Research Article | Open Access
Yong Ye, Hua Liu, Yu-mei Wei, Ming Ma, Kai Zhang, "Dynamic Study of a Predator-Prey Model with Weak Allee Effect and Delay", Advances in Mathematical Physics, vol. 2019, Article ID 7296461, 15 pages, 2019. https://doi.org/10.1155/2019/7296461
Dynamic Study of a Predator-Prey Model with Weak Allee Effect and Delay
In this paper, a prey-predator model and weak Allee effect in prey growth and its dynamical behaviors are studied in detail. The existence, boundedness, and stability of the equilibria of the model are qualitatively discussed. Bifurcation analysis is also taken into account. After incorporating the searching delay and digestion delay, we establish a delayed predator-prey system with Allee effect. The results show that there exist stability switches and Hopf bifurcation occurs while the delay crosses a set of critical values. Finally, we present some numerical simulations to illustrate our theoretical analysis.
Some researchers have conducted extensive research on the dynamics of interacting prey-predator models to understand the long-term behavior of species. A wide variety of nonlinear coupled ordinary differential equation models are proposed and analyzed for the interaction between prey and their predators. The classic predator-prey model is the Lotka-Volterra model, which was independently proposed by Lotka in the United States in 1925 and Volterra in Italy in 1926 [1, 2]. The model was developed on the basis of a single-population growth model and has wide applicability. The mathematical form of the Lotka-Volterra model isIn population dynamics,when the population density is very low, there is a positive correlation between the population unit growth rate and the population density. This phenomenon can be called the Allee effect [3–5], starting with Allee’s research . The Allee effect is classified according to the density-dependent properties at low density. If the population density is low, a strong Allee effect will appear. If the proliferation rate is positive and increases, the Allee effect will be weak. Demographic Allee effects can be either weak or strong [7, 8]. When the density is below the critical threshold, the population affected by the strong Allee effect will have a negative average growth rate. Under deterministic dynamics, we find that populations that do not exceed this threshold will be extinct. Many jobs only consider the strong Allee effect, but in the work of Allee it is clear that the Allee effect also has a weak Allee effect [8–13].
Today, it is widely believed that the Allee effect greatly increases the likelihood of local and global extinction and can produce a rich variety of dynamic effects [14–16]. And it is interesting and important to study the impact of Allee effect on the predator-prey models [17–19]. In this paper, we introduced a predator-prey model with weak Allee effect:Here, the weak Allee effect term is , where is described as a “weak Allee effect constant” (). is the prey population and is the predator population, is the intrinsic death rate of predators, is the conversion efficiency from prey to predator, is the carrying capacity, is the intrinsic growth rate of prey, and is the prey capture rate by their predators. It is more realistic to introduce time delay on the basis of traditional predator-prey model because it exists almost everywhere in biological activities and is considered as one of the reasons for the regular change of population density [20–26]. Therefore, in order to make the system established in this paper biologically closer to reality, incorporating the searching delay and digestion delay in the system (2) is interesting. Based on the above considerations, We establish a predator-prey model with time delay and weak Allee effect, as follows:where the time delay is the controlling or perturbed parameters, is the searching delay, and is the digestion delay.
The latter parts of the paper are described as follows. In Section 2, we discuss the boundedness, the stability of the equilibria, and bifurcation of the model (2) in detail. In Section 3, we investigated local stability property of interior equilibrium point of the model (3) with time delay; the Hopf bifurcation around the positive equilibrium point is also studied. In Section 4, we verify the previous theoretical derivation by numerical simulation.
2. A Predator-Prey Model with Weak Allee Effect
We easily see that model (2) exhibits three equilibrium points , , and . Here , . And for the positive equilibrium point(s), we have .
Theorem 1. For the solution of model,
Proof. We define . Then we can easily see that along the solution of system (2),Thus, we see that for all large Hence the standard comparison argument shows that
2.2. Stability Analysis
Theorem 2. (1) Trivial equilibrium point is always a saddle-node point.
(2) is stable for and is a saddle point otherwise.
(3) Coexistence equilibrium is locally asymptotically stable for and is unstable node otherwise.
