Total Vegetation Research Articles

Assessing the large-scale plant–water relations in the humid, subtropical Pearl River basin of China

Abstract. Vegetation interacts closely with water resources. Conventional field studies of plant–water relations are fundamental for understanding the mechanisms of how plants alter and adapt to environmental changes, while large-scale studies can be more practical for regional land use and water management towards mitigating climate change impacts. In this study, we investigated the changes in the total water storage (TWS), aridity index (AI) and vegetation greenness, productivity, and their interactions in the Pearl River basin since April 2002. Results show an overall increasing trend of vegetation greenness and productivity, especially in the middle reaches where TWS also increased. This region dominated by croplands was identified as the hot spot for changes and interactions between water and vegetation in the basin. Vegetation was more strongly affected by TWS than precipitation (P) at both the annual and monthly scales. Further examination showed that the influence of TWS on vegetation in dry years was stronger than wet years, while the impact of P was stronger in wet years than dry years; moreover, vegetation productivity responded slower but stronger to atmospheric dryness in dry years than wet years. The lag effects resulted in nonlinearity between water and vegetation dynamics. This study implies that vegetation in the basin uses rainwater prior to water storage until the soil becomes dry, and their dynamics indicate that vegetation development is subject to water availability, and that vegetation is not dominant in reducing water availability.

Is green space exposure beneficial in a developing country?

Previous studies have confirmed the beneficial effects of green space exposure on depressive disorders, but the majority of them were conducted in developed countries. Given the difference in utilization of green spaces in countries with agriculturally heavy economies such as Indonesia, the effects of green space remain unknown. This study aimed to investigate the association between green space exposure and depressive disorders, focusing on Indonesia. Depressive disorders data provided by the Institute for Health Metrics and Evaluation was used. Exposure to the green space was based on the normalized difference vegetation index (MODIS-NDVI) and green land-cover data provided by the Geospatial Information Agency of Indonesia. A generalized additive mixed model was conducted, followed by a sensitivity test and positive-negative control analysis, to evaluate the association. Stratified analysis was employed to assess the effects of green space on depressive disorders in areas with different levels of urbanization. Exposure to green space significantly reduced the risk of depressive disorders by up to 5% per interquartile unit increment of NDVI. Moreover, there was a 12.4% reduced risk of depressive disorders coinciding with an 8.4% increase in the proportion of forest and total vegetation. In high-urbanization areas, the results of sensitivity analysis illustrated that green spaces reduced the risk of depressive disorders by 10%. Exposure to green spaces was confirmed to be associated with a reduced risk of depressive disorders in Indonesia. Findings of this ecological study could serve as references for urban planning, considering the health benefits of green spaces in developing countries.

MadingleyR: An R package for mechanistic ecosystem modelling

AbstractAimMechanistic general ecosystem models are used to explore fundamental ecological dynamics and to assess possible consequences of anthropogenic and natural disturbances on ecosystems. The Madingley model is a mechanistic general ecosystem model (GEM) that simulates a coherent global ecosystem, consisting of photo‐autotrophic and heterotrophic life, based on fundamental ecological processes. The C++ implementation of the Madingley model delivers fast computational performance, but it (a) limits the userbase to researchers that are familiar with the intricacies of C++ programming, (b) has limited possibility to change model settings and provide model outputs required to address specific research questions, and (c) has limited reproducibility of simulation experiments. The aim of this paper is to present an R package of the Madingley model to aid with increasing the accessibility and flexibility of the model.InnovationThe MadingleyR R package streamlines the installation procedure and supports all major operating systems. MadingleyR enables users to combine multiple consecutive simulations, making case study specific modifications to MadingleyR objects along the way. Default input files are available from the package and study‐specific inputs can be easily loaded from the R environment. MadingleyR also provides functions to plot and summarize MadingleyR outputs. We provide a comprehensive description of the MadingleyR functions and workflow. We also demonstrate the applicability of the MadingleyR package using three case studies: (a) simulating the cascading effects of the loss of mega‐herbivores on food‐web structure, (b) simulating the impacts of increased land‐use intensity on the total biomass of different feeding guilds by restricting the total vegetation biomass available for feeding and (c) simulating the impacts of an intensive land‐use scenario on a continental scale.Main conclusionsThe MadingleyR package provides direct accessibility to simulations with the mechanistic ecosystem model Madingley and is flexible in its application without a loss in performance.

