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Statistical Modelling of Marine Fish Populations and Communities

Panikian, Garabet (2016) Statistical Modelling of Marine Fish Populations and Communities. PhD thesis, University of York.

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Abstract

Sustainable fisheries management require an understanding of the relationship between the adult population and the number of juveniles successfully added to that population each year. The process driving larval survival to enter a given stage of a fish population is highly variable and this pattern of variability reflects the strength of density-dependent mortality. Marine ecosystems are generally threatened by climate change and overfishing; the coupling of these two sources have encouraged scientists to develop end-to-end ecosystem models to study the interactions of organisms at different trophic levels and to understand their behaviours in response to climate change. Our understanding of this important and massively complex system has been constrained historically by the limited amount of data available. Recent technological advances are beginning to address this lack of data, but there is an urgent need for careful statistical methodology to synthesise this information and to make reliable predictions based upon it. In this thesis I developed methodologies specifically designed to interpret the patterns of variability in recruitment by accurately estimating the degree of heteroscedasticity in 90 published stock-recruitment datasets. To better estimate the accuracy of model parameters, I employed a Bayesian hierarchical modelling framework and applied this to multiple sets of fish populations with different model structures. Finally, I developed an end-to-end ecological model that takes into account biotic and abiotic factors, together with data on the fish communities, to assess the organisation of the marine ecosystem and to investigate the potential effects of weather or climate changes. The work developed within this thesis highlights the importance of statistical methods in estimating the patterns of variability and community structure in fish populations as well as describing the way organisms and environmental factors interact within an ecosystem.

Item Type: Thesis (PhD)
Academic Units: The University of York > Computer Science (York)
Identification Number/EthosID: uk.bl.ethos.713339
Depositing User: Mr Garabet Panikian
Date Deposited: 10 May 2017 08:23
Last Modified: 21 May 2020 09:53
URI: http://etheses.whiterose.ac.uk/id/eprint/17063

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