Assessing non-parametric and area-based methods for estimating regional species richness
Article first published online: 17 MAY 2012
© 2012 International Association for Vegetation Science
Journal of Vegetation Science
Volume 23, Issue 6, pages 1006–1012, December 2012
How to Cite
Xu, H., Liu, S., Li, Y., Zang, R., He, F. (2012), Assessing non-parametric and area-based methods for estimating regional species richness. Journal of Vegetation Science, 23: 1006–1012. doi: 10.1111/j.1654-1103.2012.01423.x
- Issue published online: 7 NOV 2012
- Article first published online: 17 MAY 2012
- Manuscript Accepted: 11 APR 2012
- Manuscript Received: 24 OCT 2011
- State Forestry Administration. Grant Numbers: 201104057, 200804001
- National Nonprofit Institute Research Grant of CAF. Grant Numbers: CAFYBB2011004, RITFYWZX200902
- National Natural Science Foundation of China. Grant Numbers: 30430570, 30590383
- Area-based methods;
- Estimation of species richness;
- Maximum entropy;
- Non-parametric methods;
- Regional scale
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).
Tropical forest of Jianfengling Reserve, Hainan Island, China.
We extrapolated species richness on 12 estimators up to a larger regional scale – the reserve (472 km2) – where 164 25 m × 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 (Sest) with MaxEnt.
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.
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.