Evergreen Broadleaf Forest Research Articles

Future changes and driving factors of global peak vegetation growth based on CMIP6 simulations

Accurate detection and attribution of changes in global peak vegetation growth at the annual scale are prerequisites for characterising the productivity of terrestrial ecosystems and developing strategies for the sustainable management of ecosystems. This study examined the long-term global normalised difference vegetation index during the baseline period (1982–2015) and found widespread greening in 70% of global vegetated areas in response to climate warming. However, climate change is not the only cause of global greening. The spatial variability in the response of global vegetation to environmental factors has not been well established. The Cubist model was used to investigate the relationship between peak vegetation growth and environmental variables. The results showed that 64% of the spatial variation in greening/browning can be explained by climate (including precipitation and temperature), followed by atmospheric components of nitrogen deposition and carbon dioxide concentration (17%), terrain properties (12%), and soil properties (7%). By incorporating future climate and atmospheric component projections from the Coupled Model Intercomparison Project Phase 6 into the model, enhanced vegetation greening was predicted globally, particularly in evergreen needle-leaf forests and grasslands, from 2081 to 2100. Many browning changes were predicted in evergreen and deciduous broadleaf forests, mixed forests, and around areas influenced by human land use. Overall, these findings reveal that environmental factors have relevant integrated impacts on vegetation dynamics under climate change and should be considered during the design of local mitigation and adaptation management strategies.

Light use efficiency models incorporating diffuse radiation impacts for simulating terrestrial ecosystem gross primary productivity: A global comparison

Estimating dynamic changes in gross primary productivity (GPP) of terrestrial ecosystems has always been challenging. Indeed, light use efficiency (LUE) models are extensively employed to capture GPP dynamics. Improved big-leaf LUE models by introducing a cloudiness scalar and two-leaf LUE models by dividing the canopy into shaded and sunlit leaves are two improved families for addressing the widely-recognized effects of diffuse radiation on terrestrial ecosystem GPP. However, the global performance in simulating GPP dynamics between such models has not been evaluated comprehensively. Here, we assess and compare the global performance of the ten LUE models considering diffuse radiation impacts (CFLUX, DIFFUSE, CI-LUE, CCW, Wang's Model, CI-EF, TL-LUE, TL-LUEn, DTEC, and RTL-LUE) at 102 flux sites in a standardized framework. Results indicate that LUE models considering diffuse radiation impacts can explain 46.7–63.6% of daily GPP variability obtained by the eddy covariance technique. Seven of the ten models show a better performance across all ecosystems than MOD17, despite the relatively poor performance of three (i.e., DIFFUSE, Wang's Model, and DTEC). The seven improved models exhibiting a similar explanation of GPPEC perform poorly in evergreen broadleaf forests and croplands but well in deciduous broadleaf forests. Although two-leaf models with higher leaf area index (LAI) sensitivity and better model structure have greater potential to describe diffuse radiation fertilization (DRF), their performance in capturing GPP dynamics is limited by the uncertainties of remote sensing data. This study indicates the importance of accurate LAI for two-leaf models and emphasizes the necessity of incorporating diffuse radiation impacts into the global GPP estimates in the future.

Investigating Vegetation Types Based on the Spatial Variation in Air Pollutant Concentrations Associated with Different Forms of Urban Forestry

Globally, rapid urbanisation is one of the major drivers for land-use changes, many of which have a marked impact on urban air quality. Urban forestry has been increasingly proposed as a means of reducing airborne pollutants; however, limited studies have comparatively assessed land-use types, including urban forestry, for their relationship with air pollution on a city scale. We, thus, investigated the spatial relationships between three air pollutant concentrations, NO2, SO2, and PM10, and different land uses and land covers across a major city, by constructing a yearly average model combining these variables. Additionally, relationships between different vegetation types and air pollutant concentrations were investigated to determine whether different types of vegetation are associated with different air pollutants. Parklands, water bodies, and more specifically, broadleaf evergreen forest and mangrove vegetation were associated with lower pollutant concentrations. These findings support urban forestry’s capabilities to mitigate air pollution across a city-wide scale.