Mapping groundwater-dependent ecosystems in arid Central Asia: Implications for controlling regional land degradation

Groundwater-dependent ecosystems (GDEs) exist all over the world, especially in water-limited regions. To achieve better water management, it is necessary to map and identify GDEs. Central Asia (CA) is one of the most arid regions in the mid-latitudes and one of the major regions with shallow groundwater tables. However, the role of groundwater in the impacts of climate change and regional anthropogenic activities on environmental risks, especially regional desertification, is inadequately understood due to the limited available research on GDEs. In the present study, a remote sensing-based method was used for mapping GDEs in regional CA, and three means—overlay analysis, correlation analysis, and the water balance method—were adopted to validate the accuracy of the mapping outcomes. Our results indicated that: 1) GDEs were concentrated around large lakes and in central Kazakhstan (between 46°N and 50°N latitudes), and areas “Very Likely” and “Likely” to be GDEs accounted for 36.89%, and 28.85% of the total natural vegetation areas, respectively; 2) at the watershed scale, the Sarysu Basin had the largest proportion (94.02% of the area) of potential GDEs while the Ysyk-Kol Basin had the lowest proportion (17.84%); 3) all the three validation methods indicated a good performance for our GDE mapping results. We concluded that the remote sensing-based GDE identification method can be considered a potential approach for mapping GDEs regionally. Better recognition of relationships among groundwater availability, ecosystem health and groundwater management policies should be developed by conducting further studies, to protect GDEs and to prevent regional land degradation.

Dynamics of ecosystem carbon stocks in a chronosequence of nitrogen‐fixing Nepalese alder (Alnus nepalensis D. Don.) forest stands in the central Himalayas

AbstractNitrogen‐fixing Nepalese alder (Alnus nepalensis D. Don.) is a rapidly‐growing pioneer tree species that often forms pure stands in areas affected by landslides, but also occurs mixed with other late‐successional species in the central Himalayas. In this study, we assessed the distribution of carbon in vegetation and soil in a chronosequence of A. nepalensis (3–270 years old) forest stands and evaluated its correlation with the stand and alder total basal area (TBA). The study was conducted in six different naturally occurring forest stands in a chronosequence, viz., alder‐early regenerating (AER), alder‐late regenerating (ALR), alder young‐mixed (AYM), alder mature‐oak (Quercus leucotrichophora) mixed (AMOM), alder mature‐rhododendron (Rhododendron arboreum) mixed (AMR), and alder old‐oak (Q. leucotrichophora) mixed (AOOM) forests. The chronosequence consisted of three young unreserved, AER, ALR, and AYM (age 3–25) and three older reserved, AMOM, AMR, and AOOM (age 80–270) stands. We used structural equation modelling (SEM) to quantify the contribution of various carbon pools on ecosystem carbon stock. Forest carbon stock significantly increased (p < 0.05) with forest age and TBA, indicating that succession is the driving factor of the forest ecosystem processes, carbon accumulation, and therefore, change in vegetation biomass carbon and soil carbon in the chronosequence of A. nepalensis forests. Total vegetation biomass carbon (tree, shrubs, herbs, and litter) contained ~12.54–289.85 Mg ha−1 (79.11–57.77% total carbon stock), and soil carbon (depths of 0–10 cm in AER, 0–30 cm in AYM, 0–100 cm in remaining forest stands) contained ~3.31–210.13 Mg ha−1 (20.8–42% total carbon stock) and positively increased with stand and A. nepalensis TBA. The tree layer biomass carbon stock was considerably higher than the understory (shrubs, herbs, and forest floor). The soil carbon stock was reported to be influenced by the successional stages. Therefore, these findings reflect that the A. nepalensis forest development in the central Himalayas lead to storage of large amount of carbon stock in both plant biomass and soil.

