Terrestrial evapotranspiration (ET) is a key component of the water cycle and energy balance. While the Earth system models (ESMs) in the Coupled Model Intercomparison Project Phase 6 (CMIP6) project that global ET will increase through the end of this century, the magnitude of this change remains highly uncertain because future trends cannot be directly validated against historical observations. This partly explains why the Intergovernmental Panel on Climate Change Sixth Assessment Report assigns only “medium confidence” to projected hydrological changes over the same period. Here, we provide more reliable future (2015–2099) global ET projections by leveraging water-balance observations in world’s large river basins. We show that future global ET trends are significantly correlated with historical basin-wide ET trends across certain ESMs skillful in capturing long-term trends. Applying an Emergent Constraint approach, we find that the raw CMIP6 ESM ensemble overestimates future global ET trend by 30% under both low- and medium-emission scenarios, and by 43% under high-emission scenario. Even for a subset of 18 well-performing ESMs, the unconstrained ensemble overpredicts the future global ET trend by 15–22% relative to the constrained estimates under three emission scenarios. The observational constraint also reduces the inter-model variance in future global ET projections by approximately one-third. Our conclusions are robust to the precipitation dataset in the water-balance calculation, projection length, or the ESM selection, although the absolute constrained trend values vary slightly with these choices. These findings enhance confidence in future global ET changes and underscore the crucial role of historical trends measured at the basin scale in refining global hydrological projections.

ABSTRACT Urbanization strongly alters soil environments and may reshape soil bacterial communities (SBC), yet their responses across long-term urbanization stages remain unclear. Using Shenzhen, China, a subtropical city that urbanized within 40 years, we investigated SBC diversity, composition, and co-occurrence network stability across different land-use types and urbanization stages. Soil bacterial α-diversity showed no significant differences between natural forests and urban ecosystems. However, bacterial composition shifted markedly during urbanization, with dominant taxa changing from Acidobacteriota and Verrucomicrobiota in forests to Actinobacteriota , Chloroflexi , and Firmicutes in urban soils. SBC composition remained strongly associated with soil physicochemical properties rather than converging across urbanization stages, indicating dominant environmental filtering effects. Network analyses further revealed non-linear changes in resistance and resilience stability along the urbanization chronosequence, associated with shifts in network topology and soil properties. Together, our results show that urbanization does not drive a uniform trajectory of SBC assembly; instead, soil environmental conditions under different land-use types primarily shape bacterial composition and potential stability type. IMPORTANCE Urbanization is rapidly transforming soil ecosystems worldwide, but its impacts on soil bacterial communities remain poorly understood. We show that urbanization in a subtropical city caused major shifts in dominant bacterial taxa. Urban soils were characterized by a transition from oligotrophic to copiotrophic taxa, accompanied by contrasting changes in network-inferred resistance and resilience stability. These patterns were primarily driven by changes in soil properties associated with land-use change, highlighting the importance of environmental filtering in shaping urban soil microbiomes. Our findings improve understanding of how urbanization restructures underground ecosystems and their potential stability under long-term environmental change.

Rising atmospheric CO2 is widely expected to enhance plant biomass, yet the mechanisms that partition this effect between physiological regulation and structural adjustment remain poorly understood. Here we use a process-based ecohydrological model to disentangle these pathways across the Yellow River Basin (YRB). We show that biomass increases nonlinearly with rising CO2 in YRB, approximating a logarithmic response, but is dominated by stomatal conductance-mediated regulation rather than structural change. When averaging across the whole YRB, CO2-driven biomass gains rise from 3.2 g C m(-2) at 400 ppm to 62.8 g C m(-2) at 1100 ppm, with stomatal regulation accounting for >91% of the increase and structural contributions inferred from leaf area index remaining small (<9%) across all scenarios. Spatially, the strongest biomass responses to CO2 emerge in the southeastern basin and the Hetao region, whereas muted responses occur over the Mu Us Sand Land and along the northeastern margin of the Tibetan Plateau; this pattern is largely explained by the stomatal conductance pathway, with irregular spatial heterogeneity in the structural effect. Responses also differ among ecosystem types, peaking in croplands, followed by forests, grasslands and deserts, and stomatal effects exceed structural contributions in all ecosystems. These results indicate that CO2-enhanced biomass and carbon uptake in the Yellow River Basin are governed chiefly by stomatal regulation, highlighting the need for region-specific carbon-climate assessments that explicitly represent physiological processes alongside ecosystem heterogeneity.

