Abstract
The plankton community is classified into three category of species, namely, non-toxic phytoplankton (NTP), toxic phytoplankton (TPP) and zooplankton. In this work we have introduced a mathematical model for the interaction of NTP, TPP and zooplankton population in an open marine system. We have incorporated two time delays and observed important mathematical characteristics of the proposed model such as positivity, boundedness, stability and Hopf-bifurcation for all possible combinations of both the delays at the interior equilibrium point of the model system. It is noted that increase in gestation delay may lead to the destabilization of stationary points through the creation of limit cycles. Various numerical simulations are performed to validate the analytical findings obtained here.
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05 February 2019
In the original publication, Theorem 4.6 has been published incorrectly.
References
Saha T, Bandyopadhyay M (2009) Dynamical analysis of toxin producing phytoplankton–zooplankton interactions. Nonlinear Anal Real World Appl 10:314–332
Smayda T (1997) What is a bloom? A commentary. Limnol Oceonogr 42:1132–1336
Anderson DM (1989) Toxic algae blooms and red tides: a global perspective. In: Okaichi T, Anderson DM, Nemoto T (eds) Red tides biology, environmental science and toxicology. Elsevier, New York, pp 11–16
Hallegraeff GM (1993) A review of harmful algae blooms and the apparent global increase. Phycologia 15:79–99
Smayda T (1990) Novel and nuisance phytoplankton blooms in the sea: evidence for a global epidemic. In: Graneli E, Sundstrom B, Edler L, Anderson DM (eds) Toxin marine phytoplankton. Elsevier, New York, pp 29–40
Blaxter JHS, Southward AJ (1997) Advance in marine biology. Academic Press, London
Mukhopadhyay B, Bhattacharyya R (2008) Role of gestation delay in a plankton fish model under stochastic fluctuations. Math Biosci 215:26–34
Stoermer EF, Smol JP (1999) The diatoms. Cambridge University Press, Cambridge
Chattopadhyay J, Sarkar RR, Mandal S (2002) Toxin producing plankton may act as a biological control for planktonic blooms-field study and mathematical modeling. J Theor Biol 215:333–344
Roy S, Alam S, Chattopadhyay J (2006) Competitive effects of toxin-producing phytoplankton on the overall plankton populations in the Bay of Bengal. Bull Math Biol 68(8):2303–2320
Sarkar RR, Chattopadhayay J (2003) The role of environmental stochasticity in a toxic phytoplankton–non-toxic phytoplankton–zooplankton system. Environmetrics 14:775–792
Roy S, Bhattacharya S, Das P, Chattopadhyay J (2007) Interaction among non-toxic phytoplankton, toxic phytoplankton and zooplankton: inferences from field observations. J Biol Phys 33:1–17
Jang SR-J, Baglama J, Rick J (2006) Nutrient-phytoplankton–zooplankton models with a toxin. Math Comput Model 43(1–2):105–118
Roy S (2009) The coevolution of two phytoplankton species on a single resource: allelopathy as a pseudo-mixotrophy. Theor Popul Biol 75(1):68–75
MacDonald M (1989) Biological delay system: linear stability theory. Cambridge University Press, Cambridge
Beretta E, Kuang Y (2002) Geometric stability switch criteria in delay differential systems with delay dependent parameters. SIAM J Math Anal 33(5):1144–1165
Mondal S, Maiti A, Samanta GP (2018) Effects of fear and additional food in a delayed predator–prey model. Biophys Rev Lett. https://doi.org/10.1142/S1793048018500091
Sharma S, Mondal A, Pal AK, Samanta GP (2018) Stability analysis and optimal control of avian influenza virus A with time delays. Int J Dyn Control 6(3):1351–1366
Chattopadhyay J, Sarkar RR, El Abdllaoui A (2002) A delay differential equation model on harmful algal blooms in the presence of toxic substances. J Math Appl Med Biol 19(2):137–161
Hale JK (1969) Ordinary differential equations. Wiley, New York
Hale JK (1977) Theory of functional differential equations. Springer, Heidelberg
Das A, Samanta GP (2018) Stochastic prey–predator model with additional food for predator. Physica A 512:121–141
Das A, Samanta GP (2018) Modeling the fear effect on a stochastic prey–predator system with additional food for the predator. J Phys A Math Theor 51:465601
Mondal A, Pal AK, Samanta GP (2018) On the dynamics of evolutionary Leslie–Gower predator–prey eco-epidemiological model with disease in predator. Ecol Genet Genom. https://doi.org/10.1016/j.egg.2018.11.002
Saha S, Samanta GP (2019) Modelling and optimal control of HIV/AIDS prevention through PrEP and limited treatment. Physica A 516:280–307
Sangeeta S, Samanta GP (2018) Influence of dispersal and strong Allee effect on a two-patch predator–prey model. Int J Dyn Control. https://doi.org/10.1007/s40435-018-0490-3
Sangeeta S, Alakes M, Samanta GP (2018) A Michaelis–Menten predator–prey model with strong Allee effect and disease in prey incorporating prey refuge. Int J Bifurc Chaos 28(6):1850073
Yang X, Chen L, Chen J (1996) Permanence and positive periodic solution for the single-species nonautonomous delay diffusive models. Comput Math Appl 32(4):09–116
Birkhoff G, Rota GC (1982) Ordinary differential equations. Ginn, Boston
LaSalle JP (1976) The stability of dynamical systems. Society for Industrial and Applied Mathematics, Philadelphia
Gard TC, Hallam TG (1979) Persistence in food web-1, Lotka–Voltterra food chains. Bull Math Biol 41:302–315
Freedman HI, Rao VSH (1983) The tradeoff between mutual interference and time lag in predator prey models. Bull Math Biol 45:991–1004
Acknowledgements
The authors are grateful to the anonymous referees and Editor in Chief Prof. Jian-Qiao Sun for their careful reading, valuable comments and helpful suggestions, which have helped them to improve the presentation of this work significantly. The first author (Ashok Mondal) is thankful to the University Grants Commission, India for providing SRF (RGNF). The second author (A. K. Pal) acknowledges financial support from UGC, India (MRP No.-PSW-128/15-16 (ERO)).
