Testing unidimensional species distribution models to forecast and hindcast changes in marsh vegetation over 40 years
文献类型:期刊论文
作者 | Lou, Yanjing1,2; Liu, Ying3; Tang, Zhanhui4; Jiang, Ming1; Lu, Xianguo1; Rydin, Hakan2 |
刊名 | ECOLOGICAL INDICATORS |
出版日期 | 2019-09-01 |
卷号 | 104页码:341-346 |
ISSN号 | 1470-160X |
关键词 | Environmental change Extended Huisman-Olff-Fresco models (eHOF) Generalized additive models (GAM) Herbaceous marsh Model evaluation Prediction Water depth Wetlands |
DOI | 10.1016/j.ecolind.2019.05.024 |
通讯作者 | Lou, Yanjing(louyj@neigae.ac.cn) |
英文摘要 | Species distribution models (SDM) predicting changes in species occurrences and abundance are increasingly being used as a tool in biogeography and conservation biology. However, we have little information on their predictive performance. Here we used archive-recorded predictor and field-observational verifier data associated with water level to evaluate the performance of response curves over 40 years for marsh plant species in Northeast China. A consensus approach (AUC: area-under-curve) was used as the test measure for internal evaluation and external evaluation (forecast and hindcast). Our results demonstrated that there is no significant differences between internal and external evaluation, and they both showed reasonable accuracy (AUC=0.73, respectively). There was considerable variation across species and projection direction in model accuracy, and accuracy of model fitting in internal evaluation and restricting the environmental range of data in different time periods may impact the performance of model projection over time. The performance of generalized additive models (GAM) is similar with that of extended Huisman-Olff-Fresco models (eHOF). Cover model is a little better than presence/absence models in prediction over time. Our findings provide some guidelines for the use of SDM for predictions under environmental change. |
资助项目 | National Natural Science Foundation of China[41671109] ; National Natural Science Foundation of China[41371107] ; National key Research and Development Project of China[2016YFC0500403] |
WOS研究方向 | Biodiversity & Conservation ; Environmental Sciences & Ecology |
语种 | 英语 |
出版者 | ELSEVIER SCIENCE BV |
WOS记录号 | WOS:000470966000035 |
源URL | [http://119.78.100.138/handle/2HOD01W0/8139] |
专题 | 中国科学院重庆绿色智能技术研究院 |
通讯作者 | Lou, Yanjing |
作者单位 | 1.Chinese Acad Sci, Northeast Inst Geog & Agroecol, Key Lab Wetland Ecol & Environm, 4888 Shengbeida Rd, Changchun 130102, Jilin, Peoples R China 2.Uppsala Univ, Evolutionary Biol Ctr, Dept Ecol & Genet, Norbyvagen 18D, SE-75236 Uppsala, Sweden 3.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, 266 Fangzheng Ave, Chongqing 400714, Peoples R China 4.Northeast Normal Univ, Sch Environm, 2555 Jingyue St, Changchun 130117, Jilin, Peoples R China |
推荐引用方式 GB/T 7714 | Lou, Yanjing,Liu, Ying,Tang, Zhanhui,et al. Testing unidimensional species distribution models to forecast and hindcast changes in marsh vegetation over 40 years[J]. ECOLOGICAL INDICATORS,2019,104:341-346. |
APA | Lou, Yanjing,Liu, Ying,Tang, Zhanhui,Jiang, Ming,Lu, Xianguo,&Rydin, Hakan.(2019).Testing unidimensional species distribution models to forecast and hindcast changes in marsh vegetation over 40 years.ECOLOGICAL INDICATORS,104,341-346. |
MLA | Lou, Yanjing,et al."Testing unidimensional species distribution models to forecast and hindcast changes in marsh vegetation over 40 years".ECOLOGICAL INDICATORS 104(2019):341-346. |
入库方式: OAI收割
来源:重庆绿色智能技术研究院
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