[Objective]To clarify the response and interaction of soil element content and function at different depths under Pinus pumila forest to severe lightning fire interference,and to provide data reference and theoretical basis for the restoration of Pinus pumila ecosystem after severe lightning fire interference.[Method]The study focused on the soil leaching and deposition layers in the severely lightning damaged area of Pinus pumila in the Greater Khingan Range.The carbon,nitrogen,phosphorus,and trace nutrient content of the soil were measured in the year and year after the fire,and the stoichiometric ratio was calculated.One-way ANOVA,Spearman correlation,and Mantel test were used to calculate differences and correlations;Random forest importance ranking is used to explain the contribution of soil elements to stoichiometry;The partial least squares structural equation model is used to demonstrate the comprehensive effect relationship between soil spatial pattern,recovery time,soil elements,and soil stoichiometry before and after fire disturbance.[Result]The content and stoichiometry of carbon,nitrogen,phosphorus,copper,and mercury in the soil leaching layer and sediment upper layer showed significant responses to fire interference and fire exposure time.Fire interference increased the variability of trace nutrients in the leaching layer,and the changes in soil element content showed a lag with soil depth.The content of trace nutrients in soil is significantly correlated with soil carbon,nitrogen,phosphorus,and their stoichiometric ratios,and they can also significantly explain and predict soil stoichiometric ratios.[Conclusion]Fire interference inhibits the mineralization rate of organic matter in leached soil and increases the availability of phosphorus in soil sediments by high temperature burning and changing the source of substances.The soil element content undergoes significant changes in the short term after fire due to leaching,and lags as the soil layer deepens.Severe fire disturbance weakened the direct effect of soil spatial pattern on soil elements,enhanced the direct effect of recovery time on soil element content,and resulted in significant effects of soil spatial pattern and recovery time on soil stoichiometry.

Flame shape and heat flux are crucial indicators for evaluating forest combustion conditions. Variations in surface fire spread rate caused by changes in fuel and terrain conditions correspond to distinct flame shape and heat flux characteristics. However, little is known about the relationships among flame shape, heat flux, and surface fire spread rate after alterations in fuel conditions (such as moisture content and load) and slope. We selected Pinus koraiensis, P. sylvestris var. mongolica, Quercus mongolica, and Juglans mandshurica in Heilongjiang Province, China, whose surface fuel bed structures and fuel types differ. We analyzed the impacts of changes in fuel moisture content, load, and slope on the surface fire spread rate, flame shape, and heat flux characteristics. Additionally, we evaluated the relationships among flame shape, heat flux, and surface fire spread rate under varying moisture content, load, and slope conditions. Moisture content is negatively correlated with the surface fire spread rate, flame shape, and heat flux characteristics, while fuel load exhibits the opposite trend. Slope is positively correlated only with flame size (height and length) and indicators of total and convective heat fluxes. Increases in moisture content and slope exert direct negative and positive impacts on surface fire spread rate respectively. In contrast, an increase in fuel load produces only an indirect positive impact on the spread rate through the peak total heat flux and flame height, with no significant direct effect. Both increases in moisture content and slope indirectly negatively affect the spread rate through flame height. However, the negative impact of slope is offset by its positive effect through the peak total heat flux, resulting in an overall positive indirect effect. The surface fire spread rate directly responds to changes in moisture content and slope and indirectly enhances its responsiveness through flame shape and heat flux. In contrast, the response of the spread rate to changes in fuel load is mediated by flame shape and heat flux. This study enhances the understanding of the theoretical mechanisms by which changes in fuel and terrain conditions influence the surface fire spread rate.

