Vegetation Types Research Articles

Assessment of potential toxic elements in soils, sediments, and vegetation in the surroundings of Anapa, Russia.

The study presented here reports the concentration of major, trace, and rare earth elements in soil, sediments, and vegetation samples collected from 13 locations around Anapa City located on the northern coast of the Black Sea in Russia. The neutron activation analysis technique has been used to fulfill this objective. Along with this, the bioconcentration and translocation factors were calculated. Overall, the content of 31 elements was detected in soil and sediments while 20 elements were determined in three types of vegetation: macroalgae (Cystoseira sp. and Ulva sp.), aquatic plants (Phragmites australis), and sea grass (Zostera sp.). The quantified concentration followed the order soil > sediment > vegetation. The phytotoxic levels for Zn, V, Mn, and Fe have been quantified as the highest. Bromine was the most abundant and accumulated in Phragmites australis. Based on the results obtained from this investigation, there is a possibility of contamination in the study area.

Long-term variations in ecosystem water use efficiency in the Tibetan Plateau: Vegetation types, attribution methods and main drivers

Ecosystem water use efficiency (WUE) is a key indicator for understanding the response of carbon–water processes to environmental changes. However, as affected by uncertainty of WUE estimation and attribution, the individual contributions of environmental drivers to WUE changes and the underlying mechanisms remain unclear on the Tibetan Plateau (TP). Here, a theory-based analytical WUE model was modified by introducing an optimal stomatal behavior model, and used to estimate monthly WUE and their drivers during 1982–2018 for main vegetation types on the Tibetan Plateau (TP). Rationality of three mainstream analytical attribution methods—analytical-based partial derivative method (APDM), regression-based partial derivative method (NPDM), and climate elasticity method (CEM)—was examined. Furtherly, the contributions of various environmental drivers to WUE variation trend were estimated for various vegetation types. Results indicate that: (1) The modified model performed well in estimating WUE against with observed WUE at five eddy-covariance (EC) flux stations, which decreased the uncertainty of WUE estimation. WUE increased significantly in the TP with a slope of 0.49 × 10-2 g C kg−1 H2O yr−1 during 1982–2018 with forests increased most rapidly; (2) Only APDM captured both the variation and magnitude of WUE trends, and both NPDM and CEM failed to reproduce the magnitude of WUE trends, though CEM was better than NPDM benefited from its nonlinear structure. Through comparing these methods, we found that the nonlinear direct effects of these forcing variables on WUE change could be higher than their indirect effects caused by their interactions. (3) There are some differences in the main drivers of WUE trends for various vegetation types. Increase in WUE was mainly driven by the increase in leaf area index (LAI) for both steppes and meadows, while decreased air water vapor pressure (VPD) and elevated CO2 showed higher positive effects for broad-leaf forests. The distinct response and extremely scarce EC observations of forests suggest that more attention should be paid to forests in the TP. This study could help in selection of attribution methods and understanding the mechanisms of carbon–water coupling of alpine ecosystems under a changing climate.

Detection of Parasites in Locally Sourced Fresh Fruits and Vegetables Using Various Diagnostic Techniques

A prospective study was conducted from September 2022 to March 2023 to examine the occurrence and contamination rates of different parasitic stages on fresh edible fruits and vegetables in local markets of Erbil city. Eight different types of fruits and vegetables were selected from eight markets, and 144 samples were analyzed for parasites using direct sedimentation, indirect floatation (ZnSO4), Kinyoun, and trichrome staining methods. The highest total frequencies of parasites were detected using the direct sedimentation method (18.75%), followed by the indirect floatation method (6.25%), Kinyoun technique (5.55%), and modified trichrome staining (4.16%). A. lumbricoides (Ova) was detected with 4.16% using direct sedimentation, while no positive detections were observed with other techniques. Kinyoun technique identified Cryptosporidium spp. oocysts. For E. histolytica 1.3% were positive using the direct sedimentation method, while the other three methods showed no positive detections. Similar results were observed for G. lamblia where only the modified trichrome staining technique detected 0.69% with trophozoites and 1.3% with cysts. Additionally, the direct sedimentation method revealed the presence of nematodes at 2.77%, but no positive detections were made with other methods. Concerning Taeniid eggs, the indirect floatation identified 0.69% with ova, while other methods showed no positive detections. Lastly, the direct sedimentation method yielded 6.25% with unidentified flagellate protozoa, but no positive detections were found using the indirect floatation method, Kinyoun technique, or modified trichrome staining technique. The study concluded that Kinyoun and trichrome techniques are the gold standard for detecting different parasite stages.

