应用生态学报最新文献

The enhancement of carbon sequestration in terrestrial ecosystems is regarded as one of the most effective measures for mitigating global carbon emissions and climate change. Compared to that at the local scale, watershed terrestrial ecosystems at the watershed scale typically exhibit the characteristics of more complex hydrological processes, intense anthropogenic disturbance, independence and intact. The Dongting Lake basin, as one of the most representative watershed in China, exhibits low carbon sink stability but significant carbon sequestration potential. We reviewed current research on the spatiotemporal patterns, carbon storage, sequestration potential, and carbon storage stability along the Dongting Lake basin, and proposed future research prospects. Currently, the mea-surement and monitoring of terrestrial ecosystem carbon sinks primarily rely on conventional models, with the limitations of inconsistent validation standards, relatively low precision, and neglecting anthropogenic disturbances. Data sources are predominantly confined to land use and remote sensing imagery, which often suffer from insufficient spatial resolution and untimely updates, leading to considerable uncertainties in carbon sink estimation. Overall, forest ecosystems are the primary contributors to carbon sequestration across the basin, while farmland and wetland ecosystems exhibit substantial carbon sequestration potential. Further attention should also be directed toward the complex hydrological conditions and regional characteristics. There is a critical need to develop carbon cycle models that couple watershed hydrological processes with biogeochemical cycles. Additionally, we require a systematic assessment and quantification of the mechanisms underlying the influences of human activities on ecosystem carbon sequestration. Such efforts are essential for more accurately evaluating the carbon sequestration function, potential, and multi-scale drivers of the terrestrial ecosystem in the Dongting Lake basin, thereby offering scientific support for achieving China's "Dual Carbon" goals.

Reseeding, fertilization, and fencing are widely used restoration measures for degraded natural grasslands. Soil organic carbon fractions serve as key indicators for evaluating carbon turnover and sequestration during the restoration of degraded grasslands. Clarifying the impacts of various restoration measures on soil organic carbon fractions can provide a scientific basis for selecting appropriate restoration strategies. Based on 269 data pairs from 73 papers, we evaluated the effects of three restoration measures-reseeding, fertilization, and fencing-on soil organic carbon fractions in degraded natural grasslands in China. The results showed that reseeding significantly increased soil total organic carbon by 18.7%, dissolved organic carbon by 12.4%, and easily oxidizable carbon by 17.7%. Fertilization significantly increased easily oxidizable carbon by 15.5% and light fraction organic carbon by 11.5%, but significantly reduced microbial biomass carbon by 15.5%. Fencing significantly increased dissolved organic carbon by 12.7%, microbial biomass carbon by 17.8%, and particulate organic carbon by 14.7%, while significantly reduced light fraction organic carbon by 9.7%. Under different environmental conditions, reseeding significantly enhanced soil organic carbon content, whereas fencing markedly enhanced soil microbial biomass carbon. In contrast, the effects of fertilization on soil organic carbon fractions exhibited considerable uncertainty. Correlation analysis indicated that soil dissolved organic carbon, light fraction organic carbon, and mineral associated organic carbon significantly increased with increasing total soil organic carbon content, whereas microbial biomass carbon, easily oxidizable carbon, and particulate organic carbon remained relatively stable. Soil moisture and ammonium content are key factors influencing changes in soil organic carbon during the restoration of degraded grasslands.

