Vegetation Types Research Articles

Seasonal variations in psychophysiological stress recovery from street greenery: A virtual reality study on vegetation structures and configurations

The benefits of street greenery for well-being in urban development are widely recognized. However, few studies have explored seasonal variations in the psychophysiological stress recovery effects of different street greenery. To address this gap, we conducted field research in the Guanzhong region of China and created virtual streetscapes representing different seasons using Mars 2020 software. This study categorized vegetation into various layers based on their biological attributes: deciduous and evergreen trees for the arbor layer, evergreen and colourful foliage hedges for the shrub layer, observable and unobservable flowering herbs for the herb layer. They were then classified into Tree, Tree-Shrub, Tree-Herb, and Tree-Shrub-Herb structures, all of which had different seasons and vegetation configurations. 1,136 participants were recorded for psychophysiological changes in different street greenery virtual reality scenarios. The results were as follows: 1. Streets with greenery exhibited greater stress recovery effects than those without greenery. Seasonally, the physiological stress recovery effects were greater in autumn and winter, and there was an interaction between season, structure, configuration factors. 2. Overall, the Tree-Shrub structure was most effective for stress recovery, the Tree-Shrub-Herb structure was least effective. However, the Tree-Shrub-Herb structure proved to be the best in winter. 3. Rich color and stable green coverage were most effective for improving psychological and physiological stress recovery, respectively. However, the optimal vegetation types and configurations for stress recovery vary with specific viewing periods within each season. These findings offer a theoretical foundation and practical reference for designing health-oriented street greenery and seasonal landscapes in future.

Riparian vegetation influences aquatic greenhouse gas production in an agricultural landscape

Although riparian vegetation is widely acknowledged for its positive impact on soil and water quality and its role in regulating terrestrial greenhouse gas emissions in agricultural landscapes, there remains a gap in understanding how different types of riparian vegetation affect aquatic greenhouse gas production. Thus, the objective of this study was to investigate whether the type of vegetation within riparian zones influenced aquatic environmental factors, subsequently impacting aquatic greenhouse gas emissions. To address this, we measured greenhouse gases in the aquatic environment bordered by riparian zones with herbaceous vegetation (GRS) compared to undisturbed natural riparian forests dominated by deciduous (UNF-D) or coniferous (UNF-C) vegetation or a rehabilitated riparian forest (RH). Our findings indicate that aquatic CO2 concentrations were not influenced (p < 0.05) by vegetation type ranging from 9 g L−1 to 11 g L−1. In contrast, aquatic CH4 concentrations were significantly lower (p < 0.05) in treed riparian zones, ranging from 14 μg L−1 to 24 μg L−1, compared to a riparian zone with herbaceous vegetation (34 μg L−1). However, we observed significantly higher (p < 0.05) aquatic N2O concentrations in treed riparian zones (9.5 μg L−1 to 10.3 μg L−1), particularly those dominated by coniferous vegetation (23.0 μg L−1), compared to the riparian zone with herbaceous vegetation (7.7 μg L−1). The total CO2-C equivalent (i.e., CO2 + CH4 + N2O) was highest in the riparian zone with coniferous trees (UNF-C: 10,717 mg CO2-Ceq L−1), followed by the GRS (9494 mg CO2-Ceq L−1), RH (9423 mg CO2-Ceq L−1) and UNF-D (9,183 mg CO2-Ceq L−1) riparian zone. Moreover, riparian vegetation was influenced by various environmental factors that likely controlled physicochemical and biological processes related to the production of greenhouse gases within the aquatic environment.

Real-time lettuce-weed localization and weed severity classification based on lightweight YOLO convolutional neural networks for intelligent intra-row weed control

