Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.556194
Title: Inference from gene to population : propagating uncertainty in estimates of population characteristics through ecological scales
Author: Chipperfield, Joseph Daniel
Awarding Body: University of York
Current Institution: University of York
Date of Award: 2010
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Abstract:
A current trend in population biology is the increasing realisation of the effect of individual variability on some of the big patterns of population dynamics. Simultaneously, the field of population genetics continues to develop a sophisticated theoretical basis for the inference of large-scale population dynamics from information derived from the smallest ecological unit, that of the gene. This thesis aims to contribute to the synthesis of these two fields by outlining a series of novel methods that can be used in the scaling up of genetic information to individual dynamics, and, eventually, to inference of patterns of the population. A critical feature of the methods described here is the preservation and propagation of uncertainty in estimates at each stage of the analysis. The thesis begins by introducing an estimation procedure for the calculation of allele frequencies when observation error means that frequencies cannot be directly observed. Genotyping errors can also prove troublesome in the field of parentage analysis, the basis of many models of inference of population-level processes. Any assignment errors made at this stage can be disastrous for any inference build upon these assignments. I describe a novel method of conducting parentage analysis, extend these methods for a series of common marker types and arbitrary ploidy, and show how uncertainty in parentage allocations can be propagated robustly to further stages of analysis. I review a set of new methods that may prove useful for the fitting of individual-based models to real data. I describe how these methods can be applied in the context of individual-based modelling and describe an extension of the methods to efficiently handle common data used to parametrise individual-based models. I discuss that individual-based models may provide a key bridging discipline between the field of traditional population ecology and population genetics. Finally, I describe a method to use information on dispersal collected at the individual-level to inform population-level estimate of immigration and emigration rates of spatially-explicit models of population dynamics.
Supervisor: Dytham, Calvin ; Thomas, Chris ; Bridle, Jon ; Butlin, Roger Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.556194  DOI: Not available
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