Search Results (349)

In order to precisely improve the quality of major tree species in northern China, near-natural differentiated management has been gradually introduced into forestry practice, aiming to optimize forest structure, enhance forest quality, and promote nutrient cycling and water conservation. As an essential element of forest ecosystems, soil microbes contribute to biodiversity preservation and nutrient turnover in soils. This study selected three typical forest types (Quercus acutissima forest, Pinus tabulaeformis forest, and Pinus tabulaeformis × Quercus mixed forest) that have been managed with target trees on Zhongtiao Mountain. Using 16S/ITS rRNA high-throughput sequencing, this study systematically assessed the influences of forest type and soil depth (0–60 cm) on the soil properties and microbial communities. The results showed that the fungal alpha diversity indices were the highest in Pinus tabulaeformis forest, which decreased with soil depth. Actinobacteriota exhibited the greatest relative abundance in mixed forest, whereas Ascomycota predominated in the Pinus tabulaeformis forest. The microbial co-occurrence network exhibited greater complexity compared to the pure forest. Microbial carbon and nitrogen cycling functions showed strong correlation with soil pH and nutrient levels. Symbiotrophs dominated the fungal community, and ectomycorrhizae were significantly abundant in mixed forests. pH is the dominant factor driving changes in microbial communities. In summary, the mixed forest improved soil nutrients, enhanced the complexity of microbial networks, and supported higher ectomycorrhizal abundance. These findings provide practical guidance for improving soil health and stability of forest ecosystems through near-natural management. Full article

Soil moisture serves as a critical factor in the hydrological cycle, affecting plant growth, ecosystem health, and groundwater reserves. Current methods for monitoring and predicting it fail to account for the complexities introduced by climatic variations and other influencing factors, such as the effects of atmospheric interference and data gaps, leading to reduced prediction accuracy. To address these challenges, this study introduces a novel soil moisture prediction model based on remote sensing and deep learning, utilizing the Adaptive Group Radial Lasso Regularized Basis Function Networks (AGRL-RBFN) optimized by the Hierarchical Correlated Spider Wasp Optimizer (HCSWO) and incremental learning (IL) techniques. The proposed method for monitoring soil moisture utilizes hyperspectral and soil moisture data from a 2017 campaign in Karlsruhe, encompassing variables such as datetime, soil moisture percentage, soil temperature, and remote sensing spectral bands. The proposed methodology begins with comprehensive preprocessing of historical remote sensing data to fill gaps, reduce noise, and correct atmospheric disturbances. It then employs a unique seasonal mapping and grouping technique, enhanced by the AdaK-MCC method, to analyze the impact of climatic changes on soil moisture patterns. The model’s innovative feature selection approach, using HCSWO, identifies the most significant predictors, ensuring optimal data input for the AGRL-RBFN model. The model achieves an impressive accuracy of 98.09%, a precision of 98.17%, a recall of 97.24%, and an F1-score of 98.95%, outperforming existing methods. Furthermore, it attains a mean absolute error (MAE) of 0.047 in gap filling and a Dunn Index of 4.897 for clustering. Although successful in many aspects, the study did not investigate the relationship between soil moisture levels and specific crops, which presents an opportunity for future research aimed at enhancing smart agricultural practices. Furthermore, the model can be refined by integrating a wider range of datasets and improving its resilience to extreme weather conditions, thereby providing a reliable tool for climate-responsive agricultural management and water conservation strategies. Full article