The Penman–Monteith–Leuning (PML) model is a widely recognized diagnostic framework for estimating coupled terrestrial evapotranspiration (ET) and gross primary production (GPP). To address the critical need for high-fidelity, long-term, and near-present eco-hydrological records, we developed the PML-V2.2 dataset, spanning from 1982 to 2025. Driven by observation-constrained Multi-Source Weighted-Ensemble Precipitation (MSWEP) and Multi-Source Weather (MSWX) meteorological variables, the dataset comprises three complementary products: (1) PML-V2.2a, an 8 d 500 m MODIS/VIIRS satellite-based product (2000–2024 and 2012–2025) optimized for near-present monitoring (updated annually); (2) PML-V2.2b, a half-month 0.1° AVHRR-based product (1982–2020) anchoring long-term climate attribution; and (3) PML-V2.2c, a consolidated half-month 0.1° record integrating the above products for seamless 44-year continuity (1982–2025). Our methodological framework features an expanded bottom-up calibration using 208 flux sites (∼ 1400 site-years) across various plant functional types (PFTs) and a refined parameterization that explicitly distinguishes between irrigated and rainfed croplands. This distinction effectively mitigated systematic biases in agricultural regions, reducing ET and GPP estimation errors by 8.7 % and 16.2 %, respectively. Performance evaluation reveals high accuracy across PFTs (cross-validation Nash-Sutcliffe Efficiency, NSE > 0.60, absolute bias < 5 %), while top-down water-balance validation across 56 large river basins during 1982–2016 and 152 basins during 2003–2020 confirms high reliability (NSE: 0.89–0.91) as compared with other products. The MODIS- and VIIRS-based PML-V2.2a datasets are internally consistent, and exhibit high agreement with PML-V2.2b during their overlapping period (NSE = 0.90 and 0.79 for annual ET and GPP anomalies), ensuring a seamless transition across satellite epochs. Based on the consolidated PML-V2.2c dataset, global terrestrial ET and GPP during 1982–2025 are estimated at 65.8 × 103 km3 yr−1 (with 58.2 % from transpiration) and 143.4 PgC yr−1, respectively. Long-term analysis reveals significant (p < 0.05) increasing trends in GPP (0.343 PgC yr−2) and ET (0.019 × 103 km3 yr−2) during 1982–2025, where vegetation greening impact on ET is partially offset by physiological water saving under rising atmospheric CO2, consequently enhancing water use efficiency. By bridging the gap between satellite epochs, PML-V2.2 provides an internally consistent long-term global dataset for hydrology, ecology, and other Earth science studies. The dataset is freely accessible, with the 500 m resolution PML-V2.2a product hosted on Google Earth Engine, and all 0.1° PML-V2.2a/b/c versions archived at the National Tibetan Plateau Data Center under https://doi.org/10.11888/Terre.tpdc.303314 (Xu et al., 2026).

Vegetation change (VC) plays a crucial role in driving fluctuations in ecosystem biomass. However, VC encompasses either natural growth (NG) or artificial restoration (AR), and the specific effects of these two processes on biomass remain inadequately understood. The present study employed an ecohydrological model to evaluate the impacts of NG and AR on biomass in the Chinese Yellow River Basin (YRB) from 1998 to 2020. The results indicate that, over the past 23 years, NG led to 10.3 g C m-2 (8.4 %) increase in biomass, while AR resulted in somewhat less increase in biomass with just 6.6 g C m-2 (5.0 %). Overall, VC contributed to a 13.7 % increase in biomass. In most (84.8 %) regions within the YRB, VC positively contributed to biomass enhancement. The biomass increases attributed to NG mainly occur in the northern and northwestern YRB, encompassing 46.3 % of the total basin area. In contrast, regions where AR dominated biomass accumulation are spatially opposite to those primarily influenced by NG, with mostly being located in the southeastern and western YRB. The biomass responses to VC, NG, and AR exhibited pronounced differences across different ecosystem types. Farmlands experienced the most substantial biomass enhancement, followed by forest and grassland ecosystems, while desert ecosystem was the least affected. These findings suggested that, for the whole YRB, NG is more effective than AR in carbon sequestration. This is especially true for the northern and northwestern YRB where climate is more arid. However, in the southeastern YRB and the margianl area of Tibetan Plateau, AR is fine for the ecosystem health and biomass restoration in the region.

