AIM:To analyze ultrasound biomicroscopy(UBM)images using random forest network to find new features to make predictions about vault after implantable collamer lens(ICL)implantation.METHODS:A total of 450 UBM images we...AIM:To analyze ultrasound biomicroscopy(UBM)images using random forest network to find new features to make predictions about vault after implantable collamer lens(ICL)implantation.METHODS:A total of 450 UBM images were collected from the Lixiang Eye Hospital to provide the patient’s preoperative parameters as well as the vault of the ICL after implantation.The vault was set as the prediction target,and the input elements were mainly ciliary sulcus shape parameters,which included 6 angular parameters,2 area parameters,and 2 parameters,distance between ciliary sulci,and anterior chamber height.A random forest regression model was applied to predict the vault,with the number of base estimators(n_estimators)of 2000,the maximum tree depth(max_depth)of 17,the number of tree features(max_features)of Auto,and the random state(random_state)of 40.0.RESULTS:Among the parameters selected in this study,the distance between ciliary sulci had a greater importance proportion,reaching 52%before parameter optimization is performed,and other features had less influence,with an importance proportion of about 5%.The importance of the distance between the ciliary sulci increased to 53% after parameter optimization,and the importance of angle 3 and area 1 increased to 5% and 8%respectively,while the importance of the other parameters remained unchanged,and the distance between the ciliary sulci was considered the most important feature.Other features,although they accounted for a relatively small proportion,also had an impact on the vault prediction.After parameter optimization,the best prediction results were obtained,with a predicted mean value of 763.688μm and an actual mean value of 776.9304μm.The R²was 0.4456 and the root mean square error was 201.5166.CONCLUSION:A study based on UBM images using random forest network can be performed for prediction of the vault after ICL implantation and can provide some reference for ICL size selection.展开更多
Forest net primary productivity （NPP） is a key parameter for forest monitoring and management. In this study, monthly and annual forest NPP in the northeastern China from 1982 to 2010 were simulated by using Carnegi...Forest net primary productivity （NPP） is a key parameter for forest monitoring and management. In this study, monthly and annual forest NPP in the northeastern China from 1982 to 2010 were simulated by using Carnegie-Ames-Stanford Approach （CASA） model with normalized difference vegetation index （NDVI） sequences derived from Advanced Very High Resolution Radiometer （AVHRR） Global Invento y Modeling and Mapping Studies （GIMMS） and Terra Moderate Resolution Imaging Spectroradiometer （MODIS） products. To address the problem of data inconsistency between AVHRR and MODIS data, a per-pixel unary linear regres- sion model based on least ~;quares method was developed to derive the monthly NDVI sequences. Results suggest that estimated forest NPP has mean relative error of 18.97% compared to observed NPP from forest inventory. Forest NPP in the northeastern China in- creased significantly during the twenty-nine years. The results of seasonal dynamic show that more clear increasing trend of forest NPP occurred in spring and awmnn. This study also examined the relationship between forest NPP and its driving forces including the climatic and anthropogenic factors. In spring and winter, temperature played the most pivotal role in forest NPR In autumn, precipitation acted as the most importanl factor affecting forest NPP, while solar radiation played the most important role in the summer. Evaportran- spiration had a close correlation with NPP for coniferous forest, mixed coniferous broadleaved forest, and broadleaved deciduous forest. Spatially, forest NPP in the Da Hinggan Mountains was more sensitive to climatic changes than in the other ecological functional re- gions. In addition to climalie change, the degradation and improvement of forests had important effects on forest NPP. Results in this study are helpful for understanding the regional carbon sequestration and can enrich the cases for the monitoring of vegetation during long time series.展开更多
Detecting small forest fire targets in unmanned aerial vehicle(UAV)images is difficult,as flames typically cover only a very limited portion of the visual scene.This study proposes Context-guided Compact Lightweight N...Detecting small forest fire targets in unmanned aerial vehicle(UAV)images is difficult,as flames typically cover only a very limited portion of the visual scene.This study proposes Context-guided Compact Lightweight Network(CCLNet),an end-to-end lightweight model designed to detect small forest fire targets while ensuring efficient inference on devices with constrained computational resources.CCLNet employs a three-stage network architecture.Its key components include three modules.C3F-Convolutional Gated Linear Unit(C3F-CGLU)performs selective local feature extraction while preserving fine-grained high-frequency flame details.Context-Guided Feature Fusion Module(CGFM)replaces plain concatenation with triplet-attention interactions to emphasize subtle flame patterns.Lightweight Shared Convolution with Separated Batch Normalization Detection(LSCSBD)reduces parameters through separated batch normalization while maintaining scale-specific statistics.We build TF-11K,an 11,139-image dataset combining 9139 self-collected UAV images from subtropical forests and 2000 re-annotated frames from the FLAME dataset.On TF-11K,CCLNet attains 85.8%mAP@0.5,45.5%mean Average Precision(mAP)@[0.5:0.95],87.4%precision,and 79.1%recall with 2.21 M parameters and 5.7 Giga Floating-point Operations Per Second(GFLOPs).The ablation study confirms that each module contributes to both accuracy and efficiency.Cross-dataset evaluation on DFS yields 77.5%mAP@0.5 and 42.3%mAP@[0.5:0.95],indicating good generalization to unseen scenes.These results suggest that CCLNet offers a practical balance between accuracy and speed for small-target forest fire monitoring with UAVs.展开更多
Patterns and drivers of species–genetic diversity correlations(SGDCs)have been broadly examined across taxa and ecosystems and greatly deepen our understanding of how biodiversity is maintained.However,few studies ha...Patterns and drivers of species–genetic diversity correlations(SGDCs)have been broadly examined across taxa and ecosystems and greatly deepen our understanding of how biodiversity is maintained.However,few studies have examined the role of canopy structural heterogeneity,which is a defining feature of forests,in shaping SGDCs.Here,we determine what factors contribute toα-andβ-species–genetic diversity correlations(i.e.,α-andβ-SGDCs)in a Chinese subtropical forest.For this purpose,we used neutral molecular markers to assess genetic variation in almost all adult individuals of the dominant tree species,Lithocarpus xylocarpus,across plots in the Ailaoshan National Natural Reserve.We also quantified microhabitat variation by quantifying canopy structure heterogeneity with airborne laser scanning on 201-ha subtropical forest plots.We found that speciesα-diversity was negatively correlated with geneticα-diversity.Canopy structural heterogeneity was positively correlated with speciesα-diversity but negatively correlated with geneticα-diversity.These contrasting effects contributed to the formation of a negativeα-SGDC.Further,we found that canopy structural heterogeneity increases speciesα-diversity and decreases geneticα-diversity by reducing the population size of target species.Speciesβ-diversity,in contrast,was positively correlated with geneticβ-diversity.Differences in canopy structural heterogeneity between plots had non-linear parallel effects on the two levels ofβ-diversity,while geographic distance had a relatively weak effect onβ-SGDC.Our study indicates that canopy structural heterogeneity simultaneously affects plot-level community species diversity and population genetic diversity,and species and genetic turnover across plots,thus drivingα-andβ-SGDCs.展开更多
Making the distinction between different plantation tree species is crucial for creating reliable and trustworthy information, which is critical in forestry administration and upkeep. Over the years, forest delineatio...Making the distinction between different plantation tree species is crucial for creating reliable and trustworthy information, which is critical in forestry administration and upkeep. Over the years, forest delineation and mapping have been done using the conventional techniques, such as the utilization of ground truth facts together with orthophotos. These techniques have been proven to be very precise, but they are expensive, cumbersome, and challenging to employ in remote regions. To resolve this shortfall, this research investigates the potential of data from the commercial, PlanetScope CubeSat and the freely available, Sentinel 2 data from Copernicus to discriminate commercial forest tree species in the Usutu Forest, Eswatini. Two approaches for image classification, Random Forest (RF) and the Support Vector Machine (SVM) were investigated at different levels of the forest database classification which is the genus (family of tree species) and species levels. The result of the study indicates that, the Sentinel 2 images had the highest species classification accuracy compared to the PlanetScope image. Both classification methods achieved a 94% maximum OA and 0.90 kappa value at the genus level with the Sentinel 2 imagery. At the species level, the Sentinel 2 imagery again showed highly acceptable results with the SVM method, with an OA of 82%. The PlanetScope images performed badly with less than 64% OA for both RF and SVM at the genus level and poorer at the species level with a low OA figure, 47% and 53% for the SVM and RF respectively. Our results suggest that the freely available Sentinel 2 data together with the SVM method has a high potential for identifying differences between commercial tree species than the PlanetScope. The study uncovered that both classification methods are highly capable of classifying species under the gum genus group (esmi, egxu, and egxn) using both imageries. However, it was difficult to separate species types under the pine genus group, particularly discriminating the hybrid species such as pech and pell since pech is a hybrid species for pell.展开更多