Competition for nutrients between intra-row weeds and cultivated vegetables is a major contributor to reduced crop yields. Compared with manual weeding, intelligent robots can improve the efficiency of weeding operations. Developing real-time and reliable robotic systems for weed control in vegetable fields is a significant challenge due to the complexity of real-time identification, localization, and classification of vegetables as well as various weed species. The main purpose of this study was to propose a high-performance, lightweight deep learning model and an intra-row weed severity classification algorithm for real-time lettuce identification and weed severity classification. In this study, a scaling factor (τ = 0.5) was chosen to lightweight the YOLOv7 model. A new Multimodule-YOLOv7-L lightweight model was then developed by combining ECA and CA attention mechanisms, ELAN-B3 and DownC modules. The overall performance of the Multimodule-YOLOv7- L was better than that of other deep learning models, including YOLOv7, YOLOv7-Tiny, YOLOv8m, YOLOv5n-Cabbage, SE: YOLOv5x, YOLOv5s_Ghb, MST-YOLO_CBAM, Citrus-YOLOv7, Pineapple-YOLOv7, MS-YOLOv7 and CBAM-YOLOv7. The precision, recall, mAP@0.5, F1-score, model weight and FPS of the Multimodule-YOLOv7- L model were 97.5 %, 95.7 %, 97.1 %, 96.6 %, 18.4 MB and 37.3 FPS (Image resolution about 3000 × 3000), respectively. An intra-row weed severity classification algorithm based on the Multimodule-YOLOv7-L model was proposed for use in a new mechanical-laser collaborative intra-row weeding robot. The developed algorithm achieved a classification accuracy of 100 % in eight lettuce weed scenarios, with the processing time of a single image ranging from 4-13 ms. The results of this study provided valuable reference for the development of intelligent robots for intra-row weed control. The algorithm proposed in this article can be obtained at https://github.com/H777R/The-intra-row-weed-severity-classification-algorithm.git.

Altitudinal Effects on Soil Microbial Diversity and Composition in Moso Bamboo Forests of Wuyi Mountain.

Moso bamboo (Phyllostachys edulis) forest is a key ecosystem and its soil microbial community plays a crucial role in maintaining the ecosystem's functions, but it is very vulnerable to climate change. An altitude gradient can positively simulate environmental conditions caused by climate change, and hence, it provides an efficient means of investigating the response of soil microorganisms to such climatic changes. However, while previous research has largely concentrated on plant-soil-microorganism interactions across broad altitudinal ranges encompassing multiple vegetation types, studies examining these interactions within a single ecosystem across small altitudinal gradients remain scarce. This study took Moso bamboo forests at different altitudes in Wuyi Mountain, China, as the research object and used high-throughput sequencing technology to analyze the soil microbial community structure, aiming to elucidate the changes in soil microbial communities along the altitude gradient under the same vegetation type and its main environmental driving factors. This study found that the structure of bacterial community was notably different in Moso bamboo forests' soil at varying altitudes, unlike the fungal community structure, which showed relatively less variance. Bacteria from Alphaproteobacteria phylum were the most dominant (14.71-22.91%), while Agaricomycetes was the most dominating fungus across all altitudinal gradients (18.29-30.80%). Fungal diversity was higher at 530 m and 850 m, while bacterial diversity was mainly concentrated at 850 m and 1100 m. Redundancy analysis showed that soil texture (sand and clay content) and available potassium content were the main environmental factors affecting fungal community structure, while clay content, pH, and available potassium content were the main drivers of bacterial community structure. This study demonstrates that the altitude gradient significantly affects the soil microbial community structure of Moso bamboo forest, and there are differences in the responses of different microbial groups to the altitude gradient. Soil properties are important environmental factors that shape microbial communities. The results of this study contribute to a deeper understanding of the impact of altitude gradient on the soil microbial community structure of Moso bamboo forests, thus providing support for sustainable management of Moso bamboo forests under climate change scenarios.

Soil Organic Matter and Bulk Density: Driving Factors in the Vegetation-Mediated Restoration of Coastal Saline Lands in North China