Diversity, structure, and composition of melliferous and non-melliferous vegetation surrounding meliponaries of the Yucatan Peninsula, Mexico

Background: Although the loss of Melipona beecheii colonies in meliponaries suggests insufficient availability of melliferous blooming plants, there is limited knowledge about the diversity and conditions of the surrounding vegetation. Question: What is the diversity, structure, and composition of the melliferous and non-melliferous vegetation surrounding meliponaries, and how does it affect the availability of food sources for bees? Study site and dates: The vegetation surrounding nine meliponaries, three in each political state of the Yucatán Peninsula encompassing the main vegetation types, was studied in 2022 and 2023. Methods: Four 150-meter-long transects with the point-centered quadrants method were traced in each meliponary to estimate plant species composition, the availability of melliferous and blooming plants (IVI), diversity parameters (Hill-numbers), tree density, and diameter per strata. Results: 312 taxa, 250 genera, and 73 plant families were recorded. In five meliponaries, blooming melliferous plants accounted for less than 9 % of the IVI, with one meliponary having no blooming species. The highest diversity was found in a meliponary surrounded by semi-evergreen forest. The high stratum had a mean tree height of 5.5 (SD ± 2.9) meters and 3,390 (SD ± 2,702) trees/hectare across vegetation types. The tree diameter was lowest in meliponaries located in the semi-deciduous forest. Conclusions: The meliponaries are surrounded by young secondary vegetation with high density of small trees and predominance of the low stratum. We found a similar vegetation diversity among meliponaries and scarce blooming melliferous plants. Human activity seems to impact plant diversity and food availability for M. beecheii.

Leaf Functional Traits and Their Influencing Factors in Six Typical Vegetation Communities

Leaf Functional Traits and Their Influencing Factors in Six Typical Vegetation Communities

The Effectiveness of Public Green Open Space Capabilities in Reducing Flooding

The Palopo City watershed could not accommodate the high discharge of rainfall that had fallen previously. The Green Open Space (GOS) had an ecological function by absorbing rainwater. Urban green open spaces absorbed surface flow water in the past, reducing the risk of flooding. In the past, this research measured the effectiveness of three green open spaces (Islamic Center, Pancasila Field, and Salubulo Field) in reducing flooding in Palopo City. We collected data on vegetation types, infiltration capacity, and runoff discharge. Data analysis involved the analysis of infiltration capacity (F), runoff discharge (Q), and green open space effectiveness. In the three green space locations, the following vegetation types were found: Katapang (Terminalia catappa), Palm (Dypsis lutescens), Trambesi (Samanea Saman), and Grass (Cyperus rotundus). The highest total infiltration capacity in the Salubulo Field Green Space resulted in 155 m3/hour (fast category). In the past, the most significant runoff discharge's total value was GOS Pancasila Field: 713.4 mm3/minute. The study concluded that the Salubulo Field Green Space had the highest infiltration capacity (160 m3/hour) and runoff discharge (69 mm3/minute), making it the most effective in reducing flooding in Palopo City in the past. This was > 1 (effectiveness criteria achieved), and the space was dominated by vegetation (trees and grasses).

Spatial and Temporal Dynamics in Vegetation Greenness and Its Response to Climate Change in the Tarim River Basin, China

