Use of evergreen and deciduous plants by nocturnal-roosting birds:A case study in Beijing

With continually increasing urbanization,the land cover in urban areas continues to change,resulting in the loss of biodiversity.Birds are highly sensitive to changes in habitat.Most forest birds perch on plants that provide increased safety to reduce the risk of predation,and small birds may also consider insulation when using roosting plants in winter because of cold weather.Landscaping plants thus shape the nocturnal roosting environment of urban birds,and proper planting is essential for the survival of birds at night.The use of roosting plants by urban birds should therefore be studied to provide a reference for landscaping.In the current study,we observed 1865 nocturnal roosting birds in Beijing from 2021 to 2022,with 23 species of birds from 12 families and 45 species of plants from 22 families recorded.Juniperus chinensis exhibited the highest bird rarity-weighted richness,followed by Fraxinus pennsylvanica,Phyllostachys propinqua,Pinus tabuliformis,and Ulmus pumila.The diameter at breast height,tree height,and crown width of plants used by birds was largest in summer and smallest in winter,and the perch height of birds was the highest in spring and summer and the lowest in winter.Birds used the highest proportion of deciduous plants in summer and the highest proportion of evergreen plants in winter.A significant seasonal difference in the use of evergreen and deciduous plants by small birds was noted,with a preference for deciduous plants in summer and evergreen plants in winter,while this preference was not found in large birds.These findings indicate that evergreen plants provide a vital nocturnal roosting environment for small birds in winter.To provide a better nocturnal roosting habitat for urban birds,we recommend paying attention to the combination of evergreen and deciduous plants when carrying out landscape construction.

Coupling and Long-term Change Characteristics of Forest Carbon Sink and Forestry Economic Development in China

[Objectives]To analyze the changes in of forest carbon sink and forestry economic development,provide reference for relevant management decisions,ecological governance and resource and environment management,and promote the development of green low-carbon economy in China.[Methods]Based on the data of six forest resource inventories from 1989 to 2018 and related studies,the comprehensive evaluation model of forest carbon sink and forestry economic development,the coupling degree model of forest carbon sink and forestry economic development,and the coupling coordination degree model of forest carbon sink and forestry economic development were adopted.The coupling degree of forest carbon sink and forestry economic development from 1992 to 2018 was analyzed.Stepwise regression and ARIMA model were used to analyze the influencing factors and lagging characteristics of forest carbon sink.The coupling degree between forest carbon sink and forestry economic development in China from 2019 to 2030 was predicted by autoregression and ADF test.The coupling between forest carbon sink and forestry economic development in China and its long-term change characteristics were also discussed in this study.[Results](i)The investment of ecological construction and protection,the actual investment of forestry key ecological projects,GDP and the import of forest products had a significant impact on forest resources carbon stock.The total output value of forestry industry,the actually completed investment of forestry key ecological projects and the export volume of forest products had a significant impact on the forest carbon sink,and the actually completed investment of forestry key ecological projects has the greatest impact on the two.(ii)The impact of actually completed investment of forestry key ecological projects had a lag of 2 years on the forest resources carbon stock and a lag of 1 year on the forest carbon sink.When investing in forest carbon sink,it is necessary to make a good plan in advance,and do a good job in forest resources management and time optimization.(iii)From 1992 to 2018,the coupling degree of forest resources carbon stock,forest carbon sink and long-term development of forestry economy in China was gradually increasing.Although there were some fluctuations in the middle time,the coupling degree of forest resources carbon stock and the long-term development of forestry economy increased by 9.24%annually,and the degree of coupling coordination increased from"serious imbalance"in 1992 to"high-quality coordination"in 2018.From 1993 to 2018,the coupling degree of forest carbon sink and long-term development of forestry economy increased by 9.63%annually,slightly faster than the coupling coordination degree of forest resources carbon stock and long-term development of forestry economy.The coordination level also rose from level 2 in 1993 to level 10 in 2018.(iv)The prediction shows that the coupling coordination degree of forest resources carbon stock,forest carbon sink and the long-term development of forestry economy would increase from 2019 to 2030.The coupling coordination degree(D)values of both were close to 1,the coordination level was also 10 for a long time,and the degree of coupling coordination was also maintained at the"high-quality coordination"level for a long time.[Conclusions]Forest has multiple benefits of society,economy and ecology,and forest carbon sink is only a benefit output.The long-term coupling analysis of forest carbon sink and forestry economic development is a key point to multiple benefit analysis.The analysis shows that the spillover effect and co-evolution effect of forest carbon sink in China are significant.From 1992 to 2018,the coupling coordination degree of forest carbon sink and forestry economic development was gradually rising.The prediction analysis also indicate that the coupling coordination degree between the forest carbon sink and the long-term development of forestry economy will remain at the level of"high-quality coordination"for a long time from 2019 to 2030.Therefore,improving the level of forest management and maintaining the current trend of increasing forest resources are the key to achieving the goal of carbon peaking and carbon neutrality in China.

