Collaborative forest management (CFM) is a form of forest governance in which local communities are involved in the management and decision-making processes related to forest resources. It is believed that forests und...Collaborative forest management (CFM) is a form of forest governance in which local communities are involved in the management and decision-making processes related to forest resources. It is believed that forests under such management are better in tree diversity and conservation status and thus hold more carbon stocks. The study assessed the impact of CFM on carbon stocks, tree species diversity & tree species density in Mabira Central Forest Reserve. Data were collected from plots that were systematically laid in the different purposively selected forest areas. The study findings show that there is no difference in stem density and carbon stocks between CFM and non-CFM areas. CFM areas had lower species richness compared to non-CFM areas. CFM areas, however, exhibited more species diversity than non-CFM areas. Climax colonization may favor a few dominant species over others, hence lowering species diversity despite the number of species being many in the understory, hence at the same time increasing species richness. Likewise, disturbance in CFM area may affect natural colonization and favor the emergency of many species either naturally or through assisted regeneration by reforestation, hence increasing diversity, whereas artificial selection of preferred species through harvesting may lower species richness, as observed. Recommendations for improving collaborative forest management (CFM) areas include implementing targeted interventions to enhance carbon sequestration, such as promoting reforestation and afforestation with high-carbon-storing species and strengthening monitoring and evaluation frameworks to assess carbon stock changes over time. Additionally, efforts should focus on enhancing biodiversity conservation by implementing more stringent protection measures and reducing human disturbance while encouraging community participation in biodiversity monitoring and conservation education.展开更多
Forests are facing several challenges related to forest deforestation mostly due to the actions of man. The study used a CA-Markov model to examine land use/land cover dynamics from 1986 to 2022, as well as estimate f...Forests are facing several challenges related to forest deforestation mostly due to the actions of man. The study used a CA-Markov model to examine land use/land cover dynamics from 1986 to 2022, as well as estimate future changes from 2022 to 2052 in the Mount Nlonako forest and peripheries. Three types of Landsat images (Landsat 4 - 5 Thematic Mapper (TM) images of 1986 and 2004, and Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI-TIRS) image of 2022) were used for diachronic analysis. The results revealed six major land use/land cover classes namely: Dense forest, Clear forest, Farmland, Savannah, Built-up Area and Bare floor. Accuracy rates for land use/land cover classification ranged from 89.85% to 93.11%. The prediction model was accepted with an overall satisfaction rate of 84.08%. The Dense Forest class has been steadily decreasing from 138320.94 ha (75.42%) in 1986 to 84161.34 ha (45.89%) in 2022, corresponding to a total loss of 54159.6 ha (29.53%) over the 36-year period and is projected to reach 39028.34 ha (21.28%) in 2052 corresponding to a future loss of 45133 ha (24.61%) over a period of 30 years. Anthropogenic factors (mainly agriculture and industrial logging) and natural factors (excess rainfall) were responsible for the degradation of the area. Regardless of the limitations of the CA-Markov model due to the non integration of socio-economic factors, this study is a crucial alert to decison and policy makers to undergo protection procedures for this area to be protected, thereby involving the local communities in the management and restoration of the area through participatory management.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
The proliferation of robot accounts on social media platforms has posed a significant negative impact,necessitating robust measures to counter network anomalies and safeguard content integrity.Social robot detection h...The proliferation of robot accounts on social media platforms has posed a significant negative impact,necessitating robust measures to counter network anomalies and safeguard content integrity.Social robot detection has emerged as a pivotal yet intricate task,aimed at mitigating the dissemination of misleading information.While graphbased approaches have attained remarkable performance in this realm,they grapple with a fundamental limitation:the homogeneity assumption in graph convolution allows social robots to stealthily evade detection by mingling with genuine human profiles.To unravel this challenge and thwart the camouflage tactics,this work proposed an innovative social robot detection framework based on enhanced HOmogeneity and Random Forest(HORFBot).At the core of HORFBot lies a homogeneous graph enhancement strategy,intricately woven with edge-removal techniques,tometiculously dissect the graph intomultiple revealing subgraphs.Subsequently,leveraging the power of contrastive learning,the proposed methodology meticulously trains multiple graph convolutional networks,each honed to discern nuances within these tailored subgraphs.The culminating stage involves the fusion of these feature-rich base classifiers,harmoniously aggregating their insights to produce a comprehensive detection outcome.Extensive experiments on three social robot detection datasets have shown that this method effectively improves the accuracy of social robot detection and outperforms comparative methods.展开更多
Forest ecosystems play key roles in mitigating human-induced climate change through enhanced carbon uptake;however,frequently occurring climate extremes and human activities have considerably threatened the stability ...Forest ecosystems play key roles in mitigating human-induced climate change through enhanced carbon uptake;however,frequently occurring climate extremes and human activities have considerably threatened the stability of forests.At the same time,detailed accounts of disturbances and forest responses are not yet well quantified in Asia.This study employed the Breaks For Additive Seasonal and Trend method-an abrupt-change detection method-to analyze the Enhanced Vegetation Index time series in East Asia,South Asia,and Southeast Asia.This approach allowed us to detect forest disturbance and quantify the resilience after disturbance.Results showed that 20%of forests experienced disturbance with an increasing trend from 2000 to 2022,and Southeast Asian countries were more severely affected by disturbances.Specifically,95%of forests had robust resilience and could recover from disturbance within a few decades.The resilience of forests suffering from greater magnitude of disturbance tended to be stronger than forests with lower disturbance magnitude.In summary,this study investigated the resilience of forests across the low and middle latitudes of Asia over the past two decades.The authors found that most forests exhibited good resilience after disturbance and about two-thirds had recovered to a better state in 2022.The findings of this study underscore the complex relationship between disturbance and resilience,contributing to comprehension of forest resilience through satellite remote sensing.展开更多