Abstract Lake evaporation, as a key hydrological process on the Tibetan Plateau (TP), is highly uncertain in estimation and lacks a systematic evaluation. Taking Siling Co, the largest lake in Tibet, as an example, this study comprehensively evaluated 30 lake evaporation models from five groups (Combination, Solar radiation–temperature, Dalton, Temperature–daylength, Temperature) based on eddy covariance observations to determine their applicability and rank them. Results show that the Dalton group models outperformed others, with the mass transfer (4) model achieving the lowest root-mean-square deviation (RMSD) (0.16 mm day −1 ) and highest Nash–Sutcliffe efficiency (NSE) (0.71). The Combination group models, despite their theoretical robustness, significantly overestimated evaporation (e.g., Penman–Brutsaert model overestimated by 76.7%) when the lake heat storage term G was calculated from net radiation. However, when G was replaced with observed values, Combination models improved drastically (RMSD < 1.0 mm day −1 , NSE > 0.86). We propose a data-driven selection framework 1) with water temperature profiles ( G available), using Combination models; 2) with only wind/humidity, using Dalton models; and 3) Temperature/Solar radiation–temperature models should be used with caution in deep TP lakes. Although these findings are derived solely from observations during the 2014 open-water season at Siling Co, they still provide valuable insights for evaporation estimation in large, deep lakes across the TP and other sparsely instrumented high-elevation regions.

Climatic aridity interplays with ecohydrological aridity, generally showing synchronous increasing trends under global warming. However, these changes can be decoupled and even exhibit contrasting temporal variations. The spatial patterns and underlying mechanisms of such contrasting aridity changes remain poorly understood. Utilizing satellite and climate data, we demonstrate that nearly one-fifth (22.3%) of global vegetated drylands showed contrasting trends in climatic and ecohydrological aridity over the past four decades. Approximately 60% of these areas experienced an increase in climatic aridity but a decrease in ecohydrological aridity, while others exhibited opposite temporal trends. These divergences stem primarily from elevated atmospheric CO2 levels, which exert opposite effects on ecohydrological aridity via vegetation structure and canopy stomatal conductance. These findings highlight the nonlinear nature of vegetation-climate interactions in drylands and provide new insight into water and carbon cycling in global drylands under climate change.

The air urban heat island (UHI) poses significant challenges to urban environments. While vegetation is a recognized mitigation strategy, the in-situ cooling effects of different vegetation types, such as forests and grasslands, across diverse climatic regions remain poorly quantified. This study addresses this gap using a station-pair regression approach to evaluate vegetation's cooling efficacy in three major Chinese cities: Beijing, Shanghai, and the Mega-city Cluster in the Pearl River Delta (MCPRD). We developed multiple linear regression models using summer data from 2016 to 2020. These models predict the air temperature difference between paired meteorological sites based on spatial differences in vegetation conditions, derived from high-resolution Sentinel-2 imagery, distance to large water body, wind speed, and elevation. The model performed well, achieving coefficient of determination of 0.71, 0.58 and 0.82 for Beijing, Shanghai and MCPRD, respectively. Crucially, our results show that compared to completely urbanized land, existing vegetation significantly lowers air temperature by 2.43 °C in Beijing, 0.58 °C in Shanghai, and 2.74 °C in the MCPRD. This research quantifies the variable cooling benefits of urban vegetation across different climate zones, providing an empirical basis for optimizing green infrastructure in urban planning to enhance thermal comfort.

Reliable quantification of global water-cycle components, such as river flow and land evapotranspiration, remains a major challenge. Here we refine estimates of global water partitioning by combining outputs from multiple Earth system models with river flow observations from 50 large basins, applying the emergent constraint approach. Between 1980 and 2014, global river flow was (39.1 ± 5.4) × 103 km3 yr−1, with a river flow-to-precipitation ratio of 0.35 ± 0.03, both lower than previous estimates. Land evapotranspiration reached (73.4 ± 6.2) × 103 km3 yr−1. Under climate change, we project global river flow to rise by 7.8 ± 5.5 mm per year per degree of warming. This estimate, refined through the emergent constraint method, is 9.3

