文献类型：期刊文献

英文题名：Assessing non-parametric and area-based methods for estimating regional species richness

作者：Xu, Han[2] Liu, Shirong[1] Li, Yide[2] Zang, Runguo[1] He, Fangliang[3]

第一作者：许涵

通信作者：Liu, SR[1]

机构：[1]Chinese Acad Forestry, Res Inst Forest Ecol Environm & Protect, Beijing 100091, Peoples R China;[2]Chinese Acad Forestry, Res Inst Trop Forestry, Guangzhou 510520, Guangdong, Peoples R China;[3]Univ Alberta, Dept Renewable Resources, Edmonton, AB T6R 2H1, Canada

年份：2012

卷号：23

期号：6

起止页码：1006-1012

外文期刊名：JOURNAL OF VEGETATION SCIENCE

收录：;Scopus(收录号：2-s2.0-84869080608);WOS:【SCI-EXPANDED(收录号:WOS:000310798600002)】；

基金：This work was conducted during the visit of Han Xu to the University of Alberta in 2009. The study was supported by the State Forestry Administration (201104057, 200804001), National Nonprofit Institute Research Grant of CAF (CAFYBB2011004, RITFYWZX200902), National Natural Science Foundation of China (30430570, 30590383), the China Institute of the University of Alberta and the GEOIDE of Canada, a CFERN and GENE award for ecological papers. The authors are grateful to Mingxian Lin, Jianhui Wu, Zhang Zhou, Tushou Luo, and Dexiang Chen from the Research Institute of Tropical Forestry, Jinhua Mo from Jianfengling Bureau of Forestry and Guangjian Li from the Jianfengling National Reserve for their support.

语种：英文

外文关键词：Area-based methods; Estimation of species richness; Maximum entropy; Non-parametric methods; Regional scale

摘要：Questions Many methods have been developed to estimate species richness but few are useful for estimating regional richness. We compared the performance of commonly used non-parametric and area-based estimators with a particular focus on testing a newly developed but little tested maximum entropy method (MaxEnt). Location Tropical forest of Jianfengling Reserve, Hainan Island, China. Methods We extrapolated species richness on 12 estimators up to a larger regional scale the reserve (472km2) where 164 25mx25m quadrats were distributed on a grid of 160km2 within the tropical forest. We also analysed the effects of base (or anchor) scale A0 on the species richness estimated (Sest) with MaxEnt. Results Six non-parametric methods underestimated the species richness, while six area-based methods overestimated the species richness. The accuracy of the MaxEnt estimate (Sest) was improved with the increase of base scale A0. Conclusions Our findings suggest non-parametric methods should not be used to estimate richness across heterogeneous landscapes but can be used in well-defined sampling areas. Jack2 is the best of the six non-parametric methods, while the logistic model and the MaxEnt method seem to be the best of the six area-based methods. Improvements to the MaxEnt method are possible but that will require reformulation of the method by considering speciesabundance distributions other than log-series and more general spatial allocation rules.