Search Results (496)

The living roots of woody plants in forests play a crucial role in sustaining the soil temperature equilibrium. However, there is limited research investigating the effects of soil temperature balance disruption, influenced by living roots, on soil microarthropods, especially in the context of global climate change. To address this knowledge gap, we conducted a three-year in situ simulation experiment involving either experimental warming or root trenching treatments to mimic environmental changes and their impacts on soil microarthropod communities in a temperate forest ecosystem in Northeast China. Statistical analysis focused on assessing the abundance and family richness of Collembola and Acari. Warming increased soil temperature, while root trenching had contrasting effects. In the absence of root trenching, warming positively influenced Collembola but negatively affected Acari. Conversely, when combined with root trenching, warming had a diminished impact on both Collembola and Acari. Our findings demonstrate that the interactive effects of warming on soil microarthropod communities vary depending on the presence or absence of root trenching. Specifically, within the context of root trenching treatment compared to no-root trenching treatment, warming exhibited a comparatively attenuated influence on soil microarthropod communities. Overall, living roots play a pivotal role in mediating soil temperature conditions, which significantly impact soil microarthropod communities in the context of global climate change. Full article

Betula. platyphylla Sukaczev (B. platyphylla) forest is an important montane forest type. Global warming has impacted its distribution. However, how it affects suitable distribution across ecoregions and corresponding biodiversity protection measures remains unclear. This study used the Maxent model to analyze the suitable distribution and driving variables of B. platyphylla forest in China and its four ecoregions. The minimum cumulative resistance (MCR) model was applied to construct corridors nationwide. Results show that B. platyphylla forest in China is currently mainly distributed in the four ecoregions; specifically, in Gansu and Shaanxi Province in Northwest China, Heilongjiang Province in Northeast China, Sichuan Province in Southwest China, and Hebei Province and Inner Mongolia Autonomous Region in North China. Precipitation and temperature are the main factors affecting suitable distribution. With global warming, the suitable areas in China including the North, Northwest China ecoregions are projected to expand, while Northeast and Southwest China ecoregions will decline. Based on the suitable areas, we considered 45 corridors in China, spanning the four ecoregions. Our results help understand dynamic changes in the distribution of B. platyphylla forest in China under global warming, providing scientific guidance for montane forests’ sustainable development. Full article

Understanding the detailed spatiotemporal variations in soil organic carbon (SOC) stocks is essential for assessing soil carbon sequestration potential. However, most existing studies predominantly focus on topsoil SOC stocks, leaving significant knowledge gaps regarding critical zones, depth-dependent variations, and key influencing factors associated with deeper SOC stock dynamics. This study adopted a comprehensive methodology that integrates random forest modeling, equal-area soil profile analysis, and space-for-time substitution to predict depth-specific SOC stock dynamics under climate warming in Northeast China’s forest ecosystems. By combining these techniques, the approach effectively addresses existing research limitations and provides robust projections of soil carbon changes across various depth intervals. The analysis utilized 63 comprehensive soil profiles and 12 environmental predictors encompassing climatic, topographic, biological, and soil property variables. The model’s predictive accuracy was assessed using 10-fold cross-validation with four evaluation metrics: MAE, RMSE, R², and LCCC, ensuring comprehensive performance evaluation. Validation results demonstrated the model’s robust predictive capability across all soil layers, achieving high accuracy with minimized MAE and RMSE values while maintaining elevated R² and LCCC scores. Three-dimensional spatial projections revealed distinct SOC distribution patterns, with higher stocks concentrated in central regions and lower stocks prevalent in northern areas. Under simulated warming conditions (1.5 °C, 2 °C, and 4 °C increases), both topsoil (0–30 cm) and deep-layer (100 cm) SOC stocks exhibited consistent declining trends, with the most pronounced reductions observed under the 4 °C warming scenario. Additionally, the study identified mean annual temperature (MAT) and normalized difference vegetation index (NDVI) as dominant environmental drivers controlling three-dimensional SOC spatial variability. These findings underscore the importance of depth-resolved SOC stock assessments and suggest that precise three-dimensional mapping of SOC distribution under various climate change projections can inform more effective land management strategies, ultimately enhancing regional soil carbon storage capacity in forest ecosystems. Full article

