Affordable Access

deepdyve-link
Publisher Website

The Genetic Architecture of Fitness Drives Population Viability during Rapid Environmental Change.

Authors
  • Kardos, Marty
  • Luikart, Gordon
Type
Published Article
Journal
The American Naturalist
Publisher
The University of Chicago Press
Publication Date
May 01, 2021
Volume
197
Issue
5
Pages
511–525
Identifiers
DOI: 10.1086/713469
PMID: 33908831
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

AbstractThe rapid global loss of biodiversity calls for improved predictions of how populations will evolve and respond demographically to ongoing environmental change. The heritability (h2) of selected traits has long been known to affect evolutionary and demographic responses to environmental change. However, effects of the genetic architecture underlying the h2 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 h2 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 h2 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.

Report this publication

Statistics

Seen <100 times