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Global dynamics of population-toxicant models with nonlocal dispersals

  • Li Ma and De Tang EMAIL logo
Published/Copyright: August 13, 2025

Abstract

In the article, we first investigate a population-toxicant model with nonlocal dispersal. Compared to the local dispersal (random dispersal), nonlocal dispersal brings some difficulties. One of the difficulties is that principal eigenvalue might not exist. Another difficulty is that the solution orbits lose their compactness. Motivated by previous studies, we overcome these difficulties and establish the global dynamics by spectral analysis, the methods of lower and upper solutions, and the theory of abstract monotone dynamical system. Then, we generalize these results to more general case that the dispersal strategies are mixture of local and nonlocal dispersals. Finally, we point out that we have successfully expanded the upper bound of the critical indicator given in the study by Zhou and Huang without advection terms.

MSC 2010: 35K57; 35K61; 37C65; 92D25

1 Introduction

As is well known, water pollution issues are one of the increasingly hot topics of global concern. In this case, toxic substances are discharged into rivers, streams, lakes, and other body of water, such as pesticides, plastics, pathogens, and chemicals. Toxins in environments bring adverse effects on various levels of ecosystems, including Biodiversity, population growth, community structure, etc. From an environmental and protection perspective, risk assessments of exposed populations are crucial in polluted ecosystems.

In order to better study pollution issues, many mathematicians and ecologists propose appropriate mathematical models on this topic. In these classical models, some include matrix population models (e.g., [11,19,3436]) and also the ordinary differential equation model (e.g., [13,1517,22,23]), and their purpose is to study how environmental toxicants affect populations in polluted aquatic ecosystems. In recent decades, researchers have found that many organisms exhibit diffusion phenomena, this makes diffusion mechanisms an ecology’s most important topic. As early as 1951, Skellam [33] first introduced random diffusion to represent dispersal strategies, and there exists a wealth of studies on the subject [5,32]. A number of researchers have realized that how ecological communities are shaped by spatial interactions is a challenge both theoretically and empirically, and understanding the role of space has been identified as an important factor [10,29]. Researching the effects of environmental toxicants on the dynamics of river populations, Zhou and Huang [40] proposed a toxic model with advection term in one dimension domain:

(1) U 1 t = d 1 2 U 1 x 2 β 1 U 1 x + U 1 ( r ( x ) U 1 b U 2 ) , x ( 0 , L ) , t > 0 , U 2 t = d 2 2 U 2 x 2 β 2 U 2 x + h ( x ) c U 1 U 2 g U 2 , x ( 0 , L ) , t > 0 , d 1 U 1 x ( 0 , t ) β 1 U 1 ( 0 , t ) = U 1 x ( L , t ) = 0 , t > 0 , d 2 U 2 x ( 0 , t ) β 2 U 2 ( 0 , t ) = U 2 x ( L , t ) = 0 , t > 0 , U 1 ( x , 0 ) = U 1 0 ( x ) , 0 , U 2 ( x , 0 ) = U 2 0 ( x ) 0 , 0 , x ( 0 , L ) ,

where U 1 ( x , t ) (resp. U 2 ( x , t ) ) is the population (resp. toxicant) density at location x and time t ; d 1 and d 2 are the diffusion rates of population and toxicant, respectively; β 1 and β 2 denote the advection rates of population and toxicant, respectively; r characterizes the natural growth rate of population; the effect rates of toxicant on population is b ; h denotes input rate of the toxicant; c describes the uptake rate of population on toxicant; and g is the output rate of the contaminant. Zero flux at the upstream end and free flow downstream were assumed. Applying the eigenvalue analysis and a monotone dynamical system theory, they established the global dynamics under appropriate conditions, which gave sufficient conditions on parameter b that leads to population persistence or extinction.

Recently, Deng et al. [9] proposed the following model with negative toxicant-taxis to consider the case that moving from areas with high levels of toxicants to areas with low levels of toxicants may improve survival, growth, and reproduction chances

U 1 t = d 1 Δ U 1 + χ ( U 1 U 2 ) + U 1 ( r U 1 b U 2 ) , x Ω , t > 0 , U 2 t = d 2 Δ U 2 + h ( x ) c U 1 U 2 g U 2 , x Ω , t > 0 , U 1 n ( x , t ) = U 2 n ( x , t ) = 0 , x Ω , t > 0 ,

where r is a constant and χ is the taxis coefficient. They successfully established the global well-posedness and proved the global dynamics by constructing appropriate Lyapunov functions.

While random dispersal is widely used in biological models, it is clearly oversimplified when it comes to describing the movements of many organisms. However, it is well known in ecology that long range dispersals may occur [4,6,7], typical examples include birds flying, seeds spreading, and pollen spreading. In response to this motivation, mathematicians have introduced a novel diffusion mode-nonlocal dispersal, which differs from traditional random diffusion. A widely adopted formulation that incorporates such long-range dispersal is represented by the following nonlocal diffusion operator [3,12,14,24]:

(2) i ν = Ω l i ( x , z ) ν ( z ) d z Ω l i ( z , x ) d z ν ( x ) , i = 1 , 2 ,

where the kernels l i ( x , z ) ( i = 1 , 2 ) characterize the rate at which organisms transition from point z to point x .

It is natural for us to consider what is the nature of the interaction between a population and a toxicant in contaminated environments when we take the nonlocal dispersal factor into account? In what ways does this alteration influence the persistence and spatial distribution of the population? To explore these inquiries, we study the following nonlocal dispersal model:

(3) U 1 t = d 1 1 [ U 1 ] + U 1 ( r ( x ) U 1 b U 2 ) , x Ω , t 0 , U 2 t = d 2 2 [ U 2 ] + h ( x ) c U 1 U 2 g U 2 , x Ω , t 0 , U 1 ( x , 0 ) = U 1 0 ( x ) , 0 , x Ω , U 2 ( x , 0 ) = U 2 0 ( x ) , 0 , x Ω ,

where all the biological explanations for all parameters are the same as in the previous model (1). 1 and 2 defined in (2) denote the nonlocal dispersal. The objective of this study is to elucidate the roles of the effect coefficient of toxicants on population growth b and the uptake coefficient c within the ecological interactions of competing species through the classification of global dynamics described in equation (3).

In the article, unless stated otherwise, we will suppose that

  1. r ( x ) , h ( x ) C ( Ω ¯ ) , r , 0 , and h > 0 on Ω ¯ .

  2. For i = 1 , 2 , l i ( x , z ) 0 belongs to C ( R n × R n ) and l i ( x , x ) > 0 in R n . Moreover,

    R n l i ( x , z ) d z = R n l i ( z , x ) d z = 1 .

  3. For i = 1 , 2 , l i ( x , z ) is symmetric, that is, l i ( x , z ) = l i ( z , x ) .

It should be noted that there exist many difficulties in the nonlocal dispersal cases. One of the most important reasons is that principal eigenvalue might not exist and hence, it further constrains our research on assessing the local stability through linearized analysis. The second important reason is that the compactness of solutions orbits is a natural result in the random dispersal model, while this condition is not met in the n o n l o c a l model (3) since lacks of regularity. For the two-species competition model with l o c a l   d i s p e r s a l s , to describe the global dynamics, the crucial aspect is to establish that all positive steady states exhibit linear stability. Refer to [21] and associated references, where the compactness of solution orbits is identified as a necessary condition. However, in the study of n o n l o c a l models, this cannot be achieved. In addition, the reaction terms have distinct forms and are asymmetric, and reveal that our model is a competitive system between the population and the toxicant, which is very different from the traditional Lotka-Volterra competition models [18,25,27,28,38,39,41] and thus, the previous argument is not applicable.

For the sake of clarity, set C ( Ω ¯ ) + = { u C ( Ω ¯ ) u 0 } and = L ( Ω ) × L ( Ω ) . To effectively illustrate our primary findings and methodologies, some clarifications are necessary. Let ( U 1 ( x ) , U 2 ( x ) ) be a nonnegative steady state of (3) and we know that there are at most two cases.

  • ( U 1 , U 2 ) = ( 0 , U ˜ 2 ) is called a semi-trivial steady state, where U ˜ 2 is the unique positive solutions to single-species model:

    d 2 2 [ U 2 ] + h ( x ) g U 2 = 0 , x Ω .

  • U 1 > 0 , U 2 > 0 , and ( U 1 , U 2 ) is called a positive steady state.

