My work is centered around the detection of sites under selection using a machine learning-based software called SWIF(r) that uses Composite of Multiple Signals (CMS). The great benefit of this method is that it employs a number of selection scan statistics at once (for example iHS, nSL, XP-EHH, Fst) without assuming their dependence or independence from each other and greatly outperforms individual statistics. It is interesting to scientists who work in conservation since it improves the detection threshold of selection in genes and can assist in the detection of adaptation of wild species to their environment.