Hopf Bifurcation in a Diffusive Predator-Prey Model with Predation-Driven Allee Effect and Gestation Time Delay ()
1. Introduction
The direct predation relationship between prey and predators is one of the most important relationships in population dynamics in nature. Currently, many scholars have studied this relationship by establishing predator-prey models [1] [2]. Many scholars have also considered the impact of the Allee effect on predator-prey models [3] [4]. In [5] authors proposed a predator-prey model with predation-driven Allee effect with the following form.
(1)
where
and
respectively represent the populations of the prey and the predator. The significance of specific parameters can be referred to in reference [5]. They studied the local stability, Bogdanov-Tankens bifurcation, and Hopf bifurcation near the coexisting equilibrium point.
In nature, the spatial arrangement of populations is uneven, and diffusion phenomena often occur. Therefore, it is wise to introduce the reaction-diffusion to predator-prey model [6] [7]. In [8], Mi Y Y, Song C and Wang Z C investigated the steady-state patterns and dynamical behaviors of a modified Leslie-Gower predator-prey model with diffusion, which incorporates density-dependent movement in the predators. They validated the existence of regular solutions with the uniform-in time bound and analyzed the global and local stability of the spatially homogeneous co-existence steady state under certain parameter conditions. In [9], Yang W S examined a diffusive predator-prey model with no-flux boundary condition and modified Holling-Tanner functional response. He confirmed persistence of the system by obtaining a sufficient condition. Moreover, he used a comparison method to confirm sufficient conditions for the global asymptotical stability of the system’s unique positive equilibrium.
In addition, time delay phenomenon is also widely present [11] [12]. In [10], Guo S J applied the S-1-equivariant degree method to a Hopf bifurcation problem for functional differential equations with a state-dependent delay. He used the homotopy invariance of S-1-equivariant degree, then the linearization of the system at a stationary state is extracted and translated into a bifurcation invariant. In [13], authors considered the fractional-order Leslie-Gower model with a single time delay and Holling type II functional response, they determined the stability range and bifurcation points by analytic extrapolation with regarding time delay as a bifurcation parameter. In [14], authors studied a predator-prey model with a Holling type II functional response and gestation delay. Focusing on the effect of predation-induced fear, they proved positivity boundedness and permanence under certain parametric conditions. We consider the following model with diffusion and the gestation time delay of predator.
(2)
where
and
are self diffusion coefficients of prey and predators.
is the gestation time delay,
is the carrying capacity of prey, the term
describes the predator-driven Allee effect in prey.
is the conversion coeffcient.
The structure of this article is as follows. In Section 2, the stability of the positive equilibrium and time delay inducing Hopf bifurcation are analyzed. In Section 3, some numerical simulations are given.
2. Stability Analysis
The existence of equilibrium points has been discussed in [5], then we present the relevant results as follow. The model (2) has three boundary equilibrium
,
and
. If
and
, then the model (2) obtain an unique positive equilibrium
, where
is the positive root of the following equation and
.
(3)
If
, then the model (2) may have two positive equilibria.
Denote a positive equilibrium of (2) by
. Linearize system (2) at
has the following form
(4)
where
and
(5)
2.1. The Non-Delay Model
When the time delay
and diffusion
. The characteristic equation are
(6)
where
The roots of (6) come from
(7)
It easy to know that the roots of (6) have negative real parts if and only if
and
(H2)
hold. When
near
, Equation (6) has a pair of complex eigenvalues
with
We have
Obviously, ODE system of (2) without delay undergoes a Hopf bifurcation at
when
.
Theorem 1 When (H2) holds, for ODE system of (2) without delay the following statements are true.
i) If
and
, the equilibrium
is local asymptotically stable.
ii) If
and
, the equilibrium
is local asymptotically stable.
iii) If
and
, the ODE system of (2) without delay undergoes Hopf bifurcation at
.
When
, we define the real-valued Sobolev space
and the complexification of
:
The linearized system of (2) without delay at
has the following form
Then the linearized operator of the steady state evaluated at
is
with the domain
.