Vegetation in ecologically sensitive regions has experienced significant alterations due to global climate change. The underlying mechanisms remain somewhat obscure owing to the spatial heterogeneity of influencing factors, particularly in the Tarim River Basin (TRB) in China. Therefore, this study targets the TRB, analyzing the spatial and temporal dynamics of vegetation greenness and its climatic determinants across multiple spatial scales. Utilizing Normalized Difference Vegetation Index (NDVI) data, vegetation greenness trends over the past 23 years were assessed, with future projections based on the Hurst exponent. Partial correlation and multiple linear regression analyses were employed to correlate NDVI with temperature (TMP), precipitation (PRE), and potential evapotranspiration (PET), elucidating NDVI’s response to climatic variations. Results revealed that from 2000 to 2022, 90.1% of the TRB exhibited an increase in NDVI, with a significant overall trend of 0.032/decade (p < 0.01). The difference in NDVI change across sub-basins and vegetation types highlighted the spatial disparity in greening. Notable greening predominantly occurred near rivers at lower elevations and in extensive cropland areas, with projections indicating continued greening in some regions. Conversely, future trends mainly suggested a shift towards browning, particularly in higher-elevation areas with minimal human influence. From 2000 to 2022, the TRB experienced a gradual increase in TMP, PRE, and PET. The latter two factors were significantly correlated with NDVI, indicating their substantial role in greening. However, vegetation sensitivity to climate change varied across sub-basins, vegetation types, and elevations, likely due to differences in plant characteristics, hydrothermal conditions, and human disturbances. Despite climate change influencing vegetation dynamics in 51.5% of the TRB, its impact accounted for only 25% of the total NDVI trend. These findings enhance the understanding of vegetation ecosystems in arid regions and provide a scientific basis for developing ecological protection strategies in the TRB.

Impacts of forest canopy heterogeneity on plot-scale hydrometeorological variables - Insights from an experiment in the humid boreal forest with the Canadian Land Surface Scheme

High latitude regions, including the circumpolar boreal biome, are experiencing important changes in the availability of usable surface water because of climate change. In this context, an adequate representation of the land-atmosphere interaction is critical to ensure optimal management of current and future water resources, forest management, and climate prediction. However, the task is particularly intricate in high-latitude boreal forest, as land surface model faces several challenges due to the unique environmental conditions and ecological characteristics. The objective of this study is to quantify the impact of forest landscape heterogeneity, specifically stand leaf-area index (LAI), soil texture, and drainage regime, on surface water and energy balance in a small boreal high-latitude sub-catchment. To this end, hydrometeorological conditions at seventeen 20×20 m plots in a 1-km2 boreal forest sub-basin are simulated using the Canadian Land Surface Scheme (CLASS), a land surface model, at the point scale. The subplot-scale soil texture, drainage regime, and vegetation characteristics and type are based as closely as possible on field measurements and observations for the 17 plots. The model-driven experiment comprises two sets of simulations using CLASS, each employing the same model setup and run for the 17 experimental plots. The main set employs meteorological forcing from a local micrometeorological tower within the sub-basin to investigate the plot-to-plot variability of albedo, energy fluxes, and soil state variables. A second set of simulations is conducted using meteorological forcing from the ERA5-Land reanalysis, which spans from 1986 to 2022. This data provides a longer time series, enabling a more accurate representation of the interannual climatic variability in the sub-basin. The results of the main and secondary sets of CLASS simulations are used to assess the plot-to-plot and temporal variability of several key hydrometeorological variables by calculating a monthly spread. In brief, the following conclusions and broader implications can be drawn from the findings: i) The simulated total annual evapotranspiration remains relatively uniform between plots despite notable variation in its partitioning from plot to plot. ii) In the presence of a full snowpack, the albedo exhibits substantial heterogeneity at the subplot scale, linked to the canopy's LAI. iii) Local soil properties, drainage regime, and vegetation structure and type exhibit substantial influence on the plot-to-plot variability in soil water content. iv) When parameterized with localized observations and measurements, CLASS can represent and be responsive to the complex dynamics of energy and water fluxes at the plot scale within the heterogeneous surface of boreal forests.

Analysing the capacity of multispectral indices to map the spatial distribution of potential post-fire soil losses based on soil burn severity

