The scale of the underlying data compilation is one of the study’s most striking features. The researchers gathered 2,311 in-situ measurements of woody fine root biomass and 3,580 measurements of herbaceous fine root biomass from peer-reviewed studies spanning the country. These field observations, collected from tropical forests in the south to boreal woodlands in the north, and from alpine meadows on the Qinghai–Tibet Plateau to arid grasslands in the northwest, form the empirical backbone of the new dataset. For decades, such measurements have sat scattered across hundreds of individual papers, each describing a single site at a single moment in time. By pooling them, the team converted a fragmented literature into a coherent national picture—one that can be compared, modeled, and validated at scales far beyond any individual field campaign.

Turning thousands of point measurements into a continuous map requires a modeling bridge, and the researchers built theirs from machine learning. They trained random forest models—a technique that combines hundreds of decision trees, each trained on random subsets of the data and environmental predictors, to produce robust estimates even when relationships between variables are nonlinear—to predict fine root biomass in the surface soil layer, defined as the top 20 centimeters. Crucially, they built separate models for woody ecosystems and herbaceous ecosystems, recognizing that trees and grasses deploy their roots under fundamentally different constraints. Environmental covariates, including climate, soil properties, and vegetation characteristics, provided the spatial context that allowed the models to extrapolate from the measurement sites to every one-kilometer grid cell across China.

But surface biomass alone does not tell the full story. Fine roots are distributed through the soil profile in patterns that vary enormously with species, climate, and water availability, and this vertical structure matters enormously for carbon accounting. To capture it, the team modeled a vertical attenuation coefficient—a parameter, conventionally denoted k, that describes how quickly root biomass declines with depth. By pairing the predicted surface-layer biomass with these depth-distribution functions, the researchers generated estimates of fine root biomass in successive 20-centimeter layers reaching down to two meters, for every grid cell, for all woody and herbaceous ecosystems in China, and for each year from 2005 through 2025. The result is a four-dimensional picture of the country’s underground biomass: across longitude, latitude, depth, and time.

The models were rigorously evaluated against independent observations, and the performance figures are impressive for a variable as noisy as root biomass. The random forest models explained approximately 86 percent of the observed variation in surface fine root biomass in woody ecosystems and roughly 81 percent in herbaceous ecosystems. The team also reported that their predictions reasonably captured interannual dynamics—meaning the models did not merely reproduce average conditions but tracked year-to-year fluctuations in the observations. For a quantity that is notoriously difficult and labor-intensive to measure, involving careful excavation, sieving, and separation of living fine roots from soil, this level of predictive skill represents a substantial methodological achievement.

The mapped patterns reveal a continent-scale geography of root life. Woody fine root biomass density is generally higher in southern China than in central and northern regions, a gradient consistent with the greater productivity and denser vegetation of the country’s humid subtropical forests. Herbaceous fine root biomass, by contrast, reaches its highest densities on the Qinghai–Tibet Plateau, where cold-adapted grasslands concentrate a remarkable share of their biomass below ground—an evolutionary strategy for surviving grazing, frost, and short growing seasons. These contrasting regional hotspots underline a key message of the study: belowground biomass cannot be inferred from aboveground greenness alone, and the two storages obey different rules in different biomes.

Perhaps the most scientifically provocative finding concerns depth. The analysis showed that woody plants send proportionally more of their fine root biomass deeper into the soil in arid regions, a pattern consistent with the physiological imperative to reach reliable water sources when precipitation is scarce. Herbaceous species, meanwhile, maintained consistently surface-concentrated rooting patterns across all climate zones, keeping their fine roots packed into the uppermost soil layers regardless of how dry their environment became. This divergence means that the same meter of soil beneath a desert shrubland and beneath a steppe grassland holds very different amounts of carbon-bearing root tissue—and that global models assuming a universal root-depth profile are likely misplacing carbon in both cases.

The environmental drivers of fine root biomass also split along vegetation lines in ways that matter for future projections. Woody fine root biomass was more strongly influenced by root traits and nutrient availability, suggesting that the chemical and architectural properties of tree species, together with soil fertility, set the ceiling on how much fine root mass a forest maintains. Herbaceous fine root biomass, in contrast, tracked climatic conditions more closely, implying that grassland roots may respond more directly and rapidly to warming and shifting precipitation regimes. As climate change alters both temperature belts and nutrient cycles, these differing sensitivities could cause woody and herbaceous ecosystems to diverge in their belowground carbon dynamics over the coming decades.

