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Assessing non‐parametric and area‐based methods for estimating regional species richness
Author(s) -
Xu Han,
Liu Shirong,
Li Yide,
Zang Runguo,
He Fangliang
Publication year - 2012
Publication title -
journal of vegetation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 115
eISSN - 1654-1103
pISSN - 1100-9233
DOI - 10.1111/j.1654-1103.2012.01423.x
Subject(s) - species richness , quadrat , ecology , geography , estimator , principle of maximum entropy , statistics , environmental science , mathematics , biology , shrub
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 km 2 ) – where 164 25 m × 25 m quadrats were distributed on a grid of 160 km 2 within the tropical forest. We also analysed the effects of base (or ‘anchor‘) scale A 0 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 A 0 . 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.