
Citation: Patricia Morcillo, Maria Angeles Esteban, Alberto Cuesta. Mercury and its toxic effects on fish[J]. AIMS Environmental Science, 2017, 4(3): 386-402. doi: 10.3934/environsci.2017.3.386
[1] | Kuang-Hui Lin, Yuan Lou, Chih-Wen Shih, Tze-Hung Tsai . Global dynamics for two-species competition in patchy environment. Mathematical Biosciences and Engineering, 2014, 11(4): 947-970. doi: 10.3934/mbe.2014.11.947 |
[2] | Jin Zhong, Yue Xia, Lijuan Chen, Fengde Chen . Dynamical analysis of a predator-prey system with fear-induced dispersal between patches. Mathematical Biosciences and Engineering, 2025, 22(5): 1159-1184. doi: 10.3934/mbe.2025042 |
[3] | Ali Mai, Guowei Sun, Lin Wang . The impacts of dispersal on the competition outcome of multi-patch competition models. Mathematical Biosciences and Engineering, 2019, 16(4): 2697-2716. doi: 10.3934/mbe.2019134 |
[4] | Xizhuang Xie, Meixiang Chen . Global stability of a tridiagonal competition model with seasonal succession. Mathematical Biosciences and Engineering, 2023, 20(4): 6062-6083. doi: 10.3934/mbe.2023262 |
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[7] | Nancy Azer, P. van den Driessche . Competition and Dispersal Delays in Patchy Environments. Mathematical Biosciences and Engineering, 2006, 3(2): 283-296. doi: 10.3934/mbe.2006.3.283 |
[8] | Jinyu Wei, Bin Liu . Coexistence in a competition-diffusion-advection system with equal amount of total resources. Mathematical Biosciences and Engineering, 2021, 18(4): 3543-3558. doi: 10.3934/mbe.2021178 |
[9] | B. Spagnolo, D. Valenti, A. Fiasconaro . Noise in ecosystems: A short review. Mathematical Biosciences and Engineering, 2004, 1(1): 185-211. doi: 10.3934/mbe.2004.1.185 |
[10] | S.A. Gourley, Yang Kuang . Two-Species Competition with High Dispersal: The Winning Strategy. Mathematical Biosciences and Engineering, 2005, 2(2): 345-362. doi: 10.3934/mbe.2005.2.345 |
Dispersal of organisms is a topic of central interest in ecology and evolutionary biology. Its effects on the size, stability, and interactions of populations, as well as biological invasions and the geographical distribution of populations have attracted considerable studies. Investigation on dispersal strategies which are evolutionarily stable has been the fundamental research goal for theoretical ecologists [7,26]. The relationship between diffusion rates, spatial heterogeneity, and coupling from competition of species is the target of several recent works. To tackle these problems, continuous diffusion models expressed by reaction-diffusion systems have been considered in [1,2,3,6,9,13,14,19,20,23,25]. On the other hand, discrete diffusion models represented by systems of ODEs have been investigated in [4,5,11,12,24,31].
Concerning the interaction between diffusion rates and the heterogeneity of the environment and mutant invasion, the following competitive Lotka-Volterra model was investigated in [18,19]:
ut=μΔu+u[α(x)−u−v],vt=μΔv+v[β(x)−u−v], | (1.1) |
under homogeneous Neumann boundary condition, where μ is the diffusion rate and functions α(x) and β(x) express the spatially dependent intrinsic growth rates or reproductive rates of u- and v-species, respectively. Therein, to study the effect of spatially heterogeneous growth rates on the competitive dynamics, the difference between intrinsic growth rates of two species was set as
α(x)=β(x)+τg(x), |
where g(x) is a function describing resource difference between two species from the viewpoint of spatial heterogeneity, and τ>0 measures the magnitude of the difference. The case g(x)>0 on the considered domain was studied in [18], whereas the situation that g(x) changes sign was investigated in [19]. The assumption in [19],
∫Ωg(x)dx>0, | (1.2) |
means that the mutant u-species has better average reproductive rate than v-species, and thus the total population of u-species has higher growth rate than that of v-species when two populations are identical in the whole space Ω. However, under such a circumstance, u-species possibly fails to invade when rare for certain level of diffusion rate. Mathematically, stability of semitrivial solutions (˜u,0) and (0,˜v), which depend on the magnitudes of μ and τ, was analyzed in [19]. The stability may switch according to the varying diffusion rate μ. In particular, by measuring the level of mutation with the value of τ, theoretical analysis for the cases of tiny and large mutation was established therein. In the former case (0<τ≪1), multiple switches of global convergence to different equilibria was derived and the relationship between the bifurcation value of the diffusion rate and the value of τ was also established, while in the latter case (τ≫1), only once switch of global convergence was observed.
The influence from magnitudes of diffusion rates on the competition outcome has been another topic of interest. It has been shown in [9] that the slower diffuser always prevails if the two species interact identically with the environment, see also [12,20,23]. To focus on the effect of diffusion rates, the birth rates for all competing species were set equal to the carrying capacity of the environment, see [2,5,6,13,15].
Models for competitive species with dispersal expressed by discrete diffusion are also very appealing. Indeed, organisms are distributed in space, often in patches of habitat scattered over a landscape and region, and the distribution is determined by the pattern of movement between these patches. More specifically, it is interesting to see how possible interaction outcome, which can be competitive exclusion and coexistence, depends on the diffusion rates and the birth rates.
As early as in 1934, Gause [10] formulated the competitive exclusion law which in particular states that the species with a larger birth rate will outcompete the other one, if the other properties are the same. Concerning these issues, Gourley and Kuang [11] then asked how does diffusion affect the competition outcomes of two competing species that are identical in all respects other than their strategies on how they spatially distribute their birth rates. They studied the following ODE system as a model for two neutrally competing species on two patches of habitat:
{du1dt=u1(α1−u1−v1)+d(u2−u1)du2dt=u2(α2−u2−v2)+d(u1−u2)dv1dt=v1(β1−u1−v1)+d(v2−v1)dv2dt=v2(β2−u2−v2)+d(v1−v2) | (1.3) |
where ui (resp., vi) is the population density of species-u (resp., -v) in patch i, i=1,2; the linear birth rates α1,α2,β1,β2 are positive parameters, and there is a diffusion between the two patches with same diffusivity (dispersal rate) d for both species. The two species differ only in their birth rates. Let (ˉu1,ˉu2,0,0) denote the semitrivial equilibrium with extinct v-species. The following conjectures on the global dynamics of system (1.3) were posed in [11]:
Conjecture 1. Assume that in system (1.3), β1−σ=α1<β1<β2<α2=β2+σ with 0<σ<β1, and d is sufficiently large. If u1(0)+u2(0)>0, then
limt→∞(u1(t),u2(t),v1(t),v2(t))=(ˉu1,ˉu2,0,0). |
Conjecture 2. Assume that in system (1.3), β1−σ=α1<β1<β2<α2=β2+σ with 0<σ<β1, and d is small enough so that (1.3) has a positive steady state e∗. If u1(0)+u2(0)>0 and v1(0)+v2(0)>0, then
limt→∞(u1(t),u2(t),v1(t),v2(t))=e∗. |
These conjectures, if true, suggest that the species that can concentrate its birth in a single patch wins, if the diffusion rate is larger than a critical value. That is, the winning strategy is to focus as much birth in a single patch as possible. In [24], the following global dynamics and bifurcation were established, which include confirmation of Conjectures 1 and 2:
Theorem 1.1. Suppose that the following condition holds in system (1.3),
(C′):0<α1=β1−σ1<β1<β2<α2=β2+σ2with0<σ1≤σ2. |
Then there is a constant ˜d>0 which can be expressed or estimated by the birth rates, so that if d≥˜d, (ˉu1,ˉu2,0,0) is globally asymptotically stable among the initial data in R4+ satisfying u1(0)+u2(0)>0; if d<˜d, (1.3) has a unique positive steady state (u∗1,u∗2,v∗1,v∗2) which is globally asymptotically stable among the initial data in R4+ satisfying u1(0)+u2(0)>0 and v1(0)+v2(0)>0.
Note that α1+α2 and β1+β2 measure the average birth rates of species u and v, respectively. The condition of Theorem 1.1 means α1<β1<β2<α2 and β1+β2≤α1+α2, and indicates that the birth rate of u-species is larger than that of v-species in the second patch, and less than that of v-species in the first patch, whereas the average birth rate of u-species is larger than or equal to that of v-species. For the situation with identical average birth rate: α1+α2=β1+β2, i.e., the case in these conjectures, Theorem 1.1 implicates that the two species coexist in a slow diffusion environment, whereas in a fast diffusion environment, the species that can concentrate its birth in a single patch drives the other species into extinction. Convincingly, the same scenario prevails when u has further competitive advantage that its average birth rate is larger than v-species: α1+α2>β1+β2. Along with such finding is that the semitrivial equilibrium (0,0,ˉv1,ˉv2) is always unstable for any diffusion rate d, as was stated in Proposition 3.11 of [24]. It becomes very interesting to see what happens when α1+α2<β1+β2, i.e., v-species has larger average birth rate.