Weed Management of Kenaf (Hibiscus Cannabinus) Through Intercropping Leafy Vegetables And Cultural Practices

A field experiment was conducted at Jute Research Experimental Station, Manikganj, Bangladesh during 2017 and 2018 cropping seasons to study the feasibility of intercropping leafy vegetables at early stage of the fibre crop: kenaf along with conventional weeding methods for better weed suppression and higher productivity. A Randomized Complete Block Design (RCBD) was followed consisting of eight treatments with three replications. Three leafy vegetables: red amaranth, jute shak and kangkong were used as intercrop with kenaf following 1 hand weeding and 1 hand hoeing alternately. Sole kenaf (weeded twice) was also grown as control. The total weed vegetation was comprised of grasses 49%, broadleaved 33% and sedge only 18%. Performance of kangkong as intercrop was found better in terms of weed suppression. Hand weeding was found more efficient as a cultural weeding method in reducing weed density. Although intercropping resulted in significant yield reduction of kenaf but increased net return and benefit-cost ratio (BCR) by increasing kenaf equivalent yield (KEY). The land equivalent ratio (LER) was found higher in all intercropping treatments than sole kenaf which indicated more efficient utilization of land under intercropping. Red amaranth intercropping+1 hand weeding resulted the maximum gross return (TK.1,36,200 ha-1) while the highest gross margin (Tk. 59,390 ha-1) and BCR (1.78) was obtained from jute shak intercropping+1 hoeing. Considering the production cost, monetary return and productivity, Kenaf intercropped with Jute shak+1 hoeing was found better than other intercropping treatments. SAARC J. Agric., 19(1): 165-176 (2021)

Raw EPIC Data Calibration

Earth Polychromatic Imaging Camera (EPIC) raw level-0 (L0) data in one channel is a 12-bit 2,048 × 2,048 pixels image array plus auxiliary data such as telemetry, temperature, etc. The EPIC L1a processor applies a series of correction steps on the L0 data to convert them into corrected count rates (level-1a or L1a data): Dark correction, Enhanced pixel detection, Read wave correction, Latency correction, Non-linearity correction, Temperature correction, Conversion to count rates, Flat fielding, and Stray light correction. L1a images should have all instrumental effects removed and only need to be multiplied by one single number for each wavelength to convert counts to radiances, which are the basis for all higher-level EPIC products, such as ozone and sulfur dioxide total column amounts, vegetation index, cloud, aerosol, ocean surface, and vegetation properties, etc. This paper gives an overview of the mathematics and the pre-launch and on-orbit calibration behind each correction step.

Fine‐scale functional metacommunity dynamics: Analysing the role of disturbance‐driven environmental variability in grasslands

AbstractQuestionPlant community dynamics are influenced by microvariations of the environment and of processes acting at fine spatio‐temporal scales like plant–plant interactions, local dispersal and phenology. However, descriptions and understanding of diversity changes both within year and over short distances are still required. We used a dynamic metacommunity framework to answer the following question: what is the relative influence of environmental filtering, dispersal limitation and ecological drift on fine‐scale grassland community dynamics under different disturbance regimes?LocationData were collected in a long‐term experiment located in the Massif Central, France (45°43′23″ N, 03°1′21″ E, 880 m a.s.l.).MethodsWe monitored fine‐scale spatio‐temporal community changes in small plots (i.e. local communities) distributed within six grassland paddocks (i.e. metacommunities) subject to two treatments (mown or grazed). We simultaneously monitored three key environmental variables: light, nitrogen and water, to assess how environmental heterogeneity impacts species compositional changes. We then inferred the metacommunity drivers of these changes in taxonomic and functional composition using a path analysis.ResultsManaged grasslands are dynamic within a year, and spatially heterogeneous within a paddock, with spatial and temporal β diversities totalling more than half of the taxonomic diversity. Mowing events caused sudden decreases of total vegetation cover resulting in taxonomical and functional changes. In grazed paddocks, community changes were mainly spatial due to spatially heterogeneous light‐structured filtering conditions. In both treatments, environmental heterogeneity only partially explained community changes that were also greatly impacted by dispersal limitation, but not ecological drift.ConclusionsOur study demonstrates that the fine‐scale analysis of community spatio‐temporal changes allows disentangling the relative importance of environmental filtering, dispersal limitation and ecological drift in community dynamics. Applying this approach to communities experiencing other kinds of disturbance regimes may allow gaining a general understanding of plant community assembly processes.