Let K be defined as C ( Ω ¯ ) + × ( C ( Ω ¯ ) + ) , representing the standard cone used in the analysis of competitive systems, which has a nonempty interior described by Int K = Int C ( Ω ¯ ) + × ( Int C ( Ω ¯ ) + ) . The standard partial order relations induced by K , K \ { ( 0,0 ) } , and Int K are represented by K , < K , K , respectively. More precisely, for any i = 1 , 2 and i , W i C ( Ω ¯ ) + ,

( 1 , W 1 ) K ( 2 , W 2 ) i.e. , 1 2 and W 2 W 1 ; ( 1 , W 1 ) < K ( 2 , W 2 ) i.e. , ( 1 , W 1 ) K ( 2 , W 2 ) and ( 1 , W 1 ) ( 2 , W 2 ) ; ( 1 , W 1 ) K ( 2 , W 2 ) i.e. , 1 < 2 and W 2 < W 1

in Ω ¯ . Following the approach as reported in [2,20], one can show that if ( U 1 0 ( x ) , U 2 0 ( x ) ) < K ( U ¯ 1 0 ( x ) , U ¯ 2 0 ( x ) ) , then system (3) generates a monotone dynamical system and we know

( U 1 ( x , t ) , U 2 ( x , t ) ) < K ( U ¯ 1 ( x , t ) , U ¯ 2 ( x , t ) ) , t > 0 ,

where ( U 1 0 ( x ) , U 2 0 ( x ) ) , ( U ¯ 1 0 ( x ) , U ¯ 2 0 ( x ) ) C ( Ω ¯ ) + \ { 0 } × C ( Ω ¯ ) + \ { 0 } , and ( U 1 ( x , t ) , U 2 ( x , t ) ) (resp. ( U ¯ 1 ( x , t ) , U ¯ 2 ( x , t ) ) ) is the unique positive solution of system (3) with initial condition ( U 1 0 ( x ) , U 2 0 ( x ) ) (resp. ( U ¯ 1 0 ( x ) , U ¯ 2 0 ( x ) ) ).

Fortunately, due to the classical abstract monotone theory, system (3) with n o n l o c a l   d i s p e r s a l s still has a more detailed assertion regarding the global dynamics.

Theorem 1.1

Assume that b h m g 2 c h M 2 . For all nonnegative and nontrivial initial conditions, we have the following statements.

  1. If ( 0 , U ˜ 2 ) exhibits linear instability, then system (3) possesses a unique positive steady state in C ( Ω ¯ ) × C ( Ω ¯ ) .

  2. If ( 0 , U ˜ 2 ) exhibits linearly stable or neutrally stable, then ( 0 , U ˜ 2 ) is globally asymptotically stable.

Resorting to the above solution structure of Theorem 1.1 and verifying either the absence or uniqueness of positive steady states is determined directly by the properties of nonlocal operators and through arguments involving contradiction, we have the following important global result.

Theorem 1.2

For all nonnegative and nontrivial initial conditions, the subsequent assertions regarding system (3)are valid.

  1. Assume that c c * .

    1. If 0 < b < b * , then there exists a unique positive steady state of system (3) in C ( Ω ¯ ) × C ( Ω ¯ ) that is globally asymptotically stable.

    2. If b * b h m g 2 c h M 2 , then ( 0 , U ˜ 2 ) is globally asymptotically stable.

    3. If b > h m g 2 c h M 2 , then ( 0 , U ˜ 2 ) is locally stable. Moreover, for any b [ b ˇ * , ) , ( 0 , U ˜ 2 ) is globally asymptotically stable.

  2. Assume that c > c * .

    1. If 0 < b h m g 2 c h M 2 , then there exists a unique positive steady state of system (3) in C ( Ω ¯ ) × C ( Ω ¯ ) that is globally asymptotically stable.

    2. If h m g 2 c h M 2 < b < b * , then system (3)contains at least one (locally stable) positive steady state in .

    3. If b > b * , then ( 0 , U ˜ 2 ) is locally stable. Furthermore, for any b [ b ˇ * , ) , ( 0 , U ˜ 2 ) is globally asymptotically stable.

Here c * = h m g 2 b * h M 2 , h m = min x Ω ¯ h ( x ) , h M = max x Ω ¯ h ( x ) , r M = max x Ω ¯ r ( x ) , b * is given in Lemma3.1(i), and b ˇ * satisfies

(4) λ 1 * d 1 , r b ˇ * h m g + c r M = 0 .

In numerous species, dispersion encompasses both local migration and a minor fraction of long-distance migration. Refer [37] and references therein. A famous example is a genetic model featuring partial panmixia, in which the diffusion term integrates both local and nonlocal dispersal; here the nonlocal dispersal serves as an approximation for long-distance migration. Furthermore, to gain a clearer insight into the competitive benefits of various dispersal strategies, Kao et al. [30,31] studied the model in which one species moves solely through random walk, while the other employs a non-local dispersal strategy. Motivated by these work, we propose the following model with more general dispersal:

(5) U 1 t = d 1 [ α 1 1 [ U 1 ] + ( 1 α 1 ) Δ U 1 ] + U 1 ( r ( x ) U 1 b U 2 ) , x Ω , t > 0 , U 2 t = d 2 [ α 2 2 [ U 2 ] + ( 1 α 2 ) Δ U 2 ] + h ( x ) c U 1 U 2 g U 2 , x Ω , t > 0 , ( 1 α 1 ) U 1 n = ( 1 α 2 ) U 2 n = 0 , x Ω , t > 0 , U 1 ( x , 0 ) = U 1 0 ( x ) , 0 , x Ω , U 2 ( x , 0 ) = U 2 0 ( x ) , 0 , x Ω .

We note here that if α i = 1 ( i = 1 , 2 ), then ( 1 α i ) U i n = 0 on Ω always holds. Obviously, system (3) is the special case of system (5) with α 1 = α 2 = 1 .

Using a method similar to that employed in the proof of Theorem 1.2, one can derive the following outcome regarding the global dynamics of system (5).

Theorem 1.3

For all nonnegative and nontrivial initial conditions, the subsequent assertions regarding system (5)are true.

  1. Assume that c c ˜ .

    1. If 0 < b < b ˜ , then system (5)admits a unique positive steady state, which is globally asymptotically stable.

    2. If b ˜ b h m g 2 c h M 2 , then ( 0 , U ˜ 2 ) is globally asymptotically stable.

    3. If b > h m g 2 c h M 2 , then ( 0 , U ˜ 2 ) is locally stable. Moreover, for any b [ b ˜ * , ) , ( 0 , U ˜ 2 ) is globally asymptotically stable.

  2. Assume that c > c ˜ .

    1. If 0 < b h m g 2 c h M 2 , then there exists a unique positive steady state of system (5), which is globally asymptotically stable.

    2. If h m g 2 c h M 2 < b < b ˜ , then system (5)possesses at least one (locally stable) positive steady state.

    3. If b > b ˜ , then ( 0 , U ˜ 2 ) is locally stable. Furthermore, for any b [ b ˜ * , ) , ( 0 , U ˜ 2 ) is globally asymptotically stable.

Here c ˜ = h m g 2 b ˜ h M 2 , b ˜ is given in Proposition4.1and b ˜ * satisfies λ ˜ 1 d 1 , α 1 , r b ˜ * h m g + c r M = 0 .

The remainder of this study is structured as follows: Section 2 examines the nonlocal model for a single species and demonstrates the existence and uniqueness of a semi-trivial steady state of system (3). Section 3 is devoted to showing the main Theorems 1.1 and 1.2. In Section 4, we analyze system (5) and establish Theorem 1.3. We give a short conclusion in Section 5.

2 Single species models with nonlocal dispersal

In this section, we consider two single species models

(6) d 1 1 [ U 1 ] + U 1 ( r ( x ) U 1 ) = 0 in Ω

and

(7) d 2 2 [ U 2 ] + h ( x ) g U 2 = 0 in Ω ,

where r , h C ( Ω ¯ ) , r , 0 , and h > 0 on Ω ¯ .

In order to investigate the existence and uniqueness of positive solutions of (6), we need to examine the local stability of the zero solution, which is decided by the signs of

λ * ( d 1 , r ) = sup { e λ λ σ ( d 1 1 + r ) } ,

where d 1 1 + r is an operator from C ( Ω ¯ ) to C ( Ω ¯ ) and σ denotes the spectral of operator d 1 1 + r . If λ is an eigenvalue of operator d 1 1 + r that has a positive and continuous eigenfunction, we refer to λ as the principal eigenvalue. By [2,8,24,26], λ * ( d 1 , r ) can be characterized as

λ * ( d 1 , r ) = sup ψ L 2 ( Ω ) \ { 0 } Ω ( d 1 ψ 1 [ ψ ] + r ψ 2 ) d x Ω ψ 2 d x .

Choosing 1 as a test function, one finds that λ * ( d 1 , r ) > 0 due to r ( x ) , 0 . Therefore, by [2, Theorem 2.1] and maximum principle, we obtain the following result.

Lemma 2.1

Problem (6) possesses a unique positive solution U ˜ 1 C ( Ω ¯ ) . Moreover, U ˜ 1 < r M on Ω ¯ , where r M = max x Ω ¯ r ( x ) .

Next, we demonstrate the existence and uniqueness of positive solutions for problem (7).

