Selection of the best Protected Areas Network using an Intelligent Algorithm (Case Study: Mazandaran Province)

To preserve wildlife habitats and populations, it is normal practice to select representative natural areas. The aim of this research is prioritization of candidate sites for environmental protection in Mazandaran Province. For this purpose, 26 forest cover types, habitats of 8 mammal species and important distribution areas for 4 groups of birds were used as input criteria. Simulated annealing was used for prioritization through Marxan software. The first scenario looked into the efficiency of current network of protected areas to satisfy 30 percent of the protection criteria. The result showed that the current network of protected areas only provided the set goals for 8 protection criteria. In the second scenario, the best regions for conservation were selected for supplementation of current protected areas network. The result for the beast scenario showed that, to satisfy 30 percent of the protection criteria, 28 percent of the province should be set aside. Hence, addition of 186918.04 hectares to the currently protected areas is necessary for preservation. In the third scenario, the best regions to achieve 30 percent of the protection criteria were used, masking out the current protected areas. The result for the best scenario showed that, 18.43 percent of the Province is necessary for preservation. Of this amount, only 24.17 percent overlapped with the current protected areas. The result of this research is useful for identification of gaps in the current protected areas network and selection of the best regions for efficient environmental protection.