Abies nephrolepis and Picea jezoensis are native Pinaceae trees distributed in high mountainous regions of Northeast Asia (typically above ~1000 m a.s.l. on the Korean peninsula, northeastern China, Sakhalin, and the Russian Far East) and southern boreal forests, vulnerable to climate change and human disturbances, necessitating accurate habitat identification for effective conservation. While protected areas (PAs) are essential, merely expanding existing ones often fail to protect populations under human pressure and climate change. Using species distribution models with current and projected climate data, we mapped potential habitats across Northeast Asia. Spatial clustering analyses integrated with PA and land cover data helped identify optimal sites and priorities for new conservation areas. Ensemble species distribution models indicated extensive suitable habitats, especially in southern Sikhote-Alin, influenced by maritime-continental climates. Specific climate variables strongly affected habitat suitability for both species. The Kamchatka peninsula consistently emerged as an optimal habitat under future climate scenarios. Our study highlights essential environmental characteristics shaping the habitats of these species, reinforcing the importance of strategically enhancing existing PAs, and establishing new ones. These insights inform proactive conservation strategies for current and future challenges, by focusing on climate refugia and future habitat stability. Full article

Drought impacts agricultural production and regional sustainable development. Accordingly, timely and accurate drought monitoring is essential for ensuring food security in rain-fed agricultural regions. Alternating drought and flood events frequently occur in the Heilongjiang River Basin, the largest grain-producing area in Far East Asia. However, spatiotemporal variability in drought is not well understood, in part owing to the limitations of the traditional Temperature Vegetation Dryness Index (TVDI). In this study, an Improved Temperature Vegetation Dryness Index (ITVDI) was developed by incorporating Digital Elevation Model data to correct land surface temperatures and introducing a constraint line method to replace the traditional linear regression for fitting dry–wet boundaries. Based on MODIS (Moderate-resolution Imaging Spectroradiometer) normalized vegetation index and land surface temperature products, the Heilongjiang River Basin, a cross-border basin between China, Mongolia, and Russia, exhibited pronounced spatiotemporal variability in drought conditions of the growing season from 2001 to 2023. Drought severity demonstrated clear geographical zonation, with a higher intensity in the western region and lower intensity in the eastern region. The Mongolian Plateau and grasslands were identified as drought hotspots. The Far East Asia forest belt was relatively humid, with an overall lower drought risk. The central region exhibited variation in drought characteristics. From the perspective of cross-national differences, the drought severity distribution in Northeast China and Inner Mongolia exhibits marked spatial heterogeneity. In Mongolia, regional drought levels exhibited a notable trend toward homogenization, with a higher proportion of extreme drought than in other areas. The overall drought risk in the Russian part of the basin was relatively low. A trend analysis indicated a general pattern of drought alleviation in western regions and intensification in eastern areas. Most regions showed relatively stable patterns, with few areas exhibiting significant changes, mainly surrounding cities such as Qiqihar, Daqing, Harbin, Changchun, and Amur Oblast. Regions with aggravation accounted for 52.29% of the total study area, while regions showing slight alleviation account for 35.58%. This study provides a scientific basis and data infrastructure for drought monitoring in transboundary watersheds and for ensuring agricultural production security. Full article

