期刊
JOURNAL OF VEGETATION SCIENCE
卷 23, 期 6, 页码 1006-1012出版社
WILEY
DOI: 10.1111/j.1654-1103.2012.01423.x
关键词
Area-based methods; Estimation of species richness; Maximum entropy; Non-parametric methods; Regional scale
资金
- State Forestry Administration [201104057, 200804001]
- National Nonprofit Institute Research Grant of CAF [CAFYBB2011004, RITFYWZX200902]
- National Natural Science Foundation of China [30430570, 30590383]
- China Institute of the University of Alberta
- GEOIDE of Canada
- CFERN
- GENE
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.
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