Frequent forest fires cause serious damage to ecosystems and socioeconomic systems, increasing the importance of fire prevention and risk assessment. Forest fuel is a fundamental determinant of forest fire behavior and a key component of fire risk management. However, a systematic synthesis of its global research evolution and emerging scientific challenges remains relatively insufficient. On the basis of 1257 publications retrieved from the Web of Science Core Collection (2010-2025) with the themes of "wildfire fuel" and "forest fuel," this study employed CiteSpace for bibliometric analysis to systematically investigate research trends, collaboration patterns, and thematic evolution. The results show that forest fuel research has exhibited sustained growth overall, with notable peaks in 2016 and 2020, and reaching a historical high in 2023. The United States dominated both in publication output and institutional collaboration networks, forming a core research cluster together with Australia and Canada. Keyword co-occurrence and burst analyses revealed a shift in research hotspots-from early focus on forest fuel models and risk assessment at the wild-urban interface (WUI)-toward concerns about climate-change-driven fire seasonality, fuel moisture dynamics, and emergency response issues, reflecting the growing influence of climate change on wildfire patterns. Notably, this study identified several critical research gaps, including limitations in cross-regional integration of fuel moisture studies, insufficient attention to ignition prevention in WUI residential settings, and a lack of reproducible, open bibliometric workflows. By systematically mapping the knowledge structure and evolutionary trajectory of forest fuel research, this study provides a globally informed knowledge framework for the future advancement of forest fuel science and its deeper integration with forest fire management and policy making.

Ledum palustre (L. palustre) is widely used in drug development because of its antibacterial and analgesic effects. However, wild L. palustre is often affected by wildfires, resulting in unstable yields. Forest fires represent a major disturbance in northern forest ecosystems and profoundly affect shrub vegetation and its associated rhizosphere microbial communities. In this study, we investigated a fire chronosequence (1991, 2004, 2012, 2017, and 2020) to systematically examine the morphological traits of L. palustre, rhizosphere soil physicochemical properties, and microbial community characteristics and to identify the key drivers underlying these patterns. The results revealed that postfire recovery time significantly influenced the morphological traits of L. palustre. The biomass, branch number, basal diameter, and plant height of the shrubs at the 1991 burned site increased by 270.49%, 36.11%, 79.32%, and 191.36%, respectively (p < 0.05). From unburned soils, 29 bacterial and 29 fungal isolates were obtained, with Bacillus sp. and Oidiodendron sp. being the dominant culturable bacterial and fungal taxa, respectively. With increasing postfire recovery time, soil moisture, total nitrogen, ammonium, nitrate, soil organic carbon, acid phosphatase (AP) and N-acetyl-β-D-glucosaminidase (NAG) activity significantly decreased. Early fire disturbance markedly altered soil microbial abundance and community composition, leading to an overall decrease in bacterial α diversity. The bacterial community structure at the 2020 burn site and the fungal community structure at the 2012 burn site significantly differed. Mantel tests revealed significant positive correlations between branch number and basal diameter (p < 0.01) and significant negative correlations between plant height and stem density (p < 0.001). Soil carbon and hydrolysable nitrogen were significantly positively correlated with AP and NAG activities (p < 0.001). Moreover, soil physicochemical properties significantly shaped soil microbial community structures, with bacterial communities in early postfire sites driven by total carbon and nitrogen (p < 0.05), whereas fungal communities in the 2012 burned site were influenced primarily by β-N-acetylglucosaminidase (BG) activity (p < 0.05). Fire disturbance drives successional changes in the rhizosphere microbial community structure and function by altering the soil nutrient status and enzyme activity, which in turn influences the morphological traits of L. palustre. This study provides a theoretical basis for improving the yield of L. palustre by exploring the variation in rhizosphere microorganisms.

