soil clay content during the growing season. The interaction of vegetation types and soil textures can explain 37% of the change in the time-lag effect of the NPP response to PPT on spatial pattern. Our findings suggested that preceding precipitation influences vegetation growth at the early stages of growth, while preceding drought influences vegetation growth in the later stages of growth. The spatial pattern of the time lag was significantly influenced by interaction between vegetation type and soil texture factors. This study highlights the importance of considering the time-lag effects of climate conditions and underlying drivers in further improving the prediction accuracy of NPP and carbon sinks in temperate semiarid forest–grassland ecotones.

A data-driven estimate of litterfall and forest carbon turnover and the drivers of their inter-annual variabilities in forest ecosystems across China

Strong influences of climate and land-cover changes on terrestrial ecosystems urgently need to re-estimate forest carbon turnover time (τ_forest), i.e., the residence time of carbon (C) in the living forest carbon reservoir in China, to reduce uncertainties in ecosystem carbon sinks under ongoing climate change. However, in absence of accurate carbon loss (e.g., forest litterfall), τ_forest estimate based on the non-steady-state assumption (NSSA) in forest ecosystems across China is still unclear. In this study, thus, we first compiled a litterfall dataset with 1025 field observations, and applied a Random Forest (RF) algorithm with the linkage of gridded environmental variables to predict litterfall from 2000 to 2019 with a fine spatial resolution of 1 km and a temporal resolution of one year. Finally, τ_forest was also estimated with the data-driven litterfall product. Results showed that RF algorithm could well predict the spatial and temporal patterns of forest litterfall with a model efficiency of 0.58 and root mean square error of 78.7 g C m^-2 year^-1. Mean litterfall was 205.4 ± 1.1 Tg C year^-1 (mean ± standard error) with a significant increasing trend of 0.65 ± 0.14 Tg C year^-2 from 2000 to 2019 (p < 0.01), indicating an increasing carbon loss from litterfall. Mean τ_forest was 26.2 ± 0.1 years with a significant decreasing trend of -0.11 ± 0.02 years (p < 0.01) from 2000 to 2019. Climate change dominated the inter-annual variability of τ_forest in high latitude areas, and land-cover change dominated the regions with intensive human activities. These findings suggested that carbon loss from vegetation to the atmosphere becomes more quickly in recent decades, with significant implication for vegetation carbon cycling-climate feedbacks. Meanwhile, the developed litterfall and τ_forest datasets can serve as a benchmark for biogeochemical models to accurately estimate global carbon cycling.

Bacterial Communities of Forest Soils along Different Elevations: Diversity, Structure, and Functional Composition with Potential Impacts on CO2 Emission

Soil bacteria are important components of forest ecosystems, there compostion structure and functions are sensitive to environmental conditions along elevation gradients. Using 16S rRNA gene amplicon sequencing followed by FAPROTAX function prediction, we examined the diversity, composition, and functional potentials of soil bacterial communities at three sites at elevations of 1400 m, 1600 m, and 2200 m in a temperate forest. We showed that microbial taxonomic composition did not change with elevation (p = 0.311), though soil bacterial α-diversities did. Proteobacteria, Acidobacteria, Actinobacteria, and Verrucomicrobia were abundant phyla in almost all soil samples, while Nitrospirae, closely associated with soil nitrogen cycling, was the fourth most abundant phylum in soils at 2200 m. Chemoheterotrophy and aerobic chemoheterotrophy were the two most abundant functions performed in soils at 1400 m and 1600 m, while nitrification (25.59% on average) and aerobic nitrite oxidation (19.38% on average) were higher in soils at 2200 m. Soil CO2 effluxes decreased (p < 0.050) with increasing elevation, while they were positively correlated (r = 0.55, p = 0.035) with the abundances of bacterial functional groups associated with carbon degradation. Moreover, bacterial functional composition, rather than taxonomic composition, was significantly associated with soil CO2 effluxes, suggesting a decoupling of taxonomy and function, with the latter being a better predictor of ecosystem functions. Annual temperature, annual precipitation, and pH shaped (p < 0.050) both bacterial taxonomic and functional communities. By establishing linkages between bacterial taxonomic communities, abundances of bacterial functional groups, and soil CO2 fluxes, we provide novel insights into how soil bacterial communities could serve as potential proxies of ecosystem functions.