Coastal saline soils are an important soil resource that, when restored, can enhance arable land and preserve the natural ecology. With the aim of improving the use of coastal saline soils, we conducted a spot survey at Bohai coastal saline land to investigate the differences in soil properties between different vegetation types. The soil physical and chemical properties of various vegetation types, including Aeluropus sinensis, Imperata cylindrica, Tamarix chinensis, Lycium chinense, Hibiscus moscheutos, Helianthus annuus, Gossypium hirsutum, and Zea mays, were examined at two depth layers: 0–20 cm and 20–40 cm, and in two seasons, spring and autumn. The soil properties were compared with bare land as a control. The results indicated that the electrical conductivity, total soil salt content, sodium adsorption ratio, and bulk density of soils with vegetation cover were lower than those with bare land. On the other hand, soil pH, organic matter content, mean weight diameter, and saturated hydraulic conductivity were higher. The redundancy analysis results revealed a substantial positive correlation between soil pH, saturated hydraulic conductivity, water content, mean weight diameter, and organic matter content, as well as a significant positive correlation between soil electrical conductivity, total soil salt content, sodium adsorption ratio, and bulk density. Soil pH, saturated hydraulic conductivity, water content, mean weight diameter, organic matter content, and soil electrical conductivity, total soil salt content, sodium adsorption ratio, and bulk density were negatively correlated. The results of the structural equation model and variance decomposition showed that soil organic matter and bulk density were the key factors affecting the degree of soil salinization, and compared with their independent effects, their combined effect on soil salinization was greater. This study’s conclusions can provide a point of reference for further research on the mechanisms of soil improvement and desalinization in coastal saline land.

Dynamic interplay of soil parameters and CO2 sequestration in Solanku-runi Freshwater Pond Wetland, Tamil Nadu, India

This study investigates the dynamic interplay between soil temperature, pH levels, and CO2 sequestration across ten sample sites over a four-month periods from January to April 2018 in Solankuruni freshwater pond wetland in Madurai District, Tamil Nadu, India. The soil temperature fluctuations, ranging from 31°C to 44°C, reveal distinct site-specific patterns, with sample site six consistently exhibiting the highest temperatures. Soil temperature exhibited notable variations, influenced by factors such as soil composition, and microclimate variations. Regarding pH level, the range between 6.23 to 8.1 display variability influenced by factors like soil composition and anthropogenic influences, study site five consistently leading in pH levels. Soil CO2 sequestration varying from 1517.857 g/m² to 3357.143 g/m², highlight the influence of soil microbial activity, vegetation cover, and soil moisture content, ninth site consistently showing the highest sequestration rates. The findings reveal substantial variations in factors such as soil composition, vegetation type, microclimate, and anthropogenic activities, prominence the intricate nature of soil dynamics. These results stress the importance of understanding the interconnections among soil properties to create effective climate change mitigation strategies. Moreover, the research provides valuable understanding of the intricate connections between soil properties, emphasizing the necessity for region-specific studies to inform comprehensive environmental policies. Understanding these dynamic soil processes is crucial for advancing sustainable land management practices and boosting soil carbon sequestration in wetland ecosystems.

Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies

Abstract Wildfires pose a significant ecological, environmental, and socioeconomic challenge in southeastern Europe. The preservation of wildlands is not only essential but also a foremost priority for Montenegro, a country recognized as the world’s first ecological state. Consequently, the development of optimal methodologies and models is of paramount importance to enhance fire protection measures. With this objective in mind, this study strives to create a wildfire susceptibility model on a national scale for Montenegro. The study employed seven natural and anthropogenic causative criteria: vegetation type; aspect; slope; elevation; climate classification; distance from road; and population. The modeling process integrates both natural and anthropogenic causal criteria, employing the Fuzzy Analytic Hierarchy Process (F-AHP) and Frequency Ratio (FR) within geoinformatics environment. The outcomes of the F-AHP model reveal that 72.84% of the total area is categorized as having high to very high susceptibility. Conversely, based on the FR model, only 29.07% of the area falls within these susceptibility levels. In terms of validation, the area under curvature values indicates good performance of the F-AHP model. In contrast, the FR model demonstrates poor performance. These novel findings, pertaining to Montenegro at a national scale, offer valuable insights for preemptive wildfire safeguarding efforts. Moreover, the methodologies employed, with necessary modifications, hold potential for application in geographically diverse regions.

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.

