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Optimal predator control policy and weak Allee effect in a delayed prey–predator system

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Abstract

In this article, a system of delay differential equations to represent the predator–prey dynamics with weak Allee effect in the growth of predator population is discussed. The delay parameter regarding the time lag corresponds to the predator gestation period. Mathematical features such as uniform persistence, permanence, stability, Hopf bifurcation at the interior equilibrium point of the system are analyzed and verified by numerical simulations. Bistability between different equilibrium points is properly discussed. The chaotic behaviors of the system are recognized through bifurcation diagram, Poincare section, and maximum Lyapunov exponent. By constructing a suitable Lyapunov functional for the time-delayed model, global asymptotic stability analysis of the positive equilibrium points has been performed separately. It can be observed that the Allee parameter \(\theta \) can destabilize the non-delay system, whereas \(\theta \) and the attack rate of predator can stabilize the time-delayed model and can control the chaotic oscillations through period-halving bifurcation. The optimal predator control policy with Allee parameter (\(\theta \)) as the control parameter is also discussed.

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References

  1. Dickman, C.R.: Overview of the Impacts of Feral Cats on Australian Native Fauna. Australian Nature Conservation Agency, Canberra (1996)

    Google Scholar 

  2. Dickman, C.R.: Impact of exotic generalist predators on the native fauna of Australia. Wildl. Biol. 2, 185–195 (1996)

    Google Scholar 

  3. Rolls, E.C.: They All Ran. Wild The Story of Pests on the Land in Australia. Angus and Robertson, Sydney (1969)

    Google Scholar 

  4. Glen, A.S., Dickman, C.R.: Effects of bait-station design on the uptake of baits by non-target animals during control programmes for foxes and wild dogs. Wildl. Res. 30, 147–149 (2003)

    Article  Google Scholar 

  5. Berec, L., Angulo, E., Courchamp, F.: Multiple Allee effects and population management. Trends Ecol. Evol. 20, 185–191 (2006)

    Google Scholar 

  6. Boukal, D.S., Berec, L.: Modelling mate-finding Allee effects and populations dynamics, with applications in pest control. Popul. Ecol. 51, 445–458 (2009)

    Article  Google Scholar 

  7. Gascoigne, J.C., Lipccius, R.N.: Allee effect driven by predation. J. Appl. Ecol. 41, 801–810 (2004)

    Article  Google Scholar 

  8. Yamanaka, T., Liebhold, A.M.: Spatially implicit approaches to understanding the manipulation of mating success for insect invasion management. Popul. Ecol. 51, 427–444 (2009)

    Article  Google Scholar 

  9. Allee, W.C.: Anim. Aggreg. A study in general sociology. University of Chicago Press, Chicago (1931)

    Google Scholar 

  10. Ferdy, J.B., Austerlitz, F., Moret, J., Gouyon, P.H., Godelle, B.: Pollinator-induced density dependence in deceptive species. Oikos 87, 549–560 (1999)

    Article  Google Scholar 

  11. Courchamp, F., Grenfell, B., Clutton-Brock, T.: Impact of natural enemies on obligately cooperatively breeders. Oikos 91, 311–322 (2000)

    Article  Google Scholar 

  12. Courchamp, F., Berec, L., Gascoigne, J.: Allee Effects in Ecology and Conservation. Oxford University Press, Oxford (2008)

    Book  Google Scholar 

  13. Wang, M.H., Kot, M.: Speeds of invasion in a model with strong or weak Allee effects. Math. Biosci. 171, 83–97 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  14. Biswas, S., Sasmal, S.K., Saifuddin, Md, Chattopadhyay, J.: On existence of multiple periodic solutions for Lotka–Volterra’s predator–prey model with Allee effects. Nonlinear Stud. 22(2), 189–199 (2015)

    MATH  MathSciNet  Google Scholar 

  15. Pablo, A.: A general class of predation models with multiplicative Allee effect. Nonlinear Dyn. 78(1), 629–648 (2014)

    Article  MATH  MathSciNet  Google Scholar 

  16. Peng, F., Kang, Y.: Dynamics of a modified Leslie–Gower model with double Allee effects. Nonlinear Dyn. 80, 1051–1062 (2015)

    Article  MATH  MathSciNet  Google Scholar 

  17. Sun, G.Q.: Mathematical modeling of population dynamics with Allee effect. Nonlinear Dyn. 85(1), 1–12 (2016)

    Article  MathSciNet  Google Scholar 

  18. Rocha, J.L., Fournier-Prunaret, D., Taha, A.K.: Big bang bifurcations and Allee effect in blumbergs dynamics. Nonlinear Dyn. 77(4), 1749–1771 (2014)

    Article  MATH  MathSciNet  Google Scholar 

  19. Dong, T., Liao, X.: Bogdanov–Takens bifurcation in a trineuron BAM neural network model with multiple delays. Nonlinear Dyn. 71(3), 583–595 (2013)

    Article  MATH  Google Scholar 

  20. Sarwardi, S., Haque, M., Mandal, P.K.: Ratio-dependent predator–prey model of interacting population with delay effect. Nonlinear Dyn. 69, 817–836 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  21. Wang, J., Jiang, W.: Bifurcation and chaos of a delayed predator–prey model with dormancy of predators. Nonlinear Dyn. 69, 1541–1558 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  22. Xu, C., Tang, X., Liao, M., He, X.: Bifurcation analysis in a delayed Lotka–Volterra predator–prey model with two delays. Nonlinear Dyn. 66, 169–183 (2011)

    Article  MATH  Google Scholar 

  23. MacDonald, N.: Biological Delay Systems: Linear Stability Theory. Cambridge University Press, Cambridge (1989)

    MATH  Google Scholar 

  24. Kuang, Y.: Delay Differential Equation with Applications in Population Dynamics. Academic Press, New York (1993)

    MATH  Google Scholar 

  25. Freedman, H.I., Rao, V.S.H.: The tradeoff between mutual interference and time lag in predator prey models. Bull. Math. Biol. 45, 991–1004 (1983)

    Article  MATH  MathSciNet  Google Scholar 

  26. Freedman, H.I., So, J., Waltman, P.: Coexistence in a model of competition in the chemostat incorporating discrete time delays. SIAM J. Appl. Math. 49, 859–870 (1989)

