Role of Space
- Pp. 65-92 (28)Vikas Rai
History of pattern formation dates back to 1952 when A. M. Turing pointed out that diffusion can destabilize an otherwise stable system to give rise to spatial patterns. Since then the instability has been studied in ecological, chemical and biochemical systems. An alternative to reaction–diffusion systems is meta–population models which assume that species can be thought as distributed in different spatial pockets connected by spatial processes such as migration and dispersal. Murdoch et al. (1992) explored the model proposed by Godfray and Pacala who assumed that within patch dynamics is described by Lotka–Volterra model. Spatial differences were created by making the prey birth rate in patch 2 (&#945;2) greater than that in patch 1(&#945;1). Prey moves symmetrically from one patch to the other; i.e., z1 &#61; z2 . The meta-population is neutrally stable when birth rates of prey are equal. When significant difference in birth rates is created, oscillations in prey abundance in two patches become increasingly less correlated. This is associated with per capita prey immigration into a patch becoming increasingly temporally density–dependent. The density dependence arises as the number of immigrants into a patch is weakly correlated with the number of residents in the patch. Cellular automata simulation of a reaction–diffusion system obeying rules by Ebenhoh shows that fractals are present in fish school motion. Lewis and Collaborators developed a modeling approach which enables us to find out invasion speed of a biological invasion. This approach involves setting up an integro–differential equation which needs a dispersal kernel to be specified.