Green Patches Research Articles

Unlocking Urban Breathability: Investigating the Synergistic Mitigation of PM2.5 and CO2 by Community Park Green Space in the Built Environment Using Simulation

Reducing carbon emissions and controlling air pollution is a dual challenge for China in addressing climate change. Analyzing the synergistic relationship between PM2.5 and CO2 in urban green spaces has become an important part of promoting pollution control. The study investigated the influence and synergistic relationship between the spatial pattern of community parks on PM2.5 and CO2 in Xi’an City, Shaanxi Province, through practical measurement and ENVI-met/Open Studio simulation calculations. The results showed that: (1) Within the sphere of influence, community parks exhibit a positive synergy varying with distance, peaking at 400 m and declining as 300 m > 500 m > 200 m > 100 m. (2) The green space rate, total edge (TE), and mean patch shape index (SHAPE_MN) positively influence the synergistic mitigation of PM2.5 and CO2, with a defined maximum impact boundary. The strongest synergistic reduction of PM2.5 and CO2 occurs at a green space rate of 85%, TE1200, and SHAPE1.2, with optimal influence boundaries of 300 m, 200 m, and 100 m, respectively. This conclusion demonstrates the key role of green space in community parks in the synergistic abatement and provides a scientific basis and practical guidance for the planning and design of urban green space under the goal of “dual-carbon”.

THE ROLE AND IMPACT OF VEGETATION ON THE URBAN FABRIC. CASE OF GUELMA CITY

In cities, land use changes caused by various human activities can affect how natural ecosystems function. In this context, it is increasingly important for cities to consider the role of vegetation in preserving a sustainable environment. A diachronic analysis of landscape changes was applied to assess the presence and distribution of vegetation to determine whether it was evolving or regressing. This phenomenon was studied within the current administrative boundaries of the city of Guelma (Algeria). For this purpose, as an approach based on landscape metrics, several Landsat TM and ETM+ remote sensing satellite images were used throughout the period 1987-2019. A set of landscape indices, including NP, AREA_MN, LPI, PLAND, AI and LSI, were calculated to map land cover, the mechanism of land cover and vegetation change, and their impact on the urban ecosystem. The geo-statistical procedure was carried out using a geographic information system Qgis combined by statistical software using Fragstats to calculate various landscape metrics at class level for the analysis of fragmentation . The results of the landscape metric analysis show that the decrease in average area and the increase in the number of green patches are important indicators of land degradation, meaning that the mechanism of landscape degradation and transformation is progressive. This underlines the need to give particular attention to land use and land cover in the region to ensure the sustainable allocation of natural resources.

Tree community structure of selected green patches of Guwahati, Assam, India with special reference to spatio-temporal changes in vegetation

Tree community structure of selected green patches of Guwahati, Assam, India with special reference to spatio-temporal changes in vegetation

Study on temperature regulation function of green spaces at community scale in high-density urban areas and planning design strategies