Comparison between the ASTER and ECOSTRESS global emissivity datasets

High spatial resolution global land surface emissivity datasets are valuable for various applications. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) is the only accessible global emissivity data with a high spatial resolution (∼100 m). Since the launch of the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) in June 2018, another high spatial resolution GED can be generated. A comprehensive comparison is needed to investigate the consistency between the ASTER and ECOSTRESS GEDs before subsequent applications and the release of the ECOSTRESS GED. We conducted a first-ever comparison between the ASTER and ECOSTRESS GEDs in two ways: 1) global spatial comparison and 2) validation using emissivity measurements at 9 sand dune and 2 vegetated sites. The results reveal that the ECOSTRESS emissivities in bands 2 and 4 are in good agreement with ASTER GED, with mean absolute biases (MAB) of ∼ 1 % averaged over the globe. The emissivity difference is the largest for snow and ice in these two bands. For band 5, the discrepancy is relatively larger with a MAB of 1.7 %. The largest emissivity difference is found over evergreen broadleaf forests in humid regions. Comparison with measurements reveals an average root-mean-square error (RMSE) of ECOSTRESS emissivity of ∼ 3 % among all the nine sand sites for all the 3 bands. At the two vegetated sites, the average RMSE is ∼ 1.5 %. The absolute biases are below 3 % in all the bands. The average RMSE between ASTER and ECOSTRESS GEDs among all sand dune sites is 1.29 %. The evaluation results demonstrate a good agreement between these two emissivity retrievals overall and high accuracy of the ECOSTRESS GED.

PROJECTION OF CLIMATE CHANGE IMPACTS ON NET PRIMARY PRODUCTIVITY OF THE LEGAL AMAZON – BRAZIL

The Amazon Rainforest is one of the main carbon sinks (CO2) on the planet. However, recently, due to anthropic activities and climate change, it has lost its stability in CO2 absorption. Therefore, understanding the dynamics of future climate change scenarios becomes essential. We assess the influence of future climate change scenarios on NPP (biomass) levels in the Amazon Forest using ML models. The tested models were Bayesian, Linear Model, and Random Forest. The current scenario was evaluated using 19 bioclimatic covariates (worldclim dataset). While the future scenarios were based on RCPs 2.6 and 8.5 (based on the models of the MIROC5 and HadGEM2-ES models). Random Forest had the best performance statistic (R² = 0.71 in training and 0.68 in holdout-test). The climate change scenarios will imply an increase in the average NPP for the Amazon forest, especially with greater intensification in RCP 2.6 (2 and 7.69 % for the HadGEM2-ES and MIROC5 models, respectively). Forests (Evergreen Broadleaf Forests areas) will have greater carbon fixation capacity. In general, the Amazon forest will have increased carbon fixation capacity by the end of the century.

Effects of food availability and climate on the activity budget of Shortridge's langur (Trachypithecus shortridgei) in the Drung Valley, Gaoligong Mountains, China.

Food availability and climate represent environmental factors that affect species' social behavior, ranging patterns, diet, and activity budget. From August 2012 to September 2013, we examined the effects of seasonal changes in food availability, temperature, and rainfall on the diet and behavioral ecology of Shortridge's langur (Trachypithecus shortridgei) an Endangered primate species inhabiting moist evergreen broadleaf forests in the Eastern Himalayas. Our field site represents the northernmost latitudinal distribution of this species. Data were collected using scan sampling at 10 min intervals, and analyzed based on generalized linear models. The results indicate that the langurs experienced two feeding peaks (9:00 and 17:00) and two traveling peaks (10:00 and 19:00) during each day. Periods of rest, mainly occurred between 10:00 and 13:00, and overnight. Feeding accounted for 38.5% of the daily activity budget, followed by resting (35%), traveling (24.5%), and socializing (2%). During periods when young leaves were most available, the langurs increased feeding time on young leaves (35% vs. 4%). During periods of maximum fruit availability, the langurs decreased total time spent feeding (36.6% vs. 40.4%), devoted more time to traveling (28.1% vs. 21%), and increased time spent consuming fruit (49.1% vs. 11.8%). During the winter, the langurs increased their consumption of mature leaves (44.5%) and reduced time spent traveling (20.2% vs. 25.4%). Overall, time spent resting was greatest in the spring (47.5%), time spent feeding was greatest during the summer (51.1%), and time spent in traveling was greatest in the autumn (33.2%). The frequency of social interactions remained relatively constant throughout the year. Foraging effort was greatest in the summer, when fruits dominated the diet. Like other species of temperate langurs, T. shortridgei devoted less time to resting, more time to feeding, and was characterized by a greater year-round foraging effort than tropical/subtropical langurs.