Late spring frost (LSF) poses a threat to temperate forest ecosystems; however, its combined effects with soil properties on triterpene glycosides in the buds of valuable shrubs are still unclear. In this study, natural Aralia elata (Miq.) Seem. populations were investigated in 15 counties in Heilongjiang and Jilin provinces in Northeast China. Buds were sampled in 3–5 cm length and used for determining triterpene glycosides (TGs) of Araloside VI, Araloside V, and 4-F8 (structural analogs) in spring of 2023. LSF in Heilongjiang showed longer days reaching 20 °C (CD20) (6.0 ± 2.5 d), LSF number (NLSF) (1.8 ± 0.5 times) and duration (DLSF) (21.5 ± 5.2 d), and days of temperature rise (DTR) (15.9 ± 3.8 d) compared to Jilin (4.4 ± 0.4 d, 1.2 ± 0.4 times, 17.4 ± 3.9 d, 12.0 ± 3.3 d, respectively). Araloside VI (0.30–0.59%) was positively driven by DLSF but negatively driven by DTR. Araloside V (0.04–0.17%) and 4-F8 (0.09–0.44%) were positively influenced by the lowest temperature, DTR, and CD20, negatively influenced by NLSF, and slightly influenced by organic matter. In LSF-prone regions, soil organic matter and nutrient availability do not need to be enriched, and soil pH should be higher than 5.7 if high TGs are the objective in A. elata buds. Full article

Jilin Province is an important ecological security barrier and major grain-producing region in northeast China, playing a crucial role in ensuring ecological security and promoting regional sustainable development. This study examines ecological resilience from three dimensions: resistance, adaptability, and resilience. Based on multi-source data from 2000 to 2020, an ecological resilience indicator system was constructed. Spatial autocorrelation and OPGD models were employed to analyze temporal and spatial evolution and the driving mechanisms. The results indicate that ER exhibits an overall spatial pattern of “high in the east, low in the west, and under pressure in the central region.” The eastern mountainous areas demonstrate high and stable resilience, while the central plains and western ecologically fragile regions exhibit weaker resilience. In terms of resistance, the eastern mountainous regions are primarily forested, with high and sustained ESV, while the western sandy edge regions primarily have low ESV, making ecosystems susceptible to disturbance. In terms of adaptability, the large-scale farmland landscapes in the central regions exhibit strong disturbance resistance, while water resource adaptability in the western ecologically fragile regions has locally improved. However, adaptability in the eastern mountainous regions is relatively low due to development impacts. In terms of resilience, the eastern core regions possess stable recovery capabilities, while the central and western regions generally exhibit lower resistance with fluctuating changes. Between 2000 and 2020, the ecological resilience Moran’s I index slightly decreased from 0.558 to 0.554, with the spatial aggregation pattern remaining largely stable. Among the driving factors, DEM remains the most stable. The influence of NDVI has weakened, while temperature (TEM) and NPP-VIIRS have become more significant. Overall, factor interactions have grown stronger, as reflected by the q-value rising from 0.507 to 0.5605. This study provides theoretical support and decision-making references for enhancing regional ecological resilience, optimizing ecological spatial layout, and promoting sustainable ecosystem management. Full article

Fire regime (FR) is a key element in the study of ecosystem dynamics, supporting natural resource management planning by identifying gaps in fire patterns in time and space and planning to assess ecological conditions. Due to the insufficient consideration of integrated characterization factors, especially the insufficient research on fire season types (FST), the current understanding of the spatial heterogeneity of fire patterns in China is still limited, and it is necessary to use FST as a key dimension to classify FR zones more accurately. This study extracted 13 fire characteristic variables based on Moderate Resolution Imaging Spectroradiometer (MODIS) burned area data (MCD64A1), active fire data (MODIS Collection 6), and land cover data (MCD12Q1) from 2001 to 2023. The study systematically analyzed the frequency, intensity, spatial distribution and seasonal characteristics of fires across China. By using data normalization and the k-means clustering algorithm, the study area was divided into five types of FR zones (FR 1–5) with significant differences. The burned areas of the five FR zones account for 67.76%, 13.88%, 4.87%, 12.94%, and 0.55% of the total burned area across the country over the 23-year study period, respectively. Among them, fires in the Northeast China Plain and North China Plain cropland areas (FR 1) exhibit a bimodal distribution, with the peak period concentrated in April and June, respectively; the southern forest and savanna region (FR 2) is dominated by high-frequency, small-scale, unimodal fires, peaking in February; the central grassland region (FR 3) experiences high-intensity, low-frequency fires, with a peak in April; the east central forest region (FR 4) is characterized by low-frequency, high-intensity fires; and the western grassland region (FR 5) experiences low-frequency fires with significant inter-annual fluctuations. Among the five zones, FST consistently ranks within the top five contributors, with contribution rates of 0.39, 0.31, 0.44, 0.27, and 0.55, respectively, confirming that the inclusion of FST is a reasonable and necessary choice when constructing FR zones. By integrating multi-source remote sensing data, this study has established a novel FR classification system that encompasses fire frequency, intensity, and particularly FST. This approach transcends the traditional single-factor classification, demonstrating that seasonal characteristics are indispensable for accurately delineating fire conditions. The resultant zoning system effectively overcomes the limitations of traditional methods, providing a scientific basis for localized fire risk warning and differentiated prevention and control strategies. Full article

