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Interpreting the Evolutionary Regression: The Interplay Between Observational and Biological Errors in Phylogenetic Comparative Studies

Regressions of biological variables across species are rarely perfect. Usually, there are residual deviations from the estimated model relationship, and such deviations commonly show a pattern of phylogenetic correlations indicating that they have biological causes. We discuss the origins and effect... Full description

Journal Title: Systematic biology 2012-05-01, Vol.61 (3), p.413-425
Main Author: Hansen, Thomas F
Other Authors: Bartoszek, Krzysztof
Format: Electronic Article Electronic Article
Language: English
Subjects:
Quelle: Alma/SFX Local Collection
Publisher: England: Oxford University Press
ID: ISSN: 1063-5157
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recordid: cdi_swepub_primary_oai_research_chalmers_se_29a5347c_ce05_4040_a457_47f2df5e504d
title: Interpreting the Evolutionary Regression: The Interplay Between Observational and Biological Errors in Phylogenetic Comparative Studies
format: Article
creator:
  • Hansen, Thomas F
  • Bartoszek, Krzysztof
subjects:
  • Adaptation
  • Adaptation
  • allometry
  • major-axis regression
  • measurement error
  • phylogenetic comparative method
  • phylogenetic inertia
  • reduced major-axis regression
  • structural equation
  • allometry
  • Bias
  • Biological adaptation
  • Biological evolution
  • Biological Sciences
  • Biological taxonomies
  • Biologiska vetenskaper
  • brain-size
  • Comparative analysis
  • contrasts
  • Earth Science with specialization in Environmental Analysis
  • Error rates
  • Estimation bias
  • Evolution
  • Evolutionary Biology
  • Evolutionsbiologi
  • Geovetenskap med inriktning mot miljöanalys
  • interspecific data
  • Linear Models
  • Linear regression
  • major-axis regression
  • Matematik
  • Matematisk statistik
  • Mathematical Statistics
  • Mathematics
  • measurement error
  • Measurement errors
  • metaanalysis
  • Modeling
  • models
  • Models, Biological
  • Natural Sciences
  • Naturvetenskap
  • patterns
  • phylogenetic comparative method
  • Phylogenetics
  • Phylogeny
  • Probability Theory and Statistics
  • Regression Analysis
  • Sannolikhetsteori och statistik
  • selection
  • Species
  • Statistical discrepancies
  • Statistics
  • Statistik
  • within-species variation
ispartof: Systematic biology, 2012-05-01, Vol.61 (3), p.413-425
description: Regressions of biological variables across species are rarely perfect. Usually, there are residual deviations from the estimated model relationship, and such deviations commonly show a pattern of phylogenetic correlations indicating that they have biological causes. We discuss the origins and effects of phylogenetically correlated biological variation in regression studies. In particular, we discuss the interplay of biological deviations with deviations due to observational or measurement errors, which are also important in comparative studies based on estimated species means. We show how bias in estimated evolutionary regressions can arise from several sources, including phylogenetic inertia and either observational or biological error in the predictor variables. We show how all these biases can be estimated and corrected for in the presence of phylogenetic correlations. We present general formulas for incorporating measurement error in linear models with correlated data. We also show how alternative regression models, such as major axis and reduced major axis regression, which are often recommended when there is error in predictor variables, are strongly biased when there is biological variation in any part of the model. We argue that such methods should never be used to estimate evolutionary or allometric regression slopes.
language: eng
source: Alma/SFX Local Collection
identifier: ISSN: 1063-5157
fulltext: fulltext
issn:
  • 1063-5157
  • 1076-836X
  • 1076-836X
url: Link


