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Scenario modelling - step by step

The general approach is to develop empirical models where relevant proxies for human pressures are used as predictors for biodiversity (A in figure below). Then, the spatial distribution of e.g. fish habitats (response variable) can be predicted at the current state by using complete layers of predictors (B). After that, by defining a change in the predicting pressure variable and a time-scale (C) the fitted model can be used to predict spatial distributions under alternative management scenarios (D). Finally, economic values of biodiversity in the various scenarios can be valuated, using an appropriate valuation method (E).

The principle of scenario modellingScenario modelling. The principle is to incorporate ecological impacts of human pressures in statistical models for spatial predictions of biodiversity. In this way, effects due to future changes in human pressures can be predicted and economically valuated.

Modelling methods

In order to minimize methodological errors, habitat distributions of the four study species were modelled using three separate techniques in an ensemble approach. The techniques were Maximum entropy modelling (MaxEnt), Generalized Additive Modelling (GAM) and Random Forest (rF).


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