Terrestrial ecosystems are experiencing rapid and unprecedented changes driven by global warming, precipitation changes, increasing atmospheric CO₂, nitrogen enrichment, and land-use change. Understanding and predicting ecosystem responses to these interacting factors requires integrative approaches that combine empirical observations with advanced analytical and modeling frameworks. Over the past several decades, Luo Ecolab and its collaborators have pioneered a suite of innovative research methodologies, including long-term manipulative field experiments, global-scale meta-analyses, data assimilation, matrix-based modeling, and artificial intelligence (AI)-enabled data–model integration. In this review, we synthesize key advances across six thematic areas and demonstrate how these complementary approaches enhance mechanistic understanding and predictive capacity in ecosystem ecology. Specifically, experimental warming studies reveal that ecosystem responses are dynamic and frequently constrained by water and nutrient availability, and subject to acclimation over time. Meta-analyses provide robust quantitative syntheses across ecosystems, identifying consistent yet context-dependent effects of global change on ecosystem productivity, soil carbon cycling, and greenhouse gas emissions. Data assimilation bridges observations and process-based models, reducing uncertainty and improving predictions at site, regional, and global scales. The matrix modeling framework offers a unifying mathematical structure for carbon cycle models, enabling efficient computation, traceability analysis, and systematic diagnosis of model uncertainty. Emerging AI approaches, particularly knowledge-guided machine learning, further advance the integration of big data with ecological theory, unlocking new pathways for scientific discovery. Collectively, these advances demonstrate that ecosystem responses to global change are governed by complex interactions among climate, nutrient availability, and microbial processes. By integrating empirical data, theoretical frameworks, and computational innovations, this body of work provides a robust foundation for next-generation ecosystem modeling and ecological forecasting in an era of accelerating environmental change.

Although soil organic carbon (SOC) is a continuum of progressively decomposed compounds with diverse molecular structures, classifying SOC into particulate organic carbon (POC) and mineral-associated organic carbon (MAOC) has been suggested to improve our understanding of SOC vulnerability to environmental changes. Incubation experiments have been extensively employed as a powerful approach to the investigation of SOC decomposition, as this method can isolate specific effects from covariations in the field. Here, we proposed a method to separate the decomposition rates and temperature sensitivities (Q₁₀) of POC and MAOC from bulk soil incubation data using the Bayesian Markov Chain Monte Carlo technique. The data used to validate our method was collected from a ~2600 m altitudinal transect in the Eastern Himalayas. We found that reactive iron plus aluminum oxides had a significant negative effect on the decomposition rate of MAOC but had no effect on the decomposition rate of POC. The negative effect of reactive iron plus aluminum oxides on Q₁₀ for POC was stronger than that on Q₁₀ for MAOC. In addition, the relative values of Q₁₀ for POC and MAOC depended upon elevation, challenging the assumed higher Q₁₀ for POC than that for MAOC from the carbon quality temperature hypothesis. Overall, the proposed approach will improve our mechanistical understanding of soil MAOC and POC dynamics in response to environmental changes.

Temperature sensitivity of soil microbial respiration (or soil organic carbon decomposition) is crucial in determining the dynamics of soil organic carbon or carbon cycle-climate feedback under global warming. Though increasing plant species diversity (PSD) has been found to stimulate soil organic carbon accumulation during ecological restoration or afforestation, whether this carbon sink can persist under global warming largely depends on the responses of temperature sensitivity of microbial respiration to PSD. Nevertheless, the latter remains unknown so far. Here, soil microbial respiration and its temperature sensitivity were investigated based on an incubation experiment with soils collected in 43 plots covering a natural gradient of PSD as indexed by Shannon-Weiner index ranging from 0.15 to 3.53 in a subtropical forest with calcareous soil. Increasing PSD stimulated soil microbial respiration by increasing soil water content and microbial biomass but decreasing fungal/bacterial biomass ratio. Nevertheless, PSD had no significant effect on the temperature sensitivity of microbial respiration due to the contrasting effects of soil biotic and abiotic variables. Increasing PSD stimulated temperature sensitivity by enhancing soil pH, or suppressed temperature sensitivity via decreasing fungal/bacterial biomass ratio. These findings suggest that increasing PSD would stimulate microbial respiration along with soil organic carbon accumulation, but not alter the carbon cycle-climate feedback when implementing ecological restoration or afforestation. Additionally, this study, for the first time, provides the mechanism underlying the response of temperature sensitivity of microbial respiration to PSD, which should be considered when assessing the impacts of PSD on soil organic carbon dynamics under climate change.

