Local Bifurcation Analysis of the Holling-Tanner Predator-Prey Model with Predator-Taxis and Fear Effects ()
1. Introduction
In ecosystems, the coexistence and evolution of species are influenced by multiple factors, including population migration, interspecific interactions, and environmental heterogeneity. In recent years, mathematical studies of predator-prey models have garnered increasing attention, where diffusion, chemotaxis, and fear effects are considered key mechanisms affecting the spatial distribution and dynamic behavior of populations.
In modeling predator-prey interactions, chemotaxis describes the directed movement response of organisms to external stimuli (such as food or predation risk). Kareiva and Odell [1] were among the first to propose a prey-taxis model to describe the aggregation behavior of predators towards areas of higher prey density. Subsequently, numerous studies have further explored predator-prey models incorporating prey-taxis [2]-[6], with reference [6] investigating prey-taxis in the Holling-Tanner predator-prey model. This study revealed the significant impact of the taxis coefficient as a bifurcation parameter on the existence of non-normal steady-state solutions and elucidated the crucial role of prey-taxis in the formation of spatial patterns.
In addition to the active search of predators for prey, prey also employ various anti-predation strategies to evade predation risk. Most species possess the ability to perceive predation risk and adjust their behavior accordingly [7]. Among these, predator-taxis is an important anti-predation behavior, where prey migrate towards areas of lower predator density in response to perceived predation risk. Previous research has proposed and analyzed predator-prey models that incorporate predator-taxis [8]-[12].
Moreover, fear effects are also significant factors influencing population dynamics. Wang et al. [13] were the first to introduce fear effects into a predator-prey model with a Holling-II functional response, finding that higher levels of fear could stabilize predator-prey dynamics by eliminating periodic solutions, while lower levels of fear might lead to multiple limit cycles through subcritical Hopf bifurcation, resulting in bistability. Subsequent studies [14]-[17] have further explored the impact of fear effects in various predator-prey models.
Inspired by the aforementioned studies, this paper considers a more complex predator-prey model. Unlike the research in reference [6], which focuses on predator foraging behavior (i.e., prey-taxis), this paper introduces both predator-taxis and fear effects into the Holling-Tanner predator-prey model. Additionally, the model incorporates environmental factors of spatial heterogeneity, where the intra-specific competition coefficient of predators,
, depends on spatial location, thereby reflecting the complexity of real ecological environments. Building upon these motivations, we consider the full time-dependent model, which is a parabolic system describing the evolution of prey density
and predator density
(1)
This paper is devoted to the analysis of its steady-state problem, which yields the stationary solutions that dictate the long-term spatial patterns. Therefore, we investigate the following elliptic system
(2)
Let Ω be a bounded domain in
with a smooth boundary
, and let
denote the unit outward normal vector on the boundary. The variables
and
represent the population densities of the prey and predator, respectively. The positive constants
and
are their random diffusion coefficients. The advection term
models predator-taxis, describing the directed movement of the prey away from regions of high predator density. The coefficient
represents the prey’s sensitivity to predation risk. The same risk perception triggers a fear effect, which reduces the prey’s reproduction rate; this is captured in the term
, where
scales the impact of fear on the birth rate
. The parameter
signifies the natural mortality rate of the prey, and
is the intra-specific competition coefficient among prey. The expression
denotes the Holling-II functional response of the predator. The term
represents
the predator’s intrinsic growth rate, and
indicates intra-specific competition among predators, where
is a spatially dependent coefficient and
is a half-saturation constant for the prey-dependent carrying capacity. The term
represents biomass conversion, with
being the conversion efficiency. The homogeneous Neumann boundary conditions signify that the system is isolated at the boundary.