In recent years, the frequency of extreme high-temperature events has gradually increased. To better understand the impact of urban green space coverage's spatial pattern on land surface temperature (LST), this study selected three sample areas along the urban-rural gradient in Shanghai. Using LST inversion and resampling methods, LST data for different grid sizes were obtained for spring, summer, and winter in the 2010s, 2015s, and 2020 s. A boosting regression tree model was employed to determine the key indicators affecting LST and effective cooling thresholds. The impact of green quality, structure, and pattern at the community scale on LST was discussed, providing feasible suggestions for green space planning and design in different urban spaces. The study found significant differences in the spatial patterns of green areas and LST among the three sample regions. The quantity and integrity of green spaces in the Huangpu, Minhang, and Songjiang sample areas have progressively improved, with overall green coverages of 19 %, 38 %, and 43 %, respectively. As grid size increases, the relative influence of fractional vegetation coverage (FVC) on LST generally decreases, whereas the relative influence of green structure and patterns gradually increases. Taking the summer of the 2020 s as an example, the influence of FVC on the three study sample areas was 80.79, 83.36, and 87.18 at a 30 m grid size, which decreased to 48.87, 40.59, and 47.64 at a 120 m grid size. The green structure's impact rose from 13.09, 15.22, and 10.6–30.01, 51.82, and 38.5; the influence of green patterns increased from 6.12, 1.42, and 2.21–21.13, 7.6, and 13.86. Key indicators affecting LST include FVC (Fractional Vegetation Coverage), AREA (Green Patch Area), PD (Patch Density), COHESION (Patch Cohesion Index), and ED (Edge Density). High temperatures in summer are one of the ecological issues that need special attention in Shanghai's green space design. Setting the green space proportion to 35 % while avoiding fragmentation and low vegetation coverage can achieve effective cooling. This study's main advancement lies in utilizing machine learning algorithms to identify the principal green spatial pattern impact factors and key thresholds influencing LST at the community scale in Shanghai. The related results and proposed strategies provide a research framework and strong basis for special regulations in urban green space system planning concerning urban thermal environments. They offer references for urban renewal and new town planning to address climate change and urban high-temperature issues, such as clear requirements for optimal green space area at the community scale, community park spatial layout and quantity regulations, and urban park planning design suggestions. This study also highlights obstacles in the practical application of planning and design strategies, which can help avoid difficulties in implementing planning policies. It attempts to set goals for community-scale vegetation planning in China from a policy perspective and provides relevant recommendations.

Identifying pollinator‐friendly sites within urban green spaces for sustainable urban agriculture

AbstractIntroductionThe relentless urban expansion has led to the depletion of forests and agricultural lands. Yet, pinpointing viable zones for urban agriculture, particularly concerning pollinator presence, remains a formidable task for city planners.Materials and MethodsOur study introduces a novel methodology to identify optimal locations for cultivating pollinator‐dependent crops. Leveraging species distribution models (SDMs), we generated habitat suitability maps for 243 pollinating insects, including bees (41 species), butterflies (140 species), and hoverflies (62 species) across South Korea. Subsequently, employing Graphab 2.8 software, we categorised 10 major cities in South Korea based on the connectivity of green space patches. Clusters exhibiting greater green space coverage, size, and connectivity displayed higher modularity values. Utilising ArcGIS software's zonal function, we computed key statistical parameters—minimum, maximum, mean, and sum values—of the pollinator taxa maps within each cluster.ResultsOur analysis unveiled a robust positive correlation between cluster modularity and the cumulative distribution of pollinators. Regions with heightened modularity exhibited richer pollinator populations, suggesting their suitability for urban agriculture for pollinator‐dependent crops.ConclusionThis study proposes that employing graph theory‐based clustering of urban green spaces can effectively delineate areas conducive to urban agriculture. This strategic identification of green space clusters with optimal pollinator abundance holds promise for urban agricultural planning and sustainability initiatives.

A multi-scale context-aware and batch-independent lightweight network for green tide extraction from SAR images

ABSTRACT The outbreaks of green tide have caused severe harm to the marine environment and human society. Synthetic Aperture Radar (SAR) plays an important role in green tide monitoring by virtue of its high resolution and cloud-free nature. The existing green tide extraction methods still face challenges in identifying multi-scale green tide patches due to noise interference, uneven greyscale and blurred boundaries in SAR images. Meanwhile, the practical application of deep learning methods with high precision is limited due to the complexity of the model and the large amount of computation. Therefore, we propose a multi-scale context-aware and batch-independent lightweight green tide extraction network called MBL-Net. A novel lightweight heterogeneous backbone is designed to extract multi-scale discriminative features and improve segmentation efficiency by using multi-scale selection kernel (MSK) modules and lightweight stages. Meanwhile, Triplet attention module is introduced to improve the internal consistency of the green tide region and suppress the effect of speckle noise. Then, the mixed pooling-based channel prior module (MCPM) is used to expand the receptive field of the network and extract the fine green tide structure by fusing multi-scale features. In addition, Filter Response Normalisation (FRN) is innovatively applied for feature normalization in the decoding stage, eliminating batch dependency. In order to verify the effectiveness of the proposed method, a dataset is built using the Sentinel-1 images of the Yellow Sea, China, from 2019 to 2021. The experimental results show that the proposed method achieves an overall accuracy of 98.59% with 0.970 G FLOPs and 3.525 M parameters, which ensures high precision and improves green tide detection efficiency. Compared with several representative networks, this method can capture more details of green tide with fewer parameters and faster calculation speed.