Agricultural soils have great potential to sequester carbon, mitigating climate change while enhancing soil health. Subsoil layers are particularly promising for long-term carbon storage due to their lower carbon density and slower carbon turnover compared to topsoil. The reduced subsoil carbon density primarily results from limited carbon inputs at depth, while slower turnover is driven by 1) stronger physiochemical constraints on microbial decomposition, and 2) limited availability of high-quality, energy-rich substrates. These factors underscore the opportunities to target management practices that either increase carbon inputs to subsoil layers or reduce carbon turnover rates to enhance subsoil carbon sequestration. Advancing this field requires understanding the vertical distribution of carbon input quality and quantity, as well as the processes driven vertical carbon transport within soil profiles. Additionally, it is critical to elucidate how substrate properties (e.g., energy and nutrient content) and vertical environmental constraints (e.g., hydrothermal regimes and oxygen availability) influence microbial efficiency. Addressing these knowledge gaps will enable the design of effective management practices, unlocking the full potential of whole-profile carbon sequestration in agricultural systems.

Soil organic carbon (SOC) plays a vital role in mitigating climate change. While fertilization can substantially influence SOC, its impact on SOC storage in arbuscular mycorrhizal (AM)-dominated forests remains uncertain. To address this knowledge gap, we conducted a meta-analysis of 631 observations from 28 published studies to examine SOC responses to fertilization in bamboo forests dominated by AM fungi. Contrary to numerous previous meta-analyses, our results revealed that fertilization significantly decreased SOC by 4.46%. Specifically, inorganic nitrogen (N) fertilizers negatively affected SOC by disrupting the soil N:P:K stoichiometric balance, which can contribute to soil degradation and potentially impair the role of AM fungi in regulating soil carbon dynamics. In contrast, organic and compound N fertilizers showed no significant effect on SOC due to external nutrient inputs and additional C offsetting these negative impacts. The effects of fertilization on SOC varied depending on the level and duration of fertilization, as well as soil depth. Low-level and long-term fertilization resulted in significant SOC losses, particularly in the subsoil. Furthermore, our correlation analysis indicated that MAP, soil pH, MBC, NH₄⁺-N, and AK were key drivers of SOC responses to fertilization. Our findings offer a new perspective that contrasts with previous studies, showing that N fertilization significantly reduces SOC in bamboo forests. This underscores the need for future investigation into the mechanisms by which AM fungi regulate SOC dynamics. Consequently, we recommend using organic or compound N fertilizers to maintain SOC storage and contribute to climate change mitigation efforts.

Due to the air pollutant emissions, acid rain in southern China may strongly influence soil organic carbon (SOC) decomposition and stabilization by disrupting microbial communities and associated enzymatic processes. In this study, we conducted a field experiment in a subtropical forest in southern China, by implementing simulated acid rain (SAR) treatments with varying pH levels (4.5 as a control, 4.0, 3.5, and 3.0) to investigate the effects of acid rain on soil microbial community composition, carbon (C)-degrading enzyme activities, an both labile and stable SOC fractions. Results showed that SAR treatments significantly altered the soil microbial community and reduced several key C-degrading enzyme activities (e.g., β-glucosidase by −22% − −55%, phenol oxidase by −32% − −71%, and peroxidase by −4% − −71%). Accordingly, SAR treatments increased liable SOC content by 3%−150% and stable SOC content by 1%−52%, leading to increases in total SOC content by 5%−29%. These findings demonstrate that acid rain can suppress soil microbial productions of C-degrading enzymes, thereby promoting the accumulation of both labile and stable SOC fractions. The differential responses of labile and stable SOC fractions to prolonged acid rain exposure may have important implications for the long-term sequestration and stability of SOC in subtropical forests soils in southern China.

Understanding how the terrestrial carbon cycle responds to temperature rise is of great importance in studies on global climate change and carbon budget. This study collected eddy covariance (EC) data from 27 FLUXNET sites with over 10 years of continuous observations in mid- and high- latitude ecosystems to assess the temperature sensitivities of gross primary production (GPP), ecosystem respiration (ER), and net ecosystem production (NEP) simulated by eight terrestrial carbon cycle models. Results showed that the temperature sensitivities of GPP, ER, and NEP were highest in spring and wet regions. The eight models could well capture seasonal patterns of the temperature sensitivities, but failed to reproduce their spatial variations, which are critical for representing regional differences in carbon–climate feedbacks. This limitation can lead to biased estimates of ecosystem carbon dynamics under future warming scenarios. Overall, model-simulated temperature sensitivities of GPP and NEP were significantly lower than those derived from EC measurements, which resulted in overestimation of carbon losses under climate warming. It indicates that current terrestrial ecosystem models overestimate the negative effects of warming on ecosystem carbon sink. These findings highlight the biases in the simulation of temperature sensitivities and an urgent need to improve the temperature response modeling in order to obtain accurate predictions of carbon cycle dynamics.

