Evergreen Broadleaf Forest Research Articles

Global mapping of fractional tree cover for forest cover change analysis

Fractional tree cover facilitates the characterization of forest cover changes using satellite data. However, there are still substantial challenges in generating fractional tree cover datasets that satisfy the requirements of interannual stability for forest cover change monitoring. In this study, a global annual fractional tree cover dataset, named as GLOBMAP Fractional Tree Cover, was generated from MODIS observations with a resolution of 250 m during the period of 2000–2022. The tree cover estimation was improved relative to conventional global tree cover mapping methods by developing highly discriminative input features and near global sampling training data. MODIS annual observations were realigned at the pixel level to eliminate phenological differences among regions. The realigned annual series were condensed into twelve features showing high separability between trees and herbaceous vegetation to reduce the dimension of features. A global covering training dataset, comprising 465.88 million sample points, was extracted across the globe through the aggregation and combination of forest/land cover maps from ESA WorldCover, GlobeLand30, PALSAR FNF, and ESRI Land Cover to improve the representativeness of training data. A feed-forward neural network was calibrated to predict fractional tree cover from MODIS data. The spatial pattern of the estimation results was generally consistent with the CGLS-LC100 product at global scale, with the average MAE in global vegetated areas of 12.50 %, while our dataset provided extended temporal coverage. The interannual stability of the estimated tree cover series was improved compared to MODIS vegetation continuous fields products for deciduous broadleaf forests, evergreen broadleaf forests, and mixed forests, with the global average value of mean absolute deviation (MAD) in tree cover series reduced by 5.8 %, 46.9 %, and 18.3 %, respectively. The estimation results were assessed using globally distributed validation data around BELMANIP 2.1 sites and those derived from the USGS circa 2010 global land cover reference dataset, leading to the R2 values of 0.93 and 0.73, MAE of 4.24 % and 10.55 %, and RMSE of 11.45 % and 17.98 %, respectively. This dataset could enhance the capability to monitor forest cover changes, particularly gradual changes such as forest recovery.

Overridingly increasing vegetation sensitivity to vapor pressure deficit over the recent two decades in China

Vapor pressure deficit (VPD) shows significant spatial and temporal variability in the context of global climate change, which is important for studying the implications of climate change on the structure and function of ecosystems to analyze the effects of VPD on vegetation dynamics. Spatial patterns of vegetation sensitivity to VPD have been recently investigated, however, the feedback of different vegetation types to VPD may vary depending on physiological characteristics, it is unclear how different types influence the sensitivity to VPD. In this study, the ERA5-Land reanalysis time-series dataset was used to analyze the spatial and temporal trends of VPD under different vegetation types. It was found that VPD showed an increasing trend in vegetated areas over the past 20 years with large spatial heterogeneity, generally increasing with drying conditions. On this basis, the spatial patterns of vegetation sensitivity to VPD and temporal trends in sensitivity were evaluated over the past 20 years in China using the enhanced vegetation index (EVI) and near-infrared reflectance of vegetation (NIRv) which can describe vegetation dynamics. The results show that the sensitivities under the two indices have high spatial consistency, with northeastern and central China showing positive sensitivities and southern China showing negative sensitivities, respectively. The positive sensitivities are relatively high for Deciduous Broadleaf Forests (DBF), Deciduous Needleleaf Forests (DNF), Grasslands (GL), and Croplands (CL) types, while the negative sensitivities are larger for Shrublands (SL) and Savannas (SA) types. Under different climatic zones, the sensitivity of CL and GL are independent of climatic zones (both showing positive), while the sensitivity of SL is negative in the Humid zone and positive in the Semi-Arid zone. Temporally, the sensitivity showed a slow increasing trend over the last 20 years. In terms of vegetation types, sensitivities of Evergreen Broadleaf Forests (EBF), DBF, GL and CL types showed a significant increasing trend (p < 0.05), except for the SL type, which showed a significant decreasing trend (p < 0.05). The trends of sensitivity are not affected by the differences in vegetation types (all of them show an increasing trend) under arid and semi-arid conditions, while dry sub-humid and humid have a greater impact on sensitivity trends. The finding of an overall increase in sensitivity suggests a mechanism of erratic change in vegetation growth under climate change. Notably, the increased sensitivity of certain vegetation types (especially GL and CL) suggests that these may become progressively vulnerable to increased VPD as global climate change persists, with the risk of moving from facilitation to inhibition of photosynthesis.