The area burned in Spain exceeded historical records in 2022, when exceptionally warm conditions influenced wildfire events. The predicted intensification of wildfire regimes includes an increase in frequency, severity, and size. Therefore, a study of the wildfires that occurred in 2022 is necessary to understand their behaviour and possible environmental impacts. The objective of this study is to analyse the applicability of using spectral indices and Geographic Information System (GIS) approaches to map the spatial distribution and estimate potential soil losses using Sentinel-2 imagery and fire severity field data. Soil losses were estimated using an empirical model based on soil burn severity data collected in the field after wildfire. The relationship between the Normalized Difference Infrared Index (NDII), Difference Normalized Wildfire Ash Index (dNWAI), and the Blue Normalized Difference Vegetation Index (BNDVI) with the estimated soil losses was then evaluated. In addition, the influence of different time scales of the satellite images was analysed. The first period considered (Date I) ranges from 8 to 20 days after the beginning of the wildfire, which coincides with the field data collection. The second period considered (Date II) ranges from 28 to 35 days after the start of the wildfire. The results obtained showed a significant dependence relationship between the BNDVI index (using satellite images of Date I) and the estimated soil losses (R2 = 0.756), while the results of the NDII (R2 = 0.31) and dNWAI (R2 = 0.061), showed no spatial relationship with the estimated soil losses. Three of the largest wildfires in 2022 in Spain were analysed, and the results showed strong correlations of BNDVI index for Folgoso do Courel (R2 = 0.808), for Carballeda de Valedorras (R2 = 0.906), and for Sierra de la Culebra (R2 = 0.939). In addition, these results allowed the mapping and quantification of potential soil losses in areas where fire severity was high, totalling ∼2,50,000 Mg ha−1 in Folgoso do Courel, ∼3,70,000 Mg ha−1 in Carballeda de Valdeorras, and ∼4,70,000 Mg ha−1 in Sierra de la Culebra. Moreover, BNDVI values for estimating soil loss vary by vegetation type, and there is a positive correlation between severity classes and the BNDVI index. This approach can inform post-fire land management decisions in future wildfires and could be applied to other regions.

A novel AMSR2 retrieval algorithm for global C-band vegetation optical depth and soil moisture (AMSR2 IB): Parameters' calibration, evaluation and inter-comparison

Effective monitoring of soil and vegetation properties on a global scale is essential for better understanding climate changes, hydrological dynamics, and ecological processes. Passive microwave remote sensing at C-band radio frequency, with long observation period and relatively high penetration capability, has been widely used to retrieve soil moisture (SM) and vegetation optical depth (C-VOD). The retrieval process is generally achieved by inversion of the τ-ω radiative transfer model, which depends on crucial parameters such as effective scattering albedo (ω) and soil roughness (HR) for accurate retrievals. Current SM/C-VOD retrieval algorithms, such as the Land Parameter Retrieval Model (LPRM), predominantly rely on globally-constant ω and HR values, ignoring the inherent sensitivity of those parameters to varying soil conditions and vegetation types. To evaluate the impact of ω and HR variables on SM and C-VOD retrievals and to improve their accuracy, this study proposed and evaluated a novel retrieval approach from AMSR2 C-band observations during 2017–2020 using the C-band Microwave Emission of the Biosphere (C-MEB) model. We evaluated two new retrieval algorithms, considering either a globally-constant calibration or a land cover-based calibration of ω and HR. As a benchmark for the calibration, we optimized the values of ω and HR by evaluating the retrieved SM against in situ measurements from the International Soil Moisture Network (ISMN) and OzNet hydrological monitoring networks. The main originality compared to previous algorithms is that i) it includes a comprehensive calibration exploring the optimal values of ω and HR, applicable globally or tailored to specific land cover; ii) field SM measurements were leveraged to constrain the calibrated value of ω and HR.For the globally-constant calibration, the optimal values of ω = 0.05 and HR = 0.1 were found to yield the best results. For the land cover-based calibration, an inverse relationship between ω/HR and canopy height was observed, with ω ranging from 0.04 to 0.06 and HR ranging from 0.1 to 0.7 for heights between 0 and 30 m. The algorithm employing a land cover-based calibration (INRAE Bordeaux 2, IB2) exhibited better performance than the one utilizing a globally-constant calibration (INRAE Bordeaux 1, IB1) in evaluating retrieved SM against in situ measurements, as well as in evaluating C-VOD vs various vegetation variables including aboveground biomass (AGB), tree cover, canopy height and several optical vegetation indices. Comparison with LPRM suggested that our IB2 C-VOD retrievals present improved performances in terms of both spatial and temporal results with all considered vegetation variables (spatial correlation (R) between various vegetation variables and C-VOD of 0.76–0.83 for IB2 vs 0.69–0.79 for LPRM), and exhibited lower saturation effects when compared with AGB. In addition, the IB2 SM produced lower root mean squared error (RMSE) (0.147 vs 0.217 m3/m3), bias (−0.03 vs 0.09 m3/m3), and ubRMSE (0.066 vs 0.067 m3/m3) when compared with in situ measurements, although it showed a lower R compared to LPRM SM.