Lemma 2.2

Problem (7) possesses a unique positive solution U ˜ 2 C ( Ω ¯ ) . Furthermore, u ˜ 2 < h M g on Ω ¯ , where h M = max x Ω ¯ h ( x ) .

Proof

Take h M g and 0 as the upper and lower solutions, respectively. Using the same arguments as that in proving [2, Theorem 2.1], one can derive that Lemma 2.2 holds.□

3 Two species competition model with nonlocal dispersal

For ease of reference, we express the n o n l o c a l   o p e r a t o r s defined in (2) in the following manner:

(8) i [ U ] = Ω l i ( x , z ) U ( z ) d z a i ( x ) U ( x ) ,

where a i ( x ) = Ω l i ( z , x ) d z , for i = 1 , 2 . Lemma 2.2 indicates that system (3) consistently admits one semi-trivial steady state ( 0 , U ˜ 2 ) . To begin with, the linearized operator of system (3) at ( 0 , U ˜ 2 ) is

(9) D ( 0 , u ˜ 2 ) ϕ 1 ϕ 2 = d 1 1 [ ϕ 1 ] + ( r b U ˜ 2 ) ϕ 1 d 2 2 [ ϕ 2 ] c U ˜ 2 ϕ 1 g ϕ 2 .

Let

(10) μ ( 0 , U ˜ 2 ) μ ( 0 , U ˜ 2 ) ( d 1 , r b U ˜ 2 ) = sup { e λ λ σ ( d 1 1 + ( r b U ˜ 2 ) ) } = sup 0 ψ L 2 ( Ω ) Ω ( d 1 ψ 1 [ ψ ] + ψ 2 ( r b U ˜ 2 ) ) d x Ω ψ 2 d x ,

which determines the local stability/instability of ( 0 , U ˜ 2 ) :

if μ ( 0 , U ˜ 2 ) < 0 , = 0 , > 0 , then ( 0 , U ˜ 2 ) is linearly stable , neutrally stable , linearly unstable .

To stress the dependence on parameters of μ ( 0 , U ˜ 2 ) ( d 1 , r b U ˜ 2 ) , by the expression of (10), we have the following monotonic result.

Lemma 3.1

  1. Fixing all parameters except b and regarding μ ( 0 , U ˜ 2 ) ( d 1 , r b U ˜ 2 ) as a function of b, μ ( 0 , U ˜ 2 ) ( d 1 , r b U ˜ 2 ) is strictly monotonic decreasing with respect to b, i.e., if b 1 < b 2 , then μ ( 0 , U ˜ 2 ) ( d 1 , r b 1 U ˜ 2 ) > μ ( 0 , U ˜ 2 ) ( d 1 , r b 2 U ˜ 2 ) . Moreover, there is a critical point b * ( 0 , ) such that

    μ ( 0 , U ˜ 2 ) < 0 , i f b ( b * , ) , = 0 , i f b = b * , > 0 , i f b ( 0 , b * ) ,

    i.e., ( 0 , U ˜ 2 ) is linearly unstable if b < b * , neutrally stable if b = b * and linearly stable if b > b * .

  2. Fixing all parameters except r and regarding μ ( 0 , U ˜ 2 ) ( d 1 , r b U ˜ 2 ) as a function of r , if r 1 , r 2 in Ω , then μ ( 0 , U ˜ 2 ) ( d 1 , r 1 b U ˜ 2 ) < μ ( 0 , U ˜ 2 ) ( d 1 , r 2 b U ˜ 2 ) .

Remark 3.1

We remark here that b * is independent of c .

We next introduce some definitions and basic properties which will be beneficial for demonstrating the main results.

Definition 3.1

We say ( U 1 , U 2 ) C ( Ω ¯ ) × C ( Ω ¯ ) is a lower (upper) solution of system (3) if

d 1 1 [ U 1 ] + U 1 ( r ( x ) U 1 b U 2 ) ( ) 0 , x Ω , d 2 2 [ U 2 ] + h ( x ) c U 1 U 2 g U 2 ( ) 0 , x Ω .

The following result follows from some standard arguments and [20, Proposition 2.2].

Lemma 3.2

Let ( U ¯ 1 , U ¯ 2 ) and ( U ̲ 1 , U ̲ 2 ) be the upper and lower solutions of system (3), respectively, with U ¯ 1 , U ¯ 2 , U ̲ 1 , U ̲ 2 > 0 . Then,

  1. The solution of (3) with initial value ( U ¯ 1 , U ¯ 2 ) decreases in t according to the competitive order.

  2. The solution of (3) with initial value ( U ̲ 1 , U ̲ 2 ) increases in t according to the competitive order.

Similar to that of ([1], Lemmas 2.3 and 2.5), we reach the following result.

Lemma 3.3

The subsequent statements are true.

  1. If μ ( 0 , U ˜ 2 ) > 0 , then there is a positive real number ε 1 such that for any ε ( 0 , ε 1 ) and δ ( 0 , ε 1 ) , there exists a lower solution ( U ̲ 1 , U ̲ 2 ) of system (3)satisfying

    0 < U ̲ 1 < ε , U ̲ 2 = ( 1 + δ ) U ˜ 2 .

  2. There is a positive real number ε 2 such that for any 0 < δ ε 2 , there exists an upper solution ( U ¯ 1 , U ¯ 2 ) of system (3) satisfying

    U ¯ 1 = U ˜ 1 , U ¯ 2 = ( 1 δ ) U ́ 2 < ( 1 δ ) U ˜ 2 ,

    where U ́ 2 ( x ) is the unique positive solution to the following problem:

    d 2 2 [ U 2 ] + h ( x ) c U ˜ 1 U 2 g U 2 = 0 .

    We note here that similar to Lemma 2.2, one can obtain the existence and uniqueness of u ́ 2 ( x ) .

Proof

Here we use a similar method as in the proof of [1, Lemma 2.3] and [20, Proposition 2.6]. For any ε > 0 , there exists a function r ε C ( Ω ¯ ) satisfying r r ε L < ε and the principal eigenvalue μ ε of

d 1 1 [ ψ ] + ψ ( r ε b U ˜ 2 ) = μ ψ

exists associated with the eigenfunction 0 < ψ ε C ( Ω ¯ ) and ψ ε L = 1 . Moreover, μ ( 0 , U ˜ 2 ) μ ε < ε . Then, we will show that ( U ̲ 1 , U ̲ 2 ) = ( ε ψ ε , ( 1 + δ ) U ˜ 2 ( x ) ) is a lower solution of system (3) when ε and δ are small enough.

Obviously, we see that

d 1 1 [ ε ψ ε ] + ε ψ ε ( r ε + ( r r ε ) ε ψ ε b ( 1 + δ ) U ˜ 2 ) = ε ψ ε ( μ ( 0 , U ˜ 2 ) + ( μ ε μ ( 0 , U ˜ 2 ) ) + ( r r ε ) ε ψ ε b δ U ˜ 2 ) > 0 .

On the other hand, for small ε and δ , we also have

d 2 2 [ ( 1 + δ ) U ˜ 2 ] + h ( x ) c ε ψ ε ( 1 + δ ) U ˜ 2 g ( 1 + δ ) U ˜ 2 = δ h ( x ) c ε ψ ε ( 1 + δ ) U ˜ 2 < 0 .

Thus, ( U ̲ 1 , U ̲ 2 ) = ( ε , ( 1 + δ ) U ˜ 2 ( x ) ) is a lower solution of system (3).

(ii) Next we turn to show that ( U ¯ 1 , U ¯ 2 ) = ( U ˜ 1 , ( 1 δ ) U ́ 2 ) is an upper solution of system (3) when δ is small enough.

Obviously, for small δ , we see that

d 1 1 [ U ˜ 1 ] + U ˜ 1 ( r ( x ) U ˜ 1 b ( 1 δ ) U ́ 2 ) = b U ˜ 1 ( 1 δ ) U ́ 2 < 0 .

On the other hand, we also have

d 2 2 [ ( 1 δ ) U ́ 2 ] + h ( x ) c U ˜ 1 ( 1 δ ) U ́ 2 g ( 1 δ ) U ́ 2 = δ h ( x ) > 0 .

Thus, ( U ̲ 1 , U ̲ 2 ) = ( U ˜ 1 , ( 1 δ ) U ́ 2 ) is an upper solution of system (3).□

Lemma 3.4

If there is a coexistence steady state ( U 1 , U 2 ) of system (3) in , then ( U 1 , U 2 ) satisfies

0 < U 1 < U ˜ 1 r M a n d h m g + c r M < U ́ 2 < U 2 < U ˜ 2 < h M g ,

where r M = max x Ω ¯ r ( x ) , h m = min x Ω ¯ h ( x ) , and h M = max x Ω ¯ h ( x ) .

Proof

The results follow directly from the approach of the upper and lower solutions method and maximum principle.□

Based on the above preparations, we can characterize the global dynamics of the competition model (3) with ( 0 , U ˜ 2 ) .

