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dc.contributor.author
Johnson, Thomas F.
dc.contributor.author
Isaac, Nick J. B.
dc.contributor.author
Paviolo, Agustin Javier
dc.contributor.author
González Suárez, Manuela
dc.date.available
2022-09-08T18:00:53Z
dc.date.issued
2021-01
dc.identifier.citation
Johnson, Thomas F.; Isaac, Nick J. B.; Paviolo, Agustin Javier; González Suárez, Manuela; Handling missing values in trait data; Wiley Blackwell Publishing, Inc; Global Ecology and Biogeography; 30; 1; 1-2021; 51-62
dc.identifier.issn
1466-822X
dc.identifier.uri
http://hdl.handle.net/11336/168014
dc.description.abstract
Aim: Trait data are widely used in ecological and evolutionary phylogenetic comparative studies, but often values are not available for all species of interest. Traditionally, researchers have excluded species without data from analyses, but estimation of missing values using imputation has been proposed as a better approach. However, imputation methods have largely been designed for randomly missing data, whereas trait data are often not missing at random (e.g., more data for bigger species). Here, we evaluate the performance of approaches for handling missing values when considering biased datasets. Location: Any. Time period: Any. Major taxa studied: Any. Methods: We simulated continuous traits and separate response variables to test the performance of nine imputation methods and complete-case analysis (excluding missing values from the dataset) under biased missing data scenarios. We characterized performance by estimating the error in imputed trait values (deviation from the true value) and inferred trait–response relationships (deviation from the true relationship between a trait and response). Results: Generally, Rphylopars imputation produced the most accurate estimate of missing values and best preserved the response–trait slope. However, estimates of missing data were still inaccurate, even with only 5% of values missing. Under severe biases, errors were high with every approach. Imputation was not always the best option, with complete-case analysis frequently outperforming Mice imputation and, to a lesser degree, BHPMF imputation. Mice, a popular approach, performed poorly when the response variable was excluded from the imputation model. Main conclusions: Imputation can handle missing data effectively in some conditions but is not always the best solution. None of the methods we tested could deal effectively with severe biases, which can be common in trait datasets. We recommend rigorous data checking for biases before and after imputation and propose variables that can assist researchers working with incomplete datasets to detect data biases and minimize errors.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Wiley Blackwell Publishing, Inc
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/
dc.subject
BHPMF
dc.subject
FUNCTIONAL TRAIT
dc.subject
IMPUTATION
dc.subject
LIFE-HISTORY TRAIT
dc.subject
MAR
dc.subject
MCAR
dc.subject
MISSING DATA
dc.subject
MNAR
dc.subject
MULTIPLE IMPUTATION CHAINED EQUATIONS
dc.subject
RPHYLOPARS
dc.subject.classification
Ecología
dc.subject.classification
Ciencias Biológicas
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Handling missing values in trait data
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2022-05-06T16:47:42Z
dc.journal.volume
30
dc.journal.number
1
dc.journal.pagination
51-62
dc.journal.pais
Reino Unido
dc.journal.ciudad
Londres
dc.description.fil
Fil: Johnson, Thomas F.. University of Reading; Reino Unido
dc.description.fil
Fil: Isaac, Nick J. B.. Centre For Ecology And Hydrology; Reino Unido
dc.description.fil
Fil: Paviolo, Agustin Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú; Argentina. Centro de Investigaciones del Bosque Atlántico; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina
dc.description.fil
Fil: González Suárez, Manuela. University of Reading; Reino Unido
dc.journal.title
Global Ecology and Biogeography
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1111/geb.13185
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/10.1111/geb.13185
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