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Cited by 485

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Within-Species Variation and Measurement Error in Phylogenetic Comparative Methods

Ives, Anthony R., Midford, Peter E., Garland, Theodore·2007·Systematic Biology
DOI 10.1080/10635150701313830
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Abstract

Most phylogenetically based statistical methods for the analysis of quantitative or continuously varying phenotypic traits assume that variation within species is absent or at least negligible, which is unrealistic for many traits. Within-species variation has several components. Differences among populations of the same species may represent either phylogenetic divergence or direct effects of environmental factors that differ among populations (phenotypic plasticity). Within-population variation also contributes to within-species variation and includes sampling variation, instrument-related error, low repeatability caused by fluctuations in behavioral or physiological state, variation related to age, sex, season, or time of day, and individual variation within such categories. Here we develop techniques for analyzing phylogenetically correlated data to include within-species variation, or "measurement error" as it is often termed in the statistical literature. We derive methods for (i) univariate analyses, including measurement of "phylogenetic signal," (ii) correlation and principal components analysis for multiple traits, (iii) multiple regression, and (iv) inference of "functional relations," such as reduced major axis (RMA) regression. The methods are capable of incorporating measurement error that differs for each data point (mean value for a species or population), but they can be modified for special cases in which less is known about measurement error (e.g., when one is willing to assume something about the ratio of measurement error in two traits). We show that failure to incorporate measurement error can lead to both biased and imprecise (unduly uncertain) parameter estimates. Even previous methods that are thought to account for measurement error, such as conventional RMA regression, can be improved by explicitly incorporating measurement error and phylogenetic correlation. We illustrate these methods with examples and simulations and provide Matlab programs.

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Metadata

Authors
Ives, A. R., Midford, P. E., & Garland, T.
Venue
Systematic Biology
Publisher
Oxford University Press (OUP)
Published
2007-04-01
Volume
56
Issue
2
Pages
252-270
ISSN
  • 1063-5157 (ISSN-L)
  • 1076-836X
DOI
10.1080/10635150701313830
OpenAlex
W2028562213
Cited by
485
References
4
FWCI
29.54
Type
journal article
Language
en
Open access
closed

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