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Environmental data - the predictors

To be able to accurately map biodiversity through predictive modelling, it is important to use relevant and powerful environmental variables as predictors. In order to provide advice on which variables to use, PREHAB has empirically explored and evaluated a number of environmental variables in different parts of the Baltic Sea.

Many environmental factors affect the abundance of benthic species and habitats and are thus potentially useful predictors. We classified these factors into five categories: geographical location, bottom topography, wave exposure, type of substrate and hydrographic variables (see column to the right).

Although our analyses were not complete in all case-study areas, the results suggest that the importance of various types of predictors were consistent among the areas, but partly different depending on which type of biodiversity you want to map. In addition, environmental variables that were important in modelling the abundance or percentage cover of species or habitats were also important for modelling distribution patterns of the same species or habitats. Use the menu on the left and read more about predictors for vegetation, invertebrates and fish.

A general conclusion is the crucial role of detailed information on water depth and bottom substrate. An important message to any commissioning authority or other user is therefore that efforts to provide accurate data on depth and substrate, at the same resolution as the biological data, are likely to greatly influence the quality of the resulting maps of biodiversity.


Five predictor categories

PREHAB found the following five predictor categories useful for modelling benthic species and habitats in the Baltic Sea:

  • geographical location (longitude and latitude)
  • bottom topography (depth, slope, curvature and aspect)
  • wave exposure (regular and depth-attenuated)
  • bottom substrate (rocky, non-mobile and soft, mobile)
  • hydrography (salinity, temperature, pH and Secchi depth)

Evaluation of predictors

To evaluate the usefulness of different types of environmental predictors PREHAB has performed predictive modelling of large data sets from four case study areas. Also, we have reviewed and synthesised previolusly published records of species-environment relationships in coastal areas of the Baltic Sea region.

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