By the [15], we konw that the eigenvalues of
are given by the eigenvalues of
for
. where
The characteristic equation of
is
(8)
with
(9)
the eigenvalues of Equation (8) are given by
(10)
It is obvious that when
(H3)
holds, all the roots of (8) have negative real parts. We make a hypothesis
(H4)
and denote
Then the following statements are true.
Theorem 2. Suppose (H2) holds, for system (2) with
,
i) If (H3) holds, then the equilibrium
is asymptotically stable;
ii) If (H4) and
hold, then the equilibrium
is asymptotically stable;
iii) If (H4) hold,
and
hold, then the equilibrium
is asymptotically stable;
iv) If (H4) hold,
and
hold, then the equilibrium
is Turing unstable;
v) If
, when
, for
, the system (2) undergoes Hopf bifurcation at
.
Proof. From [15], (i), (ii), (iii), (iv) are easy to argue, we only prove (v).
We assume
are eigenvalues of (6), then
,
. By straightforward computation, we obtain
. If
are eigenvalues of (6), then
, we could obtain
. Obviously,
is monotonically decreasing with regard to
, then there is a
, we have
for
and
for
.
Substitution
into
yields
By
, there yields an integer
then
when
. Define
, such that the last statement holds.
□
2.2. The delay model
When the time delay
. The characteristic equation is
(11)
where
In this section, we assume
and
hold. When
, we can easily get
, then 0 is not a characteristic root of (11). Then we have the following lemma.
Lemma 3. Suppose
and
hold, then Eq. (11) has a couple of purely imaginary roots
(
) at
, where
,
,
.
Proof.
(
) is a root of Eq. (11) if and only if
satisfies
Then we have
which lead to
(12)
and the roots of (12) are
Obviously,
and
. Obviously, there is a
such
for
. Then
and
for
. Based on the above discussion, the lemma holds.
□
Denote
. Based on the above analysis, we have the following theorem.
Theorem 4. Suppose
and
hold, for system (2), the following statements are true.
i) If
, then
is local asymptotically stable;
ii) If
, then equilibrium
is unstable;
iii)
(
) are Hopf bifurcation values of system (2).
Proof. Denote
is the root of (11), which satisfy
and
when
is near
. Then we can obtain the following transversality condition. Differentiating two sides of (11) with respect
, we have
Then
where
. Therefore the transversal condition hold. The content of the theorem is obviously valid.
□
3. Direction and Stability of Hopf bifurcation
In this section, we use center manifold theorem and normal form theorem of partial functional differential equations to analyze the stability of the bifurcating periodic solution and direction of Hopf bifurcation. Denote
,
,
and
. Thus, in the phase space
, (2) can be transformed into an abstract form
(13)
with
, and
, where , with
, and
(14)
(15)
Next, we study the linear equation of (13).
From the subsection 2.2, are characteristic eigenvalues of equation
(16)
From Riesz representation, we can get a
matrix function
,
, such that , for
.
We can choose
(17)
Let
be the infinitesimal generators of semigroup included by the solutions of equation (16) and
be the formal adjoint of
under the bilinear paring
(18)
for
.
has a pair of purely imaginary eigenvalues , Let
and
be the characteristic subspaces of
respectively, and they are also characteristic subspaces of
and
. Therefore,
is the adjoint matrix of
, we have
. Obviously, and
are bases of
corresponding to
, and
are bases of
corresponding to
, where
Let
and , for
, we have
For
, we have
Then we can obtain the following equation by (18)
Define
and construct a new basis
for
by . Then
, Besides, define
, with
and
, for
. Hence, we have for
,
,
and
. From [15], we have
(19)
where
The solution of (19) can be expressed as
(20)
with
From center manifold theorem, the solution of (13) can be expressed as
(21)
Denote
, and notice that
. Then we have
Thus, Equation (21) become
(22)
where
From [16], we can know
(23)
with
(24)
Denote
(25)
(26)
From (23) and (26), we have
(27)
and
with
Therefore,
(28)
(29)
(30)
where
Denote
, observe that
.