Lemma 3.5

For all nonnegative and nontrivial initial conditions, the subsequent two statements are true.

  1. If μ ( 0 , U ˜ 2 ) > 0 , then system (3) possesses at least one positive steady state in . Moreover, if system (3)possesses a unique positive steady state in C ( Ω ¯ ) × C ( Ω ¯ ) , then it is globally asymptotically stable.

  2. If μ ( 0 , U ˜ 2 ) < 0 and there is no positive steady state of system (3), then ( 0 , U ˜ 2 ) is globally asymptotically stable.

Proof

For ease of reference, we will use ( U ̲ 1 ( x , t ) , U ̲ 2 ( x , t ) ) , and ( U ¯ 1 ( x , t ) , U ¯ 2 ( x , t ) ) to represent the solutions of system (3) with initial values ( U ̲ 1 0 ( x ) , U ̲ 2 0 ( x ) ) , and ( U ¯ 1 0 ( x ) , U ¯ 2 0 ( x ) ) , respectively. Set ε 0 = min { ε 1 , ε 2 } , where ε 1 and ε 2 are given in Lemma 3.3. By employing the comparison principle to the second equation in system (3), for any initial value ( U 1 0 , U 2 0 ) C ( Ω ¯ ) × C ( Ω ¯ ) with U 1 0 , 0 and U 2 0 , 0 in Ω , it is trivial to prove that there is a positive constant T 1 such that

(11) 1 ε 0 2 U ́ 2 < U 2 ( x , t ) < ( 1 + ε 0 2 ) U ˜ 2 for t T 1 .

Then, applying the comparison principle to the first equation in system (3), one can derive that there is a constant ε 3 ( 0 , ε 0 ) such that

(12) 0 < U 1 ( x , T ) < ( 1 ε 3 2 ) U ˜ 1 for T T 1 .

Next we show statement (i) is valid. Note that μ ( 0 , U ˜ 2 ) > 0 , according to Lemma 3.3, system (3) possesses lower and upper solutions denoted by ( U ̲ 1 0 ( x ) , U ̲ 2 0 ( x ) ) and ( U ¯ 1 0 ( x ) , U ¯ 2 0 ( x ) ) , respectively, satisfying

( U ̲ 1 0 ( x ) , U ̲ 2 0 ( x ) ) < K ( U 1 ( x , T ) , U 2 ( x , T ) ) < K ( U ¯ 1 0 ( x ) , U ¯ 2 0 ( x ) ) .

Using the comparison principle for monotone systems, we obtain

(13) ( U ̲ 1 ( x , t ) , U ̲ 2 ( x , t ) ) < K ( U 1 ( x , T + t ) , U 2 ( x , T + t ) ) < K ( U ¯ 1 ( x , t ) , U ¯ 2 ( x , t ) ) .

According to Lemma 3.2, it can be concluded that, in the context of competitive order, ( U ̲ 1 ( x , t ) , U ̲ 2 ( x , t ) ) transitions to a positive solution ( U ̲ 1 * ( x ) , U ̲ 2 * ( x ) ) of system (3) as t increases while ( U ¯ 1 ( x , t ) , U ¯ 2 ( x , t ) ) transitions to a positive solution ( U ¯ 1 * ( x ) , U ¯ 2 * ( x ) ) of system (3) as t decreases point wisely. Therefore, the establishment of positive steady states is confirmed.

Furthermore, if the positive steady state is unique, one has

( U ̲ 1 * ( x ) , U ̲ 2 * ( x ) ) = ( U 1 * ( x ) , U 2 * ( x ) ) = ( U ¯ 1 * ( x ) , U ¯ 2 * ( x ) ) C ( Ω ¯ ) × C ( Ω ¯ ) .

In view of Theorem 7.13 [14], one obtains

lim t U ̲ 1 ( x , t ) = lim t U ¯ 1 ( x , t ) = U 1 * ( x ) in L ( Ω )

and

lim t U ̲ 2 ( x , t ) = lim t U ¯ 2 ( x , t ) = U 2 * ( x ) in L ( Ω ) .

Combining with (13), we see that ( U 1 * , U 2 * ) is globally convergent.

Now, we turn to show (ii). From (11), (12), and Lemma 3.3 (ii), it can be concluded that system (3) possesses an upper solution ( U ¯ 1 0 ( x ) , U ¯ 2 0 ( x ) ) satisfying

( U 1 ( x , T ) , U 2 ( x , T ) ) < K ( U ¯ 1 0 ( x ) , U ¯ 2 0 ( x ) ) .

Again resorting to the comparison principle, we have

(14) ( U 1 ( x , T + t ) , U 2 ( x , T + t ) ) < K ( U ¯ 1 ( x , t ) , U ¯ 2 ( x , t ) ) .

Note the assumption that system (3) has no positive steady states, this along with Lemma 3.2 suggests that ( U ¯ 1 ( x , t ) , U ¯ 2 ( x , t ) ) decreases in t to ( 0 , U ˜ 2 ) point wisely. Due to Theorem 7.13 [14], we can conclude that

lim t U ¯ 1 ( x , t ) = 0 and lim t U ¯ 2 ( x , t ) = U ˜ 2 in L ( Ω ) .

By (14), one has lim t U 1 ( x , t ) = 0 . Finally, it is trivial to show that

lim t U 2 ( x , t ) = U ˜ 2 in L ( Ω ) .

Lemma 3.6

If b h m g 2 c h M 2 , then any positive steady state ( U 1 , U 2 ) of system (3) in is included in C ( Ω ¯ ) × C ( Ω ¯ ) .

Proof

First of all, define L 1 ( ) and L 2 ( ) : Ω ¯ [ 0 , ) by

L 1 ( x ) = Ω l 1 ( x , z ) U 1 ( z ) d z , L 2 ( x ) = Ω l 2 ( x , z ) U 2 ( z ) d z , x Ω ¯ .

For any given x Ω ¯ and 1 , 2 R , set O ( x , 1 , 2 ) :

O ( x , 1 , 2 ) = d 1 ( L 1 ( x ) a 1 ( x ) 1 ) + 1 ( r ( x ) 1 b 2 ) d 2 ( L 2 ( x ) a 2 ( x ) 2 ) + h ( x ) c 1 2 g 2 ,

where

a 1 ( x ) = Ω l 1 ( x , z ) d z , a 2 ( x ) = Ω l 2 ( x , z ) d z .

Then, O ( x , U 1 ( x ) , U 2 ( x ) ) = 0 for all x Ω ¯ . By maximum principle, one sees that

(15) U 2 L ( Ω ) h M g .

Clearly, for any x Ω ¯ and 1 , 2 > 0 with O ( x , 1 , 2 ) = 0 , we have

r ( x ) 1 b 2 = d 1 ( L 1 ( x ) 1 a 1 ( x ) ) , x Ω , h ( x ) 2 c 1 g = d 2 ( L 2 ( x ) 2 a 2 ( x ) ) , x Ω .

Thus,

O ( x , 1 , 2 ) ( 1 , 2 ) = d 1 L 1 ( x ) 1 1 b 1 c 2 d 2 L 2 ( x ) 2 h ( x ) 2 .

This suggests that if b h m g 2 c h M 2 , then

det O ( x , 1 , 2 ) ( 1 , 2 ) = d 1 d 2 L 1 ( x ) L 2 ( x ) 1 2 + d 1 L 1 ( x ) h ( x ) 1 2 + d 2 L 2 ( x ) 1 2 + 1 h ( x ) 2 b c 1 2 > h ( x ) 1 2 b c 1 2 = 1 2 ( h ( x ) b c 2 2 ) 1 2 h m b c ( h M g ) 2 0 ,

where we have used (15). Employing the implicit function theorem, U 1 ( ) and U 2 ( ) are C 0 functions on Ω ¯ and hence, ( U 1 , U 2 ) is a positive steady state of system (3).□

The following result is crucial for understanding the global dynamics. Our reasoning depends on examining the properties of nonlocal operators and certain integral relationships inspired by [2,40].

Proposition 3.1

Assume that b h m g 2 c h M 2 . Then, system (3) has at most one positive steady state in C ( Ω ¯ ) × C ( Ω ¯ ) .

Proof

It is enough to demonstrate that if system (3) has two positive steady states ( U 1 * , U 2 * ) and ( U 1 * , U 2 * ) , then the two positive solutions must be equal, i.e., ( U 1 * , U 2 * ) = ( U 1 * , U 2 * ) . By Lemma 3.4, one can derive that

( U 1 * , U 2 * ) < K ( U ¯ 1 , U ¯ 2 ) and ( U 1 * , U 2 * ) < K ( U ¯ 1 , U ¯ 2 ) ,

where ( U ¯ 1 , U ¯ 2 ) is defined in Lemma 3.3. Adopting a similar approach to that in proving Lemma 3.5, one obtains that system (3) possesses a positive solution ( U 1 , U 2 ) satisfying

( U 1 * , U 2 * ) K ( U 1 , U 2 ) and ( U 1 * , U 2 * ) K ( U 1 , U 2 ) .