Proof. Let So, the Jacobian matrix for the model (2) is given by whereSo we get First, it can be concluded by calculating the Jacobian matrix of the model (2) at given by And hence is always a saddle-node point. Then, by evaluating the Jacobian matrix of the model (2) at , we find First eigenvalue is negative; hence is stable if implying , and is a saddle point when . Finally, the Jacobian matrix for the model (2) evaluated at is given by The characteristic polynomial is where and . Thus, we have the following conclusions. (1) If and , then the positive equilibrium is locally asymptotically stable. (2) If and , then the positive equilibrium is unstable.
Theorem 3. is globally stable when .
Proof. Consider the Lyapunov function: The derivative of along the solution of the model is
2.3. Bifurcation Analysis
2.3.1. Transcritical Bifurcation
Theorem 4. The model enters into transcritical bifurcation around at , where .
Proof. One of the eigenvalues of will be zero if which gives . At this point, the other eigenvalue is . If and denote the eigenvectors corresponding to the eigenvalue 0 of the matrices and , respectively, then we obtain and , where , , .Therefore, by the Sotomayor theorem, we can find that the model experiences transcritical bifurcation at around the axial equilibrium .
2.3.2. Hopf Bifurcation
From Theorem 2, model (2) undergoes bifurcation if . The purpose of this section is to show that model (2) undergoes a Hopf bifurcation if . We analyze the Hopf bifurcation occurring at by choosing as the bifurcation parameter. DenoteWhen , we have . Thus, the Jacobian matrix has a pair of imaginary eigenvalues . Let be the roots of ; thenandBy the Poincare-Andronov-Hopf Bifurcation Theorem, we know that model (2) undergoes a Hopf bifurcation at when . However, the detailed nature of the Hopf Bifurcation needs further analysis of the normal form of the model. Set and , to as origin of coordinates . We have the following model:where , , , , andwhere and are smooth functions of and at least of order four. Now, using the transformation, , we obtainwheresoSetwhereSoIf , the equilibrium is destabilized through a Hopf bifurcation that is supercritical and Hopf bifurcation is subcritical otherwise.
3. Delayed Model with Weak Allee Effect
Let , ; then the model (3) can be expressed as in the following matrix form after linearization:
3.1. Stability Analysis
The characteristic polynomial iswhereLetSoLet , , and .
Theorem 5. Assume , when ; we have the following conclusions. (1) When and , the positive equilibrium is locally asymptotically stable. (2) Hopf bifurcation occurs when passes the critical value
Proof. The characteristic equation is Next we suppose that is a solution of for some ; then we have Then Where , , we know So we get Separate real and imaginary partsThenSo We assume thatIf and , then all roots of equation have negative real parts for all ; that is, the equilibrium is locally asymptotically stable: where If , and , there is a unique positive solution; the equilibrium is unstable. Also if , , and , then there are two positive solutions. We have So Then, we get It shows that if and , has a pair of imaginary eigenvalues ,
Next verify the cross-sectional conditions: According to At this time, , where is the value of at . We get Then
Theorem 6. Assume , when ; we have the following conclusions. (1) If , the positive equilibrium is locally asymptotically stable. (2) When , if , the positive equilibrium is locally asymptotically stable; if , the positive equilibrium is unstable. (3) Hopf bifurcation occurs when passes the critical value
Proof. The characteristic equation is Next we suppose that is a solution of for some ; then we have Then Where , , we know So we get Separate real and imaginary partsThenSo We assume thatWe can easily find If , the positive equilibrium is locally asymptotically stable,, and the positive equilibrium is unstable. We have So Then, we get It shows that if, has a pair of imaginary eigenvalues ,
Next verify the cross-sectional conditions: According to At this time,, where is the value of at . We get Then
Theorem 7. Assume , when ; we have the following conclusions. (1) When and and , the positive equilibrium is locally asymptotically stable. (2) Hopf bifurcation occurs when passes the critical value
Proof. The characteristic equation is Next we suppose that is a solution of for some ; then we have Where , , we know So we get Separate real and imaginary partsLet . ThenSo