Rapid global warming significantly affects terrestrial carbon (C) cycling, altering climate feedbacks and amplifying uncertainty in ecosystem C balance projection under future climates. Although the direct impacts of warming on ecosystem C fluxes through changes in temperature and moisture are well studied, the indirect effects mediated by warming-induced shifts in plant communities remain poorly understood. A key challenge lies in quantifying these community-level changes under warming and linking them to ecosystem C cycling. Traditional frameworks based on broad functional groups or life forms often fail to capture species-specific responses to warming. In contrast, plant traits provide a mechanistic understanding of how plant species and communities respond to warming and regulate ecosystem C fluxes. This review highlights the potential of a trait-based framework to bridge this knowledge gap, unifying plant community responses to warming with their impacts on ecosystem C fluxes. By identifying key community-level traits and/or trait combinations, such a framework can improve predictions of ecosystem C cycling and improve model performance under future climate scenarios.

Anthropogenic perturbations have profoundly changed global nitrogen (N) cycling, jeopardizing ecosystem sustainability and human well-being. Accurately understanding soil available N dynamics is critical for enhancing N use efficiency and mitigating nitrous oxide (N₂O) emissions. Although mechanistic insights into soil inorganic N transformations and N₂O production have advanced significantly at microscales, their dynamics at macroscales remain elusive, hindering predictive accuracy in Earth system models. Here, we propose a hierarchical framework that integrates environmental factors and microbial traits to scale up soil N processes, bridging the micro-macro research gap. This framework by embedding microbial traits into empirical models can improve the accuracy of N projections. Crucially, coupling relevant N processes (e.g., mineralization, nitrification, and denitrification) with the hierarchical framework is essential to better project N₂O emissions and inorganic N dynamics at macroscales. Achieving this potential requires not only big data but also substantial computational power. Emerging approaches, such as Bayesian approaches, deep learning architectures, convergent cross-mapping techniques, and digital twin simulations, offer new opportunities to integrate heterogeneous data sets and refine model parameterization for macroscale predictions. Our framework advances the theoretical foundation for scaling soil N processes, with direct applications in improving the precision of projections for global N₂O emissions and soil inorganic N dynamics.

Climate change and nitrogen (N) application significantly influence agricultural productivity and soil greenhouse gas emissions. However, the interactive effects of interannual climate variability and N fertilization legacy on corn yield and soil nitrous oxide (N₂O) emissions remain inadequately understood. In this study, we employed the DeNitrification-DeComposition (DNDC) model to simulate corn yield and soil N₂O emissions over a 40-year period (1981–2020). We designed a series of experiments by adjusting climate year data to quantify interannual variability in corn yield and soil N₂O emissions, while also disentangling the contributions of climate variability and N legacy effects. The results revealed substantial interannual variability in both corn yield and soil N₂O emissions. Corn yield was primarily driven by changes in growing season precipitation, while soil N₂O emissions were influenced by precipitation, exchangeable ammonium N (NH₄⁺), and nitrification-denitrification processes. Severe drought strongly reduced corn yield, while soil N₂O emissions exhibited a gradual yet pronounced legacy effect of N application, increasing from 1.69 to 7.85 kg N·ha⁻¹ over the 40-year period. This study highlights the relatively weak influence of interannual climate variability compared to the stronger legacy effects of N application on crop yield and soil N₂O emissions, providing valuable insights for sustainable agricultural and environmental management.

Arbuscular mycorrhizal fungi (AMF) play a crucial role in ecosystem carbon storage and climate change mitigation. However, the relationship between AMF and soil organic carbon (SOC) dynamics under nitrogen (N) deposition remains poorly understood. In this global meta-analysis, we synthesized data from 438 observations across 45 studies. Results demonstrated a general decrease in AMF abundance under N addition treatments: both AMF biomass and root colonization rate declined by 11%. Specifically, AMF biomass decreased significantly in temperate (−19%), tropical/subtropical (−10%), and alpine (−8%) ecosystems. Similarly, root colonization rate declined in temperate (−8%), alpine (−3%), and tropical/subtropical (−2%) zones. Interestingly, AMF diversity remained unchanged. Additionally, N addition significantly increased SOC storage (by 3%) and soil available N (by 12%), while it decreased soil available phosphorous (by 5%) and soil pH (by 2%). The responses of AM fungal traits and soil properties varied depending on fertilizer types, ecosystem types, and climate conditions. Meta-regression analysis identified local atmospheric N deposition rates as the most significant factor influencing AM fungal traits (based on multi-model inference). Moreover, we found that AMF abundance, including AMF biomass and root colonization rate, had no significant relationship with SOC variations (p > 0.1, R² < 0.1). This lack of a direct relationship, coupled with the concurrent decline in AMF abundance and increase in SOC, indicates that N deposition may disrupt the typical linkage between AMF and SOC dynamics, potentially leading to their decoupling.

