Prediction of potential invasion range of alien plant Peperomia pellucida in China
In recent years, new alien plants have constantly been invading China as a result of an increase of foreign exchanges.Peperomia pellucida (L.) Kunth, a species that originated from tropical America, is one of the alien species recently found in Shanghai. Its potential geographical distribution range...
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Zhejiang University Press
2013-11-01
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| Series: | 浙江大学学报. 农业与生命科学版 |
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| Online Access: | https://www.academax.com/doi/10.3785/j.issn.1008-9209.2013.01.083 |
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| author | DONG Xu CHEN Xiuzhi LOU Yuxia GUO Shuiliang |
| author_facet | DONG Xu CHEN Xiuzhi LOU Yuxia GUO Shuiliang |
| author_sort | DONG Xu |
| collection | DOAJ |
| description | In recent years, new alien plants have constantly been invading China as a result of an increase of foreign exchanges.Peperomia pellucida (L.) Kunth, a species that originated from tropical America, is one of the alien species recently found in Shanghai. Its potential geographical distribution range and habitats of the species are still unknown. Scientists have applied a variety of ecological niche models to predict the risk of exotic plant invasions in China. Among these ecological niche models, maximum entropy (MaxEnt) model has higher accuracy of predicted results with small sample size.According to 12 environmental variables from the global climate environment database (http://www.worldclim.org/) and 649 occurrence records of P. pellucida in the world from the global biodiversity database (http://data.gbif.org/welcome.htm) and the Chinese Virtual Herbarium (http://www.cvh.org.cn/cms/), a prediction of P. pellucida potential distribution was conducted using MaxEnt model and ArcGis 9.3 software. In this prediction, 12 environmental variables were used, including precipitation of wettest month, mean diurnal temperature range, isothermality, precipitation of warmest quarter, mean temperature of coldest quarter, variance in precipitation seasonality, precipitation of coldest quarter, temperature annual range, altitude, precipitation of driest month, mean temperature of warmest quarter, and mean temperature of driest quarter. When modeling, the occurrence data and environmental variables were firstly imported into the MaxEnt, and 75% of the occurrence data to predict the risk (training data) and the other (testing data) to test the accuracy were used. The raster layer of P. pellucida was gotten in the global potential distribution in ASCII format, then was imported into ArcGis for further analyses, and the potential suitable areas of P. pellucida in China was gotten. Finally, the result was confirmed by the ROC (receiver operating characteristics) curve analytical method, and the AUC (area under the ROC curve) of the training data and testing data was as high as 0.956 and 0.963, respectively.The predicted result showed that precipitation of the wettest month, isothermality, mean temperature of coldest quarter, precipitation of coldest quarter, altitude were the main environmental variables that affected the invasion risk of P. pellucida. The distribution probability of P. pellucida increased with the increase of precipitation of wettest month, isothermality and mean temperature of coldest quarter. The low altitude regions in China were of higher risk for P. pellucida to invade. Meanwhile, a calculation method to evaluate comprehensively the invasion risk was put forward. According to the invasion grades for P. pellucida in 34 provinces, municipalities, and metropolis (directly under the jurisdiction of the central government) in China, the country is divided into four regions including high risk area (Taiwan, HongKong, Macao, Guangdong, Guangxi and Hainan), moderate risk area (Shanghai, Fujian and Yunnan), low risk area (Zhejiang, Hubei, Jiangsu, Anhui, Jiangxi, Tibet, Hu’nan, Sichuan and Guizhou) and non-suitable area (Inner Mongolia, Beijing, Heilongjiang, Jilin, Tianjin, Ningxia, Shandong, Shanxi, Xinjiang, Hebei, He’nan, Gansu, Liaoning, Chongqing, Shaanxi and Qinghai).In a conclusion, P. pellucida has a wide range of potential distribution regions including Taiwan, HongKong, Guangdong, Guangxi and Hainan. The actual distribution of P. pellucida is far narrower than its current maximum range of potential distribution, suggesting that it may continue to spread. Therefore, we should pay more attentions to the harming effects of the weed, strengthen monitoring and integrated management. |
| format | Article |
| id | doaj-art-c17213186c6d4e3b9c0495e1dd2f538c |
| institution | DOAJ |
| issn | 1008-9209 2097-5155 |
| language | English |
| publishDate | 2013-11-01 |
| publisher | Zhejiang University Press |
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| series | 浙江大学学报. 农业与生命科学版 |