This paper explores the synergistic effect of a model combining Elastic Net and Random Forest in online fraud detection.The study selects a public network dataset containing 1781 data records,divides the dataset by 70...This paper explores the synergistic effect of a model combining Elastic Net and Random Forest in online fraud detection.The study selects a public network dataset containing 1781 data records,divides the dataset by 70%for training and 30%for validation,and analyses the correlation between features using a correlation matrix.The experimental results show that the Elastic Net feature selection method generally outperforms PCA in all models,especially when combined with the Random Forest and XGBoost models,and the ElasticNet+Random Forest model achieves the highest accuracy of 0.968 and AUC value of 0.983,while the Kappa and MCC also reached 0.839 and 0.844 respectively,showing extremely high consistency and correlation.This indicates that combining Elastic Net feature selection and Random Forest model has significant performance advantages in online fraud detection.展开更多
With the popularization of microgrid construction and the connection of renewable energy sources to the power system,the problem of source and load uncertainty faced by the coordinated operation of multi-microgrid is ...With the popularization of microgrid construction and the connection of renewable energy sources to the power system,the problem of source and load uncertainty faced by the coordinated operation of multi-microgrid is becoming increasingly prominent,and the accuracy of typical scenario predictions is low.In order to improve the accuracy of scenario prediction under source and load uncertainty,this paper proposes a typical scenario identification model based on random forests and order parameters.Firstly,a method for ordinal parameter identification and quantification is provided for the coordinated operating mode of multi-microgrids,taking into account source-load uncertainty.Secondly,the dynamic change characteristics of the order parameters of the daily load curve,wind and solar curve,and load curve of typical scenarios are statistically analyzed to identify the key order parameters that have the most significant impact on the uncertainty of the load.Then,the order parameters and seasonal distribution are used as features to train a random forest classification model to achieve efficient scenario prediction.Finally,the simulation of actual data from a provincial distribution network shows that the proposed method can accurately classify typical scenarios with an accuracy rate of 92.7%.Additionally,sensitivity analysis is conducted to assess how changes in uncertainty levels affect the importance of each order parameter,allowing for adaptive uncertainty mitigation strategies.展开更多
The net primary productivity(NPP)of forest ecosystems plays a crucial role in regulating the terrestrial carbon cycle under global climate change.While the temporal effect driven by ecosystem processes on NPP variatio...The net primary productivity(NPP)of forest ecosystems plays a crucial role in regulating the terrestrial carbon cycle under global climate change.While the temporal effect driven by ecosystem processes on NPP variations is well-documented,spatial variations(from local to regional scales)remain inadequately understood.To evaluate the scale-dependent effects of productivity,predictions from the Biome-BGC model were compared with moderate-resolution imaging spectroradiometer(MODIS)and biometric NPP data in a large temperate forest region at both local and regional levels.Linear mixed-effect models and variance partitioning analysis were used to quantify the effects of environmental heterogeneity and trait variation on simulated NPP at varying spatial scales.Results show that NPP had considerable predictability at the local scale,with a coefficient of determination(R^(2))of 0.37,but this predictability declined significantly to 0.02 at the regional scale.Environmental heterogeneity and photosynthetic traits collectively explained 94.8%of the local variation in NPP,which decreased to 86.7%regionally due to the reduced common effects among these variables.Locally,the leaf area index(LAI)predominated(34.6%),while at regional scales,the stomatal conductance and maximum carboxylation rate were more influential(41.1%).Our study suggests that environmental heterogeneity drives the photosynthetic processes that mediate NPP variations across spatial scales.Incorporating heterogeneous local conditions and trait variations into analyses could enhance future research on the relationship between climate and carbon cycles at larger scales.展开更多
Upper Andean tropical forests are renowned for their extraordinary biodiversity and heterogeneous environmental conditions.Despite the critical role of litter decomposition in carbon and nutrient cycles,its dynamics i...Upper Andean tropical forests are renowned for their extraordinary biodiversity and heterogeneous environmental conditions.Despite the critical role of litter decomposition in carbon and nutrient cycles,its dynamics in this region remains unexplored at finer scales.This study investigates how micro site conditions influence litter decomposition of 15 upper Andean species over time.A reciprocal translocation field experiment was conducted over 18 months in 14 permanent plots within four sites in Colombian Andean mountain forests.Each plot contained three litterbeds(microsites),each with the 15 species,harvested at 3,6,12 and 18 months,totaling 2520 litterbags.Different forest variables,including canopy openness,leaf area index,slope and depth of litter,were measured in each litterbed.ANOVAs and linear mixed models were used to assess variation between sites and plots respectively,while multiple linear regression analyses evaluated the effects of forest variables on decay rates over time at the micro site scale.Results showed differences in absolute decay rates between sites but consistent relative decay rates,indicating varying magnitudes of decomposition,yet maintaining the same order based on their litter quality.Decay rates varied between species,with more variation in labile species compared to recalcitrant ones.Despite substantial variation in forest characteristics within sites,their influence on litter decomposition was minimal and declined over time.This suggests that,at finer spatial scales,the forest microenvironment plays a lesser role in litter decomposition,with litter quality emerging as the primary driver.This study is a step towards understanding the fine-scale dynamics of litter decomposition in upper Andean tropical forests,highlighting the intricate interplay between microenvironmental factors and decomposition processes.展开更多
Frequent droughts pose considerable threat to global forest carbon uptake,but little is known about the response of forest carbon fluxes in climatic transition zones to seasonal drought.In this study,the responses of ...Frequent droughts pose considerable threat to global forest carbon uptake,but little is known about the response of forest carbon fluxes in climatic transition zones to seasonal drought.In this study,the responses of carbon fluxes to seasonal drought in two natural forests(Quercus aliena var.acute serrata Maxim and Pinus tabuliformis Carr.)in the Baotianman Nature Reserve were investigated.The Q.aliena forest exhibited a high resilience with stable gross primary productivity(GPP).However,ecosystem respiration(Re)significantly declined by 18.4%compared with normal years,leading to an increase in net carbon sequestration capacity of 4.1%.This resilience was attributed to its deep root system accessing soil water(SWC_(50cm))to sustain stomatal openness,coupled with the efficient utilization of photosynthetically active radiation to drive photosynthesis.In contrast,the P.tabuliformis forest,which relied on shallow soil moisture(SWC_(20cm)),experienced simultaneous decreases in both GPP and Re during drought,with a sharply greater decrease in GPP,resulting in low net carbon sink capacity.Further analysis revealed that the Q.aliena forest prioritized carbon assimilation through a deep water-stomatal synergy strategy(anisohydric behavior),whereas the P.tabuliformis forest adopted an isohydric strategy favoring water conservation at the expense of carbon fixation efficiency.These findings highlight distinct mechanisms underlying drought adaptation between forest types,providing critical insight into optimizing forest carbon cycle models and selecting drought-resistant species under the influence of climate change.展开更多
Soil fertility and forest structure influence tree carbon stocks.However,it remains unclear how tree mycorrhizal types affect these relationships.This study addressed the question of how aboveground and belowground tr...Soil fertility and forest structure influence tree carbon stocks.However,it remains unclear how tree mycorrhizal types affect these relationships.This study addressed the question of how aboveground and belowground tree carbon stocks in soils with different mycorrhizal types are affected by soil fertility and forest structure.Tree demographic data were used from a 21.12-ha study area collected over a ten-year period(2009-2019),covering 43species of woody plants and more than 50,000 individuals.Relationships between tree carbon stock,soil fertility and forest structure(stand density,diameter variation,species diversity and spatial distribution)were examined,as well as whether these relationships differed between arbuscular mycorrhiza and ectomycorrhizal mycorrhiza groups in a typical temperate conifer and broad-leaved mixed forest.We found that total tree carbon stock was positively impacted by variations in stand density and tree diameter but negatively influenced by soil fertility,tree species diversity and uniform angle index.Soil fertility promoted carbon stock of trees associated with arbuscular mycorrhiza(AM)but inhibited the carbon stock of trees with ectomycorrhizal mycorrhiza fungi(EcM).Carbon stock of AM trees was mainly influenced by soil fertility,while carbon stock of EcM trees was influenced by stand density.Our findings show that mycorrhizae types mediate the impact of stand structure and soil fertility on tree carbon stocks and provides new evidence on how forest tree carbon stocks may be enhanced based on the types of mycorrhizal associations.Tree species with different mycorrhizal types can be managed in different ways.展开更多
