Climate warming can affect freeze–thaw cycle (FTCs) patterns in northern high-latitude regions and may affect permafrost carbon emissions. The response of carbon release and microbial communities to FTCs has not been well characterized. Here, we conducted laboratory incubation experiments to investigate the relationships among carbon emissions, bacterial community, and soil variables in a permafrost-affected forest–wetland ecotone in Northeast China. The emission rates of CO₂ and CH₄ increased during the FTCs. FTC amplitude, FTC frequency, and patch type had significant effects on carbon emissions. FTCs increased the contents of soil DOC, NH₄⁺-N, and NO₃⁻-N but reduced bacterial alpha diversity. CO₂ emissions were mainly affected by bacterial alpha diversity and composition, and the inorganic nitrogen content was the important factor affecting CH₄ emissions. Our findings indicated that FTCs could significantly regulate CO₂ and CH₄ emissions by reducing bacterial community diversity and increasing the concentration of available soil substrates. Our findings shed new light on the microorganism-substrate mechanisms regulating the response patterns of the soil carbon cycle to FTCs in permafrost regions.

Sediment grain-size distribution (GSD) provides rich information about sedimentary sources and can potentially do the same with regard to environmental and climatic changes. However, neither traditional descriptive statistics nor curving-fitting methods can fully address its complexity. We selected the Greater Khingan Range in northeastern China as the study area and used parameterized end-member analysis (EMA) of the GSD of four drilling cores to extract different end-member (EM) components. The results show that EM1 (mode particle size (Mo): 1.26–1.66 μm) originates from weathering and pedogenesis. The EMs with Mo values of 4.37–5.01 μm represent components transported by the upper westerly wind. EMs with Mo values of 7.58 μm and 11.48 μm represent wet dust deposition and dry dust deposition. The wind transport of particles in winter consists of low-level near-source transport and local-source transport (possibly from the flood plain of the Amur River). Due to the limitations of the EM model, the two sources have one or two EM components: AEM3 + AEM4, BEM3 + BEM4, CEM4, and DEM4. DEM5 is the only large particle-size component and may represent coarse-grained detritus generated via rock weathering. The components related to the winter monsoon and the ¹⁴C dating data suggest a weak-strong-weak-strong trend of the winter monsoon since the Marine Isotope Stage 3a (MIS 3a). Our results suggest that the strengthening of the winter monsoons in the previous few thousand years has caused the transportation of coarser grain sizes and further exacerbated permafrost degradation, providing a scientific reference for understanding climate change and the formation and evolution of permafrost in the Greater Khingan Mountains since the MIS 3a.

The stability of soil organic carbon (SOC) in high-latitude permafrost regions plays a critical role in the global carbon balance. However, a systematic understanding of SOC pool fractions and their response to warming across different wetland types in the Great Hing’an Mountains remains lacking. In this study, soil samples were collected from forested, shrub, and herbaceous wetlands at depths of 0–60 cm and incubated at 5, 10 and 15 °C. A three-pool first-order kinetic model was employed to analyze SOC mineralization characteristics, carbon pool fractions, and influencing factors. The results showed that SOC mineralization rates exhibited a pattern of rapid increase followed by a peak and gradual decline over time, decreased with soil depth, and increased with temperature. The mineralization potential followed the order of shrub wetlands > herbaceous wetlands > forest wetlands. The temperature sensitivity (Q₁₀) was lowest in the deep soil layer of shrub wetlands (1.2), whereas a deeper soil layer of forest wetlands exhibited the highest Q₁₀ value (3.5). Across the three wetland types, SOC was dominated by the inert carbon pool (61–72%), with forest wetlands showing the highest proportion of inert carbon (72%). The active carbon pool in shrub wetlands was most sensitive to warming, while herbaceous wetlands had the largest inert carbon stock. Soil pH was significantly negatively correlated with the inert carbon pool, whereas soil moisture content showed a significantly positive correlation. Path analysis further revealed that SOC had the largest total effect on inert carbon accumulation, whereas available nitrogen and pH showed the strongest direct associations with Q₁₀. Wetland type was indirectly associated with inert carbon stocks through its influence on soil moisture, pH, SOC, and available nitrogen. These results highlight that both direct and indirect pathways jointly influence SOC stability in permafrost wetlands. Overall, Wetland type and soil physicochemical properties jointly regulate SOC stability and its response to warming. These results suggest that although forest wetlands possess stronger carbon stability, their stable carbon pools may become increasingly vulnerable under climate warming.

Accurate estimation of forest soil organic carbon (SOC) is considered critical for understanding terrestrial carbon cycling and supporting climate change mitigation strategies. However, the canopy block, intricate vertical structure of forests, and the constraints of single-source remote sensing data have presented considerable obstacles for estimating forest SOC. This study proposes a forest SOC estimation and uncertainty analysis (ForSOC-UA) framework to enhance forest SOC estimation and quantify its uncertainty in the natural secondary forests of northern China by integrating hyperspectral imagery (ZY-1F), synthetic aperture radar data (Sentinel-1), and environmental covariates (such as topography, vegetation, and soil indices). The performance of traditional machine learning models (RF, SVM, and CNN), geographically weighted regression (GWR), and a geographically weighted random forest (GWRF) model was compared across three different soil depths (0–5 cm, 5–10 cm, and 10–30 cm). The results showed that GWRF consistently outperformed all other models across all soil depth layers, with the highest accuracy achieved using multi-source data (R² = 0.58, RMSE = 27.49 g/kg, rRMSE = 0.31). Analysis of feature importance revealed that soil moisture, terrain characteristics, and Sentinel-1 polarization attributes were the primary predictors, while spectral derivatives in the red and near-infrared bands from ZY-1F also played a significant role for forest SOC estimation. The uncertainty analysis indicated a forest SOC estimation uncertainty of 37.2 g/kg in the 0–5 cm soil layer, with a decreasing trend as depth increased. This pattern is associated with the vertical spatial distribution of the measured forest SOC. This integrated approach effectively captures spatial heterogeneity and nonlinear relationships between feature and forest SOC, while also assessing estimation uncertainty, so providing a robust methodology for predicting forest SOC. The ForSOC-UA framework addresses the uncertainty quantification of SOC estimation at different vertical depths based on machine learning, providing methodological enhancements for the assessment of large-scale forest SOC and the monitoring of carbon sinks within forest ecosystems.

Plant litter decomposition drives soil biogeochemical cycling, yet how warming alters interactions among litter quality, microbial communities, and soil C, N, and P transformations remains unclear. We conducted a 90-day soil microcosm incubation at 15, 25, and 35 °C using single and mixed litter of Calamagrostis angustifolia and Carex lasiocarpa from the Xingkai Lake wetland to assess warming effects on decomposition, microbial communities, and C, N, and P dynamics. Warming significantly accelerated decomposition, with the highest decomposition constant observed for mixed litter at 35 °C. Mixed litter showed greater temperature sensitivity at 25–35 °C, whereas decomposition rates were weakly correlated with initial litter C/N, C/P, and N/P ratios. Warming and decomposition time reshaped microbial communities, with bacterial diversity increasing over time and fungal diversity declining at 35 °C. Despite accelerated mass loss, C, N, and P dynamics remained asynchronous, with net C release in some treatments, net N accumulation, and litter-dependent P responses. Three-way ANOVA, RDA, and partial least squares structural equation modeling were used to examine associations among environmental factors, microbial attributes, litter decomposition, and soil C, N, and P status. Overall, warming modifies relationships among litter quality, microbial communities, and C, N, and P transformations, providing insights into freshwater wetland responses to climate warming.