The Critical Role of Soil Ecological Stoichiometric Ratios: How Does Reforestation Improve Soil Nitrogen and Phosphorus Availability?

The ecological stoichiometric characteristics of soil elements have greatly enhanced our understanding of the circulation of soil nutrients. However, there is limited knowledge regarding the alteration of carbon, nitrogen, and phosphorus stoichiometric ratios in deep soil after afforestation. To examine the variations in stoichiometric ratios of soil elements with different vegetation types, restoration times, and soil depths, we collected soil samples from grassland, Caragana korshinskii shrubland, and Picea asperata forestland at different stand ages (10a, 25a, and 40a) in Xining City, which is located on the Loess Plateau. Our results showed that, at 25a, the carbon-to-nitrogen (C:N) and carbon-to-phosphorus (C:P) ratios were significantly higher in the grassland soil than under other vegetation types, whereas the nitrogen-to-phosphorus (N:P) ratio had no significant difference among the three vegetation types. At 40a, the ratios of soil C:N, C:P, and N:P in the shrubland were the highest. With the increasing of the restoration time, the ratios of soil C:N, C:P, and N:P in grassland with 25a became higher than for 40a or 10a. The ratios in the shrubland were highest at 40a, followed by 25a and then 10a, while the ratios in the forestland showed no significant difference. At 40a, the soil C:N, C:P, and N:P ratios of shrubland were highest at the soil depth of 40-100 cm. The soil C:N, C:P, and N:P ratios showed positive correlations with soil ammonium nitrogen and nitrate nitrogen, and the soil N:P ratios showed a negative correlation with soil available phosphorus. Plant diversity significantly influenced the soil stoichiometric ratio of the upper soil layer. In the upper soil layer (0-40 cm), species richness showed a positive correlation with soil C:N, C:P, and N:P ratios, and the Margalef index exhibited a positive correlation with soil C:N and C:P ratios. The results of this study indicate that the stoichiometric ratio and nutrient availability of Caragana korshinskii shrubland were the highest over time. Therefore, these findings can be served as a valuable reference for local revegetation and ecological restoration.

Global-scale improvement of the estimation of terrestrial gross primary productivity by integrating optical and microwave remote sensing with meteorological data

Photosynthesis (a key ecological process) is measured based on gross primary productivity (GPP), emphasizing the criticality of accurate GPP estimation to climate change research. The extant remote sensing-based approaches for GPP estimation were typically based on optical remote sensing data, neglecting the potential supplementary information from microwave remote sensing data. Thus, based on the random forest algorithm, we developed a GPP model through the integration of optical and microwave remote sensing with meteorological data (OMM-GPP). The software and tools used for data processing, modeling, and analysis are the standard and third-party libraries based on the Python Language. Our OMM-GPP model was trained and validated using GPP data (referred to as “observed GPP”) retrieved from carbon dioxide fluxes measured at 137 flux towers. The results indicated that GPP estimation by integrating optical and microwave data with meteorological data was more accurate than GPP estimation by integrating single-source remote sensing data (optical or microwave data) with meteorological data across eight vegetation types. The model performed well on daily and monthly scales, with determination coefficients (R2) (root-mean-square errors, RMSE) of 0.85 (1.52 gC m−2 d−1) and 0.83 (1.49 gC m−2 d−1), respectively, which increased (decreased) by 0.17–0.03 (0.58–0.12 gC m−2 d−1) and 0.11–0.02 (0.39–0.10 gC m−2 d−1) compared with the R2 (RMSE) values obtained by integrating single-source remote sensing data with meteorological variables. Further, the OMM-GPP model effectively captured the seasonal variations in daily and monthly GPP across most vegetation types. The eight-day-scale comparison of the model with the VODCA2GPP dataset revealed its enhanced performances, increasing R2 by 0.33 and decreasing RMSE by 0.97 gC m−2 d−1. Overall, the integration of microwave and optical remote sensing data with meteorological data can enhance GPP estimation accuracy, as demonstrated by the established OMM-GPP, across different vegetation types.

