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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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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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
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
and
where
By the Riesz representation theorem, there exists a function \(\eta (\theta , \mu )\) of bounded variation for \(\theta \in [-1, 0]\) such that
In fact,
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
and
Then system (45) is of the form
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
and a bilinear inner product
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
Then, \(q(0)=(1, u)^\mathrm{T}\),
and
where
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,
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
On the center manifold \(\mathbf {C}_{0} \), we have
where
\(\text {z}\) 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 \(\mu =0\),
so \(\dot{\text {z}}=i\rho _{0}\tau ^{*}\text {z}+g(\text {z}, \overline{\text {z}})\) with
Then from Eq. (52),
Thus, from Eq. (53),
Comparing the coefficients with (53) one can obtain that
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),
where
Expanding the above series and comparing the corresponding coefficients,
From Eq. (57), for \(\theta \in [-1, 0),\)
Comparing the coefficients with (58) gives that
and
Since \(q(\theta )=(1, u)^\mathrm{T} e^{i\rho _{0}\tau ^{*}\theta }\),
where \(E_{1}=(E_{1}^{(1)}, E_{1}^{(2)}) \in \mathbf {R}^{2}\) is a constant vector.
Similarly, from Eqs. (59) and (62),
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),
and
where \(\eta (\theta )=\eta (0, \theta )\). From (59),
and
Noting that
and
and putting (63) and (67) into (65),
which implies that
It follows that
where
Similarly, putting (64) and (68) into (66),
which implies that
and hence,
where
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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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DOI: https://doi.org/10.1007/s11071-017-3854-x