Cellular automata based simulation of random versus selective harvesting strategies in predator–prey systems

Abstract

The stability and productivity properties of different harvesting strategies have been analyzed through various approaches. The previous studies mostly applied the spatially lumped models and focused on different harvested species. In this research, the stochastic cellular automata based predator–prey model (EcoCA) was upgraded to investigate the difference between random harvest and the selective harvest strategies of the predator–prey complex. Three groups of numerical experiments have been conducted: (1) joint harvest with a constant effort and a density limitation, (2) joint harvest with constant quota, (3) joint harvest with a constant effort and without density limitation. In each group, both random harvest and selective harvest have been adopted to examine their differences. The statistical results of the simulations showed that the selective harvest is more stable than random harvest. Moreover, the selective harvest resulted in a longer cyclic period. For both random and selective harvest, the system is more stable if a density limitation is imposed. When the constant quota was adopted, the system is unstable. However, it collapsed more slowly for selective harvest than for random harvest. In conclusion, the selective harvest is a more sustainable strategy to a predation and harvesting system. The results are important to sustainable management of ecosystems, in particular to many situations of conservation biology and natural resources' exploitation. The research also demonstrated the advantages of cellular automata to simulate more realistic predation–harvesting system.