If ( U 1 * , U 2 * ) ( U 1 * , U 2 * ) , we have ( U 1 * , U 2 * ) < K ( U 1 , U 2 ) . Next we let ξ = U 1 U 1 * and η = U 2 U 2 * . Hence, ξ 0 , η 0 , ξ η 0 , and ( ξ , η ) satisfies

(16) d 1 1 [ ξ ] + ξ ( r U 1 b U 2 ) U 1 * ξ b U 1 * η = 0 , d 2 2 [ η ] c U 1 * η c U 2 ξ g η = 0 .

Using the first equation of system (3) satisfied by U 1 , we derive

d 1 ( U 1 1 [ ξ ] ξ 1 [ U 1 ] ) = U 1 U 1 * ( ξ + b η ) ,

and thus,

(17) d 1 Ω ( U 1 1 [ U 1 * ] + U 1 * 1 [ U 1 ] ) ξ 2 U 1 U 1 * d x = Ω ξ 2 ( ξ + b η ) d x .

We will show that Ω ξ 2 ( ξ + b η ) d x 0 . By assumption ( H 3 ) , one can infer

Ω × Ω l 1 ( x , z ) [ U 1 ( x ) U 1 * ( z ) U 1 ( z ) U 1 * ( x ) ] d x d z = 0 .

In view of the left-hand side of (17), one has

(18) d 1 Ω ( U 1 1 [ U 1 * ] + U 1 * 1 [ U 1 ] ) ξ 2 U 1 U 1 * d x = d 1 Ω × Ω l 1 ( x , z ) [ U 1 * ( z ) U 1 ( x ) U 1 * ( x ) U 1 ( z ) ] ( U 1 ( x ) U 1 * ( x ) ) 2 U 1 ( x ) U 1 * ( x ) d x d z = d 1 Ω × Ω l 1 ( x , z ) [ U 1 * ( z ) U 1 ( x ) U 1 * ( x ) U 1 ( z ) ] U 1 ( x ) U 1 * ( x ) + U 1 * ( x ) U 1 ( x ) d x d z .

By exchanging the position of variables x and z , we have

(19) d 1 Ω ( U 1 1 [ U 1 * ] + U 1 * 1 [ U 1 ] ) ξ 2 U 1 U 1 * d x = d 1 Ω × Ω l 1 ( z , x ) [ U 1 * ( x ) U 1 ( z ) U 1 * ( z ) U 1 ( x ) ] U 1 ( z ) U 1 * ( z ) + U 1 * ( z ) U 1 ( z ) d x d z .

From (18) and (19), we obtain

d 1 Ω ( U 1 1 [ U 1 * ] + U 1 * 1 [ U 1 ] ) ξ 2 U 1 U 1 * d x = d 1 2 Ω × Ω l 1 ( x , z ) [ U 1 * ( z ) U 1 ( x ) U 1 * ( x ) U 1 ( z ) ] U 1 ( x ) U 1 * ( x ) U 1 ( z ) U 1 * ( z ) + U 1 * ( x ) U 1 ( x ) U 1 * ( z ) U 1 ( z ) d x d z = d 1 2 Ω × Ω l 1 ( x , z ) [ U 1 ( x ) U 1 * ( z ) U 1 * ( x ) U 1 ( z ) ] 2 1 U 1 ( z ) U 1 ( x ) 1 U 1 * ( z ) U 1 * ( x ) d x d z 0 .

This implies Ω ξ 2 ( ξ + b η ) d x 0 .

Similarly, by applying (16) and the second equation of system (3), we have

d 2 ( U 2 2 [ η ] η 2 [ U 2 ] ) = h ( x ) η + c U 2 U 2 * ξ ,

which gives that

d 2 Ω ( U 2 2 [ η ] η 2 [ U 2 ] ) η 2 U 2 U 2 * d x = Ω η 2 U 2 U 2 * ( h ( x ) η + c U 2 U 2 * ξ ) d x .

Similar to the previous analysis and noting the fact that U 2 U 2 * , one has

(20) Ω η 2 U 2 U 2 * ( h ( x ) η + c U 2 U 2 * ξ ) d x = d 2 Ω ( U 2 2 [ η ] η 2 [ U 2 ] ) η 2 U 2 U 2 * d x = d 2 2 Ω × Ω l 2 ( x , z ) [ U 2 * ( z ) U 2 ( x ) U 2 ( z ) U 2 * ( x ) ] 2 1 U 2 ( z ) U 2 ( x ) 1 U 2 * ( z ) U 2 * ( x ) d x d z 0 .

Therefore, we have

(21) Ω ξ 2 ( ξ + b η ) d x 0 , Ω η 2 U 2 U 2 * ( h ( x ) η + c U 2 U 2 * ξ ) d x 0 .

By multiplying the second equation of (21) by b 3 h M 2 g 2 h m , then subtracting the first equation of (21), one has

0 Ω b 3 h M 2 η 2 g 2 h m U 2 U 2 * ( h ( x ) η + c U 2 U 2 * ξ ) d x Ω ξ 2 ( ξ + b η ) d x Ω b 3 η 3 d x + Ω b 3 c h M 2 η 2 ξ g 2 h m d x Ω ξ 2 ( ξ + b η ) d x Ω b 3 η 3 d x + Ω b 2 η 2 ξ d x Ω ξ 2 ( ξ + b η ) d x = Ω ( ξ + b η ) 2 ( b η ξ ) d x 0 ,

where we have used ξ > 0 , η < 0 , b h m g 2 c h M 2 , and U 2 , U 2 * < h M g . Thus, we have

ξ = η 0 ,

which contradicts ξ η 0 . The proof of Proposition 3.1 is completed.□

3.1 Proof of Theorems 1.1 and 1.2

In this section, we establish the primary theorems.

Proof of Theorem 1.1

Statement (i) is a direct consequence of Lemmas 3.5, 3.6, and Proposition 3.1.

Next, we prove statement (ii). Based on Lemma 3.5 (ii), to demonstrate that ( 0 , U ˜ 2 ) is globally asymptotically stable, it is sufficient to establish that system (3) has no positive steady state. If not, we assume that system (3) admits a positive steady state ( U 1 , U 2 ) , satisfying

d 1 1 [ U 1 ] + U 1 ( r U 1 b U 2 ) = 0 , x Ω d 2 2 [ U 2 ] + h ( x ) c U 1 U 2 g U 2 = 0 , x Ω .

Let ( U 1 * , U 2 * ) = ( 0 , U ˜ 2 ) and ξ = U 1 U 1 * = U 1 > 0 , η = U 2 U 2 * = U 2 U ˜ 2 < 0 .

Using the same arguments to derive (20), one has

Ω η 2 U 2 U 2 * ( h ( x ) η + c U 2 U 2 * U 1 ) d x = Ω η 2 U 2 U 2 * ( h ( x ) η + c U 2 U 2 * ξ ) d x = d 2 Ω ( U 2 2 [ η ] η 2 [ U 2 ] ) η 2 U 2 U 2 * d x = d 2 2 Ω × Ω l 2 ( x , z ) [ U 2 ( x ) U 2 * ( z ) U 2 ( z ) U 2 * ( x ) ] 2 1 U 2 ( x ) U 2 ( z ) 1 U 2 * ( x ) U 2 * ( z ) d x d z 0 .

Recalling that μ ( 0 , U ˜ 2 ) 0 , we have

0 μ ( 0 , U ˜ 2 ) = sup 0 ψ L 2 Ω [ d 1 ψ 1 [ ψ ] + ψ 2 ( r b U ˜ 2 ) ] d x Ω ψ 2 d x Ω [ d 1 U 1 1 [ U 1 ] + U 1 2 ( r b U ˜ 2 ) ] d x Ω U 1 2 d x = Ω [ u 1 2 ( r U 1 b U 2 ) + U 1 2 ( r b U ˜ 2 ) ] d x Ω U 1 2 d x = Ω u 1 2 [ U 1 + b ( U 2 U ˜ 2 ) ] d x Ω U 1 2 d x = Ω ξ 2 [ ξ + b η ] d x Ω ξ 2 d x .

Thus, we derive two inequalities as follows:

(22) Ω η 2 U 2 U 2 * ( h ( x ) η + c U 2 U 2 * ξ ) d x 0 , Ω ξ 2 [ ξ + b η ] d x 0 .