Global warming is widely expected to intensify the hydrological cycle. However, observational evidence for accelerated evapotranspiration (ET), a key terrestrial water cycle and energy balance component, remains ambiguous. Here, we constrained diverse datasets using basin-scale ET estimates covering 47% of global land to reveal a hidden asymmetry. We found a small global change in ET [-0.06 ± 0.44 mm year-2 (millimeters per year squared), P > 0.10] from 2000 to 2022, resulting from strongly opposing hemispheric trends. The Northern Hemisphere shows noticeable ET increases (0.91 ± 0.46 mm year-2, P < 0.05), primarily driven by vegetation greening, whereas the Southern Hemisphere exhibits strong declines (-2.63 ± 0.46 mm year-2, P < 0.05) due to precipitation deficits. Our observation-constrained projections indicate that this asymmetry is likely to persist through 2050. The small global ET trend masks a marked compensation between northern greening and southern drying, suggesting that current models may overestimate homogenized water cycle intensification. Our study provides profound implications for global food security, carbon sequestration, drought risks, and regional climate adaptation.

The streamflow change within the Loess Plateau is of great importance, given its complex driving mechanisms and diverse human activities. While it is widely recognized that human activities play a crucial role in impacting streamflow in this region, the specific contributions of each sort of human activity remain poorly understood. This is especially true for the coal mining since this process is particularly difficult to quantify, and thus, it remains unknown whether it is more important than another widely studied process-vegetation restoration-in streamflow changes. Here, we further improved our newly-developed model (SIMHYD-PML) by incorporating the groundwater seepage, thereby allowing it to explicitly depict the impacts of coal mining and vegetation restoration on streamflow changes. Using two typical basins in the Loess Plateau as examples, three modeling scenarios-vegetation change and coal mining, vegetation change without coal mining, and a non-impact scenario-were implemented to assess whether coal mining contributes more to streamflow changes than vegetation change. Our results show that increased groundwater seepage due to coal mining and enhanced evapotranspiration due to vegetation restoration are indeed two key factors contributing to the streamflow reduction in both basins. Specifically, from 2000 to 2020, coal mining accounted for 58.6 %-63 % (6.3-12.3 mm yr-1), and vegetation restoration accounted for 37.0 %-41.4 % (4.5-7.2 mm yr-1) of the total streamflow reduction. This suggests that coal mining has a greater impact on streamflow processes than vegetation restoration, making it the primary driver of streamflow changes in both catchments. This study enhances our understanding of the impacts of coal mining on streamflow, offering valuable guidelines for water resource management in regions heavily dependent on coal resources.

Changes in vegetation have pronounced effects on water and carbon cycles. In the past few decades with significant warming, vegetation in the Tibetan Plateau (TP) has become overall greening, particularly in its northeastern part. However, the effects of these changes on land-atmosphere water and carbon exchanges in the TP remain insufficiently understood. Here, we use a water-carbon coupled model, Penman-Monteith-Leuning Version 2, to quantify the direct impacts of vegetation change on evapotranspiration (ET) and gross primary production (GPP) in the Yellow River Source (YRS) region, a greening hotspot in the northeastern TP. We show that ET and GPP in the YRS increased significantly from 1982 to 2018, with trends of 1.72 +/- 0.21 mm yr(-2) and 3.96 +/- 0.55 gC m(-2) yr(-2) (both p < 0.001), respectively. The change in leaf area index (LAI) was the dominant driver of GPP's increase, contributing 79 %, followed by atmospheric CO2 concentration and the climatic factors. However, vegetation greening had a limited impact (11 %) on ET since the increases in plant transpiration (E-c) and canopy evaporation (E-i) were largely offset by the decline in soil evaporation (E-s). Instead, the climatic factors contributed most (72 %) to ET change over the past 37 years. Nevertheless, vegetation changes played a key role in altering ET components, with LAI contributing nearly 40 % to trends in E-s and E-c, and over 70 % to the E-i trend over the past 37 years. Our results highlight the distinct roles that vegetation plays in regulating land-atmosphere water and carbon exchanges at high altitudes.