Corn Biomass Estimation by Integrating Remote Sensing and Long-Term Observation Data Based on Machine Learning Techniques

The accurate and timely estimation of regional crop biomass at different growth stages is of great importance in guiding crop management decision making. The recent availability of long time series of remote sensing data offers opportunities for crop monitoring. In this paper, four machine learning models, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), and extreme gradient boosting (XGBoost) were adopted to estimate the seasonal corn biomass based on field observation data and moderate resolution imaging spectroradiometer (MODIS) reflectance data from 2012 to 2019 in the middle reaches of the Heihe River basin, China. Nine variables were selected with the forward feature selection approach from among twenty-seven variables potentially influencing corn biomass: soil-adjusted total vegetation index (SATVI), green ratio vegetation index (GRVI), Nadir_B7 (2105–2155 nm), Nadir_B6 (1628–1652 nm), land surface water index (LSWI), normalized difference vegetation index (NDVI), Nadir_B4 (545–565 nm), and Nadir_B3 (459–479 nm). The results indicated that the corn biomass was suitably estimated (the coefficient of determination (R2) was between 0.72 and 0.78) with the four machine learning models. The XGBoost model performed better than the other three models (R2 = 0.78, root mean squared error (RMSE) = 2.86 t/ha and mean absolute error (MAE) = 1.86 t/ha). Moreover, the RF model was an effective method (R2 = 0.77, RMSE = 2.91 t/ha and MAE = 1.91 t/ha), with a performance comparable to that of the XGBoost model. This study provides a reference for estimating crop biomass from MOD43A4 datasets. In addition, the research demonstrates the potential of machine learning techniques to achieve a relatively accurate estimation of daily corn biomass at a large scale.

A methodological approach for the assessment of basic crown parameters in Scots pine stands

Among other measurement techniques applied for the assessment of leaf area index, direct methods are still valued as the most accurate measures and often implemented as calibration tools. Even though more attention has been given to indirect measurements of tree crown properties in forest ecosystems over the last decades, the present study was designed to discuss the direct (destructive) and indirect (non-destructive) methods used for the assessment of crown measures in the stands defoliated from 20 to 90%. The stands with similar stand characteristics and representing relatively wide range of defoliation served as an appropriate target for the assessment of foliage mass variations.
 Overall, this study showed that the foliage mass or its surface area and defoliation at the stand level can be determined by the conventional methods used for the assessment of defoliation in forest monitoring programme as well as the PAR transmission methods. The findings showed that needle surface area decreased with the increase of tree defoliation; however, the changes of branch and stem surface areas were insignificant. Otherwise, the branch and shoot area contribute significantly to the total vegetation surface area at least in Scots pine stands. This study also strengthened the idea that the indirect measurement of vegetation area index underestimated vegetation area index at least in Scots pine stands defoliated less than 60%. The multivariate regression models were developed using tree diameter at breast height and tree crown defoliation ranges to estimate needle surface area. Keywords: Pinus sylvestris, crown defoliation, needle area index, regression model