Multiplying the first equation of (22) by b 3 h M 2 g 2 h m and subtracting the second one, one has

(23) 0 Ω b 3 h M 2 η 2 g 2 h m U 2 U 2 * ( h ( x ) η + c U 2 U 2 * ξ ) d x Ω ξ 2 ( ξ + b η ) d x < Ω b 3 η 3 d x + Ω b 2 η 2 ξ d x Ω ξ 2 ( ξ + b η ) d x = Ω ( ξ + b η ) 2 ( b η ξ ) d x 0 ,

where we have used Lemma 3.4. This is impossible. Therefore, system (3) does not possess positive steady state and hence, ( 0 , U ˜ 2 ) is globally asymptotically stable.□

Proof of Theorem 1.2

We will begin by demonstrating statement (i). Due to the definitions of c * = h m g 2 b * h M 2 , if c c * , then b * h m g 2 c h M 2 .

(i1) If 0 < b < b * < h m g 2 c h M 2 , then for all nonnegative and nontrivial initial conditions, according to Lemma 3.1 (i) and Theorem 1.1, system (3) possesses a unique positive steady state in C ( Ω ¯ ) × C ( Ω ¯ ) that is globally asymptotically stable.

(i2) If b * b h m g 2 c h M 2 , then for all nonnegative and nontrivial initial conditions, it follows from Lemma 3.1 (i) and Theorem 1.1 that ( 0 , U ˜ 2 ) is globally asymptotically stable.

(i3) If b > h m g 2 c h M 2 , then b > b * and ( 0 , U ˜ 2 ) is locally stable by Lemma 3.1 (i). If b b ˇ * , by Lemmas 3.1 and 3.4, we have b ˇ * > b * . Thus, ( 0 , U ˜ 2 ) is locally stable. Using a similar approach to that in proving Lemma 3.5, to prove that ( 0 , U ˜ 2 ) is globally asymptotically stable, it is sufficient to demonstrate that system (3) does not possess any positive steady state. If not, assume that system (3) admits a positive solution ( U 1 , U 2 ) in . Recalling that ( U 1 , U 2 ) satisfies

d 1 1 [ U 1 ] + U 1 ( r U 1 b U 2 ) = 0 , x Ω , d 2 2 [ U 2 ] + h ( x ) c U 1 U 2 g U 2 = 0 , x Ω ,

we conclude that

(24) λ 1 * ( d 1 , r U 1 b U 2 ) = 0 .

Using Lemma 3.4 and b b ˇ * , one obtains

r b ˇ * h m g + c r M > r U 1 b U 2 .

Similar to Lemma 3.1 (monotonicity), one can show that

λ 1 * d 1 , r b ˇ * h m g + c r M > λ 1 * ( d 1 , r U 1 b U 2 ) = 0 ,

which contradicts (4). Therefore, ( 0 , U ˜ 2 ) is globally asymptotically stable.

By applying arguments analogous to those used in proving statement (i), it can be shown that statement (ii) holds, which concludes the proof.□

4 Two species competition model with more general dispersal

In this section, we explore the global dynamics of (5). For i = 1 , 2 , let

λ ˜ ( d i , α i , r ) = sup { e λ λ σ ( d i [ α i i [ ] + ( 1 α i ) Δ ] + r ) } .

Then, λ ˜ ( d i , α i , r ) can be characterized by

λ ˜ ( d i , α i , r ) = sup ψ L 2 ( Ω ) \ { 0 } Ω ( d i [ α i i [ ϕ i ] ( 1 α i ) ψ i 2 ] + r ψ 2 ) d x Ω ψ 2 d x .

Similar to the arguments as that in Section 2, due to the assumption of H 1 , one has that system (5) always admits one semi-trivial steady state ( 0 , U ˜ 2 ) . Based on the variational characterization, one can derive the local stability of ( 0 , U ˜ 2 ) .

Proposition 4.1

There exists a positive critical value b ˜ which is independent of c such that

λ ˜ ( d 1 , α 1 , r b U ˜ 2 ) > 0 , if b ( b ˜ , ) , = 0 , if b = b ˜ , < 0 , if b ( 0 , b ˜ ) ,

i.e., ( 0 , U ˜ 2 ) is linearly stable as long as b > b ˜ , neutrally stable as long as b = b ˜ , and linearly unstable as long as b < b ˜ .

Parallel results to Lemmas 3.2,3.3,3.4, and 3.5 can be established similarly. For simplicity of presentation, we do not state here. If system (5) admits a positive solution ( U 1 , U 2 ) and α i [ 0 , 1 )   ( i = 1 , 2 ) , then u i C 2 ( Ω ) by the regularity theory of the elliptic operator.

To obtain the global dynamics of system (5), we first establish the key result similar to Proposition 3.1.

Proposition 4.2

Assume that b h m g 2 c h M 2 . Then, system (5) possesses at most one positive steady state.

Proof

If it is not true, i.e., system (5) admits two positive steady states ( U 1 * , U 2 * ) and ( U 1 * , U 2 * ) satisfying

d 1 [ α 1 1 [ U 1 ] + ( 1 α 1 ) Δ U 1 ] + U 1 ( r ( x ) U 1 b U 2 ) = 0 , x Ω , d 2 [ α 2 2 [ U 2 ] + ( 1 α 2 ) Δ U 2 ] + h ( x ) c U 1 U 2 g U 2 = 0 , x Ω , ( 1 α 1 ) U 1 n = ( 1 α 2 ) U 2 n = 0 , x Ω .

Resorting to Proposition (3.1), one may assume that

( U 1 * , U 2 * ) > K ( U 1 * , U 2 * ) .

Let ξ = U 1 * U 1 * and η = U 2 * U 2 * . We have ξ 0 , η 0 , and ξ η 0 . Similar to the proof of Proposition 3.1, it suffices to show that the following formulas

(25) Ω ξ 2 ( ξ + b η ) d x 0 , Ω η 2 u 2 * U 2 * ( h ( x ) η + c u 2 * U 2 * ξ ) d x 0

hold. Note that ( ξ , η ) satisfies

(26) Ω ξ 2 ( ξ + b η ) d x = d 1 Ω ( U 1 * ( α 1 [ U 1 * ] + ( 1 α ) Δ U 1 * ) + U 1 * ( α 1 [ U 1 * ] + ( 1 α ) Δ U 1 * ) ) ξ 2 U 1 * U 1 * d x = d 1 α Ω × Ω l 1 ( x , z ) [ U 1 * ( z ) U 1 * ( x ) U 1 * ( x ) U 1 * ( z ) ] ( U 1 * ( x ) U 1 * ( x ) ) 2 U 1 * ( x ) U 1 * ( x ) d x d z + d 1 ( 1 α ) Ω ( U 1 * Δ U 1 * U 1 * Δ U 1 * ) ( U 1 * ( x ) U 1 * ( x ) ) 2 U 1 * ( x ) U 1 * ( x ) d x = d 1 2 Ω × Ω l 1 ( x , z ) [ U 1 * ( z ) U 1 * ( x ) U 1 * ( x ) U 1 * ( z ) ] 2 1 U 1 * ( z ) U 1 * ( x ) 1 U 1 * ( z ) U 1 * ( x ) d x d z + d 1 ( 1 α ) Ω ( U 1 * ) U 1 * ( U 1 * ) U 1 * 2 1 U 1 * 2 ( x ) 1 ( U 1 * ) 2 ( x ) d x 0 .

Clearly, the other inequality in (25) can be handled similarly. Multiplying the second equation of (25) by b 3 h M 2 g 2 h m and subtracting the first equation of (25), one also has

0 Ω b 3 h M 2 η 2 g 2 h m U 2 U 2 * ( h ( x ) η + c U 2 U 2 * ξ ) d x Ω ξ 2 ( ξ + b η ) d x Ω ( ξ + b η ) 2 ( b η ξ ) d x 0 ,

where we also have used ξ 0 , η 0 , b h m g 2 c h M 2 , and U 2 , U 2 * < h M g . Thus, we have

ξ = η 0 ,

which contradicts ξ η 0 . Thus, Proposition 4.2 holds.□

Proof of Theorem 1.3

By employing arguments to those used in the proof of Theorem 1.2, it can be demonstrated that Theorem 1.3 holds and we omit the details.□

5 Conclusion

In this study, we put forward new nonlocal diffusion models (3) and (5) and characterize the global dynamics of them. Model (3) comprises two nonlocal diffusion equations: one regulates the population’s dispersal and growth affected by toxicants, while the other outlines the dispersal, input, and decay of the toxicant. In the study process, we face many new difficulties in the nonlocal dispersal cases, for example, the local stability analysis cannot be used by primary eigenvalue analysis like the local diffusion case, and the nonlocal dispersal is lack of regularity such that the compactness of solutions orbits is not a natural result, and the reaction terms have distinct forms and are asymmetric, and so on. Based on some special skills, the linear stability analysis of the semi-trivial solution, the methods of lower-upper solutions, implicit function theorem, and the abstract monotone dynamical theory, we have overcome these difficulties and explore the existence and stability of steady states, which yields sufficient conditions that lead to population persistence or extinction. Theorems 1.2 and 1.3 describe the global dynamics of population-toxicant models (3) and (5) with the nonlocal dispersal operator and even the mixed dispersal strategies, respectively. Of course, these conclusions also apply to local diffusion model (1) when the advection rates β 1 = β 2 = 0 . Specially, Zhou and Huang [40] investigated model (1) with advection term in one dimension domain, and they also established the global dynamical results by proposing an important critical value

(27) κ 0 sup σ ( 0 , 1 ) min 1 c max Ω ¯ e β 1 d 1 β 2 d 2 x 1 1 σ , h m g 2 h M 2 1 σ .

