Vegetation Types Research Articles

Interrelationships of biological spectra, life-form, landform and functional vegetation type in the riparian forests of a tropical river

Interrelationships of biological spectra, life-form, landform and functional vegetation type in the riparian forests of a tropical river

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.

Perseverance of management is needed – Efficient long-term strategy of Reynoutria management

One of the most problematic invasive species in Europe are knotweeds from genus Reynoutria (Fallopia) which have significant negative impact on the native communities as well on human activities. Therefore, they are a target of many control programmes. Due to their high regeneration potential, their management is problematic, and only chemical treatment is reported to be sufficiently effective. The aim of this paper was to describe and analyse the patterns of Reynoutria invasion under long-term chemical treatment with glyphosate-based herbicide in The Morávka river floodplain, Czech Republic. The data covers 17 years of management which started with the European project “Preservation of alluvial forest habitats in the Morávka river basin”. We focus on (i) assessment of Reynoutria distribution during long-term management, (ii) analysis of the change of distribution according to the habitat, and (iii) discussion of the optimal management strategy based on the long-term data. Distribution data was obtained using GNSS field mapping. Before the start of the study in 2007, Reynoutria stands covered 29% of the study area (96.9 ha). As a result of systematic whole area chemical management, the extent decreased to 19.6% (65.3 ha) in 2009, and even reached 14.5% (48.2 ha) in 2013, three years after its end. Due to implementation of local chemical management in the following years, the area of Reynoutria was maintained at similar level, with minimum value 41.8 ha in 2018 and a slight increase in recent mapping in 2023. Beside the extent, the structure and coverage of invaded sites was analysed. There was a clear trend of fragmentation of larger polycormons with high coverage into many smaller and less dense ones as a result of chemical spraying. The average size of Reynoutria stand decreased from 0.61 ha in 2007 to half in 2013 (0.32 ha) to 0.15 ha in 2023. Testing of the effects of time, habitat, and biotope did not reveal significant differences of changes of extent and abundance over different environments (forest, open, bare ground), which indicates that there are no differences in reaction to management in the studied habitat and vegetation types. Our study provides a robust and unique overview of the invasion, reinvasion, and suppression dynamics for an important invasive species. If herbicide management is used, chemical treatment must be quite long-term as even three years of intensive glyphosate foliar spray application was not sufficient for the complete eradication of Reynoutria. Therefore, we propose the following procedure for effective chemical management of Reynoutria: 1) In largely infested sites, the first step is to reduce the distribution of Reynoutria stands to isolated polycormons. This phase can last 3–5 years. 2) After reaching the state of sparse distribution of Reynoutria, we recommend herbicide application only in periods of every 3–5 years depending on the local context and rate of regrowth. 3) At sites exposed to soil disturbances, where the soil is contaminated by fragments of Reynoutria rhizomes, there is a need to apply herbicide immediately to target newly resprouting individuals.

Haymaking complemented by moderate disturbances can sustain and restore species-rich alpine to subalpine grasslands

AbstractThe high plant diversity in alpine to subalpine grasslands is threatened by the abandonment of land use. In addition, changing environmental conditions might lead to vegetation shifts even when traditional land use is maintained, as observed in grasslands in Switzerland during the last decades. Maintaining and restoring the diversity of such grasslands might therefore require modified management methods. We conducted a six-year experiment to assess the responses of plant species richness, mean ecological indicator values, and vegetation composition to five management treatments, including scraping as additional management measure: haymaking (in autumn), haymaking complemented by scraping (i.e. manual raking) in autumn, haymaking complemented by scraping in spring, only scraping in spring, and abandonment of land use. We hypothesized that haymaking complemented by scraping in either season would remove additional biomass and increase species richness by creating open patches that can reduce inter-specific competition and promote species establishment. We found positive effects of haymaking complemented by scraping on plant species richness and habitat quality, indicated by the increased mean indicator value for light. Abandonment showed the opposite effects and increased mean indicator values for nutrients. Interestingly, haymaking combined with scraping in autumn promoted the development of the vegetation towards the composition similar to the resident vegetation type. Our findings show that extensive land use is essential to maintain species-rich alpine to subalpine grasslands. Further, they imply that modified land use can compensate for the negative developments such as reduced habitat quality and species richness caused by environmental changes and help restore the vegetation.

