Running the analysis

SMD algorithms and the ensemble modelling

Species distribution models can be fitted using different techniques. The most used ones can be dived in two categories: regression-based techniques (generalized linear model, generalized additive model and multivariate adaptive regression splines) and machine learning techniques (maximum entropy, genetic algorithm for rule set production, gradient boosting machines, random forest, support vector machines).
You can choice to use one of these techniques or more than one. Different techniques often lead to results that can be also very different. This can happen because each method relies to different assumptions. To know the advantage and limitation of each technique that you will use is a good starting point for a proper interpretation of the output. However, several studies demonstrated that results of different model techniques can be so variable limiting their usefulness in a forecasting framework forecasting (Araújo & New, 2006). A possible solution is to combine multiple models in an ensemble model that present advantages over single models, particularly when the aim of SDM is prediction and forecasting (Araújo & New, 2006).