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Environmental data - the predictors

Based on project results, PREHAB has produced a Baltic-wide synthesis on the performance of different types of environmental variables. This is in short our conclusions on their usefulness for modelling bentic species and habitats.

A. Vegetation

For modelling benthic macrophytes (seagrass and macroalgae), PREHAB found that predictors of all categories are useful.

  • For modelling macrophyte abundance or coverage, all five predictor categories comprise powerful predictors.
  • For macrophyte distribution (presence/absence), substrate and bottom topography provide better predictions than hydrography, location and exposure.


B. Invertebrates

In terms of macroinvertebrates, similar patterns =? as for macrophytes were found in both classification and regression analyses. For modelling distribution and abundance, in both cases, exposure seems to be a bit less useful than other predictor categories.

  • For invertebrate abundance or coverage, bottom topography and substrate provide better predictions than location and hydrography. Exposure was clearly the least powerful predictor category.
  • For invertebrate distribution (presence/absence), substrate was the most important predictor followed by location, bottom topography and hydrography. Exposure was clearly the least powerful predictor category.


C. Fish
Similar as in modelling vegetation and invertebrates, all predictor categories were found useful for predicting fish abundance and distribution. Exposure was a bit less important than the other categories.

  • For fish abundance or coverage, location and hydrography were clearly better predictors than bottom topography and exposure. Noteworthy is that substrate was not included as a predictor in regression analyses of fish.
  • For fish distribution (presence/absence), substrate, hydrography and location were clearly better predictors than bottom topography and exposure.

Five predictor categories

PREHAB has evaluated the usefulness of five broad predictor categories for modelling benthic species and habitats:

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

Scientific background

Synthesis of previously published results and predictive modelling of large data sets from PREHAB´s case study areas, provides a scientific basis for evaluating the usefulness of environmental predictors for modelling benthic species and habitats.
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