In this paper we will examine such interesting situation, i.e., system (1.3) under condition
(C):0<α1=β1−σ1<β1<β2<α2=β2+σ2with0<σ2<σ1. |
This condition means α1<β1<β2<α2, and α1+α2<β1+β2, due to (β1+β2)−(α1+α2)=σ1−σ2>0. That is, the birth rate α2 of u-species in the second patch is the biggest among all species and patches, but the average birth rate of v-species is larger than that of u-species; one may also regard this as that v-species has more total resources than u-species. Then we ask how the magnitude of the dispersal rate d is related to the species persistence or extinction. With the framework of monotone dynamics, we shall target the global dynamics of system (1.3) and the bifurcation with respect to d, under condition (C).
It turns out that the dynamical scenarios are richer than the case under condition (C′). In particular, equilibrium (0,0,ˉv1,ˉv2) switches from being unstable to stable, as d increases. On the other hand, there are up to two stability changes for equilibrium (ˉu1,ˉu2,0,0), as d increases. That is, the property described as monotone relation between the stability of (ˉu1,ˉu2,0,0) and the diffusion rate d no longer holds, cf. [19]. The main results will be summarized in Theorem 4.2. There are two dynamical scenarios (see Figure 1): (ⅰ) Under σ2β2<σ1β1, there exists an d∗3>0, so that the positive steady state (u∗1,u∗2,v∗1,v∗2) is globally attractive for d<d∗3 and the semitrivial equilibrium (0,0,ˉv1,ˉv2) becomes globally attractive for d≥d∗3. (ⅱ) Under σ2β2>σ1β1, there exist d∗1,d∗2,d∗3 with 0<d∗1<d∗2<d∗3, so that (u∗1,u∗2,v∗1,v∗2) is globally attractive for d<d∗1 or d∗2<d<d∗3, (ˉu1,ˉu2,0,0) is globally attractive for d∗1≤d≤d∗2, and (0,0,ˉv1,ˉv2) becomes globally attractive for d≥d∗3. In addition, d∗1,d∗2,d∗3 can be estimated in terms of the system parameters. Our analytical work on the model strongly suggests that, in a fast diffusion (large dispersal) environment, a species will prevail if its average birth rate is larger than the other competing species; in a slow diffusion (small dispersal) environment, the two species can coexist or one species that has the greatest birth rate among both species and patches, even with smaller average birth rate, will be able to persist and drive the other species to extinction.
We note that α1+α2>β1+β2 in system (1.3) is analogous to condition (1.2) in PDE system (1.1). The present study, with α1+α2<β1+β2, can be compared to the results in [19] with u and v reversed. Systems with two competing species over two patches with different dispersal rates and more general competition coupling have been considered in [21,29,30]. While the effect of competition was studied in [29], herein we aim at investigating the influence of both dispersal rate and birth rates on the population dynamics and assume the same ability of competition for two species in (1.3). Predator-prey dynamics on two-patch environments were investigated in [8,16,22].
This presentation is organized as follows. In Section 2, we characterize the existence of positive equilibrium for system (1.3). In Section 3, we analyze the stability of the semitrivial equilibria. In Section 4, we discuss the existence of positive steady state representing coexistence of two species and extinction of one species, depending on the magnitude of dispersal rate. Four numerical examples illustrating the present theory are given in Section 5. We summarize our results with some discussions in Sections 6. For reader's convenience, we review in Appendix Ⅰ the monotone dynamics theory which is to be applied to obtain our results. Some qualitative properties of the semitrivial equilibria for system (1.3) reported in [24] are recalled in Appendix Ⅱ.
In this section, we characterize the conditions under which the positive equilibrium (u∗1,u∗2,v∗1,v∗2) of system (1.3) exists. There are five parameters α1,α2,β1,β2,d in system (1.3), which generate a complication of analysis for such existence. We first derive the following magnitude relationships which are required in the main result, Theorem 2.1, of this section.
Lemma 2.1. The following parameter relationships hold under condition (C).
(ⅰ) 1σ1−σ2<β1β2σ2β22−σ1β21 if and only if (σ2β22−σ1β21)(σ1β1−σ2β2)>0.
(ⅱ) 0<α1α2σ2α22−σ1α21<β1β2σ2β22−σ1β21, provided σ2β22−σ1β21>0.
(ⅲ) σ1σ1−σ2<β2σ1+σ2, provided σ1β1>σ2β2 and β2−β1≥σ1+σ2.
Proof. Recall that σ1>σ2 in condition (C).
(ⅰ) We compute
β1β2σ2β22−σ1β21−1σ1−σ2=β1β2(σ1−σ2)−(σ2β22−σ1β21)(σ2β22−σ1β21)(σ1−σ2)=(β1+β2)(σ1β1−σ2β2)(σ2β22−σ1β21)(σ1−σ2). |
Thus, β1β2σ2β22−σ1β21−1σ1−σ2>0 if and only if (σ2β22−σ1β21)(σ1β1−σ2β2)>0.
(ⅱ) Suppose σ2β22−σ1β21>0. Then
σ2α22−σ1α21>σ2β22−σ1α21>σ2β22−σ1β21>0. |
The assertion follows from
α1α2σ2α22−σ1α21−β1β2σ2β22−σ1β21=(σ2α2β2+σ1α1β1)(α1β2−α2β1)(σ2α22−σ1α21)(σ2β22−σ1β21)<0, |
due to α1β2−α2β1=α1β2−(β2+σ2)(α1+σ1)<0.
(ⅲ) If σ1β1>σ2β2, then
β2σ1+σ2−σ1σ1−σ2=σ1β2−σ2β2−σ1(σ1+σ2)σ21−σ22>σ1β2−σ1β1−σ1(σ1+σ2)σ21−σ22=σ1[(β2−β1)−(σ1+σ2)]σ21−σ22≥0, |
provided β2−β1≥σ1+σ2. The assertion thus follows.
The following parameter condition is to be used throughout the discussions:
Condition(P):1σ1+σ2<σ1β1σ2β22−σ1β21. |
Certainly condition (P) holds only if σ2β22−σ1β21>0. And a direct computation shows that condition (P) is equivalent to σ2β22−σ1β21>0 with
σ2β22<σ1β1(β1+σ1+σ2). | (2.1) |
Accordingly, if condition (P) holds, Lemma 2.1(ⅰ) can be recast as
1σ1−σ2<β1β2σ2β22−σ1β21⇔σ2β2<σ1β1; |
for convenience of later use, we put this relationship as
σ1σ2σ1−σ2<σ1σ2β1β2σ2β22−σ1β21⇔σ2β2<σ1β1. | (2.2) |
The condition of Lemma 2.1(ⅲ): σ1β1>σ2β2 and β2−β1≥σ1+σ2 implies
σ1σ1−σ2<β2σ1+σ2. |
Then, by combining condition (P), we obtain
σ1σ2σ1−σ2<σ2β2σ1+σ2<σ1σ2β1β2σ2β22−σ1β21. | (2.3) |
On the other hand, combining σ1σ2σ1−σ2>σ1σ2β1β2σ2β22−σ1β21, i.e. σ2β2>σ1β1 by (2.2), with condition (P) yields
σ2β2σ1+σ2<σ1σ2β1β2σ2β22−σ1β21<σ1σ2σ1−σ2. | (2.4) |
Therefore, by imposing condition (P) additionally, the following relationships can be concluded.
Lemma 2.2. Assume that conditions (C) and (P) hold.
(ⅰ) If σ2β2<σ1β1 and β2−β1≥σ1+σ2, then
σ1σ2σ1−σ2<σ2β2σ1+σ2<σ1σ2β1β2σ2β22−σ1β21. |
(ⅱ) If σ2β2>σ1β1, then
σ2β2σ1+σ2<σ1σ2β1β2σ2β22−σ1β21<σ1σ2σ1−σ2. |
It is obvious that the terms in the inequalities in Lemma 2.2 can be simplified. But it is convenient to keep these forms.
Remark 1. In Lemma 2.2, with (2.1), the condition in (ⅱ) : σ2β2>σ1β1 leads to σ1β1β2<σ2β22<σ1β1(β1+σ1+σ2), and thus β2−β1<σ1+σ2, which is contrary to the condition β2−β1≥σ1+σ2 in (ⅱ). That is, the condition in (ⅰ) and the condition in (ⅱ) are opposite cases under assumption (P). In addition, the condition in Lemma 2.2(ⅰ): σ2β2<σ1β1 and β2−β1≥σ1+σ2 further indicates
σ2β22<σ1β1(β1+σ1+σ2)≤σ1β1β2, | (2.5) |
via (2.1).
We characterize the existence of positive equilibrium for system (1.3) in the following theorem.
Theorem 2.1. Consider system (1.3) under conditions (C) and (P).
(ⅰ) Under σ2β2<σ1β1 and β2−β1≥σ1+σ2, there exists an d∗3>0 so that the system has a unique positive equilibrium (u∗1,u∗2,v∗1,v∗2) if and only if 0<d<d∗3.
(ⅱ) Under σ2β2>σ1β1, there exist d∗1,d∗2,d∗3>0, with d∗1<d∗2<d∗3, so that the system has a unique positive equilibrium (u∗1,u∗2,v∗1,v∗2) if and only if 0<d<d∗1 or d∗2<d<d∗3.
