Vegetation Distribution Research Articles

A Prediction of Estuary Wetland Vegetation with Satellite Images

Estuarine wetlands are the transition zone between marine, freshwater, and terrestrial ecosystems and are more ecologically fragile. In recent years, the spread of exotic vegetation, specifically Spartina alterniflora, in the Yellow River estuary wetlands has significantly encroached upon the habitats of native species such as Phragmites australis, Suaeda glauca Bunge, and Tamarix chinensis Lour. With advances in land prediction modeling, predicting wetland vegetation distribution can aid management and decision-making for ecological restoration. We selected the core area as the study object and coupled the hydrological model MIKE 21 with the PLUS model to predict the potential future distribution of invasive and dominant species in the region. (1) Based on the fine classification results from satellite images of GF1/G2/G5, we gained an understanding of the changes in wetland vegetation types in the core area of the reserve in 2018 and 2020. (2) Using public data such as ERA5 and GEO as input for basic environmental data, using MIKE 21 to provide high-spatial-resolution hydrodynamic parameters for the PLUS model as an environmental driver, we modeled the spatial distribution of various wetland vegetation in the Yellow River estuary wetland in Dongying under different artificial restoration measures. (3) We predicted the 2022 distribution of typical vegetation in the region, used the classification results of GF6 as the actual distribution, compared the spatial distribution with the actual distribution, and obtained a kappa coefficient of 0.78; the predicted values of the model are highly consistent with the true values. This study combines the fine classification results of vegetation based on hyperspectral remote sensing, the construction of a coupled model, and the prediction effect of typical species, providing a reference for constructing and optimizing the vegetation prediction model of estuarine wetlands. It also allows scientific and effective decision-making for the management of ecological restoration of delta wetlands.

Watershed: a more efficient sampling unit for mountain camera traps.

Wildlife camera trap (CT) surveys typically employ two-dimensional equal-area grid sampling, which often neglects the influence of complex mountainous terrain on species distribution, potentially yielding misleading outcomes. A watershed, incorporating diverse habitats from high to low elevations and from rivers to ridges, aligns with complex mountains. Monitoring based on watersheds might address this. In southwest China's mountain forests, under comparable sampling intensities, we contrasted the capture rate (CR), species richness, and relative abundance index (RAI) of dominant species among watershed, 1 × 1km² grid, and elevation gradient patterns. Also, habitat factor correlations and heterogeneities were analyzed. Results reveal higher CR, species richness, and habitat heterogeneity in the watershed pattern. The elevation gradient pattern shows more stable species and RAI than the grid pattern. In small-scale mountains, topographic factors indirectly affect CT survey results via vegetation distribution. Analysis of similarities (ANOSIM) indicates significant differences in species and community among watersheds. Using watersheds as sampling units for CTs can match the mountains' elevation differences and complex topography well, aids in capturing wildlife diversity and understanding mountain species distribution. Therefore, we recommend that the spatial sample design in mountainous areas should be based on watersheds, taking elevation gradients and topography into consideration.

A Universal Framework for Near-Real-Time Detection of Vegetation Anomalies from Landsat Data