Empirical analysis of Beijing forestry industrial structure and its competitiveness under the background of low carbon economy

With Shift-Share Method, the study implemented an empirical analysis of Beijing's forestry industrial structure and its regional competitiveness around China from 2002 to 2011.The results indicated that the development tendency of forestry industry structure will be gradually transferred from "primarysecondary –tertiary industry" into "tertiary-secondary-primary industry". As the study suggested that the basic position of the primary forestry industry should be strengthened and promoted, deep processing capacity and resources utilization of the secondary industry should be further developed, and the leading role of the tertiary industry should be further enhanced in accordance with the low carbon concept.

Deposition of water-soluble inorganic ions in PM_(2.5) in a typical forestry system in Beijing,China

Background: Rapid economic development in China has resulted in an increase in severe air pollution in city groups such as the Beijing-Tianjin-Hebei Metropolitan Region. PM2.5(fine particles with an aerodynamic equivalent diameter of 2.5 μm or less) is one of the most important pollutants. The deposition process is an important way of removing particles from the air. To evaluate the effect of an urban forest on atmospheric particle removal, a concentration gradient method was used to measure the deposition velocities of water-soluble inorganics in PM2.5 in two national forest parks in Beijing, China. The following eight water-soluble inorganic ions in PM2.5 were investigated: sodium, ammonium, potassium, magnesium, calcium, chloride, nitrate, and sulfate.Methods: Samples were taken from two sites in Beijing from the 7 th to the 15 th May, 2013. The concentrations of water-soluble inorganic ions were analyzed with ion chromatography. We used the concentration gradient technique to estimate the deposition flux and velocity. To determine the relationships between leaf traits and particle accumulation, typical leaf samples from each selected species were studied using scanning electron microscopy.Results: The total deposition flux and total deposition velocity during the daytime were higher than those at night.Sulfate showed the biggest deposition flux and velocity at both study sites, whereas the other ions showed different trends at each site. Result from higher proportion of coniferous to broadleaved trees, the total deposition flux of the eight ions measured in Jiufeng National Forest Park was greater than that in Olympic Forest Park.Conclusions: The deposition velocity was affected by meteorological conditions such as wind speed, temperature,and humidity. The deposition velocity was also influenced by tree species. The surface of plants is an important factor influencing particle deposition. The results of this study may help in assessing the effects of forestry systems on particle removal and provide evidence for urban air pollution control and afforestation of urban areas.

Spatial patterns of the main trees in a plant micro-reserve constructed in the 2022 Beijing Winter Olympics mountainous competition zone

Several plant micro-reserves were established to preserve the vegetation in local mountain areas during the construction of the Yanqing competition zone of the 2022 Beijing Winter Olympics.The spatial patterns of the main species in one of the micro-reserves and the factors affecting these patterns were characterized in this study.The distribution of arbor species was found to be mostly aggregated,especially at fine scales(<5 m).Minor species were found to be more aggregated than the major species in each forest layer.The spatial patterns were found to be affected by habitat heterogeneity,intraspecific relationships,interspecific competition,and seed dispersal limitation.Habitat heterogeneity was found to affect large-scale spatial patterns,and its effects were observed throughout population development.Interspecific competition is another factor affecting the distribution of the species,and its effects were stronger during the later stages of population development.Habitat heterogeneity was found to affect competition among species and is key for species coexistence.Both these processes are affected by the seed dispersal limitation,and intraspecific relationships are a legacy of seed dispersal.The point patterns can be used as a tool for the initial assessment of the status of communities within micro-reserves.The consideration of these relationships in the development,management,and formulation of policies for micro-reserves in mountainous areas will facilitate the achievement of conservation goals.The careful consideration of habitat conditions when selecting sites for micro-reserves establishment can promote species conservation.