Wildfire is a major disturbance of terrestrial carbon cycling, yet its effects on functionally distinct soil carbon pools remain poorly resolved at the global scale. Here, we synthesize 210 studies (1,050 observations) to quantify wildfire-induced changes in particulate organic carbon (POC) and mineral-associated organic carbon (MAOC) in surface soil (to 25 cm depth). Wildfire significantly reduced both fractions, with disproportionately larger losses in POC ( − 24.0%) compared to MAOC ( − 9.5%), indicating greater vulnerability of labile carbon pools relative to mineral-stabilized carbon. Temporal analyses reveal that carbon losses do not peak immediately after wildfire but intensify over the first two decades (~20–25 years), followed by gradual recovery, highlighting prolonged post-wildfire ecosystem adjustment. Across sites, the magnitude of carbon loss is primarily controlled by initial soil carbon content, climate, and time since wildfire. Scaling these responses globally, we estimate cumulative wildfire-induced soil organic carbon losses of 885.92 ± 12.75 Tg between 1997 and 2023. Our findings demonstrate that wildfire induces persistent, fraction-specific destabilization of soil carbon, underscoring the critical need for global carbon–climate models to explicitly represent distinct particulate and mineral-associated carbon dynamics to accurately project future land–atmosphere feedbacks under intensifying fire regimes. Labile particulate organic carbon is more vulnerable to wildfire loss than mineral-associated organic carbon, with soil carbon losses intensifying over the first two decades after fire and totalling about 886 Tg globally during 1997–2023, based on a global meta-analysis of 210 studies.

Against the backdrop of global climate change, wildfires have emerged as key disturbance factors accelerating permafrost degradation. However, how wildfires affect ground-surface deformation, including spatial patterns and potential driving mechanisms, remains unclear. Therefore, in this study, the permafrost region in the northern Da Xing’an Mountains affected by the catastrophic Great Black Dragon Fire (1987) is taken as a case study. On the basis of Sentinel-1 SAR imagery acquired from 2016 to 2021, the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique was employed to derive surface deformation rates. These rates were combined with historical fire severity (dNBR) and topographic factors. Random forest and spatial autocorrelation analyses were used to evaluate the long-term association between wildfire disturbance and surface deformation and its potential controls. The results indicate that (1) thirty-five years after the wildfire, vegetation in the permafrost region had not fully recovered to prefire levels; (2) surface deformation from 2016 to 2021 was dominated by subsidence overall. When unburned patches within the same region were used as controls for climate-driven background subsidence, the proportion of areas experiencing severe subsidence (annual rate ≤ −50 mm yr−1) reached 12.86% in high-severity fire zones, compared with 10.21% in unburned areas, suggesting that high-severity fires may amplify regional background subsidence; and (3) the random forest model had low explanatory power (R2 = 0.03) and was therefore used for exploratory comparison of the selected predictors rather than for accurate prediction of surface deformation. Among the selected variables, dNBR had the greatest relative importance, followed by terrain ruggedness and slope, whereas the remaining spatial variability may reflect unmeasured hydrological and subsurface controls. This study provides a quantitative basis for understanding the wildfire-induced “abrupt degradation” of permafrost, defined here as disturbance-driven acceleration of thaw and subsidence beyond gradual climate-driven degradation, and contributes to understanding carbon–climate feedback mechanisms in permafrost regions.

Forest surface fuels are a key factor influencing fire behavior, and their spatial heterogeneity is strongly affected by forest type, climate, and topography. In this study, we innovatively introduced Active Learning into the surface fuel estimation framework. By integrating multi-source satellite remote sensing data—including optical spectral features, radar-derived variables, and ancillary topographic and climatic data—we constructed Extreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR) models to estimate four classes of surface fuels (1h, 10h, herbaceous, and shrub) in forest ecosystems along the China–Mongolia border. Model training was first conducted using Stratified Ten-fold Cross-Validation (ST10CV), followed by optimization through the Euclidean-Based Diversity Active Learning (EBD-AL) strategy. The results showed that the XGB model optimized with EBD better explained the variability of dead 1h fuels, with the mean R2 increasing from 0.20 to 0.53. The EBD optimization yielded relatively weak improvements for 10h dead woody fuels. For herbaceous fuels, the mean R2 of the XGB-EBD model increased from 0.20 to 0.50. For shrub fuels, the XGB-EBD optimization improved the mean R2 from 0.07 to 0.42, indicating a substantial enhancement in the model's explanatory power. These findings demonstrate that combining multi-source remote sensing data with Active Learning in a Machine Learning framework can significantly improve the spatial predictive accuracy of forest surface fuel load, providing reliable data support for forest fire risk assessment and management.