Assessment of climatic influences on net primary productivity along elevation gradients in temperate ecoregions

Elevation gradients significantly influence net primary productivity (NPP), but the relationship between elevation, climate variables, and vegetation productivity remains underexplored, particularly in diverse ecological zones. This study quantifies the impact of elevation and climatic variables on NPP in northern Pakistan, hypothesizing that elevation modulates NPP through its influence on temperature and precipitation patterns. Using remote sensing data (MODIS ERA5) and advanced ecological models like the Eddy Covariance-Light Use Efficiency (EC-LUE) model and the Thornthwaite Memorial Model (TMM), we analyzed Gross Primary Productivity (GPP) dynamics across various vegetation types and elevations from 2001 to 2023. Our findings show a mean annual NPP of 323.46 g C m-2 a-1, with an annual increase of 5.73 g C m-2 a-1. Significant elevation-dependent variations were observed, especially in mid-elevation zones (401 to 1600 meters), where NPP increased at a rate of 0.174 g C m-2 a-1 per meter (R² = 0.808, p < 0.01). In contrast, higher elevations (2800-5200 meters) exhibited a decline in NPP, decreasing by -0.171 g C m-2 a-1 per meter (R² = 0.905, p < 0.001). Temperature and precipitation were key drivers, with precipitation positively correlating with NPP across all vegetation types, particularly in Evergreen Needleleaf and Broadleaf Trees. The EC-LUE model's GPP estimates closely matched MODIS data (R² = 0.82), demonstrating the model's reliability. These findings highlight the critical role of elevation and climatic factors in vegetation productivity and underscore the need for targeted ecological management and conservation strategies. The insights from this research are vital for global climate adaptation policies and sustainable development goals, contributing to ecological resilience and carbon sequestration efforts worldwide.

Influences of climate change on carbon and water fluxes of the ecosystem in the Qinling Mountains of China

Climate change is one of the foremost challenges confronting the contemporary world. Carbon and water cycles and their interconnections of ecosystems are playing a pivotal role in assessing the impacts of climate change on ecosystems. The Qinling Mountains of China represent a focal area for research on global climate change and regional adaptation strategies. Based on historical and CMIP6 (Coupled Model Intercomparison Project Phase 6) future climatic data, the Biome-BGC model, which was sufficiently optimized with the Biome-BGC-PEST package, was used to simulate the historical and future trends of GPP (Gross primary productivity), ET (Evapotranspiration), and WUE (Ecosystem water use efficiency) in the Qinling Mountains, so as to elucidate the dynamics and spatiotemporal variations of carbon and water fluxes amidst climate change. The results showed that the GPP, ET, and WUE of the Qinling Mountains heterogeneously distributed in space, due to the dramatic disparities in carbon and water fluxes among different vegetation types in the Qinling Mountains. Among the various vegetation types, evergreen needle-leaved forest had the highest annual mean value of WUE. In the historical period of 1979-2018, deciduous broad-leaved forest had the highest annual mean value of GPP and ET, while evergreen needle-leaved forest would dominate in the future (2021-2100). For the annual GPP and ET flux variations, different vegetation cover types followed varying peak patterns in the Qinling Mountains. Deciduous broad-leaved forest, evergreen needle-leaved forest, and shrub meadow showed ’single-peak’ pattern annually, whereas the crop area displayed ’double-peak’ pattern due to the different growing seasons of crops. There also were temporal variations for the carbon and water fluxes within the same vegetation type. Future projections suggested a continuous increase in GPP and ET fluxes, underscoring the consistent role of the Qinling Mountains in water resource conservation and carbon sink amid evolving climatic conditions.

Identification of critical drought thresholds affecting vegetation on the Mongolian Plateau

The increasing frequency of drought events due to global climate change has significant impacts on ecosystems, often resulting in reduced vegetation productivity. However, vegetation responses to drought often exhibit non-linear threshold phenomena, where vegetation growth sharply decreases once water stress reaches a certain level. Our study, conducted on the Mongolian Plateau, focuses on revealing the drought response thresholds and distribution patterns of different vegetation types. These thresholds mark critical turning points from high drought resistance to high vulnerability in vegetation response and serve as early warning indicators, crucial for accurately assessing how terrestrial ecosystems adapt to climate change. We utilized remote sensing observation datasets and inversion datasets spanning from 2001 to 2020, including six representative vegetation indices. The results indicate that: (1) Over 70 % of the Mongolian Plateau experiences low vegetation growth (<10th) predominantly influenced by drought, with low vegetation growth often occurring when water availability falls below the 30th percentile; (2) Areas with higher vegetation coverage and relatively moist climates have lower proportions of drought thresholds, implying that more complex ecosystems can better mitigate water stress, while background climatic conditions also regulate vegetation response to water availability; (3) According to multi-model climate predictions, the overall probability of drought risk on the Mongolian Plateau is projected to significantly increase during 2080–2100, especially in drought-prone regions. These findings provide important insights into better understanding the impacts of drought on ecosystems. Additionally, they emphasize the urgency of taking early measures to adapt to future drought risks.

