Anas Altartouri

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.

Spatial neighborhood effect and scale issues in the calibration and validation of a dynamic model of Phragmites australis distribution - A cellular automata and machine learning approach

We developed a dynamic model of the distribution of Phragmites australis, a plant that has spread intensively on Finnish coasts. The model employs cellular automata and utilizes machine learning to provide the transition rules. We examined the effects that various cell sizes and neighborhood extents had on pattern detection and model behavior. We obtained the transition probabilities using boosted regression trees in a way that accounts for the spatial arrangement of the neighboring cells. The results show the influence of the scale settings on the ability to detect and simulate patterns of Phragmites dynamics. The introduced method of quantifying the neighborhood effect, based on the spatial arrangement of the neighboring cells, displayed potential for capturing directional influences within the neighborhood. Our study addresses the close-range effect on the distribution of Phragmites, and it can be linked with models of water quality to predict future distributions under various scenarios of land-cover change. We develop a dynamic model of Phragmites distribution using cellular automata.We investigate patterns of distribution and spread at different scale settings.We obtain cellular automata transition rules using boosted regression trees.We present a model of neighborhood effect that captures directional influences.

Geospatial Web Services for Responding to Ecological Risks Posed by Oil Spills

The increased maritime oil transportation raises the risk of marine oil spill accidents. An oil spill accident can cause severe harm to the ecosystem. Adequate contingency planning for oil spill and efficient combating operations require ecological data and knowledge to be integrated in tools that facilitate the decision making. A great deal of the decisions required during these operations is of spatial nature, such as defining areas with high priority for safeguarding. Also, real time decision making is required in the operations. All of this calls for spatial and on-sit accessible tools. The authors analyze and discuss geospatial Web services and an application develop[ed for responding to the ecological risk posed by oil spills. The case study presented in this paper concerns the Gulf of Finland and the Finnish Archipelago Sea. The results indicate that geospatial services are an efficient method to deliver ecological knowledge and information for oil spill combating.