Differences in leaf photoprotection strategies of tree species at different successional stages in subtropical forests under seasonal climate change and their relationship to construction cost strategies

Differences in response patterns to spatio-temporal changes in environmental stressors mediated by seasonal climatic differences reflect the underlying adaptation strategies of plants, and can provide key information on how tree species respond to seasonal climatic changes at different successional stages. However, in forests dominated by subtropical broadleaf evergreen species, the relationship between leaf photoprotection strategies and their construction cost strategies in different seasons remains unclear. It limits the assessment of the impact of seasonal climate change on community growth and exploration of potential drivers of community succession. In this study, we measured photoprotective substances, chlorophyll fluorescence, photosynthetic pigments and leaf construction cost (CC) in leaves of three mid-successional and three late-successional tree species in a subtropical broadleaf evergreen forest in different seasons. Our data showed that the young leaves of mid-successional trees had higher photoprotection ability in summer compared to winter, and were therefore better adapted to high temperature and high light conditions in summer, but not to low temperature and high light conditions in winter. The young leaves of late-successional trees had strong and flexible photoprotection in both summer and winter, which ensured normal growth of the young leaves under high temperature and high light in summer, and under low temperature and high light in winter. In addition, the photoprotection of the leaves was positively correlated with CC, suggesting that an increase in photoprotective capacity of plants was accompanied by an increase in its investment in CC. This was a response of the plants to environmental stress and the sacrifices it needed to make to maintain its survival. The differential growth patterns of late-successional species, which could grow normally in summer and winter, and mid-successional species, which were adapted to grow in summer but not in winter, may be key clues to the gradual replacement of mid-successional species by late-successional species.

Spatiotemporal dynamic of subtropical forest carbon storage and its resistance and resilience to drought in China.

Subtropical forests are rich in vegetation and have high photosynthetic capacity. China is an important area for the distribution of subtropical forests, evergreen broadleaf forests (EBFs) and evergreen needleleaf forests (ENFs) are two typical vegetation types in subtropical China. Forest carbon storage is an important indicator for measuring the basic characteristics of forest ecosystems and is of great significance for maintaining the global carbon balance. Drought can affect forest activity and may even lead to forest death and the stability characteristics of different forest ecosystems varied after drought events. Therefore, this study used meteorological data to simulate the standardized precipitation evapotranspiration index (SPEI) and the Biome-BGC model to simulate two types of forest carbon storage to quantify the resistance and resilience of EBF and ENF to drought in the subtropical region of China. The results show that: 1) from 1952 to 2019, the interannual drought in subtropical China showed an increasing trend, with five extreme droughts recorded, of which 2011 was the most severe one; 2) the simulated average carbon storage of the EBF and ENF during 1985-2019 were 130.58 t·hm-2 and 78.49 t·hm-2, respectively. The regions with higher carbon storage of EBF were mainly concentrated in central and southeastern subtropics, where those of ENF mainly distributed in the western subtropic; 3) The median of resistance of EBF was three times higher than that of ENF, indicating the EBF have stronger resistance to extreme drought than ENF. Moreover, the resilience of two typical forest to 2011 extreme drought and the continuous drought events during 2009 - 2011 were similar. The results provided a scientific basis for the response of subtropical forests to drought, and indicating that improve stand quality or expand the plantation of EBF may enhance the resistance to drought in subtropical China, which provided certain reference for forest protection and management under the increasing frequency of drought events in the future.

