1. Introduction
In the past few decades, the dynamic behaviors of competition-diffusion systems (see [
1]) in homogeneous or heterogeneous environments have been extensively studied. Until 2017, He and Ni [
2,
3] studied the dynamics of two organisms by changing their random diffusion coefficients, resource carrying capacity and competitiveness, and they also described the global dynamics of two organisms. Their research has made outstanding contributions to the competition-diffusion systems. For the competition model of two organisms, either both organisms survive or win with the extinction of the other organisms (see [
4,
5,
6]). In 2019, Du et al. [
7,
8] studied a Lotka-Volterra competition system with periodic habitat advection. From a biological point of view, this pulsating travel front provided a way for two competing species to interact in heterogeneous habitats. Based on the assumption that the resource function in spatial variables is decreasing, Lou et al. [
9] described the competition between two aquatic organisms with different diffusion strategies for the same resource in the Lotka-Volterra reaction-diffusion-advection system in 2019. Md. Kamrujjaman [
10] studied the impact of diffusion strategies on the outcome of competition between two populations while the species are distributed according to their respective carrying capacities in competition-diffusion systems. However, in the competition-diffusion-advection systems, the study of different species with different distribution functions will be more complex. Tang and Chen [
11] and Xu et al. [
12] studied the population dynamics of competition between two organisms from the perspective of river ecology in 2020. One interesting feature of their system was that the boundary conditions at the upstream end and downstream end can represent the net loss of individuals. In some cases, both organisms leave the site of competition, neither coexisting nor becoming extinct. Such an environment is important enough to demonstrate how organisms change their density and survival time in competition (see [
13]). In 2021, Ma and Guo [
14] described the feature of the coincidence of bifurcating coexistence steady-state solution branches and the effect of advection on the stability of the bifurcating solution. However, it is worthwhile to point out that all the aforementioned works focus on the global dynamic behaviors of competition-diffusion systems (see [
10,
15,
16]) or advection systems (see [
17,
18]), in which the diffusion rates and spatial carrying capacity are changed, or the periodic habitat of advection systems is studied, or the upstream and downstream boundary conditions are changed.
Motivated by the effort of the aforementioned studies, we investigate the problem of the global directed dynamic behaviors of a Lotka-Volterra advection system between two organisms in heterogeneous environments, where two organisms are competing for different fundamental resources, their advection and diffusion strategies follow the dispersal towards a positive distribution, and the functions of inter-specific competition ability are variable.
Hence, we discuss the following global dynamics of the advection system:
where
and
are the population densities of biological organisms, location
and time
, which are supposed to be nonnegative;
correspond to the dispersal rates of two competing organisms
U and
V, respectively. ∇ is the gradient operator.
correspond to the advection rates of two competing organisms
U and
V, and
are the nonconstant functions and represent the advective direction. The intrinsic growth rates of the two competing organisms are bounded functions
and
, respectively, two positive distributions are
.
show the inter-specific competition ability. The habitat
is a bounded smooth domain in
,
;
n denotes the outward unit normal vector on the boundary
. For the sake of simplicity, we can suppose the initial data
and
not identically zero. The system (1) satisfies no-flux boundary conditions.
When
, the system (1) becomes the advection system studied by Zhou and Xiao [
19]:
where
and
are positive constants.
is the usual Laplace operator. If
, readers can take a look at the relevant literature [
20] and for the case
, please see the references [
21,
22,
23,
24,
25].
If
, the system (2) becomes a diffusion model (see [
2,
3,
5,
26]):
According to the research of the above models, the purpose of our paper is to deal with a more broader model (1) in a high spatial dimensions. In this system, we consider that the diffusion and advection strategies follow the dispersal towards a positive distribution, growth rates and competitiveness of the two organisms are different. Thus, we have the following basic assumptions in this paper.
, , and are positive constants;
;
, in ;
is nonconstant, and , are also nonconstant.
Conditions
ensure that the distribution of resources is heterogeneous for two species and the positivity is imposed here to guarantee the existence of two semi-trivial steady states for later discussion convenience. Under the conditions of
, we show a complete classification of the global dynamics of the system (1). The rest of this paper is arranged as follows. In
Section 2, we mainly do some preparatory work. Some related properties of the system (1) are deduced from the properties of a single organisms model (4). Besides, some lemmas are proved. In
Section 3, we investigate our main results. By using principal eigenvalue theory, we obtain the linear stability of coexisting steady states (see Theorem 2). Then, the most important thing is that in virtue of the monotone dynamical system theory (see [
4]), we show the classification of global dynamic behaviors (see Theorem 3). A discussion on the main results and problems that deserve future investigation is presented in
Section 4.
2. Preliminaries
In order to describe our main results, we show a competition-diffusion-advection system for a single organisms as follows:
where
and
,
is bounded. According to the relevant description in [
27] and the case that
, there is a unique positive steady state
in the system (4). If we apply this result to the system (1) and the conditions
, there are two semi-trivial steady states
and
respectively.
