J Plant Ecol ›› 2008, Vol. 1 ›› Issue (3): 161-171.doi: 10.1093/jpe/rtn016

• Research Articles • Previous Articles     Next Articles

Simulating the evolution of a clonal trait in plants with sexual and vegetative reproduction

Markus Fischer1,2,*, Eckart Winkler3 and Bernhard Schmid4   

  1. 1 Institute of Plant Sciences, University of Bern, Altenbergrain 21, CH-3013 Bern, Switzerland; 2 Institute for Biochemistry and Biology, University of Potsdam, Maulbeerallee 1, D-14469 Potsdam, Germany; 3 Department of Ecological Modelling, Helmholtz Centre for Environmental Research UFZ, PO Box 500 135, D-04301 Leipzig, Germany; 4 Institute of Environmental Sciences, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
  • Received:2007-12-31 Accepted:2008-06-04 Online:2008-07-04 Published:2008-08-25
  • Contact: Fischer, Markus E-mail:markus.fischer@ips.unibe.ch

Abstract: Aims Phenotypic optimality models neglect genetics. However, especially when heterozygous genotypes are fittest, evolving allele, genotype and phenotype frequencies may not correspond to predicted optima. This was not previously addressed for organisms with complex life histories.
Methods Therefore, we modelled the evolution of a fitness-relevant trait of clonal plants, stolon internode length. We explored the likely case of an asymmetric unimodal fitness profile with three model types. In constant selection models (CSMs), which are gametic, but not spatially explicit, evolving allele frequencies in the one-locus and five-loci cases did not correspond to optimum stolon internode length predicted by the spatially explicit, but not gametic, phenotypic model. This deviation was due to the asymmetry of the fitness profile. Gametic, spatially explicit individual-based (SEIB) modeling allowed us relaxing the CSM assumptions of constant selection with exclusively sexual reproduction.
Important findings For entirely vegetative or sexual reproduction, predictions of the gametic SEIB model were close to the ones of spatially explicit non-gametic phenotypic models, but for mixed modes of reproduction they approximated those of gametic, not spatially explicit CSMs. Thus, in contrast to gametic SEIB models, phenotypic models and, especially for few loci, also CSMs can be very misleading. We conclude that the evolution of traits governed by few quantitative trait loci appears hardly predictable by simple models, that genetic algorithms aiming at technical optimization may actually miss the optimum and that selection may lead to loci with smaller effects in derived compared with ancestral lines.

Key words: clonal plants, ecological and evolutionary modelling, genetic variation, life-history evolution, optimal life histories, simulation model

摘要:
Aims Phenotypic optimality models neglect genetics. However, especially when heterozygous genotypes are fittest, evolving allele, genotype and phenotype frequencies may not correspond to predicted optima. This was not previously addressed for organisms with complex life histories.
Methods Therefore, we modelled the evolution of a fitness-relevant trait of clonal plants, stolon internode length. We explored the likely case of an asymmetric unimodal fitness profile with three model types. In constant selection models (CSMs), which are gametic, but not spatially explicit, evolving allele frequencies in the one-locus and five-loci cases did not correspond to optimum stolon internode length predicted by the spatially explicit, but not gametic, phenotypic model. This deviation was due to the asymmetry of the fitness profile. Gametic, spatially explicit individual-based (SEIB) modeling allowed us relaxing the CSM assumptions of constant selection with exclusively sexual reproduction.
Important findings For entirely vegetative or sexual reproduction, predictions of the gametic SEIB model were close to the ones of spatially explicit non-gametic phenotypic models, but for mixed modes of reproduction they approximated those of gametic, not spatially explicit CSMs. Thus, in contrast to gametic SEIB models, phenotypic models and, especially for few loci, also CSMs can be very misleading. We conclude that the evolution of traits governed by few quantitative trait loci appears hardly predictable by simple models, that genetic algorithms aiming at technical optimization may actually miss the optimum and that selection may lead to loci with smaller effects in derived compared with ancestral lines.

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