Analysis of Urban Thermal Environment Improved by Blue-Green Spaces on the Landsat Data: A Case Study on Tianjin

China’s urbanization is characterized by rapid speed, large scale and high energy consumption, which causes a series of ecological problems. Among these problems, urban thermal environment is particularly prominent, and the most intuitive reflection is the heat island effect. It is of great significance to optimize the urban layout to give full play to the cooling effect of blue-green spaces for improving urban thermal environment. Compared with other methods, it is more extensive and feasible. However, the current research on urban thermal environment mostly focuses on revealing the driving effects of different land use, and lacks of in-depth discussions on blue-green spaces. Tianjin is located in the northeast of the North China Plain, and has a typical warm temperate sub-humid monsoon climate. This paper takes the six districts of Tianjin City as the research area, covering Heping District, Nankai District, Hongqiao District, Hebei District, Hexi District and Hedong District, with a total area of about 181.18 square kilometers. The typical climate and urbanization characteristics of Tianjin can provide reference for cities with the same characteristics. Firstly, this paper had adopted the Landsat changing scenario of the city gained during the periods of 2004, 2011 and 2017 to formulate the land surface temperature by Mono-window Algorithm, and divided the results into seven temperature zones by standard deviation classification method. In regard to blue-green spaces, this paper had identified Landsat satellite data by Maximum Likelihood Classifier, and classified land use into three categories: blue spaces, green spaces, and non-blue and green spaces. And, next, this paper had studied the correlation between the landscape pattern of blue-green spaces and urban thermal environment by Landscape Pattern Analysis, and used Moving-window Analysis to compare the relationship between thermal environment and blue-green spaces at different spatial scales. And so the research results prove that, the high-temperature regions of Tianjin has been expanding along with the expansion and agglomeration of the urban areas and across the river space restrictions, while the low-temperature regions of the city are mainly concentrated in the blue-green spaces, during the periods of 2004, 2011 and 2017. Furthermore, in the landscape pattern level, there is a significant negative correlation between patch perimeter index of blue-green spaces and land surface temperature, and a significant positive correlation between patch perimeter area ratio index of blue-green spaces and land surface temperature. In the multi-scale spatial pattern, the correlation between the Percent of Landscape of blue spaces patches and land surface temperature is the highest in the 1500 meters × 1500 meters sample, and the Percent of Landscape of green spaces patches and land surface temperature is the highest in the 300 meters × 300 meters sample. In addition, the improvement efficiency of thermal environment in blue spaces is higher. In the end, the optimization suggestions of adjusting the layout of blue-green spaces to effectively improve the urban thermal environment problem are proposed. By optimizing the blue-green space landscape patterns at the optimal scale, increasing the blue spaces in the northern high temperature range, widening and transforming the existing waterways, constructing riverside greenways and other strategies to effectively deal with the thermal environmental problems, improving the ecology and livability of the city. It is hoped to provide new ideas and references for the construction of ecological and livable city under the background of rapid urbanization. In the research process, due to the limitation of data sources, calculation methods and other factors, the relevant conclusion is still limited at the spatial scale. In the future research, the thermal environment and the pattern of blue-green spatial landscape at the fine scale will be studied.