Extreme fire behavior can lead to a transition from an initial low-intensity quasi-steady fire spread state to a new state with increased rates of spread (ROS) and burning intensity. Eruptive fire, a typical extreme fire behavior, occurs frequently in canyons. This study systematically conducted a series of experiments under different topographic conditions to explore the combustion dynamics of canyon fires and the mechanism of eruptive fires. The fire head direction in both symmetric and one-sided canyon fires deviated from the line of maximum slope and shifted toward the canyon centerline. The ROS of symmetric canyon fires exhibited dynamic characteristics, with eruptive fire observed at alpha = 30 degrees, where the ROS increased sharply. In contrast, one-sided canyon fires and two-point-ignited upslope fires spread at approximately a steady rate. Through thermal modelling and analysis of symmetric canyon fires spread, it was found that significant convective heating occurred ahead of the fire front during eruptive fires. The interaction between the two lateral flame fronts and the chimney effect induced by canyon terrain collectively contributed to a significant enhancement of convective heating, which is identified as the key mechanism of eruptive fires in canyons.

Wildfires increasingly influence boreal forest soil greenhouse gas (GHG) dynamics through pyrocarbon (PyC) production. However, the mechanisms by which fire-altered microbial communities regulate the temperature sensitivity (Q10) of post-fire GHG fluxes remain poorly understood. Here, we quantified seasonal CO₂, CH₄, and N₂O fluxes in a Dahurian larch (Larix gmelinii) forest, applying a field manipulation (10 t/ha PyC addition) to burned and unburned soils to simulate post-fire legacies. Key findings were as follows: (1) Fire increased soil CO2 emissions (+6.5% global warming potential, GWP), reduced CH4 uptake and decreased N2O emissions, collectively elevating GWP. (2) PyC application amplified divergent GHG responses; in unburned soils, it increased CO2 emissions (+33.6% GWP) while reducing CH4 uptake and N2O emissions; in burned soils, it suppressed CO2/N2O emissions (−26.9% GWP) and enhanced CH4 uptake. (3) Microbial community shifts drove Q10 variations, projecting increasing GWP differences under warming between PyC-amended burned/unburned soils. Crucially, PyC application in intact forests exacerbated GHG emissions, undermining carbon sequestration goals, whereas naturally occurring PyC partially mitigated fire-induced GWP increases. These results challenge conventional PyC management strategies. Our results indicate that the direct application of PyC to unburned boreal forest soils as a carbon sequestration strategy may not be effective for climate mitigation as it can amplify GHG emissions and exacerbate the greenhouse effect. Conversely, PyC left in the areas after fires appear to help mitigate some of the negative impacts of fire on soil GHG fluxes. This highlights the need to consider PyC from wildfires as an active soil component rather than merely a fire residue and suggests that its role in post-fire ecosystem recovery warrants further attention in the context of climate change mitigation strategies.

Aims Biodiversity plays an important role in regulating ecosystem functioning.Fire is an important ecological factor in forest ecosystems,and can significantly affects both above-and below-ground biodiversity,as well as ecosystem functions.However,how biodiversity affects ecosystem multifunctionality(EMF)after prescribed burning,such as forest biomass accumulation and nutrient availability,is still less understood. Methods In this study,we investigated Pinus koraiensis plantations in Hongqi Forestry Farm,Hegang,Heilongjiang,four years after the prescribed burning was conducted in 2018(when the forest environment has stabilized).We used structural equation modeling to assess the relationships of understory plant diversity(species and functional diversity,efficiency and quantity traits)and soil microbial diversity(fungi and bacteria)with EMF. Important findings We found that prescribed burning increased both understory plant diversity and EMF.Out of various above and belowground diversity metrics,Traitquantity(i.e.,total leaf nitrogen per unit area)and functional diversity(i.e.,functional dispersion(FDis)based on leaf dry matter content)were significantly and positively correlated with EMF,while the effect of belowground microbial diversity on EMF was not significant.The prescribed burning explained the highest proportion of variations in EMF(33.7％),followed by Traitquantity(27.5％)and functional diversity(13.9％).The results suggest that in P.koraiensis plantations,enhancing nutrient accumulation and trait diversity in the understory layer is an effective strategy to improve EMF after prescribed burning.Meanwhile,in forest management in the context of global change,prescribed burning is not only an effective way to reduce forest fire risks,but may also play a positive role in maintaining understory biodiversity and EMF.