When our research model is limited to local dispersal, i.e., β 1 = β 2 = 0 , then the critical value κ 0 can be simplified as

κ 0 * sup σ ( 0 , 1 ) min 1 c 1 1 σ , h m g 2 h M 2 1 σ ,

which is smaller than the critical value h m g 2 c h M 2 in our article, by some elementary analysis as shown below:

Claim: max κ 0 * , h m g 2 c h M 2 = h m g 2 c h M 2 . To show this claim, we require the conclusion stated below:

Lemma 5.1

For any 0 < σ < 1 ,

κ 0 * = h m g 2 h M 2 , i f 1 c 1 h m g 2 h M 2 , 1 c , i f 1 c 1 h m g 2 h M 2 , h m g 2 c h M 2 , i f 1 c , h m g 2 h M 2 < 1 o r 1 c , h m g 2 h M 2 > 1 .

Proof

We first show that statement (i) is true. Let h m g 2 c h M 2 = C h , ι = 1 σ , ι = 1 1 σ . We will consider three cases to complete the proof.

Case I: If 1 c 1 h m g 2 h M 2 , then 1 c 1 C h . Clearly,

κ 0 * sup σ ( 0 , 1 ) min 1 c 1 1 σ , h m g 2 h M 2 1 σ = sup ι , ι ( 1 , ) , 1 ι + 1 ι = 1 min 1 c ι , ( C h ) ι = sup ι , ι ( 1 , ) , 1 ι + 1 ι = 1 { ( C h ) ι } = C h ,

Case II: If 1 c 1 < h m g 2 h M 2 , then 1 c 1 < C h . Similarly, we can derive κ 0 * = 1 c .

Case III: If 1 c > h m g 2 h M 2 > 1 or h m g 2 h M 2 > 1 c > 1 or 1 c < h m g 2 h M 2 < 1 or h m g 2 h M 2 < 1 c < 1 , that is, 1 c > C h > 1 or C h > 1 c > 1 or 1 c < C h < 1 or C h < 1 c < 1 . We only discuss one of these cases, since other cases can be proved similarly. If 1 c > C h > 1 , then there exists a unique pair ι * , ι * ( 1 , ) satisfying 1 c ι * = ( C h ) ι * . Next, we will show that

κ 0 * = 1 c ι * = ( C h ) ι * .

For any 1 < ι < ι * , we have ι * > ι > 1 and hence,

κ 0 * sup σ ( 0 , 1 ) min 1 c 1 1 σ , h m g 2 h M 2 1 σ = sup ι , ι ( 1 , ) , 1 ι + 1 ι = 1 min 1 c ι , ( C h ) ι sup ι , ι ( 1 , ) , 1 ι + 1 ι = 1 { ( C h ) ι } sup ι , ι ( 1 , ) , 1 ι + 1 ι = 1 { ( C h ) ι * } = ( C h ) ι * = 1 c ι * .

If ι > ι * > 1 , then 1 < ι < ι * and hence,

κ 0 * = sup ι , ι ( 1 , ) , 1 ι + 1 ι = 1 min 1 c ι , ( C h ) ι sup ι , ι ( 1 , ) , 1 ι + 1 ι = 1 1 c ι sup ι , ι ( 1 , ) , 1 ι + 1 ι = 1 1 c ι * = 1 c ι * = ( C h ) ι * .

By a simple computation, we conclude that ι * = 1 + ln 1 c ln C h and

1 c ι * = ( C h ) ι * = h m g 2 c h M 2 1 + ln 1 c ln h m g 2 h M 2 = C h 1 + ln 1 c ln C h = 1 c C h = h m g 2 c h M 2 .

Therefore, the proof is complete.□

Due to Lemma 5.1, we conclude that

  1. if 1 c 1 h m g 2 h M 2 , then κ 0 * = h m g 2 h M 2 1 c h m g 2 h M 2 ;

  2. if 1 c 1 < h m g 2 h M 2 , then κ 0 * = 1 c 1 c h m g 2 h M 2 ;

  3. other cases, κ 0 * = 1 c h m g 2 h M 2 .

In a word,

max κ 0 * , h m g 2 c h M 2 = h m g 2 c h M 2 .

This suggests that we have successfully improved the critical value of [40, Lemma 3.9] when β 1 = β 2 = 0 .

However, there still exists some in-depth problems to the further study of the present work, at certain levels. First, some important results such as Theorem 1.2 (resp. Theorem 1.3) indeed gives the global dynamics of system (3) (resp. (5)) when b ( 0 , h m g 2 c h M 2 ] ( b ˇ * , ) (resp. b ( 0 , h m g 2 c h M 2 ] ( b ˜ * , ) ), and it also answers the local results of system (3) (resp. (5)) when b ( h m g 2 c h M 2 , b ˇ * ] (resp. b ( h m g 2 c h M 2 , b ˜ * ] ), yet, within this range, it does not discuss the global dynamics of system (3) (resp. (5)). Second, we only discuss the nonlocal operators 1 , 2 with the symmetric kernels, what about the other nonlocal diffusion strategy, for instance, the asymmetric situation? Besides, if we put the delay into consideration what about the dynamics? These problems will be considered carefully in future work.

Acknowledgments

We are deeply grateful to the referees for carefully reading our paper and giving us various corrections and insightful suggestions/comments, which greatly improve the precision of our results and exposition of our manuscript.

  1. Funding information: Ma’s research was supported by the National Natural Science Foundation of China (Nos. 12161003, 12361099, and 12301269), Guangdong Basic and Applied Basic Research Foundation (No. 2025A1515012064), and Guangdong Provincial Department of Education Innovation Team Project (No. 2023KCXTD063). Tang’s research was supported by Guangdong Basic and Applied Basic Research Foundation (Nos. 2023A1515012553, 2025A1515012045) and National Natural Science Foundation of China (No. 11901596).

  2. Author contributions: All authors contributed equally to this work.

  3. Conflict of interest: The authors state no conflict of interest.

  4. Data availability statement: Data sharing is not applicable to this article as no new data were created or analyzed in this study.

References

[1] X. Bai and F. Li, Global dynamics of a competition model with nonlocal dispersal II: The full system, J. Differential Equations 258 (2015), 2655–2685, https://doi.org/10.1016/j.jde.2014.12.014. Search in Google Scholar

[2] X. Bai and F. Li, Classification of global dynamics of competition models with nonlocal dispersals I: symmetric kernels, Calc. Var. Partial Differential Equations 57 (2018), 1–35, https://doi.org/10.48550/arXiv.1704.02728. Search in Google Scholar

[3] H. Berestycki, J. Coville, and H. Vo, Persistence criteria for populations with non-local dispersion, J. Math. Bio. 72 (2016), 1693–1745, https://doi.org/10.1007/s00285-015-0911-2. Search in Google Scholar PubMed

[4] M. L. Cain, B. G. Milligan, and A. E. Strand, Long-distance seed dispersal in plant populations, Am. J. Bot. 87 (2000), 1217–1227, https://doi.org/10.2307/2656714. Search in Google Scholar

[5] R. S. Cantrell and C. Cosner, Spatial Ecology via Reaction-diffusion Equations. John Wiley & Sons, Ltd., Chichester, 2003, 10.1002/0470871296. Search in Google Scholar

[6] J. S. Clark, Why trees migrate so fast: confronting theory with dispersal biology and the paleorecord, Am. Nat. 152 (1998), 204–224, 10.1086/286162. Search in Google Scholar PubMed

[7] J. S. Clark, C. Fastie, G. Hurtt, S. T. Jackson, C. Johnson, G. A. King, et al., Reids paradox of rapid plant migration, BioScience 48 (1998), 13–24, https://doi.org/10.2307/1313224. Search in Google Scholar

[8] J. Coville, On a simple criterion for the existence of a principal eigenfunction of some nonlocal operators, J. Differential Equations, 249 (2010), 2921–2953, https://doi.org/10.1016/j.jde.2010.07.003. Search in Google Scholar

[9] X. Deng, Q. Huang, and Z.-A. Wang, Global dynamics and pattern formation in a diffusive population-toxicant model with negative toxicant-taxis, SIAM J. Appl. Math. 83 (2023), no. 6, 2212–2236, https://doi.org/10.1137/22M1510881. Search in Google Scholar