2018年浙江古田山24公顷亚热带常绿阔叶林动态监测样地林冠结构和地形数据集

The canopy structure and topographic variables jointly influence the spatiotemporal variation of habitats for important species. However, there remains a considerable scarcity of precise forest canopy structure and topographic data obtained from large-scale forest dynamic plots. LiDAR data, acquired via near-surface remote sensing platforms, facilitates precise computation of both canopy structure and topographic variables. This study selected a 24-hectare dynamic plot in the subtropical evergreen broadleaf forest, located in the core conservation area of Qianjiangyuan National Park, as its focal point for survey. We conducted an analysis and quality control process on the point cloud data in 2018 using a near-surface remote sensing platform for this area, aiming to acquire corresponding canopy structure and topography data. Ground data collected through Real-Time Kinematic (RTK) surveying were used to verify and validate the accuracy of the digital elevation model (DEM). The analysis revealed a mean square error of elevation of 0.07 meters, suggesting a high level of accuracy of the dataset. The 24-hectare subtropical evergreen broad-leaved forest dynamic plot represents a typical low-altitude evergreen broad-leaved forest in the central subtropical region. This dataset can provide essential data support for the monitoring and research of biodiversity in subtropical broadleaved evergreen forests.

The roles of environmental filtering and competitive exclusion in the plant community assembly at Mt. Huangshan are forest-type-dependent

Integrating trait and phylogenetic data are increasingly being used to enhance our understanding of the underlying community assembly processes and optimize the interpretation of unmeasured, phylogenically conserved traits. The biodiversity of Huangshan, China, was higher, but it was suffered from some damage, such as biological invasion and human disturbance. We sampled 176 species from three different forests (EBF: evergreen broadleaf forest; DBF: deciduous broadleaf forest; MNBF: mixed needleleaf and broadleaf forest) which are the representative communities of Mt. Huangshan. Then, we measured six functional traits (leaf thickness, leaf area, specific leaf area, leaf carbon concentration, leaf nitrogen concentration and leaf phosphorus concentration) and constructed a molecular phylogeny tree to assess community structure using species trait-phylogenetic distances. Results showed that six traits are more phylogenetically convergent than predicted, which was not closely related to the evolutionary history of species. The contribution of phylogenetic distance and functional distance to community construction was equal in total layer and more importance was given to functional distances for tree and shrub layer, indicating that closely related species tended to be phenotypically different. The functional trait-phylogenetic of EBF and MNBF was clustering, while DBF exhibited functional trait-phylogenetic over-dispersion. In other words, environmental filtering appears to be the dominant force in EBF and MNBF, while competitive exclusion appears to become more important in DBF. Our study also indicates that spatial factors resulted in a higher explanatory power than environmental factors. In according to better protect forest biodiversity, species coexistence mechanisms should be incorporated into the management of forest communities and to predict their presentation in future environmental scenarios.

Improving the Simulation Accuracy of the Net Ecosystem Productivity of Subtropical Forests in China: Sensitivity Analysis and Parameter Calibration Based on the BIOME-BGC Model