Evaluation and comparison of VIIRS dark target and deep blue aerosol products over land

The dark target (DT) and deep blue (DB) algorithms have been applied to the VIIRS to construct a long-term climate data recording of atmospheric aerosols. This study provides the first evaluation and comparison of two updated VIIRS aerosol products over global land based on Version 3 Level 1.5 AERONET measurements. Overall, both AOD products agree well with AERONET measurements with correlation coefficients >0.85 and over 75 % of AOD matchups falling within the expected error (EE). The DB product is superior to the DT product with more AOD matchups meeting the EE. Meanwhile, the DB AOD performs better in spatiotemporal accuracy, adaption to different aerosol types, and addresses the effects of large view geometry. DT AOD shows higher accuracy in the summer months in the northern hemisphere and evergreen broadleaf forest areas than DB AOD. The two products exhibit similar spatial distribution patterns of AOD, but higher values are seen in the DT product, especially in Asia and Western North America. The spatial completeness of the DB AOD is higher than DT, especially in brighter surface regions. In contrast, higher temporal completeness of the DT AOD is found in vegetated areas. A case study of the Western United States wildfires in 2020 indicates both products can capture extreme smoke aerosols. In addition, the DB AOD shows a distinct advantage in spatial and temporal continuity and in displaying the regional transport of smoke aerosols. However, the accuracy of the AOD retrieval for both products still needs to be improved in sparse vegetation regions, northern hemisphere summers, fine particle aerosols, and during extreme aerosol events. The overall evaluation of DB SSA and AE products shows that they are unsuitable for quantitative scientific research. This study can provide users a guide on how to select VIIRS aerosol products for different research purposes.

亚热带森林演替阶段下不同光环境对芒萁光合生理的影响

PDF HTML阅读 XML下载 导出引用 引用提醒 亚热带森林演替阶段下不同光环境对芒萁光合生理的影响 DOI: 10.5846/stxb202112273661 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（31971623）；浙江省"尖兵""领雁"研发攻关计划项目（2022C02019） Effects of different light environments on photosynthetic physiology of Dicranopteris dichotoma under successional stages in subtropical forests Author: Affiliation: Fund Project: The National Natural Science Foundation of China (31971623)； 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:光环境的变化是群落演替进程中植物进入和退出群落的重要因素。为研究亚热带森林次生演替不同阶段的光照对芒萁光合生理的影响，对灌草丛、马尾松林和常绿阔叶林这3种群落内盆栽芒萁的光响应过程和叶绿素荧光诱导动力学参数进行了研究。结果表明：芒萁在光强为400 μmol m-2 s-1左右且日变化幅度较小的马尾松林下净光合速率（Pn）最高，在光强50 μmol m-2 s-1左右的常绿阔叶林下次之，在400-1800 μmol m-2 s-1左右波动的灌草丛内最低。这是因为灌草丛内芒萁J点相对可变荧光（Vj）、K点标准化后的相对可变荧光（Wk）显著升高，以吸收光能为基础的光系统Ⅱ性能指数（PSⅡ）（PIABS）显著降低，而热耗散参数（φDo）基本不变，即强光破坏了芒萁光合机构PSⅡ的供体侧和受体侧，使其产生了光抑制。常绿阔叶林内芒萁叶绿素荧光诱导动力学参数与马尾松林下差异不明显，且最大净光合速率（Pmax）下降是光强变弱导致的，但是暗呼吸速率（Rd）、光补偿点（LCP）和光饱和点（LSP）均随之降低，可见芒萁光合机构对该光强有一定的适应性。由此可知，芒萁喜光但不耐强光，对弱光具有一定耐受性。 Abstract:The light changes of forest succession play important roles in the presence or absence of plant species in communities. To reveal the effects of light on photosynthetic physiology of Dicranopteris dichotoma at different stages of secondary succession in subtropical forests, light response process and chlorophyll fluorescence induction kinetic parameters of D. dichotoma growing in three different communities (scrub, Pinus massoniana forest, and evergreen broadleaf forest) were investigated in this experiment. The results showed that the net photosynthetic rate (Pn) was the highest in the P. massoniana forest with a light intensity of about 400 μmol m-2 s-1 and a small daily variation, followed by the evergreen broadleaf forest with a light intensity of about 50 μmol m-2 s-1, and the lowest in the scrub with a fluctuation of about 400-1800 μmol m-2 s-1. The lowest Pn in the scrub was because high light disrupted donor and acceptor sides of photosystem II (PSII) of D. dichotoma, and resulted in photoinhibition of D. dichotoma, which were proved by significantly increased relative variable fluorescence (Vj) at point J, normalized relative variable fluorescence (Wk) at point K, and significantly decreased PSII performance index (PIABS), and unchanged heat dissipation parameter (φDo). No significant differences in chlorophyll fluorescence induction kinetics between the D. dichotoma growing in evergreen broad-leaved forest and P. massoniana forest indicated that its PSII was not damaged, and the decrease of the maximum net photosynthetic rate (Pmax) was caused by the low light. At the same time, the reduced dark respiration rate (Rd), light compensation point (LCP), and light saturation point (LSP) showed that D. dichotoma photosynthetic system could adapt to the low light in the evergreen broad-leaved forest. This study showed that D. dichotoma was a light-loving but not light-tolerant species, with the modest low-light tolerance. 参考文献 相似文献 引证文献

