Sometimes the expression ‘Numerical Ecology’ is restricted to the use of adequate statistical methods to analyse different kinds of ecological and environmental data. But here the term ‘Numerical Ecology’ is considered in a very global approach and refer to the final use of the computer to solve many problems encountered in ecological systems. However, these methods are generic and can be applied to any field giving a universal meaning to these contributions. Due to the diversity of the theories and techniques developped and applied by Sami Souissi in his resaerch, this section is subdivided to 3 different categories:

 Computer simulations: mainly the indivdiual-based modelling approch using multi-agent systems and the Object-Oriented computer programming. Several algorithms and methods, including artificial intelligence are implemented in these simulation platform used not only in ecology but also in epidemiology as well as social, and economical sciences.

 Statistical methods: they are based on the statistical theory and its application in the field of environemental and ecosystem studies. Some methods, such as the new multivariate Bayesian mapping are developped by Sami Souissi and have been applied to different domains. The validity and robustness of the results and our conclusions are dependent on the quantity and quality of the data we are using. Unfortunately, even the most powerful statistical methods cannot compensate for the data poverty and/or biases accumulated during sampling procedure. For these reasons, Sami Souissi created an innovative teaching module that emphasizes the importance of sampling strategies upstream of any environmental or ecological project. You can see the description of this module here. 

 Mathematical methods: this sub-section refers to the use of applied mathematics in ecological modelling. Among these techniques the use of dynamical systems, such as difefrential or partial equations was applied by Sami Souissi during his earlier career including his PhD research project.