Why does this matter beyond China’s borders? Earth system models that simulate the global carbon cycle must partition photosynthesized carbon among leaves, wood, and roots, and must decide how quickly root carbon enters the soil through turnover. Poorly constrained root distributions propagate uncertainty into estimates of soil carbon stocks and their feedbacks to climate. An observation-constrained, spatially explicit benchmark of the kind produced by Zheng and colleagues gives modelers something they have rarely possessed: a gridded ground truth against which simulated root biomass and its vertical arrangement can be tested, corrected, and improved. The dataset’s yearly coverage from 2005 to 2025 adds a temporal dimension that can support studies of how belowground carbon storage is already shifting as climate and land use change.

The dataset itself has been made openly available, archived on Figshare, and the underlying preprint is undergoing open review at Earth System Science Data, a journal dedicated to publishing data products of lasting value to the geosciences. The authors suggest that this resource will serve as a benchmark for quantifying belowground carbon stocks and their dynamics, but its applications extend further—to nutrient cycling studies, hydrological modeling, land restoration planning, and the verification of carbon offset schemes that depend on honest accounting of what lies beneath. For a country that spans tropical reefs, karst forests, alpine meadows, and deserts, a unified, vertically resolved map of fine roots is more than a technical curiosity. It is a detailed portrait of the hidden half of China’s ecosystems, and an invitation to look more carefully at the roots that quietly run the planet’s most important carbon exchange.

Subject of Research: Vertically resolved fine root biomass mapping across China's ecosystems

Article Title: A High-Resolution Gridded Dataset of Vertically Resolved Fine Root Biomass Across China

Article References: Zheng, W., Zhao, M., Li, W., Zhang, D., Tang, W., Luo, J., Jiao, S., Li, F., & Zhang, H. (2026). A High-Resolution Gridded Dataset of Vertically Resolved Fine Root Biomass Across China. https://doi.org/10.5194/essd-2026-777

Image Credits: AI Generated

DOI: 10.5194/essd-2026-777

Keywords: fine root biomass, soil carbon, random forest, machine learning, Qinghai-Tibet Plateau, root depth distribution, Earth System Science Data, gridded dataset, carbon cycle, China ecosystems, belowground biomass, ecosystem modeling

The research, led by Dhia Zalfa Zahira, Anjar Dimara Sakti, and Ketut Wikantika of Institut Teknologi Bandung, goes beyond simply counting lost trees. It tracks how much carbon those forests actually stored — both above ground in trunks, branches, and leaves, and below ground in root systems — and how that storage changed year by year. Most previous studies of the region offered static snapshots for a single year or focused only on aboveground biomass. By modelling both aboveground and belowground biomass carbon across five time points, the team has produced one of the most dynamic pictures yet of a tropical carbon reservoir in decline.

Defining what counts as an intact forest was itself a technical feat. The researchers started with the Hansen tree cover dataset derived from Landsat imagery, flagging any pixel with at least 30 percent canopy cover in 2000. They then filtered out oil-palm plantations using a global planting-year dataset and removed cropland using the MODIS land cover product. From that baseline, they iteratively subtracted three drivers of loss — stand-replacing deforestation, new cropland, and new oil-palm planting — at five-year intervals, generating intact forest maps for 2000, 2005, 2010, 2015, and 2020. This multi-layer filtering matters because oil-palm plantations, though tree-covered, are not ecologically intact forests; they typically replace natural forest and store far less carbon.

With the forest maps in hand, the team turned to a Random Forest regression model, an ensemble machine learning method that aggregates predictions from hundreds of decision trees. Thirteen environmental predictors fed the model, spanning vegetation indices such as NDVI and NIRv, productivity metrics including gross and net primary productivity, leaf area index, and the fraction of absorbed photosynthetically active radiation, along with climate variables like land surface temperature and precipitation, and topographic features including elevation, slope, and a topographic wetness index. All layers were processed at 300-metre resolution and aggregated to 1.5-by-1.5-kilometre grids to reduce noise and pixel misalignment.