In addition,
σ1σ2α1α2σ2α22−σ1α21<d∗1<σ1σ2β1β2σ2β22−σ1β21σ1σ2σ1−σ2<d∗2<σ1√σ1σ2+σ1σ2σ21−σ22(α2−α1)σ1σ2σ1−σ2<d∗3<σ1σ1−σ2(α2−α1). |
Proof. System (1.3) has a positive equilibrium (u∗1,u∗2,v∗1,v∗2) if and only if
(α1−u∗1−v∗1)+d(u∗2u∗1−1)=0,(α2−u∗2−v∗2)+d(u∗1u∗2−1)=0,(β1−v∗1−u∗1)+d(v∗2v∗1−1)=0,(β2−v∗2−u∗2)+d(v∗1v∗2−1)=0, | (2.6) |
are satisfied for u∗1,u∗2,v∗1,v∗2>0. Let (u∗1,u∗2,v∗1,v∗2) be a solution of (2.6) and denote
a:=u∗2u∗1,b:=v∗2v∗1. | (2.7) |
Combining each pair of equations in (2.6), we obtain
−σ1+d(a−b)=0,σ2+d(1a−1b)=0. | (2.8) |
This yields
ab=σ1σ2=:k, | (2.9) |
and k>1, as 0<σ2<σ1. Substituting b=k/a and a=k/b into (2.8) respectively leads to
a=σ1+√σ21+4kd22d,b=−σ1+√σ21+4kd22d. | (2.10) |
We thus express a,b in terms of system parameters, and it can be computed that b2<k<a2 and a>1. We substitute (2.7) into (2.6) and obtain
(α1−u∗1−v∗1)+d(a−1)=0,a(α2−au∗1−bv∗1)+d(1−a)=0, | (2.11) |
(β1−v∗1−u∗1)+d(b−1)=0,b(β2−bv∗1−au∗1)+d(1−b)=0. | (2.12) |
Solving the two equations in (2.11), we have
{u∗1=1a2−k[(aα2−ad+d)−k(α1+ad−d)]v∗1=(α1+ad−d)−u∗1. | (2.13) |
On the other hand, solving the two equations in (2.12), we obtain
{u∗1=1k−b2[(bβ2−bd+d)−b2(β1+bd−d)]v∗1=(β1+bd−d)−u∗1. | (2.14) |
In fact, (2.13) and (2.14) are equivalent, as it can be seen by (2.8) that α1+ad−d=β1+bd−d and
1a2−k[(aα2−ad+d)−k(α1+ad−d)]=1k−b2[(bβ2−bd+d)−b2(β1+bd−d)]. |
Herein, α1+ad−d>0 since a>1. From (2.13) and (2.14), we obtain
v∗1=1a2−k[a2(α1+ad−d)−(aα2−ad+d)]=1k−b2[k(β1+bd−d)−(bβ2−bd+d)]. |
Observe that u∗1,v∗1>0 imply u∗2,v∗2>0, due to (2.7) and a,b>0. Hence, system (1.3) has a unique positive equilibrium if and only if
u∗1=1a2−k[(aα2−ad+d)−k(α1+ad−d)]=1k−b2[(bβ2−bd+d)−b2(β1+bd−d)]>0, | (2.15) |
and
v∗1=1a2−k[a2(α1+ad−d)−(aα2−ad+d)]=1k−b2[k(β1+bd−d)−(bβ2−bd+d)]>0. | (2.16) |
To explore the range of d where u∗1,v∗1>0, we denote u∗1(d),v∗1(d) to express the dependence of u∗1,v∗1 on d. Let us discuss the positivity of v∗1 first. The terms in the brackets of (2.16) can be recast as
{a2(α1+ad−d)−(aα2−ad+d)=a2α1−aα2+d(a2+1)(a−1)k(β1+bd−d)−(bβ2−bd+d)=kβ1−bβ2+d(k+1)(b−1). | (2.17) |
We define two functions to discuss the positivity of v∗1:
Fv∗(d):=a2α1−aα2+d(a2+1)(a−1),Gv∗(d):=kβ1−bβ2+d(k+1)(b−1). |
It follows from (2.16) and (2.17) that
Fv∗(d)=a2−kk−b2Gv∗(d). |
In addition, v∗1>0 if and only if Fv∗(d)>0 if and only if Gv∗(d)>0, due to b2<k<a2. In the following discussions (a)-(e), we analyze the ranges of d within which Fv∗(d) and Gv∗(d) take positive or negative values. For some situations, analyzing Fv∗ is more convenient than Gv∗, whereas the convenience is reverse in other cases.
(a) Fv∗(d)>0 if d<σ1σ2α1α2σ2α22−σ1α21: It can be seen that aα1>α2 implies Fv∗(d)>0, due to a>1. On the other hand, aα1>α2 is actually
a=σ1+√σ21+4kd22d>α2α1, |
which is equivalent to d<σ1σ2α1α2σ2α22−σ1α21.
(b) Fv∗(d)>0 if d>σ1√σ1σ2+σ1σ2σ21−σ22(α2−α1): If b>1, i.e. d>σ1σ2σ1−σ2 by (2.10), then a<k since ab=k. From b2<k<a2, we have 1<b<√k<a<k. Then
Fv∗(d)=a2α1−aα2+d(a2+1)(a−1)>kα1−kα2+d(k+1)(√k−1)>0, |
if d>k(k+1)(√k−1)(α2−α1)=σ1√σ1σ2+σ1σ2σ21−σ22(α2−α1). It is clear that
σ1√σ1σ2+σ1σ2σ21−σ22(α2−α1)=σ1√σ1σ2+σ1σ2σ21−σ22(β2−β1)+σ1√σ1σ2+σ1σ2σ1−σ2>σ1σ2σ1−σ2. |
(c) Gv∗(d)>0 if σ1σ2σ1−σ2<d<σ1σ2β1β2σ2β22−σ1β21: Obviously, Gv∗(d)>0 if kβ1−bβ2>0 and b>1, which is
kβ1β2>b=−σ1+√σ21+4kd22d>1. |
This is equivalent to
σ1σ2σ1−σ2<d<σ1σ2β1β2σ2β22−σ1β21, |
by (2.10). Such value of d exists provided σ1σ2σ1−σ2<σ1σ2β1β2σ2β22−σ1β21.
(d) Gv∗(d)<0 if σ1σ2β1β2σ2β22−σ1β21<d<σ1σ2σ1−σ2: Gv∗(d)<0 provided kβ1−bβ2<0 and b<1, which is
kβ1β2<b=−σ1+√σ21+4kd22d<1, |
and equivalent to σ1σ2β1β2σ2β22−σ1β21<d<σ1σ2σ1−σ2, provided σ1σ2β1β2σ2β22−σ1β21<σ1σ2σ1−σ2.
(e) G′v∗(d)<0 if d<min{σ1σ2σ1−σ2,σ2β2σ1+σ2} and G′v∗(d)>0 if d>max{σ1σ2σ1−σ2,σ2β2σ1+σ2}: From (2.10), we compute
b′=b′(d)=σ1bd√σ21+4kd2>0, | (2.18) |
and G′v∗(d)=(k+1)(b−1)+b′(kd−β2+d). It can be seen that G′v∗(d)<0, provided b<1 and kd−β2+d<0, which are equivalent to d<σ1σ2σ1−σ2 and d<σ2β2σ1+σ2. On the contrary, G′v∗(d)>0, if b>1 and kd−β2+d>0, which are equivalent to d>σ1σ2σ1−σ2 and d>σ2β2σ1+σ2.
For case (ⅰ), we will show that v∗1(d)>0 for all d>0 if σ2β2<σ1β1 and β2−β1≥σ1+σ2. Recall Lemma 2.2(ⅰ): σ1σ2σ1−σ2<σ2β2σ1+σ2<σ1σ2β1β2σ2β22−σ1β21. From the above (c), (e), we summarize
{Gv∗(d)>0ifσ1σ2σ1−σ2<d<σ1σ2β1β2σ2β22−σ1β21G′v∗(d)<0ifd<σ1σ2σ1−σ2G′v∗(d)>0ifd>σ2β2σ1+σ2. | (2.19) |
In addition, at d=σ1σ2σ1−σ2, i.e., b=1, we have Gv∗(σ1σ2σ1−σ2)=kβ1−β2>0, thanks to σ2β2<σ1β1. Therefore, from (2.19), we see that Gv∗(d)>0 for all d>0, namely, v∗1(d)>0 for all d>0, under σ2β2<σ1β1, β2−β1≥σ1+σ2, and condition (P).