Carbon Materials Advancing Microorganisms in Driving Soil Organic Carbon Regulation

Carbon emission from soil is not only one of the major sources of greenhouse gases but also threatens biological diversity, agricultural productivity, and food security. Regulation and control of the soil carbon pool are political practices in many countries around the globe. Carbon pool management in engineering sense is much bigger and beyond laws and monitoring, as it has to contain proactive elements to restore active carbon. Biogeochemistry teaches us that soil microorganisms are crucial to manage the carbon content effectively. Adding carbon materials to soil is thereby not directly sequestration, as interaction of appropriately designed materials with the soil microbiome can result in both: metabolization and thereby nonsustainable use of the added carbon, or-more favorably-a biological amplification of human efforts and sequestration of extra CO₂ by microbial growth. We review here potential approaches to govern soil carbon, with a special focus set on the emerging practice of adding manufactured carbon materials to control soil carbon and its biological dynamics. Notably, research on so-called "biochar" is already relatively mature, while the role of artificial humic substance (A-HS) in microbial carbon sequestration is still in the developing stage. However, it is shown that the preparation and application of A-HS are large biological levers, as they directly interact with the environment and community building of the biological soil system. We believe that A-HS can play a central role in stabilizing carbon pools in soil.

Variability of ecosystem carbon source from microbial respiration is controlled by rainfall dynamics

Soil heterotrophic respiration (R _h) represents an important component of the terrestrial carbon cycle that affects whether ecosystems function as carbon sources or sinks. Due to the complex interactions between biological and physical factors controlling microbial growth, R _h is uncertain and difficult to predict, limiting our ability to anticipate future climate trajectories. Here we analyze the global FLUXNET 2015 database aided by a probabilistic model of microbial growth to examine the ecosystem-scale dynamics of R _h and identify primary predictors of its variability. We find that the temporal variability in R _h is consistently distributed according to a Gamma distribution, with shape and scale parameters controlled only by rainfall characteristics and vegetation productivity. This distribution originates from the propagation of fast hydrologic fluctuations on the slower biological dynamics of microbial growth and is independent of biome, soil type, and microbial physiology. This finding allows us to readily provide accurate estimates of the mean R _h and its variance, as confirmed by a comparison with an independent global dataset. Our results suggest that future changes in rainfall regime and net primary productivity will significantly alter the dynamics of R _h and the global carbon budget. In regions that are becoming wetter, R _h may increase faster than net primary productivity, thereby reducing the carbon storage capacity of terrestrial ecosystems.

Greening of the earth does not compensate for rising soil heterotrophic respiration under climate change

Stability of the soil carbon (C) pool under decadal scale variability in temperature and precipitation is an important source of uncertainty in our understanding of land-atmosphere climate feedbacks. This depends on how two opposing C-fluxes-influx from net primary production (NPP) and efflux from heterotrophic soil respiration (R_h )-respond to covariation in temperature and precipitation. There is scant evidence to judge whether field experiments which manipulate both temperature and precipitation align with Earth System Models, or not. As a result, even though the world is generally greening, whether the resultant gains in NPP can offset climate change impacts on R_h , where, and by how much, remains uncertain. Here, we use decadal-scale global time-series datasets on NPP, R_h , temperature, and precipitation to estimate the two opposing C-fluxes and address whether one can outpace the other. We implement machine-learning tools on recent (2001-2019) and near-future climate scenarios (2020-2040) to assess the response of both C-fluxes to temperature and precipitation variation. We find that changes in C-influx may not compensate for C-efflux, particularly in wetter and warmer conditions. Soil-C loss can occur in both tropics and at high latitudes since C-influx from NPP can fall behind C-efflux from R_h . Precipitation emerges as the key determinant of soil-C vulnerability in a warmer world, implying that hotspots for soil-C loss/gain can shift rapidly and highlighting that soil-C is vulnerable to climate change despite widespread greening of the world. The direction of covariation between change in temperature and precipitation, rather than their magnitude, can help conceptualize highly variable patterns in C-fluxes to guide soil-C stewardship.

Spatiotemporal Variations and Risk Analysis of Chinese Typhoon Disasters

Typhoons are a product of air-sea interaction, which are often accompanied by high winds, heavy rains, and storm surges. It is significant to master the characteristics and pattern of typhoon activity for typhoon warning and disaster prevention and mitigation. We used the Kernel Density Estimation (KDE) index as the hazard index; the probability of exceeding, or reaching, return period or exceeding a certain threshold was used to describe the probability of hazard occurrence. The results show that the overall spatial distribution of typhoon hazards conforms to a northeast-southwest zonal distribution, decreasing from the southeast coast to the northwest. Across the six typical provinces of China assessed here, data show that Hainan possesses the highest hazard risk. Hazard index is relatively high, mainly distributed between 0.005 and 0.015, while the probability of exceeding a hazard index greater than 0.015 is 0.15. In light of the four risk levels assessed here, the hazard index that accounts for the largest component of the study area is mainly distributed up to 0.0010, all mild hazard levels. Guangdong, Guangxi, Hainan, Fujian, Zhejiang, and Jiangsu as well as six other provinces and autonomous regions are all areas with high hazard risks. The research results can provide important scientific evidence for the sustainable development of China’s coastal provinces and cities. The outcomes of this study may also provide the scientific basis for the future prevention and mitigation of marine disasters as well as the rationalization of related insurance.