Predicting alien herb invasion with machine learning models: biogeographical and life-history traits both matter
文献类型：期刊论文
| 作者 | Chen, Leiyi3; Peng, Shaolin; Yang, Bin1 |
| 刊名 | BIOLOGICAL INVASIONS
 |
| 出版日期 | 2015 |
| 卷号 | 17期号:7页码:2187-2198 |
| 关键词 | Risk assessment Invasiveness Native range distribution size Germination rate Seed weight |
| ISSN号 | 1387-3547 |
| DOI | 10.1007/s10530-015-0870-y |
| 文献子类 | Article |
| 英文摘要 | Identifying the variables associated with invasiveness is a core task for developing risk assessment models to predict invasion potential. However, quantitative models with both biogeographical and life-history variables for invasion risk assessment in China are limited. We hypothesized that (1) compared to statistical algorithms, some machine learning models could offer a promising quantitative approach with high accuracy for potential invader prediction; (2) native range distribution size, origins and life-history traits co-determine an alien plant's performance in the latter invasion stage. In this study, we used four machine learning models [classification and regression tree (CART), multivariate adaptive regression spline (MARS), random forest (RF) and multiple additive regression tree (MART)] and two traditional statistical algorithms [logistic regression (LR) and linear discriminant analysis (LDA)] to assess the relative importance of biogeographical and trait variables in the naturalized-invasion stage of 150 invasive and 87 non-invasive herb plants in China. Our results showed that good performance was the case for all predictive models (AUROC ranges from 0.68 to 0.87), which had overall mean performance value ranging from 0.66 to 0.82. Compared with traditional statistical algorithms, MART and RF models have a consistently higher accuracy, indicating that these two models could be used as alternative quantitative approaches for risk assessment. Additionally, both biogeographical (native range distribution size) and life-history traits (seed weight) were screened out by the models, suggesting their high correlation with plant invasiveness and important roles in risk assessment. |
| 学科主题 | Biodiversity Conservation ; Ecology |
| 出版地 | DORDRECHT |
| 电子版国际标准刊号 | 1573-1464 |
| WOS关键词 | WEED RISK-ASSESSMENT ; PLANT INVASIVENESS ; AUSTRALIAN ACACIAS ; RESIDENCE TIME ; NATIVE RANGE ; SUCCESS ; CHINA ; DISTRIBUTIONS ; PERFORMANCE ; TOOL |
| WOS研究方向 | Science Citation Index Expanded (SCI-EXPANDED) |
| 语种 | 英语 |
| WOS记录号 | WOS:000355685700020 |
| 出版者 | SPRINGER |
| 资助机构 | National Natural Science Foundation of China [31030015, 31400364] ; Open Project of the State Key Laboratory of Biocontrol [SKLBC12K09] ; Scientific Research Fund of Hongda Zhang of Zhongshan University |
| 源URL | [http://ir.ibcas.ac.cn/handle/2S10CLM1/25701]  |
| 专题 | 植被与环境变化国家重点实验室 |
| 作者单位 | 1.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 2.Peking Univ, MOE Lab Earth Surface Proc, Coll Urban Environm Sci, Beijing 100871, Peoples R China 3.Sun Yat Sen Univ, Sch Life Sci, State Key Lab Biocontrol, Guangzhou 510006, Guangdong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Leiyi,Peng, Shaolin,Yang, Bin. Predicting alien herb invasion with machine learning models: biogeographical and life-history traits both matter[J]. BIOLOGICAL INVASIONS,2015,17(7):2187-2198. |
| APA | Chen, Leiyi,Peng, Shaolin,&Yang, Bin.(2015).Predicting alien herb invasion with machine learning models: biogeographical and life-history traits both matter.BIOLOGICAL INVASIONS,17(7),2187-2198. |
| MLA | Chen, Leiyi,et al."Predicting alien herb invasion with machine learning models: biogeographical and life-history traits both matter".BIOLOGICAL INVASIONS 17.7(2015):2187-2198. |
入库方式： OAI收割
来源：植物研究所
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