Urban forests are essential components of green infrastructure,however,rapid urbanization-induced changes in landscape patterns may affect their ecosystem services through complex ecological processes.A total of 184 s...Urban forests are essential components of green infrastructure,however,rapid urbanization-induced changes in landscape patterns may affect their ecosystem services through complex ecological processes.A total of 184 sample plots in the built-up areas of Nanchang,China,were used as research sites.Urbanization intensities were categorized by the rate of impervious surface area,and forest types were classified into landscape and relaxation forest,attached forest(AF),road forest(RF),and ecological public welfare forest.This study aimed to explore the spatial variations in vegetation characteristics and landscape pattern indices of different forest types under rapid urbanization.The results indicated that the largest patch index(LPI),aggregation index(AI),and percentage of landscape(PLAND)in RF and AF were lower than those in the other forest types(p<0.05).With increasing urbanization intensity,the mean perimeter-area ratio increased by 130.84%,whereas the PLAND,LPI,and AI decreased by 22−86%(p<0.05).Redundancy analysis and variation partitioning suggested that the interpretation rate of landscape pattern indices for variations in vegetation characteristics increased from low to heavy urbanization areas.Especially,the landscape shape index,patch connection index,PLAND,and mean patch size were significantly correlated with vegetation characteristics(e.g.,tree richness,herb coverage,and tree height).In the future,appropriate landscape layout superiority cases should be considered in different urbanization areas and forest types;for instance,increasing the patch connection index will beneficially improve the diversity of trees and herbs in heavy urbanization areas and the RF.This study serves as a reference for maximizing the ecosystem services of urban forests.展开更多
Stand age plays a crucial role in forest biomass estimation and carbon cycle modeling.Assessing the uncertainty of stand age prediction models and identifying the key driving factors in the modeling process have becom...Stand age plays a crucial role in forest biomass estimation and carbon cycle modeling.Assessing the uncertainty of stand age prediction models and identifying the key driving factors in the modeling process have become major challenges in forestry research.In this study,we selected the Shaanxi-Gansu-Ningxia region of Northeast China as the research area and utilized multi-source datasets from the summer of 2019 to extract information on spectral,textural,climatic,water balance,and stand characteristics.By integrating the Random Forest(RF)model with Monte Carlo(MC)simulation,we constructed six regression models based on different combina-tions of features and evaluated the uncertainty of each model.Furthermore,we investigated the driving factors influencing stand age modeling by analyzing the effects of different types of features on age inversion.Model performance and accuracy were assessed using the root mean square error(RMSE),mean absolute error(MAE),and the coefficient of determination(R^(2)),while the relative root mean square error(rRMSE)was employed to quantify model uncertainty.The results indicate that the scenarios with more obvious improve-ment in accuracy and effective reduction in uncertainty were Scenario 3 with the inclusion of climate and water balance information(RMSE=25.54 yr,MAE=18.03 yr,R^(2)=0.51,rRMSE=19.17%)and Scenario 5 with the inclusion of stand characterization informa-tion(RMSE=18.47 yr,MAE=13.05 yr,R^(2)=0.74,rRMSE=16.99%).Scenario 6,incorporating all feature types,achieved the highest accuracy(RMSE=17.60 yr,MAE=12.06 yr,R^(2)=0.77,rRMSE=14.19%).In this study,elevation,minimum temperature,and diameter at breast height(DBH)emerged as the key drivers of stand-age modeling.The proposed method can be used to identify drivers and to quantify uncertainty in stand-age estimation,providing a useful reference for improving model accuracy and uncertainty assessment.展开更多
Reforestation initiatives are often limited by insufficient seeds,a problem exacerbated by natural variability in tree flowering and seed production and climate change and other environmental challenges.Innovative and...Reforestation initiatives are often limited by insufficient seeds,a problem exacerbated by natural variability in tree flowering and seed production and climate change and other environmental challenges.Innovative and adaptive solutions such as in vitro propagation are thus needed.Tissue culture can provide high-quality propagation material for tree conservation and mass propagation,but faces technical,economic,regulatory,and social barriers.Obstacles related to the academia-industry interface and to stakeholder concerns are discussed and actions suggested to overcome these barriers to realize the full potential of tree micropropagation.These include refining techniques to improve efficiency and reduce costs;establishing collaborations among researchers,industry,and foresters;and reducing points of contention and misinformation regarding genetic diversity and public perception.International collaborative initiatives,exemplified by the EU COST Action CA21157 COPYTREE,are elementary for achieving these goals.展开更多
The root-to-shoot(R/S)ratio is a critical indicator of the balance between root biomass and shoot biomass,representing the ecological strategies and adaptive responses of plants to environmental conditions.However,the...The root-to-shoot(R/S)ratio is a critical indicator of the balance between root biomass and shoot biomass,representing the ecological strategies and adaptive responses of plants to environmental conditions.However,the patterns of change in community R/S ratios during forest succession and their response to moisture levels across broad geographic gradients remains unclear.Based on forest biomass data from a national field inventory of 5,825 plots conducted across China between 2011 and 2015,this study looked into allocating biomass shoots and roots at the early,middle,and late stages of growth in plantations and succession in natural forests,and evaluated how moisture availability influences this allocation.The results revealed a significant decline in R/S ratios from early to late stages for both plantations and natural forests.Shoot and root biomass in plantations grew isometrically during the early and middle succession stages but shifted to allometric growth in the late stage,with the slope of the log-transformed shoot-root biomass relationship differing significantly across growth stages.Natural forests,in contrast,maintained isometric growth across successional stages,showing no significant variation in the slope of the log-transformed shoot-root biomass relationship.Environmental factors,particularly moisture levels,strongly influenced R/S ratios.Moisture levels significantly affected size-corrected R/S ratios,particularly in the middle stage of plantations and the early and middle stages of natural forests,supporting the hypothesis of optimal allocation.These findings suggest that in water-limited regions,forest management should prioritize drought-tolerant,deep-rooted native species,encourage mixed-species planting in the early stage,and reduce logging intensity in mature plantations.Conserving natural forests to maintain successional dynamics is essential for long-term ecological resilience.These findings emphasize the importance of balancing productivity with ecological sustainability by adapting practices to specific environments and forest types under climate change.展开更多
Soil bacteria are integral to ecosystem functioning,significantly contributing to nutrients cycling and organic matter decomposition,and enhancing soil structure.This research considered the composition and dynamics o...Soil bacteria are integral to ecosystem functioning,significantly contributing to nutrients cycling and organic matter decomposition,and enhancing soil structure.This research considered the composition and dynamics of soil bacterial communities under different vegetation types(native Quercus brantii Lindl.and Amygdalus scoparia Spach,and non-native Pinus eldarica Medw.and Cupressus arizonica Greene.)in Zagros mountain area of Iran.This study involved a comparative analysis of soil culturable heterotrophic bacterial communities in spring(wet season)and summer(dry season)to clarify the effects of seasonal changes and vegetation on the dynamics of soil microorganisms.Soil samples were randomly collected under the canopies of various tree species and a control area,yielding a total of 48 composite samples analyzed for bacterial composition.Results indicated that 11 Gram-negative(e.g.,Citrobacter freundii,Enterobacter cloacae,Escherichia coli,Klebsiella oxytoca,Klebsiella pneumoniae,etc.)and 2 Gram-positive(Staphylococcus epidermidis and Staphylococcus aureus)bacteria were identified,showing significant seasonal variation.Specifically,53.85%of bacterial species were common to both seasons,with notable shifts in community composition observed between spring and summer,highlighting a higher abundance of Gram-negative species in spring.Bacterial community structure was significantly influenced by vegetation type,with various tree species shaping distinct microbial assemblages.Moreover,Pearson's correlations revealed that soil properties,particularly pH,phosphorus,and moisture content,were critical drivers of bacterial diversity and abundance.Our findings underscore the dynamic nature of soil bacterial communities in response to seasonal and vegetation changes,emphasizing the importance of repeated temporal sampling for accurate assessments of microbial diversity.Understanding these microbial dynamics is essential for improving soil management strategies and enhancing ecosystem resilience,particularly in arid and semi-arid areas where environmental fluctuations play a pivotal role.This research not only confirms our hypotheses but also enhances our understanding of soil biogeochemical processes and informs future vegetation management practices.展开更多