Lemma 1. Assume that and , is bounded. The elliptic boundary value Problem:has a unique positive solution denoted by θ. Proof. It is known in [
27] that the problem (5) admits a solution and the solution is positive, denoted by
, owning to the positivity of
. Next, assume that
,
are any two positive solutions of (5) and
. It is not difficult to see that
Therefore, . □
To give a complete classification of the global dynamic system (1), we define
Based on the approach in [
2], we define
We first recall the well-known Krein-Rutman Theorem:
Theorem 1 (Krein-Rutman Theorem [
28])
. Let X be a Banach space, a total cone and a compact linear operator that is positive (i.e., ) with positive spectral radius . Then is an eigenvalue with an eigenvector . Moreover, is an eigenvalue of with an eigenvector . In order to better describe the linear stability of semi-trivial steady states, we give the definition of elliptic eigenvalue problem:
where
and
Let
Since
A is uniformly strongly elliptic operator, we declare that the operator
A satisfies the conditions in Theorem 1. An eigenvalue
of the problem (8) is called a principal eigenvalue if
and for any eigenvalue
with
, we have
. Hence, the problem (8) has a principal eigenvalue, denoted by
, and its corresponding eigenfuntion
in
. The principal eigenvalue is expressed as
Next, we give a useful lemma related to eigenvalue comparison results, which is used for Lemma 3 and Theorem 3.
Lemma 2 ([
5])
. If within Ω, then and the equality holds if and only if in Ω. According to the description of theory of monotone semi-flow in the literature [
6], let
X denote the standard Banach space consisting of all continuous functions from
to
, i.e.,
, and
be the set of all non-negative continuous functions from
to
. Define
as the usual cone for the study of competitive systems with nonempty interior. Then we define the notion of linear stability of a given steady state
. Linearizing the steady state problem of (1) at
, we obtain
Similar to the scalar problem (8), we can define the principal eigenvalue for the system (10), that is, an eigenvalue
of the problem (10) is called a principal eigenvalue if
and for any eigenvalue
with
, we have
. Based on the approach in [
6], by using Theorem 1, the problem (10) has a principal eigenvalue
. In fact, we can select the corresponding eigenfunction
, which satisfies
in
. Here, for the convenience of readers to better understand the problem (10), we provide a simple illustration. Let us do this simple transformation
then the problem (10) can be changed to
which is a linear cooperative elliptic system. Suppose now
L is the elliptic operator, let
According to [
28,
29], the problem (11) has
coefficients and is strictly uniformly elliptic in the bounded domain
which has
boundary. Let
K be the positive cone in
consisting of nonnegative functions. For any
, then we can deduce that
defined by
is a positive compact linear operator. By applying Theorem 1 for positive compact linear operators and the Neumann type boundary condition, the problem (11) admits a principal eigenvalue
, and the corresponding eigenfunction
can be chosen to satisfy
and
in
. Notice that
is the solution of the problem (11). Moreover, since the off-diagonal elements
and
are strictly positive in
, it can be further concluded that
is simple and it is the unique eigenvalue corresponding to a pair of strictly positive eigenfunctions, i.e.,
and
in
. In fact, we have
and
in
due to Hopf boundary lemma, which in turn allows us to choose
in
. See [
30] using semi-group theory and [
31] using maximum principle, [
1,
6] for detailed explanation. For the principal eigenvalue theory of general linear cooperative elliptic systems, we refer the interested readers to [
29]. If
is an eigenvalue of (10) and the boundary condition is Neumann type, then
in the coexistence case.
Based on [
26], (Corollary 2.10), the following lemma is about the linear stability of
and
.
Lemma 3. The linear stability of , and in the system (1) are determined by the sign of , and
Proof. For the linear stability of
, when
in (10), we have
Let
be an principal eigenvalue of (12) with the eigenfunction
. We get
If
, then
belonging to an eigenvalue of the second equation in (12), is real and the inequality
holds. Perhaps, if
, then
and
is an eigenvalue of the first equation, we get -4.6cm0cm
Due to the fact that
is real and satisfies
. It follows
If now , letting be the first eigenfunction corresponding to , then is an eigenvalue of (12) with the eigenfunction , which deduces .
Suppose that
. Let
be the first eigenfunction corresponding to
, then
is an eigenvalue of (12) with the eigenfunction
, that means
. Here
satisfies
The existence of
is inferred from
So our claim is right. Owing to (6) and (9), it is inferred that
. Hence, according to Lemma 2, we gain
then
has the same sign as the first eigenvalue
. Applying the definition of
and linear stability, we deduce that the linear stability of
is determined by the sign of
.
Through completely similar arguments, we demonstrate that the stability of and , is determined by respectively. □
Remark 1. From the variational characteristics of the first eigenvalue, we can see that is linearly unstable for any .
Therefore, we give equivalent descriptions of (7) below:
The neutrally stable case is defined as follows
By the definition, it is easy to see =.
In the following, “” is used to mean that the steady state is globally asymptotically stable among all non-negative and not identically zero initial conditions.
Lemma 4 ([
5])
. For any , assume that hold and every coexistence steady state of the system (1)
, if it exists, is asymptotically stable. Then one of the following alternatives holds:- (i)
There exists a unique coexistence steady state of (1) that is .
- (ii)
The system (1) has no coexistence steady state and either one of or is , while the other is unstable.