For case (ⅱ), if σ2β2>σ1β1, we will show that Gv∗(d)>0, and hence v∗1(d)>0, for d in certain range. Recall Lemma 2.2(ⅱ): σ2β2σ1+σ2<σ1σ2β1β2σ2β22−σ1β21<σ1σ2σ1−σ2. With the above (a), (b), (d), (e), we obtain
{Fv∗(d)>0ifd<σ1σ2α1α2σ2α22−σ1α21Gv∗(d)<0ifσ1σ2β1β2σ2β22−σ1β21<d<σ1σ2σ1−σ2Fv∗(d)>0ifd>σ1√σ1σ2+σ1σ2σ21−σ22(α2−α1) | (2.20) |
and
{G′v∗(d)<0ifd<σ2β2σ1+σ2G′v∗(d)>0ifd>σ1σ2σ1−σ2. | (2.21) |
Furthermore, if d≥σ2β2σ1+σ2, i.e. d(k+1)≥β2, and d≤σ1σ2σ1−σ2, i.e. b≤1, we have
Gv∗(d)=kβ1−bβ2−d(k+1)(1−b)≤kβ1−bβ2−β2(1−b)=kβ1−β2<0, |
by σ2β2>σ1β1. To summarize, Gv∗(d)<0 if σ2β2σ1+σ2≤d≤σ1σ2σ1−σ2. Therefore, from (2.20) and (2.21), there exists a unique d∗1>0 so that Gv∗(d)>0 if d<d∗1 and Gv∗(d∗1)=0, where
σ1σ2α1α2σ2α22−σ1α21<d∗1<σ1σ2β1β2σ2β22−σ1β21, |
and there exists a unique d∗2>0 so that Gv∗(d)>0 if d>d∗2, where
σ1σ2σ1−σ2<d∗2<σ1√σ1σ2+σ1σ2σ21−σ22(α2−α1). |
The two cases for the assertion of v∗1(d)>0 are thus concluded. Now let us discuss the positivity of u∗1(d). From (2.15), we have
{(aα2−ad+d)−k(α1+ad−d)=aα2−kα1−d(k+1)(a−1),(bβ2−bd+d)−b2(β1+bd−d)=b(β2−bβ1)+d(b2+1)(1−b). | (2.22) |
Let
Fu∗(d):=aα2−kα1−d(k+1)(a−1),Gu∗(d):=b(β2−bβ1)+d(b2+1)(1−b). |
Then
Fu∗(d)=a2−kk−b2Gu∗(d). |
In addition, u∗1>0 if and only if Fu∗(d)>0 if and only if Gu∗(d)>0, due to b2<k<a2. Let us discuss the signs of Fu∗(d) and Gu∗(d) in the following (a')-(d').
(a') Gu∗(d)>0 if d≤σ1σ2σ1−σ2: It is clear that b≤1 implies Gu∗(d)>0, and b≤1 is equivalent to d≤σ1σ2σ1−σ2. Thus, Gu∗(d)>0 if d≤σ1σ2σ1−σ2.
(b') Fu∗(d)<0 if d>σ1σ1−σ2(α2−α1): If b>1, then from 1<b<√k<a<k, we have
Fu∗(d)=aα2−kα1−d(k+1)(a−1)<aα2−aα1−d(a+1)(a−1)=a(α2−α1)−d(a2−1)<k(α2−α1)−d(k−1)<0, |
if d>kk−1(α2−α1)=σ1σ1−σ2(α2−α1).
(c') Case (ⅰ): σ2β2<σ1β1, i.e., σ1σ2σ1−σ2<σ1σ2β1β2σ2β22−σ1β21 by (2.2). We claim that F′u∗(d)<0 for d≤σ2β2σ1+σ2 and G′u∗(d)<0 for d>σ2β2σ1+σ2. Notably,
F′u∗(d)=[β2+σ2−d(k+1)]a′−(k+1)(a−1) | (2.23) |
and
G′u∗(d)=−(b2+1)(b−1)−2bb′d(b−1)−b′[d(b2+1)+2bβ1−β2], | (2.24) |
by direct computations, where a′=a′(d),b′=b′(d). For the first term of (2.23), we see that
β2+σ2−d(k+1)≥σ2>0ifd≤σ2β2σ1+σ2. |
In addition, from (2.10), we compute
a′=−σ1ad√σ21+4kd2<0. | (2.25) |
Hence, in (2.23), we confirm F′u∗(d)<0 for d≤σ2β2σ1+σ2, due to a>1 and a′<0. Next, we discuss the third term of G′u∗(d) in (2.24), and claim that
d(b2+1)+2bβ1−β2>0ifd>σ2β2σ1+σ2. |
From (2.10) and b′>0 shown in (2.18), a direct computation shows
b>−σ1(σ1+σ2)+√σ21(σ1+σ2)2+4σ1σ2β222σ2β2ifd>σ2β2σ1+σ2. |
For d>σ2β2σ1+σ2, we compute directly
d(b2+1)+2bβ1−β2>12σ2β2(σ1+σ2)[σ21(σ1+σ2)2+2σ1σ2β22+2σ22β22]−σ1β1(σ1+σ2)σ2β2−β2+12σ2β2(σ1+σ2)[(2β1−σ1)(σ1+σ2)√σ21(σ1+σ2)2+4σ1σ2β22]=2β1−σ12σ2β2[√σ21(σ1+σ2)2+4σ1σ2β22−σ1(σ1+σ2)]>0. | (2.26) |
Note that σ1σ2σ1−σ2<σ2β2σ1+σ2, according to Lemma 2.2(ⅰ). As seen in (b) above, b>1 is equivalent to d>σ1σ2σ1−σ2. Thus, we see from (2.24) that G′u∗(d)<0 for d>σ2β2σ1+σ2, due to (2.26), b>1, and b′>0.
(d') Case (ⅱ): σ2β2>σ1β1, i.e. σ1σ2σ1−σ2>σ1σ2β1β2σ2β22−σ1β21 by (2.2). We claim that the third term of (2.24): d(b2+1)+2bβ1−β2>0 for d≥σ1σ2σ1−σ2. If so, then it can be seen from (2.24) and b′>0, that G′u∗(d)<0 for d≥σ1σ2σ1−σ2 which is equivalent to b≥1. For d≥σ1σ2σ1−σ2, we obtain
d(b2+1)+2bβ1−β2≥2σ1σ2σ1−σ2+2β1−β2=1σ1−σ2[2σ1σ2+2σ1β1−2σ2β1−σ1β2+σ2β2]>1σ1−σ2[2σ1σ2+3σ1β1−2σ2β1−σ1β2]>1σ1−σ2[2σ1σ2+3σ1β1−2σ2β1−σ1(β1+σ1+σ2)]=2β1−σ1>0, |
due to β1>σ1>0 and β2−β1<σ1+σ2, mentioned in Remark 1.
For case (ⅰ), we summarize properties (a')-(c'):
{Gu∗(d)>0ifd≤σ1σ2σ1−σ2Fu∗(d)<0ifd>σ1σ1−σ2(α2−α1)F′u∗(d)<0ford≤σ2β2σ1+σ2G′u∗(d)<0ford>σ2β2σ1+σ2. |
Recall Lemma 2.2(ⅰ): σ1σ2σ1−σ2<σ2β2σ1+σ2<σ1σ2β1β2σ2β22−σ1β21, and that Gu∗(d) and Fu∗(d) have identical sign. There are two possibilities:
(Ⅰ) Fu∗(σ2β2σ1+σ2)≥0, i.e., Gu∗(σ2β2σ1+σ2)≥0: As G′u∗(d)<0 for d>σ2β2σ1+σ2 and Fu∗(d)<0 if d>σ1σ1−σ2(α2−α1), there exists a unique d∗3>0 such that Gu∗(d∗3)=0, where
σ2β2σ1+σ2≤d∗3<σ1σ1−σ2(α2−α1). |
(Ⅱ) Fu∗(σ2β2σ1+σ2)<0, i.e., Gu∗(σ2β2σ1+σ2)<0: As Fu∗(d)>0 for d≤σ1σ2σ1−σ2, F′u∗(d)<0 for d≤σ2β2σ1+σ2, and G′u∗(d)<0 for d>σ2β2σ1+σ2, we confirm that there exists a unique d∗3>0 such that Gu∗(d∗3)=0, where
σ1σ2σ1−σ2<d∗3<min{σ2β2σ1+σ2,σ1σ1−σ2(α2−α1)}. |
Both (Ⅰ) and (Ⅱ) indicate that there exists a unique d∗3>0 such that Gu∗(d∗3)=0, Gu∗(d)>0 if d<d∗3, and Gu∗(d)<0 if d>d∗3, where
σ1σ2σ1−σ2<d∗3<σ1σ1−σ2(α2−α1). |
For case (ⅱ), from the above (a'), (b'), and (d'), we summarize
{Gu∗(d)>0ifd≤σ1σ2σ1−σ2Fu∗(d)<0ifd>σ1σ1−σ2(α2−α1)G′u∗(d)<0ifd≥σ1σ2σ1−σ2. |
We thus conclude that there exists a unique d∗3>0 such that Gu∗(d∗3)=0, Gu∗(d)>0 if d<d∗3, and Gu∗(d)<0 if d>d∗3, where
σ1σ2σ1−σ2<d∗3<σ1σ1−σ2(α2−α1). |
From (2.13), we see that v∗1→α1+ad−d>0 as u∗1→0+, i.e., u∗1 and v∗1 can not be zero simultaneously. From the above discussions, we confirm that d∗2<d∗3.
Combining the above discussions of two scenarios for v∗1(d)>0, and one single scenario for u∗1(d)>0, the assertions are thus justified, see Figure 2.
