摘要：
为清晰认知儿童友好环境领域的研究范畴和发展过程,以2010-2023年Web of Science核心合集数据库中收录的与儿童友好环境相关的文献为研究数据,借助文献计量软件CiteSpace绘制儿童友好环境知识图谱,对儿童友好环境研究的发展态势、合作特征、关键词共现、时区分析等四个方面进行系统分析.以期能为深入理解儿童友好环境的内涵和未来建设方向提供参考.
摘要：
Marine eutrophication, primarily driven by nutrient over input from agricultural runoff, wastewater discharge, and atmospheric deposition, leads to harmful algal blooms (HABs) that pose a severe threat to marine ecosystems. This review explores the causes, monitoring methods, and control strategies for eutrophication in marine environments. Monitoring techniques include remote sensing, automated in situ sensors, modeling, forecasting, and metagenomics. Remote sensing provides large-scale temporal and spatial data, while automated sensors offer real-time, high-resolution monitoring. Modeling and forecasting use historical data and environmental variables to predict blooms, and metagenomics provides insights into microbial community dynamics. Control treatments encompass physical, chemical, and biological treatments, as well as advanced technologies like nanotechnology, electrocoagulation, and ultrasonic treatment. Physical treatments, such as aeration and mixing, are effective but costly and energy-intensive. Chemical treatments, including phosphorus precipitation, quickly reduce nutrient levels but may have ecological side effects. Biological treatments, like biomanipulation and bioaugmentation, are sustainable but require careful management of ecological interactions. Advanced technologies offer innovative solutions with varying costs and sustainability profiles. Comparing these methods highlights the trade-offs between efficacy, cost, and environmental impact, emphasizing the need for integrated approaches tailored to specific conditions. This review underscores the importance of combining monitoring and control strategies to mitigate the adverse effects of eutrophication on marine ecosystems.
通讯机构：
[Zhang, G ] C;Cent South Univ Forestry & Technol, Coll Forestry, Changsha 410004, Peoples R China.
关键词：
Subpixel scale;Forest fire identification;Data fusion;Satellite remote sensing
摘要：
Forest disaster fires pose a serious threat to forest resources and people's lives and property, and may also cause ecological disasters and social crises. There is a phenomenon of forest fire omission error, due to combustibles incomplete combustion in the forest fires early stage, low ground temperature and weak infrared radiation energy, and influence of forest canopy shading. Smoke detection based on satellite imagery is imperative for forest fire detection. Therefore, when forest fire occurs, forest fire smoke detection combined with forest fire infrared radiation monitoring can greatly reduce the phenomenon of forest fire omission error. Meteorological satellite imagery is commonly used for near real-time forest fire monitoring, thanks to its high temporal resolution. However, due to the low spatial resolution of meteorological satellite imagery and a large number of mixed pixels, it cannot accurately locate forest fire, and we introduced the concept of subpixel mapping, which is studied in detail in the article (https://doi.org/10.3390/rs14102460) we published. In this paper, the Himawari9 satellite imagery is used to detect forest fire smoke at subpixel scale based on the Modified Pixel Swapping Algorithm (MPSA) and detect forest fire infrared radiation at subpixel scale based on the Mixed-Pixel Unmixing integrated with Pixel-Swapping Algorithm (MPU-PSA), respectively, obtaining forest fire smoke detection and infrared radiation monitoring results, and average value of forest fire identification accuracy is 41.78 % and 83.96 %, respectively. The Threshold-Weighted Fusion (TWF) method is used to fuse subpixel scale forest fire smoke detection result and forest fire infrared radiation monitoring result, and average value of forest fire identification accuracy is increased from 83.96 % (forest fire infrared radiation monitoring alone) to 93.97 %. Results show that fusion of subpixel scale forest fire smoke detection result and forest fire infrared radiation monitoring result can greatly improve spatial positioning accuracy of forest fire monitoring and reduce phenomenon of forest fire omission error.
通讯机构：
[Ji, L ; Yang, LL] C;Cent South Univ Forestry & Technol, Sch Forestry, Changsha 410004, Peoples R China.