Multilevel Change of Urban Green Space and Spatiotemporal Heterogeneity Analysis of Driving Factors

Analyzing the change trend of urban green space (UGS) and exploring related driving forces can provide scientific reference for sustainable development in rapidly urbanizing areas. However, the spatial and temporal driving mechanisms of the drivers on UGS patterns at different scales are still not deeply understood. Based on the GlobeLand30 land cover data, nighttime lighting data and spatial statistics from 2000 to 2020, this study analyzed the size, shape and diversity of UGS in Guangzhou at the urban level, gradient level and township level with multiple landscape indices. Diversity means the richness of UGS patch types. The selected indices include percent of landscape (PLAND), largest path index (LPI), landscape shape index (LSI), aggregation index (AI) and Shannon’s diversity index (SHDI). The spatiotemporal heterogeneity of the drivers was then explored using the spatiotemporal weighted regression (GTWR) method. Results showed the following: (1) During 2000−2020, the total amount of UGS in Guangzhou increased slightly and then decreased gradually. UGS was mainly transferred into artificial surfaces (lands modified by human activities). (2) The UGS landscape showed a non-linear trend along the urban–rural gradient and fluctuated more in the interval of 20–60% urbanization level. PLAND, LPI and AI decreased significantly in areas with higher levels of urbanization. LSI increased and SHDI decreased significantly in areas with lower levels of urbanization. At township level, the landscape indices showed significant spatial autocorrelation. They transformed from discrete changes at the edge and at the junction of the administrative district to large-scale aggregated change, especially in northern areas. (3) The size of UGSs was mainly influenced by natural factors and population density, but their shape and diversity were mainly influenced by socio-economic factors. More regular shapes of green patches were expected in higher urbanization areas. Population agglomeration positively influenced green space patterns in the northeastern and southern regions (Zengcheng, Conghua and Nansha). Meanwhile the negative influence of urban expansion on the green space pattern in the central and southern regions decreased over time. This study contributes to an in-depth understanding of how the key factors affect the different changes of UGS with time and space and provides methodological support for the long-term zoning planning and management of UGS.

The Influence of Spatial Heterogeneity of Urban Green Space on Surface Temperature

Urban green space (UGS) has been recognized as a key factor in enhancing the urban ecosystem balance, particularly in arid areas. It is often considered an effective means to mitigate the urban heat island (UHI) effect. In this study, the reference comparison method was utilized to optimize the process of nighttime lighting data; the random forest classification method was employed to extract UGS data; and the radiative transfer method was applied in land surface temperature (LST) inversion. Additionally, moving window analysis was conducted to assess the robustness of the results. The objective of this research was to analyze the spatial distribution characteristics of UGS and LST and to explore their bivariate local spatial autocorrelations by calculating four landscape metrics, including the aggregation index (AI), edge density (ED), patch density (PD), and area-weighted mean shape index (Shape_am). It was found that the distribution of UGS in the study area was uneven, with higher temperatures in the eastern and western regions and lower temperatures in the central and southern regions. The results also revealed that ED, PD, and Shape_am were negatively correlated with LST, with correlation coefficients being −0.469, −0.388, and −0.411, respectively, indicating that UGS in these regions were more effective in terms of cooling effect. Conversely, AI was found to be positively correlated with LST (Moran’ I index of 0.449), indicating that surface temperatures were relatively higher in regions of high aggregation. In essence, the fragmented, complex, and evenly distributed green patches in the study area provided a better cooling effect. These findings should persuade decision makers and municipal planners to allocate more UGS in cities for UHI alleviation to improve quality of life and enhance recreational opportunities.

The unequal connectivity of parks and tree canopy in 10 U.S. cities

The connectivity of urban green space has a strong influence on the benefits that green space provides—more connected green space has been linked to greater biodiversity, increased recreational opportunities, improved air quality, and increased noise attenuation. Previous urban green space equity research largely focuses on the total amount or accessibility of green space, ignoring connectivity and other measures of configuration that also impact green space equity. Taking both a demographic and spatial approach, we examine whether green space (i.e., park and tree canopy) area, patch size, and connectivity are distributed equally in ten U.S. cities. We find that not only are wealthy, college-educated, and predominately White census tracts more likely to have greater total green space, the green space patches are larger and more connected. This inequality was stronger for tree canopy than for parks, pointing to the need for policy interventions to improve tree canopy connectivity in areas of systematic disinvestment.