The dynamics of calcium(Ca)and magnesium(Mg)in the forest floor and topsoil caused by anthropogenic and natural processes continue to be a concern in temperate forests.However,the impacts of abiotic and biotic variabl...The dynamics of calcium(Ca)and magnesium(Mg)in the forest floor and topsoil caused by anthropogenic and natural processes continue to be a concern in temperate forests.However,the impacts of abiotic and biotic variables as well as their interactions remain unclear,especially in areas undergoing long-term forest restoration.In this study,Ca and Mg concentrations in the forest floor and topsoil from 239 forest plots across the Loess Plateau were measured,and the effects of forest types,climate,soil properties,stand characteristics and nitrogen deposition were explored.The results showed significantly higher Ca concentrations in the forest floor(20.68±8.04 mg/g)than in the topsoil(13.28±12.83 mg/g),whereas Mg exhibited the inverse pattern(3.64±1.09 and 10.11±2.51 mg/g,respectively).The effect of forest types was only significant on forest floor Ca,and Ca concentrations were higher in broadleaf and mixed forests than in coniferous forests.Overall,Ca and Mg concentrations in forest floor and topsoil increased with latitudes while decreased with elevations,and the significance of the trends varied among forest types.Forest floor Ca and Mg were mainly influenced by environmental variables aboveground,i.e.,basal area(BA)and mean annual precipitation(MAP),respectively;topsoil Ca and Mg were more affected by soil properties(soil C/N and pH,respectively).Those suggested a depletion of Ca belowground was associated with forest growth and enriched soil nitrogen,and the leaching of mobile Mg was correlated with rainfall and soil acidification.Besides,the impact of environmental variables on Ca-Mg balance(Ca/Mg ratio)belowground was primarily through the regulation of Ca.Elucidating the influence of environmental variables will improve our ability to predict future changes in base cations and thus forest soil health in the greening vegetated Loess Plateau.展开更多
Evaluating the effectivenes s of forest restoration projects is crucial for designing adaptive restoration strategies.However,existing studies have primarily focused on ecological outcomes while overlooking cost input...Evaluating the effectivenes s of forest restoration projects is crucial for designing adaptive restoration strategies.However,existing studies have primarily focused on ecological outcomes while overlooking cost inputs.This gap can lead to increased uncertainties in restoration planning.Here we investigated forest dynamics in China's Upper Yangtze River Basin(UYRB)using kernel Normalized Difference Vegetation Index(kNDVI),Leaf Area Index(LAI),Gross Primary Productivity(GPP),Ku-band Vegetation Optical Depth(Ku-VOD)time series and climate data from1982 to 2020.Subsequently,we employed a residual trend analysis integrating temporal effects to determine the relative contributions of climate change and human activities to forest dynamics before and after the implementation of forest restoration engineering in 1998.Additionally,we developed an Afforestation Efficiency Index(AEI)to quantitatively assess the cost efficiency of afforestation projects.Results indicated that forest in the UYRB showed sustained increases during 1982-2020,with most areas experiencing greater growth after 1998 than before.Temporal effects of climatic factors influenced over 42.7%of the forest,and incorporating time-lag and cumulative effects enhanced climate-based explanations of forest variations by 1.61-24.73%.Human activities emerged as the dominant driver of forest dynamics post 1998,whereas climate variables predominated before this period.The cost-effectiveness of forest restoration projects in the UYRB typically ranges from moderate to high,with higher success predominantly observed in the northeastern and eastern counties,while the central,western,and northwestern counties mainly showed relatively low efficiency.These findings stress the need for assessing forest restoration outcomes from both ecological and cost perspectives,and can offer valuable insights for optimizing the layout of forest restoration initiatives in the UYRB.展开更多
Slope units are divided according to the real topography and have clear geological characteristics,making them ideal units for evaluating the susceptibility to geological disasters.Based on the results of automaticall...Slope units are divided according to the real topography and have clear geological characteristics,making them ideal units for evaluating the susceptibility to geological disasters.Based on the results of automatically and manually corrected hydrological slope unit division,the Longhua District,Shenzhen City,Guangdong Province,was selected as the study area.A total of 15 influencing factors,namely Fluctuation,slope,slope aspect,curvature,topographic witness index(TWI),stream power index(SPI),topographic roughness index(TRI),annual average rainfall,distance to water system,engineering rock group,distance to fault,land use,normalized difference vegetation index(NDVI),nighttime light,and distance to road,were selected as evaluation indicators.The information volume model(IV)and random points were used to select non-geological disaster units,and then the random forest model(RF)was used to evaluate the susceptibility to geological disasters.The automatic slope unit and the hydrological slope unit were compared and analyzed in the random forest and information volume random forest models.The results show that the area under the curve(AUC)values of the automatic slope unit evaluation results are 0.931 for the IV-RF model and 0.716 for the RF model,which are 0.6%(IV-RF model)and 1.9%(RF model)higher than those for the hydrological slope unit.Based on a comparison of the evaluation methods based on the two types of slope units,the hydrological slope unit evaluation method based on manual correction is highly subjective,is complicated to operate,and has a low evaluation accuracy,whereas the evaluation method based on automatic slope unit division is efficient and accurate,is suitable for large-scale efficient geological disaster evaluation,and can better deal with the problem of geological disaster susceptibility evaluation.展开更多
Soil greenhouse gas(GHG)emissions contribute profoundly to global warming;however,how plant detritus input alters GHG emissions is poorly understood.Here,we used detritus input and removal treatments(i.e.,DIRT:control...Soil greenhouse gas(GHG)emissions contribute profoundly to global warming;however,how plant detritus input alters GHG emissions is poorly understood.Here,we used detritus input and removal treatments(i.e.,DIRT:control,CK;double litter,DL;no roots with double litter,NRDL;no litter,NL;no roots,NR;no roots and no litter,NRNL)to assess the effects of litter and root inputs on soil CO_(2),CH_(4),and N_(2)O fluxes in soils in a coniferous(Pinus yunnanensis)and a broad-leaf forest(Quercus pannosa)in a subalpine region in southwestern China.Litter addition increased CO_(2) emissions on average 22.22%,but did not significantly alter CH_(4) uptake and N_(2)O emission compared to the CK.Litter removal(NL and NRNL)significantly reduced CO_(2) emissions on average 30.22%and N_(2)O emissions on average 31.16%from both forest soils,but did not significantly affect soil CH_(4) uptake.Root removal(NR and NRNL)generally decreased these three soil GHG fluxes.Changes inβ-1,4-glucosidase(BG)involved in C and phospholipid fatty acid(PLFAs)biomass were projected to influence CO_(2) emissions,while soil microclimates(temperature and moisture)combined with BG activity mainly regulated CH_(4) uptake.Alterations in dissolved organic nitrogen,microbial biomass nitrogen and BG were mainly responsible for changes in N_(2)O emissions.Interestingly,coniferous forest soil seemed to promote CH_(4) uptake more than the broad-leaf forest soil,but CO_(2) and N_(2)O fluxes were not significantly affected by the forest types.As expected,litter addition significantly increased the warming potential,while litter removal relatively lowered it.These findings revealed the divergent roles of plant detritus input and forest type in shaping soil GHG fluxes,thereby providing insights into forest management and predicting contributions of subalpine forests to global warming.展开更多
文摘AIM:To analyze ultrasound biomicroscopy(UBM)images using random forest network to find new features to make predictions about vault after implantable collamer lens(ICL)implantation.METHODS:A total of 450 UBM images were collected from the Lixiang Eye Hospital to provide the patient’s preoperative parameters as well as the vault of the ICL after implantation.The vault was set as the prediction target,and the input elements were mainly ciliary sulcus shape parameters,which included 6 angular parameters,2 area parameters,and 2 parameters,distance between ciliary sulci,and anterior chamber height.A random forest regression model was applied to predict the vault,with the number of base estimators(n_estimators)of 2000,the maximum tree depth(max_depth)of 17,the number of tree features(max_features)of Auto,and the random state(random_state)of 40.0.RESULTS:Among the parameters selected in this study,the distance between ciliary sulci had a greater importance proportion,reaching 52%before parameter optimization is performed,and other features had less influence,with an importance proportion of about 5%.The importance of the distance between the ciliary sulci increased to 53% after parameter optimization,and the importance of angle 3 and area 1 increased to 5% and 8%respectively,while the importance of the other parameters remained unchanged,and the distance between the ciliary sulci was considered the most important feature.Other features,although they accounted for a relatively small proportion,also had an impact on the vault prediction.After parameter optimization,the best prediction results were obtained,with a predicted mean value of 763.688μm and an actual mean value of 776.9304μm.The R²was 0.4456 and the root mean square error was 201.5166.CONCLUSION:A study based on UBM images using random forest network can be performed for prediction of the vault after ICL implantation and can provide some reference for ICL size selection.