Remark 2. (Ⅰ) Under conditions (C) and (P), the proof of Theorem 2.1 actually indicate:
(ⅰ) If σ2β2<σ1β1 and β2−β1≥σ1+σ2, then (u∗1,u∗2,v∗1,v∗2)→(0,0,ˉv1,ˉv2), as d→(d∗3)−, i.e., the positive equilibrium (u∗1,u∗2,v∗1,v∗2) degenerates and merges into the semitrivial equilibrium (0,0,ˉv1,ˉv2) at d=d∗3.
(ⅱ) If σ2β2>σ1β1, then (u∗1,u∗2,v∗1,v∗2)→(ˉu1,ˉu2,0,0), as d→(d∗1)−, i.e., the positive equilibrium (u∗1,u∗2,v∗1,v∗2) degenerates and merges into the semitrivial equilibrium (ˉu1,ˉu2,0,0) at d=d∗1; (ˉu1,ˉu2,0,0)→(u∗1,u∗2,v∗1,v∗2), as d→(d∗2)+, i.e., the semitrivial equilibrium (ˉu1,ˉu2,0,0) becomes the positive equilibrium (u∗1,u∗2,v∗1,v∗2) as the value of d increases through d∗2; (u∗1,u∗2,v∗1,v∗2)→(0,0,ˉv1,ˉv2), as d→(d∗3)−, i.e., the positive equilibrium (u∗1,u∗2,v∗1,v∗2) again degenerates and merges into the semitrivial equilibrium (0,0,ˉv1,ˉv2) at d=d∗3.
(Ⅱ) In Theorem 2.1(ⅱ), combining σ2β2>σ1β1 and condition (P) yields β2−β1<σ1+σ2, which is contrary to condition β2−β1≥σ1+σ2 in case (ⅰ), as mentioned in Remark 1.
(Ⅲ) Although the same symbol d∗3 is used in Theorem 2.1 (ⅰ) and (ⅱ), they represent different values under assumptions in (ⅰ) and (ⅱ), respectively.
(Ⅳ) With the setting a:=u∗2u∗1,b:=v∗2v∗1, and subsequently ab=σ1σ2=:k, we always have b2<k<a2. Notably, in [24], 0<k≤1 under assumption σ1≤σ2, and hence b<1. This is disparate from the situation in Theorem 2.1 that k>1, and hence a>1, due to σ1>σ2.
In this section, we analyze the stability of the semitrivial equilibria for system (1.3). We denote by (ˉu1,ˉu2,0,0) and (0,0,ˉv1,ˉv2) the semitrivial (boundary) equilibria for system (1.3), and by ˉui(d) and ˉvi(d),i=1,2, to express the dependence of ˉui and ˉvi on d. In Appendix Ⅱ, we recall some properties of semitrivial equilibria of system (1.3) in Propositions 3.7-3.10 of [24], which are independent of the order between σ1 and σ2. Herein, we add the following additional properties for the semitrivial equilibria, which shall be employed to discuss the stability of semitrivial equilibria.
Proposition 3.1. (ⅰ) If α1<α2, then ˉu′1(d)>0,ˉu′2(d)<0, ˉu″1(d)<0, and ˉu″2(d)>0, for all d>0.
(ⅱ) If β1<β2, then ˉv′1(d)>0,ˉv′2(d)<0, ˉv″1(d)<0, and ˉv″2(d)>0, for all d>0.
Proof. (ⅰ) (ˉu1,ˉu2,0,0) is an equilibrium of (1.3) if and only if ˉu1 and ˉu2 satisfy
ˉu1(α1−ˉu1)+d(ˉu2−ˉu1)=0ˉu2(α2−ˉu2)+d(ˉu1−ˉu2)=0. | (3.1) |
Differentiating (3.1) with respect to d, we obtain
(α1−2ˉu1−d)ˉu′1+dˉu′2+ˉu2−ˉu1=0(α2−2ˉu2−d)ˉu′2+dˉu′1+ˉu1−ˉu2=0, | (3.2) |
where ˉu′i,i=1,2, represent the derivatives of ˉui with respect to d. Thus,
ˉu′1=(α2−2ˉu2)(ˉu1−ˉu2)(α1−2ˉu1−d)(α2−2ˉu2−d)−d2, | (3.3) |
ˉu′2=(α1−2ˉu1)(ˉu2−ˉu1)(α1−2ˉu1−d)(α2−2ˉu2−d)−d2. | (3.4) |
Note that
(α1−2ˉu1−d)(α2−2ˉu2−d)−d2=(α1−2ˉu1)(α2−2ˉu2)−d(α1−2ˉu1)−d(α2−2ˉu2)>0, |
by Proposition A.3 (in Appendix Ⅱ). Thus ˉu′1>0 and ˉu′2<0. More detailed descriptions for ˉu1 and ˉu2 can be found in Proposition A.5. We further differentiate (3.3) with respect to d, and obtain
(α1−2ˉu1−d)ˉu″1+dˉu″2=2ˉu′1−2ˉu′2+2(ˉu′1)2(α2−2ˉu2−d)ˉu″2+dˉu″1=2ˉu′2−2ˉu′1+2(ˉu′2)2. |
Thus,
ˉu″1=2(α2−2ˉu2−d)[ˉu′1−ˉu′2+(ˉu′1)2]−d[ˉu′2−ˉu′1+(ˉu′2)2](α1−2ˉu1−d)(α2−2ˉu2−d)−d2ˉu″2=2(α1−2ˉu1−d)[ˉu′2−ˉu′1+(ˉu′2)2]−d[ˉu′1−ˉu′2+(ˉu′1)2](α1−2ˉu1−d)(α2−2ˉu2−d)−d2. |
Let us focus on the numerators. For ˉu″1, we have
(α2−2ˉu2−d)[ˉu′1−ˉu′2+(ˉu′1)2]−d[ˉu′2−ˉu′1+(ˉu′2)2]=(α2−2ˉu2−d)(ˉu′1)2+(α2−2ˉu2)(ˉu′1−ˉu′2)−d(ˉu′2)2<0, |
due to ˉu′1>0, ˉu′2<0 for all d>0, and Proposition A.3. Thus, ˉu″1<0. For ˉu″2, with (3.5) and (3.6), we have
(α1−2ˉu1−d)[ˉu′2−ˉu′1+(ˉu′2)2]−d[ˉu′1−ˉu′2+(ˉu′1)2]=(α1−2ˉu1−d)(ˉu′2)2+(α1−2ˉu1)(ˉu′2−ˉu′1)−d(ˉu′1)2=(α1−2ˉu1−d)[(α1−2ˉu1)(ˉu2−ˉu1)(α1−2ˉu1−d)(α2−2ˉu2−d)−d2]2+(α1−2ˉu1)[(α1−2ˉu1)(ˉu2−ˉu1)(α1−2ˉu1−d)(α2−2ˉu2−d)−d2−(α2−2ˉu2)(ˉu1−ˉu2)(α1−2ˉu1−d)(α2−2ˉu2−d)−d2]−d[(α2−2ˉu2)(ˉu1−ˉu2)(α1−2ˉu1−d)(α2−2ˉu2−d)−d2]2=(ˉu2−ˉu1)[(α1−2ˉu1−d)(α2−2ˉu2−d)−d2]2⋅{(α1−2ˉu1−d)(α2−ˉu1−ˉu2)(α1−2ˉu1)2−d(α1−2ˉu1)3+(α2−2ˉu2)2[(α1−2ˉu1−d)(α1−2ˉu1)−d(ˉu2−ˉu1)]}. |
For the first two terms in the bracket,
(α1−2ˉu1−d)(α2−ˉu1−ˉu2)(α1−2ˉu1)2−d(α1−2ˉu1)3>0, |
by Proposition A.3. For the third term, using (3.1), we have
(α2−2ˉu2)2[(α1−2ˉu1−d)(α1−2ˉu1)−d(ˉu2−ˉu1)]=(α2−2ˉu2)2[(α1−2ˉu1)2−d(α1−2ˉu1)−d(ˉu2−ˉu1)]=(α2−2ˉu2)2{[d(1−ˉu2ˉu1)−ˉu1]2−d[d(1−ˉu2ˉu1)−ˉu1]−d(ˉu2−ˉu1)}=(α2−2ˉu2)2{d2(1−ˉu2ˉu1)2−d2(1−ˉu2ˉu1)+dˉu2+ˉu21}>0, |
since ˉu1<ˉu2. Thus, ˉu″2>0.
Part (ⅱ) can be obtained by arguments similar to those for (ⅰ), using
ˉv1(β1−ˉv1)+d(ˉv2−ˉv1)=0ˉv2(β2−ˉv2)+d(ˉv1−ˉv2)=0. | (3.5) |
This completes the proof.
Propositions A.3-A.6, in Appendix Ⅱ, and Proposition 3.1 are independent of the order between σ1 and σ2. Some of the following properties for the semitrivial equilibria hold under σ2<σ1. The following notations will be helpful to recognize various related quantities:
d1:=σ1σ2(σ21+σ22+σ2β2−σ1β1)(σ1−σ2)(σ21+σ22),d2:=σ1σ2σ1−σ2,d3:=β1β2(σ2β1+σ1β2)(β2−β1)(β21+β22),d4:=σ1σ2β1β2σ2β22−σ1β21,d5:=√σ1σ2(√σ2α2−√σ1α1)(σ1+σ2)(√σ1−√σ2). |
Proposition 3.2. Under conditions (C) and (P), the following relationships among parameters hold:
(Ⅰ) ˉu2ˉu1(d) is strictly decreasing with respect to d.
