Dynamics of Modified Leslie-Gower Predator-Prey Model with Predator Harvesting

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2 International Journal of Basic & Applied Sciences IJBAS-IJENS Vol:13 No:05 56 II. BOUNDEDNESS AND PERMANENCE As mentioned in the previous section, we are interested only in the dynamics of system (2) in the closed first quadrant. It is easy to see that the positive -axis and the positive -axis are both invariant under the flow. This implies that all orbits that start in will not leave In other words; the closed first quadrant is positively invariant under the flow generated by system (2). A. Boundedness of Solutions Now we prove that all solutions of system (2) with initial conditions (3) are trapped in a finite region subset. We use the following two lemmas (see [10, 21]) to prove the boundedness and permanence of system (2). Lemma 2.1 If and with then Observe that Lemma 2.1 is quantitatively equivalent to the following lemma [10]. Lemma 2.2 If and with then for all B. Permanence In what follows, we prove permanence of system (2) under a certain condition. First, we recall the definition of permanence, see [22]. Definition 2.1 System (2) with initial conditions (3) is permanent if there are positive constants and such that each positive solution of the system satisfies:, - Theorem 2.2 System (2) with initial conditions (3) is permanent if Proof. Observe that for sufficiently large value of Thus, from the prey equation of (2), we have In particular,, - for all Theorem 2.1 All solutions of system (2) with initial conditions (3) are uniformly bounded. where Proof. Since are both positive, from (2) we can write From the predator equation of (2), we can write From Lemma 2.2, we have ( ) Lemma 2.1 implies that From the predator equation of system (2) we have Form (4) and (5), together with Lemma 2.1, we obtain [ ] Lemma 2.2 implies that Putting

3 International Journal of Basic & Applied Sciences IJBAS-IJENS Vol:13 No:05 57 implies the permanence of system (2) III. EXISTENCE AND STABILITY OF SOLUTIONS The general Jacobian matrix of system (2) is given by Eigenvalues of are and Hence the result follows. where * + ( [ ]) B. Positive Equilibria Two possible interior equilibrium points of system (2) are and, where for A. Trivial Equilibria System (2) possesses three trivial equilibrium points that appear for any choice of positive values of parameters: 1) the origin O(0,0); 2) the predator extinction point ; 3) the prey extinction point. The following simple lemma gives the number of equilibria of system (2) in the interior of the first quadrant. The proof is straightforward and is being omitted. Lemma 3.1 i. System (2) has no equilibrium in the interior of the first quadrant if ii. System (2) has two equilibria in the interior of the first quadrant, which are and if for Theorem 3.1 For system (2), a) the origin is always unstable; b) the equilibrium is always a saddle point; c) the equilibrium is stable if and a saddle point if Proof. a) The Jacobian matrix of the system at the origin is given by Eigenvalues of are and. Thus, the origin is unstable. b) At the Jacobian matrix is Eigenvalues of are and. We conclude that is a saddle-point for any choice of positive values of parameters. c) At the Jacobian matrix is given by Fig. 1. Phase portraits of system (2) without periodic orbits: (a) no equilibrium in the interior of the first quadrant. All orbits are attracted to equilibrium ; (b) one stable focus point appears in the interior of the first quadrant; (c) two equilibria coexist in the interior of the first quadrant: one stable focus and one saddle point.

5 International Journal of Basic & Applied Sciences IJBAS-IJENS Vol:13 No:05 59 Again from (7), we have. / It is clear that is in the interior of the first quadrant if two inequalities in b) hold. C. Hopf Bifurcations In Figure 1 we see that equilibrium is a focus point. At eigenvalues of the Jacobian matrix are complex conjugates. Thus, it is possible that by changing parameter values, these eigenvalues are purely imaginary. In this case, the equilibrium point is a center-type non-hyperbolic equilibrium. Numerically, we find some parameter values at which the point has purely imaginary eigenvalues. Thus, system (2) may undergo Hopf bifurcation. The trace of the Jacobian matrix of system (2) at is given by The case when [ ] corresponds to the Andronov-Hopf bifurcation at which the equilibrium point changes its stability with appearance/disappearance of a periodic orbit. Let The set is a Hopf bifurcation manifold of system (2). The stability of the periodic orbit created by the Hopf bifurcation is determined by the first Lyapunov value, see [4]. By calculating using Mathematica, we obtain that is negative at some points of (we do not write the expression of in this paper, since it is too long). This means that system (2) undergoes subcritical Hopf bifurcations. As an implication, we have obtained a proof of the following proposition. Figure 3 illustrates a scenario of the subcritical Hopf bifurcation in system (2). Proposition 4.1 a) If the parameter is in then the equilibrium is a stable weak focus. b) There exist some parameter values such that system (2) has at least one stable periodic orbit. D. Bogdanov-Takens Bifurcations The linear condition for Bogdanov-Takens bifurcation is that the Jacobian matrix (6) has double-zero eigenvalues with nontrivial nilpotent part. At a Bogdanov-Takens bifurcation point, a Hopf curve and a saddle-node curve are tangent to each other. There is also a homoclinic bifurcation occurring at another tangent curve, compare [17]. On the set { } the Jacobian matrix has double-zero eigenvalues with nontrivial nilpotent part. Thus, the system undergoes Bogdanov-Takens bifurcations on. As a consequence, we have the following. Proposition 4.2 There exist values of the parameters such that system (2) has a unique unstable limit cycle for some parameter values, and system (2) has an unstable homoclinic loop for other parameter values. Fig. 3. A scenario of codimension one subcritical Hopf bifurcation of system (2): (a) a stable focus exists in the interior of the first quadrant; (b) a stable periodic orbit appears when parameter values cross the region. The stable focus becomes unstable. V. DISCUSSION In this paper, we have extended previous research on the modified Leslie-Gower predator-prey system by imposing quadratic harvesting components in the predator equation. We have shown that system (2) can have one stable equilibrium point in the interior of the first quadrant. This means that coexistence of both populations is possible under the quadratic predator harvesting policy. We also show that all solutions are trapped in a finite domain in the interior of the first quadrant and that the system exhibits interesting dynamics around the