The novelty of this work with respect to the existing literature is twofold. While studies like [6] incorporate prey-taxis into the Holling-Tanner framework, and [8] -[12] analyze predator-taxis models, our work uniquely couples predator-taxis and fear effects through the same sensitivity coefficient
, which appears simultaneously in the cross-diffusion term
and the fear-modified growth rate
. This dual coupling creates a strongly nonlinear mathematical structure absent in all the aforementioned works. More importantly, unlike the constant-coefficient competition terms in [6] [8]-[12], we introduce a spatially degenerate intra-specific competition coefficient
for the predator. This degeneracy, characterized by
in a subregion
, models a refuge scenario where predator competition completely vanishes locally. The vanishing of
fundamentally alters the mathematical framework: it does not merely perturb a uniform state but gives rise to a new class of semi-trivial solutions and bifurcation structures, posing significant analytical challenges in establishing a priori bounds—a task that is central to our present analysis.
For the spatially dependent parameter
, we assume it exhibits degeneracy within the region Ω, specifically that
satisfies
(3)
where
is a smooth closed subdomain within Ω. From a biological perspective, condition (3) indicates that
is a suitable subregion for the survival of the predator population, as the intra-specific competition pressure among predators completely vanishes in the subregion
.
The strong coupling characteristics of the model (2) primarily stem from two key factors: first, the system includes the cross-diffusion term
; and second, the taxis coefficient
appears simultaneously in the reaction term on the right-hand side of the equations. This dual association renders the model (2) a strongly coupled partial differential equation system, posing significant challenges for subsequent theoretical analysis.
The main objective of this paper is to investigate the uniform boundedness, local bifurcation structure, and local asymptotic stability of the positive steady-state solutions of the aforementioned model. Specifically, we first establish a priori estimates for the positive solutions, from which we prove the uniform boundedness of the positive steady-state solutions. Next, taking the growth rate of the prey population
as a bifurcation parameter, we utilize the Crandall-Rabinowitz local bifurcation theorem to analyze the existence of positive solution branches bifurcating from the semi-trivial solution curve, and further discuss the linear stability of the branching solutions.
The primary contribution of this paper is a rigorous theoretical analysis, establishing uniform boundedness, existence via bifurcation, and local asymptotic stability for the positive steady-state solutions. We emphasize that the analytical thresholds derived herein—particularly the bifurcation parameter
and the stability criteria—are not merely abstract mathematical conditions; ecologically, they delineate critical regimes for species coexistence. Specifically, when the prey growth rate
crosses the bifurcation point, the system transitions from a predator-free state to a coexistence state, thereby predicting the precise conditions under which both species can persist in the heterogeneous landscape. The local stability or instability of the bifurcating solution translates directly into ecological forecasts: a stable positive steady state corresponds to the formation of a stationary spatial pattern of coexistence, whereas instability signals the onset of cyclical population dynamics or localized extinctions, especially near the boundary of the predator refuge
where the degeneracy of
takes effect. We acknowledge that this analytical work serves as a necessary theoretical foundation for subsequent numerical investigations. Without the a priori estimates and stability thresholds established here, any computational exploration of pattern formation would lack a solid mathematical basis, particularly given the degeneracy of the competition coefficient
in the refuge region. The numerical visualization of these spatial patterns and the ecological implications of parameter variations will be addressed in our future work.
2. Existence and a Priori Estimates of Positive Solutions
First, we define several symbols that will be used in the subsequent sections of this paper. Let
be a differential operator,
be a Hölder continuous function on Ω, and
be a positive constant. Denote
and
as the first eigenvalues of the operator
in the domain Ω under Dirichlet and Neumann boundary conditions, respectively. When
, we omit the operator
and the function
, denoting
and
; when only
, we omit the operator
, denoting
and
.
Furthermore, the norms in the spaces
(
) and
are defined as follows
According to Theorem 2.2 (i) in [18], when
, the boundary value problem
(4)
has a unique positive solution
; when
, there are no positive solutions to this problem, where the function
satisfies condition (3). This indicates that when
, the model (2) has a semi-trivial solution
. Additionally, when
, the model (2) also has another semi-trivial solution
, and for any parameter values, the model always has the trivial solution
.
Next, we will prove that
(5)
is a necessary condition for the existence of positive solutions of the model (2), as stated in the following lemma.
Lemma 2.1 The model (2) has positive solutions only if (5) holds.
