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Development and phenotypic correlations: the evolution of tooth shape in Sorex araneus
1School of Biological Sciences, Queen Mary, University of London, London E1 4NS, UK.
Evolution & Development
|January 12, 2005
Summary
Developmental processes minimally constrain molar shape evolution in common shrews (Sorex araneus). Phenotypic covariance patterns evolve rapidly, influenced more by immediate factors than by developmental interactions.
Area of Science:
- Evolutionary biology
- Developmental biology
- Quantitative genetics
Background:
- Phenotypic covariation, represented by variance-covariance (P) matrices, is shaped by underlying developmental processes.
- Understanding the evolution of these P matrices over microevolutionary timescales is crucial for evolutionary studies.
Purpose of the Study:
- To investigate whether morphogenetic processes drive common patterns of phenotypic covariation in molar shape.
- To assess the evolution of molar shape P matrices in five populations of the common shrew (Sorex araneus) over microevolutionary timescales.
Main Methods:
- Matrix correlation, matrix disparity, and common principal component analysis (CPCA) were employed to analyze P matrix evolution.
- A computer model estimated theoretical covariance introduced by developmental interactions.
- Rarefaction analysis assessed the impact of sample size on P matrix comparisons.
Main Results:
- Significant but small changes in covariance structure were observed among shrew populations.
- Developmental processes explained a relatively small proportion of the observed molar shape covariance.
- Developmental principal components (PCs) were infrequently associated with common principal components (CPCs).
- Sample size critically affected P matrix comparisons, with different thresholds for matrix correlation/disparity and CPCA.
Conclusions:
- Molar shape P matrices evolve rapidly and are only loosely constrained by development.
- Factors more proximate than development likely dominate the shared covariance of molar shape.
- Akaike Information Criterion (AIC) demonstrated better performance than jump-up for CPCA evaluation at smaller sample sizes (n < 30).