A more reliable species richness estimator based on the Gamma–Poisson model

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A more reliable species richness estimator based on the Gamma–Poisson model https://t.co/mGuuPsA6w0 @thePeerJ https://t.co/GrN9ZGu167
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Ecology

Main article text

 

Introduction

Materials & Methods

Simulation study and results

Species composition generated from the theoretical abundance model

  1. The Chao1 estimator (Sobs+f212f2): using the number of singletons and doubletons to estimate the number of unseen species.

  2. The first-order jackknife estimator (Sobs + f1): using the number of singletons to estimate the number of undetected species.

  3. The parametric estimator proposed by Chao & Bunge (2002); see Eq. (2).

  4. The parametric estimator proposed by Lanumteang & Böhning (2011); see Eq. (6).

  5. The newly proposed richness estimator; see Eq. (5).

Using data sets as true assemblages

Discussion

Conclusions

Supplemental Information

Newly proposed estimator is a bias-corrected Chao1 estimator

DOI: 10.7717/peerj.14540/supp-1

Newly proposed estimator is nearly unbiased estimator in homogeneous model

DOI: 10.7717/peerj.14540/supp-2

R code for Tables1-7 and Figure1

DOI: 10.7717/peerj.14540/supp-3

Additional Information and Declarations

Competing Interests

The author declares that they have no competing interests.

Author Contributions

Chun-Huo Chiu conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Data Availability

The following information was supplied regarding data availability:

The three datasets are available from three published articles:

- Miller & Wiegert (1989), DOI: https://doi.org/10.2307/1938408: https://esajournals.onlinelibrary.wiley.com/doi/abs/10.2307/1938408.

- Fisher, Corbet & Williams (1943), DOI: https://doi.org/10.2307/1411: https://www.jstor.org/stable/1411?seq=1#metadata_info_tab_contents.

- Bonham, Mesibov & Bashford (2002), DOI: https://doi.org/10.1016/S0378-1127(00)00717-9: https://www.sciencedirect.com/science/article/pii/S0378112700007179.

Funding

This work was supported by the Taiwan Ministry of Science and Technology, Grant/Award Number: (109-2118-M-002-003-MY2). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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