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

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 (472 km2) - where 164 25 m x 25 m quadrats were distributed on a grid of 160 km2 within the tropical forest. We also analysed the effects of base (or 'anchor') scale A₀ on the species richness estimated (S_est) 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 (S_est) 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 species-abundance distributions other than log-series and more general spatial allocation rules.