Air Pollution Exposure Based on Nighttime Light Remote Sensing and Multi-source Geographic Data in Beijing

Air pollution is a problem that directly affects human health,the global environment and the climate.The air quality index(AQI)indicates the degree of air pollution and effect on human health;however,when assessing air pollution only based on AQI monitoring data the fact that the same degree of air pollution is more harmful in more densely populated areas is ignored.In the present study,multi-source data were combined to map the distribution of the AQI and population data,and the analyze their pollution population exposure of Beijing in 2018 was analyzed.Machine learning based on the random forest algorithm was adopted to calculate the monthly average AQI of Beijing in 2018.Using Luojia-1 nighttime light remote sensing data,population statistics data,the population of Beijing in 2018 and point of interest data,the distribution of the permanent population in Beijing was estimated with a high precision of 200 m×200 m.Based on the spatialization results of the AQI and population of Beijing,the air pollution exposure levels in various parts of Beijing were calculated using the population-weighted pollution exposure level(PWEL)formula.The results show that the southern region of Beijing had a more serious level of air pollution,while the northern region was less polluted.At the same time,the population was found to agglomerate mainly in the central city and the peripheric areas thereof.In the present study,the exposure of different districts and towns in Beijing to pollution was analyzed,based on high resolution population spatialization data,it could take the pollution exposure issue down to each individual town.And we found that towns with higher exposure such as Yongshun Town,Shahe Town and Liyuan Town were all found to have a population of over 200000 which was much higher than the median population of townships of51741 in Beijing.Additionally,the change trend of air pollution exposure levels in various regions of Beijing in 2018 was almost the same,with the peak value being in winter and the lowest value being in summer.The exposure intensity in population clusters was relatively high.To reduce the level and intensity of pollution exposure,relevant departments should strengthen the governance of areas with high AQI,and pay particular attention to population clusters.

黄河流域生态保护迈向高质量发展的特征——植被绿度、生产力和降水利用效率的差异性变化

Globally,vegetation has been changing dramatically.The vegetation-water dynamic is key to understanding ecosystem structure and functioning in water-limited ecosystems.Continual satellite monitoring has detected global vegetation greening.However,a vegetation greenness increase does not mean that ecosystem functions increase.The intricate interplays resulting from the relationships between vegetation and precipitation must be more adequately comprehended.In this study,satellite data,for example,leaf area index(LAI),net primary production(NPP),and rainfall use efficiency(RUE),were used to quantify vegetation dynamics and their relationship with rainfall in different reaches of the Yellow River Basin(YRB).A sequential regression method was used to detect trends of NPP sensitivity to rainfall.The results showed that 34.53%of the YRB exhibited a significant greening trend since 2000.Among them,20.54%,53.37%,and 16.73%of upper,middle,and lower reach areas showed a significant positive trend,respectively.NPP showed a similar trend to LAI in the YRB upper,middle,and lower reaches.A notable difference was noted in the distributions and trends of RUE across the upper,middle,and lower reaches.Moreover,there were significant trends in vegetation-rainfall sensitivity in 16.86%of the YRB’s middle reaches—14.08%showed negative trends and 2.78%positive trends.A total of 8.41%of the YRB exhibited a marked increase in LAI,NPP,and RUE.Subsequently,strategic locations reliant on the correlation between vegetation and rainfall were identified and designated for restoration planning purposes to propose future ecological restoration efforts.Our analysis indicates that the middle reach of the YRB exhibited the most significant variation in vegetation greenness and productivity.The present study underscores the significance of examining the correlation between vegetation and rainfall within the context of the high-quality development strategy of the YRB.The outcomes of our analysis and the proposed ecological restoration framework can provide decision-makers with valuable insights for executing rational basin pattern optimization and sustainable management.

