Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/155946
Share/Export:
logo share SHARE logo core CORE BASE
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE
Title

Statistical Quantification of Individual Differences (SQuID): an educational and statistical tool for understanding multilevel phenotypic data in linear mixed models

AuthorsAllegue, H.; Araya-Ajoy, Yimen G.; Dingemanse, N.J.; Dochtermann, N.A.; Garamszegi, László Z. CSIC ORCID; Nakagawa, S.; Réale, D.; Schielzeth, H.; Westneat, D.F
KeywordsReaction norm
Personality
Phenotypic plasticity: Phenotypic equation
Mixed-effects modelling
Multivariate phenotypes
Multidimensional plasticity
Model fitting
Repeatability
Trait correlations
Variance components
Multilevel data
Individual differences
Issue Date2017
PublisherJohn Wiley & Sons
CitationMETHODS IN ECOLOGY AND EVOLUTION 8: 257- 267 (2017)
AbstractPhenotypic variation exists in and at all levels of biological organization: variation exists among species, among-individuals within-populations, and in the case of l within-populations abile traits, within-individuals. Mixed-effects models represent ideal tools to quantify multilevel measurements of traits and are being increasingly used in evolutionary ecology. Mixed-effects models are relatively complex, and two main issues may be hampering their proper usage: (i) the relatively few educational resources available to teach new users how to implement and interpret them and (ii) the lack of tools to ensure that the statistical parameters of interest are correctly estimated. In this paper, we introduce Statistical Quantification of Individual Differences (SQuID), a simulation-based tool that can be used for research and educational purposes. SQuID creates a virtual world inhabited by subjects whose phenotypes are generated by a user-defined phenotypic equation, which allows easy translation of biological hypotheses into quantifiable parameters. Statistical Quantification of Individual Differences currently models normally distributed traits with linear predictors, but SQuID is subject to further development and will adapt to handle more complex scenarios in the future. The current framework is suitable for performing simulation studies, determining optimal sampling designs for user-specific biological problems and making simulation-based inferences to aid in the interpretation of empirical studies. Statistical Quantification of Individual Differences is also a teaching tool for biologists interested in learning, or teaching others, how to implement and interpret linear mixed-effects models when studying the processes causing phenotypic variation. Interface-based modules allow users to learn about these issues. As research on effects of sampling designs continues, new issues will be implemented in new modules, including nonlinear and non-Gaussian data.
URIhttp://hdl.handle.net/10261/155946
DOI10.1111/2041-210X.12659
Identifiersdoi: 10.1111/2041-210X.12659
issn: 2041-210X
Appears in Collections:(EBD) Artículos

Files in This Item:
File Description SizeFormat
Allegueetal2016MethodEcolEvol.pdf869,44 kBAdobe PDFThumbnail
View/Open
Show full item record
Review this work

WEB OF SCIENCETM
Citations

25
checked on Dec 2, 2021

Google ScholarTM

Check

Altmetric

Dimensions


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.