Soil organic carbon and nitrogen in a carboniferous spoil heap as a function of vegetation type and reclamation treatment

AbstractEvaluating the impact of vegetation types and reclamation methods on soil organic carbon and nitrogen in carboniferous spoil heaps is critical for selecting the best vegetation type and reclamation method to improve ecosystem services in a changing climate. This paper presents the relationship between vegetation types (woodland, forbland, and grassland) and reclamation techniques (barren rock, topsoil application, succession, and cultivation) on soil organic carbon (SOC) and total nitrogen (TN) in developing soils on carboniferous rocks in coal mine heaps. Soil samples were collected from the litter layer (Oi + Oe) and the A horizons (0–10 cm). The results revealed that vegetation types and reclamation methods significantly affected SOC and TN stocks. Woodland exhibited higher SOC and TN in the Oi + Oe horizons than other vegetation types. Topsoil application and cultivation resulted in the highest SOC and TN stocks in the A horizons (0–10 cm) under woodland and forbland compared to succession on bare carboniferous rock. In grassland, there was no significant difference in SOC stock under topsoil application and cultivation; however, significantly higher TN stock was observed in the 0–10 cm areas with topsoil application compared to succession on bare carboniferous rock. Based on the results, topsoil application is recommended to improve SOC if the mining site is restored using woodland. Conversely, grassland exhibits a similar amount of SOC stock with or without topsoil application. Considering the difficulty of obtaining topsoil, we suggest that grasses are optimal for SOC stock in the studied mining sites, followed by forbs.

Effects of vegetation types on soil wetting pattern and preferential flow in arid mountainous areas of northwest China

Understanding the mechanisms governing the infiltration of precipitation into soil is crucial in eco-hydrological processes. However, the effect of vegetation types on the wetting front depth and velocity is poorly understood. Here, we analyzed 1234 infiltration events based on a large-scale long-term in-situ soil moisture monitoring network in arid mountainous area of northwest China. Our results show that the proportion of preferential flow was the largest in shrub (52.38 %), followed by alpine meadow (36.55 %), grassland (11.51 %), and barren (0.70 %). The wetting front velocity was consistent with the order of the proportion of preferential flow, with values of 11.42, 4.96, 2.32, and 1.16 cm/h, respectively. The mean velocity of preferential flow events was 2.05 times (0.06–71 times) higher in the shallow soil layer and 3.86 times (0.3–68 times) higher in the deep soil layer compared to matrix flow events. The wetting front depth was shallowest in alpine meadow (14.31 cm), followed by barren (15.70 cm), grassland (18.95 cm), and shrub (39.81 cm). Moreover, the wetting front depth and velocity reach their peak values in summer, primarily influenced by precipitation. Random Forests analysis results demonstrate that preferential flow is the primary factors influencing the profile wetting front depth, with control factors varying across different soil depths, soil water characteristic curve in shallow soil layers, and vegetation in deep soil layers, respectively. Meanwhile, soil organic carbon emerged as the most important factor impacting wetting front velocity. These findings contribute to a deeper understanding of infiltration processes in arid mountainous areas and offer a theoretical foundation for refining and enhancing mountain hydrological models.

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.

