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Function-valued traits in evolution
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DOI：10.1098/rsif.2012.1032.png)
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摘要
En 中文
Many biological characteristics of evolutionary interest are not scalar variables but continuous functions. Given a dataset of function-valued traits generated by evolution, we develop a practical, statistical approach to infer ancestral function-valued traits, and estimate the generative evolutionary process. We do this by combining dimension reduction and phylogenetic Gaussian process regression, a non-parametric procedure that explicitly accounts for known phylogenetic relationships. We test the performance of methods on simulated, function-valued data generated from a stochastic evolutionary model. The methods are applied assuming that only the phylogeny, and the function-valued traits of taxa at its tips are known. Our method is robust and applicable to a wide range of function-valued data, and also offers a phylogenetically aware method for estimating the autocorrelation of function-valued traits.
Keyword:
comparative analysis
Ornstein-Uhlenbeck process
non-parametric Bayesian inference
functional phylogenetics
ancestral reconstruction
functional Gaussian process regression
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