Carbon and Nitrogen Stocks in Soil under Native Pastures in the Pantanal Wetland Biome, Brazil

The Pantanal has a high diversity of native pastures that provide food for many wild and domestic animals. Pantanal cattle raising is practiced in an extensive grazing-based system that varies according to the flood levels in the area. This study aimed to evaluate the fractions of soil organic matter in areas of native pastures under different uses and to quantify C and N stocks in sandy soils of the Pantanal. Soil samples from three native pastures differentiated by the predominance of Hymenachne amplexicaulis, Axonopus purpusii, and Mesosetum chaseae under different land use systems (continuous grazing and no grazing for five years) were collected and used to quantify the contents of carbon, nitrogen, and humic fractions. The dynamics of SOM are modified in grazed areas of the Pantanal, with influence on C and N, including their stocks. Native pastures of Axonopus purpusii and Hymenachne amplexicaulis showed an increase in organic matter after five years without grazing, while Mesosetum chaseae showed lower soil density and nitrogen levels. The highest C stock was observed in ungrazed areas of H. amplexicaulis (127.41 Mg ha−1 in the 0–40 cm layer). The dynamics of nitrogen in Pantanal pastures are influenced by the type of vegetation and land management, with higher nitrogen content in the surface layer (0–10 cm) and an increasing C/N ratio with soil depth, indicating lower nitrogen availability.

Quantifying the biodiversity and ecosystem service outcomes of karst ecological restoration: A meta-analysis of South China Karst

Decades of restoration efforts have greened South China Karst, the largest and most concentrated karst area in the world. However, it is unknown whether and how the observed increase in vegetation coverage could enhance biodiversity and the provision of ecosystem services (ES). In this study, we performed a meta-analysis of 108 studies with 6505 observations to quantify the influence of karst ecological restoration on biodiversity and ES provision and to uncover key factors that may influence restoration outcomes. We found that restored karst lands on average provided 29 % and 18 % higher biodiversity and ES than degraded karst lands, respectively, but 14 % and 29 % lower than intact reference karst lands. However, the outcomes of karst ecological restoration were mediated by restoration strategy, vegetation type, and restoration age. Natural restoration actions often had more positive effects on biodiversity and ES than reconstructive restoration actions. Karst restoration outcomes also increased with the progress of vegetation succession and increasing restoration age. Furthermore, ES outcomes were positively correlated with biodiversity outcomes, demonstrating the importance of incorporating the biodiversity-ecosystem functioning relationship into karst restoration actions. Overall, our study suggests that karst ecological restoration has substantial potential to improve biodiversity and the provision of ES, but restoration outcomes are context-dependent. These findings may inform evidence-based decision making for biodiversity conservation and sustainable development in karst regions, especially with the UN Decade on Ecosystem Restoration 2021–2030 underway.

Recreational Assessment of Green Belt Forests of the Ulaanbaatar City

The classification of forest vegetation of the Tuul river basin within the green belt of the Ulaanbaatar city. Two associations (Linnaeo borealis–Pinetum sibiricae and Vaccinio vitis-idaeae–Laricetum sibiricae) were classified Vaccinio–Piceetea. Diagnostic signs and characteristics of the ecological and geographical peculiarities of the units were presented, anthropogenic transformation was assessed. To determine the models of succession processes, the series of permanent sample plots were established in the communities of the Linnaeo borealis–Pinetum sibiricae association, located at different distances from the city, in taxation units with unequal intensity of recreational load. Recreational forest assessment was calculated according to the approach by V.S. Romanov and L.N. Rozhkov (1974). In the Linnaeo borealis–Pinetum sibiricae association, large areas were occupied by moderately transformed communities. The Vaccinio vitis-idaeae–Laricetum sibiricae association is characterized by being weakly transformed forests. Different stages of anthropogenic pressure were formed serial communities, unequal in structure and species diversity. Serial plant communities of allogeneic succession were developed according to 2 models of geitogenesis — tolerance (Linnaeo borealis–Pinetum sibiricae ↔ Rhytidium rugosum; Linnaeo borealis-Pinetum sibiricae ↔ Chamaenerion angustifolium) and inhibition (Linnaeo borealis–Pinetum sibiricae ↔ Betula rotundifolia). A comprehensive assessment of the recreational potential was based on three indicators: natural conditions, air hygiene and landscaping. All activities aimed at increasing the recreational potential should contribute to the improvement of these indicators. A prerequisite for increasing the recreational forest assessment should be functional zoning of the territory with the allocation of infrastructure, walking and sanitary protective zones. Forest management is of great importance in increasing the recreational potential of forests. The forest management project for the organization of recreational forests should be aimed at the formation of sustainable and aesthetically attractive forest plantations.

