File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Quantifying sample completeness and comparing diversities among assemblages

TitleQuantifying sample completeness and comparing diversities among assemblages
Authors
KeywordsCompleteness
Diversity
Evenness
Hill numbers
Sample coverage
Issue Date2020
PublisherWiley-Blackwell Publishing Asia. The Journal's web site is located at https://onlinelibrary.wiley.com/journal/14401703
Citation
Ecological Research, 2020, v. 35 n. 2, p. 292-314 How to Cite?
AbstractWe 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 control 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 uncertainty 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.
Persistent Identifierhttp://hdl.handle.net/10722/294285
ISSN
2023 Impact Factor: 1.7
2023 SCImago Journal Rankings: 0.713
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChao, A-
dc.contributor.authorKubota, Y-
dc.contributor.authorZelený, D-
dc.contributor.authorChiu, CH-
dc.contributor.authorLi, CF-
dc.contributor.authorKusumoto, B-
dc.contributor.authorYasuhara, M-
dc.contributor.authorThorn, S-
dc.contributor.authorWei, CL-
dc.contributor.authorCostello, MJ-
dc.contributor.authorColwell, RK-
dc.date.accessioned2020-11-23T08:29:10Z-
dc.date.available2020-11-23T08:29:10Z-
dc.date.issued2020-
dc.identifier.citationEcological Research, 2020, v. 35 n. 2, p. 292-314-
dc.identifier.issn0912-3814-
dc.identifier.urihttp://hdl.handle.net/10722/294285-
dc.description.abstractWe 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 control 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 uncertainty 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.-
dc.languageeng-
dc.publisherWiley-Blackwell Publishing Asia. The Journal's web site is located at https://onlinelibrary.wiley.com/journal/14401703-
dc.relation.ispartofEcological Research-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectCompleteness-
dc.subjectDiversity-
dc.subjectEvenness-
dc.subjectHill numbers-
dc.subjectSample coverage-
dc.titleQuantifying sample completeness and comparing diversities among assemblages-
dc.typeArticle-
dc.identifier.emailYasuhara, M: yasuhara@hku.hk-
dc.identifier.authorityYasuhara, M=rp01474-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1111/1440-1703.12102-
dc.identifier.scopuseid_2-s2.0-85082187210-
dc.identifier.hkuros318828-
dc.identifier.volume35-
dc.identifier.issue2-
dc.identifier.spage292-
dc.identifier.epage314-
dc.identifier.isiWOS:000522097200003-
dc.publisher.placeAustralia-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats