Quantifying sample completeness and comparing diversities among assemblages

By A. Chao, Yasuhiro Kubota, D. Zelený, Chun‐Huo Chiu, Ching‐Feng Li, Buntarou Kusumoto, M. Yasuhara, S. Thorn, Chih‐Lin Wei, Mark John Costello and 1 more. Ecological research, 2020

337 citations recorded by the source corpus.

We develop a novel class of measures to quantify sample completeness of a biological survey. The class of measures is parameterized by an order q ≥ 0 to con-trol for sensitivity to species relative abundances. When q = 0, species abundances are disregarded and our measure reduces to the conventional measure of completeness, that is, the ratio of the observed species richness to the true richness (observed plus undetected). When q = 1, our measure reduces to the sample coverage (the proportion of the total number of individuals in the entire assemblage that belongs to detected species), a concept developed by Alan Turing in his cryptographic analysis. The sample completeness of a general order q ≥ 0 extends Turing's sample coverage and quantifies the proportion of the assemblage's individuals belonging to detected species, with each individual being proportionally weighted by the ( q − 1)th power of its abundance. We propose the use of a continuous profile depicting our proposed measures with respect to q ≥ 0 to characterize the sample completeness of a survey. An analytic estimator of the diversity profile and its sampling uncer-tainty based on a bootstrap method are derived and tested by simulations. To compare diversity across multiple assemblages, we propose an integrated approach based on the framework of Hill numbers to assess (a) the sample completeness profile, (b) asymptotic diversity estimates to infer true diversities of entire assemblages, (c) non-asymptotic standardization via rarefaction and extrapolation, and (d) an evenness profile. Our framework can be extended to incidence data. Empirical data sets from several research fields are used for illustration.

Subfields: Ecological Modeling, Ecology, Genetics

View Paper: https://www.semanticscholar.org/paper/147dcdae1754b9764594ae7b180bb120a2da9f10

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecological Modeling

1. A. Chao
2. Chun‐Huo Chiu
3. M. Dornelas
4. Anne E. Magurran
5. Faye Moyes
6. P. A. Henderson
7. Jérôme Chave
8. L. Jost

In Ecology

1. Chun‐Huo Chiu
2. A. Chao
3. P. A. Henderson
4. Anne E. Magurran
5. M. Dornelas
6. Faye Moyes
7. L. Jost
8. Nicholas Gotelli

In Genetics

1. Chun‐Huo Chiu
2. A. Chao
3. L. Jost

Citations in the corpus

• Measuring temporal change in alpha diversity: A framework integrating taxonomic, phylogenetic and functional diversity and the iNEXT.3D standardization (2021, methodology): “Most importantly, analyses based on multiple incidence data are less sensitive to clustering or aggregation of individuals, compared to those based on abundance data (Chao et al., 2020; Colwell et al., 2012).”