关键词：
Functional genes;Nitrogen deposition;Phoebe bournei young plantations;Rhizosphere soil;Soil bacterial community
摘要：
Anthropogenic nitrogen (N) deposition is expected to increase substantially and continuously in terrestrial ecosystems, endangering the balance of N and phosphorus (P) in P-deficient subtropical forest soil. Despite the widely reported responses of the microbial community to simulated N deposition, there is limited understanding of how N deposition affects the rhizosphere soil processes by mediating functional genes and community compositions of soil bacteria. Here, five levels of simulated N deposition treatments (N0, 0 g m- 2 center dot yr- 1; N1, 100 g m- 2 center dot yr- 1; N2, 200 g m- 2 center dot yr- 1; N3, 300 g m- 2 center dot yr- 1; and N4, 400 g m- 2 center dot yr- 1) were performed in a 10-year-old Phoebe bournei plantation. Quantitative microbial element cycling smart chip technology and 16S rRNA gene sequencing were employed to analyze functional gene compositions involved in carbon (C), N, and P cycling, as well as rhizosphere bacterial community composition. N deposition significantly influenced C cycling relative abundance of genes in the rhizosphere soil, especially those involved in C degradation. Low and moderate levels (100-300 g m- 2 center dot yr- 1) of N deposition promoted the relative abundance of the C decomposition-related genes (e. g., amyA, abfA, pgu, chiA, cex, cdh, and glx), whereas high N deposition (400 g m- 2 center dot yr- 1) suppressed enzyme (e. g., soil invertase, soil urease, and soil acid phosphatase) activities, affecting the C cycling processes in the rhizosphere. Simulated N deposition affected the functional genes associated with C, N, and P cycling by mediating soil pH and macronutrients. These findings provide new insights into the management of soil C sequestration in P. bournei young plantations as well as the regulation of C, N, and P cycling and microbial functions within ecosystems.
摘要：
Soil bacteria, as integral components of the soil microbial community, play pivotal roles in biogeochemical cycles and ecosystem functions in boreal forests. The altitudinal patterns of soil bacteria have been widely reported, but their community assembly is uncertain. Here, we investigated the soil bacterial community attributes (diversity, taxonomic and functional composition, and bacterial interactions) and ecological processes associated with community assembly on Mt. Oakley in the northern Greater Khingan Mountains via Illumina MiSeq sequencing and functional annotation tools. The alpha diversity indices of the soil bacteria exhibited a progressively decreasing trend with increasing altitude across seasons. The relative abundance of the dominant bacterial taxa was more sensitive to altitude than to season. More complex (more nodes and links) bacterial interactions were detected at the lowest and highest altitudinal sites, as well as in September. The variations in the taxonomic and functional compositions of the soil bacteria induced by altitude were mainly driven by the variations in soil pH and extracellular enzyme activities. Stochastic (dispersal limitation and drift) processes largely controlled the soil bacterial community assemblages across spatiotemporal scales. The community assemblages of soil bacteria were affected by altitude-induced changes in the taxonomic composition and functional groups. Collectively, our results have significant implications for understanding bacterial biogeography and community assembly mechanisms along altitudinal gradients in boreal forest ecosystems.
摘要：
The distribution of species is mostly influenced by climate synergistic effects and land use. The prediction of endangered species is dependent on fine-scale environmental features, especially in forests. The capture of fine-scale suitable habitats is bounded by low spatial resolution and coarse categorization. In this study, we improved the land use information and forest spatial detail through high forest thematic resolution land use data. To understand the relative influence of environmental factors, suitable habitat models for Chinese endangered tree species (Firmiana danxiaensis) under three climate and high forest thematic resolution land use conditions was constructed. We also assess F. danxiaensis 's response to climate and land use, and differential performance in land use on the suitable habitat. The area of suitable habitat for F. danxiaensis grows slowly under the SSP1-2.6 (shared social pathway scenario) scenario, decreases by 21.33% from the present to 2090 under the SSP2-4.5 scenario, and expands significantly under the SSP5-8.5 scenario. We conclude that high forest thematic resolution land use is beneficial in capturing species' requirements for specific habitats and especially necessary in predicting endangered species. At the same time, the distribution of suitable habitats for species is primarily driven by climate, and limited their development by land use. The study shows that the synthetic effects of climate and land use change on F. danxiaensis are positive, with a marked trend toward the northeast. As a consequence, some endangered species are able to benefit from future SSPs scenarios, in particular they respond more strongly to the SSP5-8.5 scenario than to the other scenarios. Assessing the role of changes in the climate and high forest thematic resolution land use on F. danxiaensis will help promote sustainable land use management and contribute to the conservation policy for Chinese endangered species.