Vegetation anomalies are frequently occurring and may greatly affect ecological functions. Many near-real-time (NRT) detection methods have been developed to detect these anomalies in a timely manner whenever a new satellite observation is available. However, the undisturbed vegetation conditions captured by these methods are only applicable to a particular pixel or vegetation type, resulting in a lack of universality. Also, most methods that use single characteristic parameter may ignore the multi-spectral expression of vegetation anomalies. In this study, we developed a universal framework to simultaneously detect various vegetation anomalies in NRT from Landsat observations. Firstly, Landsat surface reflectance data from the Benchmark Land Multisite Analysis and Intercomparison of Products (BELMANIP) sites were selected as a reference vegetation dataset to calculate the normalized difference vegetation index (NDVI) and the normalized burn ratio (NBR), which describe vegetation conditions from the perspectives of greenness and moisture, respectively. After the elimination of cloud-contaminated pixels, the high-quality NDVI and NBR data over the BELMANIP sites were further normalized in order to remove the differences in the growth of the varying vegetation. Based on the normalized NDVI and NBR, kernel density estimation (KDE) was used to create a universal measure of undisturbed vegetation, which described the uniform spectral frequency distribution of different undisturbed vegetation with a series of accumulated probabilities on a monthly basis. Whenever a new Landsat observation is collected, the vegetation anomalies are determined according to the universal measure in NRT. To demonstrate the potential of this framework, three study areas with different anomaly types (deforestation, fire event, and insect outbreak) in distinct ecozones (rainforest, coniferous forest, and deciduous broad-leaf forest) were used. The quantitative analyses showed generally high overall accuracies (>90% with the kappa >0.82). The user accuracy for the fire event and the producer accuracy for the earlier insect infestation were relatively lower. The accuracies may be affected by the complexity of the land surface, the quality of the Landsat image, and the accumulated probability threshold.

Mapping Forest Types along Ecological Gradient in Pakistan

Abstract Environmental variables influence the spatial distribution, pattern and structure of vegetation in complex mountainous landscape along varied geographical conditions. Present study explored the spatial distribution of four forest types across ecological gradient based on field data, climatic, topographic, and soil variables based on stepwise linear regression (SLR), decision trees (DT), random forests (RF), and Maxent modeling. Results showed that climatic variables. Results showed that climatic variables (particularly annual precipitation, precipitation of warmest and coldest quarter) have achieved the highest predictive capability for forest types mapping and outperformed other explanatory variables (topographic and edaphic). Among the rest of variables, elevation, sand contents and SOC have achieved good correlation and contained useful information in order explain forest type spatial distribution. Analysis of regression models revealed that RF has achieved the highest correlation (R2=0.923) and lowest RMSE 0.54, followed by the SLR model in which R2 value has been progressively increased from 0.41 (error 2.02) to 0.917 (0.77) with respect four different predictors models, each separate developed for topographic (n=5), soil (n-11), climatic (n=11) and combined of all datasets (n=27). CHAID DT showed that annual precipitation was the most important predictor for forest type classification with risk estimate of 0.412 (std error 0.31) and 0.478 (std error 0.52) for training and validation respectively. Maxent modeling showed impressive predictive performance of all forest types (STPF, MTF and DTF) along ecological gradient with average AUC values of 0.968, 0.918, and 0.940 respectively and climatic variables have highest gain compared to topographic and soil predictors. Present study suggests that mapping of forest types through machine learning algorithms may be improved by incorporating other explanatory variables such microclimate, soil types, nutrients, anthropogenic, demographic factors and spectral indices.

Long-term reconstructed vegetation index dataset in China from fused MODIS and Landsat data

The vegetation index is a key satellite-based variable used to monitor global vegetation distribution and growth. However, existing vegetation index datasets face limitations in achieving both high spatial and temporal resolution, restricting their application potential. This study revised a machine learning spatiotemporal fusion model (InENVI) to produce a high-resolution NDVI dataset with 8-day temporal and 30 m spatial resolution, covering China from 2001 to 2020. A total of 432,230 Landsat scenes were processed, enhancing data quality and accuracy. The dataset was validated using 255,000 samples across 6 geographical regions, showing strong performance in capturing spatiotemporal NDVI variations. Additionally, the dataset effectively addresses Scan Line Corrector-off stripes in Landsat 7 imagery. This dataset enables reliable annual NDVI estimates for China at a 30-m resolution and is available for reuse through an open data repository.