Study on wind damage grade model for eucalyptus plantation in Guangxi, China

Wind damage is one of the dominant natural disasters that have the greatest impact on the growth of plantations. However, little attention has been paid to the wind damage grade model. Based on eucalyptus plantations from national forest inventory data and field investigations in Guangxi, ten significant factors that are susceptible to wind damage were selected to perform multiple linear regression analysis on wind damage grade model. Through assessing the level of wind damage based on forest characteristic and site conditions, we aimed to reduce the loss of economic benefits caused by wind damage. The results showed that the age group of eucalyptus trees, slope, soil thickness, management grade, and total vegetation coverage had significant effects on the wind damage of eucalyptus trees in this area. Therefore, it is necessary to protect the vegetation coverage of forest land and to strengthen the management of forest land to improve the wind resistance of eucalyptus forests in the future.

Effects comparison of co-occurring Vachellia tree species on understory herbaceous vegetation biomass and soil nutrient: Case of semi-arid savanna grasslands in southern Ethiopia

The spread of woody plant encroachment is a major concern as it adversely affects herbaceous plant community composition in a savanna ecosystem. Assessing the canopy effects of co-occurring encroaching and non-encroaching woody plant species on the understory herbaceous vegetation biomass and the soil nutrient status can improve our understandings of the link between biological invasions and herbaceous plant community ecology. We examined the canopy and adjacent inter-canopy effects of encroaching and non-encroaching Vachellia species which was previously known as Acacia on herbaceous vegetation biomass in Southern Ethiopia. A total of 12 encroaching (Vachellia drepanolobium) and 12 non-encroaching (Vachellia tortilis) tree species were selected both at an upland and a bottomland site. A 1 m × 1 m quadrat was used to investigate the canopy effects of individual tree species and inter-canopy habitats on herbaceous vegetation biomass. Soil samples were collected under the canopy of selected tree species and the adjacent inter-canopy habitat at a depth of 0–10 cm and 10–20 cm both at the upland and bottomland site. With fixed effect ANOVA, we evaluated the effects of landforms (upland or bottomland), tree species (Vachellia drepanolobium or Vachellia tortilis) and habitats (canopy or inter-canopy habitat) on total herbaceous biomass, perennial herbaceous species biomass and annual herbaceous species biomass. There was no significant (P > 0.05) difference between the canopy and inter-canopy habitat of V. drepanolobium and V. tortilis in terms of the total herbaceous biomass and annual herbaceous vegetation at the bottomland position. The canopy of V. drepanolobium had a negative effect on the biomass production of herbaceous perennial species both at the bottomland and upland site. On the contrary, the canopy of V. tortilis had a significantly positive (P < 0.05) effect on the biomass production of herbaceous perennial plants at the bottomland site. Generally, more soil organic matter accumulation was recorded under the canopy of V. tortilis than it was either under the canopy of V. drepanolobium or in the open savanna grasslands. Our findings indicated that encroaching tree species canopy and co-occurring non-encroaching tree species canopy had a predominantly different influence on herbaceous vegetation biomass. It seemed that encroaching tree species in savannas had a high acquisition and competition for resources. The findings can help improve our understanding on the herbaceous biomass predictions of different life forms in a community of encroached grasslands and non-encroached grasslands in southern Ethiopia.

Improving the efficiency and accuracy of evaluating aridland riparian habitat restoration using unmanned aerial vehicles

