Browsing by Subject "recruitment"
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(2021)Oil spills in aquatic environments are devastating disasters with both biological and economic impacts. Fish populations are among the many subjects of these impacts. In literature, there are numerous assessments of oil spill impacts on fish populations. From all applied research methods, the focus of this thesis is on Bayesian methods. In prior research, several Bayesian models have been developed for assessing oil spill impacts on fish populations. These models, however, have focused on the assessment of impacts from past spills. They have not been used for predicting impacts of possible future oil spills. Furthermore, the models have not utilized data from laboratory studies. Some examples can be found of models assessing economic impacts of oil spills on fish populations however, none of them assess the economic impacts that follow from decreases in biomass. The aim of this thesis is to develop a Bayesian bioeconomic prediction model, which would be able to predict oil spill impacts on Baltic Sea main basin herring population, and the consequential economic impacts on fishermen. The idea is to predict the impacts of several hypothetical oil spill scenarios. As a result of this thesis, a bioeconomic prediction model was developed, which can predict both biological and economic impacts of oil spills on Baltic Sea main basin herring through additional oil induced mortality of herring eggs. The model can be applied to other fish populations in other regions as well. The model utilizes laboratory studies for assessing population level impacts. The model can be used for both assessing risks of the impacts of possible future oil spills, and for decision analysis after a spill has already occurred. Furthermore, the model can be used for assessing unknown aspects of past oil spills. The economic predictions can be used, for example, to estimate the compensations that could possibly be paid to fishermen. In the future, the prediction model should be developed further, especially regarding its stock-recruitment relationship assumptions. In addition, the model’s assumptions regarding the calculation of oil induced additional mortality and the economic impacts, should be expanded.
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(2021)The science of fish stock assessment is one that is very resource and labor intensive, with stock assessment models historically being based on data that causes a model to overestimate the strength of a population, sometimes with drastic consequences. The need of cost-effective assessment models and approaches increases, which is why I looked into using Bayesian modeling and networks as an approach not often used in fisheries science. I wanted to determine if it could be used to predict both recruitment and spawning stock biomass of four fish species in the north Atlantic, cod, haddock, pollock and capelin, based on no other evidence other than the recruitment or biomass data of the other species and if these results could be used to lower the uncertanties of fish stock models. I used data available on the RAM legacy database to produce four different models with the statistical software R, based on four different Bayes algorithms found in the R-package bnlearn, two based on continuous data and two based on discrete data. What I found was that there is much potential in the Bayesian approach to stock prediction and forecasting, as our prediction error percentage ranged between 1 and 40 percent. The best predictions were made when the species used as evidence had a high correlation coefficient with the target species, which was the case with cod and haddock biomass, which had a unusually high correlation of 0.96. As such, this approach could be used to make preliminary models of interactions between a high amount of species in a specific area, where there is data abundantly available and these models could be used to lower the uncertanties of the stock assessments. However, more research into the applicability for this approach to other species and areas needs to be conducted.
Now showing items 1-2 of 2