Background The spread of canyon fire often involves sudden acceleration, which is related to eruptive fire.Aims The purpose of the study is to explore the pattern of fire line evolution and rate of spread (ROS) with topographic conditions in canyon fire, and to clarify the critical conditions for and mechanism of eruptive fire.Methods A systematic experimental study on canyon fire was conducted by igniting dead pine needles with a point ignition.Key results Four different types of fire line contours were identified under different topographic conditions. When the central slope angle alpha >= 15 degrees, the direction of the fire head gradually deviates from the line of maximum slope and moves to the center line, and this deviation increases with alpha. Accordingly, ROS along the center line also exhibits dynamic characteristics, and ROS increases with alpha and the lateral slope angle delta. The critical conditions for eruptive fire are alpha = 27.5 degrees and delta = 20 degrees.Conclusions When eruptive fire occurs, there is significant convective heating ahead of the fire front. This strong convective heating is the basic mechanism for eruptive fire in canyons.Implications Our results may provide a theoretical basis to assist fire commanders to make decisions.

Climate change and human activity are increasing the frequency of wildfires in peatlands and threatening permafrost peatland carbon pools. In Northeast China, low-severity prescribed fires are conducted annually on permafrost peatlands to reduce the risk of wildfires. These fires typically do not burn surface peat but lead to the loss of surface vegetation and introduction of pyrogenic carbon. However, the long-term effects of repeated low-severity fires on soil carbon stability in these ecosystems remain unclear. Thus, we conducted low-severity prescribed fire experiments over 3 years in the permafrost peatlands of the Great Khingan Mountains. Our findings showed a gradual decline in the total carbon content, primarily due to the reduction in free particulate organic matter (fPOM). Initially, fPOM was higher in the burned sites but decreased with repeated burning. Chemical analyses revealed a 32% increase in the aromaticity of the fPOM at the burned sites, which diminished the thermal stability of the soil. Furthermore, both prescribed fires and the addition of pyrogenic carbon reduced biological stability while increasing enzyme activity and CO2 production, which was attributed to the introduction of post-fire pyrogenic carbon. These results suggest that low-severity fires compromise the stability of permafrost peatlands, particularly because the pyrogenic carbon input alters the chemical composition of the soil carbon fraction.

Global warming increases the freeze-thaw (FT) cycles, however, the impact of increased FT cycles on the environmental behavior of arsenic (As) in soils and the toxic effect of As to microorganisms are still unknown. Herein, the influence of FT cycles on As forms, available As, and microbial community structure in paddy soils was investigated. After 60 FT cycles, the content of exchangeable As and residual state As decreased by 1.77% and 14.18%, respectively, while the carbonate-bound As, iron-manganese oxide-bound As, and organic-bound As increased by 4.53%, 6.5%, and 5.35%, respectively. The available As in soil and As(III) in soil water increased by 6.53 mg/kg and 38.9 mu g/L, respectively. High throughput sequencing data indicated that FT cycles reduced Alpha diversity and significantly changed Beta diversity of soil microorganisms. FT cycles considerably enhanced the relative abundance of Sphingomonas and Lysobacter. Kyoto Encyclopedia of Genes and Genomes function predictions revealed that FT cycles significantly activated cellular gene segments involved in soil bacterial immunological disorders, cell motility, parasite infectious diseases, and neurological diseases. This study would serve as a reference for future study on environmental behavior and toxic effects of heavy metals in farm soils of seasonal FT aeras.

