In this paper we present a weighted mixture distribution component counts (MDCC) approach for estimating total number of species. The proposed method combines conditional estimates of component counts from several candidate mixture distributions and uses bootstrap for confidence interval estimation. The distribution specification is flexible and can be adjusted to suit a variety of datasets. Smoothing techniques can also be incorporated to improve modeling of sparse data. The method is tested by a simulation study and applied to two microbiome datasets for illustration. Simulation results indicate improved bias, mean squared error and confidence interval coverage relative to comparison methods, as well as robustness to underlying data structure.
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Office dust microbiome data is available from Qiita (http://qiita.microbio.me, Study ID: 10423). Plant microbiome data is available from Dryad (https://datadryad.org/stash/dataset/doi:10.5061/dryad.g60r3).
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We would like to thank the Associate Editor and anonymous reviewer for their comments and feedback, which improved the substance and presentation of this manuscript.
Handling Editor: Pierre Dutilleul.
Appendix A: simulation scenarios
Appendix A: simulation scenarios
See Table 4.
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Shestopaloff, K., Xu, W. & Escobar, M.D. Estimating total species using a weighted combination of expected mixture distribution component counts. Environ Ecol Stat (2020). https://doi.org/10.1007/s10651-020-00452-6
- Mixture distribution
- Statistical ecology
- Total species
- Unobserved species
- Weighted estimator