Anisotropic Green Tide Patch Information Extraction Based on Deformable Convolution

Green tides are marine disasters caused by the explosive proliferation or high concentration of certain large algae in seawater, which causes discoloration of the water body. Accurate monitoring of its distribution area is highly important for early warning and the protection of marine ecology. However, existing deep learning methods have difficulty in effectively identifying green tides with anisotropic characteristics due to the complex and variable shapes of the patches and the wide range of scales. To address this issue, this paper presents an anisotropic green tide patch extraction network (AGE-Net) based on deformable convolution. The main structure of AGE-Net consists of stacked anisotropic feature extraction (AFEB) modules. Each AFEB module contains two branches for extracting green tide patches. The first branch consists of multiple connected dense blocks. The second branch introduces a deformable convolution module and a depth residual module based on a multiresolution feature extraction network for extracting anisotropic features of green tide patches. Finally, an irregular green tide patch feature enhancement module is used to fuse the high-level semantic features extracted from the two branches. To verify the effectiveness of the AGE-Net model, experiments were conducted on the MODIS Green Tide dataset. The results show that AGE-Net has better recognition performance, with F1-scores and IoUs reaching 0.8317 and 71.19% on multi-view test images, outperforming other comparison methods.

Evaluating the impact of urban morphology on urban vitality: an exploratory study using big geo-data

ABSTRACT The United Nations has proposed Sustainable Development Goals (SDGs), of which SDG11 aims to “make cities and human settlements inclusive, safe, resilient and sustainable”. This is in line with Urban Vitality's objectives. This study proposes a quantitative framework to evaluate the impact of urban morphology on urban vitality. In this framework, a proxy that is more reflective of economic and human activities is proposed for depicting urban vitality based on geographic big data; then we use a Multi–scale Geographically Weighted Regression (MGWR) regression considering spatial heterogeneity to analyze the different effects of urban form on urban vitality. Taking the Beijing Sixth Ring Road area as the study region, the results indicate that tall, large–area, multi–functional buildings have a significantly positive impact on urban vitality. These areas exhibit single–function buildings and high levels of greenery, leading to inefficient space utilization. Increasing road network density, as well as the density of transportation and public facilities, also positively influences urban vitality. However, commercial density has a negative impact on workday vitality. Furthermore, excessive green space density, a high proportion of the largest green patches, and complex green space borders all contribute to a reduction in urban vitality.

Detection of Sarcocystis hominis, Sarcocystis bovifelis, Sarcocystis cruzi, Sarcocystis hirsuta and Sarcocystis sigmoideus sp. nov. in carcasses affected by bovine eosinophilic myositis

Bovine eosinophilic myositis is an inflammatory myopathy characterized by multiple focal or diffuse grey to green patches leading to condemnation of affected carcasses. Although its etiology is still uncertain, there is evidence that Sarcocystis species may play a role in the development of eosinophilic myositis. The goal of the present study was to identify Sarcocystis spp. in intralesional and extralesional tissues of condemned cattle carcasses, in order to evaluate the possible role of different bovine Sarcocystis spp. in the etiology of bovine eosinophilic myositis. Muscle samples (n = 100) of 26 affected carcasses were collected in Northern Italy. One to five samples with lesions and two aliquots of tissue without lesions were collected from each carcass; lesions were grossly categorized in green focal lesions and green diffuse patches. Genomic DNA was extracted and analyzed by multiplex-PCR targeting different Sarcocystis spp. Unidentified species were characterized morphologically (light microscopy, histology), ultrastructurally (scanning and transmission electron microscopy) and on the molecular level (complete 18S rRNA gene and partial cox1 gene sequencing). A bovine eosinophilic myositis prevalence of 0.017% was visually assessed by routine carcass inspection between 2014 and 2019 in Italy (184/1,108,150 slaughtered cattle). Out of 26 carcasses, 25 revealed the presence of at least one Sarcocystis species (96.2%). The presence of Sarcocystis spp. DNA was significantly more frequent in intralesional than in extralesional samples. Considering the different species, Sarcocystis bovifelis and Sarcocystis hominis were significantly more frequent in intralesional (41.7% and 50%, respectively) than in extralesional samples (1.9% and 15.4%, respectively), while there was no significant difference between the presence of Sarcocystis cruzi and Sarcocystis hirsuta in intralesional (27.1% and 2.1%, respectively) and extralesional (30.8% and 1.9%, respectively) samples. The presence of an unnamed Sarcocystis sp. showing thick-walled (3.7–5.4 μm) cysts with densely packed, flattened, undulating and narrow protrusions, which showed an S-shape in side view, was recorded in the diaphragm of two carcasses. Genomic DNA from individual sarcocysts isolated from the diaphragm was successfully amplified and further sequenced. Sequence comparison revealed <94.6% and 83.4% identity at 18S rRNA and cox1 genes, respectively, with other named Sarcocystis spp., while the phylogenetic analysis clearly separated the unnamed Sarcocystis sp. from the other Sarcocystis spp. using cattle as intermediate hosts. The present study contributes to the understanding of the importance of different Sarcocystis spp. in the pathogenesis of bovine eosinophilic myositis. The results emphasize the association of Sarcocystis hominis and Sarcocystis bovifelis with bovine eosinophilic myositis and highlight the presence of a new Sarcocystis sp. using cattle as intermediate hosts. The name Sarcocystis sigmoideus sp. nov. is proposed for the newly described Sarcocystis species.