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Appendices
Appendix A
Proof of the Proposition
Proof
Now we study the local stability behaviour of the equilibrium points by computing corresponding variational matrix:
After substituting \(E_i = (X_1,X_2,Y); i = 0, 1, 2, 3\) into (A.1), we obtain the eigenvalues for each equilibrium point: (A) \(E_0 = (0, 0, 0)\) is always unstable since eigenvalues associated with (A.1) at \(E_0\) are as follows:
(B) \(E_1 = (\frac{k_1}{\alpha _1}, 0, 0)\) is locally asymptotically stable if \(k_2<\frac{\beta _{21}k_1}{\alpha _1}\) and \(\frac{\gamma _1k_1}{\alpha _1}<\delta \), thereafter eigenvalues associated with (A.1) at \(E_1\) are given by
(C) \(E_2 = \left( 0,\frac{k_2}{\alpha _2},0\right) \) is locally asymptotically stable if \(k_1<\frac{\beta _{12}k_2}{\alpha _2}\). For this situation the eigenvalues associated with (A.1) at \(E_2\) are given by
(D) \(E_3 = ({\hat{X}}_1,{\hat{X}}_2,0)\) is locally asymptotically stable if \(\gamma _1{\hat{X}}_1<\delta +\gamma _2{\hat{X}}_2\ \hbox {and}\ A_{1}>0, B_{1}>0\). The eigenvalues associated with (A.1) at \(E_3\) are given by
and the other two eigenvalues are the roots of the equation:
where
Then all the roots of \(\lambda ^2+A_1\lambda +B_1=0\) are negative or have negative real parts if \(2(\alpha _1{\hat{X}}_1+\alpha _2{\hat{X}}_2)+\beta _{12}{\hat{X}}_2+\beta _{21}{\hat{X}}_1>k_1+k_2\) and \((k_1-2\alpha _1{\hat{X}}_1-\beta _{12}{\hat{X}}_2) (k_2-2\alpha _2{\hat{X}}_2-\beta _{21}{\hat{X}}_1)>\beta _{12}\beta _{21}{\hat{X}}_1{\hat{X}}_2\). Therefore, \(E_3\) exists and is locally asymptotically stable.
(E) \(E_4 = (\tilde{X_1},0,{\tilde{Y}})\) is locally asymptotically stable if \(\beta _{21}\tilde{X_1}+\gamma _2{\tilde{Y}}>k_2\ \hbox {and}\ A_{2}>0, B_{2}>0\). The eigenvalues associated with (A.1) at \(E_3\) are given by
and the other two eigenvalues are the roots of the equation:
where
Then all the roots of \(\lambda ^2+A_2\lambda +B_2=0\) are negative or have negative real parts if \(2\alpha _1\tilde{X_1}+\gamma _1{\tilde{Y}}+\delta >k_1+\gamma _1\tilde{X_1}\) and \(k_1\gamma _1\tilde{X_1}+2\alpha _1\tilde{X_1}\delta +\gamma _1\delta {\tilde{Y}}>k_1\delta +2\alpha _1\gamma _1\tilde{X_1}^2\). Therefore, \(E_4\) exists and is locally asymptotically stable.
(F) Again, the Jacobian matrix of the model (3.2) at its interior equilibrium point \(E^*(X_1^*,X_2^*,Y)\) can be written as follows:
The corresponding characteristic equation is given by
where
By Routh Hurwitz’s criterion, all the eigenvalues of \(V(E^*)\) have negative real parts if
Therefore, \(E^*\) exists and is locally asymptotically stable if \(\alpha _2\gamma _1^2+\gamma _2\gamma _1\beta _{21}>\alpha _1\gamma _2^2+\beta _{12}\gamma _1\gamma _2\ \hbox {and}\ A_{2}A_{3}-A_{4}>0\). \(\square \)
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Mondal, A., Pal, A.K. & Samanta, G.P. Rich dynamics of non-toxic phytoplankton, toxic phytoplankton and zooplankton system with multiple gestation delays. Int. J. Dynam. Control 8, 112–131 (2020). https://doi.org/10.1007/s40435-018-0501-4
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DOI: https://doi.org/10.1007/s40435-018-0501-4