The Genetic Architecture of Fitness Drives Population Viability during Rapid Environmental Change
Author(s) -
Marty Kardos,
Gordon Luikart
Publication year - 2021
Publication title -
the american naturalist
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.954
H-Index - 205
eISSN - 1537-5323
pISSN - 0003-0147
DOI - 10.1086/713469
Subject(s) - genetic architecture , biology , population , trait , heritability , population size , allele , environmental change , selection (genetic algorithm) , population viability analysis , evolutionary biology , quantitative trait locus , genetics , ecology , climate change , demography , gene , computer science , machine learning , endangered species , sociology , habitat , programming language
AbstractThe rapid global loss of biodiversity calls for improved predictions of how populations will evolve and respond demographically to ongoing environmental change. The heritability ( h 2 ) of selected traits has long been known to affect evolutionary and demographic responses to environmental change. However, effects of the genetic architecture underlying the h 2 of a selected trait on population responses to selection are less well understood. We use deterministic models and stochastic simulations to show that the genetic architecture underlying h 2 can dramatically affect population viability during environmental change. Polygenic trait architectures (many loci, each with a small phenotypic effect) conferred higher population viability than genetic architectures with the same initial h 2 and large-effect loci under a wide range of scenarios. Population viability also depended strongly on the initial frequency of large-effect beneficial alleles, with moderately low initial allele frequencies conferring higher viability than rare or already-frequent large-effect alleles. Greater population viability associated with polygenic architectures appears to be due to higher short-term evolutionary potential compared with architectures with large-effect loci. These results suggest that integrating information on the trait genetic architecture into quantitative genetic and population viability analysis will substantially improve our understanding and prediction of evolutionary and demographic responses following environmental change.
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