Subtropical forests have strong carbon sequestration potential; however, the spatiotemporal patterns of their carbon sink are unclear. The BIOME-BGC model is a powerful tool for forest carbon sink estimation while the numerous parameters, as well as the localization, limit their application. This study takes three typical subtropical forests (evergreen broadleaf forest, EBF; evergreen needleleaf forest, ENF; and bamboo forest, BF) in China as examples, assesses the sensitivity of 43 ecophysiological parameters in the BIOME-BGC model both by the Morris method and the extended Fourier amplitude sensitivity test (EFAST), and then evaluates the net ecosystem productivity (NEP) estimation accuracy based on the dataset of the fiveFi long-term carbon flux sites of those three typical forests from 2000 to 2015. The results showed that (1) both sensitivity analysis methods can effectively screen out important parameters affecting NEP simulation while the Morris method is more computationally efficient and the EFAST is better in the quantitative evaluation of sensitivity. (2) The highly sensitive parameters obtained using the two methods are basically the same; however, their importance varies across sites and vegetation types, e.g., the most sensitive parameters are k for the EBF and ENF and Ract25 for the BF, respectively. (3) The optimized parameters successfully improved the NEP simulation accuracy in subtropical forests, with average correlation coefficients increased by 25.19% and normalized root mean square error reduced by 21.74% compared with those simulated by original parameters. This study provides a theoretical basis for the optimization of process model parameters and important technical support for accurate NEP simulations of subtropical forest ecosystems.

Comparative assessment of leaf photosynthetic capacity datasets for estimating terrestrial gross primary productivity

The maximum Rubisco carboxylation rate normalized to 25 °C (Vcmax25) is a key parameter in terrestrial biosphere models for simulating carbon cycling. Recently, global distributions of Vcmax25 have been derived through various methods and different data, including field measurements, ecological optimality theory (EOT), leaf chlorophyll content (LCC), and solar-induced chlorophyll fluorescence (SIF). However, direct validation poses challenges due to high uncertainty arising from limited ground-based observations. This study conducted an indirect evaluation of four Vcmax25 datasets by assessing the accuracy of gross primary productivity (GPP) simulated using the Biosphere-atmosphere Exchange Process Simulator (BEPS) at both site and global scales. Results indicate that, compared to utilizing Vcmax25 fixed by plant functional types (PFT) derived from field measurements, incorporating Vcmax25 derived from SIF and LCC (SIF + LCC), or solely LCC, into BEPS significantly reduces simulated errors in the annual total GPP, with a 23.2 %–25.1 % decrease in the average absolute bias across 196 FLUXNET2015 sites. Daily GPP for evergreen needleleaf forests, deciduous broadleaf forests, shrublands, grasslands, and croplands shows a 7.8 %–27.6 % decrease in absolute bias, primarily attributed to reduced simulation errors during off-peak seasons of vegetation growth. Conversely, the annual total GPP error simulated using EOT-derived Vcmax25 increases slightly (2.2 %) compared to that simulated using PFT-fixed Vcmax25. This is primarily due to a significant overestimation in evergreen broadleaf forests and underestimation in croplands, despite slight increased accuracy for other PFTs. The global annual GPP simulated using Vcmax25 with seasonal variations (i.e., LCC Vcmax25 and SIF + LCC Vcmax25) yields a 4.3 %–7.3 % decrease compared to that simulated using PFT-fixed Vcmax25. Compared to FLUXCOM and GOSIF GPP products, the GPP simulated based on SIF + LCC Vcmax25 and LCC Vcmax25 demonstrates better consistency (R2 = 0.91–0.93, RMSE = 314.2–376.6 g C m−2 yr−1). This study underscores the importance of accurately characterizing the spatiotemporal variations in Vcmax25 for the accurate simulation of global vegetation productivity.

Ground‐dwelling mammal and bird diversity in the southern Annamites: Exploring complex habitat associations and the ghost of past hunting pressure

AbstractThe Langbian Plateau, a biodiversity hotspot in the southern Annamites of Viet Nam, has undergone extensive hunting pressure. However, the limited information on the effects of overexploitation on the current status and community composition of wildlife hinders effective conservation efforts, including the implementation of targeted patrols to reduce snaring. In this study, we conducted a camera‐trapping survey across the Langbian Plateau, consisting of a broadleaf evergreen and coniferous habitat mosaic. We recorded 46 ground‐dwelling mammals and birds, including several threatened Annamite endemics. Using multi‐species Royle‐Nichols model and landscape covariates, we found higher richness in broadleaf evergreen forest located in more remote and less rugged areas. We then used species responses to covariates to predict species distribution and identify high‐priority areas for conservation. Furthermore, we constructed diversity profiles that indicated higher biodiversity in broadleaf evergreen forest compared to the coniferous forest. Finally, we used a dissimilarity index to assess the level of defaunation, revealing 16% of the community had been lost, with higher levels of defaunation for threatened and larger‐sized species. Our findings provide insights into the status, distribution, and occurrence of the ground‐dwelling mammal and bird communities in the Langbian Plateau, and can help stakeholders design more effective conservation strategies to protect existing populations.