    Article  MATH  MathSciNet  Google Scholar 

  27. Wang, W., Ma, Z.: Harmless delays for uniform persistence. J. Math. Anal. Appl. 158, 256–268 (1991)

    Article  MATH  MathSciNet  Google Scholar 

  28. Xua, R., Gan, Q., Ma, Z.: Stability and bifurcation analysis on a ratio-dependent predator–prey model with time delay. J. Comput. Appl. Math. 230, 187–203 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  29. Ghosh, K., Samanta, S., Biswas, S., Rana, S., ELmojtaba, I.M., Kesh, D.K., Chattopadhyay, J.: Stability and bifurcation analysis of an eco-epidemiological model with multiple delays. Nonlinear Stud. 23(2), 167–208 (2016)

    MATH  MathSciNet  Google Scholar 

  30. Huang, G., Takeuchi, Y.: Global analysis on delay epidemiological dynamic models with nonlinear incidence. J. Math. Biol. 63(1), 125–139 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  31. Mukandavire, Z., Garira, W., Chiyaka, C.: Asymptotic properties of an HIV/AIDS model with a time delay. J. Math. Anal. Appl. 330(2), 916–933 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  32. Ma, J., Song, X., Jin, W., Wang, C.: Autapse-induced synchronization in a coupled neuronal network. Chaos Solitons Fractals 80, 31–38 (2015)

    Article  MATH  MathSciNet  Google Scholar 

  33. Qin, H., Wu, Y., Wang, C., Ma, J.: Emitting waves from defects in network with autapses. Commun. Nonlinear Sci. Numer. Simul. 23(1), 164–174 (2015)

    Article  MATH  MathSciNet  Google Scholar 

  34. Yao, C., Ma, J., Li, C., He, Z.: The effect of process delay on dynamical behaviors in a self-feedback nonlinear oscillator. Commun. Nonlinear Sci. Numer. Simul. 39, 99–107 (2016)

    Article  MathSciNet  Google Scholar 

  35. Biswas, S., Sasmal, S.K., Samanta, S., Saifuddin, Md, Ahmed, Q.J.K., Chattopadhyay, J.: A delayed eco-epidemiological system with infected prey and predator subject to the weak Allee effect. Math. Biosci. 263, 198–208 (2015)

    Article  MATH  MathSciNet  Google Scholar 

  36. Biswas, S., Samanta, S., Chattopadhyay, J.: Cannibalistic predator–prey model with disease in predator—a delay model. Int. J. Bifurc. Chaos 25(10), 1550130 (2015)

    Article  MATH  MathSciNet  Google Scholar 

  37. Liao, M.X., Tang, X.H., Xu, C.J.: Bifurcation analysis for a three-species predator–prey system with two delays. Commun. Nonlinear Sci. Numer. Simul. 17, 183–194 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  38. Meng, X.Y., Huo, H.F., Zhang, X.B., Xiang, H.: Stability and Hopf bifurcation in a three-species system with feedback delays. Nonlinear Dyn. 64, 349–364 (2011)

    Article  MathSciNet  Google Scholar 

  39. Pal, N., Samanta, S., Biswas, S., Alquran, M., Al-Khaled, K., Chattopadhyay, J.: Stability and bifurcation analysis of a three-species food chain model with delay. Int. J. Bifurc. Chaos 25(09), 1550123 (2015)

    Article  MATH  MathSciNet  Google Scholar 

  40. Li, L.: Bifurcation and chaos in a discrete physiological control system. Appl. Math. Comput. 252, 397–404 (2015)

  41. Sun, G.Q., Zhang, J., Song, L.P., Jin, Z., Li, B.L.: Pattern formation of a spatial predator prey system. Appl. Math. Comput. 218(22), 11151–11162 (2012)

    MATH  MathSciNet  Google Scholar 

  42. Li, L.: Periodic solutions in reaction diffusion equations with time delay. Chaos Solitons Fractals 78, 33–38 (2015)

    Article  MATH  MathSciNet  Google Scholar 

  43. Li, L.: Patch invasion in a spatial epidemic model. Appl. Math. Comput. 258, 342–349 (2015)

    MATH  MathSciNet  Google Scholar 

  44. Li, L., Jin, Z., Li, J.: Periodic solutions in a herbivore-plant system with time delay and spatial diffusion. Appl. Math. Model. 40(7), 4765–4777 (2016)

    Article  MathSciNet  Google Scholar 

  45. Sun, G.Q., Wang, S.L., Ren, Q., Jin, Z., Wu, Y.P.: Effects of time delay and space on herbivore dynamics: linking inducible defenses of plants to herbivore outbreak. Sci. Rep. 5, 11246 (2015)

    Article  Google Scholar 

  46. Sun, G.Q., Jusup, M., Jin, Z., Wang, Y., Wang, Z.: Pattern transitions in spatial epidemics: mechanisms and emergent properties. Phys. Life Rev. 19, 43–73 (2016)

    Article  Google Scholar 

  47. Sun, G.Q., Wu, Z.Y., Wang, Z., Jin, Z.: Influence of isolation degree of spatial patterns on persistence of populations. Nonlinear Dyn. 83(1–2), 811–819 (2016)

    Article  MathSciNet  Google Scholar 

  48. Sun, G.Q., Wang, C.H., Wu, Z.Y.: Pattern dynamics of a Gierer Meinhardt model with spatial effects. Nonlinear Dyn. 88(2), 1385–1396 (2017)

    Article  Google Scholar 

  49. Biswas, S., Saifuddin, M., Sasmal, S.K., Samanta, S., Pal, N., Ababneh, F., Chattopadhyay, J.: A delayed prey–predator system with prey subject to the strong Allee effect and disease. Nonlinear Dyn. 84, 1569–1594 (2016)

    Article  MATH  MathSciNet  Google Scholar 

  50. Biswas, S., Sasmal, S.K., Samanta, S., Saifuddin, M., Pal, N., Chattopadhyay, J.: Optimal harvesting and complex dynamics in a delayed eco-epidemiological model with weak Allee effects. Nonlinear Dyn. 87(3), 1553–1573 (2016)

    Article  Google Scholar 

  51. Morozov, A., Petrovskii, S., Li, B.L.: Bifurcations and chaos in a predator–prey system with the Allee effect. Proc. R. Soc. B 271, 1407–1414 (2004)

    Article  Google Scholar 

  52. Saifuddin, M., Biswas, S., Samanta, S., Sarkar, S., Chattopadhyaya, J.: Complex dynamics of an eco-epidemiological model with different competition coefficients and weak Allee in the predator. Chaos Solitons Fractals 91, 270–285 (2016)