[Objective]The contents of soil carbon(C),nitrogen(N)and phosphorus(P)along with their stoichiometric ratios are varied due to the varying nutrient uptake and utilization strategies among plantations of various tree species,which in turn can affect soil microbial activity.However,whether soil microorganisms adapt to these changes by adjusting their biomass and extracellular enzyme stoichiometric ratios remains uncertain.This study aims to explore the effects of plantations of various tree species on soil-microbe-exoenzyme C∶N∶P stoichiometric ratios and to investigate the correlations among soil-microbe-exoenzyme stoichiometry.[Methods]An investigation into the contents of C,N and P,as well as microbial biomass C(Cmic),N(Nmic),and P(Pmic)was conducted,and the activities of C-(β-1,4-glucosidase+β-D-cellosidase,BG+CBH),N-(β-1,4-N-acetylglucosaminidase,NAG),and P-(acid phosphatase,ACP)acquiring extracellular enzymes for microorganisms at 0-40 cm depth in four native tree species plantations were determined.These plantations included conifers Pinus massoniana,deciduous broad-leaved Liquidambar formosana,Devergreen broad-leaved Schima superba and Elaeocarpus decipiens located in the hilly region of central Hunan Province and shared a common soil development and management history.[Results]1)Plantations of different tree species significantly affected soil C,N,P contents,microbial biomass,extracellular enzyme activity;Cmic∶Nmic∶Pmic and EEAC∶N∶P.Cmic∶Pmic ratios in P.massoniana plantations and L.formosana plantations were significantly higher than those in S.superba plantations and E.decipiens plantations,indicating that microorganisms competed with plants for soil available P.The utilization rate of soil P was low,especially in L.formosana plantations.NAG and EEAN∶P in S.superba plantations were the highest,suggesting that microorganism were obviously limited by N there.ACP in E.decipiens plantations was higher,EEAC∶N and EEAC∶P were also higher than those in plantations of other tree species,while EEAN∶P was the lowest,indicating that microorganisms were most restricted by C and P there.2)There was no significant correlation between soil C:N:P and microbial biomass,extracellular enzyme C∶N∶P,while only Cmic∶Nmic and EEAC∶N,Cmic∶Pmic and EEAC∶P showed significant negative correlation,indicating that there was no covariance between soil C∶N∶P and microbial biomass C∶N∶P.There was a significantly positive correlation between soil C∶N∶P and C∶Pimb,a significantly negative correlation between Cmic∶Nmic∶Pmic and C∶N∶Pimb,and a significantly positive correlation between C∶Nimb and EEAC∶N,which confirmed the influence of C,N and P differences between soil and microorganisms on the stoichiometric ratios of extracellular enzymes.3)Existing biomass of the litter layer had significant effects on soil C,N,P contents,Pmic,Cmic∶Pmic,Nmic∶Pmic,BG+CBH,NAG,and EEAC∶P.[Conclusion]By influencing the contents of soil C,N and P,plantations composed of different tree species can affect microbial biomass and extracellular enzyme activities.Soil microorganisms can adapt to diverse nutrient limitations by regulating their biomass C∶N∶P ratios and synthesizing specific extracellular enzymes.The results substantiate the microbial resource allocation theory.