Mapping and Spatiotemporal Dynamics of Land-use and Land-cover Change Based on the USGS and NASA Platformsfrom MCD12Q1Landsat Imagery: A Case Study of Kinshasa, DR Congo

Kinshasa is a city with very significant urban growth. It is observed that this urban growth is concentrated in a weak area, which requires control and direction of urban expansion. To do this, it is important to understand the dynamics of cover change andland use over time. To do this, we downloaded Landsat images from the MODIS Land Cover Type Product (MCD12Q1) from 2001 to 2022, with a classification of these images based on remote sensing indices that we processed using geographic information system tools.We extracted the following classes: Evergreen Broadleaf Forests, Deciduous Broadleaf Forests, Mixed Forests, Closed Shrublands, Woody Savannas, Savannas, Grasslands, Permanent Wetlands, Croplands, Urban and Built-up Lands, Cropland/Natural Vegetation Mosaics,Barren and Water Bodies. We calculated the percentage of coverage of the classestaking into account intervalsof 3 years (7 intervals in total)by studying the relationship between the evolution curves of these classes, but also,we establishedthe relationship existing between the dynamics of these classes over time using the chord diagram. It has been observed that certain classes vary depending on others over time.

Seasonal changes in total mercury and methylmercury in subtropical decomposing litter correspond to the abundances of nitrogen-fixing and methylmercury-degrading bacteria.

Previous research has found total mercury (THg) and methylmercury (MeHg) levels increase with litterfall decay, thus suggesting litterfall decomposition plays an essential role in the biogeochemical transformation of mercury (Hg). However, it remains unclear how Hg accumulates in the decaying litter, how bacterial taxa networks vary and what roles various microorganisms play during litterfall decomposition, especially nitrogen (N)-fixing, MeHg-degrading and Hg-methylating microbes. Here, we demonstrated as degradation proceeded, a gradually-complex network evolved for litterfall bacteria for the subtropical mixed broadleaf-conifer (MBC) forest, whereas a relatively static network existed for the evergreen broadleaf (EB) forest. N-fixing and MeHg-degrading bacteria dominated throughout litterfall decomposition process, with relative abundances of N-fixing genera and nifH copies maximum and relative abundances of MeHg-degrading bacteria and merAB copies minimum in summer. Hence, N-fixing bacteria likely mediate THg increase in the decomposing litterfall, while MeHg enhancement may be regulated by aerobic MeHg-degrading microbes which can transform MeHg to inorganic divalent Hg (Hg2+) or further to elemental Hg (Hg0). Together, this work elucidates variations of N-fixing and MeHg-degrading microbes in decaying litterfall and their relationships with Hg accumulation, providing novel insights into understanding the biogeochemical cycle of Hg in the forest ecosystem.

Spatial and Temporal Patterns of Carbon and Water Use Efficiency on the Loess Plateau and Their Influencing Factors