Soil microbial carbon use efficiency (CUE) plays a critical role in carbon (C) cycling and ecosystem functioning, yet its response to nitrogen (N) deposition remains poorly understood, particularly in planted forests. This study investigates how N addition affects microbial CUE and its underlying mechanisms in Populus deltoides plantations in coastal eastern China. Using a long-term field experiment with five levels of N addition (0–30 g N·m⁻²·yr⁻¹), we measured microbial CUE, soil chemical properties, enzyme activities, and microbial community composition from 2018 to 2020. We found that N addition significantly reduced microbial CUE, primarily through N-induced stoichiometric imbalances and soil acidification. Excess N increased available N and decreased the DOC:AN ratio, driving microbial carbon limitation and reducing metabolic efficiency. Furthermore, N addition suppressed bacterial diversity and shifted microbial communities toward taxa with lower CUE. Model selection identified soil pH, available N, and DOC:AN as key predictors of microbial CUE. These findings highlight the dominant role of soil environmental factors—particularly nutrient stoichiometry and pH—in regulating microbial CUE. Our results suggest that excessive N deposition may compromise soil C sequestration in poplar plantations by altering microbial resource allocation and reducing microbial metabolic efficiency. Managing nutrient balance and maintaining microbial diversity are thus critical for sustaining soil health and carbon storage in forest ecosystems under increasing N deposition.

Soil organic carbon (SOC) is a critical component of global carbon cycling and a key regulator of soil CO₂ emission. However, the effects of agricultural activities, particularly tillage, on SOC sequestration are not fully understood. Here, we conducted a comprehensive mega-analysis of 24 individual meta-analyses to assess how conservation tillage practices, including no-till (NT), reduced tillage (RT), and mixed NT + RT, affect SOC sequestration. Overall, all conservation tillage types significantly increased SOC stocks, with RT showing the highest increase by 13.42% (effective size = 0.126), followed by NT 10.76% (0.102) and NT + RT 7.42% (0.071). Climate emerged as the dominant driver under NT, with the largest SOC increases in tropical and humid regions. Other influential factors included experimental duration, crop type, residue management, soil texture, pH, nitrogen fertilizer rate, and irrigation, all of which consistently enhanced SOC gains. SOC responses were strongest in surface layers (0–10 cm), in neutral and alkaline soils, and in coarse- to medium-textured soils. NT was especially effective in maize systems (15.57%, 0.145), the 0–10 cm soil layer (22.32%, 0.201), in neutral soils (12.87%, 0.121), and in alkaline soils (12.15%, 0.114). RT showed pronounced benefits in tropical climates, coarse and medium textured soils, and under nitrogen application and irrigation, with SOC increases up to 15.56% (0.145) in tropical regions, 18.90% (0.173) at a soil depth of 0–10 cm layer, 9.16% (0.087) in alkaline soils, 24.02% (0.215) in acidic soils, and 25.23% (0.225) in irrigated fields. Collectively, our findings demonstrate that conservation tillage substantially enhances SOC sequestration and that adopting context-specific conservation tillage practices can improve soil health while contributing to climate change mitigation.

The urgency of addressing climate change has underscored the need for precise carbon management—an approach that precisely monitors, assesses, and manages carbon emissions and sequestration at fine spatial, temporal, and sectoral scales. This perspective paper examines the current state of precise carbon management, highlighting advancements in ground-based observations, remote sensing, process-based model, and machine learning. Despite these innovations, key challenges persist, including data fragmentation and interoperability, limited geographical and temporal monitoring coverage, difficulties in integrating multi-source data sets with varying resolutions, and insufficient public engagement and decision-support infrastructure. To address these barriers, we propose a roadmap that includes the development of standardized data frameworks, expansion of monitoring networks in underrepresented regions, creation of a foundational AI model for carbon data integration, and user-centric decision-support tools to bridge science-policy gaps. Together, these proposed strategies aim to enhance the accuracy, scalability, and transparency of carbon management strategies in support of global climate goals.