Proof. We use proof by contradiction. Assume that when
, the model (2) has a positive solution
, then
must be an upper solution of the boundary value problem (4). Let
be the eigenfunction corresponding to the eigenvalue
. Extending
to zero in the entire domain Ω, for sufficiently small
,
serves as a lower solution for the boundary value problem (4). By the method of upper and lower solutions for elliptic equations, there must exist a non-negative solution between the lower solution
and the upper solution
, which is strictly positive in the interior of the domain Ω. This indicates that the boundary value problem (4) has a positive solution, contradicting the conclusion of Theorem 2.2 (i) in [18] (which states that when
, the boundary value problem (4) has no positive solutions). Therefore, the assumption is false, and the original conclusion is proved.
From Lemma 2.1, we conclude that condition (5) is necessary for the existence of positive solutions of the model (2). Hence, in the subsequent content of this paper, we will always assume that condition (5) holds.
Theorem 2.2 Let
be given positive constants satisfying the condition
. Then, when the parameter
satisfies
, there exists a positive constant
that depends only on
, such that any positive solution
of the model (2) satisfies
.
Proof. We employ a proof by contradiction. Assume that the conclusion does not hold. Then there exists a sequence
such that
, for which there exist positive solutions
of the model (2) satisfying
(6)
From the first equation of the model (2),
satisfies
Noting that
and
, we obtain
Let
be the positive solution of the ordinary differential equation
, which is given by
. By the maximum principle for elliptic equations, we find that
Thus,
, which indicates that
is uniformly bounded. Combining this with equation (6), we conclude that
as
.
Define
. Dividing both sides of the second equation of the model (2) by
, we find that
satisfies
(7)
Since
,
,
, and
, it follows that
Moreover, since
, we have
Multiplying both sides of this inequality by
and integrating over Ω using integration by parts yields
Adding to both sides, and noting that
implies , we obtain
This shows that the sequence
is uniformly bounded in
. Therefore, there exists a subsequence (still denoted as
) and a function
such that
From
and the strong convergence, using the argument in Proposition 4.1 from [18], we can deduce that
holds in
, and it is evident that
.
Next, we will prove that
holds almost everywhere in
. Multiplying both sides of equation (7) by
and integrating over Ω gives us
(8)
Since
is bounded in
and
, the right-hand side of Equation (8) is uniformly bounded with respect to
. Given that
, it follows that the left-hand side must tend to zero
Due to the boundedness of
, there exists a subsequence such that
weakly in
. Combining this with the strong convergence of
, we take the limit to obtain
Since
in
and
, we conclude that
holds almost everywhere in
.
Now consider the region
. Since
on
, Equation (7) simplifies in
to
Multiplying this equation by a test function
and integrating over
, we obtain
Taking the limit as
and using the weak convergence of
along with the boundedness of
, we find that multiplying both sides by the test function
yields
This indicates that
is a non-negative weak solution of the equation
Since
and
in
, it must be that
. By the Krein–Rutman theorem,
must be the principal eigenvalue of the operator
on
with homogeneous Dirichlet boundary conditions, i.e.,
Since
in
, we have
. By the monotonicity of eigenvalues with respect to potential functions, we obtain
Thus,
, which contradicts the assumption of the theorem that
. This contradiction indicates that the assumption made in the proof by contradiction is false, thereby proving the original statement.
3. Local Bifurcation Structure and Stability of Local Bifurcation Solutions
In this subsection, we take
as the bifurcation parameter and, using the local bifurcation theory proposed by Crandall and Rabinowitz [19], we study the local bifurcation structure of positive solutions of model (2) starting from the semi-trivial solutions
and
, as well as the stability of these local bifurcation solutions.
Now, we investigate the local bifurcation phenomena of model (2). First, we introduce the function
where
and
. Since
, we know that
is continuous and strictly increasing with respect to
. Using a reasoning similar to Theorem 2.1 in [20] and combining standard regularity theory for elliptic equations, Sobolev embedding theorem, and the boundary smoothness assumption
, we can show that there exists a constant
such that any positive solution
of model (2) satisfies
Furthermore, assuming there exists a parameter
such that
, regardless of whether
,
, or
, there exists a unique
such that
(9)
Let
, define the function spaces
and
, where
Model (2) has two semi-trivial solution curves
and
, which are given by
Next, we separately study the local bifurcation structure of positive solutions of model (2) from the semi-trivial solution curves
and
.