    Article  MATH  MathSciNet  Google Scholar 

  53. Courchamp, F., Chapuis, J.L., Pascal, M.: Mammal invaders on islands: impact, control and control impact. Biol. Rev. 78(3), 347–383 (2003)

    Article  Google Scholar 

  54. Freckleton, R.P.: Biological control as a learning process. Trends Ecol. Evol. 15(7), 263–264 (2000)

    Article  Google Scholar 

  55. Sussman, R.W., Garber, P.A.: A new interpretation of the social organisation and mating system of the Callitrichidae. Int. J. Primatol. 8, 73–92 (1987)

    Article  Google Scholar 

  56. Zhou, S.R., Liu, Y.E., Wang, G.: The stability of predator–prey systems subject to the Allee effects. Theor. Popul. Biol. 67, 23–31 (2005)

    Article  MATH  Google Scholar 

  57. Dennis, B.: Allee effects: population growth, critical density, and the chance of extinction. Nat. Resour. Model. 3, 481–538 (1989)

    Article  MATH  MathSciNet  Google Scholar 

  58. McCarthy, M.A.: The Allee effect, finding mates and theoretical models. Ecol. Model. 103, 99–102 (1997)

    Article  Google Scholar 

  59. Scheuring, I.: Allee effect increases the dynamical stability of populations. J. Theor. Biol. 199, 407–414 (1999)

    Article  Google Scholar 

  60. Wiggins, S.: Introduction to Applied Nonlinear Dynamical Systems and Chaos, vol. 2. Springer, Berlin (2003)

    MATH  Google Scholar 

  61. Huang, J., Gong, Y., Ruan, S.: Bifurcation analysis in a predator–prey model with constant-yield predator harvesting. Discrete Contin. Dyn. Syst. Ser. B 18, 2101–2121 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  62. Huang, J., Ruan, S., Song, J.: Bifurcations in a predator prey system of Leslie type with generalized Holling type III functional response. J. Differ. Equ. 257(6), 1721–1752 (2014)

    Article  MATH  MathSciNet  Google Scholar 

  63. Hassard, B., Kazarinof, D., Wan, Y.: Theory and Applications of Hopf Bifurcation. Cambridge University Press, Cambridge (1981)

    Google Scholar 

  64. Marino, S., Hogue, I.B., Ray, C.J., Kirschner, D.E.: A methodology for performing global uncertainty and sensitivity analysis in systems biology. J. Theor. Biol. 254, 178–196 (2008)

    Article  MathSciNet  Google Scholar 

  65. Sprott, J.C.: Chaos and Time Series Analysis. Oxford University Press, Oxford (2003). (Chapter 5)

    MATH  Google Scholar 

  66. Wolf, A., Swift, J., Swinney, H., Vastano, J.: Determining Lyapunov exponents from a time series. Phys. D 16, 285–317 (1985)

    Article  MATH  MathSciNet  Google Scholar 

  67. Zaman, G., Kang, Y.H., Jung, H.: Optimal treatment of an SIR epidemic model with time delay. BioSyst. 98, 43–50 (2009)

    Article  Google Scholar 

  68. Jana, S., Kar, T.: A mathematical study of a prey–predator model in relevance to pest control. Nonlinear Dyn. 74, 667–674 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  69. Kumar, D., Chakrabarty, S.P.: A comparative study of bioeconomic ratio-dependent predator–prey model with and without additional food to predator. Nonlinear Dyn. 80(1–2), 23–38 (2015)

    Article  MATH  MathSciNet  Google Scholar 

  70. Biswas, S., Subramanian, A., ELMojtaba, E.M., Chattopadhyay, J., Sarkar, S.: Optimal combinations of control strategies and cost-effective analysis for visceral leishmaniasis disease transmission. Plos One 12(2), e0172465 (2017)

    Article  Google Scholar 

  71. Allen, J.C., Schaffer, W.M., Rosko, D.: Chaos reduces species extinctions by amplifying local population noise. Nature 364, 229–232 (1993)

    Article  Google Scholar 

  72. Huisman, J., Weissing, F.J.: Biodiversity of plankton by species oscillations and chaos. Nature 402, 407–410 (1999)

    Article  Google Scholar 

  73. Reynolds, J.C., Tapper, S.C.: Control of mammalian predators in game management and conservation. Mamm. Rev. 26(2–3), 127–155 (1996)

    Article  Google Scholar 

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Acknowledgements

I would like to thank the handling editor and the reviewers for their careful reading and valuable comments. Special thanks are due to Prof. Joydev Chattopadhyay for his useful discussions.

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Correspondence to Santanu Biswas.

Appendix

Appendix

First, consider the transformation \(z_{1}(t) = S-S_{*}\), \(z_{2}(t) = P-P_{*}\).

Let \(\tau =\tau ^{*}+\mu \), \(\mu \in \mathbf {R}\). Then \(\mu =0\) is the Hopf bifurcation value of system (3). Equation (3) can be written in the form

$$\begin{aligned} \dot{z}(t)=L_{\mu }(z_{t})+F(\mu , z_{t}), \end{aligned}$$
(45)

where \(z(t)=(z_{1}(t), z_{2}(t))^\mathrm{T}\in \mathbf {R^{2}}\). For \(\psi =(\psi _{1}, \psi _{2})^\mathrm{T}\in \mathbf {C}([-1, 0], \mathbf {R^{2}_{+}})\), \(L_{\mu }: \mathbf {C}\rightarrow \mathbf {R}\) and \(F: \mathbf {R}\times \mathbf {C}\rightarrow \mathbf {R}\) are given by

$$\begin{aligned} L_{\mu }(\psi )= & {} (\tau ^{*}+\mu )A_3 \left( \begin{array}{c} \psi _{1}(0) \\ \psi _{2}(0) \\ \end{array} \right) \nonumber \\&+\,(\tau ^{*}+\mu )A_4 \left( \begin{array}{c} \psi _{1}(-1) \\ \psi _{2}(-1) \\ \end{array} \right) , \end{aligned}$$
(46)

and

$$\begin{aligned} F(\mu , \psi )=(\tau ^{*}+\mu ) A_5, \end{aligned}$$
(47)