(Ⅱ) If ˉu2ˉu1=σ1σ2, then d=d1.
(Ⅲ) If ˉu2ˉu1=β2β1, then d=d3.
(Ⅳ) If ˉu2ˉu1=√σ1√σ2, then d=d5.
(Ⅴ) (ⅰ) If σ2β2<σ1β1, then σ1σ2>β2β1 and d1<d2<d3<d4.
(ⅱ) If σ2β2>σ1β1, then σ1σ2<β2β1 and d1>d2>d3>d4.
(ⅲ) If σ2β2=σ1β1, then σ1σ2=β2β1 and d1=d2=d3=d4.
Proof. (Ⅰ) The assertion follows from ˉu′1(d)>0,ˉu′2(d)<0, as in the proof of Proposition 3.1.
(Ⅱ) If ˉu2ˉu1=σ1σ2, with ˉu1 and ˉu2 satisfying (3.1), we have
α1−ˉu1+d(σ1σ2−1)=0α2−σ1σ2ˉu1+d(σ2σ1−1)=0. |
By eliminating ˉu1, we have
d(σ31−σ21σ2−σ32+σ1σ22σ21σ2)=σ2α2−σ1α1σ1. |
Then
d=σ1σ2(σ21+σ22+σ2β2−σ1β1)(σ1−σ2)(σ21+σ22)=d1, |
due to α2=β2+σ2 and α1=β1−σ1.
Cases (Ⅲ) and (Ⅳ) can be obtained by arguments similar to those for (Ⅱ). Now let us prove (Ⅴ), and the assertions will be justified by the following (a)-(c):
(a) It is clear that
d2−d1{>0ifσ2β2<σ1β1=0ifσ2β2=σ1β1<0ifσ2β2>σ1β1. |
(b) We see that
d3−d2{>0ifσ2β2<σ1β1=0ifσ2β2=σ1β1<0ifσ2β2>σ1β1. |
as, by a direct calculation,
d3−d2=(σ1β22+σ2β21)(σ1β1−σ2β2)(σ1−σ2)(β2−β1)(β21+β22). |
(c) It holds that
d4−d3{>0ifσ2β2<σ1β1=0ifσ2β2=σ1β1<0ifσ2β2>σ1β1 |
due to
d4−d3=(σ1+σ2)β21β22(σ1β1−σ2β2)(σ2β22−σ1β21)(β2−β1)(β21+β22). |
This completes the proof.
In Appendix Ⅰ, we compute the Jacobian matrix for system (1.3). At (ˉu1,ˉu2,0,0), the Jacobian matrix is
[α1−2ˉu1−dd−ˉu10dα2−2ˉu2−d0−ˉu200β1−ˉu1−dd00dβ2−ˉu2−d], | (3.6) |
and at (0,0,ˉv1,ˉv2), the Jacobian matrix is
[α1−ˉv1−dd00dα2−ˉv2−d00−ˉv10β1−2ˉv1−dd0−ˉv2dβ2−2ˉv2−d]. | (3.7) |
First, let us focus on the stability of semitrivial equilibrium (ˉu1,ˉu2,0,0), by calculating the eigenvalues of the following submatrices in (3.6):
[α1−2ˉu1−dddα2−2ˉu2−d]and[β1−ˉu1−dddβ2−ˉu2−d]. | (3.8) |
Theorem 3.3. Assume that conditions (C) and (P) hold for system (1.3).
(ⅰ) If σ2β2<σ1β1 and β2−β1≥σ1+σ2, the semitrivial equilibrium (ˉu1,ˉu2,0,0) is unstable for all d>0.
(ⅱ) If σ2β2>σ1β1, there exist ˉd1,ˉd2>0, with ˉd1<ˉd2, so that the semitrivial equilibrium (ˉu1,ˉu2,0,0) is unstable when d<ˉd1 or d>ˉd2 and is asymptotically stable when ˉd1<d<ˉd2.
In addition,
σ1σ2α1α2σ2α22−σ1α21<ˉd1<σ1σ2β1β2σ2β22−σ1β21,σ1σ2σ1−σ2<ˉd2<σ1√σ1σ2+σ1σ2σ21−σ22(α2−α1). |
Proof. Under condition (C), the two eigenvalues of the first matrix in (3.11) are negative by Gerschgorin's Theorem and Proposition A.3. Thus, the stability of (ˉu1,ˉu2,0,0) is determined by the two eigenvalues, denoted by λ∓, of the second matrix in (3.11). By a direct calculation, the two eigenvalues are
λ∓:=12[(β1−ˉu1+β2−ˉu2−2d)∓√(β1−ˉu1−β2+ˉu2)2+4d2]. |
First, we consider λ−=λ−(d) and claim λ−(d)<0 for all d>0. From condition (C) and Proposition A.3, we have
β1−ˉu1+β2−ˉu2=(σ1−σ2)+d[2−(ˉu2ˉu1+ˉu1ˉu2)], |
and
β1−ˉu1−β2+ˉu2=(σ1+σ2)+d(ˉu1ˉu2−ˉu2ˉu1). |
Then
λ−=12[(β1−ˉu1+β2−ˉu2−2d)−√(β1−ˉu1−β2+ˉu2)2+4d2]=12[(σ1−σ2)−d(ˉu2ˉu1+ˉu1ˉu2)−√[(σ1+σ2)+d(ˉu1ˉu2−ˉu2ˉu1)]2+4d2]<12[(σ1−σ2)−d(ˉu2ˉu1+ˉu1ˉu2)−|(σ1+σ2)+d(ˉu1ˉu2−ˉu2ˉu1)|]. |
We obtain
λ−<−σ2−dˉu1ˉu2<0, |
if (σ1+σ2)+d(ˉu1ˉu2−ˉu2ˉu1)≥0, and
λ−<σ1−dˉu2ˉu1<0, |
if (σ1+σ2)+d(ˉu1ˉu2−ˉu2ˉu1)<0. Consequently, λ−(d)<0 for all d>0.
Next, we identify the sign of λ+=λ+(d). Note that λ+(d)≥0 if and only if
|β1−ˉu1+β2−ˉu2−2d|≤√(β1−ˉu1−β2+ˉu2)2+4d2, |
equivalently,
(β1−ˉu1)(β2−ˉu2)−d(β1−ˉu1+β2−ˉu2)≤0. | (3.9) |
As β1=α1+σ1 and β2=α2−σ2, (3.12) can be expressed by
(α1−ˉu1+σ1)(α2−ˉu2−σ2)−d[α1−ˉu1+α2−ˉu2+(σ1−σ2)]≤0, |
i.e.,
[d(1−ˉu2ˉu1)+σ1][d(1−ˉu1ˉu2)−σ2]−d2[2−(ˉu2ˉu1+ˉu1ˉu2)]−d(σ1−σ2)≤0, |
using (3.1). This inequality can be simplified to
d(σ2ˉu2ˉu1−σ1ˉu1ˉu2)−σ1σ2≤0. | (3.10) |
From (3.10), we define
g(d):=d(σ2ˉu2(d)ˉu1(d)−σ1ˉu1(d)ˉu2(d))−σ1σ2. | (3.11) |
Then λ+(d)≥0 if and only if g(d)≤0. According to Propositions A.3 and A.5, we have that 1<ˉu2(d)ˉu1(d)<α2α1 and ˉu2(d)ˉu1(d) decreases from α2α1 to 1 as d increases from 0 to ∞. Thus, g(0)=−σ1σ2 and g(d)→−∞ as d→∞, because of σ1>σ2. More precisely,
g(d)=d(σ2ˉu2ˉu1−σ1ˉu1ˉu2)−σ1σ2<d(σ2α2α1−σ1α1α2)−σ1σ2=d(σ2α22−σ1α21α1α2)−σ1σ2≤0,ifd≤σ1σ2α1α2σ2α22−σ1α21. | (3.12) |
Note that ˉu2(d)ˉu1(d)≤√σ1√σ2 is equivalent to d≥d5, by Proposition 3.2. Hence,
g(d)=d(σ2ˉu2ˉu1−σ1ˉu1ˉu2)−σ1σ2≤d(σ2√σ1√σ2−σ1√σ2√σ1)−σ1σ2=−σ1σ2<0,ifˉu2(d)ˉu1(d)≤√σ1√σ2. |
Thus,
g(d)<0ifd≥d5. | (3.13) |
A direct calculation yields
g′(d)=(σ2ˉu2ˉu1−σ1ˉu1ˉu2)+d[σ2(ˉu2ˉu1)′−σ1(ˉu1ˉu2)′]. | (3.14) |
We know σ2(ˉu2ˉu1)′−σ1(ˉu1ˉu2)′<0 for all d>0, due to ˉu′1>0 and ˉu′2<0, as in the proof of Proposition 3.1 or by Proposition A.5; σ2ˉu2ˉu1−σ1ˉu1ˉu2=σ2α22−σ1α21α1α2>0 when d=0, by Lemma 2.1(ⅱ); σ2ˉu2ˉu1−σ1ˉu1ˉu2→σ2−σ1<0 as d→+∞, due to Propositions A.3 and A.5. That is, σ2ˉu2ˉu1−σ1ˉu1ˉu2 decreases from σ2α22−σ1α21α1α2 to −(σ1−σ2) as d increases from 0 to +∞. On the other hand, by (3.1), we have
{d(ˉu2ˉu1)′=1−ˉu2ˉu1+ˉu′1d(ˉu1ˉu2)′=1−ˉu1ˉu2+ˉu′2. | (3.15) |
With (3.15), we reexpress (3.14) as
\begin{equation} g'(d) = \sigma_2-\sigma_1+\sigma_2\bar{u}'_{1}-\sigma_1\bar{u}'_{2}. \end{equation} | (3.16) |
It follows that
\begin{equation*} g''(d) = \sigma_2\bar{u}''_{1}-\sigma_1\bar{u}''_{2} \lt 0, \end{equation*} |