[10] J. Dockery, V. Hutson, K. Mischaikow, and M. Pernarowski, The evolution of slow dispersal rates: A reaction-diffusion model, J. Math. Biol. 37 (1998), 61–83, https://doi.org/10.1007/s002850050120. Search in Google Scholar

[11] R. A. Erickson, S. B. Cox, J. L. Oates, T. A. Anderson, C. J. Salice, and K. R. Long, A Daphnia population model that considers pesticide exposure and demography stochasticity, Ecol. Model. 275 (2014), 37–47, https://doi.org/10.1016/j.ecolmodel.2013.12.015. Search in Google Scholar

[12] P. C. Fife, An integrodifferential analog of semilinear parabolic PDEs, Partial differential equations and applications, Lecture Notes in Pure and Appl. Math., Dekker, New York, vol. 177, 1996, pp. 137–145, https://doi.org/10.3109/14659899609084991.Search in Google Scholar

[13] H. I. Freedman and J. B. Shukla, Models for the effect of toxicant in single-species and predator-prey systems, J. Math. Biol. 30 (1991), 15–30, https://doi.org/10.1007/BF00168004. Search in Google Scholar PubMed

[14] M. Grinfeld, G. Hines, V. Hutson, K. Mischaikow, and G. T. Vickers, Non-local dispersal, Differ. Integral Equ. 18 (2005), no. 11, 1299–1320, 10.57262/die/1356059743. Search in Google Scholar

[15] T. G. Hallam, C. E. Clark, and G. S. Jordan, Effects of toxicants on populations: Aqualitative approach II. First order kinetics, J. Math. Biol. 18 (1983), 25–37, 10.1007/BF00275908. Search in Google Scholar PubMed

[16] T. G. Hallam, C. E. Clark, and R. R. Lassiter, Effects of toxicants on populations: A qualitative approach I. Equilibrium environmental exposure, Ecol. Model. 18 (1983), 291–304, https://doi.org/10.1016/0304-3800(83)90019-4. Search in Google Scholar

[17] T. G. Hallam and J. T. de Luna, Effects of toxicants on populations: A qualitative approach. III. Environmental and food chain pathways, J. Theor. Biol. 109 (1984), 411–429, https://doi.org/10.1016/S0022-5193(84)80090-9. Search in Google Scholar

[18] W. Hao, K.-Y. Lam, and Y. Lou, Ecological and evolutionary dynamics in advective environments: Critical domain size and boundary conditions, Discrete Contin. Dyn. Syst. Ser. B 26 (2021), 367–400, 10.3934/dcdsb.2020283. Search in Google Scholar

[19] X. He and W.-M. Ni, Global dynamics of the Lotka-Volterra competition-diffusion system: Diffusion and spatial heterogeneity I, Comm. Pure. Appl. Math. 69 (2016), 981–1014, https://doi.org/10.1002/cpa.21596. Search in Google Scholar

[20] G. Hetzer, T. Nguyen, and W. Shen, Coexistence and extinction in the Volterra-Lotka competition model with nonlocal dispersal, Commun. Pure Appl. Anal 11 (2012), 1699–1722, 10.3934/cpaa.2012.11.1699. Search in Google Scholar

[21] S. B. Hsu, H. L. Smith, and P. Waltman, Competitive exclusion and coexistence for competitive systems on ordered Banach spaces, Trans. Amer. Math. Soc. 348 (1996), no. 10, 4083–4094, 10.1090/S0002-9947-96-01724-2. Search in Google Scholar

[22] Q. Huang, G. Seo, and C. Shan, Bifurcations and global dynamics in a toxin-dependent aquatic population model, Math. Biosci. 296 (2018), 26–35, https://doi.org/10.1016/j.mbs.2017.11.013. Search in Google Scholar PubMed

[23] Q. Huang, H. Wang, and M. A. Lewis, The impact of environmental toxins on predator-prey dynamics, J. Theor. Biol. 378 (2015), 12–30, https://doi.org/10.1016/j.jtbi.2015.04.019. Search in Google Scholar PubMed

[24] V. Huston, S. Martinez, K. Miscaikow, and G. T. Vichers, The evolution of dispersal, J. Math. Biol. 47 (2003), 483–517, https://doi.org/10.1007/s00285-003-0210-1. Search in Google Scholar PubMed

[25] K.-Y. Lam, Y. Lou, and F. Lutscher, Evolution of dispersal in closed advective environments, J. Biol. Dyn. 14 (2014), 188–212, https://doi.org/10.1080/17513758.2014.969336. Search in Google Scholar PubMed

[26] F. Li, J. Coville, and X. Wang, On eigenvalue problems arising from nonlocal diffusion models, Discrete Contin. Dyn. Syst, 37 (2017), no. 2, 879–903, 10.3934/dcds.2017036. Search in Google Scholar

[27] Y. Lou and F. Lutscher, Evolution of dispersal in open advective environments, J. Math. Biol. 69 (2014), 1319–1342, https://doi.org/10.1007/s00285-013-0730-2. Search in Google Scholar PubMed

[28] Y. Lou, X.-Q. Zhao, and P. Zhou, Global dynamics of a Lotka-Volterra competition-diffusion advection system in heterogeneous environments, J. Math. Pures Appl. 121 (2019), 47–82, https://doi.org/10.1016/j.matpur.2018.06.010. Search in Google Scholar

[29] F. Lutscher, E. Pachepsky, and M. A. Lewis, The effect of dispersal patterns on stream populations, SIAM Rev. 47 (2005), no. 4, 749–772, https://doi.org/10.1137/050636152. Search in Google Scholar

[30] C.-Y. Kao, Y. Lou, and W. Shen, Random dispersal vs. non-local dispersal, Discrete Contin. Dyn. Syst. Ser. B 26 (2010), 551–596, 10.3934/dcds.2010.26.551. Search in Google Scholar

[31] C.-Y. Kao, Y. Lou, and W. Shen, Evolution of mixed dispersal in periodic environments, Discrete Contin. Dyn. Syst. Ser. B 17 (2012), 2047–2072, 10.3934/dcdsb.2012.17.2047. Search in Google Scholar

[32] A. Okubo and S. A. Levin, Diffusion and Ecological Problems: Modern Perspectives Interdisciplinary Applied Mathematics, Vol. 14, 2nd ed. Springer, Berlin, 2001, https://doi.org/10.1007/978-1-4757-4978-6.Search in Google Scholar

[33] J. G. Skellam, Random dispersal in theoretical populations, Biometrika 38 (1951), 196–218, https://doi.org/10.2307/2332328.Search in Google Scholar

[34] J. A. Spromberg and W. J. Birge, Modeling the effects of chronic toxicity on fish populations: The influence of life-history strategies, Environ. Toxicol. Chem. 24 (2005), 1532–1540, 10.1897/04-160.1. Search in Google Scholar PubMed

[35] J. A. Spromberg and J. P. Meador, Relating chronic toxicity responses to population-level effects: A comparison of population-level parameters for three salmon species as a function of low-level toxicity, Ecol. Model. 199 (2006), 240–252, https://doi.org/10.1016/j.ecolmodel.2006.05.007. Search in Google Scholar

[36] R. A. Pastorok, S. M. Bartell, S. Ferson, and L. R. Ginzburg, Ecological Modeling in Risk Assessment: Chemical Effects on Populations, Ecosystems, and Landscapes, Lewis Publishers, Boca Raton, FL, 2001, https://doi.org/10.1201/9781420032321.Search in Google Scholar

[37] S. Petrovskii and A. Morozov, Dispersal in a statistically structured population: fat tails revisited, Am. Nat. 173 (2009), 278–289, https://doi.org/10.1086/595755. Search in Google Scholar PubMed

[38] D. Tang and Y. M. Chen, Global dynamics of a Lotka-Volterra competition-diffusion system in advective homogeneous environments, J. Differential Equations 269 (2020), 1465–1483, https://doi.org/10.1016/j.jde.2020.01.011. Search in Google Scholar

[39] P. Zhou, On a Lotka-Volterra competition system: diffusion vs advection, Calc. Var. Partial Differ. Equ. 55 (2016), 137, https://doi.org/10.1007/s00526-016-1082-8. Search in Google Scholar

[40] P. Zhou and Q. Huang, A spatiotemporal model for the effects of toxicants on populations in a polluted river, SIAM J. Appl. Math. 82 (2022), no. 1, 95–118, https://doi.org/10.1137/21M1405629. Search in Google Scholar

[41] P. Zhou, D. Tang, and D.-M. Xiao, On Lotka-Volterra competitive parabolic systems: exclusion, coexistence and bistability, J. Differential Equations 282 (2021), 596–625, https://doi.org/10.1016/j.jde.2021.02.031. Search in Google Scholar

Received: 2024-09-21
Revised: 2025-03-02
Accepted: 2025-07-14
Published Online: 2025-08-13

© 2025 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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