Stochastic spatiotemporal diffusive predator-prey systems

Guanqi Liu; Yuwen Wang

doi:10.3934/cpaa.2018005

Article Contents

2018, Volume 17, Issue 1: 67-84. Doi: 10.3934/cpaa.2018005

This issue Previous Article Infinitely many solutions for generalized quasilinear Schrödinger equations with sign-changing potential Next Article Liouville results for fully nonlinear integral elliptic equations in exterior domains

Stochastic spatiotemporal diffusive predator-prey systems

Guanqi Liu and
Yuwen Wang^,

Yuan Yung Tseng Functinal Analysis Resarch Center, Harbin Normal University, Harbin, 150025, China, School of mathematics and Statistics, Northeast Normal Unversity, Changchun, 130024, China

* Corresponding author
* Corresponding author

Received: November 30, 2016

Revised: May 31, 2017

Published: December 2017

This work is supported by the National Natural Science Foundation of China grant 11471091,11571086.

Abstract Full Text(HTML) Related Papers Cited by

Abstract

In this paper, a spatiotemporal diffusive predator-prey system with Holling type-Ⅲ is considered. By using a Lyapunov-like function, it is proved that the unique local solution of the system must be a a global one if the interaction intensity is small enough. A comparison theorem is used to show that the system can be extinction or stability in mean square under some additional conditions. Finally, an unique invariant measure for the system is obtained.

Keywords:

Global solution,
spatiotemporal stochastic Holling type III,
extinction,
stability in mean square,
invariant measure.

Mathematics Subject Classification: 35R60, 93E15, 37C40, 60H15.

Citation:

Full Text(HTML)

Related Papers

Cited by

References

[1]	P. Balasubramaniam and C. Vidhlya, Global asymptotic stability of stochastic BAM neural networks with distributed delays and reaction-diffusion terms, J. Comput. App. Math., 234 (2010), 3458-3466. doi: 10.1016/j.cam.2010.05.007.
[2]	T. Caraballo and L. Shaikhet, Stability of delay evolution equations with stochastic perturbations, Commun. Pur. Appl. Anal., 13 (2014), 2095-2113. doi: 10.3934/cpaa.2014.13.2095.
[3]	P. Chow, Stochastic Partial Differential Equations, 2$ ^{nd} $ edition, CRC Press, New York, 2014.
[4]	H. I. Freedman, Deterministic Mathematical Models in Population Ecoloty, Marcel Dekker, New York, 1980.
[5]	S. L. Hollis, R. H. Martin and M. Pierre, Global existence and boundedness in reaction-diffusion systems, SIAM J. Math. Anal., 18 (1987), 744-761. doi: 10.1137/0518057.
[6]	C. Y. Ji, D. Q. Jiang and N. Z. Shi, Analysis of a predator-prey model with modified Leslie-Goweer and Holling-type Ⅱ schemes with sotchastic perturbation, J. Math. Anal. Appl., 359 (2009), 482-498. doi: 10.1016/j.jmaa.2009.05.039.
[7]	Y. G. Kao and C. H. Wang, Global stability analysis for stochastic coupled reaction-diffusion systems on networks, Nonlinear Anal-real., 14 (2013), 1457-1465. doi: 10.1016/j.nonrwa.2012.10.008.
[8]	P. Liu, J. P. Shi and Z. A. Wang, Pattern formation of the attraction-repulsion Keller-Segel system, Discret Contin. Dyn. S., 18 (2013), 2597-2625. doi: 10.3934/dcdsb.2013.18.2597.
[9]	K. Liu, Stability of Infinite Diemnsional Stochastic Differential Equations with Applications, Chapmen & Hall\CRC, New York, 2006.
[10]	A. J. Lotka, Elements of Physical Biology, Dover Publications, 1925.
[11]	Q. Luo, F. Deng, J. Bao and Y. Fu, Stabilization of stochastic Hopfield neural network with distributed parameters, Science in China, Ser. F Information Sciences, 47 (2004), 752-762. doi: 10.1360/03yf0332.
[12]	Q. Luo and X. Mao, Sochastic population dynamics under regime switching, J. Math. Anal. Appl., 334 (2007), 69-84. doi: 10.1016/j.jmaa.2006.12.032.
[13]	X. Mao, Stochastic Differential Equations and Applications, 2$ ^{nd} $ edition, Horwood Publishing Chichester, UK, 1997. doi: 10.1533/9780857099402.
[14]	X. Mao, G. Marion and E. Renshaw, Enviromental Brownian noise suppresses explosions in population dynamis, Stochastic Process. Appl., 97 (2002), 95-110. doi: 10.1016/S0304-4149(01)00126-0.
[15]	R. M. May, Stability and Complexity in Model Ecosystems, Princeton University Press, NJ, 2001.
[16]	O. Misiats, O. Stanzhytskyi and N. K. Yip, Existence and uniqueness of invariant measures for stochastic reaction-diffusion equations in unbounded domains, J. Theor. Probab., 29 (2016), 996-1026. doi: 10.1007/s10959-015-0606-z.
[17]	G. D. Prato and J. Zabczyk, Stochastic Equations in Infinite Dimensions, Cambridge University Press, Cambridge, 1992. doi: 10.1017/CBO9780511666223.
[18]	G. D. Prato and J. Zabczyk, Ergodicity for Infinite, Cambridge University Press, Cambridge, 1996. doi: 10.1017/CBO9780511662829.
[19]	Y. Takeuchi, N. H. Dub, N.T. Hieu and K. Sato, Evolution of predator-prey systems described by a Lotka--Volterra equation under random environment, J. Math. Anal. Appl., 323 (2006), 938-957. doi: 10.1016/j.jmaa.2005.11.009.
[20]	J. Wang, Spatiotemporal Patterns of a Homogeneous Diffusive Predator-Prey System with Holling Type Ⅲ Functional Response, J. Dyn. Diff. Equ., (2016), 1-27. doi: 10.1007/s10884-016-9517-7.
[21]	J. Wang and H. Fan, Dynamics in a Rosenzweig-Macarthur predator-prey system with quiescence, Discret Contin. Dyn. S., 21 (2016), 909-918. doi: 10.3934/dcdsb.2016.21.909.
[22]	Y. Yang and D. Jiang, Dynamics of the stochastic low concentration trimolecular chemical reaction model, J. Math. Chem., 52 (2014), 2532-2545. doi: 10.1007/s10910-014-0398-x.
[23]	Y. Zhang and L. Li, Stability of numerical method for semi-linear stochastic pantograph differential equations, J. Inequal. Appl. , 1 (2016), 30. doi: 10.1186/s13660-016-0971-x.