A Logistic Regression Model for the Prediction of Vegetation Recruitment in the Kinu River, Japan

ABSTRACTVegetation overgrowth in rivers worldwide is a considerable problem because it can potentially reduce the flood‐flowing capacity and cause biodiversity loss. In this study, we developed a model to predict vegetation recruitment during the initial stages of secondary succession, which leads to vegetation overgrowth. This study chose a logistic regression model to predict vegetation recruitment because of its simplicity and lower computational load than machine learning. The model was designed for the Kinu River in Japan which is associated with extensive vegetation overgrowth. Data for the model development were obtained from unmanned aerial vehicle (UAV) aerial surveys and public databases. To ensure the model's applicability beyond the training rivers, we trained the logistic regression model across different river flows and geomorphic characteristics, including normal and flood times and gravel and sand beds. The results indicated that the logistic regression model with three explanatory variables, namely distance from the river stream, relative height, and vegetation existence history, was optimal for all rivers, with F‐measures in the range of 0.79‐0.85. In addition, using UAV imagery allows for high‐spatial resolution in predicting vegetation recruitment. The best model prediction map of vegetation recruitment demonstrated that it could accurately predict the vegetation distribution along the main river channel for gravel and sand beds. The simplicity of the present model would be advantageous when applied to other rivers with similar topographic and biological characteristics within the same river segment without hydrodynamic calculations.

Distribution of Vegetation and Soil Seed Banks Across Habitat Types in Paddy Fields Under Different Farming Practices.

Paddy field ecosystems are crucial for crop production, biodiversity conservation, and ecosystem services. Although previous studies have examined paddy field biodiversity, few have addressed how the distribution and species richness of vegetation and soil seed banks are regulated. This study investigated the distribution of wetland plants and soil seed banks in paddy fields across diverse habitat types and identified factors influencing their patterns. Surveys revealed that conventional paddy field habitats contained only a few herbicide-tolerant species (e.g., Portulaca oleracea L., Cardamine flexuosa With., and Rorippa palustris (L.) Besser). In contrast, organic paddy field habitats exhibited higher species richness and abundance. Cluster analysis and nonmetric multidimensional scaling demonstrated that soil seed bank distribution differed markedly in paddy field habitats with different farming practices and was influenced by distinct soil factors. These findings highlight the importance of understanding vegetation and soil seed bank dynamics in paddy field ecosystems to support biodiversity conservation and sustainable agriculture.

Alteration of nitrogen sink and emission by vegetation distribution in a wetland with significant change in water level.

In wetlands, hydrological conditions drive plant community distribution, forming vegetation zones with plant species and material cycling. This mediates nitrogen migration and N2O emissions within wetlands. Five vegetation zones in a large wetland were studied during flooding and drought periods. Zones including mud flat, nymphoides, phalaris, carex, and reeds, distributed sequentially with increasing water level change rate. Carbon and nitrogen densities were higher during drought period. When sediments alternated between source and sink roles annually, N2O emissions varied significantly with zones and water levels. Emission flux decreased with higher C:N ratio in sediments, approximating a threshold at 0.23μgm-2 h-1 when C:N ratio exceeded 25. Denitrifying nirS and nirK genes and anammox hzsB gene varied significantly with water level, most prominently in mud flat and nymphoides zones. Plant distribution under hydrological conditions alters soil stoichiometric ratio, influencing N2O emissions and microbial communities across vegetation zones. Therefore, in freshwater wetlands, hydraulic regulation and reduction in prolonged flooding would be an effective strategy for mitigation of greenhouse gas emissions.

Modeling Climate-Driven Vegetation Changes Under Contrasting Temperate and Arid Conditions in the Mediterranean Basin.