摘要：
为探究中国农业碳排放的时空分布特征及驱动因素,基于2000—2021年中国31个省(自治区、直辖市,不包括香港、澳门和台湾,下同)统计年鉴数据,考察水利用、土地利用和能源消耗的碳排放,利用联合国政府间气候变化专门委员会(IPCC)碳排放因子建立2000—2021年水、土地和能源3个子系统相关变量,计算各省(自治区、直辖市)农业年碳排放总量,结合莫兰指数对农业碳排放时空演变趋势及空间关联特征进行分析,并运用对数均值迪氏分解法(Logarithmic Mean Divisia Index,简称LMDI)探析农业碳排放的主要驱动因素。结果表明:1)从时序变化看,农业碳排放量整体呈倒“V”型变化趋势。2)从农业碳排放来源看,农业源碳排放中源于化肥的碳排放占比最高。3)从农业碳排放空间差异看,碳排放较大的省份(自治区、直辖市)主要集中在黄淮海区域以及中部平原等水土资源条件丰富且优质的地区,西部地区与部分直辖市(北京、上海、天津)农业碳排放量较少,高农业碳排放地区存在向北蔓延的趋势。4)农业碳排放在空间上具有集聚效应,且随着时间推移,集聚效应的显著性有所下降,其中河南、安徽、山东等省份(自治区、直辖市)具有显著的“高-高集聚”效应,北京、天津、青海等省份(自治区、直辖市)具有显著的“低-低集聚”效应。5)农业水资源经济产出因素和农业劳动力密集度因素为正向驱动因素,农业水资源经济产出因素为中国农业碳排放增加的最主要因素;农业生产效率因素、劳动力规模因素和农业水土匹配度因素为碳排放负向驱动因素,其中农业生产效率因素的碳减排贡献率最高,为中国农业碳排放减少的最主要驱动因素。基于以上结果,本文针对中国农业碳减排提出以下建议:政府应加大对低碳农业的投入,支持新型肥料和新能源农机的研发,提高水土资源利用效率。同时,要利用农业碳排放的集聚效应,推动农业集中发展和区域间合作,培养新型农业人才。
摘要：
In eukaryotic cells, epigenetic modifications of DNA and histone play an important role in the regulation of gene expression. ROXY19 is a member of the plant specific CC-type glutaredoxins (GRXs). We found previously that ROXY19, by association with the TOPLESS/TOPLESS-related (TPL/TPR) family, strongly repressed a subset of genes which were positively regulated by the class II TGA factors. Arabidopsis plants ectopic expressing ROXY19 (ROXY19OE) were hypersensitive to xenobiotic chemicals due to the silencing of detoxification pathway genes. Here, we confirmed in vivo the interaction between ROXY19 and TPL. In order to understand the molecular mechanism underlying ROXY19-TPL module mediated gene repression, DNA methylation and histone deacetylation inhibitors were applied to assess the involvement of epigenetic modifications in the silencing of detoxification pathway genes in ROXY19OE plants. Promoter regions of some ROXY19 repressed genes were not methylated in wild-type/Col-0, and the methylation status were not altered in ROXY19OE. Furthermore, we investigated the role of epigenetic modifications in the antagonistic interplay between ethylene/jasmonic acid (ET/JA) and salicylic acid (SA) signaling pathways, which were supposed to be regulated by ROXY19.
