How many consumer levels can survive in a chemotactic food chain?

Jing Liu, Chunhua Ou

Front. Math. China ›› 2009, Vol. 4 ›› Issue (3) : 495-521.

PDF(615 KB)
Front. Math. China All Journals
PDF(615 KB)
Front. Math. China ›› 2009, Vol. 4 ›› Issue (3) : 495-521. DOI: 10.1007/s11464-009-0031-7
Research Article
RESEARCH ARTICLE

How many consumer levels can survive in a chemotactic food chain?

Author information +
History +

Abstract

We investigate the effect and the impact of predator-prey interactions, diffusivity and chemotaxis on the ability of survival of multiple consumer levels in a predator-prey microbial food chain. We aim at answering the question of how many consumer levels can survive from a dynamical system point of view. To solve this standing issue on food-chain length, first we construct a chemotactic food chain model. A priori bounds of the steady state populations are obtained. Then under certain sufficient conditions combining the effect of conversion efficiency, diffusivity and chemotaxis parameters, we derive the co-survival of all consumer levels, thus obtaining the food chain length of our model. Numerical simulations not only confirm our theoretical results, but also demonstrate the impact of conversion efficiency, diffusivity and chemotaxis behavior on the survival and stability of various consumer levels.

Keywords

Food chain length / chemotaxis / stability / priori estimates / fixed-point index theory

Cite this article

Download citation ▾
Jing Liu, Chunhua Ou. How many consumer levels can survive in a chemotactic food chain?. Front. Math. China, 2009, 4(3): 495‒521 https://doi.org/10.1007/s11464-009-0031-7
This is a preview of subscription content, contact us for subscripton.