Understanding the interplay between climate change, landscape patterns, and carbon sequestration is critical for sustainable ecosystem management. This study investigates the spatiotemporal evolution of vegetation Net Primary Productivity (NPP) and landscape patterns in Inner Mongolia, China, from 2000 to 2020, and evaluates their implications for carbon sink capacity under climate change. Using remote sensing data, meteorological records, and landscape metrics (CONTAG, SPLIT, IJI), we quantified the relationships between vegetation productivity, landscape connectivity, and fragmentation. Results reveal a northeast-to-southwest gradient in NPP, with high values concentrated in forested regions of the Greater Khingan Range and low values in arid western deserts. Over two decades, NPP increased by 73% in high-productivity zones, driven by rising temperatures and ecological restoration policies. Landscape aggregation (CONTAG) and patch connectivity showed strong positive correlations with NPP, while higher fragmentation values (SPLIT, IJI) negatively impacted carbon sequestration. Climate factors, particularly precipitation variability, emerged as critical drivers of NPP fluctuations, with human activities amplifying regional disparities. We propose targeted strategies—enhancing landscape connectivity, regional differentiation management, and optimizing patch structure—to bolster climate-resilient carbon sinks. These findings underscore the necessity of integrating climate-adaptive landscape planning into regional carbon neutrality frameworks, offering feasible alternatives for mitigating climate impacts in ecologically vulnerable regions. Full article

Rapid and efficient evaluation of cultivated land quality in black soil regions at the farm scale using remote sensing techniques is crucial for resource protection. However, current studies face challenges in developing convenient and reliable models that directly leverage raw spectral reflectance. Therefore, this study develops and validates a deep learning framework specifically for this task. The framework first selects remote sensing images from typical periods using a Random Forest model in Google Earth Engine (GEE). Subsequently, the raw spectral reflectance data from these images, without any transformation into vegetation indices, are directly input into an optimized BO-Stacking-TabNet model. This model is enhanced through a two-step Stacking ensemble process and a Bayesian optimization algorithm. A case study at Shuanghe Farm in Northeast China shows that (1) compared to the BO-Stacking-TabNet model using vegetation indices as input, the BO-Stacking-TabNet model based on spectral reflectance as the input indicator achieved an improvement of 10.62% in Accuracy, 1.55% in Precision, 11.05% in Recall, and 10.18% in F1-score. (2) Compared to the original TabNet model, the BO-Stacking-TabNet model optimized by the two-step Stacking process and Bayesian optimization algorithm improved Accuracy by 2.13%, Precision by 12.59%, Recall by 1.83%, and F1-score by 2.19%. These results demonstrate the reliability of the new farm-scale black soil region cultivated land evaluation method we proposed. The method provides significant references for future research on cultivated land quality assessment at the farm scale in terms of remote sensing image data processing and model construction. Full article