A key design choice set this model apart: rather than training only on forest pixels, the researchers trained across the full gradient of Southeast Asian vegetation — forests, shrublands, grasslands, and cropland alike — using more than two million grid cells from 2010. This ensures the model learned the entire spectrum of carbon density, reducing bias at forest edges and recently disturbed areas. The data was split 80-20 into training and testing sets, with a Kolmogorov-Smirnov test confirming that both subsets covered the region geographically without spatial bias. Against the NASA reference biomass dataset, the model achieved a testing R² of 0.899 for aboveground carbon and 0.874 for belowground carbon, indicating strong predictive skill with limited overfitting.

Independent validation against the European Space Agency’s Climate Change Initiative Biomass product — built from radar observations by Sentinel-1, Envisat, and the ALOS satellites — showed moderate but consistent agreement, with R² values of 0.645 and 0.632 for the two carbon components. Among the thirteen predictors, the fraction of absorbed photosynthetically active radiation emerged as the single most influential variable, which makes physical sense: how much solar energy a canopy captures directly governs how much carbon it can fix into biomass. Land surface temperature and precipitation followed, while the classic vegetation indices contributed surprisingly little, apparently limited in their sensitivity to structural biomass in dense tropical canopies.

The carbon accounting that followed is sobering. In 2000, intact forests in the region held an estimated 26.18 megatonnes of aboveground biomass carbon and 6.61 megatonnes belowground, a combined total of roughly 32.8 megatonnes. By 2020, those figures had fallen to 15.96 and 3.96 megatonnes respectively — a drop of more than 39 percent in twenty years. The steepest losses occurred between 2010 and 2015, when forest area shrank by nearly 384,000 square kilometres and biomass carbon declined at a rate of about 1,496 megatonnes per year in total. The hardest-hit landscapes were Kalimantan and Sumatra in Indonesia, Sarawak and Sabah in Malaysia, and cropland-driven loss zones in northern Thailand, Myanmar, and parts of Papua.

To translate those ecological losses into terms policymakers cannot ignore, the team ran an indicative economic valuation under two carbon pricing scenarios. At the current average price across ASEAN+3 countries — a modest 6.45 US dollars per tonne of CO₂ equivalent — the region’s intact forest carbon was worth about 776 billion dollars in 2000, falling to roughly 471 billion by 2020. Under the globally recommended price of 75 dollars per tonne, aligned with social cost of carbon estimates, the value dropped from about 9.02 trillion dollars to 5.48 trillion. The authors are careful to stress these are illustrative, non-market figures, not credit-eligible carbon trading values, but they make the scale of the liability unmistakable: a loss of nearly 4 trillion dollars in indicative carbon assets in two decades.

The findings land at a delicate moment for climate policy. Southeast Asia harbours nearly 15 percent of the world’s tropical forests, and their continued erosion threatens not only regional biodiversity but global mitigation commitments under the Paris Agreement, REDD+ frameworks, and Sustainable Development Goal targets on sustainable forest management. There is also a real risk of a tipping dynamic: as intact forests degrade, they can flip from carbon sinks to net carbon sources, emitting more than they absorb and accelerating the very warming that stresses them. The spatially explicit maps produced by this study could help governments prioritise conservation zones where avoided deforestation yields the greatest climate benefit, and design community-based forest management schemes that recognise indigenous peoples as the primary stewards of these carbon reservoirs.

The study is candid about its limitations. The 2000 baseline may include some old plantations that the Hansen dataset cannot distinguish from natural forest; the model assumes predictor relationships stable across two decades; and the absence of explicit spatial block cross-validation means the reported accuracy figures may be somewhat optimistic. The economic valuation, by design, omits additionality, leakage, and monitoring requirements. Still, the core signal — a 59 percent collapse in intact forest area and a 39 percent loss of biomass carbon, tracked with low reported uncertainty — is difficult to dispute. Future work integrating radar-based biomass data and country-specific carbon accounting could sharpen the picture further. What this study makes clear is that Southeast Asia’s intact forests are not merely an ecological treasure; they are a quantifiable, and rapidly depreciating, planetary asset.