Quantitative assessment of the dynamics of carbon and water use efficiency on the Loess Plateau in the context of complex climate change and its driving mechanisms is important for the improvement of the regional ecological environment and the enhancement of ecological service functions. In order to assess the impact of climate change and human activities on the carbon and water use efficiency of the Loess Plateau, this study investigates the spatial and temporal rates of change in CUE, WUE, and meteorological factors at the image metric scale using one-dimensional linear fit regression, investigates the spatial correlation between CUE and meteorological factors using partial correlation analysis, and quantifies the relative contributions of human activities to CUE and WUE using residual analysis. The following are the study’s conclusions: (1) The CUE and WUE of the Loess Plateau decreased geographically from 2000 to 2020, and both the CUE and WUE of the Loess Plateau exhibited a non-significant declining trend (p > 0.05), with the CUE falling at a rate of 0.001/10a (a: year) and the WUE decreasing at a rate of 0.047/10a (a: year). (2) From 2000 to 2020, the mean values of the CUE and WUE of the Loess Plateau were 0.60 and 1.75, respectively, with a clear spatial difference. (3) CUE was favorably linked with precipitation in 56.51% of the Loess Plateau, dispersed throughout the south-central portion of the Loess Plateau and Inner Mongolia, whereas the biased association with temperature was not statistically significant and often negative. (4) An examination of residuals revealed that human activities affected the trend of CUE and WUE to some degree. Only the WUE residuals of evergreen broadleaf forests exhibited a strong upward trend that was considerably influenced by people. In conclusion, this study used remote sensing image data and meteorological data to systematically analyze the spatial and temporal dynamic patterns of carbon use efficiency and water use efficiency on the Loess Plateau over the past 21 years, as well as the characteristics of their responses to climate change and human activities, thereby providing theoretical guidance for the study of carbon and water cycles in terrestrial ecosystems on the Loess Plateau.

Applied Machine Learning Algorithms and Landsat 8 for Estimating Aboveground Carbon Stock in Evergreen Broadleaf Forest in Binh Phuoc Province

Abstract: The assessment of carbon stocks is one of the key measurements to support climate change mitigation policies. The research applied Landsat 8 satellite imagery combined with field-measurements using four machine learning methods (random forest - RF, artificial neural networks - NNET, support vector machines – SVM, and linear regression - LM) to estimate aboveground carbon in evergreen broadleaf forest in Binh Phuoc province. The field sample plots were randomly divided into training (96 plots) and testing (24 plots) data. The results showed that RF yielded the greatest precision with an R2 value above 0,9 and RMSE below 6 ton/ha on the training data, with an R2 value of 0,41 and RMSE of 11,04 ton/ha on the testing data. The estimate of forest carbon stock increased distinctly from the mean value of 59,80 ton/ha in the very poor forest to 87,78 ton/ha in the rich forest. The results found in the present study demonstrated that Landsat 8 imagery in conjunction with RF has the appropriate to estimate aboveground carbon stock in evergreen broadleaf forest-leaved in Binh Phuoc province.
 Keywords: Random forest, aboveground carbon, REDD+, forest carbon estimation.
 

Satellite-observed vegetation responses to aerosols variability

Atmospheric aerosols affect the carbon uptake of land ecosystems by altering the radiation and other environmental factors. Satellite observations have proved promising accuracies in monitoring the vegetation carbon uptake, but it remains unclear whether satellite Vegetation Indices (VIs) capture the effects of aerosols on carbon uptake or not. In this study, we employed field eddy covariance measurements to examine the capacity of Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), kernel Normalized Difference Vegetation Index (kNDVI), and Near Infrared Reflectance of terrestrial vegetation (NIRv) in reflecting the effects of aerosol pollution on Gross Primary Productivity (GPP). Results showed that all four satellite VIs can generally capture the variations of GPP induced by different aerosol loadings. Further analysis indicated that the mean VIs in different AOD bins is closely correlated with the mean GPP for all surface types, except Evergreen Broadleaf Forest (EBF). We also found that the differences of satellite VIs between different AOD bins were not only influenced by the changes of diffuse radiation, but also affected by the temperature and Vapor Pressure Deficit (VPD) changes accompanied with aerosol loadings. The results indicated that both EVI and NIRv from satellite have the potential for monitoring the responses of vegetation productivity to aerosol loadings directly.

Mixed-litter effects of fresh leaf semi-decomposed litter and fine root on soil enzyme activity and microbial community in an evergreen broadleaf karst forest in southwest China.