Over recent decades, coastal wetlands in Jiangsu Province have faced multiple challenges, including overfishing, reclamation for aquaculture, wetland shrinkage, and biodiversity loss. Implementing wetland ecological restoration proves crucial for mitigating the degradation of coastal wetland ecosystems. Quantifying ecosystem service values and establishing rational ecological compensation standards provide essential references for ecological compensation research and alleviating human–land conflicts. The Jianchuan Ecological Restoration Project, located in Dafeng District of Yancheng City, Jiangsu Province, employs integrated wetland, woodland, and farmland construction to rebuild biodiversity, enhance water conservation capacity, and improve water purification functions, thereby significantly boosting regional ecological services. Results have demonstrated that the total ecosystem service value of this project reaches CNY 76.2896 million, with climate regulation representing the highest value (CNY 68.1496 million, 89.33% of total). Subsequent values include biodiversity maintenance (3.40%), water purification (3.31%), and food production (2.95%), while carbon sequestration/oxygen release (0.96%) and soil retention (0.05%) show relatively lower contributions. Notably, this project innovatively integrates carbon finance mechanisms through “carbon sink loans”, achieving efficient transformation of ecological value from “paper accounts” to market realization. This study establishes a scientific foundation for ecological restoration projects through ecosystem service-based value assessment and pathway exploration, offering both theoretical framework and practical references. Full article

China has adopted extensive restoration practices to improve ecosystem function. The efficiency of these restoration efforts remains unclear, which may hinder the supply of ecosystem services (ESs). In this context, this study first employed InVEST models to clarify spatio-temporal changes in five key ESs. The static and dynamic efficiencies of ecosystem service production in 130 cities from 2015 to 2021 in the Yangtze River Economic Belt (YREB) were then measured using the Super-SBM-Malmquist model, with ESs considered as outputs. The results indicated that water conservation (WC), water purification (WP), and soil retention (SR) exhibited overall declining trends, decreasing by 28.32%, 3.22%, and 10.00%, respectively, while carbon storage (CS) and habitat quality (HQ) remained steady. More than 70% of studied cities exhibited static efficiency levels below 50%, which were attributed to inefficient utilization of labor, capital, and technology. Significant spatial heterogeneity was observed, with high-efficiency cities mainly located in mountainous areas and low-efficiency cities concentrated in flat regions. The downward trend in dynamic efficiency has been reversed from a 39.02% decline in 2015–2018 to a 38.31% increase in 2018–2021, despite being adversely affected by technological regression. Finally, several policy implications are proposed, including optimizing resource allocation, introducing advanced technology and setting the intercity cooperation and complementarity mechanisms. Full article

An in-depth understanding of the spatiotemporal heterogeneity of ecosystem service (ES) trade-offs and synergies, along with their driving factors, is crucial for formulating key ecological restoration strategies and effectively allocating ecological environmental resources in the Hulunbuir region. This study employed an integrated analytical approach combining the InVEST model, ArcGIS geospatial processing, R software environment, and Optimal Parameter Geographical Detector (OPGD). The spatiotemporal patterns and driving factors of the interaction of four major ES functions in Hulunbuir area from 2000 to 2020 were studied. The research findings are as follows: (1) carbon storage (CS) and soil conservation (SC) services in the Hulunbuir region mainly show a distribution pattern of high values in the central and northeast areas, with low values in the west and southeast. Water yield (WY) exhibits a distribution pattern characterized by high values in the central–western transition zone and southeast and low values in the west. For forage supply (FS), the overall pattern is higher in the west and lower in the east. (2) The trade-off relationships between CS and WY, CS and SC, and SC and WY are primarily concentrated in the western part of Hulunbuir, while the synergistic relationships are mainly observed in the central and eastern regions. In contrast, the trade-off relationships between CS and FS, as well as FS and WY, are predominantly located in the central and eastern parts of Hulunbuir, with the intensity of these trade-offs steadily increasing. The trade-off relationship between SC and FS is almost widespread throughout HulunBuir. (3) Fractional vegetation cover, mean annual precipitation, and land use type were the primary drivers affecting ESs. Among these factors, fractional vegetation cover demonstrates the highest explanatory power, with a q-value between 0.6 and 0.9. The slope and population density exhibit relatively weak explanatory power, with q-values ranging from 0.001 to 0.2. (4) The interactions between factors have a greater impact on the inter-relationships of ESs in the Hulunbuir region than individual factors alone. The research findings have facilitated the optimization and sustainable development of regional ES, providing a foundation for ecological conservation and restoration in Hulunbuir. Full article