Implication of community-level ecophysiological parameterization to modelling ecosystem productivity:a case study across nine contrasting forest sites in eastern China

Parameterization is a critical step in modelling ecosystem dynamics.However,assigning parameter values can be a technical challenge for structurally complex natural plant communities;uncertainties in model simulations often arise from inappropriate model parameterization.Here we compared five methods for defining community-level specific leaf area(SLA)and leaf C:N across nine contrasting forest sites along the North-South Transect of Eastern China,including biomass-weighted average for the entire plant community(AP_BW)and four simplified selective sampling(biomass-weighted average over five dominant tree species[5DT_BW],basal area weighted average over five dominant tree species[5DT_AW],biomass-weighted average over all tree species[AT_BW]and basal area weighted average over all tree species[AT_AW]).We found that the default values for SLA and leaf C:N embedded in the Biome-BGC v4.2 were higher than the five computational methods produced across the nine sites,with deviations ranging from 28.0 to 73.3%.In addition,there were only slight deviations(<10%)between the whole plant community sampling(AP_BW)predicted NPP and the four simplified selective sampling methods,and no significant difference between the predictions of AT_BW and AP_BW except the Shennongjia site.The findings in this study highlights the critical importance of computational strategies for community-level parameterization in ecosystem process modelling,and will support the choice of parameterization methods.

Dominant woody plant species recognition with a hierarchical model based on multimodal geospatial data for subtropical forests

Since the launch of the Google Earth Engine(GEE)cloud platform in 2010,it has been widely used,leading to a wealth of valuable information.However,the potential of GEE for forest resource management has not been fully exploited.To extract dominant woody plant species,GEE combined Sen-tinel-1(S1)and Sentinel-2(S2)data with the addition of the National Forest Resources Inventory(NFRI)and topographic data,resulting in a 10 m resolution multimodal geospatial dataset for subtropical forests in southeast China.Spectral and texture features,red-edge bands,and vegetation indices of S1 and S2 data were computed.A hierarchical model obtained information on forest distribution and area and the dominant woody plant species.The results suggest that combining data sources from the S1 winter and S2 yearly ranges enhances accuracy in forest distribution and area extraction compared to using either data source independently.Similarly,for dominant woody species recognition,using S1 winter and S2 data across all four seasons was accurate.Including terrain factors and removing spatial correlation from NFRI sample points further improved the recognition accuracy.The optimal forest extraction achieved an overall accuracy(OA)of 97.4%and a maplevel image classification efficacy(MICE)of 96.7%.OA and MICE were 83.6%and 80.7%for dominant species extraction,respectively.The high accuracy and efficacy values indicate that the hierarchical recognition model based on multimodal remote sensing data performed extremely well for extracting information about dominant woody plant species.Visualizing the results using the GEE application allows for an intuitive display of forest and species distribution,offering significant convenience for forest resource monitoring.

Understory seedlings of Quercus mongolica survive by phenological escape

Understanding understory seedling regeneration mechanisms is important for the sustainable development of temperate primary forests in the context of increasingly intense climate warming events.The poor regeneration of dominant tree species,however,is one of the biggest challenges it faces at the moment.Especially,the regeneration of the shade-intolerant Quercus mongolica seedling is difficult in primary forests,which contrasts with the extreme abundance of understory seedlings in secondary forests.The mechanism behind the interesting phenomenon is still unknown.This study used in-situ monitoring and nursery-controlled experiment to investigate the survival rate,growth performance,as well as nonstructural carbohydrate (NSC) concentrations and pools of various organ tissues of seedlings for two consecutive years,further analyze the understory light availability and simulate the foliage carbon (C) gain in the secondary and primary forest.Results suggested that seedlings in the secondary forest had greater biomass allocation aboveground,height and specific leaf area (SLA) in summer,which allowed the seedling to survive longer in the canopy closure period.High light availability and positive C gain in early spring and late autumn are key factors affecting the growth and survival of understory seedlings in the secondary forest,whereas seedlings in the primary forest had annual negative carbon gain.Through the growing season,the total NSC concentrations of seedlings gradually decreased,whereas those of seedlings in the secondary forest increased significantly in autumn,and were mainly stored in roots for winter consumption and the following year's summer shade period,which was verified by the nursery-controlled experiment that simulated autumn enhanced light availability improved seedling survival rate and NSC pools.In conclusion,our results revealed the survival trade-off strategies of Quercus mongolica seedlings and highlighted the necessity of high light availability during the spring and autumn phenological periods for shade-intolerant tree seedling recruitment.