This study investigates climate change impacts on spontaneous vegetation, focusing on the Mediterranean basin, a hotspot for climatic changes. Two case study areas, Monti Sibillini (central Italy, temperate) and Sidi Makhlouf (Southern Tunisia, arid), were selected for their contrasting climates and vegetation. Using WorldClim's CMCC-ESM2 climate model, future vegetation distribution was predicted for 2050 and 2080 under SSP 245 (optimistic) and 585 (pessimistic) scenarios. Two spectral indices, NDVI (temperate area) and SAVI (arid area), served as vegetation proxies, classified into three classes using K-means (NDVI: high = mainly associated with woodlands, medium = shrublands, continuous grasslands and crops, low = discontinuous grasslands, bare soil, and rocks; SAVI: high = mainly associated with woods, olive trees, and crops, medium = shrublands and sparse olive trees, low = bare soil and saline areas). Classes validated with ESA WorldCover 2020 data and sampled via 1390 presence-only points. A set of 33 environmental variables (topography, soil, and bioclimatic) was screened using Pearson correlation matrices, and predictive models were built using four algorithms: MaxEnt, Random Forest, XG Boost, and Neural Network. Results revealed increasing temperatures and declining precipitation in both regions, confirming Mediterranean climate trends. Vegetation changes varied by area: in the temperate area, woodlands and shrublands were stable, but discontinuous grasslands expanded. In the arid area, woodlands gained suitable habitat, while bare soil declined under the pessimistic SSP 585 scenario. Both areas showed an upward shift for shrublands and grasslands. The models indicated significant shifts in areal distribution and environmental conditions, affecting habitat suitability and ecosystem dynamics. MaxEnt emerged as the most reliable algorithm for small presence-only datasets, effectively predicting habitat suitability. The findings highlight significant vegetation redistribution and altered ecosystem dynamics due to climate change, underscoring the importance of these models in planning for future ecological challenges.

Modern pollen distribution across Mizoram, northeastern India, impacted by regional climate and anthropogenic activities

Modern pollen-rain analysis was carried out for the first time across central, western and eastern regions of Mizoram, encompassing different regional climatic zones. The vegetation experiences massive ecological pressure as a consequence of environmental factors, including the monsoon winds from the Bay of Bengal, high temperature, precipitation conditions, and varied topography. Additionally, intense human activities in the state of Mizoram impact the forest landscape in the remote northeastern part of India. These include agricultural activities such as shifting cultivation ( Jhum), commercial crop plantations such as banana, rubber, oil palm, tea, etc., pastoral activities and others. We collected moss cushion samples across transects in the central, western and eastern regions of Mizoram, to comprehend the impact of natural and anthropogenic factors on the modern vegetation and resultant pollen taxa on the surface deposits. The modern pollen data were analyzed statistically, including constrained redundancy analysis (RDA). The RDA analysis revealed the impact of bioclimatic variables on the pollen record, such as isothermality, precipitation in the warmest quarter and annual precipitation. The most significant variable was isothermality (bio_3) which indicated the dominance of anthropogenically impacted sites on the species distribution. Here, we identified that an interplay of climate- and anthropogenic-driven forcing were primarily controlling the modern vegetation and pollen-rain across Mizoram. The anthropogenic indicator pollen, such Poaceae (>50 µm), Caryophylaceae, Asteroideae, Cannabaceae, and Palmae at various sites reveal the impact of anthropogenic activities on the modern vegetation. The results reveal the impact of climatic zones, altitude gradients, anthropogenic activities and vegetation distribution on the pollen yield from the surface deposits.

Spatiotemporal Evolution and Characteristics of Colluvial Landslide Deposits in Chutou Valley Based on Vegetation Analysis after Earthquake

The normalized vegetation index (NDVI) is a kind of index that characterizes vegetation development in a region. The level of NDVI can accurately reflect the development and distribution of vegetation, so it has a good and accurate indicator function in the study of vegetation. The research area of this paper is located in the mountainous area of southwest China with dense vegetation. Strong earthquake induced a large number of coseismic landslides, which not only caused serious damage to the vegetation in the region, but also caused secondary disasters such as debris flow as loose material sources. Therefore, this paper uses NDVI index to distinguish the significant differences between vegetation and deposit bodies, and obtains the evolution trend of vegetation in the study area since the Wenchuan earthquake in 2008. The evolution trend of vegetation is analyzed and compared with the results of optical remote sensing interpretation, and finally obtains the evolution law and characteristics of slide deposit bodies. Based on the above research results, NDVI is introduced into the evolution analysis of geological hazards, which provides scientific support for the analysis of debris flow source evolution by NDVI trend.