where

$$\begin{aligned} A_3= & {} \left( \begin{array}{ccc} a_1 &{}\quad a_2 \\ 0 &{}\quad a_3\\ \end{array} \right) ,\\ A_4= & {} \left( \begin{array}{ccc} 0 &{}\quad 0 \\ a_4 &{}\quad a_5\\ \end{array} \right) ,\\ A_5= & {} \left( \begin{array}{l} b_1 \psi _{1}^{2}(0)+ b_2 \psi _{1}(0)\psi _{2}(0) \\ b_3 \psi _{1}(-1)\psi _{2}(-1) +b_4 \psi _{1}(-1)\psi _{2}(0)\\ \quad \quad +b_5 \psi _{2}(-1)\psi _{2}(0)+b_6 \psi _{2}^{2}(0) \\ \end{array} \right) ,\\ a_1= & {} -ab S_*;\quad a_2=-\beta S_*;\\ a_3= & {} -\alpha S_*P_*\frac{P_*}{(\theta +P_*)^2};\\ a_4= & {} \frac{ \alpha P_*^2}{\theta +P_*};\quad a_5=\frac{ \alpha S_*P_*}{\theta +P_*};\\ b_1= & {} -2ab;\quad b_2=-\beta ;\quad b_3=\frac{ \alpha P_*}{\theta +P_*};\\ b_4= & {} -\alpha \theta \frac{P_*}{(\theta +P_*)^2};\quad b_5=-\alpha \theta \frac{S_*}{(\theta +P_*)^2};\\ b_6= & {} -\alpha \theta \frac{P_*S_*}{(\theta +P_*)^2}. \end{aligned}$$

By the Riesz representation theorem, there exists a function \(\eta (\theta , \mu )\) of bounded variation for \(\theta \in [-1, 0]\) such that

$$\begin{aligned} L_{\mu }\psi =\int _{-1}^{0} d\eta (\theta , \mu )\psi (\theta ), \ \ \ \ \text{ for } \ \ \psi \in \mathbf {C}. \end{aligned}$$
(48)

In fact,

$$\begin{aligned} \eta (\theta , \mu )= & {} (\tau ^{*}+\mu )\left( \begin{array}{ccc} a_1 &{}\quad a_2 \\ 0 &{}\quad a_3\\ \end{array} \right) \delta (\theta )\\&-(\tau ^{*}+\mu ) \left( \begin{array}{ccc} 0 &{}\quad 0 \\ a_4 &{}\quad a_5 \\ \end{array} \right) \delta (\theta +1), \end{aligned}$$

where \(\delta \) is defined by \(\delta (\theta )=\Big \{^{1, \ \ \theta =0,}_{0, \ \ \theta \ne 0.} \)

For \(\psi \in \mathbf {C}^{1}\left( [-1, 0], \mathbf {R^{3}_{+}}\right) \), define

$$\begin{aligned} A(\mu )\psi =\left\{ \begin{array}{ll} \frac{d\psi (\theta )}{d\theta } &{}\quad \ \theta \in [-1, 0) \\ \\ \int _{-1}^{0} d\eta (\mu , s)\psi (s)&{} \quad \theta =0 \end{array} \right. \end{aligned}$$

and

$$\begin{aligned} R(\mu )\psi =\left\{ \begin{array}{c} 0, \quad \theta \in [-1, 0), \\ F(\mu , \psi ), \quad \theta =0. \end{array} \right. \end{aligned}$$

Then system (45) is of the form

$$\begin{aligned} \dot{z_{t}}=A(\mu )z_{t}+R(\mu )z_{t}, \end{aligned}$$
(49)

where \(z_{t}(\theta )=z_{t}(t+\theta )\) for \(\theta \in [-1, 0]\).

For \(\phi \in \mathbf {C}^{1}([0, 1], (\mathbf {R^{3}_{+}})^{*})\), define

$$\begin{aligned} A^{*}\phi (s)=\left\{ \begin{array}{l} -\frac{d\phi (s)}{\mathrm{d}s}, \quad s \in (0, 1], \\ \int _{-1}^{0}d\eta ^\mathrm{T}(t, 0)\phi (-t), \quad s=0, \end{array} \right. \end{aligned}$$

and a bilinear inner product

$$\begin{aligned}&\langle \phi (s), \psi (\theta )\rangle =\overline{\phi }(0)\psi (0)-\int _{-1}^{0} \int _{\alpha =0}^{\theta }\nonumber \\&\quad \overline{\phi }(\alpha -\theta )\mathrm{d}\eta (\theta )\psi (\alpha )\mathrm{d}\alpha , \end{aligned}$$
(50)

where \(\eta (\theta )=\eta (\theta , 0)\). Clearly, A(0) and \(A^{*}\) are adjoint operators. We know that \(\pm i\rho _{0}\tau ^{*}\) are eigenvalues of A(0). So, they are also eigenvalues of \(A^{*}\). Now we search for the eigenvector of A(0) and \(A^{*}\) corresponding to \(i\rho _{0}\tau ^{*}\) and \(-i\rho _{0}\tau ^{*}\), respectively.

The author assumes that \(q(\theta )=(1, u)^\mathrm{T} e^{i\rho _{0}\tau ^{*}\theta }\) and \(q^{*}(s)\) are the eigenvectors of A(0) and \(A^{*}\) corresponding to \(i\rho _{0}\tau ^{*}\) and \(-i\rho _{0}\tau ^{*}\). Then, \(A(0)q(\theta )=i\rho _{0}\tau ^{*}q(\theta )\). By the definition of A(0) and from (48), it follows that

$$\begin{aligned} \tau ^{*}\left( \begin{array}{ccc} a_1-i\rho _{0} &{}\quad a_2 \\ a_4 e^{-i\rho _{0}\tau ^{*}} &{}\quad a_5 e^{-i\rho _{0}\tau ^{*}} +a_3 -i\rho _{0}\\ \end{array} \right) q(0)=\left( \begin{array}{c} 0 \\ 0 \\ \end{array} \right) . \end{aligned}$$

Then, \(q(0)=(1, u)^\mathrm{T}\),

and

$$\begin{aligned} q^{*}(s)= & {} D\left( 1, u^{*}\right) ^\mathrm{T} e^{i\rho _{0}\tau ^{*}s}, \end{aligned}$$

where

$$\begin{aligned} \begin{aligned} u&= \frac{-a_1+i\rho _{0}}{a_2},\\ u^{*}&= -\frac{a_1+i\rho _{0}}{a_4 e^{i\rho _{0}\tau ^{*}}}. \end{aligned} \end{aligned}$$
(51)

D can be chosen in such a way that \(\langle q^{*}(s), q(\theta ) \rangle =1\), \(\langle q^{*}(s), \overline{q}(\theta ) \rangle =0\).