A distinctive feature of scholarly communities today is exploring topics and concepts in interdisciplinary and international contexts. This observation is increasingly apparent and visible in advancing our thinking an...A distinctive feature of scholarly communities today is exploring topics and concepts in interdisciplinary and international contexts. This observation is increasingly apparent and visible in advancing our thinking and policies related to human/environmental worlds at local, regional, and global scales. Maps are an important part of these innovative and ongoing research approaches. In this context, we consider urban forests a topic meriting more attention of scholars studying the geographic and environmental intersections of the natural sciences with the social sciences and humanities. We construct two innovative knowledge bases, one a conceptual framework based on major themes and concepts related to mapping urban forests using key words of the first 100 results of a Google Scholar query and a second using the number of Google Scholar hyperlinks about mapping urban forests in 244 capital cities. We discovered that the constructed world maps reveal vast global unevenness in our knowledge about urban forests in hyperlink numbers and ratios, results that merit further attention by disciplinary, international and interdisciplinary scholarly communities.展开更多
Although hunting in the north-eastern Atlantic forest of Brazil began more than 500 years ago, no study to date has evaluated its impacts on the region’s mammalian fauna. For one year we carried out diurnal and noctu...Although hunting in the north-eastern Atlantic forest of Brazil began more than 500 years ago, no study to date has evaluated its impacts on the region’s mammalian fauna. For one year we carried out diurnal and nocturnal surveys using the Line Transect method in seven forest fragments varying from 7.32 ha to 469.76 ha, within a 4000 ha forest island archipelago, in Pernambuco State, Atlantic forest of northeastern Brazil. We calculated species density, population size, biomass and synergetic biomass, and recorded direct and indirect human impacts along the study transects. We recorded 44 mammalian species, of which 45.5% (n = 20) went extinct through hunting. The smallest forest fragment had the lowest richness, diversity, population size, and total biomass. It also had no synergetic biomass. The largest fragment had the highest richness, total density, and population size. There was a statistically significant relationship between fragment area and number of gunshots heard and suspended hunting platforms found;between population size and gunshots heard, suspended hunting platforms, free-roaming and feral dogs, and between total density and free-roaming and feral dogs. After more than 500 years of colonization hunting is still devastating, with larger fragments being linked to more hunters. Higher mammal abundances attracted more free-roaming and feral dogs, which have adapted to hunt wildlife on their own. Unless we protect every single forest fragment and create sustainable landscapes, we will not be able to save this hotspot’s hotspot.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 study, which took place around the Boumba-Bek National Park (BBNP) in Cameroon, was based on identifying and characterizing stakeholders in forest resources management, as well as determining the relationships be...This study, which took place around the Boumba-Bek National Park (BBNP) in Cameroon, was based on identifying and characterizing stakeholders in forest resources management, as well as determining the relationships between them, with the goal of encouraging collaborative forest resources management. Purposive sampling was adopted, in which focus group discussions, key informant interviews, semi-structured interviews, and snowball sampling were used for data collection. Focus group discussions were conducted with a total of 20 local associations involved in forest and wildlife management, Bantu traditional councils and the Baka community. Key informant interviews were conducted with local and international NGOs, forest exploitation and Sport hunting (Safari) enterprises and local public administrations that had working rapports with village communities around the BBNP. Information was generally sought on the role of stakeholders in forest management, in terms of use, protection, policy enforcement, challenges encountered in their activities and their relationships with other stakeholders. Actor linkage matrix was used to establish the relationships between different stakeholders. The identified stakeholder groups included the local community, State, international and local NGOs, economic operators (forest exploitation and sport hunting enterprises), and also the rules guiding their activities. Conflicts were rife between the community and the other stakeholders with regard to resource accessibility and use, whereas intra-community conflicts mostly resulted from cases of corruption and embezzlement linked to benefits sharing. Cases of collaboration among all the stakeholders were mostly related to anti-poaching patrols and setting of forest concession limits. There is a need to bring all stakeholders on the same platform, such as in a consultation workshop, to get their perceptions on building trust, conflict resolution and genuine collaboration in resources management.展开更多
Sacred forests play a valuable role in the conservation of local biodiversity and provide numerous ecosystem services in Cameroon. The aim of this study was to estimate floristic diversity, stand structures and carbon...Sacred forests play a valuable role in the conservation of local biodiversity and provide numerous ecosystem services in Cameroon. The aim of this study was to estimate floristic diversity, stand structures and carbon stocks in the sacred forests of Bandrefam and Batoufam (western Cameroon). The floristic inventory and the stand structures were carried out in 25 m × 25 m plots for individuals with diameters greater than 10 cm;5 m × 5 m for individuals with diameters less than 10 cm. Carbon stocks were estimated using the non-destructive method and allometric equations. The floristic inventory identified 65 species divided into 57 genera and 30 families in the Bandrefam sacred forest and 45 species divided into 42 genera and 27 families in the Batoufam sacred forest. In the Bandrefam, the most important families are Phyllanthaceae (53.98%), Moraceae (21.69%), Lamiaceae (20.15%). At Batoufam, the most important families are Phyllanthaceae (39.73%), Fabaceae (28.47%), Araliaceae (23.77%). Malacantha alnifolia (55.14%), Vitex grandifolia (18.43%), Bosqueia angolensis (15.06%) were the most important species in Bandrefam. Otherwise, Malacantha alnifolia (28%), Polyscias fulva (22.73%), Psychotria sp. (21.28%) were the most important in Batoufam. The Bandrefam sacred forest has the highest tree density (2669 stems/ha). Total carbon stock is 484.88 ± 2.28 tC/ha at Batoufam and 313.95 ± 0.93 tC/ha at Bandrefam. The economic value varies between 5858.04 ± 27.62 USD/ha in Batoufam sacred forest and 3788.51 ± 11.26 USD/ha in Bandrefam sacred forest. The number of individuals and small-diameter trees has little influence on the carbon stocks in the trees. Medium-diameter trees store the most carbon, and very large-diameter trees, which are very poorly represented, store less carbon. In another way, wood density and the basal areas influence the carbon storage of the trees.展开更多