The Wheel of Vegetation: A Spatial and Temporal Story of Vegetation Evolution in the Shennongjia Forest District

As one of the most well-preserved areas in the vertical band spectrum of vegetation in central China and even in the northern hemisphere at the same latitude, the vegetation in Shennongjia Forest District is vital to global ecological balance. In order to fully understand the vegetation change in the study area, remotely sensed data since 1990, combined with the Sen-MK test, Geo detector, and LandTrendr algorithm, were used to analyze the vegetation distribution characteristics and change trends. The results showed that: (1) the overall NDVI in the study area displayed an upward trend. (2) Vegetation disturbance occurred frequently before 2000 and decreased significantly after 2000. The most severely disturbed year was 1991 when the disturbed area amounted to 4.0851 km2, accounting for 16.76% of the total disturbed area. The analysis of the topographic environment reveals that most of the vegetation disturbances occur in areas with slopes of 15–25° and elevations of 1500–2000 m, which indicates that these areas have frequent human activity. (3) The explanatory power of different influences on vegetation changes varied, with altitude having the most significant effect and the superposition of two influences increasing the effect on vegetation change. Over the past 30 years, vegetation in the Shennongjia Forest District has shown a general trend of recovery, with natural forest protection initiatives playing a critical role in mitigating disturbance. This comprehensive study of vegetation changes in Shennongjia offers a valuable research paradigm for forest conservation and sustainable development in temperate forests at similar latitudes, providing significant insights into the protection and management of similar ecosystems.

Жирнокислотный состав и активность ацил-липидных десатураз в почках березы повислой в зимне-весенний период в Карелии и Якутии

The dynamics of the fatty acid composition and activity of acyl-lipid desaturases contained in the buds of silver birch Betula pendula Roth have been studied. The places of growth of this tree are located in contrasting natural and climatic conditions: at the same latitude – 62° N, but are separated from each other by more than 5 thousand km in the longitudinal direction – 34° E (the vicinity of Petrozavodsk) and 130° E (the vicinity of Yakutsk). It has been found that, regardless of the place of growth, in the spring-winter period, the total lipids of the buds of silver birch have been characterized by a high content of unsaturated fatty acids. At the same time, significant differences have been revealed in the composition of mono-, di- and trienoic fatty acids, the dynamics of which have largely depended on both the phase of winter-spring plant development and the degree of continentality of the climate. It has been shown that in Karelia, during the exogenous dormancy period (January–March), an increased content of dienoic fatty acids is observed in the lipids of the buds of silver birch, and by the beginning of their breaking (April–May) – trienoic fatty acids, whereas in Yakutia in the winter-spring period monoenoic and dienoic fatty acids steadily prevail. At the same time, a high activity of ω6- and ω3-desaturases (responsible for the synthesis of linoleic С18:2 and С18:3 fatty acids) has been detected in the lipids of the buds of silver birch growing in Karelia, and ω9-desaturase (catalyzing the synthesis of oleic С18:1 fatty acid) in Yakutia. It has been suggested that in permafrost conditions there is a relationship between the expression of genes controlling the formation of ω9-acyl-lipid desaturase and the resistance of the tissues of primordial organs during their intra-bud development to negative temperatures not only of the air, but also of the root-inhabited soil layer. The authors believe that the features identified in the composition of total lipids in the buds of silver birch in Yakutia compared to Karelia can be considered as one of the additional mechanisms that increase the adaptive potential of the species in permafrost conditions and allowing the representatives of the genus Betula L. to expand their area to the northern limit of the distribution of woody vegetation.

Vegetation Cover Changes and Environmental Drivers in Typical Desert Fringe Areas of Northern China