Competition between resprouting chaparral and the recruits of a serotinous conifer following stand-replacing fire

In western North America, fire regimes are shifting towards more frequent, larger, and more severe wildfire. There is concern that this will shift the vegetation type in many areas, especially on the lower, drier slopes. In northern California, mature serotinous conifers and resprouting shrub species easily regenerate in severe patches of any size. There is no consensus, however, regarding the effects of shrub competition on conifer recruitment; conifer response to shade varies with shade tolerance and abiotic factors. Many conifers and almost all chaparral shrubs are shade intolerant, and we expect shading to be the main driver of the inter-species competition between these taxa on dry low-elevation, slopes We chose to examine early post-fire regeneration of knobcone pine (Pinus attenuata), a shade intolerant serotinous conifer, because (a) as a serotinous species we could be assured of a high initial density of recruits, and (b) it is mainly found on lower-elevation slopes in a matrix of chaparral. We examined the competitive interactions of the pine and shrubs within the 2018 Carr and Delta fires at the third and fourth post-fire years, as well as at the 2008 Motion Fire at the 14th post-fire year, focusing on two measurements of shrub shading: inter-shrub porosity (% shrub cover) and intra-shrub porosity (species-specific ground-level light availability). Our response variables included recruitment success (recruits per ovulate cone) and growth (height). We only chose stands where knobcone pine was a minor pre-fire component to ensure a high density of vigorously resprouting shrubs. We found (1) there were significantly fewer pine recruits under shrubs, with the bulk of the shrub-induced mortality of knobcone pine occurring before the third growing season; (2) knobcone pine averaged about six established recruits per burned parent tree by the third year following fire; and (3), extrapolating from height reconstruction of post-fire knobcone pine regeneration from the 2008 Motion Fire, the remaining tree recruits are expected to persist and dominate the stand within a decade of the fire. We conclude that competition with shrubs on low elevation sites in northern California does have a negative effect on knobcone pine density but is insufficient to seriously impede a dramatic post-fire increase in conifer density when conifer regeneration arrives promptly following fire.

Extraction of grassland irrigation information in arid regions based on multi-source remote sensing data

Irrigation is a vital measure for maintaining grassland productivity in arid and semi-arid regions. Grasslands typically have characteristics such as unclear boundaries, complex vegetation types, and relatively small irrigation amounts, making it challenging to extract irrigation information. Currently, research on extracting grassland irrigation information is scarce. This study proposes a method for extracting grassland irrigation information using high spatiotemporal resolution (30 m, 1 day) downscaled surface soil moisture data, combined with Landsat 8/9 and Sentinel 1/2 data. This method was applied to extract the irrigation area, timing, and frequency of grasslands in the Delingha Piedmont, northwestern China. The results showed that the overall classification accuracy of irrigated grassland was 93.43 %, and the kappa coefficient was 0.91, indicating high extraction accuracy. The average values of recall, precision, and F-score for irrigation timing and frequency were 82.54 %, 72.25 %, and 77.03 %, respectively, with most irrigation events accurately identified, indicating commendable overall efficacy. The combined use of multi-source remote sensing data is crucial for the extraction of grassland irrigation information. Among these, The high spatiotemporal resolution downscaled surface soil moisture data, by providing detailed spatiotemporal surface soil moisture dynamics, demonstrate a potent capacity for capturing irrigation events, thus effectively enhancing the accuracy of grassland irrigation data extraction.