Systems Approach to Investigate the Role of Fruit and Vegetable Types on Vascular Function in Pre-Hypertensive Participants: Protocol and Baseline Characteristics of a Randomised Crossover Dietary Intervention.

The evidence on the impact of fruits and vegetable types on cardiovascular risk factors remains limited. Specifically, the utilisation of biomarkers to objectively measure dietary compliance and metabolic responses is emerging. This protocol and baseline characteristics of a pilot randomised controlled, crossover, dietary intervention study aimed to examine the effects of citrus fruits, cruciferous vegetables, or common fruits and vegetables on cardiovascular risk factors. A total of 39 volunteers with untreated prehypertension was recruited and consumed a standardised, provided diet with eight daily portions of citrus fruits and cruciferous vegetables, common fruits and vegetables, or a low fruit and vegetable diet (two portions/d, control diet) in a random order for 2 weeks each, separated by a wash-out week. A targeted cohort-based recruitment strategy was utilised and resulted in 74% of participants recruited by re-contacting preselected individuals from two cohort studies with a 15% average enrolment rate. Participants had an average age of 54.4 years (±6.1 years), BMI of 27.9 kg/m2, and BP of 135/81 mmHg and were mainly male (67%). The primary outcome was office blood pressure; secondary outcomes included arterial stiffness, lipid profiles, inflammation, cognitive function, and subjective mood. Biofluids, i.e., 24 h urine, stool, and blood samples, were collected for biomarker measurements with multiple metabolomic platforms and the gut microbial composition, together with traditional dietary biomarkers.

Computer Vision based Greenhouse Fruits and Vegetables Identification – A Review

In recent years, computer vision has emerged as a powerful tool for automating various processes in agriculture, particularly in greenhouse environments. The identification and classification of vegetables and fruits within greenhouses play a crucial role in monitoring crop growth, assessing health status, and optimizing resource utilization. This review study provides a comprehensive overview of recent advancements in computer vision techniques for the identification of vegetables and fruits in greenhouse settings. This study discusses about various methodologies and challenges in this domain, aiming to provide insights for researchers and practitioners interested in leveraging computer vision for greenhouse agriculture.

Performance and mechanisms of constructed wetland integrated microbial fuel cell for remediation and detoxification of leather tannery wastewater

Several previous studies concerned of microbial fuel cells integrated into constructed wetlands, nevertheless, their application as a convenient treatment for wastewater is still developing. In this experimental investigation, five CW-MFC systems were similarly designed, setup, and operated in a batch mode for two subsequent cycles. Each cycle lasted for 10 days to evaluate the performance of CW-MFC system for the remediation of real leather tannery wastewater (LTW). Four CW-MFCs were planted, each with different type of vegetation including Conocarpus, Arundo donax, Canna lily, and Cyperus papyrus in CW1-MFC, CW2-MFC, CW3-MFC, and CW4-MFC, respectively. The fifth CW5-MFC was maintained unplanted and considered as the control system. The performance of each CW-MFCs systems was evaluated mainly based on the removal of organic content (COD), total dissolved solid (TDS) elimination, and power generation. The results demonstrated that the four types of plants maintained healthy and no sign of wilting was observed during the 20 days of monitoring. For the first cycle of batch operation, maximum removal efficiencies of COD were 99.8%, 99.5%, 99.7%, 99.6% and 99.5% with power outputs of 10,502.8, 10,254.6, 9956.4, 10,029.6, and 9888.0 mW/m3, while, maximum TDS elimination were 46.7%, 39.7%, 60.8%, 55.5%, and 13.8% observed in CW1-MFC, CW2-MFC, CW3-MFC, CW4-MFC, and CW5-MFC, respectively. Very comparable results were observed in the second operation cycle. Results of phototoxicity test indicated that the germination of Hordeum vulgare and Triticum aestivum were 100% watered with treated effluent compared to 90% accomplished with tap water as the control solution for both types of seeds.