ABSTRACTChina is among the nations experiencing severe desertification, and several policies have been enacted to mitigate this issue. A thorough understanding of how natural processes and socioeconomic factors interact to shape the distribution of local vegetation is crucial to effectively address the widespread problem of desertification. This study investigates the dynamics of vegetation and environmental influences at the margins of deserts by utilizing MODIS data products. Through the application of the maximum synthesis method, trend analysis, and the geodetector method, this study offers a comprehensive exploration of vegetation changes and the underlying environmental factors shaping these changes. These findings indicate a discernible trend of increasing vegetation cover and notable spatiotemporal heterogeneity in desertification management in the study areas over the last 21 years. The Ta region (desert fringe of the Taklamakan Desert) presented the lowest vegetation growth rate (0.294%), followed by the Te region (desert fringe of the Tengger Desert) (0.505%), whereas the Mu region (desert fringe of the Mu Us sandy land) presented the highest growth rate (1.055%), indicating a gradient of recovery from west to east. The findings of this research indicate that the spatial pattern of vegetation distribution is determined by natural factors. However, socioeconomic factors have greater explanatory power than natural factors in explaining the local vegetation distribution. Additionally, the study underscores that the interaction between socioeconomic and natural factors holds greater significance than the influence of any single factor. These results offer valuable insights with practical implications for addressing desertification in China.

Influence of sandy beach management strategies on biological communities and wind-blown sand

ABSTRACT Sandy beaches create unique ecosystems with diverse services such as coastal protection, habitats for organisms, and recreational use. Appropriate sandy beach management is required to use these diverse services. However, management practices often degrade sandy beach ecosystems, and the ecosystem in each management practices needs to be clarified for planning a green infrastructure. This study clarified the influence of the management practices on topography, function of decreasing sand transport, vegetation, and arthropod, suggesting the management strategies needed to provide various services. Three study sites were set up by management practices at Kawashimo Beach in Hokkaido Prefecture, Japan: a natural dune site without targeted management intervention, a grading site where dunes are graded for recreational use, and an artificial dune site with created dunes to prevent sand transport. Topography, sand accumulation on the seaward side of the wind fence and spatial distribution of vegetation and arthropods were compared between the three sites. The grading site had no vegetation, whereas the artificial dune site had vegetation that would decrease sand transport and provide habitat for insects inhabiting vegetation, similar to the natural dune site. We propose that allowing space for dunes with vegetation is necessary to maintain the diverse services.

Vegetation-environment relationships in the Akdağ (Burdur) region

In this study, the distribution of woody vegetation and the characteristics of the growing environment in the Akdağ (Burdur) region were determined, and forest site classification was established. Fieldwork was conducted on 85 sample plots, and 65 woody plant species were identified. Species with a frequency value below 5% in the vegetation data matrix were excluded and 56 species were included in the analysis. Additionally, environmental variables for each sample plot were obtained. Vegetation groups were obtained through clustering and two-way indicator species analysis methods during the vegetation classification stage. Subsequently, the most suitable discriminating group was identified using the multiple response permutation procedure (MRPP). It was observed that the clustering analysis based on Jaccard-Ward’s method yielded the most suitable discriminating group. Furthermore, positive and negative indicator species were identified through indicator species analysis. Spearman correlation analysis between the best discriminating group and environmental variables revealed that elevation, temperature index, and solar radiation index variables exhibited a positive relationship, while parent material, Bio1 and Bio15 variables showed a negative relationship. Similar results were obtained in canonical correspondence analysis (CCA) applied to interpret the relationships obtained through correlation analysis using ordination methods. According to CCA, elevation was positively associated with plant distributions and vegetation groups, while Bio1 and Bio15 variables were negatively associated.

Effects of Different Spatial Distributions of Vegetation on the Hydraulic Characteristics of Overland Flow

ABSTRACTVegetation plays a crucial role in mitigating and controlling soil erosion caused by overland flow. However, variations in the hydraulic characteristics of overland flow induced by the spatial distribution of vegetation with different row and column spacings are often overlooked in existing literature, potentially leading to significant deviations in predicting these characteristics. In this study, 180 lab‐scale runoff tests were conducted to clarify the hydraulic characteristics of overland flow considering six α (the ratio of the lateral distance of vegetation to stem diameter) levels, six β (the ratio of the slope distance of vegetation to stem diameter) levels, and three slope angles (θ) under five flow discharges (Q) conditions. The results show that the observed flow regime of overland flow belongs to the transition flow regions, shifting from slow to rapid as α and/or β increase. The friction coefficient and the proportion of frictional resistance in the total flow resistance increase with increasing α and β. The local resistance dominates the total flow resistance of bare glass slopes. The local resistance coefficient ξ decreases with increasing α and β, however, it initially increases and then decreases with increasing θ. The impact of β on the local resistance is greater for gentle slopes, whereas the impact of α is more significant for steep slopes. ξ exhibits a negative correlation with Re and the ξ‐Re curves gradually level off as α or β increases, while they become steeper with increasing θ. A prediction model for the total flow resistance was established taking into account the combined effects of Re, α, β and θ, which provides better prediction performance than two other relevant models. The results obtained from this study provide valuable insights into the hydraulic characteristics of overland flow and offer clear guidance for vegetation management in controlling soil erosion on slopes with heterogeneous vegetation coverage.

