Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.596661
Title: Using computationally intensive Bayesian methods to model demographic aspects of juvenile Salmo salar L. populations in Northern Scotland
Author: Birrell, P. J.
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2010
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Abstract:
A modelling framework is developed which transfers the uncertainty inherent in estimating the size of parr populations from electrofishing data into sub-models which predict the wealth of salmon biomass and age structure of such populations. The focus of much of this thesis is on data collected on the River Conon and we find here that such a model is able to account for 36.5% (posterior median, 95% credible interval (2.8%, 58.4%)) of the observed variation in the habitat “score” – a proxy measure for the estimated size of a population. Having looked at production, the next stage involves examining migrations of smolts within freshwater. Specifically, a methodology for using RJMCMC methods for model choice when considering a range of models for dealing with capture-recapture data with subsequent captures at a smolt trap, is developed. The smolt trap allows for estimates of a migration propensity for salmon parr by recasting migrations as recoveries of dead animals within the traditional capture-recapture-recovery framework. As a by-product, estimates for over-winter, over-spring and in-migration survival probabilities based on model-averaged posterior distributions can be achieved. This methodology is then extended to study the feasibility of incorporating highly variable time-dependent covariate histories into estimates of survival and migration. Despite the presence of a low capture probability, the data appears to hold sufficient information as to be able to identify a strong relation between migration propensity and fish length, which also appears to be positively correlated with in-migration survival.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.596661  DOI: Not available
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