Hence,

$$\begin{aligned} \begin{aligned}&\langle q^{*}(s), q(\theta ) \rangle \\&\quad = \overline{D}(1, \overline{u^{*}})(1, u)^\mathrm{T}- \int _{-1}^{0}\int _{\zeta =0}^{\theta }\overline{D}(1, \overline{u^{*}})e^{-i\rho _{0}\tau ^{*}(\zeta -\theta )}\\&\qquad \quad d\eta (\theta )(1, u)^\mathrm{T}e^{i\rho _{0}\tau ^{*}\zeta } \mathrm{d}\zeta \\&\quad = \overline{D}\left[ 1+\overline{u^{*}}u-\int _{-1}^{0}(1, \overline{u^{*}} )\theta e^{i\rho _{0}\tau ^{*}\theta } d\eta (\theta )(1, u)^\mathrm{T}\right] \\&\quad = \overline{D}\left[ 1+\overline{u^{*}}u+ \tau ^{*} \overline{u^{*}}(a_4+a_5 u) e^{-i\rho _{0}\tau ^{*}} \right] . \end{aligned} \end{aligned}$$

Thus, \(\overline{D}=\frac{1}{\left[ 1+\overline{u^{*}}u+ \tau ^{*} \overline{u^{*}}(a_4+a_5 u) e^{-i\rho _{0}\tau ^{*}} \right] }\).

To describe the center manifold \(\mathbf {C}_{0} \) at \(\mu =0 \), we compute the coordinates by using the same notations and procedures as proposed by [63].

Let \( z_{t}\) be the solution of Eq. (45) when \(\mu =0 \).

Define

$$\begin{aligned} \text {z}(t)=\langle q^{*}, z_{t}\rangle , \ \ \ W(t,\theta )=z_{t}(\theta )-2Re\{\text {z}(t)q(\theta ) \}.\nonumber \\ \end{aligned}$$
(52)

On the center manifold \(\mathbf {C}_{0} \), we have

$$\begin{aligned} W(t, \theta )=W\Bigl (\text {z}(t), \overline{\text {z}}(t), \theta \Bigr ), \end{aligned}$$

where

$$\begin{aligned} W(\text {z}, \overline{\text {z}}, \theta )= & {} W_{20}(\theta )\frac{\text {z}^{2}}{2}+ W_{11}(\theta )\text {z}\overline{\text {z}}\\&+W_{02}(\theta )\frac{\overline{\text {z}}^{2}}{2} + W_{30}(\theta ) \frac{\text {z}^{3}}{6}+\cdot \cdot \cdot , \end{aligned}$$

\(\text {z}\) and \(\overline{\text {z}}\) are local coordinates for center manifold \(\mathbf {C}_{0}\) in the direction of \(q^{*}\) and \(\overline{q}^{*}\). Here W is real when \(z_{t}\) is real. For solution \(z_{t} \in \mathbf {C}_{0}\) of Eq. (45), since \(\mu =0\),

$$\begin{aligned} \begin{aligned}&\dot{\text {z}}(t) \\&\quad = i\rho _{0}\tau ^{*}\text {z}+ \Bigl \langle \overline{q}^{*}(\theta ), F\Bigl (0, W(\text {z}, \overline{\text {z}},\theta )+2Re\{\text {z}q(\theta )\} \Bigr ) \Bigr \rangle \\&\quad = i\rho _{0}\tau ^{*}\text {z}+\overline{q}^{*}(0)F\Bigl (0, W(\text {z}, \overline{\text {z}},0)+2Re\{\text {z}q(0)\} \Bigr )\\&\quad {\mathop {=}\limits ^{\text {def}}} i\rho _{0}\tau ^{*}\text {z}+\overline{q}^{*}(0)F_{0}(\text {z}, \overline{\text {z}}), \end{aligned} \end{aligned}$$

so \(\dot{\text {z}}=i\rho _{0}\tau ^{*}\text {z}+g(\text {z}, \overline{\text {z}})\) with

$$\begin{aligned} g(\text {z}, \overline{\text {z}})= & {} \overline{q}^{*}(0)F_{0}(\text {z}, \overline{\text {z}})=g_{20}\frac{\text {z}^{2}}{2}+ g_{11}\text {z}\overline{\text {z}}\nonumber \\&+g_{02}\frac{\overline{\text {z}}^{2}}{2} + g_{21} \frac{\text {z}^{2}\overline{\text {z}}}{2}+\cdot \cdot \cdot . \end{aligned}$$
(53)

Then from Eq. (52),

$$\begin{aligned} \begin{aligned} z_{t}(\theta )&=(z_{1t}(\theta ), z_{2t}(\theta )) = W(t,\theta )+2Re\{\text {z}(t)q(\theta ) \} \\&= W_{20}(\theta )\frac{\text {z}^{2}}{2}+ W_{11}(\theta )\text {z}\overline{\text {z}} +W_{02}(\theta )\frac{\overline{\text {z}}^{2}}{2}\\&\quad +\,(1, u)^\mathrm{T} e^{i\rho _{0}\tau ^{*}\theta } \text {z}+ (1, \overline{u})^\mathrm{T} e^{-i\rho _{0}\tau ^{*}\theta } \overline{\text {z}}\\&\quad +\,O(\mid (\text {z}, \overline{\text {z}})\mid ^{3}). \end{aligned} \end{aligned}$$
(54)