Thus, we have
(31)
Hence, from (24) (26) (31), we can get
, for
. When
, yields
And
, for
. For the next part, we compute
and
to get
. We have
and
satisfies
, with
(32)
We have
(33)
From (31), we can get
Hence, by (32), we have
and
with
By the definition of
and (33), we have
Thus, ,
where
By the definition of
and (33), we have
As
We get for
,
That is
with
Similarly, from (33), we have
with
Therefore, we can calculate the relevant quantities that govern the stability and direction of the branching periodic orbits.
Then we have the following theorem.
Theorem 5. For any critical value
, we have:
i) if
(respectively > 0), then the Hopf bifurcation is backward (respectively forward), in other words, the bifurcating periodic solutions exists for
(respectively
).
ii) if
(respectively > 0), then the bifurcating periodic solutions are orbitally asymptotically stable (respectively unstable).
iii) if
(respectively > 0), then the period decreases (respectively increases).
4. Numerical Simulations
Choose the parameters
(34)
The model has positive equilibria
and
, and
is always unstable. Therefore, we mainly study the equilibrium
. By direct calculation, it can be obtained
,
,
. Obviously, hypothesis (H3) holds, then from the (2) we know that the equilibrium
is local asymptotically stable when
(shown in Figure 1). In addition, we obtain
, then then from the (4) we know that the equilibrium
is local asymptotically stable when
(shown in Figure 2), and the bifurcating periodic solution exists when
(shown in Figure 3). Moreover, we have
,
,
, then from theorem (5) we can know that the Hopf bifurcation is forward, the bifurcating periodic solutions are orbitally asymptotically stable and the period of bifurcating periodic solutions increase. Besides, we change
to 0.6 and
, (shown in Figure 4), by comparing with Figure 2, we find parameter
is also an important parameter which affect the stability of Hopf bifurcation. By above results, we can know that the gestation delay can affect the stability of population, this kind of influence is not monotonous and manifest as periodic oscillations.
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Figure 1. The numerical simulations of syetem (2) with parameters in (34), and
.
Figure 2. The numerical simulations of syetem (2) with parameters in (34), and
.
Figure 3. The numerical simulations of syetem (2) with parameters in (34), and
.
Figure 4. The numerical simulations of syetem (2) with parameters in (34), and
,
.
5. Conclusion
In this paper, we devote to exploring a diffusive predator-prey model with Allee effect and gestation time delay. By analyzing the associated characteristic transcendental equation, the linear stability of the positive equilibrium is investigated. We also investigate the phenomenon Turing instability and Hopf bifurcation. Furthermore, we conducted some calculations to determine the stability and direction of Hopf bifurcation. The addition of gestation time delay enables us to gain a more comprehensive understanding of biological processes such as reproductive strategies, population dynamics, environmental adaptability, and biodiversity maintenance in organisms. By considering this time delay, the model can more accurately predict and explain complex phenomena in actual biological systems. The numerical simulations reveal that time delay leads to the instability of population numbers that were originally stable. The population size of predators and prey is evenly distributed in time and space when there is no time delay (Figure 1). Further more, we found that there is a critical value for the impact of time delay on population size. When
is weaker than this value (Figure 2), the population size is stable over time. Conversely, when
is stronger than this value (Figure 3), the population size is unstable over time. In summary, the slower predators are born, the more unstable their populations become, which may be influenced by the predator-driven Allee effect. Besides, time delay can lead to non monotonic changes in population size, manifested as periodic oscillations, Periodic oscillations may lead to the disruption of ecological balance. Adding a model with gestation delay can reveal the complexity and potential oscillatory behavior in population dynamics. The emergence of Hopf branches suggests that populations may transition from stable equilibrium states to unstable periodic oscillations, which is of great significance for understanding the stability of ecosystems, survival strategies of species, and the sustainability of ecological services. For example, in the reproductive strategies of some insects, the time interval between larval and adult development can serve as a biological indicator of time delay. By studying these models, scholars can better predict and manage population fluctuations in ecosystems.
Acknowledgements
The authors wish to express their gratitude to the editors and the reviewers for the helpful comments.