Forest ecosystems serve as pivotal components of the global carbon cycle, with canopy height representing a critical biophysical parameter for quantifying ecosystem functionality, thereby holding substantial implications for forest resource management and carbon sequestration assessments. The precise extraction of ground elevation and vegetation canopy height is essential for advancing topographic and ecological research. The Terrestrial Ecosystem Carbon Inventory Satellite (referred to as TECIS hereafter) offers unprecedented capabilities for the large-scale, high-precision extraction of ground elevation and vegetation canopy height. Using the Northeast China Tiger and Leopard National Park as our study area, we first processed TECIS data to derive topographic and canopy height profiles. Subsequently, the accuracy of TECIS-derived ground and canopy height estimates was validated using onboard light detection and ranging (LiDAR) measurements. Finally, we systematically evaluated the influence of multiple factors on estimation accuracy. Our analysis revealed that TECIS-derived ground and canopy height estimates exhibited mean errors of 0.7 m and −0.35 m, respectively, with corresponding root mean square error (RMSE) values of 3.83 m and 2.70 m. Furthermore, slope gradient, vegetation coverage, and forest composition emerged as the dominant factors influencing canopy height estimation accuracy. These findings provide a scientific basis for optimizing the screening and application of TECIS data in global forest carbon monitoring. Full article

Forest fires critically disrupt soil ecosystems by altering physicochemical properties and microbial structure-function dynamics. This study assessed short-term impacts of fire intensities (light/moderate/heavy) on microbial communities in Larix gmelinii forests one year post-fire. Using phospholipid fatty acid (PLFA) and Biolog EcoPlate analyses, we found the following: (1) fire reduced soil organic carbon (SOC), dissolved organic carbon (DOC), total nitrogen (TN), and available nitrogen/potassium (AN/AK) via pyrolytic carbon release, while heavy-intensity fires enriched available phosphorus (AP), AN, and AK through ash deposition. (2) Thermal mortality and nutrient-pH-moisture stress persistently suppressed microbial biomass and metabolic activity. Moderate fires increased taxonomic richness but reduced functional diversity, confirming “functional redundancy.” (3) Neither soil microbial biomass nor metabolic activity at the fire site reached pre-fire levels after one year of recovery. Our findings advance post-fire soil restoration frameworks and advocate multi-omics integration to decode fire-adapted functional gene networks, guiding climate-resilient forest management. Full article

Understanding plant phenology dynamics is essential for ecosystem health monitoring and climate change impact assessment. This study generated 4-day, 500 m land surface phenology (LSP) in Northeast China (NEC) from 2001 to 2021 using interpolated and Savitzky–Golay filtered kernel normalized difference vegetation index (kNDVI) derived from MODIS. Spatial patterns, trends, and climate responses of phenology were analyzed across ecoregions and vegetation. Marked spatial heterogeneity was noted: forests showed the earliest start of season (SOS, ~125^th day) and longest growing season (LOS, ~130 days), while shrublands had the latest SOS (~150^th day) and shortest LOS (~96 days). Grasslands exhibited strong east–west gradients in SOS and EOS. From 2001 to 2021, SOS of natural vegetations in NEC advanced by 0.23 d/a, EOS delayed by 0.12 d/a, and LOS extended by 0.38 d/a. Coniferous forests, especially evergreen needle-leaved forests, exhibited opposite trends due to cold-resistant traits and an earlier EOS to avoid leaf cell freezing. Temperature was the main driver of SOS, with spring and winter temperatures influencing 48.8% and 24.2% of the NEC region, respectively. Precipitation mainly affected EOS, especially in grasslands. Drought strongly influences SOS, while precipitation affects EOS. This study integrates high-resolution phenology utilizing the kNDVI with various seasonal climate drivers, offering novel insights into vegetation-specific and ecoregion-based phenological dynamics in the context of climate change. Full article

