Analysis of a Spatiotemporal Phytoplankton Dynamics: Higher Order Stability and Pattern Formation
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33122
Analysis of a Spatiotemporal Phytoplankton Dynamics: Higher Order Stability and Pattern Formation

Authors: Randhir Singh Baghel, Joydip Dhar, Renu Jain

Abstract:

In this paper, for the understanding of the phytoplankton dynamics in marine ecosystem, a susceptible and an infected class of phytoplankton population is considered in spatiotemporal domain. Here, the susceptible phytoplankton is growing logistically and the growth of infected phytoplankton is due to the instantaneous Holling type-II infection response function. The dynamics are studied in terms of the local and global stabilities for the system and further explore the possibility of Hopf -bifurcation, taking the half saturation period as (i.e., ) the bifurcation parameter in temporal domain. It is also observe that the reaction diffusion system exhibits spatiotemporal chaos and pattern formation in phytoplankton dynamics, which is particularly important role play for the spatially extended phytoplankton system. Also the effect of the diffusion coefficient on the spatial system for both one and two dimensional case is obtained. Furthermore, we explore the higher-order stability analysis of the spatial phytoplankton system for both linear and no-linear system. Finally, few numerical simulations are carried out for pattern formation.

Keywords: Phytoplankton dynamics, Reaction-diffusion system, Local stability, Hopf-bifurcation, Global stability, Chaos, Pattern Formation, Higher-order stability analysis.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1079036

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1657

References:


[1] O. Bergh, K.Y. Borsheim and G. Bratbak, High abundance of viruses is found in aquatic environment, Nature. 340 (1989), pp. 467 - 468.
[2] S.Brenner and L. Scott, The Mathematical Theory of Finite Element Methods, Applied Mathematics, Springer, New York, 1994.
[3] J. Chattopadhayay, R. Sarkar and S. Mandal, Toxin producing phytoplankton may act as biology control for planktonic bloom eld study and mathematical modelling. J.Theor.Biol. 215 (2002), pp. 333 - 344.
[4] P. Ciarlet, The Finite Element Method for Elliptic Problems., Studies in Mathematics and its Applications, North-Holland, Amsterdam, 1979.
[5] J. Dhar and A. K. Sharma, The role of viral infection in phytoplankton dynamics with the inclusion of incubation class, Nonlinear Analysis: Hybrid Systems. 4 (2010), pp. 9 - 15 .
[6] J.A. Furman, Marine viruses and their biogeochemical and ecological effects, Nature. 399 (1990), pp. 541 - 548.
[7] W. Gentleman, A. Leising, B. Frost and S. Strom, J. Murray, Functional responses for zooplankton feeding on multiple resources: A review of assumptions and biological dynamics, Deep Sea Res. 50 (2003), pp. 2847 - 2875.
[8] C. Holling, Some characteristics of simple types of predation and parasitism, Can. Entomol. 91 (1959), pp. 385 - 398.
[9] E. Holmes, M. Lewis, J. Banks and R. Veit, Partial differential equations in ecology: Spatial interactions and population dynamics, Ecology. 75 (1994), no. 1, pp. 17 - 29.
[10] M. Horst, F.M. Hilker, V. Sergei and S.V. Petrovskii, Oscilation and waves in a virally infected plankton system, Ecological complexity1. (3)(2004), pp. 211 - 223.
[11] J. Jeschke, M. Kopp and R. Tollrian, Predator functional responses: Discriminating between handling and digesting prey, Ecol. Monogr. 72 (2002), no. 1, pp. 95 - 112.
[12] A. Medvinsky, S. Petrovskii, I. Tikhonova and H. Malchow, Spatiotemporal complexity of plankton and fish dynamics,SIAM Rev. 44 (2002), no. 3, pp. 311 - 370.
[13] J. Murray, Mathematical Biology, Biomathematics Texts. Springer, Berlin, 1993.
[14] S. Petrovskii and H. Malchow, A minimal model of pattern formation in a prey-predator system, Math. Comput. Model. 29 (1999), pp. 49 - 63.
[15] V. Rai and G. Jayaraman, Is diffusion-induced chaos robust?, Curr. Sci. India. 84 (2003), no. 7, pp. 925 - 929.
[16] G. Skalski and J.F. Gilliam, Functional responses with predator interference: Viable alternatives to the Holling type II model, Ecology. 82(2001), no. 11, pp. 3083 - 3092.
[17] J. Smoller, Shock Waves and Reaction-Diffusion Equations, Grundlehren der mathematischen Wissenschaften, Springer-Verlag, New York, 1983.
[18] C.A.Suttle, A.M. Chan and M.T. Cottrell, Infection of phytoplankton by viruses and reduction of primary productivity, Nature. 347 (1990), pp.467 - 469.
[19] C. Suttle and A.M. Chan, Marine Cyanophages infecting oceanic and coastal strain of Synechococcus: Abundance , morphology, cross infectivity and growth characteristic, Mar Eco Prog.Ser. 92 (1993), pp. 99 -109.
[20] J.L. Vanetten, L.C. Lane and R.H. Meints, Viruses and virus like particles of eukaryotic algae, Microbiol Rev. 55 (1991), pp. 586 - 620.
[21] S. S. Riaz, R. Sharma, S. P. Bhattacharya and D. S. Ray, Instability and pattern formation in reaction-diffusion systems: a higher order analysis., J. Chem. Phys.,(2007), 126, 064503.
[22] Platt, T., Local phytoplankton abundance and turbulence., Deep-Sea Res. 19 (1972), 183-187.
[23] J. Thomas, Numerical Partial Differential Equations: Finite Difference Methods., Texts in Applied Mathematics. Springer, New York (1995).
[24] K.S. Cheng, S.B. Hsu and S. S. Lin, Some redults on global stability of a predator prey system., J.Math. Biol. 12 (1981), 115-126.
[25] Yuri A. Kuznetsov, Elements of applied bifurcation theory, Second Edition, Springer, (1997).