Subject of Research: Remote sensing and random forest modelling of intact forest biomass carbon dynamics and carbon economics in Southeast Asia from 2000 to 2020

Article Title: Intact forest carbon dynamics and indicative carbon economy in Southeast Asia: A remote sensing and random forest approach

Article References: Zahira, D. Z., Sakti, A. D., & Wikantika, K. (2026). Intact forest carbon dynamics and indicative carbon economy in Southeast Asia: A remote sensing and random forest approach. Environmental Advances, 26, Article 100758. https://doi.org/10.1016/j.envadv.2026.100758

Image Credits: AI Generated

DOI: 10.1016/j.envadv.2026.100758

Keywords: intact forests, Southeast Asia, biomass carbon, remote sensing, random forest, deforestation, oil palm, carbon pricing, REDD+, aboveground biomass, belowground biomass, machine learning

Methane is a potent greenhouse gas, and undrained peatlands are among the most important natural sources of it. In these waterlogged ecosystems, oxygen is scarce below the surface, so microbes decomposing old plant material produce methane in the saturated peat. Once formed, the gas can reach the atmosphere along three main routes: it can diffuse through the water and soil, it can travel through the hollow internal tissues of vascular plants such as sedges, or it can build up as free-phase gas bubbles until buoyancy forces them upward in sudden releases. While the first two pathways are relatively well studied, ebullition has remained stubbornly difficult to characterize, because it is episodic, spatially patchy, and easily missed by conventional flux chambers that sample at fixed intervals.

To capture this elusive pathway, the team deployed gas traps across areas with different plant cover types in the fen, allowing bubbles rising from the peat to be collected and quantified directly. They then partitioned the ebullition contribution from chamber-based measurements of total methane flux, relating the results to soil properties, water table position, pore water methane concentrations, and plant biomass. This dual approach, pairing in situ bubble collection with flux chamber data, is one of the few ways to reliably separate the episodic bubble flux from the steadier diffusive and plant-mediated components of the total emission.

The field results revealed a clear seasonal and spatial pattern. Ebullition peaked in the warmest months of July and August and was greatest in sedge-dominated areas where the water table sat closest to the surface. That combination makes physical sense: warmer peat accelerates microbial methane production, while a shallow water table shortens the distance a bubble must travel before it can escape, reducing the chance that it dissolves or is oxidized along the way. When the researchers built statistical models incorporating soil temperature, precipitation, water table depth, and pore water methane concentrations, these variables explained 42 percent of the spatiotemporal variation in measured ebullition volume and 61 percent of the variation in ebullition methane flux. In other words, a substantial share of the variability can be predicted from a handful of routinely measured environmental parameters, which is encouraging news for anyone trying to model wetland emissions at larger scales.

Yet a considerable fraction of the variation remained unexplained, underscoring just how heterogeneous ebullition is. Previous work at other peatlands has shown that bubbles can accumulate in discrete pockets of trapped gas within the peat profile and release in bursts triggered by falling atmospheric pressure, changes in water table, or simply the gradual growth of bubbles until they exceed a critical size. The Ontario fen study adds to this picture by showing that even within a single wetland, the timing and magnitude of bubble release differ sharply among plant communities, meaning that point measurements can badly misrepresent the true flux if they do not sample across vegetation types.

To isolate the role of plants more rigorously, the researchers brought intact water-saturated monoliths from the fen into a greenhouse, where they could control conditions and observe ebullition under different vegetation cover. The greenhouse experiment produced a surprising twist. Dense sedge cover was associated with higher belowground biomass but lower concentrations of methane in the pore water and less frequent ebullition. This counterintuitive result likely reflects the complex interplay between sedges and methane dynamics: sedge roots transport oxygen into the rhizosphere, where it can fuel methane-oxidizing microbes and suppress production, while root exudates can simultaneously provide fresh carbon substrates for methanogens. The net effect observed here was that thick sedge stands actually dampened the bubble pathway, even though sedges are often associated with high overall methane emissions through their internal transport tissues.

The numbers from the greenhouse experiment highlighted how much the ebullition contribution can swing with vegetation. Across the different cover types, ebullition accounted for between 1.9 and 46.9 percent of total methane emissions, a more than twentyfold range. In the field, the researchers estimated that ebullition contributed approximately 16.4 percent of total methane emissions from the fen. That figure is too large to ignore. Many ecosystem-scale methane budgets rely on chamber measurements or eddy covariance techniques that implicitly average over episodic events, and if one in six units of methane is leaving the peat as bubbles, campaigns that fail to capture ebullition risk systematically underestimating emissions.