Coupled effects of climate change and human activities on vegetation dynamics in the Southwestern Alpine Canyon Region of China

The driving effects of climate change and human activities on vegetation change have always been a focal point of research.However,the coupling mechanisms of these driving factors across different temporal and spatial scales remain controversial.The Southwestern Alpine Canyon Region of China(SACR),as an ecologically fragile area,is highly sensitive to the impacts of climate change and human activities.This study constructed a vegetation cover dataset for the SACR based on the Enhanced Vegetation Index(EVI)from 2000 to 2020.Spatial autocorrelation,Theil-Sen trend,and Mann-Kendall tests were used to analyze the spatiotemporal characteristics of vegetation cover changes.The main drivers of spatial heterogeneity in vegetation cover were identified using the optimal parameter geographic detector,and an improved residual analysis model was employed to quantify the relative contributions of climate change and human activities to interannual vegetation cover changes.The main findings are as follows:Spatially,vegetation cover exceeds 60%in most areas,especially in the southern part of the study area.However,the border area between Linzhi and Changdu exhibits lower vegetation cover.Climate factors are the primary drivers of spatial heterogeneity in vegetation cover,with temperature having the most significant influence,as indicated by its q-value,which far exceeds that of other factors.Additionally,the interaction q-value between the two factors significantly increases,showing a relationship of bivariate enhancement and nonlinear enhancement.In terms of temporal changes,vegetation cover shows an overall improving trend from 2000 to 2020,with significant increases observed in 68.93%of the study area.Among these,human activities are the main factors driving vegetation cover change,with a relative contribution rate of 41.31%,while climate change and residual factors contribute 35.66%and 23.53%,respectively.By thoroughly exploring the coupled mechanisms of vegetation change,this study provides important references for the sustainable management and conservation of the vegetation ecosystem in the SACR.

The clutch size,incubation behavior of Reeves’s Pheasant(Syrmaticus reevesii) and their responses to ambient temperature and precipitation

Weather conditions play a pivotal role in embryo development and parental incubation costs,potentially impacting the clutch size and incubation behavior of birds.Understanding these effects is crucial for bird conservation.Reeves’ s Pheasant(Syrmaticus reevesii) is a threatened species endemic to China,which is characterized by female-only incubation.However,there is a lack of information regarding the impact of weather conditions on clutch size and incubation behavior in this species.Using satellite tracking,we tracked 27 wild female Reeves’ s Pheasants from 2020 to 2023 in Hubei Province,China.We explored their clutch size and incubation behavior,as well as their responses to ambient temperature and precipitation.Clutch size averaged 7.75 ±1.36,had an association with average ambient temperature and average daily precipitation during the egglaying period,and was potentially linked to female breeding attempts.Throughout the incubation period,females took an average of 0.73 ±0.46 recesses every 24 h,with an average recess duration of 100.80 ±73.37 min and an average nest attendance of 92.98 ±5.27%.They showed a unimodal recess pattern in which nest departures peaked primarily between 13:00 and 16:00.Furthermore,females rarely left nests when daily precipitation was high.Recess duration and nest attendance were influenced by the interaction between daily mean ambient temperature and daily precipitation,as well as day of incubation.Additionally,there was a positive correlation between clutch size and recess duration.These results contribute valuable insights into the lifehistory features of this endangered species.

