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Keywords:

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

Abstract

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 × 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.

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 species–abundance distributions other than log-series and more general spatial allocation rules.