Theorem 3.1 Let
, and
be given by (9). The following conclusions hold
If
, then the sufficient and necessary condition for the model (2) to produce a positive solution branch from the semi-trivial solution branch
is
. Furthermore, all positive solutions of model (2) in the neighborhood of the point
lie on a smooth curve
, expressed as
where
is a constant,
and
are determined functions in the space
, and
is defined as
Additionally, when
, the solution branch
at the point
undergoes a supercritical bifurcation; when
, this bifurcation is subcritical, where the critical parameter
satisfies
If
, then the sufficient and necessary condition for the model (2) to produce a positive solution branch from the semi-trivial solution branch
is
. Furthermore, all positive solutions of model (2) in the neighborhood of the point
lie on a smooth curve
, expressed as
where
is a constant,
and
are determined functions in the space
, and
is defined as
Additionally, when
, the solution branch
at the point
undergoes a supercritical bifurcation; when
, this bifurcation is subcritical, where the critical parameter
satisfies
Proof. The proof of conclusion (i) is completely similar to that of (ii), so we will only provide a detailed proof of conclusion (ii) here. Let
, and define the nonlinear operator
as
where the component expressions are given by
Clearly, the sufficient and necessary condition for the equation
to hold is that
is a solution of model (2). Therefore, to find the positive solution branch of (2) near
, we only need to find the positive solution branch of
near
.
Let
denote the Fréchet derivative of the operator
with respect to the variables
, and direct calculation gives
where the coefficient operators
are defined as
with
.
Based on the Krein-Rutman theorem, the existence of a positive solution
satisfying
for the equation
is equivalent to the condition
. Furthermore, it can be verified that the point
is the unique bifurcation point from the semi-trivial solution branch
of model (2) producing a positive solution branch.
Let
, then
satisfies
(10)
First, we analyze the first equation of (10). Based on this equation, we construct the following Neumann boundary value eigenvalue problem
According to the previously defined notation, the principal eigenvalue of the above eigenvalue problem can be denoted as
where the parameters satisfy
and
. From the monotonicity of the principal eigenvalue of the elliptic operator, we know that the principal eigenvalue strictly increases with the potential function. Since the coefficient of
,
, it follows that
is strictly increasing with respect to
, and as
,
. Combining
, we conclude that the first equation of (10) has a unique positive solution
if and only if
. Next, we analyze the second equation of (10). From equation (4), we know that
which indicates that
. Again using the monotonicity of the principal eigenvalue, since the potential function satisfies
, we have
For the elliptic operator Δ, its principal eigenvalue is the unique maximum eigenvalue among all eigenvalues. Therefore, all eigenvalues of the operator
are less than 0, indicating that this operator is strictly negative, and consequently, its inverse
exists and is strictly negative. Thus, the second equation of (10) can be equivalently transformed into
Since
and
, combined with the negativity of the inverse operator, we conclude that
. In summary, we can prove that
, indicating that the dimension of this kernel space is
Next, we verify that
. Consider the adjoint operator
. According to functional analysis theory, the kernel space of the adjoint operator is orthogonal to the range of the original operator, i.e.,
. The matrix form of the operator
can be expressed as
where
To determine the structure of
, let
, then it satisfies
Expanding gives the following Neumann boundary value problem
(11)
First, we analyze the second equation of (11). As previously mentioned, the operator
is a strictly negative operator, and its inverse exists and is also strictly negative, hence this equation only has the trivial solution
. Substituting
into the first Equation of (11), we simplify to obtain
This can be equivalently rewritten in the form of an eigenvalue equation
Here, 0 is the unique eigenvalue of this eigenvalue equation, and the corresponding eigenvector space is one-dimensional. According to the Fredholm alternative theorem, the range of the operator is orthogonal to the kernel space of its adjoint operator
Combining the dimension of the kernel space
, we obtain
Next, we verify the transversality condition for the bifurcation. The partial derivative of the operator
with respect to the parameter
can be expressed as
Letting
and
, we substitute to obtain
Calculating the inner product of the above partial derivative operator at
with itself gives
Since this inner product is strictly greater than 0, we conclude that
Thus, the transversality condition is satisfied. Combining the results from the bifurcation theory in [19], we conclude that the point
is the unique bifurcation point from the semi-trivial solution branch
of model (2) producing a positive solution branch, and all positive solutions of model (2) in the neighborhood of this point lie on a smooth curve
, with the parametric expression given by
where
is a constant.