The findings carry particular weight for the calcareous fens that dot parts of southern Ontario and similar landscapes elsewhere. These minerotrophic wetlands, fed by groundwater rich in calcium and other ions, occupy a distinctive position in the peatland spectrum, and their hydrology is strongly shaped by the surrounding hydrogeological setting. Because fens often support lush sedge vegetation, they have frequently been treated in models as systems where plant-mediated transport dominates and bubbles matter less. The new study complicates that assumption, showing that even in sedge-rich areas, ebullition remains a meaningful emission pathway, and that dense sedge cover can actually reduce it locally while adjacent communities continue to vent methane through bubbles.

For climate modeling, the implications are twofold. First, the demonstrated predictive power of simple environmental variables suggests that ebullition could be incorporated into process-based methane models with greater confidence, using soil temperature, water table depth, precipitation, and pore water methane concentration as driving inputs. Second, the strong vegetation signal means that maps of plant community composition, which are increasingly available from satellite imagery and drone surveys, could help identify hotspots and cold spots of bubble release within a wetland. As warming accelerates methane production in northern and temperate peatlands, and as shifting precipitation regimes alter water tables, the episodic bubble pathway may become an even larger share of the total, making its accurate representation a matter of real consequence for emission projections.

The study also serves as a methodological reminder to the wetland science community. Gas traps are inexpensive and simple, yet they capture a flux component that standard chambers routinely miss or smear across sampling intervals. The researchers argue that quantifying ebullition directly in the field is essential for accurate estimation of peatland methane emissions, including from sedge-dominated areas where the bubble pathway was once thought to be negligible. As nations refine their wetland greenhouse gas inventories and restoration projects multiply across rewetted peatlands, distinguishing between methane that seeps, methane that streams through plants, and methane that erupts in bubbles will be key to knowing exactly how much warming power these carbon-rich landscapes are releasing into the atmosphere.

Subject of Research: Controls on methane ebullition as a methane emission pathway in a calcareous fen peatland

Article Title: Evaluating soil and plant controls on the contribution of ebullition to methane emission in a calcareous fen

Article References: Kangro, R. J., & Strack, M. (2026). Evaluating soil and plant controls on the contribution of ebullition to methane emission in a calcareous fen. Plant and Soil. https://doi.org/10.1007/s11104-026-09084-4

Image Credits: AI Generated

DOI: 10.1007/s11104-026-09084-4

Keywords: methane, ebullition, peatland, fen, wetland emissions, sedge, soil temperature, water table, pore water methane, belowground biomass, greenhouse gas, Plant and Soil

The research, led by Xiaoyu Fang and Chao Zhang of Northwest A&F University together with colleagues from the Chinese Academy of Sciences, examined sixty sites spanning three contrasting grassland types: alpine meadow, alpine steppe, and alpine desert steppe. These ecosystems form a natural aridity gradient across the plateau, moving from relatively moist, plant-rich meadows to harsh, water-limited desert steppes where vegetation is sparse and soils are thin. By sampling across this gradient, the team could ask a deceptively simple question: does the link between how efficiently microbes use carbon and how much organic carbon the soil actually holds hold true everywhere, or does it depend on where you look?

The answer, published online on 3 October 2026, is that it depends dramatically. Soil organic carbon declined by a striking 84.1 percent from alpine meadow to alpine desert steppe, reflecting the shrinking inputs of plant litter that fuel carbon accumulation in wetter systems. Yet microbial carbon use efficiency moved in the opposite direction, nearly doubling along the same gradient. In other words, the driest, most carbon-poor soils hosted microbial communities that were, in a narrow physiological sense, the most efficient. The microbes were doing more with less, even as the carbon bank beneath them emptied out.

This inverse pattern is not merely a curiosity of plateau ecology. It strikes at the heart of the Microbial Efficiency-Matrix Stabilization framework, an influential idea in soil science holding that carbon stabilized in soil depends on both the efficiency with which microbes process plant inputs and the capacity of soil minerals to protect the resulting microbial residues. Recent global syntheses, including a prominent 2023 analysis in Nature, have argued that microbial carbon use efficiency promotes soil carbon storage worldwide, and modeling efforts increasingly build this parameter into land-surface models used to project climate futures. If the efficiency-to-storage link is not universal, those projections may carry hidden errors in dry regions.

To probe where the relationship held and where it failed, the researchers quantified soil organic carbon and estimated microbial carbon use efficiency using an ecoenzymatic stoichiometric model, an approach that infers microbial carbon allocation from the activities of carbon-, nitrogen-, and phosphorus-acquiring extracellular enzymes. This method, while widely used, estimates the stoichiometric balance of microbial growth relative to respiration rather than tracking carbon atoms directly, and the authors situate their results within an ongoing methodological debate about how best to measure efficiency in soils. What the model revealed was a gradient of coupling: the relationship between efficiency and soil organic carbon was significantly negative in alpine meadows and alpine steppes, but vanished entirely, or decoupled, in the alpine desert steppe.