6 International Journal of Basic & Applied Sciences IJBAS-IJENS Vol:13 No:05 60 coexistence equilibria, including multiple bifurcations, periodic orbits and homoclinic orbits. Biologically, there is still a lot of work to do in this direction. For example, it would be an idea to study the behavior of model (2) when harvesting is applied to both prey and predator equations, since people usually would like to harvest both populations. We may also consider other different avenues for future research in predator-prey modeling, including an analysis of modified Leslie-Gower predator-prey models with seasonal harvesting in one or both populations. In the latter case, the system may undergo chaotic behavior where we have strange attractors. The analysis performed and techniques used in this paper can be applied to problems other than predator-prey, especially two-dimensional ordinary differential equations. [17] Y.A. Kuznetsov, Elements of Applied Bifurcation Theory, Springer-Verlag [18] D. Xiao, L. Jennings, Bifurcations of a ratio-dependent predator prey system with constant rate harvesting, SIAM Appl. Math. 65 (2005) [19] D. Xiao, W. Li and M. Han, Dynamics in a ratio-dependent predator-prey model with predator harvesting, J. Math. Anal. Appl. 324L (2006) [20] Y. Zhu and K. Wang, Existence and global attractivity of positive periodic solutions for a predator-prey model with modified Leslie- Gower Holling-type II schemes, J. Math. Anal. Appl. 384 (2011) [21] F.D. Chen, On a nonlinear non-autonomous predator-prey model with diffusion and distributed delay, J. Comput. Appl. Math. 180(1) (2005) [22] P.J. Pal, S. Sarwardi, T. Saha, P.K. Mandal, Mean square stability in a modified Leslie-Gower and Holling-type II predator-prey model, J. Appl. Math. Inform, 29(3-4) (2011) REFERENCES [1] H.R. Akcakaya, R. Arditi, L.R. Ginzburg, Ratio-dependent prediction: An abstraction that works, Ecology 79L (1995) [2] P.A. Abrams, L.R. Ginzburg, The nature of predation: Prey dependent, ratio-dependent or neither? Trends in Ecology and Evolution 15 (2000) [3] R. Arditi and L.R. Ginzburg, Coupling in predator-prey dynamics: Ratio-dependence, J. Theor. Biol. 139 (1989) [4] V.I. Arnol'd, Geometrical Methods in the Theory of Ordinary Differential Equations, Springer-Verlag [5] T.M. Asfaw, Dynamics of Generalized Time Dependent Predator Prey Model with Nonlinear Harvesting, Int. Journal of Math. Analysis, 3(30) (2009), [6] M.A. Aziz-Alaoui, M.D. Okiye, Boundedness and global stability for a predator-prey model with modified Leslie-Gower Hollingtype II schemes, Appl. Math. Lett. 16(7) (2003) [7] A.D. Bazykin, F.S. Berezovskaya, G.A. Denisov and YU.A. Kuznetzov, The influence of predator saturation effect and competition among predators on predator-prey system dynamics, Ecological Modeling, 14 (1981) [8] F. Berezovskaya, G. Karev and R. Arditi, Parametric analysis of the ratio-dependent predator-prey model, J. Math. Biol. 43 (2001) [9] H.W. Broer, V. Naudot, R. Roussarie and K. Saleh, A predatorprey model with non-monotonic response function, Regular & Chaotic Dynamics 11 (2006), [10] R.P. Gupta and P. Chandra, Bifurcation analysis of modified Leslie-Gower predator-prey model with Michaelis-Menten type rey harvesting, J. Math. Anal. Appl. 398 (2013) [11] J. Guckenheimer and P. Holmes, Nonlinear Oscillations, Dynamical Systems and Bifurcation of Vector fields, Appl. Math. Sc. 42, Springer-Verlag [12] E.J. Doedel, R.A. Champneys, T.F. Fairgrieve, Yu.A. Kuznetsov, B. Sandstede and X.J. Wang, AUTO2000: Continuation and Bifurcation Software for Ordinary Differential Equations (with HomCont), User's Guide, Concordia University, Montreal, Canada (http//indy.cs.concordia.ca). [13] W. Govaerts, Y.A. Kuznetsov and A. Dhooge, Numerical Continuation of Bifurcations of Limit Cycles in MATLAB. SIAM J. Sci Comp. 27(2005), [14] D. Hanselman, Mastering Matlab, University of Maine ( [15] K. Saleh, A Ratio-dependent Predator-prey System with Quadratic Predator Harvesting, Journal of Asian Transactions on Basic and Applied Sciences, vol. 02, no. 04, pp , [16] K. Saleh, Multiple Attractors Involving Chaos in Predator-prey Model with Non-monotonic Response Function, Journal of Basic and Applied Sciences, vol. 11, no. 4, pp , 2011.

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