(1) Background: Wetlands are ecologically significant ecosystems that support biodiversity and contribute to essential environmental functions such as water purification, carbon storage and flood regulation. However, these ecosystems face increasing pressures from land-use change and degradation, prompting the need for scalable and accurate classification methods to support conservation and policy efforts. In this research, our motivation was to test whether high-spatial-resolution PlanetScope imagery can be used with pixel-based machine learning to support the mapping and monitoring of wetlands at a national scale. (2) Methods: This study compared four machine learning classification models—Random Forest (RF), XGBoost (XGB), Histogram-Based Gradient Boosting (HGB) and a Multi-Layer Perceptron Classifier (MLPC)—to detect and map wetland areas across New Zealand. All models were trained using eight-band SuperDove satellite imagery from PlanetScope, with a spatial resolution of ~3 m, and ancillary geospatial datasets representing topography and soil drainage characteristics, each of which is available globally. (3) Results: All four machine learning models performed well in detecting wetlands from SuperDove imagery and environmental covariates, with varying strengths. The highest accuracy was achieved using all eight image bands alongside features created from supporting geospatial data. For binary wetland classification, the highest F1 scores were recorded by XGB (0.73) and RF/HGB (both 0.72) when including all covariates. MLPC also showed competitive performance (wetland F1 score of 0.71), despite its relatively lower spatial consistency. However, each model over-predicts total wetland area at a national level, an issue which was able to be reduced by increasing the classification probability threshold and spatial filtering. (4) Conclusions: The comparative analysis highlights the strengths and trade-offs of RF, XGB, HGB and MLPC models for wetland classification. While all four methods are viable, RF offers some key advantages, including ease of deployment and transferability, positioning it as a promising candidate for scalable, high-resolution wetland monitoring across diverse ecological settings. Further work is required for verification of small-scale wetlands (<~0.5 ha) and the addition of fine-spatial-scale covariates. Full article

The ecosystem services (ESs) of forests are the benefits that people derive from forest ecosystems. Their precise recognition is important for differentiating and determining the optimal principles of multifunctional forest management. The aim of this study is to identify some important ESs based on a site classification system at the lowest level—i.e., forest stands, at the forest owner level—as a tool for differentiated management. ESs were assessed within the Czech Republic and are expressed in units in accordance with the very sophisticated Forest Site Classification System. (1) Biomass production: The vertical differentiation of ecological conditions given by vegetation tiers, which reflect the influence of altitude, exposure, and climate, provides a basic overview of biomass production; the highest value is in the fourth vegetation tier, i.e., the Fageta abietis community. Forest stands are able to reach a stock of up to 900–1200 m³·ha⁻¹. The lowest production is found in the eighth vegetation tier, i.e., the Piceeta community, with a wood volume of 150–280 m³·ha⁻¹. (2) Soil conservation function: Geological bedrock, soil characteristics, and the geomorphological shape of the terrain determine which habitats serve a soil conservation function according to forest type sets. (3) The hydricity of the site, depending on the soil type, determines the hydric-water protection function of forest stands. Currently, protective forests occupy 53,629 ha in the Czech Republic; however, two subcategories of protective forests—exceptionally unfavorable locations and natural alpine spruce communities below the forest line—potentially account for 87,578 ha and 15,277 ha, respectively. Forests with an increased soil protection function—a subcategory of special-purpose forests—occupy 133,699 ha. The potential area of soil protection forests could be up to 188,997 ha. Water resource protection zones of the first degree—another subcategory of special-purpose forests—occupy 8092 ha, and there is potentially 289,973 ha of forests serving a water protection function (specifically, a water management function) in the Czech Republic. A separate subcategory of water protection with a bank protection function accounts for 80,529 ha. A completely new approach is presented for practical use by forest owners: based on the characteristics of the habitat, they can obtain information about the fulfillment of the habitat’s ecosystem services and, thus, have basic information for the determination of forest categories and the principles of differentiated management. Full article