As an important component of the global carbon cycle, forest soil organic carbon has a crucial impact on the stability of ecosystems and climate change. As one of the largest carbon pools in terrestrial ecosystems, th...As an important component of the global carbon cycle, forest soil organic carbon has a crucial impact on the stability of ecosystems and climate change. As one of the largest carbon pools in terrestrial ecosystems, the organic carbon stock in forest soils is of great significance for climate change and the health of forest ecosystems. This paper provides a comprehensive review of forest soil organic carbon, discussing its research progress, role, influencing factors, and future trends, with the aim of providing scientific evidence for forest soil carbon management to mitigate global climate change and promote the sustainable development of forest ecosystems.展开更多
To improve the efficiency of air quality analysis and the accuracy of predictions, this paper proposes a composite method based on Vector Autoregressive (VAR) and Random Forest (RF) models. In the theoretical section,...To improve the efficiency of air quality analysis and the accuracy of predictions, this paper proposes a composite method based on Vector Autoregressive (VAR) and Random Forest (RF) models. In the theoretical section, the model introduction and estimation algorithms are provided. In the empirical analysis section, global air quality data from 2022 to 2024 are used, and the proposed method is applied. Specifically, principal component analysis (PCA) is first conducted, and then VAR and Random Forest methods are used for prediction on the reduced-dimensional data. The results show that the RMSE of the hybrid model is 45.27, significantly lower than the 49.11 of the VAR model alone, verifying its superiority. The stability and predictive performance of the model are effectively enhanced.展开更多
Evaluation of water richness in sandstone is an important research topic in the prevention and control of mine water disasters,and the water richness in sandstone is closely related to its porosity.The refl ection sei...Evaluation of water richness in sandstone is an important research topic in the prevention and control of mine water disasters,and the water richness in sandstone is closely related to its porosity.The refl ection seismic exploration data have high-density spatial sampling information,which provides an important data basis for the prediction of sandstone porosity in coal seam roofs by using refl ection seismic data.First,the basic principles of the variational mode decomposition(VMD)method and the random forest method are introduced.Then,the geological model of coal seam roof sandstone is constructed,seismic forward modeling is conducted,and random noise is added.The decomposition eff ects of the empirical mode decomposition(EMD)method and VMD method on noisy signals are compared and analyzed.The test results show that the fi rstorder intrinsic mode functions(IMF1)and IMF2 decomposed by the VMD method contain the main eff ective components of seismic signals.A prediction process of sandstone porosity in coal seam roofs based on the combination of VMD and random forest method is proposed.The feasibility and eff ectiveness of the method are verifi ed by trial calculation in the porosity prediction of model data.Taking the actual coalfi eld refl ection seismic data as an example,the sandstone porosity of the 8 coal seam roof is predicted.The application results show the potential application value of the new porosity prediction method proposed in this study.This method has important theoretical guiding signifi cance for evaluating water richness in coal seam roof sandstone and the prevention and control of mine water disasters.展开更多
The coastal zone ecological restoration project has successfully restored a cumulative shoreline length of 76 km in Fanhe Harbor and Kaozhou Bay ecological restoration shoreline (mangrove forest) located in Huidong Co...The coastal zone ecological restoration project has successfully restored a cumulative shoreline length of 76 km in Fanhe Harbor and Kaozhou Bay ecological restoration shoreline (mangrove forest) located in Huidong County, Huizhou City. Additionally, 5 619.5 m of artificial shoreline has been developed as part of the ecological restoration efforts. Various methods, including UAV remote sensing, orthophoto acquisition, and analysis using ArcGIS software, were employed to assess the length, width, coverage, and other relevant indicators of the newly established ecologically restored shoreline. The findings indicate that the average width, coverage, and ecosystem stability of mangrove forests in the restored area satisfy the criteria for the acceptance of ecological shoreline restoration. Furthermore, a relatively stable ecosystem has been established for over two years. This study offers a scientific foundation for the ecological restoration of mangrove forests and holds considerable significance for the conservation and utilization of mangrove forest resources.展开更多
Forest structure is fundamental in determining ecosystem function,yet the impact of bamboo invasion on these structural characteristics remains unclear.We investigated 219 invasion transects at 41 sites across the dis...Forest structure is fundamental in determining ecosystem function,yet the impact of bamboo invasion on these structural characteristics remains unclear.We investigated 219 invasion transects at 41 sites across the distribution areas of Moso bamboo(Phyllostachys edulis)in China to explore the effects of bamboo invasion on forest structural attributes and diameter–height allometries by comparing paired plots of bamboo,mixed bamboo-tree,and non-bamboo forests along the transects.We found that bamboo invasion decreased the mean and maximum diameter at breast height,maximum height,and total basal area,but increased the mean height,stem density,and scaling exponent for stands.Bamboo also had a higher scaling exponent than tree,particularly in mixed forests,suggesting a greater allocation of biomass to height growth.As invasion intensity increased,bamboo allometry became more plastic and decreased significantly,whereas tree allometry was indirectly promoted by increasing stem density.Additionally,a humid climate may favour the scaling exponents for both bamboo and tree,with only minor contributions from topsoil moisture and nitrogen content.The inherent superiority of diameter–height allometry allows bamboo to outcompete tree and contributes to its invasive success.Our findings provide a theoretical basis for understanding the causes and consequences of bamboo invasion.展开更多
