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1 July 2009 Is Inbreeding Depression Lower in Maladapted Populations? A Quantitative Genetics Model
Ophélie Ronce, Frank H. Shaw, François Rousset, Ruth G. Shaw
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Abstract

Despite abundant empirical evidence that inbreeding depression varies with both the environment and the genotypic context, theoretical predictions about such effects are still rare. Using a quantitative genetics model, we predict amounts of inbreeding depression for fitness emerging from Gaussian stabilizing selection on some phenotypic trait, on which, for simplicity, genetic effects are strictly additive. Given the strength of stabilizing selection, inbreeding depression then varies simply with the genetic variance for the trait under selection and the distance between the mean breeding value and the optimal phenotype. This allows us to relate the expected inbreeding depression to the degree of maladaptation of the population to its environment. We confront analytical predictions with simulations, in well-adapted populations at equilibrium, as well as in maladapted populations undergoing either a transient environmental shift, or gene swamping in heterogeneous habitats. We predict minimal inbreeding depression in situations of extreme maladaptation. Our model provides a new basis for interpreting experiments that measure inbreeding depression for the same set of genotypes in different environments, by demonstrating that the history of adaptation, in addition to environmental harshness per se, may account for differences in inbreeding depression.

© 2009 The Society for the Study of Evolution.
Ophélie Ronce, Frank H. Shaw, François Rousset, and Ruth G. Shaw "Is Inbreeding Depression Lower in Maladapted Populations? A Quantitative Genetics Model," Evolution 63(7), 1807-1819, (1 July 2009). https://doi.org/10.1111/j.1558-5646.2009.00678.x
Received: 21 July 2008; Accepted: 1 February 2009; Published: 1 July 2009
JOURNAL ARTICLE
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KEYWORDS
genetic variance
maladaptation
outbreeding depression
QUANTITATIVE TRAITS
source-sink dynamics
stabilizing selection
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