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titleInterpreting the Evolutionary Regression: The Interplay Between Observational and Biological Errors in Phylogenetic Comparative Studies
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creatorHansen, Thomas F ; Bartoszek, Krzysztof
creatorcontribHansen, Thomas F ; Bartoszek, Krzysztof ; Naturvetenskapliga fakulteten ; Institutionen för matematiska vetenskaper ; Faculty of Sciences ; Göteborgs universitet ; Gothenburg University ; Department of Mathematical Sciences
descriptionRegressions of biological variables across species are rarely perfect. Usually, there are residual deviations from the estimated model relationship, and such deviations commonly show a pattern of phylogenetic correlations indicating that they have biological causes. We discuss the origins and effects of phylogenetically correlated biological variation in regression studies. In particular, we discuss the interplay of biological deviations with deviations due to observational or measurement errors, which are also important in comparative studies based on estimated species means. We show how bias in estimated evolutionary regressions can arise from several sources, including phylogenetic inertia and either observational or biological error in the predictor variables. We show how all these biases can be estimated and corrected for in the presence of phylogenetic correlations. We present general formulas for incorporating measurement error in linear models with correlated data. We also show how alternative regression models, such as major axis and reduced major axis regression, which are often recommended when there is error in predictor variables, are strongly biased when there is biological variation in any part of the model. We argue that such methods should never be used to estimate evolutionary or allometric regression slopes.
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subjectAdaptation ; Adaptation; allometry; major-axis regression; measurement error; phylogenetic comparative method; phylogenetic inertia; reduced major-axis regression; structural equation ; allometry ; Bias ; Biological adaptation ; Biological evolution ; Biological Sciences ; Biological taxonomies ; Biologiska vetenskaper ; brain-size ; Comparative analysis ; contrasts ; Earth Science with specialization in Environmental Analysis ; Error rates ; Estimation bias ; Evolution ; Evolutionary Biology ; Evolutionsbiologi ; Geovetenskap med inriktning mot miljöanalys ; interspecific data ; Linear Models ; Linear regression ; major-axis regression ; Matematik ; Matematisk statistik ; Mathematical Statistics ; Mathematics ; measurement error ; Measurement errors ; metaanalysis ; Modeling ; models ; Models, Biological ; Natural Sciences ; Naturvetenskap ; patterns ; phylogenetic comparative method ; Phylogenetics ; Phylogeny ; Probability Theory and Statistics ; Regression Analysis ; Sannolikhetsteori och statistik ; selection ; Species ; Statistical discrepancies ; Statistics ; Statistik ; within-species variation
ispartofSystematic biology, 2012-05-01, Vol.61 (3), p.413-425
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0Copyright © 2012 Society of Systematic Biologists
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descriptionRegressions of biological variables across species are rarely perfect. Usually, there are residual deviations from the estimated model relationship, and such deviations commonly show a pattern of phylogenetic correlations indicating that they have biological causes. We discuss the origins and effects of phylogenetically correlated biological variation in regression studies. In particular, we discuss the interplay of biological deviations with deviations due to observational or measurement errors, which are also important in comparative studies based on estimated species means. We show how bias in estimated evolutionary regressions can arise from several sources, including phylogenetic inertia and either observational or biological error in the predictor variables. We show how all these biases can be estimated and corrected for in the presence of phylogenetic correlations. We present general formulas for incorporating measurement error in linear models with correlated data. We also show how alternative regression models, such as major axis and reduced major axis regression, which are often recommended when there is error in predictor variables, are strongly biased when there is biological variation in any part of the model. We argue that such methods should never be used to estimate evolutionary or allometric regression slopes.
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4Biological adaptation
5Biological evolution
6Biological Sciences
7Biological taxonomies
8Biologiska vetenskaper
9brain-size
10Comparative analysis
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12Earth Science with specialization in Environmental Analysis
13Error rates
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15Evolution
16Evolutionary Biology
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18Geovetenskap med inriktning mot miljöanalys
19interspecific data
20Linear Models
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22major-axis regression
23Matematik
24Matematisk statistik
25Mathematical Statistics
26Mathematics
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28Measurement errors
29metaanalysis
30Modeling
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32Models, Biological
33Natural Sciences
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36phylogenetic comparative method
37Phylogenetics
38Phylogeny
39Probability Theory and Statistics
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41Sannolikhetsteori och statistik
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44Statistical discrepancies
45Statistics
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abstractRegressions of biological variables across species are rarely perfect. Usually, there are residual deviations from the estimated model relationship, and such deviations commonly show a pattern of phylogenetic correlations indicating that they have biological causes. We discuss the origins and effects of phylogenetically correlated biological variation in regression studies. In particular, we discuss the interplay of biological deviations with deviations due to observational or measurement errors, which are also important in comparative studies based on estimated species means. We show how bias in estimated evolutionary regressions can arise from several sources, including phylogenetic inertia and either observational or biological error in the predictor variables. We show how all these biases can be estimated and corrected for in the presence of phylogenetic correlations. We present general formulas for incorporating measurement error in linear models with correlated data. We also show how alternative regression models, such as major axis and reduced major axis regression, which are often recommended when there is error in predictor variables, are strongly biased when there is biological variation in any part of the model. We argue that such methods should never be used to estimate evolutionary or allometric regression slopes.
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