An Interseasonal Comparison of Soil Respiration in Xeric and Mesic Pine Forest Ecosystems in Central Siberia

An understanding of how boreal forest composition responds to global environmental changes is an important challenge to predicting the future global carbon balance. Boreal forests are the most significant sink for atmospheric carbon dioxide; however, their sequestration capacity is highly sensitive to ongoing climate changes. The combination of the hydrothermal conditions of a territory strongly regulates its biogeochemical processes. The carbon fluxes in boreal forests are strongly mediated by the ground vegetation cover, composed of mosses (mesic) and lichens (xeric). Despite the concurrence of xeric and mesic vegetation types, their responses to climate variations varies significantly. Soil emission is an informative indicator of ecosystem functioning. In this study, we focused on the soil CO2 dynamics during frost-free seasons with different precipitation regimes in the xeric and mesic boreal ecosystems of Central Siberia. Seasonal measurements of soil CO2 emissions were conducted during frost-free seasons using the dynamic chamber method. Our findings reveal that the precipitation regimes of each year may control the seasonal soil emission dynamics. The soil moisture is the most important driver of emissions growth in the water-limited lichen pine forest (R2adj. = 18%). The soil temperature plays the largest role in the feather moss pine forest during the dry (R2adj. = 31%) seasons, and in the lichen pine forest during the wet (R2adj. = 41%) seasons. The cumulative efflux for the xeric and mesic sites is mostly related to the hydrothermal conditions, and not to the differences in ground vegetation cover. During the dry seasons, on average, the soil CO2 emissions are 45% lower than during the wet seasons for both sites. These findings emphasize the need for estimating and including the hydrothermal characteristics of the growing season for detailed emission assessments.

Unveiling the influence of seawater intrusion and vegetation type on coastal arbuscular mycorrhizal fungal communities in China

AbstractContinuously rising sea levels pose severe threats to the diversity and stability of coastal ecosystems. However, the responses of endospheric and rhizospheric arbuscular mycorrhizal (AM) fungal communities to accelerated seawater intrusion and changing vegetation types unfortunately remains unknown. Such insights will assist in revealing the structure of AM fungi across different tidal zones, enabling the development of effective measures to conserve biodiversity. Here, we investigated changes in endospheric and rhizospheric AM fungal communities associated with four types of coastal vegetation in Dafeng City, Jiangsu Province, China, in response to sea level variations by analyzing their characteristics and connections to selected environmental factors. It was found that the AM fungal communities of coastal vegetation differed significantly in their compositions, diversity, and stability (the resilience or resistance of a community). Despite the responses of endospheric and rhizospheric AM fungi to soil environmental factors, the soil electrical conductivity, cation exchange content, and inorganic carbon were identified as core elements that influenced the characteristics of AM fungal communities between different coastal vegetation species. Lastly, the major endospheric and rhizospheric species of AM fungi were important predictors that explained much of the variation in the soil environment. Consequently, the dynamics of AM fungal communities were correlated with the plant–soil system under different conditions. Apart from the coastal vegetation species, the characteristics of AM fungal communities can also be driven by high salinity and the inorganic carbon content induced by the intrusion of seawater.

Dynamics of structural indices and organic carbon pool across a haplic acrisol under secondary tropical rainforest at Umudike Abia State

Differences in vegetation types over a landscape influences the structural behaviour and organic carbon pool of soils thereby affecting site-specific management decisions. This study aimed at establishing the differences in structural indices and organic carbon pool across a soil under secondary rainforest vegetation. Nine replicates of auger and core soil samples were randomly collected from the site at 0 – 20 cm depth. Soil samples were prepared and analysed at a laboratory. Data obtained were subjected to geospatial analysis using a Geographical Information System (GIS) software package. Results showed that changes in organic carbon storage ranged from 40 – 48 ton / ha, BD ranged from 1.39 – 1.42 mg / m3, clay flocculation index ranged from 50 – 68 %, aggregated silt + clay ranged from 9 – 12 %, clay dispersion index ranged from 32 – 42 %, dispersion ratio ranged from 26 – 32 %, mean weight diameter ranged from 0.75 – 1.05 mm, saturated hydraulic conductivity ranged from 3.1 – 3.8 cm / mins and total porosity ranged from 46.4 – 47.6 %. Regions of weak micro aggregaion dominated the land while regions of increased stability of macro aggregates were dominant. Regions of increased soil organic carbon storage occupied about half portion of the land. Greater portion of the land was noted for increased bulk density, reduced total porosity and saturated hydraulic conductivity. Consequently, there were changes in soil structural properties and organic carbon storage across the studied area.

