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Predictors for mapping fish

Fish                                                                          Photo: Maria Asplund

PREHAB has evaluated the performance of different environmental predictors belonging to five general predictor categories to explain abundance and distribution of fish and fish eggs. The number of species, predictors and areas were limited, but modelling results showed that location and hydrography were highly useful as explanatory predictor variables.

Modelling of fish abundance and distribution (presence–absence) was done using a limited number of environmental variables and species (various stages of pike, pikeperch, stickleback and herring) with different types and amount of data from the Swedish-Finnish Archipelago Sea and the Lithuanian coast.

Because of the limited number of species and locations and because of the resulting small number of predictors tested (e.g. substrate was not used at all), it is difficult to generalise about the importance of different predictor categories. Nevertheless, it was frequently observed that the geographic location and hydrographic variables (i.e. mainly Secchi depth and salinity) were quite powerful predictors of fish abundance and distribution, while depth and wave exposure were less important. Finally, it can also be mentioned that the presence of vegetation (i.e. the red alga Furcellaria, not shown in the figures) was a powerful predictor of spawning herring areas.

Predictors for modelling fish abundanceFigure 1. Relative importance of four predictor categories for modelling abundance of fish and fish eggs in the Baltic Sea. Note that substrate was not included as a predictor in regression analyses of fish abundance. 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 fish distributionFigure 2. Relative importance of five predictor categories in models of fish distribution (presence/absence). 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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Denna text är utskriven från följande webbsida:
http://prehab.gu.se/mapping/which-data-are-needed-/environmental-data/predictors-for-fish/
Utskriftsdatum: 2019-12-07