Construction of a cold island network for the urban heat island effect mitigation

The urban heat island (UHI) effect seriously challenges sustainable urban development strategies and livability. Numerous studies have explored the UHI problem from the perspective of isolated blue and green patches, ignoring the overall function of cold island networks. This study aims to explore the construction method of cold island network by integrating scattered cold island resources, rationally guiding urban planning and construction, and providing effective ideas and methods for improving the urban thermal environment. Taking the central city of Fuzhou as an example, the identification of the cold island core source (CICS) was optimized by applying relative land surface temperature (LST), morphological spatial pattern analysis, and landscape connectivity analysis. The combined resistance surface was constructed based on a spatial principal component analysis. Subsequently, the cold island network was constructed by applying circuit theory and identifying the key nodes. The results showed that the central and eastern parts of the study area experienced the most significant UHI effects and there was a tendency for them to cluster. Overall, 48 core sources, 104 corridors, 89 cooling nodes, and 34 heating nodes were identified. The average LST of the CICSs was 28.43 °C, significantly lower than the average LST of the entire study area (31.50 °C), and the 104 cold corridors were classified into three categories according to their importance. Different targeting measures should be adopted for the cooling and heating nodes to maintain the stability of the cold island network and prevent the formation of a heat network. Finally, we suggest a model for urban cold island network construction and explore methods for mitigating issues with UHI to achieve proactive and organized adaptation and mitigation of thermal environmental risks in urban areas, as well as to encourage sustainable urban development.

Unveiling the urban colonization of the Asian water monitor (Varanus salvator) across its distribution range using citizen science.

This study aims to investigate the urban colonization of the Asian water monitor (Varanus salvator) across its entire range of distribution, addressing the paucity of research on this species in urban ecosystems. The research spans the geographic range of the Asian water monitor, focusing on urbanized areas where the species accumulates more observations (Bangkok, Colombo, Jakarta, Kuala Lumpur and Singapore). We conducted a systematic review to comprehensively assess the current knowledge of the species' presence in cities. Additionally, citizen science data from repositories like GBIF (Global Biodiversity Information facility ) were utilized to analyze the distribution patterns of V. salvator in urban environments. To elucidate urban distribution and correct collection biases, observations were weighted by sampling effort, using as a proxy all squamate occurrences available from 2010-2023, including V. salvator. Despite the widespread presence of the Asian water monitor in numerous cities within its distribution range, the available studies on the topic appear to be scarce. Existing research primarily consists of descriptive reports on diet and behavior. Our findings indicate that V. salvator predominantly colonizes green patches in urban areas, such as parks and small gardens. Larger cities exhibit higher records, potentially due to both permanent populations and increased citizen science reporting. The Asian water monitor, as the largest lizard with established populations in cities, remains scarcely studied on a broader scale. However, the urban design of each city seems relevant to understand the distribution patterns within each context. Our study highlights the need for further research to explore the ecological and human dimensions associated with the species' presence in urban environments.

