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Keywords:

  • Baltic Sea;
  • boosted regression trees;
  • common reed;
  • habitat suitability;
  • machine learning;
  • species distribution models

Abstract

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.