Litter decomposition is the main process that affects nutrient cycling and carbon budgets in mixed forests. However, knowledge of the response of the soil microbial processes to the mixed-litter decomposition of fresh leaf, semi-decomposed leaf and fine root is limited. Thus, a laboratory microcosm experiment was performed to explore the mixed-litter effects of fresh leaf, semi-decomposed leaf and fine root on the soil enzyme activity and microbial community in an evergreen broadleaf karst forest in Southwest China. Fresh leaf litter, semi-decomposed litter and fine root in the Parakmeria nitida and Dayaoshania cotinifolia forests, which are unique protective species and dominant species in the evergreen broadleaf forest, were decomposed alone and in all possible combinations, respectively. Our results showed that the mass loss of fresh leaf litter in three mixed-litter treatment was significantly higher than that in two mixed-litter treatment in the P. nitida and D. cotinifolia forests. Mass loss of fine root in the single litter treatment was significantly lower in the P. nitida forest and higher in the D. cotinifolia forest compared to that in the other litter treatments. There were insignificant differences in the activities of β-glucosidase (BG) and leucine aminopeptidase (LAP) between control and mixed-litter treatment in the P. nitida forest and between control and single litter treatment in the D. cotinifolia forest. The N-acetyl-β-D-glucosaminidase (NAG) activity was significantly increased by the single litter decomposition of fresh leaf and fine root and three mixed-litter decomposition in the P. nitida and D. cotinifolia forests. The activity of acid phospomonoesterase (AP) in the decomposition of fresh leaf litter was lower in the P. nitida forest and higher in the D. cotinifolia forest compared to that in control. The most dominant soil bacteria were Proteobacteria in the P. nitida forest and were Actinobacteria and Proteobacteria in the D. cotinifolia forest. Shannon, Chao1, ACE and PD indexes in the mixed-litter decomposition of fresh leaf and semi-decomposition litter were higher than that in control in P. nitida forest. There were insignificant differences in observed species and indexes of Chao1, ACE and PD between litter treatments in the D. cotinifolia forest. Richness of mixed-litter significantly affected mass loss, soil enzyme activity and microbial diversity in the P. nitida forest. Litter N concentration and the presence of fresh leaf litter were significantly correlated with the mass loss and soil enzyme activity in the P. nitida and D. cotinifolia forests. These results indicated that the presence of fresh leaf litter showed a non-negligible influence on mixed-litter decomposition and soil enzyme activity, which might be partly explained by litter initial quality in the P. nitida and D. cotinifolia forests.

Embolism resistance explains mortality and recovery of five subtropical evergreen broadleaf trees to persistent drought.

Subtropical evergreen broadleaf forests (SEBF) are experiencing and expected to suffer more frequent and severe drought events. However, how the hydraulic traits directly link to the mortality and recovery of SEBF trees remains unclear. In this study, we conducted a drought-rewatering experiment on tree seedlings of five dominant species to investigate how the hydraulic traits were related to tree mortality and the resistance and recovery of photosynthesis (A) and transpiration (E) under different drought severities. Species with greater embolism resistance (P50 ) survived longer than those with a weaker P50 . However, there was no general hydraulic threshold associated with tree mortality, with the lethal hydraulic failure varying from 64% to 93% loss of conductance. The photosynthesis and transpiration of tree species with a greater P50 were more resistant to and recovered faster from drought than those with lower P50 . Other plant traits could not explain the interspecific variation in tree mortality and drought resistance and recovery. These results highlight the unique importance of embolism resistance in driving carbon and water processes under persistent drought across different trees in SEBFs. The absence of multiple efficient drought strategies in SEBF seedlings implies the difficulty of natural seedling regeneration under future droughts, which often occurs after destructive disturbances (e.g., extreme drought events and typhoon), suggesting that this biome may be highly vulnerable to co-occurring climate extremes.

Soil qualities and change rules of Eucalyptus grandis × Eucalyptus urophylla plantation with different slash disposals

Slash disposal changes soil quality by affecting soil properties and nutrient cycling, and the appropriate disposal approaches remain controversial. This work aimed to explore the impact of different slash disposal methods on soil qualities. For this purpose, a Eucalyptus grandis × Eucalyptus urophylla plantation that had been cultivated in 2002 and felled for the third time in 2016 was established in Hezhou City, China. Burning forest (BF, for moderate intensity fire) and no-burning forest (NF) were set in the plantation, and the native evergreen broadleaf forest near the plantation was used as the control (CK). Soils were sampled quarterly in 2017, and 27 indicators that represent soil physical, chemical, and biological properties were analyzed and compared through the analysis of the sustainability index (SI), which adopts five indices to calculate soil quality. The obtained data showed that the indicators of BF and NF, except for the total potassium content, were much lower than those of CK. The physical properties (Max-WHC, CWHC, Min-WHC, MMC, CPD, TPD) of NF were significantly better (29.07%, 30.98%, 29.61%, 52.08%, 21.89%, 19.76%) than those of BF, unlike the chemical properties of BF (SOM, TN, ACa, AFe, AMn, ACu, AZn) were significantly better than those of NF (45.61%, 81.33%, 12.78%, 23.18%, 96.13%, 144.30%, 114.04%). The enzymatic activities of NF (URE, APHO) were significantly better (43.33%, 156.58%)than those of BF, except the activities of INV (− 25.21%). Results of SI showed that the soil quality of CK was much better than that of BF, and NF the worst. But it exhibited the most unevenness of CK, followed by NF, and BF the best. The change rules of BF and NF were contrasting, and soil quality reached the same level after half a year. In summary, the soil qualities, either BF or CK, were not comparable to that of CK. BF increased the soil quality fleetly and transiently, and NF was sustainable for the eucalyptus plantation.