Intra-Annual Cumulative Effects and Mechanisms of Climatic Factors on Global Vegetation Biomes’ Growth

Previous studies have shown that climate change has significant cumulative effects on vegetation growth. However, there remains a gap in understanding the characteristics of cumulative climatic effects on different vegetation types and the underlying driving mechanisms. In this study, using the normalized difference vegetation index data from 1982 to 2015, along with accumulated temperature, precipitation, and solar radiation data, we quantitatively investigated the intra-annual cumulative effects of climatic factors on global vegetation biomes across climatic zones. We also explored the underlying driving mechanisms. The results indicate that precipitation has a longer intra-annual cumulative effect on vegetation, with effects lasting up to 12 months for large percentages of most vegetation biomes. The cumulative effect of solar radiation is mostly concentrated within 0–6 months. Temperature has a shorter cumulative effect, with no significant cumulative effect of temperature on large percentages of tree-type vegetation. Compared to other vegetation types, evergreen broadleaf forests, close shrublands, open shrublands, savannas, and woody savannas exhibit more complex cumulative climatic effects. Each vegetation type shows a weak-to-moderate correlation with accumulated precipitation while exhibiting strong-to-extremely-strong positive correlations with accumulated temperature and accumulated solar radiation. The climate-induced regulations of water, heat, and nutrient, as well as the intrinsic mechanisms of vegetation’s tolerance, resistance, and adaptation to climate change, account for the significant heterogeneity of cumulative climatic effects across vegetation biomes in different climatic zones. This study contributes to enriching the theoretical understanding of the relationship between vegetation growth and climate change. It also offers crucial theoretical support for developing climate change adaptation strategies and improving future “vegetation-climate” models.

Temporal and Spatial Dynamics in Carbon Utilization Efficiency and Driving Mechanisms in Southeast Tibet from 2012 to 2022

Carbon utilization efficiency (CUE) in terrestrial ecosystems stands as a pivotal metric for assessing ecosystem functionality. Investigating the spatiotemporal dynamics of regional CUE within the context of global climate change not only provides a theoretical foundation for understanding terrestrial carbon cycling but also furnishes essential data support for formulating sustainable management strategies at a regional scale. This study focuses on the southeastern region of Tibet. Utilizing monthly and yearly MOD17A2HGF as primary sources, we employ Thiel–Sen estimation and Mann–Kendall trend analysis to scrutinize the spatiotemporal dynamics of CUE. Systematic analysis of the stability of CUE spatiotemporal changes in the Southeast Tibet region is conducted using the coefficient of variation analysis. The Hurst model is then applied to prognosticate future CUE changes in Southeast Tibet. Additionally, a comprehensive analysis of CUE is undertaken by integrating meteorological data and land-use data. The findings reveal the following: (1) At the monthly scale, regional CUE exhibits discernible variations synchronized with the growth season, with different vegetation types displaying diverse fluctuation patterns. The high-altitude forest area manifests the least annual CUE fluctuations, while evergreen needleleaf forests and evergreen broadleaf forests demonstrate larger variations. At the yearly scale, CUE reveals a non-significant upward trend overall, but there is an augmented fluctuation observed from 2019 to 2022. (2) CUE in Southeast Tibet demonstrates sensitivity to temperature and precipitation variations, with temperature exhibiting a more pronounced and strongly correlated impact, especially in Gongjo County and Qamdo Town. Temperature and precipitation exert opposing influences on CUE changes in the Southeast Tibet region. In the southern (below 28° N) and northern (above 31° N) regions of Southeast Tibet, the response of CUE to temperature and precipitation variations differs. Moreover, over 62.3% of the areas show no sustained trend of change. (3) Vegetation type emerges as a principal factor determining the scope and features of vegetation CUE changes. Grassland and sparse grassland areas exhibit markedly higher CUE values than evergreen broadleaf forests, deciduous broadleaf forests, evergreen needleleaf forests, and deciduous needleleaf forests. Notably, the CUE fluctuation in shrublands and areas with embedded farmland vegetation surpasses that of other vegetation types.