Thus, from Eq. (53),

$$\begin{aligned} g(\text {z}, \overline{\text {z}})&=\overline{q}^{*}(0)F_{0}(\text {z}, \overline{\text {z}}) \\&=\overline{D}(1, \overline{u^{*}})\tau ^{*}\nonumber \\&\quad \left( \begin{array}{l} b_1 z_{1t}^{2}(0)+b_2 z_{1t}(0)z_{2t}(0) \\ b_3 z_{1t}(-1)z_{2t}(-1)+b_4 z_{1t}(-1)z_{2t}(0)\\ \quad \quad +b_5 z_{2t}(-1)z_{2t}(0) +b_6 z_{2t}^{2}(0)\\ \end{array} \right) \\&= \overline{D}\tau ^{*} \Big [b_1 \Big \{ z+\overline{z}+\,W_{20}^{1}(0)\frac{\text {z}^{2}}{2}+ W_{11}^{1}(0)\text {z}\overline{\text {z}}\\&\quad +W_{02}^{1}(0)\frac{\overline{\text {z}}^{2}}{2}+O(\mid (\text {z}, \overline{\text {z}})\mid ^{3})\Big \}^{2} \\&\quad + b_2 \left\{ z+\overline{z}+W_{20}^{1}(0)\frac{\text {z}^{2}}{2}+ W_{11}^{1}(0)\text {z}\overline{\text {z}} \right. \\&\quad \left. +W_{02}^{1}(0)\frac{\overline{\text {z}}^{2}}{2}+O(\mid (\text {z}, \overline{\text {z}})\mid ^{3}) \right\} \\&\quad \times \left\{ uz+ \overline{u} \ \overline{z}+W_{20}^{2}(0)\frac{\text {z}^{2}}{2}+ W_{11}^{2}(0)\text {z}\overline{\text {z}}\right. \\&\quad \left. \left. +W_{02}^{2}(0)\frac{\overline{\text {z}}^{2}}{2}+O(\mid (\text {z}, \overline{\text {z}})\mid ^{3}) \right\} \right] \\&\quad +\overline{D}\tau ^{*} \Big [b_3 \Big \{ z e^{-i\rho _{0}\tau ^{*}}+ \overline{z} e^{i\rho _{0}\tau ^{*}}+W_{20}^{1}(-1)\frac{\text {z}^{2}}{2}\\&\quad + W_{11}^{1}(-1)\text {z}\overline{\text {z}} +W_{02}^{1}(-1)\frac{\overline{\text {z}}^{2}}{2}+O(\mid (\text {z}, \overline{\text {z}})\mid ^{3})\Big \}\\&\quad \times \left\{ uz e^{-i\rho _{0}\tau ^{*}}+ \overline{u} \ \overline{z} e^{i\rho _{0}\tau ^{*}}+W_{20}^{2}(-1)\frac{\text {z}^{2}}{2}\right. \\&\left. \quad + W_{11}^{2}(-1)\text {z}\overline{\text {z}} +W_{02}^{2}(-1)\frac{\overline{\text {z}}^{2}}{2}+O(\mid (\text {z}, \overline{\text {z}})\mid ^{3})\right\} \\&\quad + b_4\left\{ z e^{-i\rho _{0}\tau ^{*}}+ \overline{z} e^{i\rho _{0}\tau ^{*}}+W_{20}^{1}(-1)\frac{\text {z}^{2}}{2}\right. \\&\left. \quad + W_{11}^{1}(-1)\text {z}\overline{\text {z}} +W_{02}^{1}(-1)\frac{\overline{\text {z}}^{2}}{2}+O(\mid (\text {z}, \overline{\text {z}})\mid ^{3})\right\} \\&\quad \times \left\{ uz+ \overline{u} \ \overline{z}+W_{20}^{2}(0)\frac{\text {z}^{2}}{2}+ W_{11}^{2}(0)\text {z}\overline{\text {z}}\right. \\&\quad \left. +W_{02}^{2}(0)\frac{\overline{\text {z}}^{2}}{2}+O(\mid (\text {z}, \overline{\text {z}})\mid ^{3}) \right\} \end{aligned}$$
$$\begin{aligned}&\quad \quad \quad \quad \quad + b_5 \{uz e^{-i\rho _{0}\tau ^{*}}+ \overline{u} \ \overline{z} e^{i\rho _{0}\tau ^{*}}+W_{20}^{2}(-1)\frac{\text {z}^{2}}{2}\nonumber \\&\quad \quad \quad \quad \quad + W_{11}^{2}(-1)\text {z}\overline{\text {z}} +W_{02}^{2}(-1)\frac{\overline{\text {z}}^{2}}{2}+O(\mid (\text {z}, \overline{\text {z}})\mid ^{3})\}\nonumber \\&\quad \quad \quad \quad \quad \times \left\{ uz+ \overline{u} \ \overline{z}+W_{20}^{2}(0)\frac{\text {z}^{2}}{2}+ W_{11}^{2}(0)\text {z}\overline{\text {z}}\right. \nonumber \\&\quad \quad \quad \quad \quad \left. +W_{02}^{2}(0)\frac{\overline{\text {z}}^{2}}{2}+O(\mid (\text {z}, \overline{\text {z}})\mid ^{3}) \right\} \nonumber \\&\quad \quad \quad \quad \quad + b_6 \left\{ uz+ \overline{u} \ \overline{z}+W_{20}^{2}(0)\frac{\text {z}^{2}}{2}+ W_{11}^{2}(0)\text {z}\overline{\text {z}}\right. \nonumber \\&\quad \quad \quad \quad \quad \left. \left. +W_{02}^{2}(0)\frac{\overline{\text {z}}^{2}}{2}+O(\mid (\text {z}, \overline{\text {z}})\mid ^{3}) \right\} ^2\right] . \end{aligned}$$
(55)

Comparing the coefficients with (53) one can obtain that

$$\begin{aligned} g_{20}=\,&2\overline{D}\tau ^{*}\Big [b_1+b_2 u +b_3 u e^{-2i\rho _{0}\tau ^{*}}+b_4 u e^{-i\rho _{0}\tau ^{*}}\nonumber \\&+b_5 u^2 e^{-i\rho _{0}\tau ^{*}} +b_6 u^2\Big ],\nonumber \\ g_{11}=\,&2\overline{D}\tau ^{*}\Big [b_1+ b_2 \mathfrak {R}\{u\} +b_3 \mathfrak {R}\{u\} +b_4 \mathfrak {R}\{u e^{i\rho _{0}\tau ^{*}}\}\nonumber \\&+b_5 |u|^2 \mathfrak {R}\{e^{i\rho _{0}\tau ^{*}}\} +b_6 |u|^2\Big ],\nonumber \\ g_{02}=\,&2\overline{D}\tau ^{*}\Big [b_1+b_2 \overline{u} +b_3 \overline{u} e^{2i\rho _{0}\tau ^{*}}+b_4 \overline{u} e^{i\rho _{0}\tau ^{*}}\nonumber \\&+b_5 \overline{u}^2 e^{i\rho _{0}\tau ^{*}} +b_6 \overline{u}^2\Big ],\nonumber \\ g_{21}=\,&\overline{D}\tau ^{*}\Big [ b_2( W_{20}^{2}(0)+ 2W_{11}^{2}(0)+\overline{u} W_{20}^{1}(0)\nonumber \\&+ 2uW_{11}^{1}(0))+2 b_1 ( W_{20}^{1}(0)+ 2W_{11}^{1}(0))\nonumber \\&+2 b_6( \overline{u}W_{20}^{2}(0)+ 2 u W_{11}^{2}(0))\nonumber \\&+b_3(2uW_{11}^{1}(-1)e^{-i\rho _{0}\tau ^{*}}+\overline{u}W_{20}^{1}(-1)e^{i\rho _{0}\tau ^{*}}\nonumber \\&+ W_{20}^{2}(-1)e^{i\rho _{0}\tau ^{*}}+ 2W_{11}^{2}(-1)e^{-i\rho _{0}\tau ^{*}})\nonumber \\&+ b_4 ( 2W_{11}^{2}(0)e^{-i\rho _{0}\tau ^{*}}+W_{20}^{2}(0)e^{i\rho _{0}\tau ^{*}}\nonumber \\&+ \overline{u}W_{20}^{1}(-1)+ 2u W_{11}^{1}(-1))\nonumber \\&+ b_5 ( 2uW_{11}^{2}(0)e^{-i\rho _{0}\tau ^{*}}+\overline{u}W_{20}^{2}(0)e^{i\rho _{0}\tau ^{*}}\nonumber \\&+ \overline{u}W_{20}^{2}(-1)+ 2u W_{11}^{2}(-1))\Big ]. \end{aligned}$$
(56)

