Dataset Information

Effect of landscape pattern on insect species density within urban green spaces in Beijing, China.

ABSTRACT: Urban green space is an important refuge of biodiversity in urban areas. Therefore, it is crucial to understand the relationship between the landscape pattern of green spaces and biodiversity to mitigate the negative effects of urbanization. In this study, we collected insects from 45 green patches in Beijing during July 2012 using suction sampling. The green patches were dominated by managed lawns, mixed with scattered trees and shrubs. We examined the effects of landscape pattern on insect species density using hierarchical partitioning analysis and partial least squares regression. The results of the hierarchical partitioning analysis indicated that five explanatory variables, i.e., patch area (with 19.9% independent effects), connectivity (13.9%), distance to nearest patch (13.8%), diversity for patch types (11.0%), and patch shape (8.3%), significantly contributed to insect species density. With the partial least squares regression model, we found species density was negatively related to patch area, shape, connectivity, diversity for patch types and proportion of impervious surface at the significance level of p < 0.05 and positively related to proportion of vegetated land. Regression tree analysis further showed that the highest species density was found in green patches with an area <500 m2. Our results indicated that improvement in habitat quality, such as patch area and connectivity that are typically thought to be important for conservation, did not actually increase species density. However, increasing compactness (low-edge) of patch shape and landscape composition did have the expected effect. Therefore, it is recommended that the composition of the surrounding landscape should be considered simultaneously with planned improvements in local habitat quality.

SUBMITTER: Su Z

PROVIDER: S-EPMC4368726 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Effect of landscape pattern on insect species density within urban green spaces in Beijing, China.

Su Zhimin Z Li Xiaoma X Zhou Weiqi W Ouyang Zhiyun Z

PloS one 20150320 3

Urban green space is an important refuge of biodiversity in urban areas. Therefore, it is crucial to understand the relationship between the landscape pattern of green spaces and biodiversity to mitigate the negative effects of urbanization. In this study, we collected insects from 45 green patches in Beijing during July 2012 using suction sampling. The green patches were dominated by managed lawns, mixed with scattered trees and shrubs. We examined the effects of landscape pattern on insect spe ...[more]

PMID: 25793897

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Project description:BackgroundUrban green spaces are important for human health, but they may expose visitors to tick-borne diseases. This not only presents a potential public health challenge but also undermines the expected public health gains from urban green spaces. The aim of this study is to assess the public health risk of tick-borne diseases in an urban green space used for recreation in Stockholm, Sweden.MethodsWe used a mixed method approach identifying both the magnitude of the tick hazard and the extent of the human exposure to tick-borne diseases. At six entry points to an urban green space, we sampled ticks and documented microhabitat conditions from five randomly assigned 2 m × 2 m plots. Surrounding habitat data was analyzed using geographical information system (GIS). Nymphs and adult ticks were tested for Borrelia burgdorferi sensu lato and Anaplasma phagocytophilum using TaqMan qPCR. Positive B. burgdorferi (s.l.) ticks were further analyzed by nested PCR amplification and sequence analysis. Population census data and visitor count data were used to estimate the degree of human exposure to tick-borne diseases. To further understand the degree to which visitors get in contact with infected ticks we also conducted interviews with visitors to green spaces.ResultsHigh tick densities were commonly found in humid broadleaved forest with low field vegetation. High pathogen prevalence was significantly correlated with increasing proportions of artificial areas. Integrating the tick hazard with human exposure we found that the public health risk of tick-borne diseases was moderate to high at most of the studied entry points. Many of the visitors frequently used urban green spaces. Walking was the most common activity, but visitors also engaged in activities with higher risk for tick encounters. Individual protective measures were connected to specific recreational activities such as picking berries or mushrooms.ConclusionsThe number of visitors can be combined with tick inventory data and molecular analyses of pathogen prevalence to make crude estimations of the public health risk of tick-borne diseases in urban green spaces. The risk of encountering infected ticks is omnipresent during recreational activities in urban green spaces, highlighting the need for public health campaigns to reduce the risk of tick-borne diseases.

