Stability and Bifurcation Analysis of the Impact of Refuge on the Dissolved Oxygen-Plankton Interaction

Authors

DOI:

https://doi.org/10.30526/37.4.3505

Abstract

The suggested mathematical model for studying the effect of refuge on the dissolved oxygen in the plankton ecosystem is based on measurements of dissolved oxygen, phytoplankton, and zooplankton populations. The aim of this work is to find out the potential equilibrium and to investigate their behaviour. The study shows that there are three points of equilibrium. The feasibility requirements and stability conditions for all steady states are determined. Using the consumption of oxygen by zooplankton as a bifurcation parameter, we test for the presence of Hopf- bifurcation for the interior equilibrium. It is shown what conditions must be met for stable limit cycles. Finally, a numerical simulation is conducted to back up the analytical findings. It shows when the stability criteria are met, the solution of the proposed system constantly oscillates around the positive stable state. In addition, the solution exhibits limit cycle behaviour for small changes in certain parameters.

References

Hull, V.; Parrella, L.; Falcucci, M. Modelling dissolved oxygen dynamics in coastal lagoons. Ecological Modelling, 2008, 211(3–4), 468–480. https://doi.org/10.1016/j.ecolmodel.2007.09.023

Misra, A. K. Modeling the depletion of dissolved oxygen in a lake due to submerged macrophytes. Nonlinear Analysis: Modelling and Control, 2010, 15(2), 185–198. https://doi.org/10.15388/NA.2010.15.2.14353

Misra, A. K.; Chandra, P.; Raghavendra, V. Modeling the depletion of dissolved oxygen in a lake due to algal bloom: Effect of time delay. Advances in Water Resources, 2011, 34(10), 1232–1238. https://doi.org/10.1016/j.advwatres.2011.05.010

Gökçe, A. A mathematical study for chaotic dynamics of dissolved oxygen-phytoplankton interactions under environmental driving factors and time lag. Chaos, Solitons & Fractals, 2021, 151, 111268. https://doi.org/10.1016/j.chaos.2021.111268

Sekerci, Y.; Petrovskii, S. Mathematical modelling of plankton–oxygen dynamics under the climate change. Bulletin of Mathematical Biology, 2015, 77(12), 2325–2353. https://doi.org/10.1007/s11538-015-0126-0

Hancke, K.; Glud, R. N. Temperature effects on respiration and photosynthesis in three diatom-dominated benthic communities. Aquatic Microbial Ecology, 2004, 37(3), 265–281.

Mandal, S.; Ray, S.; Ghosh, P. B. Modeling nutrient (dissolved inorganic nitrogen) and plankton dynamics at Sagar island of Hooghly–Matla estuarine system, West Bengal, India. Natural Resource Modeling, 2012, 25(4), 629–652. https://doi.org/10.1111/j.1939-7445.2011.00116.x

Mondal, S.; Samanta, G.; De la Sen, M. Dynamics of Oxygen-Plankton Model with Variable Zooplankton Search Rate in Deterministic and Fluctuating Environments. Mathematics, 2022, 10(10), 1641. https://doi.org/10.3390/math10101641

Turner, J.; Vollrath, F.; Hesselberg, T. Wind speed affects prey-catching behaviour in an orb web spider. Naturwissenschaften, 2011, 98, 1063–1067. https://doi.org/10.1007/s00114-011-0854-4

Das, A.; Samanta, G. P. Modeling the fear effect on a stochastic prey–predator system with additional food for the predator. Journal of Physics A: Mathematical and Theoretical, 2018, 51(46), 465601. https://doi.org/10.1088/1751-8121/aae4c6

Arditi, R.; Ginzburg, L. R. Coupling in predator-prey dynamics: ratio-dependence. Journal of Theoretical Biology, 1989, 139(3), 311–326. https://doi.org/10.1016/S0022-5193(89)80211-5

Sajan; Sasmal, S. K.; Dubey, B. A phytoplankton–zooplankton–fish model with chaos control: In the presence of fear effect and an additional food. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2022, 32(1), 13114. https://doi.org/10.1063/5.0069474

Gard, T. C.; Hallam, T. G. Persistence in food webs—I Lotka-Volterra food chains. Bulletin of Mathematical Biology, 1979, 41(6), 877–891. https://doi.org/10.1016/S0092-8240(79)80024-5

Paine, R. T. Food web complexity and species diversity. The American Naturalist, 1966, 100(910), 65–75.

Meng, X.-Y.; Xiao, L. Stability and Bifurcation for a Delayed Diffusive Two-Zooplankton One-Phytoplankton Model with Two Different Functions. Complexity, 2021, 2021, 5560157. https://doi.org/10.1155/2021/5560157

Dhar, J.; Baghel, R. S. Role of dissolved oxygen on the plankton dynamics in spatio-temporal domain. Modeling Earth Systems and Environment, 2016, 2(1), 1–15. https://doi.org/10.1007/s40808-015-0061-y

Hirsch, M. W.; Smale, S.; Devaney, R. L. Differential equations, dynamical systems, and an introduction to chaos. 2012, Academic Press.

Perko, L. Differential equations and dynamical systems (Vol. 7). 2013, Springer Science & Business Media.

LaSalle, J. P. Stability theory and invariance principles. In Dynamical Systems, 1976, 211–222. Elsevier. https://doi.org/10.1016/B978-0-12-164901-2.50021-0

Liu, Y.; Zhao, L.; Huang, X.; Deng, H. Stability and bifurcation analysis of two species amensalism model with Michaelis–Menten type harvesting and a cover for the first species. Advances in Difference Equations, 2018, 2018(1), 1–19. https://doi.org/10.1186/s13662-018-1752-2

Kuznetsov, V. A.; Makalkin, I. A.; Taylor, M. A.; Perelson, A. S. Nonlinear dynamics of immunogenic tumors: parameter estimation and global bifurcation analysis. Bulletin of Mathematical Biology, 1994, 56(2), 295–321. https://doi.org/10.1016/S0092-8240(05)80260-5

Collings, J. B. Bifurcation and stability analysis of a temperature-dependent mite predator-prey interaction model incorporating a prey refuge. Bulletin of Mathematical Biology, 1995, 57(1), 63–76. https://doi.org/10.1007/BF02458316

Yu, X.; Zhu, Z.; Li, Z. Stability and bifurcation analysis of two-species competitive model with Michaelis–Menten type harvesting in the first species. Advances in Difference Equations, 2020, 2020(1), 1–25. https://doi.org/10.1186/s13662-020-02817-4

Ali, N. Stability and bifurcation of a prey predator model with Qiwu’s growth rate for prey. International Journal of Mathematics and Computation, 2016, 27(2), 30–39.

Jawad, S. R.; Al Nuaimi, M. Persistence and bifurcation analysis among four species interactions with the influence of competition, predation and harvesting. Iraqi Journal of Science, 2023, 64(3), 1369–1390. https://doi.org/10.24996/ijs.2023.64.3.30

Mukherjee, D. Study of fear mechanism in predator-prey system in the presence of competitor for the prey. Ecology and Genetics and Genomics, 2020, 15, 100052. https://doi.org/10.1016/j.egg.2020.100052

Additional Files

Published

20-Oct-2024

Issue

Section

Mathematics

Publication Dates

Received

2023-05-21

Accepted

2023-07-09

Published Online First

2024-10-20