Evaluating vegetation vulnerability under compound dry and hot conditions using vine copula across global lands

With global warming, climate extremes are intensifying, particularly compound dry and hot events (CDHEs), which would severely impact vegetation growth. However, quantitatively evaluating vegetation vulnerability to CDHEs remains challenging in global lands. A conditional probability model was constructed using the vine copula function to evaluate responses of vegetation to CDHEs based on the Standardized Moisture Anomaly Index (SZI), the Standardized Temperature Index (STI), and the kernel Normalized Difference Vegetation Index (kNDVI). The results show that vegetation growth is more susceptible to dry and hot conditions in arid and semi-arid regions than in sub-humid and humid regions. In terms of vegetation types, shrubs, savanna, grass, and crops are more susceptible to CDHEs than to individual dry or hot events, especially in water-limited regions. In arid regions, the vegetation vulnerability for open shrubs, closed shrubs, savanna, and grass under extreme CDHEs would correspondingly increase by 45%, 23%, 15%, and 12% compared with dry conditions, indicating the necessity to consider both dry and hot conditions simultaneously when evaluating vegetation dynamics to climatic extremes. In semi-arid and sub-humid regions, broadleaf forests show higher vulnerability to CDHEs than needleleaf forests. However, evergreen broadleaf forests show the lowest vulnerability and higher resistance to CDHEs in humid regions. The Analysis of Variance (ANOVA)-based uncertainty decomposition results show that copula combination sets in vine copula trees and vine copula structures are the two main uncertainty sources, accounting for about 60%. Despite with uncertainty, the modeling results show consistent vegetation loss probability patterns simulated with different structures of the vine copula model, indicating the robustness and reliability of the results. The findings provide a profound comprehension of ecosystems’ response mechanisms to compound extremes, which are essential to developing effective mitigation and adaptation strategies against the impact of future climate change.

Optimizing China’s Afforestation Strategy: Biophysical Impacts of Afforestation with Five Locally Adapted Forest Types

Recent research has mapped potential afforestation land to support China’s goal of achieving “carbon neutrality” and has proposed tree species selection to maximize carbon uptake. However, it overlooked biophysical climatic effects, which have a more significant impact on local temperature than CO2 reduction. This study aims to present a comprehensive understanding of how afforestation in China affects local and regional climates through biophysical processes. It focuses on the latitudinal patterns of land surface temperature differences (ΔLST) between five locally adapted forest types and adjacent grasslands using satellite-based observations. Our key findings are as follows: Firstly, broadleaf forests and mixed forests exhibit a stronger cooling effect than coniferous forests due to differences in canopy structure and distribution. Specifically, the net cooling effects of evergreen broadleaf forests (EBFs), deciduous broadleaf forests (DBFs), and mixed forests (MFs) compared to grasslands are −0.50 ± 0.10 °C (mean ± 95% confidence interval), −0.33 ± 0.05 °C, and −0.36 ± 0.06 °C, respectively, while evergreen needleleaf forests (ENFs) compared to grasslands are −0.22 ± 0.11 °C. Deciduous needleleaf forests (DNFs) exhibit warming effects, with a value of 0.69 ± 0.24 °C. In regions suitable for diverse forest types planting, the selection of broadleaf and mixed forests is advisable due to their enhanced local cooling impact. Secondly, temperate forests have a net cooling effect to the south of 43° N, but they have a net warming effect to the north of 48° N compared to grasslands. We recommend caution when planting DNFs, DBFs, and MFs in northeastern China, due to the potential for local warming. Thirdly, in the mountainous areas of southwestern China, especially when planting ENFs and MFs, tree planting may lead to local warming. Overall, our study provides valuable supplementary insights to China’s existing afforestation roadmap, offering policy support for the country’s climate adaptation and mitigation efforts.