AbstractUnmanned Aerial Vehicles (UAVs) offer new opportunities for accurate, repeatable vegetation assessments, which are needed to adaptively manage restored habitat. We used UAVs, ground surveys, and satellite imagery to evaluate vegetation metrics for three riparian restoration sites along the Colorado River in Mexico and we compared the data accuracy and efficiency (cost and time requirements) between the three methods. We used an off‐the‐shelf UAV coupled with a multispectral sensor to determine Normalized Difference Vegetation Index (NDVI) and vegetation cover. We were unable to accurately classify vegetation by individual species, but by grouping riparian species of interest (cottonwood‐willow, mesquite, shrubs), we achieved high overall model accuracies of 87–96% across sites (Kappa = 0.82–0.95). Producer’s and user’s accuracies were moderate to high for target vegetation classes (69–100%). UAV and ground‐survey vegetation percent cover differed due to differences in methodologies (UAVs measure aerial cover; ground surveys measure foliar cover) and sources of error for each method. Correlations between UAV and ground survey vegetation cover were moderate (rs(90) = 0.24–0.58, p &lt; 0.05). UAV NDVI (0.50–0.61) was significantly higher than Landsat NDVI (0.40–0.45) for all sites (p &lt; 0.0001), likely due to presence of shadows with high NDVI values in UAV imagery. UAV NDVI, Landsat NDVI and UAV total vegetation cover were strongly correlated (rs(90) = 0.72–0.85, p &lt; 0.05). UAV surveys were more labor‐ and cost‐ intensive than ground surveys in the first year, but were slightly less so in the second year. We conclude that UAVs can provide efficient, accurate assessments of riparian vegetation, which can be used in restoration site management. Due to UAV limitations to assess vegetation in a multi‐layered canopy and inability to classify individual riparian species with similar spectral signals, we recommend a combined approach of UAV and ground surveys.

Promoting wildflower biodiversity in dense and green cities: The important role of small vegetation patches

The conservation and promotion of biodiversity in urban spaces has become a core concern in urban greening. As cities continue to densify, however, urban green spaces are becoming smaller and more isolated. Many hope that wildflowers, and with them also faunistic biodiversity, can be maintained through networks of small informal green spaces. Currently, there are little data available for city planners to answer the questions of how patch size and ecological connectivity affect the population persistence of wildflowers in small vegetation patches. To address these concerns, we surveyed all vegetation patches greater than 1 m2 in public spaces across seven 1 km2 quadrats distributed across the city of Zurich, Switzerland. Our survey focused on a list of 166 herbaceous vascular plant species. We analyzed vegetation patch topologies (frequency distributions of size and connectivity) and related it to alpha and beta species diversity. Dispersal traits of investigated species were used to interpret connectivity patterns. Over 75 % of patches were <20 m2 in size, however, these small patches made up only 3.4 % of total vegetation area. Species richness was significantly positively correlated with patch area. Nevertheless, taken together, small patches had similar species richness and higher beta diversity than larger patches. These small patches contribute far more than expected to overall wildflower species richness in the city – “punching above their weight”. This held true even for less common species. However, our research indicates that connecting these patches to one another and to larger green spaces is essential to unlocking their potential as habitats and potentially allowing for the natural colonization and reinforcement of existing populations. As cities densify, we must acknowledge that small vegetation patches such as tree discs or road margins are essential for the survival of wildflower populations and associated fauna, especially in highly urbanized areas.

Spatiotemporal variation and predictability of vegetation coverage in the Beijing–Tianjin–Hebei metropolitan region, China

Vegetation coverage and its dynamic response to climatic ecologic indicators are critical for the monitoring and management of terrestrial ecosystem. This paper aims to investigate the spatial and temporal variations and relationships between vegetation coverage and climatic factors during the growing season for the period of 2000–2017 in the Beijing–Tianjin–Hebei metropolitan region (BTH) of China, using the Normalized Difference Vegetation Index (NDVI) and related climate data. The multivariate linear regression model (MLR), support vector regression model (SVR), and gradient boosting regression tree model (GBRT) were applied to explore the predictability of NDVI, using climatic factors as predictors. The results showed that the mean NDVI during growing season has significantly increased in the BTH over the past 18 years, with about 66% of the total vegetation cover in the study area evidencing increasing trends. Significant increasing trends were mainly located in the northwest mountainous regions and southeastern plains, and the trends significantly decreased in big cities and the surroundings. The precipitation during growing season and summer has increased. However, no significant trend of the temperature was detected during growing season. NDVI was mainly positively correlated with precipitation at about 85.8% area of the BTH and negatively correlated with temperature during summer. Precipitation and temperature were the dominant influencing factors for vegetation growth in BTH, and therefore these factors were used as potential predictors to estimate NDVI during growing season. The comparative results of three prediction models showed that the prediction accuracy of GBRT was higher than other two models, indicating that it should be applicable to use GBRT model to predict the NDVI in the study area.