The roles of diurnal temperature in providing heat accumulation and chilling requirements for vegetation spring phenology differ.Although previous studies have established a stronger correlation between leaf onset and...The roles of diurnal temperature in providing heat accumulation and chilling requirements for vegetation spring phenology differ.Although previous studies have established a stronger correlation between leaf onset and diurnal temperature than between leaf onset and average temperature,current research on modeling spring phenology based on diurnal temperature indicators remains limited.In this study,we confirmed the start of the growing season(SOS)sensitivity to diurnal temperature and average temperature in boreal forest.The estimation of SOS was carried out by employing K-Nearest Neighbor Regression(KNR-TDN)model,Random Forest Regres-sion(RFR-TDN)model,eXtreme Gradient Boosting(XGB-TDN)model and Light Gradient Boosting Machine model(LightGBM-TDN)driven by diurnal temperature indicators during 1982-2015,and the SOS was projected from 2015 to 2100 based on the Coupled Model Intercomparison Project Phase 6(CMIP6)climate scenario datasets.The sensitivity of boreal forest SOS to daytime temperature is greater than that to average temperature and nighttime temperature.The LightGBM-TDN model perform best across all vegetation types,exhibiting the lowest RMSE and bias compared to the KNR-TDN model,RFR-TDN model and XGB-TDN model.By incorporating diurn-al temperature indicators instead of relying only on average temperature indicators to simulate spring phenology,an improvement in the accuracy of the model is achieved.Furthermore,the preseason accumulated daytime temperature,daytime temperature and snow cover end date emerged as significant drivers of the SOS simulation in the study area.The simulation results based on LightGBM-TDN model exhibit a trend of advancing SOS followed by stabilization under future climate scenarios.This study underscores the potential of diurn-al temperature indicators as a viable alternative to average temperature indicators in driving spring phenology models,offering a prom-ising new method for simulating spring phenology.展开更多
Evergreen broad-leaved forests(EBLFs) are widely distributed in East Asia and play a vital role in ecosystem stability. The occurrence of these forests in East Asia has been a subject of debate across various discipli...Evergreen broad-leaved forests(EBLFs) are widely distributed in East Asia and play a vital role in ecosystem stability. The occurrence of these forests in East Asia has been a subject of debate across various disciplines. In this study, we explored the occurrence of East Asian EBLFs from a paleobotanical perspective. By collecting plant fossils from four regions in East Asia, we have established the evolutionary history of EBLFs. Through floral similarity analysis and paleoclimatic reconstruction, we have revealed a diverse spatio-temporal pattern for the occurrence of EBLFs in East Asia. The earliest occurrence of EBLFs in southern China can be traced back to the middle Eocene, followed by southwestern China during the late Eocene-early Oligocene. Subsequently, EBLFs emerged in Japan during the early Oligocene and eventually appeared in central-eastern China around the Miocene. Paleoclimate simulation results suggest that the precipitation of wettest quarter(PWet Q, mm) exceeding 600 mm is crucial for the occurrence of EBLFs. Furthermore, the heterogeneous occurrence of EBLFs in East Asia is closely associated with the evolution of the Asian Monsoon. This study provides new insights into the occurrence of EBLFs in East Asia.展开更多
The agricultural Internet of Things(IoT)system is a critical component of modern smart agriculture,and its security risk assessment methods have garnered increasing attention from the industry.Current agricultural IoT...The agricultural Internet of Things(IoT)system is a critical component of modern smart agriculture,and its security risk assessment methods have garnered increasing attention from the industry.Current agricultural IoT security risk assessment methods primarily rely on expert judgment,introducing subjective factors that reduce the credibility of the assessment results.To address this issue,this study constructed a dataset for agricultural IoT security risk assessment based on real-world security reports.A PCARF algorithm,built on random forest principles,was proposed,incorporating ensemble learning strategies to enhance prediction accuracy.Compared to the second-best model,the proposed model demonstrated a 2.7%increase in accuracy,a 3.4%improvement in recall rate,a 3.1%rise in Area Under the Curve(AUC),and a 7.9%boost in Matthews Correlation Coefficient(MCC).Extensive comparative experiments showed that the proposed model outperforms others in prediction accuracy and robustness.展开更多
Latitudinal patterns of treeβ-diversity reveal important insights into the biogeographical processes that influence forest ecosystems.Although previous studies have extensively documentedβ-diversity within relativel...Latitudinal patterns of treeβ-diversity reveal important insights into the biogeographical processes that influence forest ecosystems.Although previous studies have extensively documentedβ-diversity within relatively small spatial extents,the potential drivers ofβ-diversity along latitudinal gradients are still not well understood at larger spatial extents.In this study,we determined whether treeβ-diversity is correlated with latitude in forests of southeastern China,and if so,what ecological processes contribute to these patterns of treeβ-diversity.We specifically aimed to disentangle the relative contributions from interspecific aggregation and environmental filtering across various spatial extents.We delineated regional communities comprising multiple nearby national forest inventory(NFI)plots around random focal plots.The number of NFI plots in a regional community served as a surrogate for spatial extent.We also used a null model to simulate randomly assembled communities and quantify the deviation(β-deviation)between observed and expectedβ-diversity.We found thatβ-diversity decreased along a latitudinal gradient and that this pattern was clearer at larger spatial extents.In addition,latitudinal patterns ofβ-deviation were explained by the degree of species spatial aggregation.We also identified environmental factors that driveβ-deviation in these forests,including precipitation,seasonality,and temperature variation.At larger spatial extents,these environmental variables explained up to 84%of theβ-deviation.Our results reinforce that ecological processes are scale-dependent and collectively contribute to theβ-gradient in subtropical forests.We recommend that conservation efforts maintain diverse forests and heterogeneous environments at multiple spatial extents to mitigate the adverse effects of climate change.展开更多
Gross primary production(GPP)is a crucial indicator representing the absorption of atmospheric CO_(2) by vegetation.At present,the estimation of GPP by remote sensing is mainly based on leaf-related vegetation indexes...Gross primary production(GPP)is a crucial indicator representing the absorption of atmospheric CO_(2) by vegetation.At present,the estimation of GPP by remote sensing is mainly based on leaf-related vegetation indexes and leaf-related biophysical para-meter leaf area index(LAI),which are not completely synchronized in seasonality with GPP.In this study,we proposed chlorophyll content-based light use efficiency model(CC-LUE)to improve GPP estimates,as chlorophyll is the direct site of photosynthesis,and only the light absorbed by chlorophyll is used in the photosynthetic process.The CC-LUE model is constructed by establishing a linear correlation between satellite-derived canopy chlorophyll content(Chlcanopy)and FPAR.This method was calibrated and validated utiliz-ing 7-d averaged in-situ GPP data from 14 eddy covariance flux towers covering deciduous broadleaf forest ecosystems across five dif-ferent climate zones.Results showed a relatively robust seasonal consistency between Chlcanopy with GPP in deciduous broadleaf forests under different climatic conditions.The CC-LUE model explained 88% of the in-situ GPP seasonality for all validation site-year and 56.0% of in-situ GPP variations through the growing season,outperforming the three widely used LUE models(MODIS-GPP algorithm,Vegetation Photosynthesis Model(VPM),and the eddy covariance-light use efficiency model(EC-LUE)).Additionally,the CC-LUE model(RMSE=0.50 g C/(m^(2)·d))significantly improved the underestimation of GPP during the growing season in semi-arid region,re-markably decreasing the root mean square error of averaged growing season GPP simulation and in-situ GPP by 75.4%,73.4%,and 37.5%,compared with MOD17(RMSE=2.03 g C/(m^(2)·d)),VPM(RMSE=1.88 g C/(m^(2)·d)),and EC-LUE(RMSE=0.80 g C/(m^(2)·d))model.The chlorophyll-based method proved superior in capturing the seasonal variations of GPP in forest ecosystems,thereby provid-ing the possibility of a more precise depiction of forest seasonal carbon uptake.展开更多