Predicting the Dry Season Habitat Occupancy of African Savannah Elephant Using Vegetation Indices and Modelling Landscape Variability in a Mesic Protected Area

ABSTRACTAfrican savannah elephants (Loxodonta africana) are key ecosystem engineers that migrate over large spatiotemporal scales foraging as they require copious amounts of food and water across habitable landscapes. Therefore a need to understand movement patterns arises in relation to vegetation type and landscape variability, moreso in forage depauparate arid areas such as Gonarezhou National Park (GNP) in Zimbabwe. The objectives of this study were to: (i) assess the performance of vegetation indices in modelling the distribution of African savannah elephants, and (ii) model future landscape variability in Gonarezhou National Park (GNP) in Zimbabwe. Maximum entropy (MaxEnt) algorithm was used to explore the relationship between vegetation indices and distribution of African savannah elephants in the GNP. The Soil Adjusted Vegetation Index (SAVI) performs better relative to other indices in modelling the distribution of African savannah elephants across all habitat types in the GNP. Cellular automata‐Artificial Neural Network (CA‐ANN) showed a significant future decrease (Kruskal Anova; p &lt; 0.05) in landscape suitable to sustain large populations of African savannah elephants in the GNP by the year 2083. Future remote sensing reveals directional insights into the future consequences of current landscape management for African savannah elephant conservation which is a crucial in the sustainability of climate threatened arid protected areas such as the GNP.

Satellite Imagery Analysis and Plane Detection

Monitoring natural vegetation and detecting planes in satellite imagery are critical tasks in remote sensing, with applications ranging from environmental conservation to security and surveillance. This project aims to develop an effective method for analyzing natural vegetation and detecting planes by utilizing satellite imagery and applying advanced image processing and machine learning techniques. By leveraging various features such as vegetation indices, texture, shape, and spectral signatures, we aim to build predictive models that accurately classify vegetation types and identify planes. This paper discusses the methodology, including data collection, preprocessing, feature extraction, model training, and evaluation, ultimately highlighting the models' performance and potential applications in real-world scenarios. Keywords— Vegetation Analysis,Deep learning ,Plane Detection.

Peatland vegetation of the Syra Pogonia massif of the Rivneskyi Nature Reserve (Polissia, Ukraine): ecological characteristics

Background. Peat bog complexes are self-sufficient ecosystems in which unique microclimatic conditions leading to a significant diversity of valuable plant communities are formed. Such communities are sensitive to the effects of climate change or any anthropogenic intervention. The lack of reliable information on their distribution on the territory of the Syra Pogonia peat-bog massif of the Rivnenskyi Nature Reserve necessitates a detailed study of the bog vegetation. Therefore, the purpose of the work was to classify the community of peatland vegetation of the Syra Pogonia massif and to determine the features of their syntaxonomic and ecological differentiation for further development of environmental management strategies. Materials and Methods. The study of the peculiarities of peatland vegetation was conducted on transects that represent the variety of local conditions of the complex system. A total of 141 relevés were analysed using the Braun–Blanquet method. The material was analysed using TURBOVEG 2.79 and JUICE 7.0.83 software. Vegetation units were separated using the method of two-factor indicator species analysis (TWINSPAN). Diagnostic species of syntaxa were determined by the fidelity coefficient phi, the fidelity threshold for which was &gt; 25 %. The statistical significance of the phi coefficient was determined with the Fisher’s test at P &lt; 0.001. The DCA-ordination method was used to identify an ecological differentiation of units. Results. Peatland vegetation of the Syra Pogonia massif of the Rivnenskyi Nature Reserve was analysed. We identified 7 associations belonging to 6 alliances, 6 orders and 4 classes and compiled a vegetation syntaxonomic scheme. The leading factor of ecological differentiation of community is humidity of the area. The greatest diversity is inherent in mesotrophic areas, rare species are found and grouped according to diffe­rent ecological conditions. The species composition of 7 associations includes 79 plant species, of which 65 are vascular and 14 are bryophytes. Using methods of phytosociological analysis, we established that the distribution of community in multi-dimensional space of ecological factors occurs under the conditions of their complex action. At the same time, changes in the humidity regime are of crucial importance for the selected syntaxa (Andromedo polifoliae-Sphagnetum magellanicі). The results of the phytoindicative analysis proved that the associations identified on the territory of the Syra Pogonia peat-bog complex are acidophilic in terms of acidity, and oligotrophic in terms of the requirements for the content of nutriens. Conclusions. As orders, and result of the analysis of relevés, 7 associations, which belong to 6 alliances, 6 orders and 4 classes were selected. Based on the results of the dataset analysis, a vegetation classification scheme was compiled. As a result of the cluster analysis of 7 associations, a significant difference between communities of watered and wet habitats was revealed. We established that the differentiation of the vegetation on the territory of the peat massif of Syra Pogonia mainly depends on the change in the moisture regime.

