中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
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
DOI10.1016/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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