On the comparison of the strength of morphological integration across morphometric datasets

Evolutionary morphologists frequently wish to understand the extent to which organisms are integrated, and whether the strength of morphological integration among subsets of phenotypic variables differ among taxa or other groups. However, comparisons of the strength of integration across datasets ar...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Evolution Jg. 70; H. 11; S. 2623 - 2631
Hauptverfasser: Adams, Dean C., Collyer, Michael L.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Blackwell Publishing Ltd 01.11.2016
Society for the Study of Evolution
Oxford University Press
Schlagworte:
Algorithms
Animals
Body Size - genetics
BRIEF COMMUNICATION
Datasets
Datasets as Topic - statistics & numerical data
Economic models
Evolution, Molecular
Genetic Variation
Geometric morphometrics
Lacertilia
Least-Squares Analysis
Lizards
Lizards - anatomy & histology
Lizards - genetics
Models, Genetic
Morphological evolution
morphological integration
Morphology
Variables
Morphological evolution
morphological integration
Geometric morphometrics
ISSN:0014-3820, 1558-5646, 1558-5646
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Evolutionary morphologists frequently wish to understand the extent to which organisms are integrated, and whether the strength of morphological integration among subsets of phenotypic variables differ among taxa or other groups. However, comparisons of the strength of integration across datasets are difficult, in part because the summary measures that characterize these patterns (RV coefficient and rPLS) are dependent both on sample size and on the number of variables. As a solution to this issue, we propose a standardized test statistic (a z-score) for measuring the degree of morphological integration between sets of variables. The approach is based on a partial least squares analysis of trait covariation, and its permutation-based sampling distribution. Under the null hypothesis of a random association of variables, the method displays a constant expected value and confidence intervals for datasets of differing sample sizes and variable number, thereby providing a consistent measure of integration suitable for comparisons across datasets. A two-sample test is also proposed to statistically determine whether levels of integration differ between datasets, and an empirical example examining cranial shape integration in Mediterranean wall lizards illustrates its use. Some extensions of the procedure are also discussed.
Bibliographie:ark:/67375/WNG-96XNFBP9-3
istex:D49514A2DD20E396779A4506935B1CCD7AFC79D5
ArticleID:EVO13045
National Science Foundation - No. DEB-1556379, DEB-1556540
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0014-3820
1558-5646
1558-5646
DOI:10.1111/evo.13045