| spelling | doaj-art-c17213186c6d4e3b9c0495e1dd2f538c2025-08-20T03:16:06ZengZhejiang University Press浙江大学学报. 农业与生命科学版1008-92092097-51552013-11-013962162810.3785/j.issn.1008-9209.2013.01.08310089209Prediction of potential invasion range of alien plant Peperomia pellucida in ChinaDONG XuCHEN XiuzhiLOU YuxiaGUO ShuiliangIn recent years, new alien plants have constantly been invading China as a result of an increase of foreign exchanges.Peperomia pellucida (L.) Kunth, a species that originated from tropical America, is one of the alien species recently found in Shanghai. Its potential geographical distribution range and habitats of the species are still unknown. Scientists have applied a variety of ecological niche models to predict the risk of exotic plant invasions in China. Among these ecological niche models, maximum entropy (MaxEnt) model has higher accuracy of predicted results with small sample size.According to 12 environmental variables from the global climate environment database (http://www.worldclim.org/) and 649 occurrence records of P. pellucida in the world from the global biodiversity database (http://data.gbif.org/welcome.htm) and the Chinese Virtual Herbarium (http://www.cvh.org.cn/cms/), a prediction of P. pellucida potential distribution was conducted using MaxEnt model and ArcGis 9.3 software. In this prediction, 12 environmental variables were used, including precipitation of wettest month, mean diurnal temperature range, isothermality, precipitation of warmest quarter, mean temperature of coldest quarter, variance in precipitation seasonality, precipitation of coldest quarter, temperature annual range, altitude, precipitation of driest month, mean temperature of warmest quarter, and mean temperature of driest quarter. When modeling, the occurrence data and environmental variables were firstly imported into the MaxEnt, and 75% of the occurrence data to predict the risk (training data) and the other (testing data) to test the accuracy were used. The raster layer of P. pellucida was gotten in the global potential distribution in ASCII format, then was imported into ArcGis for further analyses, and the potential suitable areas of P. pellucida in China was gotten. Finally, the result was confirmed by the ROC (receiver operating characteristics) curve analytical method, and the AUC (area under the ROC curve) of the training data and testing data was as high as 0.956 and 0.963, respectively.The predicted result showed that precipitation of the wettest month, isothermality, mean temperature of coldest quarter, precipitation of coldest quarter, altitude were the main environmental variables that affected the invasion risk of P. pellucida. The distribution probability of P. pellucida increased with the increase of precipitation of wettest month, isothermality and mean temperature of coldest quarter. The low altitude regions in China were of higher risk for P. pellucida to invade. Meanwhile, a calculation method to evaluate comprehensively the invasion risk was put forward. According to the invasion grades for P. pellucida in 34 provinces, municipalities, and metropolis (directly under the jurisdiction of the central government) in China, the country is divided into four regions including high risk area (Taiwan, HongKong, Macao, Guangdong, Guangxi and Hainan), moderate risk area (Shanghai, Fujian and Yunnan), low risk area (Zhejiang, Hubei, Jiangsu, Anhui, Jiangxi, Tibet, Hu’nan, Sichuan and Guizhou) and non-suitable area (Inner Mongolia, Beijing, Heilongjiang, Jilin, Tianjin, Ningxia, Shandong, Shanxi, Xinjiang, Hebei, He’nan, Gansu, Liaoning, Chongqing, Shaanxi and Qinghai).In a conclusion, P. pellucida has a wide range of potential distribution regions including Taiwan, HongKong, Guangdong, Guangxi and Hainan. The actual distribution of P. pellucida is far narrower than its current maximum range of potential distribution, suggesting that it may continue to spread. Therefore, we should pay more attentions to the harming effects of the weed, strengthen monitoring and integrated management.https://www.academax.com/doi/10.3785/j.issn.1008-9209.2013.01.083<italic>Peperomia pellucida</italic>maximum entropy modeldistributionprediction |
| spellingShingle | DONG Xu CHEN Xiuzhi LOU Yuxia GUO Shuiliang Prediction of potential invasion range of alien plant Peperomia pellucida in China 浙江大学学报. 农业与生命科学版 <italic>Peperomia pellucida</italic> maximum entropy model distribution prediction |
| title | Prediction of potential invasion range of alien plant Peperomia pellucida in China |
| title_full | Prediction of potential invasion range of alien plant Peperomia pellucida in China |
| title_fullStr | Prediction of potential invasion range of alien plant Peperomia pellucida in China |
| title_full_unstemmed | Prediction of potential invasion range of alien plant Peperomia pellucida in China |
| title_short | Prediction of potential invasion range of alien plant Peperomia pellucida in China |
| title_sort | prediction of potential invasion range of alien plant peperomia pellucida in china |
| topic | <italic>Peperomia pellucida</italic> maximum entropy model distribution prediction |
| url | https://www.academax.com/doi/10.3785/j.issn.1008-9209.2013.01.083 |
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