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. 2013 Jul 16;47(14):7624-32.
doi: 10.1021/es400382c. Epub 2013 Jul 8.

Spatial risk assessment of alien invasive plants in China

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Spatial risk assessment of alien invasive plants in China

Fan Bai et al. Environ Sci Technol. .

Abstract

The large-scale distribution patterns of alien invasive plants (AIP) can provide key information and a theoretical basis for management strategies, including the prevention of invasions, the control and eradication of established AIPs, and the identification of areas at high risk of invasion. This study aims to quantify distribution patterns of AIP in China, to develop approaches that measure the social, economic, and ecological impacts, and to identify areas that are at higher risk of plant invasion. Based on published literature, there were 384 AIPs in China, representing 233 genera from 66 families. Climatic factors were among the primary factors determining AIPs' overall distribution patterns. The majority of AIPs were tropically distributed in China, meaning that they were mainly restricted to southern China. Temperate-distributed AIPs, those distributed only or predominantly in northern China, were fewer but had higher average rates of spread than tropically distributed AIPs. Average ecological and economic impact per AIP was negatively correlated with AIP richness, meaning that areas with relatively few AIPs nevertheless have some of the most detrimental ones. Our comparative evaluation showed that the risk of invasion differed among regions of China, with high-risk areas in southern China (Yunnan, Guangxi, and Guangdong) and central coastal areas of eastern China (Shandong, Hebei, and Jiangsu). In the context of climate change, areas around latitudes of 33° N, including Hebei, Shandong, Henan, and Jiangsu, should be given more attention for the control and prevention of plant invasions. Predictions of high-risk areas for future invasions differed depending on the scale of aggregation and the evaluation index, indicating that invasive risk assessments should be based on multiple factors.

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