The potential distribution of Cereus (Cactaceae) species in scenarios of climate crises

Climate change represents one of the most critical threats to global biodiversity, impacting species worldwide. The family Cactaceae, found predominantly in xeric habitats, is particularly vulnerable to the effects of climate change. To assess the potential impacts of climate change, ecological niche models (ENMs) have become essential tools in ecology, biogeography, and conservation. Here, we used ENMs algorithms, such as MaxEnt, Generalized Linear Models (GLM), and Artificial Neural Networks (ANN), to forecast the future distribution of Cereus species (Cactaceae), focusing on their response to climate change. Our analysis targeted five species, utilizing shared socioeconomic pathways (SSPs) to project their distribution from 2030 to 2090, comparing these with models for the last glacial maximum (∼21 ka). Our results suggest that C. stenogonus K.Schum. and C. bicolor Rizzini & Mattos are prone to experience a decline in distribution range, independent of the SSP scenario. Overall, the results also indicate that the effects of anthropogenic climate change differ from those of natural Pleistocene aridification, refuting our previous expectations. This work contributes to understanding how the distribution of drought-adapted vegetation could be influenced by climate change and highlights the need for informed conservation efforts to mitigate the potential adverse effects on cactus biodiversity.

Modelling the Effects of Vegetation Distribution and Density on Hydrological Connectivity and Water Age in a River Delta

ABSTRACTWater transport timescales (WTTs) quantify how long it takes for water to travel through or remain in a system and are often cast as indicators of ecosystem function and health. Such timescales are known to be affected by vegetation in various environments. We quantify the impact of floodplain vegetation on WTTs within the Wax Lake Delta (WLD), a river delta system in Louisiana, USA, using a high‐resolution Delft3D Flexible Mesh (DFM) model incorporating vegetation‐induced flow resistance. We show that increased vegetation density leads to extended WTTs within vegetated sections of WLD while fostering flow localization and accelerating transport within distributary channels. We find that the presence or absence of floodplain vegetation significantly influences the volumetric flow directed towards the floodplain, with spatial distribution exerting more control than vegetation density. Vegetation density and spatial arrangement have minimal impact on flow directed out of the deltaic floodplain, indicating that vegetation does not constrain flow across the bayward boundary. Furthermore, network‐scale water age distribution remains largely unaffected by vegetation density and spatial arrangement, except for slight modifications in the distribution's right tail. These findings contribute to a better understanding of how vegetation affects deltaic hydrology across scales, highlighting the importance of considering multi‐scale vegetation influences for coastal restoration and management strategies.

STUDY ON THE HEALTH STATUS OF TREES IN THE USAMV BUCHAREST CAMPUS

The study explores the importance of urban vegetation, focusing on the campus of the University of Agronomic Sciences and Veterinary Medicine in Bucharest (USAMV). The 37-hectare campus serves as a biodiversity core in the heart of the city, supporting a diverse range of plant species that contribute to a favorable microclimate and support of local biodiversity.The inventory and monitoring of campus trees was done using the TreePlotter Inventory software, an advanced tool that allows the collection and analysis of data on the condition and distribution of vegetation. The results showed that most trees were in excellent or good condition. The conclusions reveal the importance of using the results of the study to develop a landscape management plan for the next 10 years.