Three-dimensional(3D)parametric measurements of individual gravels in the Gobi region using point cloud technique

Gobi spans a large area of China,surpassing the combined expanse of mobile dunes and semi-fixed dunes.Its presence significantly influences the movement of sand and dust.However,the complex origins and diverse materials constituting the Gobi result in notable differences in saltation processes across various Gobi surfaces.It is challenging to describe these processes according to a uniform morphology.Therefore,it becomes imperative to articulate surface characteristics through parameters such as the three-dimensional(3D)size and shape of gravel.Collecting morphology information for Gobi gravels is essential for studying its genesis and sand saltation.To enhance the efficiency and information yield of gravel parameter measurements,this study conducted field experiments in the Gobi region across Dunhuang City,Guazhou County,and Yumen City(administrated by Jiuquan City),Gansu Province,China in March 2023.A research framework and methodology for measuring 3D parameters of gravel using point cloud were developed,alongside improved calculation formulas for 3D parameters including gravel grain size,volume,flatness,roundness,sphericity,and equivalent grain size.Leveraging multi-view geometry technology for 3D reconstruction allowed for establishing an optimal data acquisition scheme characterized by high point cloud reconstruction efficiency and clear quality.Additionally,the proposed methodology incorporated point cloud clustering,segmentation,and filtering techniques to isolate individual gravel point clouds.Advanced point cloud algorithms,including the Oriented Bounding Box(OBB),point cloud slicing method,and point cloud triangulation,were then deployed to calculate the 3D parameters of individual gravels.These systematic processes allow precise and detailed characterization of individual gravels.For gravel grain size and volume,the correlation coefficients between point cloud and manual measurements all exceeded 0.9000,confirming the feasibility of the proposed methodology for measuring 3D parameters of individual gravels.The proposed workflow yields accurate calculations of relevant parameters for Gobi gravels,providing essential data support for subsequent studies on Gobi environments.

Performance evaluation of seven multi-label classification methods on real-world patent and publication datasets

Purpose:Many science,technology and innovation(STI)resources are attached with several different labels.To assign automatically the resulting labels to an interested instance,many approaches with good performance on the benchmark datasets have been proposed for multi-label classification task in the literature.Furthermore,several open-source tools implementing these approaches have also been developed.However,the characteristics of real-world multi-label patent and publication datasets are not completely in line with those of benchmark ones.Therefore,the main purpose of this paper is to evaluate comprehensively seven multi-label classification methods on real-world datasets.Research limitations:Three real-world datasets differ in the following aspects:statement,data quality,and purposes.Additionally,open-source tools designed for multi-label classification also have intrinsic differences in their approaches for data processing and feature selection,which in turn impacts the performance of a multi-label classification approach.In the near future,we will enhance experimental precision and reinforce the validity of conclusions by employing more rigorous control over variables through introducing expanded parameter settings.Practical implications:The observed Macro F1 and Micro F1 scores on real-world datasets typically fall short of those achieved on benchmark datasets,underscoring the complexity of real-world multi-label classification tasks.Approaches leveraging deep learning techniques offer promising solutions by accommodating the hierarchical relationships and interdependencies among labels.With ongoing enhancements in deep learning algorithms and large-scale models,it is expected that the efficacy of multi-label classification tasks will be significantly improved,reaching a level of practical utility in the foreseeable future.Originality/value:(1)Seven multi-label classification methods are comprehensively compared on three real-world datasets.(2)The TextCNN and TextRCNN models perform better on small-scale datasets with more complex hierarchical structure of labels and more balanced document-label distribution.(3)The MLkNN method works better on the larger-scale dataset with more unbalanced document-label distribution.

An ultrathin and robust single-ion conducting interfacial layer for dendrite-free lithium metal batteries

