Stochastic Modelling of Seasonal Migration Using Rewriting Systems with Spatiality
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Seasonal migration is the long-distance movement of a large number of animals belonging to one or more species that occurs on a seasonal basis. It is an important phenomenon that often has a major impact on one or more ecosystem(s). It is not fully understood how this population dynamics phenomenon emerges from the behaviours and interactions of a large number of animals. We propose an approach to the modelling of seasonal migration in which dynamics is stochastically modelled using rewriting systems, and spatiality is approximated by a grid of cells. We apply our approach to the migration of a wildebeest species in the Serengeti National Park, Tanzania. Our model relies on the observations that wildebeest migration is driven by the search for grazing areas and water resources, and animals tend to follow movements of other animals. Moreover, we assume the existence of dynamic guiding paths. These paths could either be representations of the individual or communal memory of wildebeests, or physical tracks marking the land. Movement is modelled by rewritings between adjacent cells, driven by the conditions in the origin and destination cells. As conditions we consider number of animals, grass availability, and dynamic paths. Paths are initialised with the patterns of movements observed in reality, but dynamically change depending on variation of movement caused by other conditions. This methodology has been implemented in a simulator that visualises grass availability as well as population movement.
KeywordsGrid System Seasonal Migration Ordinary Object Reaction Rule Destination Cell
This work has been supported by Macao Science and Technology Development Fund, File No. 07/2009/A3, in the context of the EAE project. Suryana Setiawan is supported by a PhD scholarship under I-MHERE Project of the Faculty of Computer Science, University of Indonesia (IBRD Loan No. 4789-IND & IDA Credit No. 4077-IND, Ministry of Education and Culture, Republic of Indonesia).
- 2.Barbuti, R., Maggiolo-Schettini, A., Milazzo, P., Cerone, A., Setiawan, S.: Modelling population dynamics using grid systems. In: MoKMaSD 2012. LNCS, vol. 7991, pp. 172–189. Springer, Heidelberg (2014)Google Scholar
- 3.Barbuti, R., Maggiolo-Schettini, A., Milazzo, P., Pardini, G., Rama, A.: A process calculus for molecular interaction maps. In: Membrane Computing and Biologically Inspired Process Calculi (MeCBIC), pp. 35–49 (2009)Google Scholar
- 8.Durier, V., Graham, P., Collett, T.S.: Snapshot memories and landmark guidance in wood ants. Curr. Biol., Elsevier Science Ltd. 13, 1614–1618 (2003)Google Scholar
- 10.Goss, P.J., Peccoud, J.: Quantitative modeling of stochastic system in molecular biology by using Petri Nets. J. Bioinform. Comput. Biol. 95, 6750–6755 (1990)Google Scholar
- 15.Milazzo, P.: Qualitative and quantitative formal modeling of biological systems. Ph.D thesis, Università di Pisa (2007)Google Scholar
- 16.Pardini, G.: Formal modelling and simulation of biological systems with spatiality. Ph.D thesis, Università di Pisa (2011)Google Scholar
- 19.Regev, A., Silverman, W., Shapiro, E.Y.: Representation and simulation of biochemical processes using the \(\pi \)-calculus process algebra. In: Proceeding of the Pacific Symposium on Biocomputing, pp. 459–470 (2001)Google Scholar