Application of genetic algorithm and greedy stepwise to select input variables in classification tree models for the prediction of habitat requirements of Azolla filiculoides (Lam.) in Anzali wetland, Iran

The aim of the present study was to predict to what extent wetland characteristics can affect the habitat requirements of an exotic species, Azolla filiculoides (Lam.) in wetland. Biotic and abiotic variables were collected at the Selkeh wildlife refuge (a protected area in Anzali wetland, northern Iran) over the study period 2007–2008. Classification tree (CT) was used to find the relationship between the wetland characteristics and the cover percentage of A. filiculoides. Genetic algorithm (GA) and greedy stepwise (GS) were combined with CT in order to select the most important variables to explain the coverage of A. filiculoides. The applied method was assessed based on the percentage of correctly classified instances (CCI) and Cohen's kappa statistics (k). Different pruning confidence factors (PCFs) were tested in order to improve the predictive results regarding the complexity and accuracy of the prediction. The results showed that the prediction was reliable in terms of both performance criteria. Yet, after variable selection, the predictive performances of the CT improved. Due to potential collinear variables in the model, the GS method was less efficient than GA. The optimization of GA and GS resulted in an easy interpretation of the selected variables. The methods showed that both structural habitat (e.g. air temperature, humidity and depth) and physico-chemical variables (e.g. nutrients) can affect the habitat requirements of A. filiculoides in the wetland but the dependence of this aquatic fern on structural habitat was well confirmed by the CT before and after variable selection. Application of the given algorithms in combination with CT thus proved to have a better capability in selecting the most important variables explaining the cover of A. filiculoides and can be used by wetland managers in their decision-making for wetland conservation and management programs.

► We model the habitat requirements of Azolla before and after variable selection.
► The models are more reliable after variable selection.
► Enlarging data and more relevant variables will further improve the prediction.
► Structural habitat variables will have more contribution to the prediction.
► Such models can be used for wetland conservation/restoration programs.