Thesis

Modelling changes in the growth, maturity, and abundance of fish in Scottish waters

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Awarding institution
  • University of Strathclyde
Date of award
  • 2017
Thesis identifier
  • T14531
Person Identifier (Local)
  • 201279564
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Abstract
  • The quantity of fish that may be sustainably harvested from the oceans depends uponhow abundant they are and, less obviously, on their rate of growth, the sizes they can attain and the sizes at which they begin reproducing. This thesis investigates whether fish stocks in Scottish waters have exhibited long term changes in growth rates, maturation scheduling or abundance. The Firth of Clyde was of particular interest, so fish stocks from this region were considered separately in each chapter. Scientific bottom trawl survey data were used to examine changes in growth rates and maturation scheduling. Trends in growth rates were determined by calculating time series of mean lengths-at-age and von Bertalanffy growth parameters. Probabilistic maturation reaction norms were used to investigate trends in maturation. The von Bertalanffy growth parameters and maturation indices were regressed against sea-surface temperature data and abundance indices derived from the survey data to determine whether trends in these variables were responsible for changes in growth and maturation. Demersal fish species were considered in chapters 2 and 3, and pelagic fish species were considered in chapter 4. Long term declines in growth rates and lengths-at-maturation were observed in several species. A length based stock assessment model was developed in chapter 5. Information on the length structure of stocks was provided by survey samples instead of catch-at-length data which is typical for such models, so the model presented here should be widely applicable since many different species are sampled by surveys. Model output was compared with existing results derived from more conventional age based models. The results were encouraging; Bayesian implementations, in particular, returned output very similar to the existing age based models.
Resource Type
DOI
Date Created
  • 2017
Former identifier
  • 9912546393502996

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