基金Under the auspices of Key Program of Chinese Academy of Sciences(No.KZZD-EW-08-02)CAS/SAFEA(Chinese Academy of Science/State Administration of Foreign Experts Affairs)International Partnership Program for Creative Research Teams(No.KZZD-EW-TZ-07)Strategic Frontier Program of Chinese Academy of Sciences-Climate Change:Carbon Budget and Relevant Issues(No.XDA05050101)
文摘Forest net primary productivity （NPP） is a key parameter for forest monitoring and management. In this study, monthly and annual forest NPP in the northeastern China from 1982 to 2010 were simulated by using Carnegie-Ames-Stanford Approach （CASA） model with normalized difference vegetation index （NDVI） sequences derived from Advanced Very High Resolution Radiometer （AVHRR） Global Invento y Modeling and Mapping Studies （GIMMS） and Terra Moderate Resolution Imaging Spectroradiometer （MODIS） products. To address the problem of data inconsistency between AVHRR and MODIS data, a per-pixel unary linear regres- sion model based on least ~;quares method was developed to derive the monthly NDVI sequences. Results suggest that estimated forest NPP has mean relative error of 18.97% compared to observed NPP from forest inventory. Forest NPP in the northeastern China in- creased significantly during the twenty-nine years. The results of seasonal dynamic show that more clear increasing trend of forest NPP occurred in spring and awmnn. This study also examined the relationship between forest NPP and its driving forces including the climatic and anthropogenic factors. In spring and winter, temperature played the most pivotal role in forest NPR In autumn, precipitation acted as the most importanl factor affecting forest NPP, while solar radiation played the most important role in the summer. Evaportran- spiration had a close correlation with NPP for coniferous forest, mixed coniferous broadleaved forest, and broadleaved deciduous forest. Spatially, forest NPP in the Da Hinggan Mountains was more sensitive to climatic changes than in the other ecological functional re- gions. In addition to climalie change, the degradation and improvement of forests had important effects on forest NPP. Results in this study are helpful for understanding the regional carbon sequestration and can enrich the cases for the monitoring of vegetation during long time series.
基金funded by the Natural Science Foundation of Hunan Province(Grant No.2025JJ80352)the National Natural Science Foundation Project of China(Grant No.32271879).
文摘Detecting small forest fire targets in unmanned aerial vehicle(UAV)images is difficult,as flames typically cover only a very limited portion of the visual scene.This study proposes Context-guided Compact Lightweight Network(CCLNet),an end-to-end lightweight model designed to detect small forest fire targets while ensuring efficient inference on devices with constrained computational resources.CCLNet employs a three-stage network architecture.Its key components include three modules.C3F-Convolutional Gated Linear Unit(C3F-CGLU)performs selective local feature extraction while preserving fine-grained high-frequency flame details.Context-Guided Feature Fusion Module(CGFM)replaces plain concatenation with triplet-attention interactions to emphasize subtle flame patterns.Lightweight Shared Convolution with Separated Batch Normalization Detection(LSCSBD)reduces parameters through separated batch normalization while maintaining scale-specific statistics.We build TF-11K,an 11,139-image dataset combining 9139 self-collected UAV images from subtropical forests and 2000 re-annotated frames from the FLAME dataset.On TF-11K,CCLNet attains 85.8%mAP@0.5,45.5%mean Average Precision(mAP)@[0.5:0.95],87.4%precision,and 79.1%recall with 2.21 M parameters and 5.7 Giga Floating-point Operations Per Second(GFLOPs).The ablation study confirms that each module contributes to both accuracy and efficiency.Cross-dataset evaluation on DFS yields 77.5%mAP@0.5 and 42.3%mAP@[0.5:0.95],indicating good generalization to unseen scenes.These results suggest that CCLNet offers a practical balance between accuracy and speed for small-target forest fire monitoring with UAVs.
基金funded by the Strategic Priority Research Program of Chinese Academy of Sciences (XDB31000000)the Joint Fund of the National Natural Science Foundation of China-Yunnan Province (U1902203)+1 种基金Major Program for Basic Research Project of Yunnan Province (202101BC070002)Southeast Asia Biodiversity Research Institute, Chinese Academy of Sciences (151C53KYSB20200019)
文摘Patterns and drivers of species–genetic diversity correlations(SGDCs)have been broadly examined across taxa and ecosystems and greatly deepen our understanding of how biodiversity is maintained.However,few studies have examined the role of canopy structural heterogeneity,which is a defining feature of forests,in shaping SGDCs.Here,we determine what factors contribute toα-andβ-species–genetic diversity correlations(i.e.,α-andβ-SGDCs)in a Chinese subtropical forest.For this purpose,we used neutral molecular markers to assess genetic variation in almost all adult individuals of the dominant tree species,Lithocarpus xylocarpus,across plots in the Ailaoshan National Natural Reserve.We also quantified microhabitat variation by quantifying canopy structure heterogeneity with airborne laser scanning on 201-ha subtropical forest plots.We found that speciesα-diversity was negatively correlated with geneticα-diversity.Canopy structural heterogeneity was positively correlated with speciesα-diversity but negatively correlated with geneticα-diversity.These contrasting effects contributed to the formation of a negativeα-SGDC.Further,we found that canopy structural heterogeneity increases speciesα-diversity and decreases geneticα-diversity by reducing the population size of target species.Speciesβ-diversity,in contrast,was positively correlated with geneticβ-diversity.Differences in canopy structural heterogeneity between plots had non-linear parallel effects on the two levels ofβ-diversity,while geographic distance had a relatively weak effect onβ-SGDC.Our study indicates that canopy structural heterogeneity simultaneously affects plot-level community species diversity and population genetic diversity,and species and genetic turnover across plots,thus drivingα-andβ-SGDCs.
文摘Making the distinction between different plantation tree species is crucial for creating reliable and trustworthy information, which is critical in forestry administration and upkeep. Over the years, forest delineation and mapping have been done using the conventional techniques, such as the utilization of ground truth facts together with orthophotos. These techniques have been proven to be very precise, but they are expensive, cumbersome, and challenging to employ in remote regions. To resolve this shortfall, this research investigates the potential of data from the commercial, PlanetScope CubeSat and the freely available, Sentinel 2 data from Copernicus to discriminate commercial forest tree species in the Usutu Forest, Eswatini. Two approaches for image classification, Random Forest (RF) and the Support Vector Machine (SVM) were investigated at different levels of the forest database classification which is the genus (family of tree species) and species levels. The result of the study indicates that, the Sentinel 2 images had the highest species classification accuracy compared to the PlanetScope image. Both classification methods achieved a 94% maximum OA and 0.90 kappa value at the genus level with the Sentinel 2 imagery. At the species level, the Sentinel 2 imagery again showed highly acceptable results with the SVM method, with an OA of 82%. The PlanetScope images performed badly with less than 64% OA for both RF and SVM at the genus level and poorer at the species level with a low OA figure, 47% and 53% for the SVM and RF respectively. Our results suggest that the freely available Sentinel 2 data together with the SVM method has a high potential for identifying differences between commercial tree species than the PlanetScope. The study uncovered that both classification methods are highly capable of classifying species under the gum genus group (esmi, egxu, and egxn) using both imageries. However, it was difficult to separate species types under the pine genus group, particularly discriminating the hybrid species such as pech and pell since pech is a hybrid species for pell.
