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Evolutionary processes in variable environments

By: Novak, Sebastian.
Material type: materialTypeLabelBookPublisher: IST Austria 2016
Contents:
Biographical Sketch
List of Publications
Personal Foreword
Abstract
List of Figures
1 Introduction
2 Stress-induced mutagenesis
3 Evolution of dispersal
4 Multivariate quantitative genetics
5 Conclusion
Bibliography
Appendix
Summary: Natural environments are never constant but subject to spatial and temporal change on all scales, increasingly so due to human activity. Hence, it is crucial to understand the impact of environmental variation on evolutionary processes. In this thesis, I present three topics that share the common theme of environmental variation, yet illustrate its effect from different perspectives. First, I show how a temporally fluctuating environment gives rise to second-order selection on a modifier for stress-induced mutagenesis. Without fluctuations, when populations are adapted to their environment, mutation rates are minimized. I argue that a stress-induced mutator mechanism may only be maintained if the population is repeatedly subjected to diverse environmental challenges, and I outline implications of the presented results to antibiotic treatment strategies. Second, I discuss my work on the evolution of dispersal. Besides reproducing known results about the effect of heterogeneous habitats on dispersal, it identifies spatial changes in dispersal type frequencies as a source for selection for increased propensities to disperse. This concept contains effects of relatedness that are known to promote dispersal, and I explain how it identifies other forces selecting for dispersal and puts them on a common scale. Third, I analyse genetic variances of phenotypic traits under multivariate stabilizing selection. For the case of constant environments, I generalize known formulae of equilibrium variances to multiple traits and discuss how the genetic variance of a focal trait is influenced by selection on background traits. I conclude by presenting ideas and preliminary work aiming at including environmental fluctuations in the form of moving trait optima into the model.
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Thesis

Biographical Sketch

List of Publications

Personal Foreword

Abstract

List of Figures

1 Introduction

2 Stress-induced mutagenesis

3 Evolution of dispersal

4 Multivariate quantitative genetics

5 Conclusion

Bibliography

Appendix

Natural environments are never constant but subject to spatial and temporal change on
all scales, increasingly so due to human activity. Hence, it is crucial to understand the
impact of environmental variation on evolutionary processes. In this thesis, I present
three topics that share the common theme of environmental variation, yet illustrate its
effect from different perspectives.
First, I show how a temporally fluctuating environment gives rise to second-order
selection on a modifier for stress-induced mutagenesis. Without fluctuations, when
populations are adapted to their environment, mutation rates are minimized. I argue
that a stress-induced mutator mechanism may only be maintained if the population is
repeatedly subjected to diverse environmental challenges, and I outline implications of
the presented results to antibiotic treatment strategies.
Second, I discuss my work on the evolution of dispersal. Besides reproducing
known results about the effect of heterogeneous habitats on dispersal, it identifies
spatial changes in dispersal type frequencies as a source for selection for increased
propensities to disperse. This concept contains effects of relatedness that are known
to promote dispersal, and I explain how it identifies other forces selecting for dispersal
and puts them on a common scale.
Third, I analyse genetic variances of phenotypic traits under multivariate stabilizing
selection. For the case of constant environments, I generalize known formulae of
equilibrium variances to multiple traits and discuss how the genetic variance of a focal
trait is influenced by selection on background traits. I conclude by presenting ideas and
preliminary work aiming at including environmental fluctuations in the form of moving
trait optima into the model.

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