The changes in soil nitrogen mineralization rate induced by litter input can determine the availability of nitrogen for plant growth in the soil. In forest ecosystems, the mixing of different species of litter can alter the chemical properties of the litter, ultimately affecting the rates of soil nitrogen transformation and cycling. In this study, litters with Fraxinus mandshurica Rupr. and Larix gmelinii (Rupr.) Kuzen. and mixed litter with Fraxinus mandshurica and Larix gmelinii were added to dark brown soil and incubated in the lab for 175 days at 25 °C. NH₄⁺-N and NO₃⁻-N contents and nitrogen mineralization rates were periodically measured to explore the effect of mixed litter addition on soil nitrogen mineralization. The results showed that compared to Larix gmelinii litter, Fraxinus mandshurica litter demonstrates higher carbon, nitrogen, and phosphorus contents while exhibiting lower lignin and cellulose contents and lower C/N and lignin/N ratios. Soil inorganic nitrogen content showed a trend of initial decrease followed by an increase. At the end of the incubation, soil NH₄⁺-N and NO₃⁻-N and the total inorganic nitrogen contents were 4.6–7.8 times, 2.2–3.4 times, and 2.9–4.3 times higher than the initial value, respectively. The soil nitrogen mineralization rate exhibited an initial rapid increase followed by stabilization. During days 7–28 of incubation, the nitrogen mineralization rates in litter addition treatments were lower than that in the control, while they were higher than that in the control during days 42–175. The soil nitrogen mineralization rate in the treatments with Fraxinus mandshurica litter and mixed litter were higher than those in the treatment with Larix gmelinii litter. The cumulative net nitrogen mineralization amounts in the Fraxinus mandshurica litter and mixed litter treatments were higher than those in the Larix gmelinii litter treatment, being 1.5 and 1.2 times those of the Larix gmelinii litter treatment, respectively. MBC and MBN presented a trend of first increasing and then decreasing, peaking on days 7 and 14 of incubation, respectively. Correlation analysis revealed that soil inorganic nitrogen content and nitrogen mineralization rate were positively correlated with the litter total nitrogen and soil microbial carbon and nitrogen and negatively correlated with litter C/N and lignin/N. The changes in soil inorganic nitrogen and nitrogen mineralization are primarily associated with soil microbial immobilization. Initially, in the treatments with litter addition, an increase in microbial biomass enhanced the immobilization of soil inorganic nitrogen. Subsequently, as litter mineralization progressed, the amount of litter decreased, leading to reduced microbial biomass and weakened immobilization. This study indicates that the interaction between litter types and soil microorganisms is the key factor affecting soil nitrogen mineralization process and soil mineral nitrogen content. These findings provide a scientific basis for soil fertility management in the forest ecosystems of Northeast China. Full article

Pine caterpillar (Dendrolimus) infestations threaten pine forests, causing severe ecological and economic impacts. Identifying the driving factors behind these infestations is essential for effective forest management. This study uses the APCIRD framework combined with an improved random forest model to analyze spatiotemporal changes in infestation risk and the driving effects of habitat factors in Northeast China. From 2019 to 2024, we applied SHapley Additive exPlanations (SHAP), frequency analysis, fitting functions, and GeoDetector to quantify the impact of key drivers, such as snow cover and soil, on infestation risk. The findings include (1) the APCIRD framework with the MLP-random forest model (MRF) accurately assesses infestation risks. MRF is composed of MLP and random forest. Between 2019 and 2024, areas with high infestation risk declined, shifting from higher to lower levels, with Eastern Heilongjiang and Southwest Liaoning remaining as key concern areas; (2) snow cover and soil factors are critical to infestation risk, with eight key habitat factors significantly affecting the risk. Their relationships with infestation risk follow complex, non-monotonic quartic and cubic patterns; (3) factors triggering high infestation risks are mostly at low to moderate levels. High-risk areas tend to have low to moderate elevation (<800 m), moderate to high solar radiation and temperature, gentle slopes (<30°), low to moderate evaporation, shallow snow depth (<0.02), moderate snow temperature (266.73–275), low to moderate soil moisture (0.2–0.3), moderate to high soil temperature (276.73–286.92), low to moderate rainfall, moderate wind speed, low leaf area index, high vegetation type, low vegetation cover, low population density, and low surface runoff. Interactions between factors provide a stronger explanation of infestation risk than individual factors. The APCIRD framework, combined with MRF, offers valuable insights for understanding the drivers of pine caterpillar infestations. Full article