A new species abundance distribution model based on model combination.

Species abundance distribution (SAD) is one of the important measures of biodiversity and one of the most significant concepts in ecology communities. Using this concept, the biologists can infer a lot of information from their collected data. In this article, we proposed a new method for predicting SAD. This method is based on the combination of several measures parameterized by machine learning techniques and decomposition of the model in sub-ranges having their proper combination. The goal is to use the combination of several individual models to design a better and more informative model. We show in this article by using many datasets representing different ecological situations that our new method is more robust and outperforms the predictive capacity of the other existing models.