Hydrology, vegetation, and soil properties as key drivers of soil organic carbon in coastal wetlands: A high-resolution study

Coastal wetlands are important blue carbon ecosystems that play a significant role in the global carbon cycle. However, there is insufficient understanding of the variations in soil organic carbon (SOC) stocks and the mechanisms driving these ecosystems. Here we analyze a comprehensive multi-source dataset of SOC in topsoil (0–20 cm) and subsoil (20–100 cm) across 31 coastal wetlands in China to identify the factors influencing their distribution. Structural equation models (SEMs) reveal that hydrology has the greatest overall effect on SOC in both soil layers, followed by vegetation, soil properties, and climate. Notably, the mechanisms driving SOC density differ between the two layers. In topsoil, vegetation type and productivity directly impact carbon density as primary sources of carbon input, while hydrology, primarily through seawater salinity, exerts the largest indirect influence. Conversely, in subsoil, hydrology has the strongest direct effect on SOC, with seawater salinity also influencing SOC indirectly through soil and vegetation mediation. Soil properties, particularly pH, negatively affect carbon accumulation, while climate influences SOC indirectly via its effects on vegetation and soil, with a diminishing impact at greater depths. Using Random Forest, we generate high-resolution maps (90 m × 90 m) of topsoil and subsoil carbon density (R2 of 0.53 and 0.62, respectively), providing the most detailed spatial distribution of SOC in Chinese coastal wetlands to date. Based on these maps, we estimate that SOC storage to a depth of 1 m in Chinese coastal wetlands totals 74.58 ± 3.85 Tg C, with subsoil carbon storage being 2.5 times greater than that in topsoil. These findings provide important insights into mechanism on driving spatial pattern of blue carbon and effective ways to assess carbon status on a national scale, thus contributing to the advancement of global blue carbon monitoring and management.

Treed field borders net-export over 82,000 more hoverflies per km every week into canola crops than herbaceous field borders, regardless of mass-flowering crop bloom

Hoverflies (Diptera: Syrphidae) provide dual ecosystem services, as the adults act as pollinators and the larvae can be predators of crop pests. Because bloom time is limited in mass-flowering crops, resources within crops for hoverfly adults can also be limited and change temporally. Therefore, hoverflies need to move between crops and their borders. It may be that some field border vegetation types support the provision of hoverflies to crops better than other vegetation types. We sought to determine how field border type (herbaceous vs. treed), canola bloom, and border vegetation structure and composition (border width, canopy cover, grass height, grass cover, plant cover, flower availability, and density of trees, shrubs, snags, stumps, and downed woody debris) affect hoverfly movement into and out of crop fields from field borders. We placed bi-directional Malaise traps in herbaceous and treed field borders at 10 fields seeded with canola, and sampled continuously from May 17 to August 20, 2021 in central Alberta, Canada. We found that field border type affected hoverfly movement such that, across the whole summer, net-export of hoverflies into crops was over 33-times higher from treed field borders (an estimated 84,699 hoverflies per km per week) than from herbaceous field borders (an estimated 2515 hoverflies per km per week). We did not find any single component of the vegetation within treed field borders that explained the difference in movement. We found more hoverfly activity in herbaceous field borders than in treed field borders during and after canola bloom, but that overall activity was equal between field border types prior to canola bloom. Treed borders had greater Hill-Shannon and Hill-Simpson diversity and evenness than herbaceous borders. Throughout the growing season, the community became dominated by Toxomerus marginatus, which drove all temporal trends. We conclude that treed field borders act as net exporters of hoverflies to canola fields and are therefore important features for optimizing the magnitude of the ecosystem services provided by hoverflies in agricultural systems.

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.