Unveiling mungbean yellow mosaic virus: molecular insights and infectivity validation in mung bean (Vigna radiata) via infectious clones.

Yellow mosaic disease (YMD) with typical symptoms of alternating bright yellow to green patches associated with stunting, downward cupping, and wrinkling has been observed in mung bean on agricultural farms in Coimbatore, Tamil Nadu, India. PCR using gene-specific primers indicated the presence of the yellow mosaic virus in symptomatic plants. Rolling circle amplification (RCA) followed by restriction digestion detected ~2.7 kb of DNA-A and DNA-B, allowing the identification of a bipartite genome. The full-length genome sequences were deposited in NCBI GenBank with the accession numbers MK317961 (DNA-A) and MK317962 (DNA-B). Sequence analysis of DNA-A showed the highest sequence identity of 98.39% to the DNA-A of mungbean yellow mosaic virus (MYMV)-Vigna radiata (MW736047), while DNA-B exhibited the highest level of identity (98.21%) to the MYMV-Vigna aconitifolia isolate (DQ865203) reported from Tamil Nadu. Recombinant analysis revealed distinct evidence of recombinant breakpoints of DNA-A within the region encoding the open reading frame (ORF) AC2 (transcription activation protein), with the major parent identified as MYMV-PA1 (KC9111717) and the potential minor parent as MYMV-Namakkal (DQ86520.1). Interestingly, a recombination event in the common region (CR) of DNA-B, which encodes the nuclear shuttle protein and the movement protein, was detected. MYMIV-M120 (FM202447) and MYMV-Vigna (AJ132574) were identified as the event's major and minor parents, respectively. This large variation in DNA-B led us to suspect a recombination in DNA-B. Dimeric MYMV infectious clones were constructed, and the infectivity was confirmed through agroinoculation. In future prospects, unless relying on screening using whiteflies, breeders and plant pathologists can readily use this agroinoculation procedure to identify resistant and susceptible cultivars to YMD.

Super food or super toxic? Turmeric and spirulina as culprits for the toxic effects of food dyes in Drosophila

Prolonged exposure to food dyes, even for those considered safe for consumption, are known to have toxic effects. However, we lack a proper understanding of the underlying compounds that are responsible for the observed toxicity. Here, we tested the toxic effects of three common commercially available natural food dyes (red, green, blue), and their main ingredients (turmeric and spirulina), on Drosophila melanogaster oviposition, larval development, and larval foraging behaviour. Larval development and egg-to-adult survival was significantly impacted by blue and green dyes. These effects were recapitulated when flies were fed with increasing concentrations of turmeric and spirulina, suggesting that turmeric is a toxic component of the food dye. Red dye, which contains neither turmeric or spirulina, had little impact on fly health and behaviour. Green and blue food dyes decreased egg laying, an effect similar to that observed in increasing concentrations of turmeric and, to a lesser extent, spirulina. When given a choice, larvae preferred to feed as follows: control > red > blue > green diet patches, a pattern inversely correlating with the previously observed toxicity. Our results show that, despite turmeric being often considered a super food, it can have toxic effects that the impact health of organisms.

Urban park attributes as predictors for the diversity and composition of spontaneous plants − A case in Beijing, China