Increasing vulnerability of vegetation to Meteorological and Groundwater drought: A Case study in Argentina

Study RegionContinental Argentina, South America. Study FocusThis study integrates multiple data sources to analyze the response of vegetation to meteorological and groundwater drought events in Argentina. It utilizes GRACE satellite data to monitor water storage variations, the GLDAS hydrological model to track soil moisture levels, and NDVI to assess vegetation health, categorized by different vegetation types. Meteorological data facilitated the computation of drought severity using the Standardized Precipitation-Evapotranspiration Index (SPEI). Anomalies in groundwater storage, derived from these data, informed the calculation of the Grace Groundwater Drought Index (GGDI). A comprehensive statistical analysis was conducted to examine the impact of meteorological (SPEI) and groundwater (GGDI) droughts on various vegetation types. New Hydrological Insights for the RegionThe study reveals that from 2002 to 2022, GRACE-TWSA and GLDAS-TWSA displayed consistent trends in Argentina. Meteorological droughts, characterized by higher frequency and intensity, predominantly affect croplands and grasslands. Compared to lagged effects, the cumulative impact of droughts was found to be more significant. Shrublands demonstrated the greatest resistance to drought, whereas vegetation in humid regions showed lower drought tolerance. Notably, the duration associated with the lag effects of meteorological droughts decreased from 2.16 months in 2002–1.52 months in 2020, suggesting an increasing sensitivity of vegetation to these droughts. This underscores the urgency of developing targeted drought resilience and water resource management strategies.

Identifying the dominant compound events and their impacts on vegetation growth in China

Terrestrial vegetation plays a vital role in global carbon recycling, but it is also affected by compound events (CEs); however, little is known about the impacts of these CEs on vegetation in terms of their occurrence and magnitude. Using meteorological observations and vegetation indices (leaf area index (LAI), gross primary productivity (GPP), and net primary productivity (NPP)) from 1981 to 2020, we explored the occurrence of 13 CEs types and identified the dominant CEs types across different eco-geographical regions of China, and quantified the response of various vegetation types to dominant CEs. We found that CEs of extreme hot-dry, extreme hot-dry-high fire weather, dry-high fire weather, and high fire weather-strong wind were the dominant types of compound events during the growing season in China, and their hazards increased at a rate of >0.1HI/10a during 1981–2020. We further detected that more than 60% of the total vegetation areas showed a strong negative correlation with compound extreme hot-dry-high fire weather-strong wind events, which was relatively higher than compound extreme hot-dry events. The response of vegetation to compound events varied at the national scale, which was related to the vegetation type, dominant compound event type, and local natural conditions. This study highlights the benefits of a multivariate perspective on compound events and reveals the regional differences in the response of vegetation to compound events, which can provide initial guidance to assess the regional compound event risk of vegetation against the background of carbon neutrality by 2060.

Do greenspaces really reduce heat health impacts? Evidence for different vegetation types and distance-based greenspace exposure

BackgroundWhile vegetation type, population density and proximity to greenspaces have been linked to human health, what type and location of greenspace matter most have remained unclear. In this context, there are question marks over investment-style metrics. ObjectivesThis paper aims at establishing what vegetation type may matter most in modifying heat-mortality associations, and what the optimal buffer distances of total and specific types of greenspace exposure associated with reduced heat-related mortality risks are. MethodsWe conducted small-area analyses using daily mortality data for 286 Territory Planning Units (TPUs) across Hong Kong and 1 × 1 km gridded air temperature data for the summer months (2005–2018). Using a case time series design, we examined effect modifications of total and specific types of greenspaces, as well as population-weighted exposure at varying buffer distances (200–4000 m). We tested the significance of effect modifications by comparing relative risks (RRs) between the lowest and highest quartiles of each greenspace exposure metric; and explored the strength of effect modifications by calculating the ratio of RRs. ResultsForests, unlike grasslands, showed significant effect modifications on heat-mortality associations, with RRs rising from 0.98 (95 %CI: 0.92,1.05) to 1.06 (1.03, 1.10) for the highest to lowest quartiles (p-value = 0.037) The optimal distances associated with the most apparent effects were around 1 km for population-weighted exposure, with the ratio of RRs being 1.424 (1.038,1.954) for NDVI, 1.191 (1.004,1.413) for total greenspace, and 1.227 (1.024,1.470) for forests. A marked difference was observed in terms of the paired area-level and optimal distance-based exposure to total greenspace and forests under extreme heat (p-values < 0.05). DiscussionOur findings suggest that greenspace, particularly nearby forests, may significantly mitigate heat-related mortality risks.