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Interpret your map

When you have obtained the final map of predicted species distribution, some care should be taken when interpreting the results, especially:

  • Predicted map based on low density of samples

(maybe link to “Which data”).
Fig below illustrates differences of predicted red alga distribution among four classification techniques. Evident differences occur in the areas with low number of samples (upper part of vegetation habitat) compared to the areas densely covered by samples (lower part of vegetation habitat). Thus, predictions are quite confident in the latter case, whereas no strict conclusions can/should be drawn in the areas with relatively low number of samples. Interpreting mapPrediction of the red alga (Furcellaria lumbricalis) distribution along the Baltic Sea Lithuanian coast by four classification methods. 
(Generalized additive models (GAM), Multivariate adaptive regression splines (MARS), Random forest (RF) and Maximum entropy modelling (MaxEnt).)

  • Measurement error propagation

Usually the environmental data are obtained from different sources (e.g. geological map, field data) with their own measurement errors. When using these data as GIS-layers for the prediction of species distribution, the final habitat map will accumulate in itself all these errors and this is called error propagation. So, if the final map error is not indicated, it must be evaluated from the description of data before interpreting the map. Otherwise it is very dangerous to draw any conclusion from map without information, how it was composed and how reliable it is.

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