Impacts of climate and land coverage changes on potential evapotranspiration and its sensitivity on drought phenomena over South Asia

AbstractUnderstanding the spatiotemporal historical drought pattern and their sensitivity effect on potential evapotranspiration (PET) and vegetation coverage changes is essential for efficient drought mitigation policies under climate change. In this study, we used the standardized precipitation evapotranspiration index (SPEI) at multiple timescales, such as SPEI‐01, SPEI‐03, SPEI‐06, SPEI‐09 and SPEI‐12; we explored their regional‐scale dry and wet annual changes across seven sub‐regions of South Asia from 1902 to 2018. Results suggest that from 1981 to 2018, the extreme drought of SPEI has increased in South Asia, which mostly affects the summer and winter growing seasons, that is, SPEI‐06 to SPEI‐12 across seven sub‐regions of South Asia. The frequency of drought events during dry and wet annual changes of SPEI had an extremely dry year starting from 1998 to 2018, which mostly affected the South Asia region. Data from the past 18 years showed that the land changing detection has increased in the forests, cultivated land, arid land, savanna and farmland; by contrast, there has been significantly reduced permanent ice and snow, mixed forests, open shrub, grasslands, permanent wetlands, water bodies and evergreen broadleaf forests. Seasonal SPEI presented diverse characteristics such as showing a dry trend in Afghanistan, India, Pakistan and Sri Lanka during autumn and winter. Afghanistan and Bhutan are wet during the summer compared with other sub‐regions of South Asia, with drought frequency occurring at 45.3% and 44.4%. Sri Lanka, Pakistan and India are the driest regions in South Asia due to their high drought frequency, duration and intensity. The correlation between PET and crop water stress index (CWSI), PET and regional ETp reduction (Er) indicated a considerably negative correlation, while a significantly positive correlation was found between CWSI and Er, NDVI and Er. This study provides a comprehensive assessment of climate and vegetation coverage changes on PET, ET and SPEI, and can help in formulating long‐term adaptive strategies to reduce the cumulative impacts of droughts.

Vertical distribution characteristics of soil organic carbon and vegetation types under different elevation gradients in Cangshan, Dali.

The Cangshan National Nature Reserve of Dali City was adopted as the research object to clarify the vertical distribution characteristics of soil organic carbon (SOC) and vegetation types at different elevations in western Yunnan. The contents of SOC, light fraction organic carbon (LFOC), heavy fraction organic carbon (HFOC), and water-soluble organic carbon (WSOC) in the 0-30 cm soil layer at different elevations (2,400, 2,600, 2,800, 3,000, 3,200, 3,400, and 3,600 m) were determined, and the above-ground vegetation types at different elevations were investigated. Results showed that the SOC content was the highest in 0-20 cm surface soil and gradually decreased with the deepening of the soil layer. It increased then decreased with the increase in elevation, and it peaked at 3,000 m. The LFOC content was between 1.28 and 7.3515 gkg-1. It exhibited a decreasing trend and little change in profile distribution. The HFOC content ranged between 12.9727 and 23.3708gkg-1; it increased then decreased with the increase in profile depth. The WSOC content was between 235.5783 and 392.3925 mgkg-1, and the response sensitivity to elevation change was weak. With the increase in elevation, WSOC/SOC and LFOC/SOC showed a similar trend, whereas HFOC presented an opposite trend. This observation indicates that the active organic carbon content at 3,600 m was lower than that at 2,400 m, and the middle elevation was conducive to the storage of active organic carbon. Meanwhile, the physical and chemical properties of soil affected the distribution of organic carbon to a certain extent. The vegetation type survey showed that the above-ground dominant species within 2,400-2,800 m were Pinus yunnanensis and Pinus armandii. Many evergreen and mixed coniferous broadleaf forests were distributed from 3,000 m to 3,200 m. Species of Abies delavayi were mainly distributed from 3,400m to 3,600m. This research serves as a reference for the study of forest soil carbon stability in high-elevation areas and plays an important role in formulating reasonable land use management policies, protecting forest soil, reducing organic carbon loss, and investigating the carbon sequestration stability of forest ecosystems.

