Estimation of minimum viable population for giant panda ecosystems with membrane computing models

Even though the giant panda’s extinction status is downgraded from endangered to vulnerable, but the animals still face an uphill battle for survival. Moreover, the number of giant pandas is also rare. With the increase of captive individuals, wild release has become an inevitable task that human face. It is important to evaluate at least how much giant pandas are remained for being released to the wild. Several methods have been presented in the literature to assess minimum viable population (MVP) of giant pandas. However, each of these methods is designed only based on population sizes. In this paper, a membrane computing model based on behavioral biology is proposed to evaluate the MVP of the species. An application of the proposed data processing to binary tree whose nodes are composed of population sizes is also proposed. An binary tree is generated by integrating the following three techniques: (a) a random technique to generate the vast majority of populations; (b) a modified binary search method to obtain an order population size sequence; (c) a modified inorder traversal to generate an order binary tree and a parent tree. This is the first attempt to apply membrane computing model to assess the MVP of giant pandas. The effectiveness of the presented general PDP system is verified by means of the numbers or amounts of subpopulations. Besides, we also provide the population distribution of the obtained MVP.