Characteristics and composition of vegetation carbon storage in natural grassland in Ning-xia, China.

We measured the total vegetation carbon stock in the widely distributed natural grassland of meadow steppe (MS), warm steppe (WS), steppe desert (SD) and desert steppe (DS)] in Ningxia, using survey and sampling method. The results showed that the average carbon rate of vege-tation, shrubs and root were 0.40, and that of litter was 0.39. The total vegetation carbon density of meadow steppe, warm steppe, steppe desert and desert steppe (including aboveground vegetation, litter and roots) was 470.26, 192.23, 117.17 and 83.36 g·m-2, while that of aboveground vegetation was 87.35, 68.50, 59.32 and 40.05 g·m-2, and that of roots was 344.29, 108.83, 50.65 and 30.29 g·m-2, litter carbon storage was 38.62,14.91, 7.19 and 13.03 g·m-2, respectively. The order of those grassland types ranked as MS＞WS＞SD＞DS. Root carbon storage contributed the most to carbon storage in meadow steppe and warm steppe, and aboveground vegetation carbon storage contributed the most to steppe desert and desert steppe. Root carbon storage showed a decreasing trend with the increases of soil depth within 40 cm soil layer. For the spatial distribution of total carbon stock, the southern part of meadow steppe and temperate steppe had obviously higher carbon stock than the middle and north part of desert steppe and steppe desert.

Effects of seasonal grazing and annual mowing on floristic composition and plant diversity in the Saral rangeland, Kurdistan, Iran

This study examined the impact of long-term (40 years) seasonal grazing and annual scythe mowing on changes in plant diversity, floristic composition and plant functional groups in the cold semi-arid rangelands of Mangahul-Zardawan located in Saral, Kurdistan. In this investigation, 900 plots (2 m2) were sampled in grazed (450 plots) and mowed sites (450 plots) to measure vegetation and plant characteristics. Data were collected in two consecutive growing seasons from late May to early June in 2017 and 2018. Both years recorded below average rainfall. The results showed that after 40 years, seasonal grazing compared to scythe mowing increased canopy cover and species richness of different functional groups including annuals, perennial grasses, perennial forbs and total vegetation. On the other hand, both the total above-ground plant material and the palatability index were higher for the mowed than the grazed sites, although scythe mowing had caused the Boraginaceae family to disappear. Based on these results, the mean of total plant material (above-ground live + standing dead + litter) was substantially higher in the mowed area, where two-year mean totals were 271 g/m2 compared with 216 g/m2 for the grazed area. In terms of a three-level palatability classification (high, medium and low), only the plants with moderate palatability differed in contribution between the sites, with mowed sites having a significantly greater proportion of this class than the grazed sites. Our results suggested that in the long term, grazing can maintain species richness and the typical floristic composition of rangelands; but when socio-economic issues have priority, mowing has more advantages. In rangelands with the same ecological and geographic conditions, users and managers can choose the most appropriate management approach based on their long- and short-term policies.

Spatiotemporal Variation of Vegetation Productivity and Its Feedback to Climate Change in Northeast China over the Last 30 Years

