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A basic assessment of residential plant diversity and its ecosystem services and disservices in Beijing, China
- Wang, Hua-Feng, Qureshi, Salman, Knapp, Sonja, Friedman, Cynthia Ross, Hubacek, Klaus
- Applied geography 2015 v.64 pp. 121-131
- allergenicity, developed countries, ecosystem services, edible species, flora, food plants, geography, herbs, infrastructure, introduced species, linear models, pollen, prices, principal component analysis, quality of life, residential areas, shrubs, socioeconomic factors, species diversity, trees, urban areas, China
- About 52% of the world's population now lives in urban areas, and 41% of urban land in developed countries is used for residential areas. The amount and quality of residential green space, an important element in urban residential infrastructure, is closely correlated to city dwellers' quality of life. The quality of green spaces is not only closely correlated to the ecosystem services they provide, but also to their disservices. In order to (i) examine how plant diversity and plant traits vary in different residential areas, (ii) determine the main socio-economic factors driving plant trait variations across different residential areas, and (iii) provide an overview on selected ecosystem services and disservices related to plant diversity, we investigated the flora and socio-economic properties of 83 residential areas in Beijing, China. We found a total of 369 plant species belonging to 99 families and 150 genera. With respect to plant traits, there were 90 annual species, 174 alien species, 169 pollen allergenic species, and 133 species with edible or pharmaceutical value. The number of perennial, alien, ornamental and edible plant species was largest in residential areas completed in the 1990s. The number of allergenic species was highest in residential areas completed prior to 1980. The Simpson, Shannon and Pielou indices for trees and shrubs were highest in areas completed in the 1990s, while those same indices for herbs were highest in residential areas completed prior to 1980. General Linear Model analyses revealed that richness increased with increasing housing price across all groups of species. Principal Component Analysis indicated that housing price and floor-area ratio are the variables that positively correlate with species richness for all groups of species.