Measuring change in biological communities: multivariate analysis approaches for temporal datasets with low sample size.

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dc.contributor.author Buckley, Hannah L
dc.contributor.author Day, Nicola J
dc.contributor.author Case, Bradley S
dc.contributor.author Lear, Gavin
dc.coverage.spatial United States
dc.date.accessioned 2022-05-11T21:38:35Z
dc.date.available 2022-05-11T21:38:35Z
dc.date.issued 2021-01
dc.identifier.citation (2021). PEERJ, 9, e11096-.
dc.identifier.issn 2167-8359
dc.identifier.uri https://hdl.handle.net/2292/59193
dc.description.abstract Effective and robust ways to describe, quantify, analyse, and test for change in the structure of biological communities over time are essential if ecological research is to contribute substantively towards understanding and managing responses to ongoing environmental changes. Structural changes reflect population dynamics, changes in biomass and relative abundances of taxa, and colonisation and extinction events observed in samples collected through time. Most previous studies of temporal changes in the multivariate datasets that characterise biological communities are based on short time series that are not amenable to data-hungry methods such as multivariate generalised linear models. Here, we present a roadmap for the analysis of temporal change in short-time-series, multivariate, ecological datasets. We discuss appropriate methods and important considerations for using them such as sample size, assumptions, and statistical power. We illustrate these methods with four case-studies analysed using the R data analysis environment.
dc.format.medium Electronic-eCollection
dc.language eng
dc.publisher PeerJ
dc.relation.ispartofseries PeerJ
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher.
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject Beta diversity
dc.subject Biodiversity
dc.subject Community variation
dc.subject Compositional change
dc.subject Multivariate analysis
dc.subject Species turnover
dc.subject Temporal change
dc.subject Temporal variability
dc.subject Time series
dc.subject Zeta diversity
dc.subject 2.5 Research design and methodologies (aetiology)
dc.subject Generic health relevance
dc.subject Science & Technology
dc.subject Multidisciplinary Sciences
dc.subject Science & Technology - Other Topics
dc.subject NULL MODEL ANALYSIS
dc.subject BETA-DIVERSITY
dc.subject RECRUITMENT LIMITATION
dc.subject DYNAMICS
dc.subject ASSEMBLAGES
dc.subject ABUNDANCE
dc.subject TURNOVER
dc.subject PATTERNS
dc.subject DISSIMILARITY
dc.subject VARIABILITY
dc.subject 06 Biological Sciences
dc.subject 11 Medical and Health Sciences
dc.title Measuring change in biological communities: multivariate analysis approaches for temporal datasets with low sample size.
dc.type Journal Article
dc.identifier.doi 10.7717/peerj.11096
pubs.begin-page e11096
pubs.volume 9
dc.date.updated 2022-04-11T21:30:50Z
dc.rights.holder Copyright: The author en
dc.identifier.pmid 33889442 (pubmed)
pubs.author-url https://www.ncbi.nlm.nih.gov/pubmed/33889442
pubs.publication-status Published
dc.rights.accessrights http://purl.org/eprint/accessRights/OpenAccess en
pubs.subtype research-article
pubs.subtype Journal Article
pubs.elements-id 851586
pubs.org-id Science
pubs.org-id Biological Sciences
dc.identifier.eissn 2167-8359
dc.identifier.pii 11096
pubs.number e11096
pubs.record-created-at-source-date 2022-04-12
pubs.online-publication-date 2021-04-08


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