We conducted a field experiment on three rice cultivation patterns, namely ratoon rice, single-cropping rice, and double-cropping rice, using hybrid rice and conventional rice varieties as materials in 2021-2022. We evaluated the ecological efficiency differences across rice cultivation patterns from multiple dimensions, including yield and material distribution, greenhouse gas emissions, carbon nitrogen footprint, and carbon balance, using closed static greenhouse gas collection and life cycle assessment methods. The results showed that the ratoon rice pattern had the highest average yield and daily average yield, followed by the double-cropping rice pattern with the lowest daily average yield, and the lowest single-cropping yield ranking second in daily average yield. In the rice ratooning system, non-structural carbohydrate (NSC) translocation amount and translocation rate in various plant parts were significantly higher in ratoon season rice than in its main crop, single-cropping rice, and both early and late crops of double-cropping rice. Across all organs, the average contribution of NSC remobilization to grain yield formation was 7.5% over the two years, being 19.9% and 12.8% higher than that of the main crop and single-cropping rice, respectively, and 67.0% and 77.0% greater than that of early and late rice in the double-cropping system. Compared with the single-cropping and double-cropping, the ratoon rice reduced CO₂ emission intensity by 2.3%-725.0% and 6.8%-732.6% in 2021 and 2022, CH₄ emission intensity by 31.2%-751.8% and 27.6%-746.4%, N₂O emission intensity by 7.4% and 4.6%, resource utilization efficiency by 23.5%-24.6% and 57.4%-57.5%, and daily economic benefits by 36.0%-35.7% and 81.9%-101.9%, respectively. In 2021 and 2022, the carbon footprint of the ratoon rice pattern increased by 31.2% and 11.2% respectively compared to single-cropping rice, and decreased by 19.1% and 28.2% respectively compared to double-cropping rice. The nitrogen footprint increased by 44.2% and 46.8% compared to single-cropping rice, and decreased by 10.1% and 15.4% compared to double-cropping rice. The carbon budget surplus of ratoon, single-cropping, and double-cropping rice were 24623.5, 13342.6, and 23772.2 kg CO₂-eq·hm^-2, respectively. It is suggested that the ratoon rice, especially the regenerated season rice, has high daily yield and low greenhouse gas emission intensity per unit yield, achieving stronger synergy between yield and carbon surplus, which is a sustainable, ecologically efficient, and environmentally friendly cropping system well-suited to rice production in Southeast China.

Grasslands of central Inner Mongolia are a crucial component of ecological security barrier in northern China. By integrating field-measured quadrat data, remote sensing imagery, and environmental variables of grasslands in central Inner Mongolia, we developed aboveground biomass estimation models using machine learning algorithms, and generated high-resolution spatial distribution datasets for the period 2000-2020. We further analyzed the spatiotemporal variations and driving factors of aboveground biomass by the trend analysis and GeoDetector methods. The results showed that among multiple machine learning models, the gradient boosting machine (GBM) algorithm demonstrated optimal performance, with the coefficient of determination (R²), mean absolute error (MAE) and root mean square error (RMSE) being 0.58, 42.40 g·m^-2, and 56.99 g·m^-2, respectively. From 2000 to 2020, aboveground biomass showed a fluctuating upward trend, with a multi-year average value of 148.72 g·m^-2. Spatially, aboveground biomass displayed a pattern of low values in the northwest and high values in the southeast. Overall, 77.9% of the region experienced increases in aboveground biomass, while only 0.3% showed significant degradation. Factor detection revealed that annual precipitation, growing season precipitation, soil nitrogen, and soil organic carbon content were the primary drivers of spatial heterogeneity in aboveground biomass, and all interactions exhibiting enhanced effects. Our results could provide scientific basis for the management and sustainable development of grassland resources in central Inner Mongolia.

Accurate identification of bare patches caused by Ochotona curzoniae disturbance is fundamental for scientifically assessing the damage level. Traditional methods for recognizing and calculating the area of bare patches are often computationally complex and inefficient. Here, we proposed a wavelet-enhanced U-shaped convolutional neural network (W-UNet) segmentation method based on deep learning for unmanned aerial vehicle (UAV) imagery segmentation, which was based on the U-shaped convolutional neural network (UNet) architecture and used the 16-layer Visual Geometry Group network (VGG16) as the backbone. We introduced the coordinate attention mecha-nism (CA) in the skip connection section to enhance the spatial localization of target regions, and wavelet transform convolution (WTConv) during the encoding stage to improve high-frequency information extraction and the recovery of fine-grained features. Additionally, we employed a composite loss function combining Focal Loss and Dice Loss to effectively address the class imbalance issues. The results showed that the proposed method achieved a mean intersection over union (MIoU) of 81.2%, mean pixel accuracy (MPA) of 89.4%, and overall accuracy (ACC) of 95.8%, significantly outperforming the conventional UNet-Vgg model. This study would provide a robust technical framework for the efficient and accurate monitoring of bare patches induced by O. curzoniae infestation.

With the development of automation technology, the advantages of remote monitoring network in the acquisition and efficient processing of ecosystem data will be more prominent and provide data support for ecological monitoring and resource management. We employed domestic low-cost sensors and intelligent data collectors to construct a remote monitoring network for soil moisture and temperature, achieving long-term continuous monitoring of multi-parameters (soil moisture, temperature, air temperature, air humidity and rainfall) and automatic data transmission. The specific installation process including: 1) Sample point selection: select representative monitoring sampling points based on the comprehensive consideration of topography, landform and vegetation type; 2) Equipment installation: install five soil hydrothermal sensors in the 2 m soil profile by field drilling method, and connect the sensors to the intelligent collector powered by 10 W solar panels through the intelligent data collector, measure and store a set of data per hour; 3) Data recording: measure and store data per hour and aggregate to a server based on the internet. The cost of this equipment is approximately 20% of that of the imported equipments. Based on this method, a network of 60 sampling points with two main soil textures, sand loess and sand, was established in Shenmu, Shaanxi Province. Actual operation and maintenance as well as verification were conducted. The results showed that the network can accurately capture the changes of soil moisture and temperature at different depths, and the sensors exhibited great measurement accuracy (R²≥0.90). The monitoring network enabled synchronous observation of air humidity and precipitation. The remote monitoring network established in this study provides a technical paradigm for the remote monitoring of soil moisture and temperature in diverse ecosystems.

The research on urban carbon emissions based on the full life cycle assessment is an important basis for formulating collaborative urban emission reduction strategies. Although there are increasingly fruitful research results, the spatial scales, industrial areas, and research methods of different studies differed greatly. We reviewed literature in both Chinese and English between 1998 and 2024, summarized the research trends and disciplinary differentiation characteristics of multiscale urban carbon emissions from a life cycle perspective, and then compared the key factors and mechanisms of the life cycle of carbon emissions at the three scales of city, block, and building based on the method of knowledge graph analysis. As the research scale shifted from macro (cities) to micro level (buildings), the methods transitioned from input-output model-based analysis to life cycle-based analysis, and the factors affecting the life cycle of carbon emissions shifted from socio-economic and urban form characteristics to buil-ding functional forms, building materials, and structures. Finally, we explored urban collaborative carbon reduction strategies from three aspects: building a multiscale carbon emission life cycle data management platform, analyzing the dynamic evolution mechanism of the life cycle of urban carbon emissions, and achieving cross-regional and multi-sectoral carbon reduction collaborative management. These strategies would provide reference for low-carbon oriented urban sustainable development and the achievement of dual carbon goals.

Under the background of balancing marine resource exploitation and ecosystem conservation, marine ranching has become an important approach to enhance the sustainable use of fishery resources and to improve coastal environmental quality. Tropical marine ranching, situated in high-temperature, high-salinity, biodiversity-rich, and disturbance-prone waters, faces greater uncertainty and management challenges in ecological security. Based on a systematic review of literature, we summarized research progress on evaluation index systems, assessment methods, and weighting approaches. Most frameworks originate from temperate and subtropical marine ranch studies. Although recent efforts have begun to address tropical contexts, less attention has been paid on high-temperature stress responses, tourism carrying pressure, and high-frequency disturbance factors. With respect to metho-dology, studies have evolved from static composite evaluations to dynamic predictions integrating causal analysis, machine learning, and system dynamics, yet validation in tropical scenarios is still limited. Weighting approaches have applied subjective, objective, and combined methods, but optimization for tropical-specific ecological factors is lacking. To address these knowledge gaps, we proposed that the following directions for future studies: 1) develo-ping a multi-source uncertainty assessment framework integrating natural and anthropogenic disturbances to enhance the accuracy of risk early-warning; 2) constructing a comprehensive evaluation system reflecting tropical ecological characteristics and industry coupling to avoid 'climate-zone transfer' bias in indicators; and 3) creating time-series-embedded tools for dynamic monitoring and adaptive management of ecological security.

