Using simulated annealing optimization algorithm for prioritizing protected areas in Alborz province, Iran

Abstract

Systematic selection and prioritization of Protected Areas (PAs) are essential for biodiversity protection, however, majority of PAs of Iran are not systematically selected. Hence, an attempt is made in this study to select and prioritize PAs in Alborz province, Iran using three types of heuristic algorithms (Simulated Annealing (SA), Greedy, and Rarity algorithms), implemented in Marxan software. Our primary objective is to select the smallest possible proportion of the study area in which the maximum conservation targets is achieved. Eight conservation criteria including five species of mammals, a community of reptiles, prey birds, and waterfowls are chosen. Furthermore, five conservation scenarios are modeled to investigate the performance of the algorithms and compactness of the selected conservation zones. Our results show that the current network of PAs in this province is not efficient in terms of achieving the conservation targets. The results also reveal that the best areas to improve the current network of PAs. The SA algorithm provides satisfactory results in all scenarios compared to the Rarity and Greedy algorithms. This study elaborates the need to modify the existing boundaries of the PAs network and to select new zones for effective biodiversity protection in this province in central Alborz.