文摘Collaborative forest management (CFM) is a form of forest governance in which local communities are involved in the management and decision-making processes related to forest resources. It is believed that forests under such management are better in tree diversity and conservation status and thus hold more carbon stocks. The study assessed the impact of CFM on carbon stocks, tree species diversity & tree species density in Mabira Central Forest Reserve. Data were collected from plots that were systematically laid in the different purposively selected forest areas. The study findings show that there is no difference in stem density and carbon stocks between CFM and non-CFM areas. CFM areas had lower species richness compared to non-CFM areas. CFM areas, however, exhibited more species diversity than non-CFM areas. Climax colonization may favor a few dominant species over others, hence lowering species diversity despite the number of species being many in the understory, hence at the same time increasing species richness. Likewise, disturbance in CFM area may affect natural colonization and favor the emergency of many species either naturally or through assisted regeneration by reforestation, hence increasing diversity, whereas artificial selection of preferred species through harvesting may lower species richness, as observed. Recommendations for improving collaborative forest management (CFM) areas include implementing targeted interventions to enhance carbon sequestration, such as promoting reforestation and afforestation with high-carbon-storing species and strengthening monitoring and evaluation frameworks to assess carbon stock changes over time. Additionally, efforts should focus on enhancing biodiversity conservation by implementing more stringent protection measures and reducing human disturbance while encouraging community participation in biodiversity monitoring and conservation education.
文摘Forests are facing several challenges related to forest deforestation mostly due to the actions of man. The study used a CA-Markov model to examine land use/land cover dynamics from 1986 to 2022, as well as estimate future changes from 2022 to 2052 in the Mount Nlonako forest and peripheries. Three types of Landsat images (Landsat 4 - 5 Thematic Mapper (TM) images of 1986 and 2004, and Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI-TIRS) image of 2022) were used for diachronic analysis. The results revealed six major land use/land cover classes namely: Dense forest, Clear forest, Farmland, Savannah, Built-up Area and Bare floor. Accuracy rates for land use/land cover classification ranged from 89.85% to 93.11%. The prediction model was accepted with an overall satisfaction rate of 84.08%. The Dense Forest class has been steadily decreasing from 138320.94 ha (75.42%) in 1986 to 84161.34 ha (45.89%) in 2022, corresponding to a total loss of 54159.6 ha (29.53%) over the 36-year period and is projected to reach 39028.34 ha (21.28%) in 2052 corresponding to a future loss of 45133 ha (24.61%) over a period of 30 years. Anthropogenic factors (mainly agriculture and industrial logging) and natural factors (excess rainfall) were responsible for the degradation of the area. Regardless of the limitations of the CA-Markov model due to the non integration of socio-economic factors, this study is a crucial alert to decison and policy makers to undergo protection procedures for this area to be protected, thereby involving the local communities in the management and restoration of the area through participatory management.
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.
基金Funds for the Central Universities(grant number CUC24SG018).
文摘The proliferation of robot accounts on social media platforms has posed a significant negative impact,necessitating robust measures to counter network anomalies and safeguard content integrity.Social robot detection has emerged as a pivotal yet intricate task,aimed at mitigating the dissemination of misleading information.While graphbased approaches have attained remarkable performance in this realm,they grapple with a fundamental limitation:the homogeneity assumption in graph convolution allows social robots to stealthily evade detection by mingling with genuine human profiles.To unravel this challenge and thwart the camouflage tactics,this work proposed an innovative social robot detection framework based on enhanced HOmogeneity and Random Forest(HORFBot).At the core of HORFBot lies a homogeneous graph enhancement strategy,intricately woven with edge-removal techniques,tometiculously dissect the graph intomultiple revealing subgraphs.Subsequently,leveraging the power of contrastive learning,the proposed methodology meticulously trains multiple graph convolutional networks,each honed to discern nuances within these tailored subgraphs.The culminating stage involves the fusion of these feature-rich base classifiers,harmoniously aggregating their insights to produce a comprehensive detection outcome.Extensive experiments on three social robot detection datasets have shown that this method effectively improves the accuracy of social robot detection and outperforms comparative methods.
基金jointly supported by the National Natural Science Foundation of China [grant number 42265012]the Funding by the Fengyun Application Pioneering Project [grant number FY-APP-ZX-2022.0221]。
文摘Forest ecosystems play key roles in mitigating human-induced climate change through enhanced carbon uptake;however,frequently occurring climate extremes and human activities have considerably threatened the stability of forests.At the same time,detailed accounts of disturbances and forest responses are not yet well quantified in Asia.This study employed the Breaks For Additive Seasonal and Trend method-an abrupt-change detection method-to analyze the Enhanced Vegetation Index time series in East Asia,South Asia,and Southeast Asia.This approach allowed us to detect forest disturbance and quantify the resilience after disturbance.Results showed that 20%of forests experienced disturbance with an increasing trend from 2000 to 2022,and Southeast Asian countries were more severely affected by disturbances.Specifically,95%of forests had robust resilience and could recover from disturbance within a few decades.The resilience of forests suffering from greater magnitude of disturbance tended to be stronger than forests with lower disturbance magnitude.In summary,this study investigated the resilience of forests across the low and middle latitudes of Asia over the past two decades.The authors found that most forests exhibited good resilience after disturbance and about two-thirds had recovered to a better state in 2022.The findings of this study underscore the complex relationship between disturbance and resilience,contributing to comprehension of forest resilience through satellite remote sensing.