The negative sign of the relationship in the wetter systems is itself instructive. In alpine meadows, where plant productivity is high and organic carbon accumulates abundantly, the factors that favor carbon storage, such as plentiful plant biomass and favorable climate, tend to coincide with lower microbial efficiency, perhaps because abundant, labile substrates allow microbes to afford the luxury of rapid respiration. In alpine steppes, the team identified belowground biomass, the mass of roots threading through the soil, as a particularly important shared control: greater root biomass promoted soil organic carbon while simultaneously reducing microbial carbon use efficiency. A single ecological variable was pushing the two quantities in opposite directions, tightening the negative coupling.

In the alpine desert steppe, however, even this inverse relationship dissolved. The controls on efficiency and the controls on carbon storage diverged almost completely. Dissolved organic nitrogen, the small pool of readily available nitrogen compounds in soil solution, emerged as an important influence on microbial carbon use efficiency, while soil pH and the composition of the fungal community ranked among the major controls of soil organic carbon. When the sets of factors governing two variables no longer overlap, the variables themselves can drift apart, and that is precisely what the data show. Efficiency in the desert steppe reflects microbial nutrient physiology; carbon storage reflects geochemistry and fungal ecology, and the two speak different languages.

The broader implication, as the authors frame it, is that microbial carbon use efficiency is not a consistent predictor of soil organic carbon storage, because the overlap between their controlling factors varies across grassland types. Aridity appears to progressively separate microbial carbon allocation from ecosystem carbon storage. As water becomes scarce, plant productivity collapses and carbon inputs dwindle, while microbial communities reorganize around nutrient scarcity and stress tolerance, potentially investing more of the limited carbon they capture into biomass to survive. The result is a microbial physiology that looks increasingly efficient on paper while the soil around it grows steadily poorer in organic carbon.

For climate modelers, the finding lands at an uncomfortable moment. Soil holds more carbon than the atmosphere and vegetation combined, and small shifts in its stability translate into large feedbacks on atmospheric carbon dioxide. Earth system models are only beginning to represent microbial physiology explicitly, and the parameters they use, including carbon use efficiency, are often calibrated from datasets that blend ecosystems of very different character. The new results suggest that a single global value, or even a smooth climatic function, may misrepresent drylands, where the coupling between microbial traits and carbon storage appears weakest. The authors point to soil-plant-microbial interactions as the essential unit of analysis for predicting terrestrial carbon dynamics under global environmental change, rather than microbial physiology alone.

The study also carries a warning for the Qinghai-Tibetan Plateau itself, a region warming faster than the global average and experiencing shifting precipitation patterns. If aridity intensifies across the plateau, alpine meadows may trend toward the steppe and desert-steppe conditions documented here, weakening the very linkages that currently tie microbial activity to carbon accumulation. The raw sequence data underpinning the work, covering bacterial 16S rRNA genes and fungal internal transcribed spacer regions, have been deposited in public archives, allowing other researchers to test whether the decoupling the team observed is a plateau phenomenon or a preview of what aridifying soils worldwide will do. Either way, the message is clear: the microbes that govern Earth’s soils cannot be counted on to follow the carbon rules we wrote for them.

Subject of Research: The relationship between microbial carbon use efficiency and soil organic carbon across alpine grassland types on the Qinghai-Tibetan Plateau

Article Title: Decoupling of microbial carbon use efficiency and soil organic carbon across alpine grassland types

Article References: Fang, X., Lei, S., Zhang, L., Song, Z., & Zhang, C. (2026). Decoupling of microbial carbon use efficiency and soil organic carbon across alpine grassland types. Plant and Soil. https://doi.org/10.1007/s11104-026-09156-5

Image Credits: AI Generated

DOI: 10.1007/s11104-026-09156-5

Keywords: microbial carbon use efficiency, soil organic carbon, alpine grasslands, Qinghai-Tibetan Plateau, aridity, soil microbiology, carbon cycle, ecoenzymatic stoichiometry, fungal community composition, belowground biomass, soil pH, climate feedbacks