The acceleration of global warming and intensifying global climate anomalies have led to a rise in the frequency of wildfires. However, most existing research on wildfire fields focuses primarily on wildfire identification and prediction, with limited attention given to the intelligent interpretation of detailed information, such as fire front within fire region. To address this gap, advance the analysis of fire front in UAV-captured visible images, and facilitate future calculations of fire behavior parameters, a new method is proposed for the intelligent segmentation and fire front interpretation of wildfire regions. This proposed method comprises three key steps: deep learning-based fire segmentation, boundary tracking of wildfire regions, and fire front interpretation. Specifically, the YOLOv7-tiny model is enhanced with a Convolutional Block Attention Module (CBAM), which integrates channel and spatial attention mechanisms to improve the model's focus on wildfire regions and boost the segmentation precision. Experimental results show that the proposed method improved detection and segmentation precision by 3.8 % and 3.6 %, respectively, compared to existing approaches, and achieved an average segmentation frame rate of 64.72 Hz, which is well above the 30 Hz threshold required for real-time fire segmentation. Furthermore, the method's effectiveness in boundary tracking and fire front interpreting was validated using an outdoor grassland fire fusion experiment's real fire image data. Additional tests were conducted in southern New South Wales, Australia, using data that confirmed the robustness of the method in accurately interpreting the fire front. The findings of this research have potential applications in dynamic data-driven forest fire spread modeling and fire digital twinning areas. The code and dataset are publicly available at https://github.com/makemoneyokk/fire-segmentation-interpretation.git.

Wildfires are natural disasters that pose substantial threats to the environment. The accurate prediction of wildfire risk levels and the timely implementation of effective mitigation measures are critical for wildfire prevention and ecological security maintenance. Fuel moisture content is an important factor affecting the spread and intensity of wildfires; however, there is currently a lack of large-scale data on surface dead fuel moisture content (DFMC). Therefore, in this study, we aimed to develop a more accurate method for retrieving DFMC by integrating multisource satellite remote sensing data with machine learning algorithms. Evaluation of six retrieval models [extreme gradient boosting (XGB), linear regression (LR), generalized additive model (GAM), random forest (RF), convolutional neural network (CNN), and long short-term memory (LSTM)] confirmed the adaptability of XGB to four forest types, achieving an average R-2 value of 0.78. In addition, we used SHAP analysis to assess the importance of the model's influencing factors and identified the significance of soil moisture content (SMC) and evapotranspiration (ET). Furthermore, comparative analysis of four input parameter combinations confirmed the pivotal role of SMC, with the integrated use of ERA5-Land reanalysis data and SMC data achieving optimal model performance while balancing large-scale applicability with accuracy. In addition, a daily DFMC dataset for the Greater Khingan Mountains was constructed by integrating soil moisture active and passive (SMAP) soil moisture data, ERA5 meteorological data, and the XGB model. In this study, we innovatively combined microwave remote sensing data with machine learning to provide a robust methodological framework for DFMC data retrieval via microwave remote sensing. Our results establish a foundation for enhancing the precision of forest fire risk early warning systems.

Pyrogenic carbon (PyC) is a common byproduct of wildfires in terrestrial systems; however, its role in fire-prone forest ecosystems, particularly cold boreal forests, remains unclear. Ecological stoichiometry is a valuable tool for studying interactions within plant-soil-microbial continuum systems, which could help us understand post-fire changes in boreal forest ecosystems. In this study, we manipulated different additions of PyC in a forest of Dahurian larch (Larix gmelinii) after a wildfire to investigate the effects of PyC on plant-soil-microbial ecological stoichiometry. We engineered PyC under controlled conditions to simulate that produced by wildfires. The experimental design included no PyC addition (C0), 0.5 kg m-2 PyC addition (C1), 1.0 kg m-2 PyC addition (C2), and no fire as a control (CK). One year after PyC manipulation, understory vegetation and soil samples (0-10 cm depth) were collected to investigate how PyC addition affects plant-soil-microbial carbon (C), nitrogen (N), and phosphorus (P) stoichiometry. Our results showed that PyC addition (C1, and C2) increased plant biomass, particularly in the green tissues (35-53 % higher than that in the CK and 59-80 % higher than that in the C0 treatment). The C2 treatment also increased the plant C and N contents but did not significantly affect plant P content. PyC addition led to an increase in soil microbial biomass N (MBN) and P (MBP), altered the microbial biomass C:N:P ratio (to 27:1:1), and disrupted the microbial dynamic balance, indicating a possible shift towards a bacterial-dominated community. In boreal forest soils, post-fire PyC manipulation elevated soil organic C (SOC) and total P (STP). As there were no changes in soil total N (STN), the addition of PyC also increased the soil C:N and C:P ratios. Our findings highlight the potential of PyC as a soil conditioner that enhances plant biomass and alters nutrient cycling in boreal forests. However, PyC effects are modulated by soil resource availability and the nutrient environment. Further studies are required to elucidate the mechanisms underlying these differential nutrient responses.