The practical application of rechargeable lithium metal batteries(LMBs) encounters significant challenges due to the notorious dendrite growth triggered by uneven Li deposition behaviors. In this work,a mechanically robust and single-ion-conducting interfacial layer, fulfilled by the strategic integration of flexible cellulose acetate(CA) matrix with rigid graphene oxide(GO) and Li F fillers(termed the CGL layer), is rationally devised to serve as a stabilizer for dendrite-free lithium(Li) metal batteries. The GCL film exhibits favorable mechanical properties with high modulus and flexibility that help to relieve interface fluctuations. More crucially, the electron-donating carbonyl groups(C=O) enriched in GCL foster a strengthened correlation with Li^(+), which availably aids the Li^(+)desolvation process and expedites facile Li^(+)mobility, yielding exceptional Li^(+) transference number of 0.87. Such single-ion conductive properties regulate rapid and uniform interfacial transport kinetics, mitigating the growth of Li dendrites and the decomposition of electrolytes. Consequently, stable Li anode with prolonged cycle stabilities and flat deposition morphologies are realized. The Li||LiFePO_(4) full cells with CGL protective layer render an outstanding cycling capability of 500 cycles at 3 C, and an ultrahigh capacity retention of 99.99% for over 220 cycles even under harsh conditions. This work affords valuable insights into the interfacial regulation for achieving high-performance LMBs.

Impacts of climate change and human activities on vegetation dynamics on the Mongolian Plateau, East Asia from 2000 to 2023

The Mongolian Plateau in East Asia is one of the largest contingent arid and semi-arid areas of the world.Under the impacts of climate change and human activities,desertification is becoming increasingly severe on the Mongolian Plateau.Understanding the vegetation dynamics in this region can better characterize its ecological changes.In this study,based on Moderate Resolution Imaging Spectroradiometer(MODIS)images,we calculated the kernel normalized difference vegetation index(kNDVI)on the Mongolian Plateau from 2000 to 2023,and analyzed the changes in kNDVI using the Theil-Sen median trend analysis and Mann-Kendall significance test.We further investigated the impact of climate change on kNDVI change using partial correlation analysis and composite correlation analysis,and quantified the effects of climate change and human activities on kNDVI change by residual analysis.The results showed that kNDVI on the Mongolian Plateau was increasing overall,and the vegetation recovery area in the southern region was significantly larger than that in the northern region.About 50.99%of the plateau showed dominant climate-driven effects of temperature,precipitation,and wind speed on kNDVI change.Residual analysis showed that climate change and human activities together contributed to 94.79%of the areas with vegetation improvement.Appropriate human activities promoted the recovery of local vegetation,and climate change inhibited vegetation growth in the northern part of the Mongolian Plateau.This study provides scientific data for understanding the regional ecological environment status and future changes and developing effective ecological protection measures on the Mongolian Plateau.

Assessing forest cover changes and fragmentation in the Himalayan temperate region: implications for forest conservation and management

This study comprehensively assessed long-term vegetation changes and forest fragmentation dynamics in the Himalayan temperate region of Pakistan from 1989 to 2019.Four satellite images,including Landsat-5 TM and Landsat-8 Operational Land Imager(OLI),were chosen for subsequent assessments in October 1989,2001,2011 and 2019.The classified maps of 1989,2001,2011 and 2019 were created using the maximum likelihood classifier.Post-classification comparison showed an overall accuracy of 82.5%and a Kappa coefficient of 0.79 for the 2019 map.Results revealed a drastic decrease in closed-canopy and open-canopy forests by 117.4 and 271.6 km^(2),respectively,and an increase in agriculture/farm cultivation by 1512.8 km^(2).The two-way ANOVA test showed statistically significant differences in the area of various cover classes.Forest fragmentation was evaluated using the Landscape Fragmentation Tool(LFT v2.0)between 1989 and 2019.The large forest core(>2.00 km^(2))decreased from 149.4 to 296.7 km^(2),and a similar pattern was observed in medium forest core(1.00-2.00 km^(2))forests.On the contrary,the small core(<1.00 km^(2))forest increased from 124.8 to 145.3 km^(2) in 2019.The perforation area increased by 296.9 km^(2),and the edge effect decreased from 458.9 to 431.7 km^(2).The frequency of patches also increased by 119.1 km^(2).The closed and open canopy classes showed a decreasing trend with an annual rate of 0.58%and 1.35%,respectively.The broad implications of these findings can be seen in the studied region as well as other global ecological areas.They serve as an imperative baseline for afforestation and reforestation operations,highlighting the urgent need for efficient management,conservation,and restoration efforts.Based on these findings,sustainable land-use policies may be put into place that support local livelihoods,protect ecosystem services,and conserve biodiversity.

