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#### Evolutionary dynamics on multi-dimensional fitness landscapes

##### MPG-Autoren

##### Externe Ressourcen

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##### Volltexte (frei zugänglich)

Gokhale_PhD_2011.pdf

(Verlagsversion), 7MB

##### Ergänzendes Material (frei zugänglich)

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##### Zitation

Gokhale, C. S. (2011). Evolutionary dynamics on multi-dimensional fitness landscapes. PhD Thesis, Christian-Albrechts-Universität, Kiel.

Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-D3F4-F

##### Zusammenfassung

Evolution is the common theme linking everything in biology from individual
alleles to languages. Darwin believed that those who were mathematically inclined
had a di erent insight and he regretted not having it. He probably
would feel grati ed knowing that now evolution has gained a solid mathematical
foundation. The general principles of evolution can be represented by precise
mathematical equations. Simplicity is invoked by making use of the minimum
factors that matter. But we cannot even imagine how many factors a single
honeybee takes into account to vouch for a particular
ower. How can we take
this complexity into account if we aim at retrieving simple tractable explanations
of biological principles? This thesis addresses this problem particularly in
two scenarios: Static and dynamic tness landscapes. A tness landscape is
a tool for visualising the the tness of a population in a space in which each
dimension is a trait a ecting the tness. The population is ever searching for
tness maxima on this landscape. This is the process of adaptation. In a static
tness landscape the tness is xed, determined by the trait combination. Here
we present results pertaining to the time required for a population to move from
one point to another on this landscape if the paths consists of broad valleys or
narrow ridges. In dynamic tness landscapes the tness is a function of the
population composition. Hence as the population moves over the landscape the
landscape changes shape and the tness maxima can be eternally moving. To
analyse frequency dependence we employ evolutionary game theory. Traditional
evolutionary game theory deals with two player games with two strategies. This
thesis invokes higher dimensions and non-linearities by studying multiple players
and strategies. Important results from the two player two strategy case are
generalised to multiple players. Finally we employ this theoretical development
to analyse a possible evolutionary application in genetic pest management.