Assessing non-parametric and area-based methods for estimating regional species richness

作者：Xu, Han; Liu, Shirong^*; Li, Yide; Zang, Runguo; He, Fangliang

来源：Journal of Vegetation Science, 2012, 23(6): 1006-1012.

DOI：10.1111/j.1654-1103.2012.01423.x

• Area-based methods
• Estimation of species richness
• Maximum entropy
• Non-parametric methods
• Regional scale

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.

• 出版日期2012-12
• 单位中国林业科学研究院森林生态环境与保护研究所; 中国林业科学研究院; 中国林业科学研究院热带林业研究所