Computational Biodiversity can broadly be understood as the effort of computational approaches for exploring, interpreting, and analyzing biodiversity data. An enormous load of growing biodiversity data needs algorithmic care for accurate data management, and therefore the term computational biodiversity comes. Instead of relying purely on presence data, the probabilistic forecast of member distribution including the regions of not occurrence can neutralize biodiversity loss by restoring potential ecosystems. This paper is aiming at revealing the perspective of computational biodiversity as a counteract for biodiversity loss by correlating the concept of dark diversity. The computation of the dark diversity is accompanied by a data mining algorithm for establishing rules with more nobility to manage the depletion of biodiversity. We generate a dataset for the Indian estuarine ecosystem and show the use of our approach by ending up with rules worthwhile for the ecologists. These would step up reinforcing biological diversity via introducing or rehabilitating specific faunal groups to an estuary under survey.