Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.663898
Title: Genetic differentiation in spatially structured populations with particular regard to the Atlantic salmon (Salmo salar L.)
Author: Wilson, Ian Joseph
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 1996
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Abstract:
The interpretation of genetic diversity within subdivided populations is a difficult problem. This thesis develops new theory for its interpretation, and applies it to the problem of within river variation in Atlantic salmon (Salmo salar L.) populations which have been the subject of many surveys of genetic diversity throughout their range. These surveys have found lower levels of genetic variation than many fish species, even those occupying similar environments such as the Brown Trout (Salmo Trutta L.). Genetic differentiation has been observed both between samples from different rivers, and from samples from different tributaries of the same river. The levels of differentiation are of considerable interest from both scientific and commercial reasons. Despite the intense study the reasons behind the levels of differentiation between populations are unclear, and it is uncertain whether there are distinct stocks of fish within rivers. As an attempt to understand this differentiation and to guide any future studies new mathematical and statistical models for the riverine environment are developed. The riverine environment within which salmon breed is unlike any of the previously produced models for genetic differentiation. This thesis shows that the branching pattern within rivers can lead to a distinct pattern of variation which is unlike that seen in linear habitats. These habitats may lead to increased differentiation between populations. A stochastic approach to genetic variation, the coalescent process, allows us to separate the processes involved in the production of genetic data, into the genealogical and mutational processes. This approach allows us more flexibility in the modelling of genetic data. The various processes which have been invoked as causes for the observed genetic variation are analysed using these new tools.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.663898  DOI: Not available
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