基金Guangdong Innovation and Entrepreneurship Training Programme for Undergraduates“Automatic Classification and Identification of Fraudulent Websites Based on Machine Learning”(Project No.:DC2023125)。
文摘This paper explores the synergistic effect of a model combining Elastic Net and Random Forest in online fraud detection.The study selects a public network dataset containing 1781 data records,divides the dataset by 70%for training and 30%for validation,and analyses the correlation between features using a correlation matrix.The experimental results show that the Elastic Net feature selection method generally outperforms PCA in all models,especially when combined with the Random Forest and XGBoost models,and the ElasticNet+Random Forest model achieves the highest accuracy of 0.968 and AUC value of 0.983,while the Kappa and MCC also reached 0.839 and 0.844 respectively,showing extremely high consistency and correlation.This indicates that combining Elastic Net feature selection and Random Forest model has significant performance advantages in online fraud detection.
基金supported by Science and Technology Project Managed by the State Grid Jiangsu Electric Power Co.,Ltd.(No.J2024163).
文摘With the popularization of microgrid construction and the connection of renewable energy sources to the power system,the problem of source and load uncertainty faced by the coordinated operation of multi-microgrid is becoming increasingly prominent,and the accuracy of typical scenario predictions is low.In order to improve the accuracy of scenario prediction under source and load uncertainty,this paper proposes a typical scenario identification model based on random forests and order parameters.Firstly,a method for ordinal parameter identification and quantification is provided for the coordinated operating mode of multi-microgrids,taking into account source-load uncertainty.Secondly,the dynamic change characteristics of the order parameters of the daily load curve,wind and solar curve,and load curve of typical scenarios are statistically analyzed to identify the key order parameters that have the most significant impact on the uncertainty of the load.Then,the order parameters and seasonal distribution are used as features to train a random forest classification model to achieve efficient scenario prediction.Finally,the simulation of actual data from a provincial distribution network shows that the proposed method can accurately classify typical scenarios with an accuracy rate of 92.7%.Additionally,sensitivity analysis is conducted to assess how changes in uncertainty levels affect the importance of each order parameter,allowing for adaptive uncertainty mitigation strategies.
基金supported by the National Key R&D Program of China(No.2023YFF1304001-01)the Science and Technology Project of the Department of Transportation of Heilongjiang Province(No.HJK2023B024-3)the Program of National Natural Science Foundation of China(No.32371870).
文摘The net primary productivity(NPP)of forest ecosystems plays a crucial role in regulating the terrestrial carbon cycle under global climate change.While the temporal effect driven by ecosystem processes on NPP variations is well-documented,spatial variations(from local to regional scales)remain inadequately understood.To evaluate the scale-dependent effects of productivity,predictions from the Biome-BGC model were compared with moderate-resolution imaging spectroradiometer(MODIS)and biometric NPP data in a large temperate forest region at both local and regional levels.Linear mixed-effect models and variance partitioning analysis were used to quantify the effects of environmental heterogeneity and trait variation on simulated NPP at varying spatial scales.Results show that NPP had considerable predictability at the local scale,with a coefficient of determination(R^(2))of 0.37,but this predictability declined significantly to 0.02 at the regional scale.Environmental heterogeneity and photosynthetic traits collectively explained 94.8%of the local variation in NPP,which decreased to 86.7%regionally due to the reduced common effects among these variables.Locally,the leaf area index(LAI)predominated(34.6%),while at regional scales,the stomatal conductance and maximum carboxylation rate were more influential(41.1%).Our study suggests that environmental heterogeneity drives the photosynthetic processes that mediate NPP variations across spatial scales.Incorporating heterogeneous local conditions and trait variations into analyses could enhance future research on the relationship between climate and carbon cycles at larger scales.
基金supported by the Universidad del Rosario(Small grant ID:IV-FPD003)。
文摘Upper Andean tropical forests are renowned for their extraordinary biodiversity and heterogeneous environmental conditions.Despite the critical role of litter decomposition in carbon and nutrient cycles,its dynamics in this region remains unexplored at finer scales.This study investigates how micro site conditions influence litter decomposition of 15 upper Andean species over time.A reciprocal translocation field experiment was conducted over 18 months in 14 permanent plots within four sites in Colombian Andean mountain forests.Each plot contained three litterbeds(microsites),each with the 15 species,harvested at 3,6,12 and 18 months,totaling 2520 litterbags.Different forest variables,including canopy openness,leaf area index,slope and depth of litter,were measured in each litterbed.ANOVAs and linear mixed models were used to assess variation between sites and plots respectively,while multiple linear regression analyses evaluated the effects of forest variables on decay rates over time at the micro site scale.Results showed differences in absolute decay rates between sites but consistent relative decay rates,indicating varying magnitudes of decomposition,yet maintaining the same order based on their litter quality.Decay rates varied between species,with more variation in labile species compared to recalcitrant ones.Despite substantial variation in forest characteristics within sites,their influence on litter decomposition was minimal and declined over time.This suggests that,at finer spatial scales,the forest microenvironment plays a lesser role in litter decomposition,with litter quality emerging as the primary driver.This study is a step towards understanding the fine-scale dynamics of litter decomposition in upper Andean tropical forests,highlighting the intricate interplay between microenvironmental factors and decomposition processes.
基金financially supported by the National Key Research and Development Program of China(2021YFD2200405)the National Natural Science Foundation of China(31930078)special funds for Baotianman Forest Ecosystem Research Station from Chinese Academy of Forestry and Ministry of Science and Technology of China。
文摘Frequent droughts pose considerable threat to global forest carbon uptake,but little is known about the response of forest carbon fluxes in climatic transition zones to seasonal drought.In this study,the responses of carbon fluxes to seasonal drought in two natural forests(Quercus aliena var.acute serrata Maxim and Pinus tabuliformis Carr.)in the Baotianman Nature Reserve were investigated.The Q.aliena forest exhibited a high resilience with stable gross primary productivity(GPP).However,ecosystem respiration(Re)significantly declined by 18.4%compared with normal years,leading to an increase in net carbon sequestration capacity of 4.1%.This resilience was attributed to its deep root system accessing soil water(SWC_(50cm))to sustain stomatal openness,coupled with the efficient utilization of photosynthetically active radiation to drive photosynthesis.In contrast,the P.tabuliformis forest,which relied on shallow soil moisture(SWC_(20cm)),experienced simultaneous decreases in both GPP and Re during drought,with a sharply greater decrease in GPP,resulting in low net carbon sink capacity.Further analysis revealed that the Q.aliena forest prioritized carbon assimilation through a deep water-stomatal synergy strategy(anisohydric behavior),whereas the P.tabuliformis forest adopted an isohydric strategy favoring water conservation at the expense of carbon fixation efficiency.These findings highlight distinct mechanisms underlying drought adaptation between forest types,providing critical insight into optimizing forest carbon cycle models and selecting drought-resistant species under the influence of climate change.
