A review of membrane computing models for ecosystems and a case study on giant pandas

Abstract

Ecosystem modeling based on membrane computing is emerging as a powerful way to study the dynamic of (real) ecological populations. These models, providing distributed parallel devices, have shown a great potential to imitate the rich features observed in the behavior of species and their interactions, key elements to understand and model ecosystems. Compared with differential equations, membrane computing models, a.k.a. P systems, can model more complex biological phenomena, due to their modularity, their ability to enclose the evolution of different environments and simulate in parallel different interrelated processes. In this paper, a comprehensive survey of membrane computing models for ecosystems is given, taking a giant panda ecosystem as an example to assess the models performance. This work aims at modeling a number of species using P systems with different membrane structure types to predict the number of individuals depending on parameters such as reproductive rate, mortality rate, and rescue or release. Firstly, the computing models are introduced conceptually, explaining the use of the rules. Next, various modeled species (including endangered animals, plants, and bacteria) are summarized, and some computer tools are presented. Then, a discussion follows on the use of P systems for ecosystem modeling. Finally, a case study on giant pandas in Chengdu Base is analyzed, concluding that the study in this field by using single environment systems can provide a valuable tool to deepen into the knowledge about the evolution of the overall ecosystem. This could ultimately help in the decision making processes of the managers of the ecosystem to increase the species diversity and modify the adaptability. Also, we should consider the impacts of natural disasters on population dynamics of species. To this purpose, the analysis performed has provided a considerably more feasible prediction data than those so far been harvested.