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Optimizing selection for function-valued traits
Journal article   Peer reviewed

Optimizing selection for function-valued traits

Jay Beder and Richard Gomulkiewicz
Journal of mathematical biology, Vol.55(5), pp.861-882
11/2007
Handle:
https://hdl.handle.net/2376/116053
PMID: 17671785

Abstract

Selection differential Function-valued trait Reproducing kernel Hilbert space Mathematics Fitness function Weak limits Selection gradient Quantitative genetics Finite-dimensional trait Gaussian process 47N60 92D15 46E22 Mathematical Biology in General Applications of Mathematics 60G15
We consider a function-valued trait z(t) whose pre-selection distribution is Gaussian, and a fitness function W that models optimizing selection, subject to certain natural assumptions. We show that the post-selection distribution of z(t) is also Gaussian, compute the selection differential, and derive an equation that expresses the selection gradient in terms of the parameters of W and of the pre-selection distribution. We make no assumptions on the nature of the ‘time–parameter t.

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