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Quantitative tests of general models for the evolution of development
1Department of Biology, Museum of Southwestern Biology, University of New Mexico, Albuquerque, New Mexico 87131, USA. anolis@unm.edu
The American Naturalist
|October 13, 2004
Summary
Evolutionary developmental biology models like the hourglass model are too complex. A simpler model of evolutionary change between adjacent developmental events adequately explains vertebrate ontogeny patterns.
Area of Science:
- Evolutionary developmental biology
- Comparative genomics
- Phylogenetics
Background:
- Several models, including the hourglass, adaptive penetrance, and early conservation models, propose different patterns of evolutionary change during vertebrate ontogeny.
- These models have historical roots but have only recently been subjected to quantitative analysis.
Purpose of the Study:
- To develop and apply quantitative phylogenetic approaches for evaluating models of evolutionary trends in ontogeny.
- To critically assess existing methods for model evaluation and propose a more accurate approach.
Main Methods:
- Development of novel quantitative phylogenetic methods to analyze developmental event data.
- Application of these methods to developmental data from 14 vertebrate species.
- Comparison of model fit using the new quantitative approaches.
Main Results:
- Existing methods for assessing evolutionary developmental models were found to be biased.
- The hourglass, adaptive penetrance, and early conservation models were found to be overly complex.
- A simpler model, positing increased evolutionary change between ontogenetically adjacent events, sufficiently explains the observed data.
Conclusions:
- The proposed quantitative phylogenetic approaches provide a more robust framework for evaluating evolutionary developmental models.
- Simpler explanations are preferred when they adequately account for observed patterns in evolutionary developmental biology.
- Evolutionary change in vertebrate ontogeny may be best characterized by a model of adjacent event transitions rather than complex stage-specific constraints.