Urban biodiversity homogenization and native species habitat loss are becoming increasingly severe. The overuse of lawn grasses and cultivars in green spaces is one of the most important drivers. Scholars have asserted that spontaneous vegetation provides various ecological benefits and should be widely incorporated in planting design. Factors influencing spontaneous plant diversity have mainly been studied in specific habitats or at the city scale, and findings surrounding the attributes of urban parks that support the diversity of spontaneous plants are lacking. This study aimed to provide a perspective on conserving spontaneous plants through inferring the urban park attributes associated with plant diversity. Twenty-two urban park samples in Beijing were examined to identify the correlation between various park variables and spontaneous vegetation. This study recorded 199 spontaneous plant species, with large historic parks containing the most number of species. Park size was a remarkable predictive variable for the diversity of spontaneous plants. Species richness and diversity in large parks were significantly higher than in small parks; not only in total richness due to the species–area relationship but also in richness and diversity at the community level independent of plot number. Microhabitat heterogeneity and the percentage of green area had the strongest positive association with the richness of spontaneous plants. In contrast, the maintenance intensity, perimeter–area ratio, and fragmentation of green area, deciduous forest, and open lawn patches had negative associations with spontaneous plant richness. Redundancy analysis demonstrated that park area, maintenance intensity, microhabitat heterogeneity, and deciduous forest patches were the most remarkable variables predicting species composition. The correlations of variables related to water and open lawn patches with spontaneous plant diversity were more evident in summer. The predicted variables were also more significantly correlated with the diversity of exotic species in summer than in spring. Perennial herbs were more sensitive to the evaluated park attributes. This study elucidates how spontaneous plants are associated with the attributes of urban parks in Beijing, with critical implications for other large cities.

Study on the Spatial and Temporal Evolution of Regional Green Space Morphology Outside Built-Up Areas based on the Google Earth Engine and Biophysical Component Modeling

The spatial pattern of regional green space is an important dimension to describe and quantitatively express the characteristics of regional green spaces outside the built-up area of a city. With the expansion of urban and rural construction land, regional green space has been continuously encroached upon. This leads to a decline in regional ecological well-being and the loss of biodiversity. Based on the remote sensing data of Shanghai city from 2000 to 2020, we quantitatively studied the spatial morphological change characteristics of regional green space outside the built-up area of Shanghai city. Firstly, with the help of the GEE platform, the optimal decoding accuracy classification method was selected through machine learning (random forest, support vector machine, classification regression tree); then, based on the biophysical component (BCI) and CA binarization, the built-up area ranges for up to five time nodes were obtained; finally, through GIS spatial data analysis and processing technology, the regional green space dynamic data of Shanghai for five time nodes were extracted. Based on the above data, an analysis index system was constructed to quantitatively analyze the spatial morphology characteristics of the regional green space outside the built-up area of Shanghai. The results show that (1) the area of regional green space outside the built-up area of Shanghai had a fluctuating growth pattern of “decreasing and then increasing”. The arable land and water areas in Shanghai decreased, and the woodland area increased steadily, while the wetland and grassland areas showed a trend of first decreasing and then increasing. (2) The regional green patch fragmentation shows a fluctuating development trend of increasing, decreasing, and increasing. (3) The change in the spatial center of gravity of the regional green space in Shanghai had a high degree of consistency with the overall green space change. The center of gravity of the grasslands in the regional green space moved substantially to the northwest, while the center of gravity of the other types remained basically unchanged. This study reveals the spatial morphology characteristics of regional green spaces and provides a research method to study the dynamic changes in regional ecological resources. The results of this study can provide a scientific basis for the identification, protection, and development of regional ecological resources.

1079. Symphyotrichum laeve (L.) Á. Löve &amp; D. Löve: Compositae

SummarySymphyotrichum laeve (L.) Á. Löve &amp; D. Löve (Compositae: Astereae: Symphyotrichinae) is described and illustrated, and the species is provided with an expanded synonymy that includes type citations, known types, and supplementary comments for the presently accepted varieties. A commentary on the tribal position of Symphyotrichum, its recognition, infrageneric classification, and the placement of S. laeve within that classification, is provided. Most recently, Symphyotrichum laeve has been divided into four varieties, although it might be more appropriate to recognise just two subspecies; the appropriate synonymy is suggested by amalgamating the two pairs' synonymies. The material grown in the North American Prairie plantings at Wakehurst Place is more aptly called S. laeve var. laeve (or subsp. laeve) largely by the conspicuous diamond‐shaped apical green patch on the phyllaries, and the phyllaries markedly gradate nature. Notes are provided on cultivation and propagation, pests and diseases, and availability. Although illustrated previously, in 1830, the plate of Aster laevigatus was somewhat stylized, and the name, possibly applicable to Lamarck's, Willdenow's or Purch's concept, at best sensu auctt. A short commentary on Oliver Atkins Farwell, author of a few infraspecific names of, is provided.