Semi-natural grasslands are some of the most species-rich habitats in Europe and provide important ecosystem services such as biodiversity conservation, carbon sequestration and soil fertility maintenance. This study investigates how different intensities of grassland management affect the composition of functional groups and soil chemical properties. Five grassland management systems were analyzed: Cut3—three cuts per year; LGI—low grazing intensity; CG—combined cutting and grazing; Cut4—four cuts per year; and HGI—high grazing intensity. The functional groups assessed were grasses, legumes and forbs, while soil samples from three depths (0–10, 10–20 and 20–30 cm) were analyzed for their chemical properties (soil organic carbon—SOC; soil total nitrogen—STN; inorganic soil carbon—SIC; soil organic matter—SOM; potassium oxide—K₂O; phosphorus pentoxide—P₂O₅; C/N ratio; and pH) and physical properties (volumetric soil water content—VWC; bulk density—BD; and porosity—POR). The results showed that less intensive systems had a higher proportion of legumes, while species diversity, as measured via the Shannon index, was the highest in the Cut4 system. The CG system tended to have the highest SOC and STN at a 0–10 cm depth, with a similar trend observed for SOC_stock at a 0–30 cm depth. The Cut4, HGI and CG systems also had an increased STN_stock. Both grazing systems had the highest P₂O₅ content. A tendency towards a higher BD was observed in the top 10 cm of soil in the more intensive systems. Choosing a management strategy that is tailored to local climate and site conditions is crucial for maintaining grassland stability, enhancing carbon sequestration and promoting long-term sustainability in the context of climate change. Full article

Southwest China, with typical karst, is one of the 36 biodiversity hotspots in the world, facing extreme ecological fragility due to thin soils, limited water retention, and high bedrock exposure. This fragility intensifies under climate change and human pressures, threatening regional sustainable development. Ecological strategic areas (ESAs) are critical safeguards for ecosystem resilience, yet their spatiotemporal dynamics and driving mechanisms remain poorly quantified. To address this gap, this study constructed a multidimensional ecological health assessment framework (pattern integrity–process efficiency–function diversity). By integrating Sen’s slope, a correlated Mann–Kendall (CMK) test, the Hurst index, and fuzzy C-means clustering, we systematically evaluated ecological health trends and identified ESA differentiation patterns for 2000–2024. Orthogonal partial least squares structural equation modeling (OPLS-SEM) quantified driving factor intensities and pathways. The results revealed that ecological health improved overall but exhibited significant spatial disparity: persistently high in southern Guangdong and most of Yunnan, and persistently low in the Sichuan Basin and eastern Hubei, with 41.47% of counties showing declining/slightly declining trends. ESAs were concentrated in the southwest/southeast, whereas high-EHI ESAs increased while low-EHI ESAs declined. Additionally, the natural environmental and human interference impacts decreased, while unique geographic factors (notably the rock exposure rate, with persistently significant negative effects) increased. This long-term, multidimensional assessment provides a scientific foundation for targeted conservation and sustainable development strategies in fragile karst ecosystems. Full article

Inner Mongolia, a typical arid and semi-arid region in northern China, has undergone significant ecological transformation over the past two decades through climate shifts and large-scale ecological restoration projects. However, the relative contributions of climate and anthropogenic drivers to these ecological changes have not been sufficiently quantified. This study presents a comprehensive quantitative evaluation of the relative contributions of climate change, land conversion, and ecological management to changes in four critical ecosystem services—carbon sequestration, hydrological regulation, soil and water conservation, and windbreak and sand fixation—between 2001 and 2020. Using the residual trend method—a technique to separate climate-driven from human-induced effects—we further decomposed human influence into land conversion and management components. The results show that climate change was the primary driver, enhancing carbon sequestration and hydrological regulation but negatively impacting erosion control, with contributions often over 90%. In contrast, human activities had more spatially variable effects; while land conversion improved several services, it also heightened the vulnerability of sand fixation functions. The analysis further revealed ecosystem-type-specific responses, where grasslands and deserts responded better to management measures and forests and croplands showed greater improvements from land conversion. These findings offer crucial insights into the differentiated mechanisms and outcomes of ecological interventions, providing a scientific basis for optimizing restoration strategies and achieving sustainable ecosystem governance in climate-sensitive regions. Full article