文摘A distinctive feature of scholarly communities today is exploring topics and concepts in interdisciplinary and international contexts. This observation is increasingly apparent and visible in advancing our thinking and policies related to human/environmental worlds at local, regional, and global scales. Maps are an important part of these innovative and ongoing research approaches. In this context, we consider urban forests a topic meriting more attention of scholars studying the geographic and environmental intersections of the natural sciences with the social sciences and humanities. We construct two innovative knowledge bases, one a conceptual framework based on major themes and concepts related to mapping urban forests using key words of the first 100 results of a Google Scholar query and a second using the number of Google Scholar hyperlinks about mapping urban forests in 244 capital cities. We discovered that the constructed world maps reveal vast global unevenness in our knowledge about urban forests in hyperlink numbers and ratios, results that merit further attention by disciplinary, international and interdisciplinary scholarly communities.
文摘Although hunting in the north-eastern Atlantic forest of Brazil began more than 500 years ago, no study to date has evaluated its impacts on the region’s mammalian fauna. For one year we carried out diurnal and nocturnal surveys using the Line Transect method in seven forest fragments varying from 7.32 ha to 469.76 ha, within a 4000 ha forest island archipelago, in Pernambuco State, Atlantic forest of northeastern Brazil. We calculated species density, population size, biomass and synergetic biomass, and recorded direct and indirect human impacts along the study transects. We recorded 44 mammalian species, of which 45.5% (n = 20) went extinct through hunting. The smallest forest fragment had the lowest richness, diversity, population size, and total biomass. It also had no synergetic biomass. The largest fragment had the highest richness, total density, and population size. There was a statistically significant relationship between fragment area and number of gunshots heard and suspended hunting platforms found;between population size and gunshots heard, suspended hunting platforms, free-roaming and feral dogs, and between total density and free-roaming and feral dogs. After more than 500 years of colonization hunting is still devastating, with larger fragments being linked to more hunters. Higher mammal abundances attracted more free-roaming and feral dogs, which have adapted to hunt wildlife on their own. Unless we protect every single forest fragment and create sustainable landscapes, we will not be able to save this hotspot’s hotspot.
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.
文摘This study, which took place around the Boumba-Bek National Park (BBNP) in Cameroon, was based on identifying and characterizing stakeholders in forest resources management, as well as determining the relationships between them, with the goal of encouraging collaborative forest resources management. Purposive sampling was adopted, in which focus group discussions, key informant interviews, semi-structured interviews, and snowball sampling were used for data collection. Focus group discussions were conducted with a total of 20 local associations involved in forest and wildlife management, Bantu traditional councils and the Baka community. Key informant interviews were conducted with local and international NGOs, forest exploitation and Sport hunting (Safari) enterprises and local public administrations that had working rapports with village communities around the BBNP. Information was generally sought on the role of stakeholders in forest management, in terms of use, protection, policy enforcement, challenges encountered in their activities and their relationships with other stakeholders. Actor linkage matrix was used to establish the relationships between different stakeholders. The identified stakeholder groups included the local community, State, international and local NGOs, economic operators (forest exploitation and sport hunting enterprises), and also the rules guiding their activities. Conflicts were rife between the community and the other stakeholders with regard to resource accessibility and use, whereas intra-community conflicts mostly resulted from cases of corruption and embezzlement linked to benefits sharing. Cases of collaboration among all the stakeholders were mostly related to anti-poaching patrols and setting of forest concession limits. There is a need to bring all stakeholders on the same platform, such as in a consultation workshop, to get their perceptions on building trust, conflict resolution and genuine collaboration in resources management.
文摘Sacred forests play a valuable role in the conservation of local biodiversity and provide numerous ecosystem services in Cameroon. The aim of this study was to estimate floristic diversity, stand structures and carbon stocks in the sacred forests of Bandrefam and Batoufam (western Cameroon). The floristic inventory and the stand structures were carried out in 25 m × 25 m plots for individuals with diameters greater than 10 cm;5 m × 5 m for individuals with diameters less than 10 cm. Carbon stocks were estimated using the non-destructive method and allometric equations. The floristic inventory identified 65 species divided into 57 genera and 30 families in the Bandrefam sacred forest and 45 species divided into 42 genera and 27 families in the Batoufam sacred forest. In the Bandrefam, the most important families are Phyllanthaceae (53.98%), Moraceae (21.69%), Lamiaceae (20.15%). At Batoufam, the most important families are Phyllanthaceae (39.73%), Fabaceae (28.47%), Araliaceae (23.77%). Malacantha alnifolia (55.14%), Vitex grandifolia (18.43%), Bosqueia angolensis (15.06%) were the most important species in Bandrefam. Otherwise, Malacantha alnifolia (28%), Polyscias fulva (22.73%), Psychotria sp. (21.28%) were the most important in Batoufam. The Bandrefam sacred forest has the highest tree density (2669 stems/ha). Total carbon stock is 484.88 ± 2.28 tC/ha at Batoufam and 313.95 ± 0.93 tC/ha at Bandrefam. The economic value varies between 5858.04 ± 27.62 USD/ha in Batoufam sacred forest and 3788.51 ± 11.26 USD/ha in Bandrefam sacred forest. The number of individuals and small-diameter trees has little influence on the carbon stocks in the trees. Medium-diameter trees store the most carbon, and very large-diameter trees, which are very poorly represented, store less carbon. In another way, wood density and the basal areas influence the carbon storage of the trees.
文摘As an important component of the global carbon cycle, forest soil organic carbon has a crucial impact on the stability of ecosystems and climate change. As one of the largest carbon pools in terrestrial ecosystems, the organic carbon stock in forest soils is of great significance for climate change and the health of forest ecosystems. This paper provides a comprehensive review of forest soil organic carbon, discussing its research progress, role, influencing factors, and future trends, with the aim of providing scientific evidence for forest soil carbon management to mitigate global climate change and promote the sustainable development of forest ecosystems.