To calculate the value of \(g_{21}\), we need to compute the values of \(W_{20}(\theta )\) and \(W_{11}(\theta )\). From Eqs. (49) and (52),

(57)

where

$$\begin{aligned} H(\text {z}, \overline{\text {z}}, \theta )= & {} H_{20}(\theta )\frac{\text {z}^{2}}{2}+ H_{11}(\theta )\text {z}\overline{\text {z}}\nonumber \\&+\,H_{02}(\theta )\frac{\overline{\text {z}}^{2}}{2}+ \cdot \cdot \cdot . \end{aligned}$$
(58)

Expanding the above series and comparing the corresponding coefficients,

$$\begin{aligned}&(A-i2\rho _{0}\tau ^{*}I)W_{20}(\theta )=-H_{20}(\theta ), \nonumber \\&\quad AW_{11}(\theta )=-H_{11}(\theta ). \end{aligned}$$
(59)

From Eq. (57), for \(\theta \in [-1, 0),\)

$$\begin{aligned} H(\text {z}, \overline{\text {z}}, \theta )= & {} -\overline{q}^{*}(0)F_{0}q(\theta )-q^{*}(0)\overline{F}_{0}\overline{q}(\theta )\nonumber \\= & {} -g q(\theta )-\overline{g}\ \overline{q}(\theta ). \end{aligned}$$
(60)

Comparing the coefficients with (58) gives that

$$\begin{aligned} H_{20}( \theta )=-g_{20}q(\theta )-\overline{g}_{02} \overline{q}(\theta ) \end{aligned}$$
(61)

and

$$\begin{aligned} H_{11}( \theta )=-g_{11}q(\theta )-\overline{g}_{11} \overline{q}(\theta ). \end{aligned}$$
(62)

From (59) and (61),

$$\begin{aligned} \dot{W}_{20}(\theta )=i2\rho _{0}\tau ^{*}W_{20}(\theta )+g_{20}q(\theta )+\overline{g}_{02} \overline{q}(\theta ). \end{aligned}$$

Since \(q(\theta )=(1, u)^\mathrm{T} e^{i\rho _{0}\tau ^{*}\theta }\),

$$\begin{aligned} W_{20}(\theta )&=\frac{ig^{}_{20}}{\rho _{0}\tau ^{*}} q(0)e^{i\rho _{0}\tau ^{*}\theta }+\frac{i\overline{g}^{}_{20}}{3\rho _{0}\tau ^{*}} \overline{q}(0)e^{-i\rho _{0}\tau ^{*}\theta }\nonumber \\&+E_{1}e^{i2\rho _{0}\tau ^{*}\theta }, \end{aligned}$$
(63)

where \(E_{1}=(E_{1}^{(1)}, E_{1}^{(2)}) \in \mathbf {R}^{2}\) is a constant vector.

Similarly, from Eqs. (59) and (62),

$$\begin{aligned} W_{11}(\theta )&=-\frac{ig^{}_{11}}{\rho _{0}\tau ^{*}} q(0)e^{i\rho _{0}\tau ^{*}\theta }\nonumber \\&+\,\frac{i\overline{g}^{}_{11}}{\rho _{0}\tau ^{*}} \overline{q}(0)e^{-i\rho _{0}\tau ^{*}\theta }+E_{2}, \end{aligned}$$
(64)

where \(E_{2}=(E_{2}^{(1)}, E_{2}^{(2)}) \in \mathbf {R}^{2}\) is a constant vector.

In what follows, \(E_{1}\) and \(E_{2}\) in (63) and (64), respectively, should be chosen appropriately. From the definition of A and (59),

$$\begin{aligned} \int _{-1}^{0}d\eta (\theta )W_{20}(\theta )=i2\rho _{0}\tau ^{*} W_{20}(0)-H_{20}(0) \end{aligned}$$
(65)

and

$$\begin{aligned} \int _{-1}^{0}d\eta (\theta )W_{11}(\theta )=-H_{11}(0), \end{aligned}$$
(66)

where \(\eta (\theta )=\eta (0, \theta )\). From (59),

$$\begin{aligned}&H_{20}(0)=-g_{02}q(0)-\overline{g}_{02}\overline{q}(0)+2 \tau ^{*}\nonumber \\&\left( \begin{array}{c} b_1+u b_2 \\ b_3 u e^{-2i\rho _{0}\tau ^{*}}+b_4 u e^{-i\rho _{0}\tau ^{*}}+b_5 u^2 e^{-i\rho _{0}\tau ^{*}} +b_6 u^2 \\ \end{array} \right) \nonumber \\ \end{aligned}$$
(67)

and

$$\begin{aligned}&H_{11}(0)=-g_{11}q(0)-\overline{g}_{11}\overline{q}(0)+2 \tau ^{*}\nonumber \\&\quad \left( \begin{array}{c} b_1+\mathfrak {R}\{u\}b_2 \\ b_3 \mathfrak {R}\{u\} +b_4 \mathfrak {R}\{u e^{i\rho _{0}\tau ^{*}}\} +b_5 |u|^2 \mathfrak {R}\{e^{i\rho _{0}\tau ^{*}}\} +b_6 |u|^2 \\ \end{array} \right) .\nonumber \\ \end{aligned}$$
(68)

