Skip to main content
Log in

Interspecific influence on mobility and Turing instability

  • Published:
Bulletin of Mathematical Biology Aims and scope Submit manuscript

Abstract

In this paper we formulate a multi-patch multi-species model in which the percapita emigration rate of one species depends on the density of some other species. We then focus on Turing instability to examine if and when this cross-emigration response has crucial effects. We find that the type of interaction matters greatly. In the case of competition a cross-emigration response promotes pattern formation by exercising a destabilizing influence; in particular, it may lead to diffusive instability provided that the response is sufficiently strong, which contrasts sharply with the well-known fact that the standard competition system does not exhibit Turing instability. In the case of prey-predator or activator-inhibitor interaction it acts against pattern formation by exerting a stabilizing effect; in particular, the diffusive instability, even though it may happen in a standard system, never occurs when the response is sufficiently strong. We conclude that the cross-emigration response is an important factor that should not be ignored when pattern formation is the issue.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Allen, J. C. (1975). Mathematical models of species interactions in time and space. Am. Nat. 109, 319–342.

    Article  Google Scholar 

  • Almirantis, Y. and S. Papageorgiou (1991). Cross-diffusion effects on chemical and biological pattern formation. J. Theor. Biol. 151, 289–311.

    Google Scholar 

  • Amann, H. (1990). Dynamics of quasilinear parabolic equations II. Reaction-Diffusion Syst., Differ. Integral Equations 3, 13–75.

    MATH  MathSciNet  Google Scholar 

  • Amann, H. (1993). Nonhomogeneous linear and quasilinear elliptic and parabolic boundary value problems, in Function Spaces, Differential Operators and Nonlinear Analysis, Teubnertexte Math. 133, H. Schmeisser and H. Triebel (Eds), Teubner: Stuttgart and Leipzig, pp. 9–126.

    Google Scholar 

  • Amann, H. (1995). Linear and Quasilinear Parabolic Problems, Vol. 1, Abstract Linear Theory, Basel, Boston, Berlin: Birkhauser Verlag.

    Google Scholar 

  • Aronson, D. G. (1985). The role of diffusion in mathematical population biology: skellam revisited, in Mathematics in Biology and Medicine, Lecture Notes in Biomathematics 57, V. Capasso (Ed.), pp. 2–6.

  • Bernstein, C. (1984). Prey and predator emigration responses in the acarine system Tetranychus urticae-Phytoseiulus persimilis. Oecologia 61, 134–142.

    Article  Google Scholar 

  • Capasso, V. and A. Di Liddo (1994). Asymptotic behaviour of reaction-diffusion systems in population and epidemic models-the role of cross-diffusion. J. Math. Biol. 32, 453–463.

    Article  MathSciNet  Google Scholar 

  • Chattopadhyay, J. and P. K. Tapaswi (1993). Order and disorder in biological systems through negative cross-diffusion of mitotic inhibitor—a mathematical model. Math. Comput. Modelling 17, 105–112.

    Article  MathSciNet  Google Scholar 

  • Chattopadhyay, J. and P. K. Tapaswi (1997). Effect of cross-diffusion on pattern formation—a nonlinear analysis. Math. Comput. Modelling 48, 1–12.

    MathSciNet  Google Scholar 

  • Crowley, P. H. (1981). Dispersal and stability of predator-prey interactions. Am. Nat. 118, 673–701.

    Article  MathSciNet  Google Scholar 

  • Durrett, R. and S. Levin (1994). The importance of being discrete (and spatial). Theor. Pop. Biol. 46, 363–394.

    Article  Google Scholar 

  • Edelstein-Keshet, L. (1987). Mathematical Models in Biology, Birkhauser Mathematics Series, New York: McGraw-Hill Inc.

    Google Scholar 

  • Farkas, M. (1997). Two ways of modeling cross diffusion. Nonlinear Anal. TMA 30, 1225–1233.

    Article  MATH  MathSciNet  Google Scholar 

  • Grindrod, P. (1996). The Theory and Applications of Reaction-Diffusion Equations, Oxford: Clarendon Press.