Understanding the interaction between forest fires and soil organic carbon (SOC) dynamics is crucial for studying the carbon cycle of boreal forest ecosystems and its influence on global climate change. In particular, the impact of forest fires on the rhizosphere soil, which is a carbon-rich and ecologically sensitive microenvironment, was particularly significant. We used the rhizosphere soil of Larix gmelinii forests, a representative species of boreal forests in China, to investigate the mechanisms by which fungal life strategies affect post-fire soil carbon mineralisation in boreal forests in China. The SOC mineralisation was determined using indoor incubation for 50 days, and bacterial and fungal communities were detected using 16S rRNA and internal transcribed spacer (ITS) gene sequencing. We found that the rhizosphere soil dominated SOC mineralisation, and carbon release from the rhizosphere soil was significantly reduced by 53.8% after fire. In addition, the number of r-strategist microorganisms increased, and the number of K-strategist microorganisms decreased after the fire, resulting in a more pronounced domination of the rhizosphere soil by the r-strategy. Notably, the ratio of Copiotrophs to Oligotrophs in rhizosphere soil fungi was negatively associated with SOC mineralisation, and this ratio increased post-fire, potentially contributing to reduced SOC mineralisation through its relationship with β-1,4-glucosidase (BG) activity. The study revealed the complex regulatory mechanisms of forest fires on soil microbial community structure and SOC mineralisation from the perspective of soil microbial life strategies, which are essential for post-fire ecological restoration strategies.

Freeze-thaw cycles (FTCs) are a typical climatic feature in northeast of China. Due to global climate change, the frequency of freezing and thawing during the autumn and winter seasons is increasing, which may affect soil physicochemical properties and biological characteristics in freeze-thaw regions. In northeast of China, selenium (Se) is used to spray paddy fields to increase Se content in rice. However, the impact of FTCs on the environmental behavior of Se in paddy soils in northeast of China is not well understood, especially under the background of increasing frequency of FTCs. In this study, indoor simulated FTCs experiment was conducted to investigate the influence of FTCs frequency, soil water content, size of soil aggregate on the Se migration and soil microorganisms in paddy soil column. The results showed that FTCs increased the proportion of microaggregates and soil organic matter content while decreased soil pH. After 60 days, the Se concentration in microaggregates and macroaggregates at 6-10 cm depth increased from 0.148 mg/kg to 0.601 mg/kg and from 0.154 mg/kg to 0.630 mg/kg, respectively; the increment were more than those of the UNFT-Se treatment group (from 0.148 mg/kg to 0.309 mg/kg and from 0.159 mg/kg to 0.318 mg/kg). Se concentration in microaggregates and macroaggregates at 16-20 cm depths increased from 0.144 mg/kg to 0.367 mg/kg and from 0.152 mg/kg to 0.378 mg/kg, respectively; the increment were more than those of the UNFT-Se treatment group (from 0.144 mg/kg to 0.196 mg/kg and from 0.168 mg/kg to 0.207 mg/kg), indicating that FTCs promoted the downward migration of Se in the soil column. Correlation analysis indicated there was a positive correlation (r = 0.458, p < 0.01) between Se concentration and organic matter as well as a negative correlation (r = -0.406, p < 0.01) between Se concentration and soil pH, respectively. Additionally, high-throughput sequencing results showed that FTCs induced changes in the soil microbial community. These findings have important implications for geochemical studies of exogenous Se in soils of the seasonally freeze-thaw aeras.