References

[1.]
Amann H. Fixed point equations and nonlinear eigenvalue problems in ordered Banach spaces. SIAM Review, 1976, 18: 620-709.
CrossRef Google scholar
[2.]
Bally M., Dung L., Jones D. A., Smith H. L. Effects of random motility on microbial growth and competition in a flow reactor. SIAM J Appl Math, 1998, 59: 573-596.
CrossRef Google scholar
[3.]
Boer M. P., Kooi B. W., Kooijman S. Multiple attractors and boundary crises in a tri-trophic food chain. Math Biosci, 2001, 169: 109-128.
CrossRef Google scholar
[4.]
Butler G. J., Hsu S. B., Waltman P. A mathematical model of the chemostat with periodic washout rate. SIAM J Appl Math, 1985, 45(3): 435-449.
CrossRef Google scholar
[5.]
Cantrell R. S., Cosner C. Models for predator-prey systems at multiple scales. SIAM Rev, 1996, 38(2): 256-286.
CrossRef Google scholar
[6.]
Chiu C., Hsu S. Extinction of top-predator in a three-level food-chain model. J Math Biol, 1998, 37(4): 372-380.
CrossRef Google scholar
[7.]
Du Y., Shi J. Allee effect and bistability in a spatially heterogeneous predator-prey model. Trans Amer Math Soc, 2007, 359(9): 4557-4593.
CrossRef Google scholar
[8.]
Dunbar S. R. Travelling wave solutions of diffusive Lotka-Volterra equations. J Math Biol, 1983, 17(1): 11-32.
CrossRef Google scholar
[9.]
Dung L. Global attractors and steady state solutions for a class of reaction-diffusion systems. J Differential Equations, 1998, 147: 1-29.
CrossRef Google scholar
[10.]
Dung L. Coexistence with chemotaxis. SIAM J Math Anal, 2000, 32: 504-521.
CrossRef Google scholar
[11.]
Dung L., Smith H. L. A parabolic system modeling microbial competition in an unmixed bio-reactor. J Differential Equations, 1996, 130(1): 59-91.
CrossRef Google scholar
[12.]
Dung L., Smith H. L. Steady states of models of microbial growth and competition with chemotaxis. J Math Anal Appl, 1999, 229: 295-318.
CrossRef Google scholar
[13.]
Freedman H. I., Ruan S. Hopf bifurcation in three-species food chain models with group defense. Math Biosci, 1992, 111(1): 73-87.
CrossRef Google scholar
[14.]
Fretwell S. D. Food chain dynamics: the central theory of ecology?. OIKOS, 1987, 50: 291-301.
CrossRef Google scholar
[15.]
Gourley S. A., Kuang Y. Two-species competition with high dispersal: the winning strategy. Math Biosci Eng, 2005, 2(2): 345-362.
[16.]
Herrero M., Velazquez J. Chemotactic collapse for the Keller-Segel model. J Math Biol, 1996, 35: 177-194.
CrossRef Google scholar
[17.]
Hofer T., Sherratt J., Maini P. K. Cellular pattern formation during Dictyostelium aggregation. Physica D, 1995, 85: 425-444.
CrossRef Google scholar
[18.]
Hsu S. B., Waltman P. Analysis of a model of two competitors in a chemostat with an external inhibitor. SIAM J Appl Math, 1992, 52(2): 528-540.
CrossRef Google scholar
[19.]
Hsu S. B., Waltman P. On a system of reaction-diffusion equations arising from competition in an unstirred chemostat. SIAM J Appl Math, 1993, 53(4): 1026-1044.
CrossRef Google scholar
[20.]
Huang J., Lu G., Ruan S. Existence of traveling wave solutions in a diffusive predator-prey model. J Math Biol, 2003, 46(2): 132-152.
CrossRef Google scholar
[21.]
Keller E. F., Segel L. A. Initiation of slime mold aggregation viewed as an instability. J Math Biol, 1970, 26: 399-415.
[22.]
Lauffenburger D., Calcagno P. Competition between two microbial populations in a non-mixed environment: effect of cell random motility. Biotech Bioengrg, 1983, 25: 2103-2125.
CrossRef Google scholar
[23.]
Lemesle V., Gouze J. L. A simple unforced oscillatory growth model in the chemostat. Bull Math Biol, 2008, 70(2): 344-357.
CrossRef Google scholar
[24.]
Li B. T., Kuang Y. Simple food chain in a Chemostat with distinct removal rates. J Math Anal Appl, 2000, 242: 75-92.
CrossRef Google scholar
[25.]
Lin C. S., Ni W. M., Takagi I. Large amplitude stationary solutions to a chemotaxis system. J Differential Equations, 1988, 72: 1-27.
CrossRef Google scholar
[26.]
Liu J., Zheng S. N. A Reaction-diffusion system arising from food chain in an unstirred Chemostat. J Biomath, 2002, 3: 263-272.
[27.]
Martiel J. L., Goldbeter A. A model based on receptor desensitization for cyclic AMP signaling in Dictyostelium cells. Biophys J, 1987, 52: 807-828.
CrossRef Google scholar
[28.]
Monk P. B., Othmer H. G. Cyclic AMP oscillations in suspensions of Dictyostelium discoideum. Proc Roy Soc Lond B, 1989, 240: 555-589.
CrossRef Google scholar
[29.]
Muratori S., Rinaldi S. Low- and high-frequency oscillations in three-dimensional food chain systems. SIAM J Appl Math, 1992, 52(6): 1688-1706.
CrossRef Google scholar
[30.]
Murray J. D. Mathematical Biology, I and II, 2002, New York: Springer-Verlag.
[31.]
Odum E. P. Trophic structure and productivity of Silver Springs. Florida, -Ecol Monogr, 1957, 27: 55-112.
CrossRef Google scholar
[32.]
Ou C., Wu J. Spatial spread of rabies revisited: influence of age-dependent diffusion on nonlinear dynamics. SIAM J Appl Math, 2006, 67(1): 138-163.
CrossRef Google scholar
[33.]
Ou C., Wu J. Persistence of wavefronts in delayed nonlocal reaction-diffusion equations. J Differential Equations, 2007, 235(1): 219-261.
CrossRef Google scholar
[34.]
Ou C., Wu J. Traveling wavefronts in a delayed food-limited population model. SIAM J Math Anal, 2007, 39(1): 103-125.
CrossRef Google scholar
[35.]
Owen M. R., Lewis M. A. How predation can slow, stop or reverse a prey invasion. Bull Math Bio, 2001, 63: 655-684.
CrossRef Google scholar
[36.]
Painter K. J., Hillen T. Volume-filling and quorum-fensing in models for chemosensitive and movement. Canadian Applied Mathematics Quarterly, 2002, 10: 501-542.
[37.]
Pang P. Y. H., Wang M. X. Strategy and stationary pattern in a three-species predatorprey model. Journal of Differential Equations, 2004, 200(2): 245-273.
CrossRef Google scholar
[38.]
Peng R., Shi J., Wang M. Stationary pattern of a ratio-dependent food chain model with diffusion. SIAM J Appl Math, 2007, 67(5): 1479-1503.
CrossRef Google scholar
[39.]
Peng R., Wang M. X. On multiplicity and stability of positive solutions of a diffusive prey-predator model. Journal of Mathematical Analysis and Applications, 2006, 316(1): 256-268.
CrossRef Google scholar
[40.]
Pimm S. L., Kitching R. L. The determinants of food chain lengths. OIKOS, 1987, 50: 302-307.
CrossRef Google scholar
[41.]
Post D. M. The long and short of food chain length. Trends in Ecology and Evolution, 2002, 17: 269-277.
CrossRef Google scholar
[42.]
Potapov A. B., Hillen T. Metastability in chemotaxis models. J Dynam Differential Equations, 2005, 17(2): 293-330.
CrossRef Google scholar
[43.]
Ruan S., He X. Global stability in chemostat-type competition models with nutrient recycling. SIAM J Appl Math, 1998, 58(1): 170-192.
CrossRef Google scholar
[44.]
Smith H. L. Competitive coexistence in an oscillating chemostat. SIAM J Appl Math, 1981, 40(3): 498-522.
CrossRef Google scholar
[45.]
Wang X. Qualitative behavior of solutions of chemotactic diffusion systems: effects of motility and chemotaxis and dynamics. SIAM J Math Anal, 2000, 31(3): 535-560.
CrossRef Google scholar
[46.]
Wang Y. F., Yin J. X. Predator-prey in an unstirred chemostat with periodical input and washout. Nonlinear Analysis: Real World Applications, 2002, 3: 597-610.
CrossRef Google scholar
[47.]
Wu J., Nie H., Wolkowicz G. S. K. A mathematical model of competition for two essential resources in the unstirred chemostat. SIAM J Appl Math, 2004, 65(1): 209-229.
CrossRef Google scholar
[48.]
Wu J., Nie H., Wolkowicz G. S. K. The effect of inhibitor on the plasmid-bearing and plasmid-free model in the unstirred chemostat. SIAM J Math Anal, 2007, 38(6): 1860-1885.
CrossRef Google scholar
[49.]
Wu J., Wolkowicz G. S. K. A system of resource-based growth models with two resources in the unstirred chemostat. J Differential Equations, 2001, 172(2): 300-332.
CrossRef Google scholar
AI Summary AI Mindmap
PDF(615 KB)

613

Accesses

2

Citations

Detail

Sections
Recommended

/