Modeling the effectiveness of oil combating from an ecological perspective - A Bayesian network for the Gulf of Finland; the Baltic Sea
ABSTRACT Maritime traffic poses a major threat to marine ecosystems in the form of oil spills. The Gulf of Finland, the easternmost part of the Baltic Sea, has witnessed a rapid increase in oil transportation during the last 15 years. Should a spill occur, the negative ecological impacts may be reduced by oil combating, the effectiveness of which is, however, strongly dependent on prevailing environmental conditions and available technical resources. This poses increased uncertainty related to ecological consequences of future spills. We developed a probabilistic Bayesian network model that can be used to assess the effectiveness of different oil combating strategies in minimizing the negative effects of oil on six species living in the Gulf of Finland. The model can be used for creating different accident scenarios and assessing the performance of various oil combating actions under uncertainty, which enables its use as a supportive tool in decision-making. While the model is confined to the western Gulf of Finland, the methodology is adaptable to other marine areas facing similar risks and challenges related to oil spills.
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ABSTRACT: A rapid increase in maritime traffic together with challenging navigation conditions and a vulnerable ecosystem has evoked calls for improving maritime safety in the Gulf of Finland, the Baltic Sea. It is suggested that these improvements will be the result of adopting a regionally effective proactive approach to safety policy formulation and management. A proactive approach is grounded on a formal process of identifying, assessing and evaluating accident risks, and adjusting policies or management practices before accidents happen. Currently, maritime safety is globally regulated by internationally agreed prescriptive rules, which are usually revised in reaction to accidents. The proactive Formal Safety Assessment (FSA) is applied to risks common to a ship type or to a particular hazard, when deemed necessary, whereas regional FSA applications are rare. An extensive literature review was conducted in order to examine the opportunities for developing a framework for the GoF for handling regional risks at regional level. Best practices were sought from nuclear safety management and fisheries management, and from a particular case related to maritime risk management. A regional approach that sees maritime safety as a holistic system, and manages it by combining a scientific risk assessment with stakeholder input to identify risks and risk control options, and to evaluate risks is proposed. A regional risk governance framework can improve safety by focusing on actual regional risks, designing tailor-made safety measures to control them, enhancing a positive safety culture in the shipping industry, and by increasing trust among all involved.Marine Policy 10/2015; 60:107-118. DOI:10.1016/j.marpol.2015.06.003
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ABSTRACT: Biodiversity is globally recognised as a cornerstone of healthy ecosystems, and biodiversity conservation is increasingly becoming one of the important aims of environmental management. Evaluating the tradeoffs of alternative management strategies requires quantitative estimates of the costs and benefits of their outcomes, including the value of biodiversity lost or preserved. This paper takes a decision-analytic standpoint, and reviews and discusses the alternative aspects of biodiversity valuation by dividing them into three categories: socio-cultural, economic, and ecological indicator approaches. We discuss the interplay between these three perspectives and suggest integrating them into an ecosystem-based management (EBM) framework, which permits us to acknowledge ecological systems as a rich mixture of interactive elements along with their social and economic aspects. In this holistic framework, socio-cultural preferences can serve as a tool to identify the ecosystem services most relevant to society, whereas monetary valuation offers more globally comparative and understandable values. Biodiversity indicators provide clear quantitative measures and information about the role of biodiversity in the functioning and health of ecosystems. In the multi-objective EBM approach proposed in the paper, biodiversity indicators serve to define threshold values (i.e., the minimum level required to maintain a healthy environment). An appropriate set of decision-making criteria and the best method for conducting the decision analysis depend on the context and the management problem in question. Therefore, we propose a sequence of steps to follow when quantitatively evaluating environmental management against biodiversity.Ecological Indicators 02/2015; 55:1-11. DOI:10.1016/j.ecolind.2015.02.034
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ABSTRACT: A maritime accident involving an oil tanker may lead to large scale mortality or reductions in populations of coastal species due to oil. The ecological value at stake is the biota on the coast, which are neither uniformly nor randomly distributed. We used an existing oil spill simulation model, an observation database of threatened species, and a valuation method and developed a software system for assessing the spatially distributed ecological risk posed by oil shipping. The approach links a tanker accident model to a set of oil spill simulations and further to a spatial ecological value data set. The tanker accident model is a Bayesian network and thus we present a case of using a Bayesian network in geographic analysis. A case in the Gulf of Finland is used for illustration of the methodology. The method requires and builds on an extensive data collection and generation effort and modeling. The main difference of our work to earlier works on using a Bayesian network in geospatial setting is that in our case the Bayesian network was used to compute the probabilities of spatial scenarios directly in a global sense while in earlier works Bayesian networks have been used for each location separately to obtain global results. The result was a software system that was used by a distributed research team.Environmental Modelling and Software 11/2014; 61:1–11. DOI:10.1016/j.envsoft.2014.06.023