Stronger increases but greater variability in global mangrove productivity compared to that of adjacent terrestrial forests.

Mangrove forests are a highly productive ecosystem with important potential to offset anthropogenic greenhouse gas emissions. Mangroves are expected to respond differently to climate change compared to terrestrial forests owing to their location in the tidal environment and unique ecophysiological characteristics, but the magnitude of difference remains uncertain at the global scale. Here we use satellite observations to examine mean trends and interannual variability in the productivity of global mangrove forests and nearby terrestrial evergreen broadleaf forests from 2001 to 2020. Although both types of ecosystem experienced significant recent increases in productivity, mangroves exhibited a stronger increasing trend and greater interannual variability in productivity than evergreen broadleaf forests on three-quarters of their co-occurring coasts. The difference in productivity trends is attributed to the stronger CO2 fertilization effect on mangrove photosynthesis, while the discrepancy in interannual variability is attributed to the higher sensitivities to variations in precipitation and sea level. Our results indicate that mangroves will have a faster increase in productivity than terrestrial forests in a CO2-rich future but may suffer more from deficits in water availability, highlighting a key difference between terrestrial and tidal ecosystems in their responses to climate change.

Trait divergence and opposite above- and below-ground strategies facilitate moso bamboo invasion into subtropical evergreen broadleaf forest.

Understanding the invasion of moso bamboo (Phyllostachys edulis) into adjacent evergreen broadleaf forest based on functional traits is crucial due to its significant influence on ecosystem processes. However, existing research has primarily focused on above- or below-ground traits in isolation, lacking a comprehensive integration of both. In this study, we conducted a trait-based analysis including 23 leaf traits and 11 root traits in three forest types - bamboo forest, mixed bamboo and broadleaf forest, and evergreen broadleaf forest - to investigate trait differences, phenotypic integration, and above- and below-ground resource strategies in bamboo and broadleaf species. Our findings demonstrated significant differences in leaf and root key traits between bamboo and broadleaf species, strongly supporting the "phenotypic divergence hypothesis". Bamboo exhibited stronger trait correlations compared to broadleaf species, indicating higher phenotypic integration. Above- and below-ground strategies were characterized by trade-offs rather than coordination, resulting in a multi-dimensional trait syndrome. Specifically, a unidimensional leaf economics spectrum revealed that bamboo with higher leaf N concentrations (LNC), P concentrations (LPC), and specific leaf area (SLA) adopted a "fast acquisitive" above-ground strategy, while broadleaf species with thicker leaves employed a "slow conservative" above-ground strategy. A two-dimensional root trait syndrome indicated a "conservation" gradient with bamboo adopting a "slow conservative" below-ground strategy associated with higher root tissue density (RTD), and broadleaf species exhibiting a "fast acquisitive" below-ground strategy linked to higher root N concentrations (RNC) and P concentrations (RPC), and a "collaboration" gradient probably ranging from broadleaf species with a "do-it-yourself" strategy characterized by high specific root length (SRL), to bamboo adopting an "outsourcing" strategy with thicker roots. In conclusion, key trait divergence from coexisting broadleaf species, higher phenotypic integration, and multi-dimensional opposite above- and below-ground resource strategies confer competitive advantages to moso bamboo, shedding light on the mechanistic understanding of its invasion into subtropical evergreen broadleaf forest and providing theoretical guidance for maintaining the stability of subtropical forest ecosystem.