Next, we derive the expression for
. Define the operator
then
where the components
are given by
According to relevant conclusions in [21], the expression for
can be derived from the following inner product relation
Here,
is a linear functional on the space
satisfying
. Thus, if
and
, the solution branch
undergoes a supercritical bifurcation at the equilibrium point
; if
and
, then this bifurcation is subcritical.
Theorem 3.2 The following conclusions hold
If
, there exists a small positive number
such that the positive solutions
bifurcating from
in model (2) are non-degenerate for
. Furthermore, if
, then
is locally asymptotically stable; if
, then
is unstable.
If
, there exists a small positive number
such that the positive solutions
bifurcating from
in model (2) are non-degenerate for
. Moreover, if
, then when
,
is locally asymptotically stable; otherwise, it is unstable.
Proof. We provide a detailed proof for (ii) since (i) can be proven similarly.
Firstly, from the proof of Theorem 3.1, there exists a constant
such that model (2) has a positive solution branch
bifurcating from
, where
. Linearizing the model (2) at the positive solution
and substituting
gives
(12)
As
when
, the operator
satisfies
, where
is given by
It is not difficult to verify that zero is the smallest eigenvalue of the operator
, with the corresponding positive eigenfunction being
, which has been defined in the proof of Theorem 3.1 (ii). Moreover, all other eigenvalues of the operator
have positive real parts that are uniformly away from zero. Therefore, by combining linear operator perturbation theory [22], we can conclude that when
is sufficiently small, the operator
has a unique principal eigenvalue
and corresponding eigenfunction
, satisfying
Furthermore, all other eigenvalues of
also have positive real parts and are uniformly away from zero. Thus, there exists a sufficiently small positive number
such that the positive solution branch
bifurcating from the trivial steady state
is non-degenerate in the interval
.
Now, we study the stability of
. From (12), we find that
satisfies
(13)
Multiplying both sides of (13) by
and integrating over Ω, we obtain the equation corresponding to
(14)
Dividing both sides of (14) by
and letting
, we obtain
where
Therefore, when
, we have
, which implies that
, indicating that the bifurcated solution is unstable. Conversely, when
, we find
, leading to
, which signifies that the bifurcated solution is locally stable.
4. Discussion
This paper focuses on a class of Holling-Tanner predator-prey models that incorporate predator attraction behavior and fear effects. Using the prey population growth rate
as a bifurcation parameter, we theoretically derive the necessary and sufficient conditions for the local bifurcation of the model from semi-trivial solutions and analyze the linear stability of the local bifurcated solutions. The results indicate that the stability of the bifurcated solutions depends on the relationship between internal system parameters (such as the intraspecific competition coefficient of prey
, the intensity of predator interference
, etc.) and their corresponding critical threshold values: when the parameter values lead to a supercritical bifurcation, small amplitude positive steady-state solutions are linearly stable, suggesting that stable spatially heterogeneous patterns can form in the vicinity of the critical parameters. Conversely, when the bifurcation is subcritical, the corresponding positive steady-state solutions are linearly unstable, indicating that the system may exhibit delayed phenomena or branching behavior of large amplitude solutions. These theoretical results not only enhance the qualitative theoretical framework of predator-prey models incorporating fear effects and chemotaxis in spatially heterogeneous environments but also provide a solid mathematical foundation for understanding the spatial distribution patterns of populations driven by fear effects in actual ecological systems.
Future research will focus on the following directions: 1) in-depth analysis of the global bifurcation structure; 2) bifurcation analysis under the combined effects of multiple parameters; 3) asymptotic behavior in degenerate critical cases; 4) numerical simulations to validate theoretical results.