摘要：
Moso bamboo (Phyllostachys edulis) forests, characterized by their rapid growth and clonal reproduction, have emerged as a significant threat to adjacent forest ecosystems. However, in China, the area, speed, and spatial distribution of moso bamboo forest expansion into other types of forests remains poorly understood. In this study, we present a case analysis of moso bamboo forests, employing a decade-long dataset from the forest second type inventory (FSTI) that utilizes transition matrices, neighboring ratio analysis, and spatio-temporal autocorrelation. This comprehensive investigation focuses on the spatio-temporal expansion of moso bamboo forests into diverse types of forests, with the aim of providing science-based recommendations for effective moso bamboo forest management. Our findings reveal that areas of moso bamboo forests have been expanding at an approximate annual rate of 2%, with an average expansion speed (including moso bamboo forests manually planted) of approximately 8 m per year. The length of moso bamboo-woodland ecotones (BWEs) increases as a consequence of moso bamboo forest expansion, indicating a sustained escalation in the extent of this expansion. Coniferous forests and evergreen broad-leaved forests are mainly invaded, accounting for around 58% of all invaded forests. The rate of moso bamboo forest expansion into different types of forests varies, although the rate remains fairly consistent within the same forest type. Moso bamboo forest expansion exhibits significant spatial heterogeneity. Furthermore, the area of moso bamboo forest intrusion into various types of forests in different provinces is notably influenced by the presence of moso bamboo forests and the proportional distribution of different forest types. The factors contributing to bamboo forest expansion encompass stand characteristics, soil attributes, light intensity, moso bamboo afforestation, forestry practices, and human disturbances.
摘要：
Xylem-associated fungus can secrete many secondary metabolites to help Aquilaria trees resist various stresses and play a crucial role in facilitating agarwood formation. However, the dynamics of endophytic fungi in Aquilaria sinensis xylem after artificial induction have not been fully elaborated. Endophytic fungi communities and xylem physio-biochemical properties were examined before and after induction with an inorganic salt solution, including four different times (pre-induction (0M), the third (3M), sixth (6M) and ninth (9M) month after induction treatment). The relationships between fungal diversity and physio-biochemical indices were evaluated. The results showed that superoxide dismutase (SOD) and peroxidase (POD) activities, malondialdehyde (MDA) and soluble sugar content first increased and then decreased with induction time, while starch was heavily consumed after induction treatment. Endophytic fungal diversity was significantly lower after induction treatment than before, but the species richness was promoted. Fungal beta-diversity was also clustered into four groups according to different times. Core species shifted from rare to dominant taxa with induction time, and growing species interactions in the network indicate a gradual complication of fungal community structure. Endophytic fungi diversity and potential functions were closely related to physicochemical indices that had less effect on the relative abundance of the dominant species. These findings help assess the regulatory mechanisms of microorganisms that expedite agarwood formation after artificial induction.
期刊：
European Journal of Forest Research,2024年143(3):955-969 ISSN：1612-4669
通讯作者：
Wu, Lichao;Lu, S;Wang, BP
作者机构：
[Wu, Lichao; Lu, Sheng; Liu, Sen; Fu, YuJia; Li, Xia] Cent South Univ Forestry & Technol, Key Lab Cultivat & Protect Nonwood Forest Trees, Minist Educ, Forestry Coll, Changsha 410004, Hunan, Peoples R China.;[Li, Peng] Guangxi Zhuang Autonomous Reg Forestry Res Inst, Nanning 530002, Guangxi, Peoples R China.;[Wang, Baoping; Qiao, Jie] Chinese Acad Forestry, Res Inst Nontimber Forestry, Zhengzhou 450003, Henan, Peoples R China.;[Li, Hui] Guangxi Diyuanzhiben Fertilizer Ind Co Ltd, Nanning 530022, Guangxi, Peoples R China.
通讯机构：
[Wu, LC; Wang, BP ; Lu, S ] C;Cent South Univ Forestry & Technol, Key Lab Cultivat & Protect Nonwood Forest Trees, Minist Educ, Forestry Coll, Changsha 410004, Hunan, Peoples R China.;Chinese Acad Forestry, Res Inst Nontimber Forestry, Zhengzhou 450003, Henan, Peoples R China.