基金supported by the Science and Technology Project of the Department of Transportation of Heilongjiang Province(HJK2023B024-3)the National Key R&D Program of China(2023YFF1304001-01)。
文摘Soil fertility and forest structure influence tree carbon stocks.However,it remains unclear how tree mycorrhizal types affect these relationships.This study addressed the question of how aboveground and belowground tree carbon stocks in soils with different mycorrhizal types are affected by soil fertility and forest structure.Tree demographic data were used from a 21.12-ha study area collected over a ten-year period(2009-2019),covering 43species of woody plants and more than 50,000 individuals.Relationships between tree carbon stock,soil fertility and forest structure(stand density,diameter variation,species diversity and spatial distribution)were examined,as well as whether these relationships differed between arbuscular mycorrhiza and ectomycorrhizal mycorrhiza groups in a typical temperate conifer and broad-leaved mixed forest.We found that total tree carbon stock was positively impacted by variations in stand density and tree diameter but negatively influenced by soil fertility,tree species diversity and uniform angle index.Soil fertility promoted carbon stock of trees associated with arbuscular mycorrhiza(AM)but inhibited the carbon stock of trees with ectomycorrhizal mycorrhiza fungi(EcM).Carbon stock of AM trees was mainly influenced by soil fertility,while carbon stock of EcM trees was influenced by stand density.Our findings show that mycorrhizae types mediate the impact of stand structure and soil fertility on tree carbon stocks and provides new evidence on how forest tree carbon stocks may be enhanced based on the types of mycorrhizal associations.Tree species with different mycorrhizal types can be managed in different ways.
基金supported by the National Natural Science Foundation of China(32460380,42007042)State Key Laboratory of Subtropical Silviculture(SKLSSKF2023-06)+2 种基金Natural Science Foundation of Jiangxi Province(20242BAB25389)National Undergraduate Innovation and Entrepreneurship Training Program(202410410029X)Jiangxi Province Graduate Student Innovation Special Fund Project(YC2024-S330).
文摘Urban forests are essential components of green infrastructure,however,rapid urbanization-induced changes in landscape patterns may affect their ecosystem services through complex ecological processes.A total of 184 sample plots in the built-up areas of Nanchang,China,were used as research sites.Urbanization intensities were categorized by the rate of impervious surface area,and forest types were classified into landscape and relaxation forest,attached forest(AF),road forest(RF),and ecological public welfare forest.This study aimed to explore the spatial variations in vegetation characteristics and landscape pattern indices of different forest types under rapid urbanization.The results indicated that the largest patch index(LPI),aggregation index(AI),and percentage of landscape(PLAND)in RF and AF were lower than those in the other forest types(p<0.05).With increasing urbanization intensity,the mean perimeter-area ratio increased by 130.84%,whereas the PLAND,LPI,and AI decreased by 22−86%(p<0.05).Redundancy analysis and variation partitioning suggested that the interpretation rate of landscape pattern indices for variations in vegetation characteristics increased from low to heavy urbanization areas.Especially,the landscape shape index,patch connection index,PLAND,and mean patch size were significantly correlated with vegetation characteristics(e.g.,tree richness,herb coverage,and tree height).In the future,appropriate landscape layout superiority cases should be considered in different urbanization areas and forest types;for instance,increasing the patch connection index will beneficially improve the diversity of trees and herbs in heavy urbanization areas and the RF.This study serves as a reference for maximizing the ecosystem services of urban forests.
基金Under the auspices of the Natural Science Foundation of China(No.32371875,32001249)。
文摘Stand age plays a crucial role in forest biomass estimation and carbon cycle modeling.Assessing the uncertainty of stand age prediction models and identifying the key driving factors in the modeling process have become major challenges in forestry research.In this study,we selected the Shaanxi-Gansu-Ningxia region of Northeast China as the research area and utilized multi-source datasets from the summer of 2019 to extract information on spectral,textural,climatic,water balance,and stand characteristics.By integrating the Random Forest(RF)model with Monte Carlo(MC)simulation,we constructed six regression models based on different combina-tions of features and evaluated the uncertainty of each model.Furthermore,we investigated the driving factors influencing stand age modeling by analyzing the effects of different types of features on age inversion.Model performance and accuracy were assessed using the root mean square error(RMSE),mean absolute error(MAE),and the coefficient of determination(R^(2)),while the relative root mean square error(rRMSE)was employed to quantify model uncertainty.The results indicate that the scenarios with more obvious improve-ment in accuracy and effective reduction in uncertainty were Scenario 3 with the inclusion of climate and water balance information(RMSE=25.54 yr,MAE=18.03 yr,R^(2)=0.51,rRMSE=19.17%)and Scenario 5 with the inclusion of stand characterization informa-tion(RMSE=18.47 yr,MAE=13.05 yr,R^(2)=0.74,rRMSE=16.99%).Scenario 6,incorporating all feature types,achieved the highest accuracy(RMSE=17.60 yr,MAE=12.06 yr,R^(2)=0.77,rRMSE=14.19%).In this study,elevation,minimum temperature,and diameter at breast height(DBH)emerged as the key drivers of stand-age modeling.The proposed method can be used to identify drivers and to quantify uncertainty in stand-age estimation,providing a useful reference for improving model accuracy and uncertainty assessment.
基金funded by COST Action CA21157“European Network for Innovative Woody Plant Cloning”www.cost.eusupported by COST(European Cooperation in Science and Technology)www.cost.eu。
文摘Reforestation initiatives are often limited by insufficient seeds,a problem exacerbated by natural variability in tree flowering and seed production and climate change and other environmental challenges.Innovative and adaptive solutions such as in vitro propagation are thus needed.Tissue culture can provide high-quality propagation material for tree conservation and mass propagation,but faces technical,economic,regulatory,and social barriers.Obstacles related to the academia-industry interface and to stakeholder concerns are discussed and actions suggested to overcome these barriers to realize the full potential of tree micropropagation.These include refining techniques to improve efficiency and reduce costs;establishing collaborations among researchers,industry,and foresters;and reducing points of contention and misinformation regarding genetic diversity and public perception.International collaborative initiatives,exemplified by the EU COST Action CA21157 COPYTREE,are elementary for achieving these goals.
基金supported by the China National Science Foundation(No.42130506,42071031)the Special Technology Innovation Fund of Carbon Peak and Carbon Neutrality in Jiangsu Province(BK20231515)+1 种基金the Spanish Government grant PID2022-140808NB-I00 funded by MICIU/AEI/https://doi.org/10.13039/501100011033the Catalan Government grants SGR 2021-1333 and AGAUR2023 CLIMA 00118.
文摘The root-to-shoot(R/S)ratio is a critical indicator of the balance between root biomass and shoot biomass,representing the ecological strategies and adaptive responses of plants to environmental conditions.However,the patterns of change in community R/S ratios during forest succession and their response to moisture levels across broad geographic gradients remains unclear.Based on forest biomass data from a national field inventory of 5,825 plots conducted across China between 2011 and 2015,this study looked into allocating biomass shoots and roots at the early,middle,and late stages of growth in plantations and succession in natural forests,and evaluated how moisture availability influences this allocation.The results revealed a significant decline in R/S ratios from early to late stages for both plantations and natural forests.Shoot and root biomass in plantations grew isometrically during the early and middle succession stages but shifted to allometric growth in the late stage,with the slope of the log-transformed shoot-root biomass relationship differing significantly across growth stages.Natural forests,in contrast,maintained isometric growth across successional stages,showing no significant variation in the slope of the log-transformed shoot-root biomass relationship.Environmental factors,particularly moisture levels,strongly influenced R/S ratios.Moisture levels significantly affected size-corrected R/S ratios,particularly in the middle stage of plantations and the early and middle stages of natural forests,supporting the hypothesis of optimal allocation.These findings suggest that in water-limited regions,forest management should prioritize drought-tolerant,deep-rooted native species,encourage mixed-species planting in the early stage,and reduce logging intensity in mature plantations.Conserving natural forests to maintain successional dynamics is essential for long-term ecological resilience.These findings emphasize the importance of balancing productivity with ecological sustainability by adapting practices to specific environments and forest types under climate change.
