Addressing climate change and biodiversity loss requires reliable scientific knowledge – accounting for uncertainties – of the environment’s current state and the consequences of management actions.
In his doctoral thesis, Doctoral Researcher Karel Kaurila developed statistical methods to refine predictions of pikeperch spawning areas by combining local fishers’ assessments with existing observational data.
The fishers’ input proved valuable, offering additional information and extending the regional coverage of the data.
Citizen observations bring a further benefit too:
“Previous research has shown that involving them in analyses can foster more positive attitudes among stakeholders towards scientific work and findings,” points out Kaurila.
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Illustration: Pinja Kettunen.
The second article in the thesis focuses on the effect of excess nutrient loads on the eutrophication of the Finnish Archipelago Sea. It is one of the biggest threats to the region’s ecosystems.
The method presented in the thesis sheds light on the uncertainties in predictions produced by the Finnish Environment Institute’s water quality simulator.
The simulator is a model used to predict nutrient load impacts in the Finnish Archipelago Sea. A clearer understanding of these uncertainties will improve comparisons between different actions aimed at reducing nutrient loads.
The published method uses uncertainty estimation to assess the probability of various nutrient load management actions achieving the water quality improvements required for ‘good ecological status’ under the European Water Framework Directive.
The results suggest that minor changes will not suffice. Achieving the required improvement in water quality in the inner Archipelago Sea would require nutrient loads in the catchment area to be halved.
Karel Kaurila's doctoral thesis ‘Hierarchical Bayesian methods for environmental for environmental policy planning’