Sensitivity index for conservation priority ranking in the oil spill response: A case study for the coastal and marine species and habitat types in the Baltic Sea.

Numerous anthropogenic stressors, such as habitat alteration and nutrient enrichment, affect coastal and marine ecosystems around the globe. An additional threat to these ecosystems is accidental oil pollution. The proactive planning of efficient oil spill response actions requires a firm understanding of the spatiotemporal distribution of ecological coastal values at stake, and how these values can be protected in case of an oil spill. In this paper, literature and expert knowledge regarding the life history attributes of coastal and marine species were used to build a sensitivity index to assess the differences in the potential of species and habitat types to be safeguarded from oil. The developed index prioritizes sensitive species and habitat types based on 1) their conservation value, 2) the oil-induced loss and recovery potential, and 3) the effectiveness of oil retention booms and protection sheets to safeguard these entities. The final sensitivity index compares the predicted difference in the state of populations and habitat types five years after an oil spill with and without protective actions. The higher the difference, the more worthwhile the management actions are. Hence, compared to other oil spill sensitivity and vulnerability indexes presented in the literature, the developed index considers the usefulness of protective measures explicitly. We apply the developed index to a case study area in the Northern Baltic Sea to demonstrate the approach. It is noteworthy that the developed index is applicable to other areas as well, as the approach is based on the biological attributes of species and habitat types instead of individual occurrences.

Modeling the role of the close-range effect and environmental variables in the occurrence and spread of Phragmites australis in four sites on the Finnish coast of the Gulf of Finland and the Archipelago Sea.

Phragmites australis, a native helophyte in coastal areas of the Baltic Sea, has significantly spread on the Finnish coast in the last decades raising ecological questions and social interest and concern due to the important role it plays in the ecosystem dynamics of shallow coastal areas. Despite its important implications on the planning and management of the area, predictive modeling of Phragmites distribution is not well studied. We examined the prevalence and progression of Phragmites in four sites along the Southern Finnish coast in multiple time frames in relation to a number of predictors. We also analyzed patterns of neighborhood effect on the expansion and disappearance of Phragmites in a cellular data model. We developed boosted regression trees models to predict Phragmites occurrences and produce maps of habitat suitability. Various Phragmites spread figures were observed in different areas and time periods, with a minimum annual expansion rate of 1% and a maximum of 8%. The water depth, shore openness, and proximity to river mouths were found influential in Phragmites distribution. The neighborhood configuration partially explained the dynamics of Phragmites colonies. The boosted regression trees method was successfully used to interpolate and extrapolate Phragmites distributions in the study sites highlighting its potential for assessing habitat suitability for Phragmites along the Finnish coast. Our findings are useful for a number of applications. With variables easily available, delineation of areas susceptible for Phragmites colonization allows early management plans to be made. Given the influence of reed beds on the littoral species and ecosystem, these results can be useful for the ecological studies of coastal areas. We provide estimates of habitat suitability and quantification of Phragmites expansion in a form suitable for dynamic modeling, which would be useful for predicting future Phragmites distribution under different scenarios of land cover change and Phragmites spatial configuration.

Combining conservation value, vulnerability, and effectiveness of mitigation actions in spatial conservation decisions: an application to coastal oil spill combating.

Increasing oil transportation and severe oil accidents in the past have led to the development of various sensitivity maps in different countries all over the world. Often, however, the areas presented on the maps are far too large to be safeguarded with the available oil combating equipment and prioritization is required to decide which areas must be safeguarded. While oil booms can be applied to safeguard populations from a drifting oil slick, decision making on the spatial allocation of oil combating capacity is extremely difficult due to the lack of time, resources and knowledge. Since the operational decision makers usually are not ecologists, a useful decision support tool including ecological knowledge must be readily comprehensible and easy to use. We present an index-based method that can be used to make decisions concerning which populations of natural organisms should primarily be safeguarded from a floating oil slick with oil booms. The indices take into account the relative exposure, mortality and recovery potential of populations, the conservation value of species and populations, and the effectiveness of oil booms to safeguard different species. The method has been implemented in a mapping software that can be used in the Gulf of Finland (Baltic Sea) for operational oil combating. It could also be utilized in other similar conservation decisions where species with varying vulnerability, conservational value, and benefits received from the management actions need to be prioritized.

Modeling the effectiveness of oil combating from an ecological perspective--a Bayesian network for the Gulf of Finland; the Baltic Sea.

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.