Project description:Land prices are the key problem of urban land management, with prices of residential land being the most sensitive and the strongest social reflection among the different land types. Exploring spatial and temporal variation of residential land prices and the effect of land market factors on residential land prices can help the government formulate targeted regulations and policies. This study analyzes the spatial and temporal evolution of residential land prices and the factors influencing the land market in the Beijing-Tianjin-Hebei region based on land transaction data from 2014-2017 using exploratory spatial data analysis (ESDA) and a geographically weighted regression (GWR) model. The results show the following: ① Residential land prices in Beijing and Tianjin are significantly higher than those in other regions, while Zhangjiakou, Chengde, and western mountainous areas have the lowest residential land prices. Over time, a development trend of residential land price polycentricity gradually emerged, and the locational correlation has gradually increased. ② Under the influence of the land finance model of local governments in China, three factors, namely, the land stock utilization rate, revenue from residential land transfers, and the growth of residential land transaction areas, have significantly contributed to the increase in residential land prices. ③ Under the land market supply and demand mechanism and government management, four indicators, namely, the land supply rate, the per capita residential land supply area, the degree of marketization of the residential land supply, and the frequency of residential land transactions, have suppressed the rise in residential land prices. ④ The overall effect of land market factors on residential land prices in the central and northern regions of Beijing, Tianjin and Hebei is stronger than that in the southern regions, which may be related to the more active land market and stricter macromanagement policies in Beijing, Tianjin and surrounding areas.

Project description:BackgroundResidential green and blue spaces may be therapeutic for the mental health. However, solid evidence on the linkage between exposure to green and blue spaces and mental health among the elderly in non-Western countries is scarce and limited to exposure metrics based on remote sensing images (i.e., land cover and vegetation indices). Such overhead-view measures may fail to capture how people perceive the environment on the site.ObjectiveThis study aimed to compare streetscape metrics derived from street view images with satellite-derived ones for the assessment of green and blue space; and to examine associations between exposure to green and blue spaces as well as geriatric depression in Beijing, China.MethodsQuestionnaire data on 1190 participants aged 60 or above were analyzed cross-sectionally. Depressive symptoms were assessed through the shortened Geriatric Depression Scale (GDS-15). Streetscape green and blue spaces were extracted from Tencent Street View data by a fully convolutional neural network. Indicators derived from street view images were compared with a satellite-based normalized difference vegetation index (NDVI), a normalized difference water index (NDWI), and those derived from GlobeLand30 land cover data on a neighborhood level. Multilevel regressions with neighborhood-level random effects were fitted to assess correlations between GDS-15 scores and these green and blue spaces exposure metrics.ResultsThe average cumulative GDS-15 score was 3.4 (i.e., no depressive symptoms). Metrics of green and blue space derived from street view images were not correlated with satellite-based ones. While NDVI was highly correlated with GlobeLand30 green space, NDWI was moderately correlated with GlobeLand30 blue space. Multilevel regressions showed that both street view green and blue spaces were inversely associated with GDS-15 scores and achieved the highest model goodness-of-fit. No significant associations were found with NDVI, NDWI, and GlobeLand30 green and blue space. Our results passed robustness tests.ConclusionOur findings provide support that street view green and blue spaces are protective against depression for the elderly in China, yet longitudinal confirmation to infer causality is necessary. Street view and satellite-derived green and blue space measures represent different aspects of natural environments. Both street view data and deep learning are valuable tools for automated environmental exposure assessments for health-related studies.

Project description:BackgroundMosquitoes inhabiting urban green spaces and cemeteries in Europe represent a crucial facet of public health concern and contribute to the ecological balance. As urbanization intensifies, these areas increasingly serve as vital habitats for various mosquito species, fostering breeding grounds and increasing the risk of disease transmission.MethodsA study was conducted in the three main cities (inland, coastal, and estuarine) of the Basque Country, northern Spain, to investigate the species composition, abundance, dynamic populations, larval habitats, and host preferences of mosquitoes in urban green spaces and cemeteries. CDC traps and dipping were used to collect mosquitoes for 2 years (2019-2020).ResultsA total of 21 mosquito species were identified, with Culex pipiens s.l. being the most abundant and widespread. The three ecological forms of Cx. pipiens were found, and Cx. pipiens pipiens was the most common in both green areas and cemeteries. Morphological identification together with molecular tools identified 65 COI sequences with high homology. The highest species richness was found in the inland city, followed by the coastal city and the estuarine city. Mosquito abundance was significantly higher in green areas compared to cemeteries and in the coastal and estuarine cities compared to the inland city. The investigation of larval breeding sites highlighted the dominance of Cx. pipiens s.l., particularly in semi-artificial ponds, diverse water-holding containers (tyres and buckets) and drainage systems in green areas; in cemeteries, most of the larvae were found in flowerpots and funerary urns. Seasonal activity exhibited variable peaks in mosquito abundance in the different cities, with a notable increase in July or August. Additionally, blood meal analysis revealed that Cx. pipiens s.l. fed on several common urban avian species.ConclusionsStudies on mosquitoes are essential to understand their role in disease transmission and to design targeted and sustainable management strategies to mitigate the associated risks.

Dataset Information

Effect of landscape pattern on insect species density within urban green spaces in Beijing, China.

Publications

Effect of landscape pattern on insect species density within urban green spaces in Beijing, China.

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