While there has been significant progress in understanding how species mixing affects leaf litter decomposition, the consequences for belowground root decomposition remains less known. This represents a critical knowledge gap, as roots are key contributors to soil carbon input. Here, we experimentally assess absorptive root decomposition in 138 paired-species combinations from 57 tree species, revealing significant non-additive mixing effects in 70% of all root combinations, with the majority of them decomposing faster than predicted from single species. Notably, non-additive effects occur only in mixtures containing at least one ectomycorrhizal species, with no net mixture effects in combinations of two arbuscular mycorrhizal species. We further find that these root mixing effects are associated with dissimilarities in condensed tannins across all mycorrhizal types and with nitrogen concentration when only ectomycorrhizal species are present. Overall, these root mixing effects are three times stronger than those documented for leaf litter decomposition in past studies. Collectively, our findings suggest that tree species mixing effects on decomposition are particularly robust belowground, especially in forests with ectomycorrhizal species of contrasting root chemistry. Absorptive root decomposition may have an essential role in how tree species mixing affects soil carbon and nutrient dynamics.

New particle formation (NPF) is the major source of atmospheric secondary aerosol. It greatly contributes to particle number concentration and potentially develops into haze events in polluted urban area. Wuhan is a highly-humid and heavily-polluted megacity in central China, its characterization of particle number size distribution (PNSD) and NPF is poorly understood. We conducted a consecutive one-year observations of PNSD in a typical urban site in Wuhan for the first time, accompanied by meteorological factors. Annual average PNC in Wuhan was 8454 +/- 4155 cm(-3), and showed slight seasonal variations. Whereas median particle size was the highest in winter and the lowest in summer. The NPF frequency was low (similar to 11 %), especially during winter (similar to 1 %), which may be due to high CS (annual average of 0.022 s(-1)), high RH (annual average of similar to 73 %), and low gaseous sulfuric acid concentration. Since RH was persistently high in Wuhan, wet CS that considers the hygroscopic growth of particles can predict the occurrence of NPF more accurately than the commonly-used dry CS. The formation rate of 10 nm particles (J(10), annual average of 0.59 cm(-3) s(-1)) and growth rate (GR) of 10-25 nm particles (annual average of 4.6 nm h(-1)) was the highest in summer and the lowest in winter, while GR (> 25nm) showed the opposite seasonal variations, indicating their different precursors or growth mechanisms. Long-distance trajectory from Northwest brings air masses with low wet CS and corresponds to the highest NPF frequency (38 %). This study provides the basis for subsequent research on particle number concentration in highly-humid cities.

Forest soils hold the largest terrestrial carbon pool, derived from dead plant tissues and transformed by soil biota. Current frameworks emphasize the role of soil microbes in highly persistent forms of carbon. However, moderately persistent forms of carbon also contribute substantially to forest soil carbon pools through the iterative effects of plant litter inputs and outputs over multi-decadal timescales. These sources of soil carbon are not well constrained. Here we synthesize published field data of the finest roots (absorptive roots) of mycorrhizal woody plants across major forest ecosystem types in the Northern Hemisphere. We estimate that, owing to fast turnover and slow decomposition, the iterative effects of absorptive roots on soil carbon accrual generate 2.4 ± 0.1 MgC ha−1 over two decades, exceeding that of leaves by 65

Since the operation of the Xiaolangdi Reservoir in 2002, the lower reaches of the Yellow River Basin have no longer experienced dried-up and the previously decreasing trend of streamflow has been reversed. However, the underlying physical mechanisms driving these hydrological changes remain unclear. In this study, we employ an improved hydrological model that integrates reservoir regulation, human water consumption, and inter-basin water diversion processes to quantify the respective contributions of climate change and various human activities to streamflow increases in each subbasin. Validation against monthly streamflow and reservoir storage observations shows that the improved hydrological model effectively simulates hydrological processes influenced by human activities, achieving NSE values ranging from 0.61 to 0.88 and PBIAS values below 17 % at nine mainstream hydrological stations. Multi-scenario modeling experiments suggest that climate-driven precipitation increases is the primary driver causing the increases in streamflow for most subbasins during 2002-2022. However, reservoir regulation exerts the dominant role in increasing streamflow in the Shizuishan-Toudaiguai subbasin. Our findings are crucial for promoting understanding of hydrological process changes in different subbasins of the Yellow River Basin.