Ecological security patterns (ESPs) are fundamental to safeguarding regional ecological integrity and enhancing human well-being. Consequently, research on conservation and restoration in critical regions is vital for ensuring ecological security and optimizing territorial ecological spatial configurations. Focusing on the Henan section of the Yellow River Basin, this study established the regional ESP and conservation–restoration framework through an integrated approach: (1) assessing four key ecosystem services—soil conservation, water retention, carbon sequestration, and habitat quality; (2) identifying ecological sources based on ecosystem service importance classification; (3) calculating a comprehensive resistance surface using the entropy weight method, incorporating key factors (land cover type, NDVI, topographic relief, and slope); (4) delineating ecological corridors and nodes using Linkage Mapper and the minimum cumulative resistance (MCR) theory; and (5) integrating ecological functional zoning to synthesize the final spatial conservation and restoration strategy. Key findings reveal: (1) 20 ecological sources, totaling 8947 km² (20.9% of the study area), and 43 ecological corridors, spanning 778.24 km, were delineated within the basin. Nineteen ecological barriers (predominantly located in farmland, bare land, construction land, and low-coverage grassland) and twenty-one ecological pinch points (primarily clustered in forestland, grassland, water bodies, and wetlands) were identified. Collectively, these elements form the Henan section’s Ecological Security Pattern (ESP), integrating source areas, a corridor network, and key regional nodes for ecological conservation and restoration. (2) Building upon the ESP and the ecological baseline, and informed by ecological functional zoning, we identified a spatial framework for conservation and restoration characterized by “one axis, two cores, and multiple zones”. Tailored conservation and restoration strategies were subsequently proposed. This study provides critical data support for reconciling ecological security and economic development in the Henan Yellow River Basin, offering a scientific foundation and practical guidance for regional territorial spatial ecological restoration planning and implementation. Full article

[Background] Brasenia schreberi is a perennial floating leaf aquatic plant with high ecological protection value and potential for economic development, and thus, its endangered mechanisms are of great concern. The rapid endangerment of this species in modern times may be primarily attributed to the deterioration of water and soil environmental conditions, as its growth relies on high-quality water and soil. [Objective] Exploring the responses of B. schreberi to water and soil conditions from the perspective of functional traits is of great significance for understanding its endangered mechanisms and implementing effective conservation strategies. [Methods] This study was conducted in the Tengchong Beihai Wetland, which has the largest natural habitat of B. schreberi in China. By measuring the key functional traits of B. schreberi and detecting the water and soil parameters at the collecting sites, the responses of these functional traits to the water and soil conditions have been investigated. [Results] (1) The growth status of B. schreberi affects the expression of its functional traits. Compared with sporadic distribution, B. schreberi in continuous patches have significantly higher stomatal conductance, intercellular CO₂ concentration, transpiration rate, and vein density, while these plants have significantly smaller leaf area and perimeter. (2) Good water quality directly promotes photosynthetic, morphological, and structural traits. However, high soil carbon, nitrogen, and phosphorus contents can inhibit the photosynthetic rate. The net photosynthetic rate is significantly positively correlated with dissolved oxygen content, pH value, ammonia nitrogen, and nitrate nitrogen contents in the water, as well as the magnesium, zinc, and silicon contents in the soil. In contrast, the net photosynthetic rate is significantly negatively correlated with the total phosphorus content in water and the total carbon, total nitrogen, and total phosphorus content in the soil. (3) Leaf area and perimeter show positive correlations with various water parameters, including the depth, temperature, pH value, dissolved oxygen content, ammonium nitrogen, and nitrate nitrogen content, yet they are negatively correlated with total phosphorus content, chemical oxygen demand, biological oxygen demand, and permanganate index of water. [Conclusions] This study supports the idea that B. schreberi thrives in oligotrophic water environments, while the notion that fertile soil is required for its growth still needs to be investigated more thoroughly. Full article