关键词：
Soil bacteria;Fertilizer application duration;Ecological network;Carbon and nitrogen cycles;Functional genes
摘要：
Paulownia fortunei, one of the world's fastest growing timber tree species, is universally applied with fertilizer as a management approach to meet the nutrient requirements for efficient cultivation. The substantial effects of fertilizer on soil microorganisms in Paulownia plantations have been empirically tested; however, the successive chronosequence of soil microbial carbon and nitrogen functional genes under different fertilizer application durations remains limited. The objective of this study was to explore the characteristics of soil microorganisms involved in carbon and nitrogen cycling and greenhouse gas (GHG) production under different fertilizer application durations. Different fertilizer treatments, i.e., the short-term group (SG) versus the long-term group (LG), and durations were applied to subtropical plantations in southern China and compared with zonal evergreen broad-leaved forests. Results showed that fertilizer treatment significantly increased the relative abundance of Acidobacteriota and the expression of nirK and nosZ. The functional groups that dominated metabolism in SG and LG treatments belonged to Actinobacteria and Acidobacteriota, respectively, suggesting that the nutrient preference of microorganisms in forest soil may change from copiotrophs to oligotrophs with increasing fertilizer application duration. Correlation network analysis showed that the communities that dominated the carbon and nitrogen cycles belonged to Actinobacteria and Acidobacteriota, respectively, and were closely related to ammonium nitrogen and available iron. Actinobacteria and Acidobacteriota were likely the major taxa that affected soil GHG production under different fertilizer application durations. We concluded that long-term fertilizer use changed the preference of microbial nutrient uptake into recalcitrant nutrients, and the sensitivity of the microbial community to nutrients gradually decreased with increasing fertilizer application time. The dominant Actinobacteria affected soil carbon and nitrogen cycles largely by stimulating denitrification to increase the release of nitrous oxide, which might lead to the loss of nitrogen components and the intensification of the GHG effect with increasing fertilizer application time.
摘要：
Accurate estimations of carbon (C), nitrogen (N), and phosphorus (P) densities in shrublands are pivotal for assessing terrestrial ecosystem carbon sequestration. Combining in-situ investigations and machine learning facilitates large-scale patterns mapping, however, which often overlooks underlying ecological regulations. Here we utilize data from 1,122 survey plots across China's shrublands and develop a novel knowledge-based deep learning framework that integrates a structural equation model (SEM) to elucidate mechanisms and construct an artificial neural network (ANN) based on these causal relationships. Results show that biomass allocation to different organs follows allometric regulations and that N and P concentrations maintain a degree of stoichiometric homeostasis following biological stoichiometry theory. This insight guides the construction of our ANN, which outperforms both SEM and other prevalent machine learning methods. By leveraging ecological theories to inform model construction, our framework not only enhances prediction accuracy and explainability but also provides a methodological blueprint for ecological research. China has set a goal to achieve carbon neutrality by 2060, and one way to achieve this is by utilizing terrestrial ecosystems, which can absorb CO2 from the atmosphere. The effectiveness of natural carbon sinks is often limited by the availability of essential nutrients such as nitrogen (N) and phosphorus (P). Shrublands are unique and contribute the most uncertainty in estimating China's carbon storage. Thus, accurately mapping shrubland vegetation C, N, and P densities is critical. Previous studies usually apply data-driven methods to scale up site information to larger scales, often failing to consider underlying ecological regulations. Here, we advance this approach by integrating deep learning (DL) with causal understanding. We found that C, N, and P allocation to different organs is relatively consistent, and their ratios maintain generally stable. These relationships are then applied to the DL algorithm. The knowledge-based DL model outperforms popular machine learning methods. Our framework not only improves the ability to predict nutrient distributions in shrublands but also serves as a blueprint for further ecological research, enhancing both the accuracy and the explainability of ecological models. Biomass and nutrient allocation follow allometry and biological stoichiometry theory Structural equation model (SEM) and artificial neural network (ANN) are combined to achieve casual interference and accurate prediction Prior knowledge-based deep learning can advance ecological modeling
