Browsing by Subject "Bayesian"
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(2020)Oil spillages represent a serious environmental hazard for flora and fauna of marine and coastal ecosystems. Though marine oil spills have decreased since the 1970s, the increasing production of petroleum goods remains a potential source of pollution due to its use and transportation. When aquatic organisms, including fish, are exposed to toxic oil compounds, this can cause sublethal morphological changes and increase mortality. In this context, herring have been frequently studied, and results suggest that particularly herrings eggs and larvae are highly susceptible to oil toxicity. In this thesis, a Bayesian meta-analysis was conducted to investigate the effects of crude and fuel oil on the mortality of herring eggs from the genus Clupea. Observations from laboratory studies, collected during a literature review, served as input for the statistical analysis. To this end, Bayesian inference modeling was applied to generate posterior probability distributions for additional mortality caused by exposure to oil mixtures. Also, oil concentration, oil type, exposure time, and temperature were analyzed to study possible correlations with mortality impacts. The results of this study suggest that acute mortality of exposed herring eggs is similar to mortality observed for individuals exposed to only small concentrations or none at all. Of all evaluated oil types, medium grade crude oil caused the most significant change in instantaneous mortality with increasing oil concentration. Generally, distinct oil types had a greater influence on mortality outcomes than temperatures at the given concentrations. For the lowest temperatures, some correlations for increased mortality were found. Overall, the unexplained variability between the reviewed studies has a relatively small influence on mortality outcomes. In conclusion, the mortality of exposed herrings eggs is most likely delayed due to sublethal effects, rather than immediate, at the modeled concentrations. Altogether, uncertainty amongst the posterior probability distributions is high, indicating a wide possibility range for the monitored parameters' actual values. The reasons for elevated uncertainty likely stem from diverse experimental setups, biological differences between tested species, relatively small sample sizes, and model-related issues. Thus, future research could consider additional variables, information from observational studies and other fish species to reduce uncertainty in mortality outcomes.
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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.
Now showing items 1-2 of 2