文摘Soil bacteria are integral to ecosystem functioning,significantly contributing to nutrients cycling and organic matter decomposition,and enhancing soil structure.This research considered the composition and dynamics of soil bacterial communities under different vegetation types(native Quercus brantii Lindl.and Amygdalus scoparia Spach,and non-native Pinus eldarica Medw.and Cupressus arizonica Greene.)in Zagros mountain area of Iran.This study involved a comparative analysis of soil culturable heterotrophic bacterial communities in spring(wet season)and summer(dry season)to clarify the effects of seasonal changes and vegetation on the dynamics of soil microorganisms.Soil samples were randomly collected under the canopies of various tree species and a control area,yielding a total of 48 composite samples analyzed for bacterial composition.Results indicated that 11 Gram-negative(e.g.,Citrobacter freundii,Enterobacter cloacae,Escherichia coli,Klebsiella oxytoca,Klebsiella pneumoniae,etc.)and 2 Gram-positive(Staphylococcus epidermidis and Staphylococcus aureus)bacteria were identified,showing significant seasonal variation.Specifically,53.85%of bacterial species were common to both seasons,with notable shifts in community composition observed between spring and summer,highlighting a higher abundance of Gram-negative species in spring.Bacterial community structure was significantly influenced by vegetation type,with various tree species shaping distinct microbial assemblages.Moreover,Pearson's correlations revealed that soil properties,particularly pH,phosphorus,and moisture content,were critical drivers of bacterial diversity and abundance.Our findings underscore the dynamic nature of soil bacterial communities in response to seasonal and vegetation changes,emphasizing the importance of repeated temporal sampling for accurate assessments of microbial diversity.Understanding these microbial dynamics is essential for improving soil management strategies and enhancing ecosystem resilience,particularly in arid and semi-arid areas where environmental fluctuations play a pivotal role.This research not only confirms our hypotheses but also enhances our understanding of soil biogeochemical processes and informs future vegetation management practices.
基金supported by the National Natural Science Foundation of China(42401054)Natural Science Foundation of Hebei Province(D2024205019)Science and Technology Project of Hebei Education Department(BJ2025014).
文摘The dynamics of calcium(Ca)and magnesium(Mg)in the forest floor and topsoil caused by anthropogenic and natural processes continue to be a concern in temperate forests.However,the impacts of abiotic and biotic variables as well as their interactions remain unclear,especially in areas undergoing long-term forest restoration.In this study,Ca and Mg concentrations in the forest floor and topsoil from 239 forest plots across the Loess Plateau were measured,and the effects of forest types,climate,soil properties,stand characteristics and nitrogen deposition were explored.The results showed significantly higher Ca concentrations in the forest floor(20.68±8.04 mg/g)than in the topsoil(13.28±12.83 mg/g),whereas Mg exhibited the inverse pattern(3.64±1.09 and 10.11±2.51 mg/g,respectively).The effect of forest types was only significant on forest floor Ca,and Ca concentrations were higher in broadleaf and mixed forests than in coniferous forests.Overall,Ca and Mg concentrations in forest floor and topsoil increased with latitudes while decreased with elevations,and the significance of the trends varied among forest types.Forest floor Ca and Mg were mainly influenced by environmental variables aboveground,i.e.,basal area(BA)and mean annual precipitation(MAP),respectively;topsoil Ca and Mg were more affected by soil properties(soil C/N and pH,respectively).Those suggested a depletion of Ca belowground was associated with forest growth and enriched soil nitrogen,and the leaching of mobile Mg was correlated with rainfall and soil acidification.Besides,the impact of environmental variables on Ca-Mg balance(Ca/Mg ratio)belowground was primarily through the regulation of Ca.Elucidating the influence of environmental variables will improve our ability to predict future changes in base cations and thus forest soil health in the greening vegetated Loess Plateau.
基金supported by the National Natural Science Foundation of China(42071238)the Jiuzhaigou Post-Disaster Restoration and Reconstruction Program(5132202020000046)+1 种基金the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)the Ministry of Science and Technology of the People's Republic of China(2019QZKK0402)。
文摘Evaluating the effectivenes s of forest restoration projects is crucial for designing adaptive restoration strategies.However,existing studies have primarily focused on ecological outcomes while overlooking cost inputs.This gap can lead to increased uncertainties in restoration planning.Here we investigated forest dynamics in China's Upper Yangtze River Basin(UYRB)using kernel Normalized Difference Vegetation Index(kNDVI),Leaf Area Index(LAI),Gross Primary Productivity(GPP),Ku-band Vegetation Optical Depth(Ku-VOD)time series and climate data from1982 to 2020.Subsequently,we employed a residual trend analysis integrating temporal effects to determine the relative contributions of climate change and human activities to forest dynamics before and after the implementation of forest restoration engineering in 1998.Additionally,we developed an Afforestation Efficiency Index(AEI)to quantitatively assess the cost efficiency of afforestation projects.Results indicated that forest in the UYRB showed sustained increases during 1982-2020,with most areas experiencing greater growth after 1998 than before.Temporal effects of climatic factors influenced over 42.7%of the forest,and incorporating time-lag and cumulative effects enhanced climate-based explanations of forest variations by 1.61-24.73%.Human activities emerged as the dominant driver of forest dynamics post 1998,whereas climate variables predominated before this period.The cost-effectiveness of forest restoration projects in the UYRB typically ranges from moderate to high,with higher success predominantly observed in the northeastern and eastern counties,while the central,western,and northwestern counties mainly showed relatively low efficiency.These findings stress the need for assessing forest restoration outcomes from both ecological and cost perspectives,and can offer valuable insights for optimizing the layout of forest restoration initiatives in the UYRB.
文摘Slope units are divided according to the real topography and have clear geological characteristics,making them ideal units for evaluating the susceptibility to geological disasters.Based on the results of automatically and manually corrected hydrological slope unit division,the Longhua District,Shenzhen City,Guangdong Province,was selected as the study area.A total of 15 influencing factors,namely Fluctuation,slope,slope aspect,curvature,topographic witness index(TWI),stream power index(SPI),topographic roughness index(TRI),annual average rainfall,distance to water system,engineering rock group,distance to fault,land use,normalized difference vegetation index(NDVI),nighttime light,and distance to road,were selected as evaluation indicators.The information volume model(IV)and random points were used to select non-geological disaster units,and then the random forest model(RF)was used to evaluate the susceptibility to geological disasters.The automatic slope unit and the hydrological slope unit were compared and analyzed in the random forest and information volume random forest models.The results show that the area under the curve(AUC)values of the automatic slope unit evaluation results are 0.931 for the IV-RF model and 0.716 for the RF model,which are 0.6%(IV-RF model)and 1.9%(RF model)higher than those for the hydrological slope unit.Based on a comparison of the evaluation methods based on the two types of slope units,the hydrological slope unit evaluation method based on manual correction is highly subjective,is complicated to operate,and has a low evaluation accuracy,whereas the evaluation method based on automatic slope unit division is efficient and accurate,is suitable for large-scale efficient geological disaster evaluation,and can better deal with the problem of geological disaster susceptibility evaluation.
基金supported by the National Natural Science Foundation of China(32130069)the National Key Research and Development Program of China(2024YFF1306700)the Scientific Research Foundation of Education Department of Yunnan Province(2024Y004).
文摘Soil greenhouse gas(GHG)emissions contribute profoundly to global warming;however,how plant detritus input alters GHG emissions is poorly understood.Here,we used detritus input and removal treatments(i.e.,DIRT:control,CK;double litter,DL;no roots with double litter,NRDL;no litter,NL;no roots,NR;no roots and no litter,NRNL)to assess the effects of litter and root inputs on soil CO_(2),CH_(4),and N_(2)O fluxes in soils in a coniferous(Pinus yunnanensis)and a broad-leaf forest(Quercus pannosa)in a subalpine region in southwestern China.Litter addition increased CO_(2) emissions on average 22.22%,but did not significantly alter CH_(4) uptake and N_(2)O emission compared to the CK.Litter removal(NL and NRNL)significantly reduced CO_(2) emissions on average 30.22%and N_(2)O emissions on average 31.16%from both forest soils,but did not significantly affect soil CH_(4) uptake.Root removal(NR and NRNL)generally decreased these three soil GHG fluxes.Changes inβ-1,4-glucosidase(BG)involved in C and phospholipid fatty acid(PLFAs)biomass were projected to influence CO_(2) emissions,while soil microclimates(temperature and moisture)combined with BG activity mainly regulated CH_(4) uptake.Alterations in dissolved organic nitrogen,microbial biomass nitrogen and BG were mainly responsible for changes in N_(2)O emissions.Interestingly,coniferous forest soil seemed to promote CH_(4) uptake more than the broad-leaf forest soil,but CO_(2) and N_(2)O fluxes were not significantly affected by the forest types.As expected,litter addition significantly increased the warming potential,while litter removal relatively lowered it.These findings revealed the divergent roles of plant detritus input and forest type in shaping soil GHG fluxes,thereby providing insights into forest management and predicting contributions of subalpine forests to global warming.