Roots profoundly influence soil carbon storage through root production, turnover, and decomposition over time. While root-derived carbon stabilization in aggregates and minerals is known, the role of slowly decomposing root fragments has been largely overlooked. We propose a new paradigm, ‘iterative effects’, integrating multigenerational root production and turnover with multistage root decomposition to address the build-up of moderately stable soil carbon forms. To inspire future studies, we develop several heuristic scenarios that differentiate root iterative effects on carbon cycling within branching root systems, across steady-state and non-steady-state ecosystems, under natural and anthropogenic disturbances, and shaped by intra- and intergenerational interactions among root processes. This theoretical framework provides novel insights into soil carbon cycling and ecosystem responses to global changes.

It is widely acknowledged that vegetation change can exert considerable influences on the hydrological processes. Vegetation change can be either natural or artificial. However, the specific effects of natural growth (NG) and artificial restoration (AR) of vegetation on surface hydrology remain unclear. Using a recently developed ecohydrological model, we demonstrate that NG and AR have contrasting spatial impacts on hydrological components across China’s Yellow River Basin, a hotspot with intensive human-driven revegetation over the recent decades. Our analysis identifies a critical annual precipitation threshold of 300 mm, below which both NG and AR have marginal hydrological impact. In regions where precipitation exceeds this threshold, NG significantly reduces evapotranspiration and increases runoff, while AR has the opposite effects. These results suggest that NG is a more sustainable strategy in areas receiving less than 300 mm of annual precipitation, while AR may be appropriate for regions with precipitation higher than this threshold. Our findings offer valuable guidance for policymakers in designing sustainable revegetation strategies tailored to local environmental conditions.

This study provides a comprehensive analysis of the hydrological dynamics and land use changes in the Hongjiannao Lake Basin from 1990 to 2023, with projections extending to 2060. By integrating advanced hydrological modeling Hydrologiska Byrans Vattenbalansavdelning (HBV), a machine learning algorithm Random Forest (RF), Cellular Automata (CA) Markov, and remote sensing data, this research offers a robust framework for understanding the interactions between climate change, anthropogenic activities, and ecosystem responses. The historical analysis revealed remarkable fluctuations in the lake's area, including a 25.5 % reduction between 2000 and 2011, followed by a recovery from 2012 to 2023. The lake area increased by 26.2 % during the recovery phase, highlighting a partial reversal of decline. Projections indicate that, under various future climate scenarios, the lake area could increase by 29 % by 2060, showcasing the resilience of the ecosystem despite ongoing climate and anthropogenic pressures. The RF model demonstrated strong predictive capabilities, with R2 values of 0.92 during 1990-2013 calibration and 0.76 during 2014-2023 validation, coupled with root mean square errors of 0.12 km2 and 0.26 km2, respectively. Additionally, the CA-Markov model predicted vegetation growth and urbanization, highlighting potential for significant landscape changes. These findings stress the need for water management strategies to preserve the lake's ecological health, advocating for the integration of climate, land use, and hydrological factors in management plans for sustainable conservation and restoration in semi-arid regions.

Home-field advantage (HFA) hypothesis proposes that leaf litter decays more rapidly in its original place than elsewhere owing to specific litter-field affinity. However, the HFA effect may vary over time and receive influences from other external factors, and it remains unclear whether the labile carbon (C) in root exudates influences the HFA effect during later decomposition stage. We aim to 1) elucidate how the HFA effect varies over time, 2) demonstrate how the HFA effect changes when stimulated by labile C at the later decomposition stage, and 3) explore how fungi affect the HFA effect. We conducted a reciprocal litter transplant experiment using two tree species, (Pinus elliottii and Cunninghamia lanceolata) with a two-phase design (early vs. late decomposition, plus glucose addition). We harvested the samples of soil and litter after decomposition for 1, 2, 4 and 6 months. Glucose (labile C) was added to soil after decomposition of 4 months. The HFA effect decreased over time, and the fungal community dissimilarity between home and away soils, especially Eurotiomycetes, affected variations in HFA. Additionally, glucose additions led to a significant increase of 15.19% in the HFA effect (p<0.05) during later decomposition stage, which was primarily associated with Sordariomycetes. Our findings implies that the HFA in litter decomposition was mainly associated with specific fungal taxa. Importantly, the introduction of labile C strengthened the HFA effect at later decomposition stage. Therefore, it cannot be overlooked that the priming effect of labile C input on the HFA effect at later decomposition stage in future research. Our two-phase design study further highlights the differences in litter decomposition between home and away soils at different decomposing stages and the regulation of HFA by specific fungal taxa and labile carbon inputs, especially in the later decomposition stage.