Evolution history dominantly regulates fine root lifespan in tree species across the world

Understanding the drivers of variations in fine root lifespan is key to informing nutrient cycling and productivity in terrestrial ecosystems.However,the general patterns and determinants of forest fine root lifespan at the global scale are still limited.We compiled a dataset of 421 fine root lifespan observations from 76 tree species globally to assess phylogenetic signals among species,explored relationships between fine root lifespan and biotic and abiotic factors,and quantified the relative importance of phylogeny,root system structure and functions,climatic and edaphic factors in driving global fine root lifespan variations.Overall,fine root lifespan showed a clear phylogenetic signal,with gymnosperms having a longer fine root lifespan than angiosperms.Fine root lifespan was longer for evergreens than deciduous trees.Ectomycorrhizal(ECM)plants had an extended fine root lifespan than arbuscular mycorrhizal(AM)plants.Among different climatic zones,fine root lifespan was the longest in the boreal zone,while it did not vary between the temperate and tropical zone.Fine root lifespan increased with soil depth and root order.Furthermore,the analysis of relative importance indicated that phylogeny was the strongest driver influencing the variation in forest fine root lifespan,followed by soil clay content,root order,mean annual temperature,and soil depth,while other environmental factors and root traits exerted weaker effects.Our results suggest that the global pattern of fine root lifespan in forests is shaped by the interplay of phylogeny,root traits and environmental factors.These findings necessitate accurate representations of tree evolutionary history in earth system models to predict fine root longevity and its responses to global changes.

Differences in leaf cuticular wax induced by whole-genome duplication in autotetraploid sour jujube

Drought-resistant plants exhibit strong water retention capability.In this regard,the autotetraploid sour jujube leaves exhibit better water retention than diploid leaves.Morphological comparisons and physiological comparisons of diploid and autotetraploid leaves showed that the autotetraploid leaves had thicker leaf cuticles and more leaf wax accumulation than the diploid leaves,which could reduce cuticle permeability and improve the drought tolerance of leaves.In this study,the cuticular wax crystalloids on the adaxial and abaxial sides of young and mature jujube leaves were observed in the two ploidy types,and unique cuticular wax crystalloids covering a large area of the cuticle on autotetraploid sour jujube leaves may provide an advantage in reducing leaf non-stomata transpiration and improving plant drought tolerance.Based on the transcriptome,115 differentially expressed genes between diploids and autotetraploids were further analyzed and found to be involved in the accumulation of cuticular wax components,including terpenoids,fatty acids,and lipids,as well as ABC transporter and wax biosynthetic process.Finally,14 genes differentially expressed between glossy autotetraploid leaves and nonglossy diploid leaves,such as LOC107414787,LOC107411574 and LOC107413721,were screened as candidate genes by qRT-PCR analysis.This findings provided insights into how polyploidization improved drought tolerance.

Predicting Users’ Latent Suicidal Risk in Social Media: An Ensemble Model Based on Social Network Relationships

Suicide has become a critical concern,necessitating the development of effective preventative strategies.Social media platforms offer a valuable resource for identifying signs of suicidal ideation.Despite progress in detecting suicidal ideation on social media,accurately identifying individuals who express suicidal thoughts less openly or infrequently poses a significant challenge.To tackle this,we have developed a dataset focused on Chinese suicide narratives from Weibo’s Tree Hole feature and introduced an ensemble model named Text Convolutional Neural Network based on Social Network relationships(TCNN-SN).This model enhances predictive performance by leveraging social network relationship features and applying correction factors within a weighted linear fusion framework.It is specifically designed to identify key individuals who can help uncover hidden suicidal users and clusters.Our model,assessed using the bespoke dataset and benchmarked against alternative classification approaches,demonstrates superior accuracy,F1-score and AUC metrics,achieving 88.57%,88.75%and 94.25%,respectively,outperforming traditional TextCNN models by 12.18%,10.84%and 10.85%.We assert that our methodology offers a significant advancement in the predictive identification of individuals at risk,thereby contributing to the prevention and reduction of suicide incidences.