by Proposition 3.1. Thus, the graph of g(d) is concave downward. Therefore, there are two possible situations based on the above analysis: (ⅰ) g(d) < 0 for all d > 0 , (ⅱ) there exist \bar{d}_{1}, \bar{d}_{2} > 0 such that g(\bar{d}_{1}) = g(\bar{d}_{2}) = 0 , and
\begin{equation*} \left\{ \begin{array}{lllll} g(d) \lt 0 \;{\rm if } \; d \lt \bar{d}_{1} {\rm or} d \gt \bar{d}_{2} & \\ g(d) \gt 0 \; {\rm if } \;\bar{d}_{1} \lt d \lt \bar{d}_{2}. & \end{array} \right. \end{equation*} |
The graphs of g(d) are illustrated in Figure 3. Accordingly, there are two possibilities for \lambda_{+} : (ⅰ) \lambda_{+} > 0 for all d > 0 , (ⅱ) there exist \bar{d}_{1}, \bar{d}_{2} > 0 such that \lambda_+ = 0 for d = \bar{d}_{1}, \bar{d}_{2} and
\begin{equation*} \left\{ \begin{array}{lllll} \lambda_{+} \gt 0 \; {\rm if } \;d \lt \bar{d}_{1} {\rm or} d \gt \bar{d}_{2} & \\ \lambda_{+} \lt 0 \; {\rm if } \; \bar{d}_{1} \lt d \lt \bar{d}_{2}. & \end{array} \right. \end{equation*} |
Now we investigate the two situations by analyzing g(d) and the stationary equation (3.1). To determine the behavior of g , we seek for its equivalent expression. Let w: = \frac{\bar{u}_2}{\bar{u}_1} . As \frac{\bar{u}_2}{\bar{u}_1}(d) is strictly decreasing with respect to d , by Proposition 3.2(Ⅰ), the one-to-one correspondence between d and w can be derived from the stationary equation for \bar{u}_1 and \bar{u}_2 in (3.1):
\begin{eqnarray} d = \frac{\alpha_2-w\alpha_1}{(w-1)(w+\frac{1}{w})}, \end{eqnarray} | (3.17) |
where 1 < w < \frac{\alpha_2}{\alpha_1} , by Proposition A.5. Then
\begin{eqnarray*} g(d)& = &d\left(\sigma_{2}\frac{\bar{u}_2}{\bar{u}_1}-\sigma_{1}\frac{\bar{u}_1}{\bar{u}_2} \right)- \sigma_{1}\sigma_{2} \\ & = & d\left( \frac{\sigma_2 w^{2}-\sigma_1}{w}\right)-\sigma_{1}\sigma_{2} \\ & = &\left( \frac{\alpha_2-w\alpha_1}{(w-1)(w+\frac{1}{w})} \right)\left( \frac{\sigma_2 w^{2}-\sigma_1}{w}\right)-\sigma_{1}\sigma_{2}\\ & = &\frac{(\alpha_2-w\alpha_1)(\sigma_2 w^{2}-\sigma_1)-\sigma_{1}\sigma_{2}(w-1)(w^{2}+1)}{(w-1)(w^{2}+1)}\\ & = :& f(w). \end{eqnarray*} |
Let us define q(w): = (\alpha_2-w\alpha_1)(\sigma_2 w^{2}-\sigma_1)-\sigma_{1}\sigma_{2}(w-1)(w^{2}+1) , which is the numerator of f(w) , and thus f(w) = \frac{q(w)}{(w-1)(w^{2}+1)} . Note that
\begin{eqnarray} q(w)& = & (\alpha_2-w\alpha_1)(\sigma_2 w^{2}-\sigma_1)-\sigma_{1}\sigma_{2}(w-1)(w^{2}+1) \\ & = & (\beta_2-w\beta_1)(\sigma_2 w^{2}-\sigma_1)+w(\sigma_1+\sigma_2)(\sigma_2w-\sigma_1), \end{eqnarray} | (3.18) |
by \beta_{1} = \alpha_{1}+\sigma_{1} and \beta_{2} = \alpha_{2}-\sigma_{2} . Thus, we have
\begin{equation} g(d) \lt 0 \Leftrightarrow f(w) \lt 0 \Leftrightarrow q(w) \lt 0, \end{equation} | (3.19) |
due to (w-1)(w^{2}+1) > 0 . In addition,
\begin{equation} f'(w) = \frac{q'(w)(w-1)(w^{2}+1)-q(w)(3w^{2}-2w+1)}{(w-1)^{2}(w^{2}+1)^{2}}, \end{equation} | (3.20) |
where
\begin{equation} q'(w) = 2\sigma_2w(\beta_2-w\beta_1)+(\sigma_1+\sigma_2)(\sigma_2w-\sigma_1)-\sigma_2\beta_1 w^{2}+\sigma_2(\sigma_1+\sigma_2)w+\sigma_1\beta_1. \end{equation} | (3.21) |
Notice that
\begin{eqnarray*} f'(w) \lt 0 \Leftrightarrow g'(d) \gt 0, \end{eqnarray*} |
according to Proposition 3.2(Ⅰ). By a direct computation in (3.18), we obtain
\begin{eqnarray} q(\frac{\sigma_1}{\sigma_2}) = \frac{\sigma_1}{\sigma^{2}_2}(\sigma_1-\sigma_2)(\sigma_2\beta_2-\sigma_1\beta_1)\left\{\begin{array}{c} \lt 0 \; {\rm if} \;\sigma_2\beta_2 \lt \sigma_1\beta_1\\ = 0 \; {\rm if} \;\sigma_2\beta_2 = \sigma_1\beta_1\\ \gt 0 \; {\rm if} \;\sigma_2\beta_2 \gt \sigma_1\beta_1, \end{array} \right. \end{eqnarray} | (3.22) |
and
\begin{eqnarray} q(\frac{\beta_2}{\beta_1}) = \frac{\beta_2}{\beta^{2}_1}(\sigma_1+\sigma_2)(\sigma_2\beta_2-\sigma_1\beta_1)\left\{\begin{array}{c} \lt 0 \; {\rm if}\; \sigma_2\beta_2 \lt \sigma_1\beta_1\\ = 0 \; {\rm if}\; \sigma_2\beta_2 = \sigma_1\beta_1\\ \gt 0 \;{\rm if}\; \sigma_2\beta_2 \gt \sigma_1\beta_1. \end{array} \right. \end{eqnarray} | (3.23) |
Case (ⅰ) \sigma_2\beta_2 < \sigma_1\beta_1 , i.e., \frac{\beta_2}{\beta_1} < \frac{\sigma_1}{\sigma_2} : We first claim that q(w) < 0 for all \frac{\beta_2}{\beta_1} < w < \frac{\sigma_1}{\sigma_2} . Consider w = \frac{\beta_2}{\beta_1}+\delta , with \delta > 0 satisfying \frac{\beta_2}{\beta_1} < w = \frac{\beta_2+\delta \beta_1}{\beta_1} < \frac{\sigma_1}{\sigma_2} . Hence, \sigma_2(\beta_2+\delta \beta_1) < \sigma_1\beta_1 . From (3.18), we obtain
\begin{eqnarray*} q(w = \frac{\beta_2+\delta \beta_1}{\beta_1})& = &-\frac{\delta}{\beta_1}[\sigma_2(\beta_2+\delta \beta_1)^{2}-\sigma_1\beta^{2}_1]\\ &-&\frac{1}{\beta^{2}_1}\left\{(\sigma_1+\sigma_2)(\beta_2+\delta \beta_1)[\sigma_1\beta_1-\sigma_2(\beta_2+\delta \beta_1)] \right\}\\ & \lt & 0, \end{eqnarray*} |
by \sigma_2\beta_2^{2}-\sigma_1\beta^{2}_1 > 0 from condition \rm (P) , and \sigma_1\beta_1 > \sigma_2(\beta_2+\delta \beta_1) .