Gross primary productivity (GPP) represents total vegetation productivity and is crucial in regional or global carbon balance. The Northeast China (NEC), abundant in vegetation resources, has a relatively large vegetation productivity; however, under obvious climate change (especially warming), whether and how will the vegetation productivity and ecosystem function of this region changed in a long time period needs to be revealed. With the help of GPP products provided by the Global LAnd Surface Satellite (GLASS) program, this paper gives an overview of the regional feedback of vegetation productivity to the changing climate (including temperature, precipitation, and solar radiation) across the NEC from 1982 to 2015. Analyzing results show a slight positive response of vegetation productivities to warming across the NEC with an overall increasing trend of GPPGS (accumulated GPP within the growing season of each year) at 4.95 g C/m2. yr−2 over the last three decades. More specifically, the growth of crops, rather than forests, contributes more to the total increasing productivity, which is mainly induced by the agricultural technological progress as well as warming. As for GPP in forested area in the NEC, the slight increment of GPPGS in northern, high-latitude forested region of the NEC was caused by warming, while non-significant variation of GPPGS was found in southern, low-latitude forested region. In addition, an obvious greening trend, as reported in other regions, was also found in the NEC, but GPPGS of forests in southern NEC did not have significant variations, which indicated that vegetation productivity is not bound to increase simultaneously with greening, except for these high-latitude forested areas in the NEC. The regional feedback of vegetation productivity to climate change in the NEC can be an indicator for vegetations growing in higher latitudes in the future under continued climate change.

Disentangling the Regional Climate Impacts of Competing Vegetation Responses to Elevated Atmospheric CO2.

Biophysical vegetation responses to elevated atmospheric carbon dioxide (CO2) affect regional hydroclimate through two competing mechanisms. Higher CO2 increases leaf area (LAI), thereby increasing transpiration and water losses. Simultaneously, elevated CO2 reduces stomatal conductance and transpiration, thereby increasing rootzone soil moisture. Which mechanism dominates in the future is highly uncertain, partly because these two processes are difficult to explicitly separate within dynamic vegetation models. We address this challenge by using the GISS ModelE global climate model to conduct a novel set of idealized 2×CO2 sensitivity experiments to: evaluate the total vegetation biophysical contribution to regional climate change under high CO2; and quantify the separate contributions of enhanced LAI and reduced stomatal conductance to regional hydroclimate responses. We find that increased LAI exacerbates soil moisture deficits across the sub‐tropics and more water‐limited regions, but also attenuates warming by ∼0.5–1°C in the US Southwest, Central Asia, Southeast Asia, and northern South America. Reduced stomatal conductance effects contribute ∼1°C of summertime warming. For some regions, enhanced LAI and reduced stomatal conductance produce nonlinear and either competing or mutually amplifying hydroclimate responses. In northeastern Australia, these effects combine to exacerbate radiation‐forced warming and contribute to year‐round water limitation. Conversely, at higher latitudes these combined effects result in less warming than would otherwise be predicted due to nonlinear responses. These results highlight substantial regional variation in CO2‐driven vegetation responses and the importance of improving model representations of these processes to better quantify regional hydroclimate impacts.

Ecological indicators and bioindicator plant species for biomonitoring industrial pollution: Eco-based environmental assessment

Industrial pollution remains a driving force to ecosystem alteration. Pollutants are released in the atmosphere interacting in turn with other components of earth system such as plant species. Despite the long-term exposition of vegetation cover to pollution is drastically devastating, less is known about the contribution of ecological indicators for its monitoring. The aims of this study are (i) to introduce the ecological indicators in assessing the cement dust impact on plant species and its biomonitoring and (ii) to screen new indicator species for phytoremediation studies. Floristic surveys were conducted in the cement plant closeness following quadrat method. Vegetation indicators such as total plant cover, perennial and annual species densities and diversity were assessed. Bioindicator species were identified using the bioaccumulation factor (BF) and translocation factor (TF). A decrease of perennial species richness and a decline of total vegetation cover by 7 times as well as a diversity decrease ranging from 2.99 to 2.31 were found pertinent indicators of land degradation in the industrial area. Annual species densities were significantly affected by cement pollution. Species like Lygeum spartum, Atractylis serratuloides and Gymnocarpos decander arise as indicators of heavy metals pollution. Pollution in the cement plant vicinity excluded sensitive species like Helianthemum kahiricum, Stipa tenassissima, Plantago coronopus. This study allowed the identification of indicator species of potential use in phytoremediation applications and emphasized the possibility of relaying on the vegetation indicators to assess the impact of cement pollution.