文摘To improve the efficiency of air quality analysis and the accuracy of predictions, this paper proposes a composite method based on Vector Autoregressive (VAR) and Random Forest (RF) models. In the theoretical section, the model introduction and estimation algorithms are provided. In the empirical analysis section, global air quality data from 2022 to 2024 are used, and the proposed method is applied. Specifically, principal component analysis (PCA) is first conducted, and then VAR and Random Forest methods are used for prediction on the reduced-dimensional data. The results show that the RMSE of the hybrid model is 45.27, significantly lower than the 49.11 of the VAR model alone, verifying its superiority. The stability and predictive performance of the model are effectively enhanced.
基金National Natural Science Foundation of China(Grant No.42274180)National Key Research and Development Program of China(2021YFC2902003).
文摘Evaluation of water richness in sandstone is an important research topic in the prevention and control of mine water disasters,and the water richness in sandstone is closely related to its porosity.The refl ection seismic exploration data have high-density spatial sampling information,which provides an important data basis for the prediction of sandstone porosity in coal seam roofs by using refl ection seismic data.First,the basic principles of the variational mode decomposition(VMD)method and the random forest method are introduced.Then,the geological model of coal seam roof sandstone is constructed,seismic forward modeling is conducted,and random noise is added.The decomposition eff ects of the empirical mode decomposition(EMD)method and VMD method on noisy signals are compared and analyzed.The test results show that the fi rstorder intrinsic mode functions(IMF1)and IMF2 decomposed by the VMD method contain the main eff ective components of seismic signals.A prediction process of sandstone porosity in coal seam roofs based on the combination of VMD and random forest method is proposed.The feasibility and eff ectiveness of the method are verifi ed by trial calculation in the porosity prediction of model data.Taking the actual coalfi eld refl ection seismic data as an example,the sandstone porosity of the 8 coal seam roof is predicted.The application results show the potential application value of the new porosity prediction method proposed in this study.This method has important theoretical guiding signifi cance for evaluating water richness in coal seam roof sandstone and the prevention and control of mine water disasters.
文摘The coastal zone ecological restoration project has successfully restored a cumulative shoreline length of 76 km in Fanhe Harbor and Kaozhou Bay ecological restoration shoreline (mangrove forest) located in Huidong County, Huizhou City. Additionally, 5 619.5 m of artificial shoreline has been developed as part of the ecological restoration efforts. Various methods, including UAV remote sensing, orthophoto acquisition, and analysis using ArcGIS software, were employed to assess the length, width, coverage, and other relevant indicators of the newly established ecologically restored shoreline. The findings indicate that the average width, coverage, and ecosystem stability of mangrove forests in the restored area satisfy the criteria for the acceptance of ecological shoreline restoration. Furthermore, a relatively stable ecosystem has been established for over two years. This study offers a scientific foundation for the ecological restoration of mangrove forests and holds considerable significance for the conservation and utilization of mangrove forest resources.
基金supported by the National Natural Science Foundation of China(No.31988102)Yunnan Province Major Program for Basic Research Project(No.202101BC070002)+1 种基金Yunnan Province Science and Technology Talents and Platform Program(No.202305AA160014)Yunnan Province Key Research and Development Program of China(No.202303AC100009)。
文摘Forest structure is fundamental in determining ecosystem function,yet the impact of bamboo invasion on these structural characteristics remains unclear.We investigated 219 invasion transects at 41 sites across the distribution areas of Moso bamboo(Phyllostachys edulis)in China to explore the effects of bamboo invasion on forest structural attributes and diameter–height allometries by comparing paired plots of bamboo,mixed bamboo-tree,and non-bamboo forests along the transects.We found that bamboo invasion decreased the mean and maximum diameter at breast height,maximum height,and total basal area,but increased the mean height,stem density,and scaling exponent for stands.Bamboo also had a higher scaling exponent than tree,particularly in mixed forests,suggesting a greater allocation of biomass to height growth.As invasion intensity increased,bamboo allometry became more plastic and decreased significantly,whereas tree allometry was indirectly promoted by increasing stem density.Additionally,a humid climate may favour the scaling exponents for both bamboo and tree,with only minor contributions from topsoil moisture and nitrogen content.The inherent superiority of diameter–height allometry allows bamboo to outcompete tree and contributes to its invasive success.Our findings provide a theoretical basis for understanding the causes and consequences of bamboo invasion.
基金Under the auspices of National Natural Science Foundation of China(No.42201374,42071359)。
文摘The roles of diurnal temperature in providing heat accumulation and chilling requirements for vegetation spring phenology differ.Although previous studies have established a stronger correlation between leaf onset and diurnal temperature than between leaf onset and average temperature,current research on modeling spring phenology based on diurnal temperature indicators remains limited.In this study,we confirmed the start of the growing season(SOS)sensitivity to diurnal temperature and average temperature in boreal forest.The estimation of SOS was carried out by employing K-Nearest Neighbor Regression(KNR-TDN)model,Random Forest Regres-sion(RFR-TDN)model,eXtreme Gradient Boosting(XGB-TDN)model and Light Gradient Boosting Machine model(LightGBM-TDN)driven by diurnal temperature indicators during 1982-2015,and the SOS was projected from 2015 to 2100 based on the Coupled Model Intercomparison Project Phase 6(CMIP6)climate scenario datasets.The sensitivity of boreal forest SOS to daytime temperature is greater than that to average temperature and nighttime temperature.The LightGBM-TDN model perform best across all vegetation types,exhibiting the lowest RMSE and bias compared to the KNR-TDN model,RFR-TDN model and XGB-TDN model.By incorporating diurn-al temperature indicators instead of relying only on average temperature indicators to simulate spring phenology,an improvement in the accuracy of the model is achieved.Furthermore,the preseason accumulated daytime temperature,daytime temperature and snow cover end date emerged as significant drivers of the SOS simulation in the study area.The simulation results based on LightGBM-TDN model exhibit a trend of advancing SOS followed by stabilization under future climate scenarios.This study underscores the potential of diurn-al temperature indicators as a viable alternative to average temperature indicators in driving spring phenology models,offering a prom-ising new method for simulating spring phenology.
