A Review of Membrane Computing Models for Complex Ecosystems and a Case Study on a Complex Giant Panda System

Ecosystem modelling based on membrane computing is emerging as a powerful way to study the dynamics of (real) ecological populations. These models, providing distributed parallel devices, have shown a great potential to imitate the rich features observed in the behaviour of species and their interactions and key elements to understand and model ecosystems. Compared with differential equations, membrane computing models, also known as P systems, can model more complex biological phenomena due to their modularity and 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 model performance. This work aims at modelling 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 involving processes as rescue or release. Firstly, the computing models are introduced conceptually, describing the main elements constituting the syntax of these systems