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Predictors for mapping invertebrates

Invertebrate                                                                          Photo: Maria Asplund

PREHAB has evaluated the performance of different environmental factors to predict the abundance and distribution of benthic invertebrates in soft and rocky habitats. The most powerful predictors were depth and substrate but occasionally other environmental factors were useful.

Abundance and distribution (presence–absence) were modelled for 27 species and groups of benthic invertebrates (e.g. bivalves, snails, crustaceans and polychaetes) from hard- and soft-substratum habitats, and with different data amounts and types (e.g. cores and video) from the Swedish-Finnish Archipelago Sea and the Lithuanian coast.

Generally, the findings showed that bottom topography (primarily depth) and bottom substrate were the most powerful predictors of both abundance and distribution of bottom-dwelling invertebrates. Exposure was clearly a less powerful predictor of Baltic Sea invertebrates compared to the other environmental predictors. On average the relative importance of depth and substrate were roughly 80 and 70 percent, respectively, see figure 1 and 2. In 50 percent of the models, bottom topography was the most important type of environmental predictor. The corresponding figure for bottom topography was 35 percent.

Preditors for modelling invertebrate abundanceFigure 1. Relative importance of predictor categories for models of invertebrate abundance or coverage of macroinvertebrates 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 invertebrate distributionFigure 2. Relative importance of five predictor categories in modelling distribution (presence/absence) of macroinvertebrates 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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