Because the loess widely used in the channel water conservancy projects in the Loess Plateau has a loose structure, low mechanical strength, and is prone to collapse when immersed in water, its comprehensive properties, such as structural strength and water resistance, must be greatly improved. Based on our previous work on the modification of Aga soil in Tibet, China, this study added hydrophobic n-dodecyltrimethoxysilane (WD10) to water glass solution (the main components are potassium silicate (K₂SiO₃) and silicic acid (H₂SiO₃) gel, referred to as PS) to obtain a composite coating PS-WD10, which was sprayed on the surface of loess blocks to achieve a full consolidation effect. We not only systematically investigated the morphology, chemical composition, and consolidation mechanism of the composite coating but also conducted in-depth and detailed research on its application performance such as friction resistance (structural strength), hydrophobicity, resistance to pure water and salt water immersion, and resistance to freeze–thaw cycles. The results showed that the PS-WD10 composite coating had better consolidation performance for loess blocks than the single coating of PS solution and WD10. For the loess block samples coated with the composite coatings, after 50 friction cycles, the weight loss rate was less than 15 wt%, and the water contact angle was above 120°. The main reason is that the good permeability of the PS solution and the excellent hydrophobicity of WD10 produce a good synergistic effect. The loess blocks coated with this composite coating are expected to replace traditional functional materials for water conservancy projects, such as cement and lime, in silt dam water conservancy projects, and also have better environmental protection and sustainability. Full article

Although economic profitability and food security often outweigh water conservation priorities in arid and semi-arid regions, this study investigates irrigation practices by evaluating water footprint and economic feasibility through a comparative analysis of water-intensive and water-efficient crops. In this context, an optimal irrigation disparity framework integrated with Internet of Things (IoT) and Machine Learning (ML) mechanisms is proposed to evaluate the effectiveness of water conservation, thereby assessing the potential for enhancing economic profitability. IoT-enabled components are employed to monitor real-time environmental—soil moisture, temperature, and weather—conditions between March and November 2023. This data is processed using a hybrid modeling approach that integrates KNN, GBT, and LSTM algorithms to predict both the duration of cultivation and the water requirements. Finally, the predicted parameters are incorporated into a multi-objective framework aimed at minimizing the disparity in water allocation per net benefit. The final results indicate that saffron required substantially less water—ranging from (19.87 to 28.65 ∗ 10⁶ m³)—compared to watermelon, which consumed (34.61 to 47.07 ∗ 10⁶ m³), while achieving a higher average net profit (33 ∗ 10⁹ IRR) relative to watermelon (31 ∗ 10⁹ IRR). Moreover, saffron consistently approached optimal values across disparity-based objective functions, averaging (0.404). These findings emphasize the dual advantages of saffron as a value-added, water-efficient crop in achieving substantial water conservation while enhancing profitability, offering actionable insights for authorities to incentivize water-efficient crop adoption through subsidies, market mechanisms, or regulatory frameworks. These strategies operationalize technical insights into actionable pathways for balancing food security, economic growth, and environmental resilience. Full article

Monoculture plantation systems face increasing challenges in sustaining ecosystem multifunctionality (EMF) under intensive management and climate change, with long-term functional trajectories remaining poorly understood. Although biodiversity–EMF relationships are well-documented in natural forests, the drivers of multifunctionality in managed plantations, particularly age-dependent dynamics, require further investigation. This study examines how stand development influences EMF in Castanopsis hystrix L. plantations, a dominant subtropical timber species in China, by assessing six ecosystem functions (carbon stocks, wood production, nutrient cycling, decomposition, symbiosis, and water regulation) of six forest ages (6, 10, 15, 25, 30, and 34 years). The results demonstrate substantial age-dependent functional enhancement, with carbon stocks and wood production increasing by 467% and 2016% in mature stand (34 year) relative to younger stand (6 year). Nutrient cycling and water regulation showed intermediate gains (6% and 23%). Structural equation modeling identified plant diversity and microbial community composition as direct primary drivers. Tree biomass profiles emerged as the strongest biological predictors of EMF (p < 0.01), exceeding abiotic factors. These findings highlight that C. hystrix plantations can achieve high multifunctionality through stand maturation facilitated by synergistic interactions between plants and microbes. Conservation of understory vegetation and soil biodiversity represents a critical strategy for sustaining EMF, providing a science-based framework for climate-resilient plantation management in subtropical regions. Full article