Characterization and Evaluation of Global Solar-Induced Chlorophyll Fluorescence Products: Estimation of Gross Primary Productivity and Phenology

As a proxy of vegetation photosynthesis, solar-induced chlorophyll fluorescence (SIF) contains rich photosynthetic information that can reveal the physiological state of vegetation and its response to the environment. Current publicly available SIF products vary in accuracy, spatiotemporal resolution, and coverage due to the different inversion algorithms and sensor characteristics. Although awareness of their performances is essential for researchers to select and use data rationally, no systematic comparative analyses of these products have been conducted. In this paper, 8 sets of widely used SIF products were systematically evaluated in terms of spatiotemporal agreement with gross primary productivity (GPP) against 3 GPP datasets (FLUXNET observations, FLUXCOM–GPP, and random forest–GPP) and the derived phenology metrics against the phenological observation data (Pan European Phenological database). Results showed that the GOSIF (757 nm) and CSIF datasets best encapsulate the spatiotemporal variability of global GPP and characterize the spatial distribution of GPP-derived phenology. The ability of SIF products to explain GPP variation changed according to ecosystem type. The ability was strong for deciduous broadleaf forests, mixed forests, and evergreen needleleaf forests, whereas it was poor for evergreen broadleaf forests. Regarding consistency with phenological observations, SIF products were substantial better at predicting the start of the growing season rather than the end or length of the growing season. The systematic evaluation of the widely used SIF products serves as a reference for subsequent studies and may also provide comprehensive information for further refinements and future development of the new SIF products.

Mechanisms of seedling community assembly in a monsoon evergreen broadleaf forest in Pu’er, Yunnan, China

Mechanisms of seedling community assembly in a monsoon evergreen broadleaf forest in Pu’er, Yunnan, China

Spatial patterns and associations of dominant species in a subtropical mid-mountain moist evergreen broadleaf forest in Gaoligong Mountains, Southwest China

Spatial patterns and associations of dominant species in a subtropical mid-mountain moist evergreen broadleaf forest in Gaoligong Mountains, Southwest China

Nutrient foraging strategies of arbuscular mycorrhizal tree species in a subtropical evergreen broadleaf forest and their relationship with fine root morphology

Nutrient foraging strategies of arbuscular mycorrhizal tree species in a subtropical evergreen broadleaf forest and their relationship with fine root morphology

Comparative and phylogenetic analysis of chloroplast genomes from ten species in Quercus section Cyclobalanopsis.

The genus Quercus L. is widely acknowledged as a significant assemblage within East Asia tropical and subtropical broadleaf evergreen forests, possessing considerable economic importance. Nevertheless, the differentiation of Quercus species is deemed arduous, and the interrelations among these species remain enigmatic. Leveraging Illumina sequencing, we undertook the sequencing and assembly of the chloroplast (cp) genomes of seven species belonging to Quercus section Cyclobalanopsis (Quercus argyrotricha, Q. augustinii, Q. bambusifolia, Q. bella, Q. edithiae, Q. jenseniana, and Q. poilanei). Furthermore, we collated three previously published cp genome sequences of Cyclobalanopsis species (Q. litseoides, Q. obovatifolia, and Q. saravanensis). Our primary objective was to conduct comparative genomics and phylogenetic analyses of the complete cp genomes of ten species from Quercus section Cyclobalanopsis. This investigation unveiled that Quercus species feature a characteristic circular tetrad structure, with genome sizes ranging from 160,707 to 160,999 base pairs. The genomic configuration, GC content, and boundaries of inverted repeats/single copy regions exhibited marked conservation. Notably, four highly variable hotspots were identified in the comparative analysis, namely trnK-rps16, psbC-trnS, rbcL-accD, and ycf1. Furthermore, three genes (atpF, rpoC1, and ycf2) displayed signals of positive selection pressure. Phylogenetic scrutiny revealed that the four sections of Cyclobalanopsis clustered together as sister taxa. The branch support values ranged from moderate to high, with most nodes garnering 100% support, underscoring the utility of cp genomic data in elucidating the relationships within the genus. Divergence time analysis revealed that Section Cyclobalanopsis represents the earliest type of Quercus genus. The outcomes of this investigation establish a foundation for forthcoming research endeavors in taxonomy and phylogenetics.