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Predictors for mapping vegetation

Macrovegetation                                                                          Photo: Maria Asplund

PREHAB has evaluated the performance of different types of environmental variables to predict coverage (abundance) and distribution of benthic macrovegetation. The results show that several environmental factors are useful as predicting variables, but high resolution data on depth and bottom substrate have a crucial role.

Modelling abundance (percent coverage) and distribution (presence–absence) of seagrass and macroalgae was done on 40 species, higher taxa or functional groups with different types and amount of data from all four case-study areas.

The main findings were that all five predictor categories were sometimes powerful predictors. Also, environmental variables that were important in modelling the abundance (percentage cover) of species or habitats were also important for modelling distribution patterns of the same species or habitats. In general, the type of substrate (i.e. different types of sediments vs rock and stones), bottom topography (mainly depth) and hydrography (mainly Secchi depth and salinity) were stronger predictors compared to geographic location or exposure. On average the relative importance of substrate and bottom topography were roughly 80 and 75 percent, respectively. Furthermore, in 40 percent of the models, substrate was the most important type of environmental predictor. The corresponding figure for topography was 30 percent.

 

Predictors for modelling vegetation abundanceFigure 1. Relative importance of different types of predictor variables in modelling abundance (or coverage) of macrophytes in the Baltic Sea. Relative importance indicates proportion in relation to the best predictor (e.g. relative importance = 0.8 means that variable importance of the predictor category of interest is 80% of the most important category).

Predictors for modelling vegetation distributionFigure 2. Relative importance of five predictor categories in modelling distribution (presence/absence) of macrophytes in the Baltic Sea. Relative importance indicates proportion in relation to the best predictor (e.g. relative importance = 0.8 means that variable importance of the predictor category of interest is 80% of the most important category).

 

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