基金supported by National Key R&D Program of China(No.2022YFF0800800)National Science Fund for Distinguished Young Scholars(No.32225005)+3 种基金National Natural Science Foundation of China(NSFC)(Nos.42072024,42320104005,42372033)the Young and Middle-aged Academic and Technical Leaders of Yunnan(No.202305AC160051)Basic Research Project of Yunnan Province(No.202401AT070222)the 14th Five-Year Plan of the Xishuangbanna Tropical Botanical Garden,Chinese Academy of Sciences(Nos.XTBG-1450101,E3ZKFF7B).
文摘Evergreen broad-leaved forests(EBLFs) are widely distributed in East Asia and play a vital role in ecosystem stability. The occurrence of these forests in East Asia has been a subject of debate across various disciplines. In this study, we explored the occurrence of East Asian EBLFs from a paleobotanical perspective. By collecting plant fossils from four regions in East Asia, we have established the evolutionary history of EBLFs. Through floral similarity analysis and paleoclimatic reconstruction, we have revealed a diverse spatio-temporal pattern for the occurrence of EBLFs in East Asia. The earliest occurrence of EBLFs in southern China can be traced back to the middle Eocene, followed by southwestern China during the late Eocene-early Oligocene. Subsequently, EBLFs emerged in Japan during the early Oligocene and eventually appeared in central-eastern China around the Miocene. Paleoclimate simulation results suggest that the precipitation of wettest quarter(PWet Q, mm) exceeding 600 mm is crucial for the occurrence of EBLFs. Furthermore, the heterogeneous occurrence of EBLFs in East Asia is closely associated with the evolution of the Asian Monsoon. This study provides new insights into the occurrence of EBLFs in East Asia.
文摘The agricultural Internet of Things(IoT)system is a critical component of modern smart agriculture,and its security risk assessment methods have garnered increasing attention from the industry.Current agricultural IoT security risk assessment methods primarily rely on expert judgment,introducing subjective factors that reduce the credibility of the assessment results.To address this issue,this study constructed a dataset for agricultural IoT security risk assessment based on real-world security reports.A PCARF algorithm,built on random forest principles,was proposed,incorporating ensemble learning strategies to enhance prediction accuracy.Compared to the second-best model,the proposed model demonstrated a 2.7%increase in accuracy,a 3.4%improvement in recall rate,a 3.1%rise in Area Under the Curve(AUC),and a 7.9%boost in Matthews Correlation Coefficient(MCC).Extensive comparative experiments showed that the proposed model outperforms others in prediction accuracy and robustness.
基金supported by the National Natural Science Foundation of China(42271317)the Innovation Research Team Project of the Natural Science Foundation of Hainan Province(422CXTD515)。
文摘Latitudinal patterns of treeβ-diversity reveal important insights into the biogeographical processes that influence forest ecosystems.Although previous studies have extensively documentedβ-diversity within relatively small spatial extents,the potential drivers ofβ-diversity along latitudinal gradients are still not well understood at larger spatial extents.In this study,we determined whether treeβ-diversity is correlated with latitude in forests of southeastern China,and if so,what ecological processes contribute to these patterns of treeβ-diversity.We specifically aimed to disentangle the relative contributions from interspecific aggregation and environmental filtering across various spatial extents.We delineated regional communities comprising multiple nearby national forest inventory(NFI)plots around random focal plots.The number of NFI plots in a regional community served as a surrogate for spatial extent.We also used a null model to simulate randomly assembled communities and quantify the deviation(β-deviation)between observed and expectedβ-diversity.We found thatβ-diversity decreased along a latitudinal gradient and that this pattern was clearer at larger spatial extents.In addition,latitudinal patterns ofβ-deviation were explained by the degree of species spatial aggregation.We also identified environmental factors that driveβ-deviation in these forests,including precipitation,seasonality,and temperature variation.At larger spatial extents,these environmental variables explained up to 84%of theβ-deviation.Our results reinforce that ecological processes are scale-dependent and collectively contribute to theβ-gradient in subtropical forests.We recommend that conservation efforts maintain diverse forests and heterogeneous environments at multiple spatial extents to mitigate the adverse effects of climate change.
基金Under the auspices of the National Key Research and Development Program of China(No.2019YFA0606603)。
文摘Gross primary production(GPP)is a crucial indicator representing the absorption of atmospheric CO_(2) by vegetation.At present,the estimation of GPP by remote sensing is mainly based on leaf-related vegetation indexes and leaf-related biophysical para-meter leaf area index(LAI),which are not completely synchronized in seasonality with GPP.In this study,we proposed chlorophyll content-based light use efficiency model(CC-LUE)to improve GPP estimates,as chlorophyll is the direct site of photosynthesis,and only the light absorbed by chlorophyll is used in the photosynthetic process.The CC-LUE model is constructed by establishing a linear correlation between satellite-derived canopy chlorophyll content(Chlcanopy)and FPAR.This method was calibrated and validated utiliz-ing 7-d averaged in-situ GPP data from 14 eddy covariance flux towers covering deciduous broadleaf forest ecosystems across five dif-ferent climate zones.Results showed a relatively robust seasonal consistency between Chlcanopy with GPP in deciduous broadleaf forests under different climatic conditions.The CC-LUE model explained 88% of the in-situ GPP seasonality for all validation site-year and 56.0% of in-situ GPP variations through the growing season,outperforming the three widely used LUE models(MODIS-GPP algorithm,Vegetation Photosynthesis Model(VPM),and the eddy covariance-light use efficiency model(EC-LUE)).Additionally,the CC-LUE model(RMSE=0.50 g C/(m^(2)·d))significantly improved the underestimation of GPP during the growing season in semi-arid region,re-markably decreasing the root mean square error of averaged growing season GPP simulation and in-situ GPP by 75.4%,73.4%,and 37.5%,compared with MOD17(RMSE=2.03 g C/(m^(2)·d)),VPM(RMSE=1.88 g C/(m^(2)·d)),and EC-LUE(RMSE=0.80 g C/(m^(2)·d))model.The chlorophyll-based method proved superior in capturing the seasonal variations of GPP in forest ecosystems,thereby provid-ing the possibility of a more precise depiction of forest seasonal carbon uptake.