Soil Property, Rather than Climate, Controls Subsoil Carbon Turnover Time in Forest Ecosystems across China

Subsoil (0.2–1 m) organic carbon (C) accounts for the majority of soil organic carbon (SOC), and SOC turnover time (τ, year) is an important index of soil C stability and sequestration capacity. However, the estimation of subsoil τ and the identification of its dominant environmental factors at a regional scale is lacking in regards to forest ecosystems. Therefore, we compiled a dataset with 630 observations to investigate subsoil τ and its influencing factors in forest ecosystems across China using the structural equation model (SEM). The results showed a large variability of subsoil τ from 2.3 to 896.2 years, with a mean (± standard deviation) subsoil τ of 72.4 ± 68.6 years; however, the results of one-way analysis of variance (ANOVA) showed that subsoil τ differed significantly with forest types (p = 0.01), with the slowest subsoil τ obtained in deciduous-broadleaf forests (82.9 ± 68.7 years), followed by evergreen-needleleaf forests (77.6 ± 60.8 years), deciduous-needleleaf forests (75.3 ± 78.6 years), and needleleaf and broadleaf mixed forests (71.3 ± 80.9 years), while the fastest subsoil τ appeared in evergreen-broadleaf forests (59.9 ± 40.7 years). Subsoil τ negatively correlated with the mean annul temperature, occurring about three years faster with a one degree increase in temperature, indicating a faster subsoil SOC turnover under a warming climate. Subsoil τ significantly and positively correlated with microbial activities (indicated by microbial C and nitrogen), highlighting the importance of microbial communities in regulating subsoil C dynamics. Climate, forest types, forest origins, vegetation, and soil variables explained 37% of the variations in subsoil τ, as indicated by the SEM, and the soil property was the most important factor affecting subsoil τ. This finding challenged previous perception that climate was the most important factor driving subsoil C dynamics, and that dominant drivers varied according to climate zones. Therefore, recognizing different dominant factors in predicting subsoil C dynamics across climate zones would improve our understanding and reduce the uncertainties regarding subsoil C dynamics in biogeochemical models under ongoing climate change.

Remote sensing strategies to monitoring land use maps with AVHRR and MODIS data over the South Asia regions.

In South Asia, annual land use and land cover (LULC) is a severe issue in the field of earth science because it affects regional climate, global warming, and human activities. Therefore, it is vitally essential to obtain correct information on the LULC in the South Asia regions. LULC annual map covering the entire period is the primary dataset for climatological research. Although the LULC annual global map was produced from the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset in 2001, this limited the perspective of the climatological analysis. This study used AVHRR GIMMS NDVI3g data from 2001 to 2015 to randomly forests classify and produced a time series of the annual LULC map of South Asia. The MODIS land cover products (MCD12Q1) are used as data from reference for trained classifiers. The results were verified using the annual map of the LULC time series, and the space-time dynamics of the LULC map were shown in the last 15years, from 2001 to 2015. The overall precision of our 15-year land cover map simplifies 16 classes, which is 1.23% and 86.70% significantly maximum as compared to the precision of the MODIS data map. Findings of the past 15years show the changing detection that forest land, savanna, farmland, urban and established land, arid land, and cultivated land have increased; by contrast, woody prairie, open shrublands, permanent ice and snow, mixed forests, grasslands, evergreen broadleaf forests, permanent wetlands, and water bodies have been significantly reduced over South Asia regions.