Traditional methods for evaluating human settlement suitability often fail to effectively characterize the nonlinear influence of indicator values on overall suitability, such as diminishing marginal effects or counterproductive outcomes from excessive input. To overcome those shortages, we proposed a Nonlinear Technique for Order Preference by Similarity to Ideal Solution (nonlinear-TOPSIS) to achieve a refined quantitative evaluation of human settlement suitability, with the central urban area of Xi'an as the study area. We constructed an evaluation framework based on urban block units as the fundamental analysis granularity, and developed an indicator system from four dimensions: natural environment, economic prosperity, living convenience, and building morphology. Based on the distributional characteristics of each indicator, we introduced nonlinear fitting methods, including power-law functions, Gaussian functions, Beta functions, and Gaussian mixture models, to characterize their nonlinear impacts and marginal effects, and then established an improved TOPSIS model based on nonlinear function to identify ideal and negative ideal solutions. Meanwhile, we compared multiple subjective and objective weighting methods to provide a more rational weight assignment for the evaluation algorithm. The results showed that the human settlement suitability index values within the research area approximated a normal distribution and exhibited a mixed spatial pattern at the parcel level. High-suitability areas covered 167.82 km²(accounting for 17.5% of the total area), mainly distributed in Beilin District, Xincheng District, and parts of Yanta District. These areas were generally characterized by moderate greening rates, appropriate building density, and well-developed living facilities. Low-suitability areas were concentrated in old urban neighborhoods and underdeveloped zones, exhibiting spatial imba-lances in greening rates, building density, and infrastructure. The spatial lag model and spatial error model further validated the applicability and robustness of the proposed evaluation method. The nonlinear-TOPSIS algorithm proposed here would enrich the theoretical framework of human settlement suitability assessment, expand the methodological approach of spatial decision support, and provide theoretical basis and methodological support for urban spatial optimization and refined governance.

In the context of global warming, we investigated the growth dynamics and climatic response mechanisms of Populus cathayana in the Jiuzhaigou region, western Sichuan Plateau, an endemic broadleaf species in China. We developed a standardized tree-ring width chronology to analyze radial growth response to climatic factors from 1959 to 2022. Moving correlation analysis was applied to assess the stability of climate-growth relationships, and growth change percentage (PGC) method was used to identify growth release and suppression events. The results showed that the tree-ring width of P. cathayana was significantly positively correlated with May-June average maximum temperature (r=0.525), mean temperature (r=0.548), and average minimum temperature (r=0.341), but significantly negatively correlated with precipitation (r=-0.260), relative humidity (r=-0.579), and cloud cover (r=-0.483) during the same period. PGC analysis revealed three significant growth release events (1937-1940, average PGC=32.8%; 1977-1978, average PGC=42.2%; 1999-2004, average PGC=43.3%) and one significant growth suppression event (2008-2010, average PGC=-28.9%). Moving correlation analysis revealed a marked shift in climate-growth relationship during the 1970s, characterized by the transition of growing-season temperatures from negative to significantly positive, while relative humidity and self-calibrated Palmer drought severity index correlations shifted from positive to significantly negative. These findings underscore the non-stationary climatic responses of P. cathayana in western Sichuan, suggesting that warmer and drier conditions in the growing season favor the radial growth.