Noting that

$$\begin{aligned} \left( i\rho _{0}\tau ^{*}I-\int _{-1}^{0}e^{i\rho _{0}\tau ^{*}\theta }d\eta (\theta ) \right) q(0)=0, \end{aligned}$$

and

$$\begin{aligned} \left( -i\rho _{0}\tau ^{*}I-\int _{-1}^{0}e^{-i\rho _{0}\tau ^{*}\theta }d\eta (\theta ) \right) \overline{q}(0)=0, \end{aligned}$$

and putting (63) and (67) into (65),

$$\begin{aligned}&\left( i2\rho _{0}\tau ^{*}I-\int _{-1}^{0}e^{i2\rho _{0}\tau ^{*}\theta }d\eta (\theta )\right) E_{1}\\&\quad =2\tau ^{*} \left( \begin{array}{c} b_1+u b_2 \\ b_3 u e^{-2i\rho _{0}\tau ^{*}}+b_4 u e^{-i\rho _{0}\tau ^{*}}+b_5 u^2 e^{-i\rho _{0}\tau ^{*}} +b_6 u^2 \\ \end{array} \right) , \end{aligned}$$

which implies that

$$\begin{aligned}&\left( \begin{array}{ccc} -a_1 +2i\rho _{0} &{}\quad -a_2\\ -a_4 e^{-2i\rho _{0}\tau ^{*}} &{}\quad -a_5 e^{-2i\rho _{0}\tau ^{*}} +a_3 +2i\rho _{0}\\ \end{array} \right) E_{1}\nonumber \\&\quad =2 \left( \begin{array}{c} b_1+u b_2 \\ b_3 u e^{-2i\rho _{0}\tau ^{*}}+b_4 u e^{-i\rho _{0}\tau ^{*}}+b_5 u^2 e^{-i\rho _{0}\tau ^{*}} +b_6 u^2 \\ \end{array} \right) .\nonumber \\ \end{aligned}$$
(69)

It follows that

$$\begin{aligned} E_{1}^{(1)}=\frac{|\Delta _{11}|}{|\Delta _{1}|}, \ \ E_{1}^{(2)}=\frac{|\Delta _{12}|}{|\Delta _{1}|}, \nonumber \\ \end{aligned}$$
(70)

where

$$\begin{aligned} \Delta _{11}= & {} 2\left( \begin{array}{ccc} b_1+u b_2 &{}\quad -a_2\\ b_3 u e^{-2i\rho _{0}\tau ^{*}}+b_4 u e^{-i\rho _{0}\tau ^{*}}+b_5 u^2 e^{-i\rho _{0}\tau ^{*}} +b_6 u^2 &{}\quad -a_5 e^{-2i\rho _{0}\tau ^{*}} +a_3 +2i\rho _{0}\\ \end{array} \right) ,\\ \Delta _{12}= & {} 2\left( \begin{array}{ccc} -a_1 +2i\rho _{0} &{}\quad b_1+u b_2 \\ -a_4 e^{-2i\rho _{0}\tau ^{*}} &{}\quad b_3 u e^{-2i\rho _{0}\tau ^{*}}+b_4 u e^{-i\rho _{0}\tau ^{*}}+b_5 u^2 e^{-i\rho _{0}\tau ^{*}} +b_6 u^2\\ \end{array} \right) ,\\ \Delta _{1}= & {} \left( \begin{array}{ccc} -a_1 +2i\rho _{0} &{}\quad -a_2\\ -a_4 e^{-2i\rho _{0}\tau ^{*}} &{}\quad -a_5 e^{-2i\rho _{0}\tau ^{*}} +a_3 +2i\rho _{0}\\ \end{array} \right) . \end{aligned}$$

Similarly, putting (64) and (68) into (66),

$$\begin{aligned} \left( \int _{-1}^{0}d\eta (\theta ) \right) E_{2}=2\tau ^{*} \left( \begin{array}{c} b_1+\mathfrak {R}\{u\}b_2 \\ b_3 \mathfrak {R}\{u\} +b_4 \mathfrak {R}\{u e^{i\rho _{0}\tau ^{*}}\} +b_5 |u|^2 \mathfrak {R}\{e^{i\rho _{0}\tau ^{*}}\} +b_6 |u|^2 \\ \end{array} \right) , \end{aligned}$$

which implies that

$$\begin{aligned} \left( \begin{array}{ccc} a_1 &{}\quad a_2\\ a_4 &{}\quad a_5+a_3\\ \end{array} \right) E_{2}=2 \left( \begin{array}{c} b_1+\mathfrak {R}\{u\}b_2 \\ b_3 \mathfrak {R}\{u\} +b_4 \mathfrak {R}\{u e^{i\rho _{0}\tau ^{*}}\} +b_5 |u|^2 \mathfrak {R}\{e^{i\rho _{0}\tau ^{*}}\} +b_6 |u|^2 \\ \end{array} \right) , \end{aligned}$$

and hence,

$$\begin{aligned} E_{2}^{(1)}=\frac{|\Delta _{21}|}{|\Delta _{2}|}, \ \ E_{2}^{(2)}=\frac{|\Delta _{22}|}{|\Delta _{2}|}, \end{aligned}$$
(71)

where

$$\begin{aligned} \Delta _{21}= & {} \left( \begin{array}{ccc} b_1+\mathfrak {R}\{u\}b_2 &{}\quad a_2\\ b_3 \mathfrak {R}\{u\} +b_4 \mathfrak {R}\{u e^{i\rho _{0}\tau ^{*}}\} +b_5 |u|^2 \mathfrak {R}\{e^{i\rho _{0}\tau ^{*}}\} +b_6 |u|^2 &{}\quad a_5+a_3\\ \end{array} \right) ,\\ \Delta _{22}= & {} \left( \begin{array}{ccc} a_1 &{}\quad b_1+\mathfrak {R}\{u\}b_2\\ a_4 &{}\quad b_3 \mathfrak {R}\{u\} +b_4 \mathfrak {R}\{u e^{i\rho _{0}\tau ^{*}}\} +b_5 |u|^2 \mathfrak {R}\{e^{i\rho _{0}\tau ^{*}}\} +b_6 |u|^2 \\ \end{array} \right) ,\\ \Delta _{2}= & {} \left( \begin{array}{ccc} a_1 &{}\quad a_2 \\ a_4 &{}\quad a_5+a_3 \\ \end{array} \right) . \end{aligned}$$

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Biswas, S. Optimal predator control policy and weak Allee effect in a delayed prey–predator system. Nonlinear Dyn 90, 2929–2957 (2017). https://doi.org/10.1007/s11071-017-3854-x

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