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.

Plant Formations Found in Xerophytic (Arid) Sparse Forests in the Azerbaijan Territory and Their Protection

Based on the ecological and geobotanical studies conducted in xerophytic (arid) sparse forests in the territory of Azerbaijan and the study of the relevant literature, a classification scheme was developed taking into account xerophytic tree species that form such groups as Caragana grandiflora DC. and Astragalus microcephalus Willd. In the course of the study, based on phytocoenological principles, a xerophytic (arid) sparse forest type of vegetation was noted in the Turyanchay Reserve, in the mountainous part of the Lankaran-Yardymli district, in Nakhchivan and the Gabala district located on the southern slope of the Greater Caucasus. It was established that this type of vegetation consists of 1 formation class, 2 formation groups and 2 associations. A geobotanical description and species composition of the formations was carried out and it was established that the sparse forests of Caraganeta-Pistacetum-Juniperosum, found in the leguminous shrubs, need protection from the negative impact of anthropogenic factors. And this, in turn, creates the basis for the conservation of plant formations found in the forest ecosystems of Azerbaijan.

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.

Potential food factors affect the fatality rate of COVID-19: an analysis using multiple linear regression

The COVID-19 pandemic has caused great harm to people around the world. This article explores the relationship between healthy diets and the fatality rate of COVID-19 from the perspective of healthy diets. This article collects data from Kaggle, pre-processes and standardizes it, conducts principal component analysis(PCA), divides it into five different dietary patterns(The contribution rate of these five dietary patterns is 72.917%, which can be interpreted as the principal component), and then constructs a multiple linear regression model. In the construction of this model, this article uses the idea of stepwise regression to remove variables that are not significant, leaving the parameters of the first and second principal components significantly non-zero. The results show that the estimated value of the parameter for the first dietary pattern in linear regression is 0.013, whereas the estimated value of the parameter for the second dietary pattern in linear regression is -0.005. Vegetable oils and vegetable products are negatively correlated with the fatality rate of COVID-19, while starchy root foods and animal products are positively correlated with it. Finally, the conclusion is drawn: In normal meals, it is more important to pay attention to the intake of vegetable foods, and try to ensure that the intake of meat foods should also be accompanied by a large amount of different types of vegetables. In the process of cooking, try to use vegetable oils such as soybean oil, olive oil, etc., which contain a lot of vitamin E and unsaturated fatty acids. For starchy root foods and certain high-protein, high-fat animal products, try to eat less in life. On the premise of ensuring good eating habits, it will have a good protective effect on COVID-19.

A typhoon-induced debris flow warning model integrating rainfall thresholds with geological factors

Typhoon-induced debris flows pose a significant threat to the southeastern coastal regions of China. The existing typhoon-induced debris flow warning models, however, are largely limited in the refinement of their rainfall thresholds and in their lack of integration with geological factors. This study proposes a typhoon-induced debris flow warning model based on refined rainfall thresholds by integrating them with geological factors. Firstly, based on the characteristic rainfall of 159 debris flows in seven typhoon rainstorms between 1999 and 2019, the rainfall thresholds of debris flow under different geological conditions were obtained. Close relationships were noticed between rainfall thresholds and the number and density of debris flow disasters. The deterministic coefficient method (DCM) and sensitivity index were used to derive the weights of the geological factors, and the geological groups of the rock masses and vegetation type were the most important geological factors for debris flows. Finally, a typhoon-induced debris flow warning model was constructed, and refined rainfall thresholds were obtained. Due to different geological backgrounds, there were significant differences in the warning rainfall thresholds for debris flow disasters in different regions of the study area. The ROC indicator showed the high accuracy of this debris flow warning model. This research provides a scientific basis for the early warning and prediction of debris flows in typhoon-prone area of China.