    Google Scholar 

  • Hastings, A. (1990). Spatial heterogeneity and ecological models. Ecology 71, 426–428.

    Article  Google Scholar 

  • Holmes, E. E., M. A. Lewis, J. E. Banks and R. R. Veit (1994). Partial differential equations in ecology: spatial interactions and population dynamics. Ecology 75, 17–29.

    Article  Google Scholar 

  • Huang, Y. and O. Diekmann (2001). Predator migration in response to prey density: what are the consequences? J. Math. Biol. 43, 561–581.

    Article  MathSciNet  Google Scholar 

  • Jansen, V. A. A. and A. L. Lloyd (2000). Local stability analysis of spatially homogeneous solutions of multi-patch systems. J. Math. Biol. 41, 232–252.

    Article  MathSciNet  Google Scholar 

  • Jorńe, J. (1974). The effect of ionic migration on oscillations and pattern formation in chemical systems. J. Theor. Biol. 43, 375–380.

    Google Scholar 

  • Lou, Y. and W.-M. Ni (1996). Diffusion, self-diffusion and cross-diffusion. J. Differ. Equations 131, 79–131.

    Article  MathSciNet  Google Scholar 

  • Marcus, M. and H. Minc (1964). A Survey of Matrix Theory and Matrix Inequalities, Boston: Allyn and Bacon Inc.

    Google Scholar 

  • Murray, J. D. (1993). Mathematical Biology, Berlin, Heidelberg, New York: Spinger.

    Google Scholar 

  • Ni, W.-M. (1998). Diffusion, cross-diffusion and their spike-layer steady states. Notices AMS 45, 9–18.

    MATH  MathSciNet  Google Scholar 

  • Okubo, A. (1980). Diffusion and Ecological Problems: Mathematical Models, Berlin: Springer.

    Google Scholar 

  • Okubo, A. and S. Levin (2000). Diffusion and Ecological Problems: Modern Perspectives, 2nd edn, Berlin: Spinger.

    Google Scholar 

  • Othmer, H. G. and L. E. Scriven (1971). Instability and dynamic patterns in cellular networks. J. Theor. Biol. 32, 507–537.

    Article  Google Scholar 

  • Pels, B. (2001). Evolutionary dynamics of dispersal in predatory mites, PhD thesis, Amsterdam University.

  • Plahte, E. (2001). Pattern formation in discrete cell lattices. J. Math. Biol. 43, 411–445.

    Article  MATH  MathSciNet  Google Scholar 

  • Sabelis, M. W. (1981). Biological control of two-spotted spider mites using phytoseid predators. Part I: Modelling the Predator-prey Interactions at the Individual Level. Wageningen: Agric Res Rep Centre for Agricultural Publication and Documentation.

    Google Scholar 

  • Segel, L. A. (1984). Taxes in cellular ecology, in Mathematical Ecology, Lecture Notes in Biomathematics 54, S. A. Levin (Ed.), pp. 407–424.

  • Segel, L. A. and J. L. Jackson (1972). Dissipative structure: an explanation and an ecological example. J. Theor. Biol. 35, 545–559.

    Article  Google Scholar 

  • Shigesada, N., K. Kawasaki and E. Teramota (1979). Spatial segregation of interacting species. J. Theor. Biol. 79, 83–99.

    Article  Google Scholar 

  • Takafuji, A. (1977). The effect of successful dispersal of a phytoseiid mite Phytoseiulus persimilis. Athias-Henriot (Acarina: phytoseiidae) on the persistence in the interactive system between the predator and its prey. Res. Popul. Ecol. 18, 210–222.

    Google Scholar 

  • Turing, A. (1952). The chemical basis of morphogenesis. Phil. Trans. R. Soc. B 237, 37–72.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yunxin Huang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Huang, Y., Diekmann, O. Interspecific influence on mobility and Turing instability. Bull. Math. Biol. 65, 143–156 (2003). https://doi.org/10.1006/bulm.2002.0328

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1006/bulm.2002.0328

Keywords

Navigation