Next, we will show that f'(\frac{\sigma_1}{\sigma_2}) < 0 and f'(\frac{\beta_2}{\beta_1}) > 0 . From (3.20) and (3.21), at w = \frac{\sigma_1}{\sigma_2} , a direct calculation yields
\begin{eqnarray*} &&q'(w)(w-1)(w^{2}+1)-q(w)(3w^{2}-2w+1)\\ & = & \frac{\sigma_1(\sigma_1-\sigma_2)}{\sigma^{2}_2}\left\{ \frac{\sigma^{2}_1}{\sigma^{2}_2}[-\sigma_2\beta_2+\sigma_2\beta_1+\sigma_2(\sigma_1+\sigma_2)]+ [-\sigma_1\beta_1+\sigma_2\beta_1+\sigma_2(\sigma_1+\sigma_2)] \right\}\\ &+& \frac{\sigma_1(\sigma_1-\sigma_2)}{\sigma^{2}_2}\left( 2\frac{\sigma_1}{\sigma_2}+1 \right)(\sigma_2\beta_2-\sigma_1\beta_1) \\ & \lt & \frac{\sigma_1(\sigma_1-\sigma_2)}{\sigma^{2}_2}\left[ -\sigma_2\left(\frac{\sigma^{2}_1}{\sigma^{2}_2}+1 \right)(\beta_2-\beta_1-\sigma_1-\sigma_2)-\left( 2\frac{\sigma_1}{\sigma_2}+1 \right)(\sigma_1\beta_1-\sigma_2\beta_2) \right]\\ & \lt & 0, \end{eqnarray*} |
because of \beta_2-\beta_1\geq \sigma_1+\sigma_2 . Thus, f'(\frac{\sigma_1}{\sigma_2}) < 0 by (3.20) and (w-1)^{2}(w^{2}+1)^{2} > 0 . Similarly, from (3.20) and (3.21), at w = \frac{\beta_2}{\beta_1} , we have
\begin{eqnarray*} &&q'(w)(w-1)(w^{2}+1)-q(w)(3w^{2}-2w+1)\\ & = &\frac{1}{\beta^{4}_1}[(\sigma_1+\sigma_2)(\sigma_1\beta_1-\sigma_2\beta_2)(2\beta^{3}_2+\beta^{3}_1-\beta_1\beta^{2}_2)]\\ &+&\frac{1}{\beta^{4}_1}\{(\beta_2-\beta_1)(\beta^{2}_1+\beta^{2}_2)[-\sigma_2\beta^{2}_2+\sigma_1\beta^{2}_1+\sigma_2\beta_2(\sigma_1+\sigma_2)]\}\\ & \gt &\frac{1}{\beta^{4}_1}[(\sigma_1+\sigma_2)(\sigma_1\beta_1-\sigma_2\beta_2)(\beta^{3}_2+2\beta^{3}_1-\beta^{2}_1\beta_2)]\\ & \gt &0, \end{eqnarray*} |
due to condition \rm (P) , i.e., \sigma_2 \beta^{2}_2 < \sigma_1 \beta_1(\beta_1+\sigma_1+\sigma_2) in (2.1). Thus, f'(\frac{\beta_2}{\beta_1}) > 0 by (3.20) and (w-1)^{2}(w^{2}+1)^{2} > 0 .
Consequently, by (3.19) and concavity of g(d) , we conclude f(w) < 0 for all 1 < w = \frac{\bar{u}_2}{\bar{u}_1} < \frac{\alpha_2}{\alpha_2} , i.e., g(d) < 0 for all d > 0 , i.e., \lambda_{+} > 0 for all d > 0 .
Case (ⅱ) \sigma_2\beta_2 > \sigma_1\beta_1 , i.e., \frac{\beta_2}{\beta_1} > \frac{\sigma_1}{\sigma_2} : We claim that q(w) > 0 for all \frac{\beta_2}{\beta_1} > w > \frac{\sigma_1}{\sigma_2} . Consider w = \frac{\sigma_1}{\sigma_2}+\delta , with \delta > 0 satisfying \frac{\beta_2}{\beta_1} > w = \frac{\sigma_1+\delta \sigma_2}{\sigma_2} > \frac{\sigma_1}{\sigma_2} . Hence, \sigma_2\beta_2 > (\sigma_1+\delta \sigma_2)\beta_1 . From (3.18), we compute
\begin{eqnarray*} q(w = \frac{\sigma_1+\delta \sigma_2}{\sigma_2})& = &\delta(\sigma_1+\sigma_2)(\sigma_1+\delta \sigma_2)\\ &+&\frac{1}{\sigma^{2}_2}[(\sigma_1+\delta \sigma_2)^{2}-\sigma_1\sigma_2][\sigma_2\beta_2-(\sigma_1+\delta \sigma_2)\beta_1]\\ & \gt & 0, \end{eqnarray*} |
owing to \sigma_1 > \sigma_2 and \sigma_2\beta_2 > (\sigma_1+\delta \sigma_2)\beta_1 . Combining (3.22) with (3.23), we have q(w) > 0 for all \frac{\beta_2}{\beta_1}\geq w\geq\frac{\sigma_1}{\sigma_2} . That is, g(d) > 0 if d_{3} \leq d \leq d_{1} , by Proposition 3.2. Recall the relationship between d and w(d) = \frac{\bar{u}_2(d)}{\bar{u}_1(d)} in Proposition 3.2. We will use the relationship to estimate \bar{d}_{1} and \bar{d}_{2} . For \frac{\bar{u}_2}{\bar{u}_1} > \frac{\beta_2}{\beta_1} , we have d < d_{3} by Proposition 3.2, and then
\begin{eqnarray*} g(d) \gt d\left( \frac{\sigma_2\beta^{2}_2-\sigma_1\beta^{2}_1}{\beta_1\beta_2} \right)-\sigma_1 \sigma_2\geq0 \; {\rm if}\; d \geq d_{4}. \end{eqnarray*} |
Thus, g(d) > 0 if d_{4} \leq d < d_{3} . According to (3.12), (3.13), and Proposition 3.2, we obtain
\begin{equation*} \left\{ \begin{array}{lll} g(d) \lt 0 \; {\rm if } \; d\leq \frac{\sigma_1 \sigma_2 \alpha_1 \alpha_2}{\sigma_2 \alpha^{2}_2-\sigma_1 \alpha^{2}_1} & \\ g(d) \gt 0 \; {\rm if } \; d_{4} = \frac{\sigma_1\sigma_2\beta_{1}\beta_{2}}{\sigma_2\beta_{2}^2-\sigma_1\beta_{1}^2}\leq d\leq \frac{\sigma_1 \sigma_2 (\sigma^{2}_1+\sigma^{2}_2+\sigma_2\beta_2-\sigma_1\beta_1) }{(\sigma_1-\sigma_2)(\sigma^{2}_1+\sigma^{2}_2)} = d_{1}\\ g(d) \lt 0 \; {\rm if } \;d\geq \frac{\sqrt{\sigma_1 \sigma_2}(\sqrt{\sigma_2}\alpha_2-\sqrt{\sigma_1}\alpha_1)}{(\sigma_1+\sigma_2)(\sqrt{\sigma_1}-\sqrt{\sigma_2})} = d_5. &\\ \end{array} \right. \end{equation*} |
In addition, we recall Lemma 2.1(ⅱ): 0 < \frac{\sigma_1 \sigma_2 \alpha_1 \alpha_2}{\sigma_2 \alpha^{2}_2-\sigma_1 \alpha^{2}_1} < \frac{\sigma_1\sigma_2\beta_{1}\beta_{2}}{\sigma_2\beta_{2}^2-\sigma_1\beta_{1}^2} = d_{4} . Accordingly, there exist \bar{d}_{1}, \bar{d}_{2} > 0 such that \lambda_{+} = 0 for d = \bar{d}_{1}, \bar{d}_{2} and
\begin{equation*} \left\{ \begin{array}{lllll} \lambda_{+} \gt 0 \;{\rm if } \; d \lt \;\bar{d}_{1} {\rm or} d \gt \bar{d}_{2} & \\ \lambda_{+} \lt 0 \;{\rm if } \; \bar{d}_{1} \lt d \lt \bar{d}_{2} & \end{array} \right. \end{equation*} |
where
\begin{align*} &\frac{\sigma_1 \sigma_2\alpha_{1}\alpha_{2}}{\sigma_2\alpha_{2}^2-\sigma_1\alpha_{1}^2 } \lt \bar{d}_{1} \lt \frac{\sigma_1\sigma_2\beta_{1}\beta_{2}}{\sigma_2\beta_{2}^2-\sigma_1\beta_{1}^2} = d_{4} \\ &d_{1} = \frac{\sigma_1 \sigma_2 (\sigma^{2}_1+\sigma^{2}_2+\sigma_2\beta_2-\sigma_1\beta_1) }{(\sigma_1-\sigma_2)(\sigma^{2}_1+\sigma^{2}_2)} \lt \bar{d}_{2} \lt \frac{\sqrt{\sigma_1 \sigma_2}(\sqrt{\sigma_2}\alpha_2-\sqrt{\sigma_1}\alpha_1)}{(\sigma_1+\sigma_2)(\sqrt{\sigma_1}-\sqrt{\sigma_2})} = d_5. \end{align*} |
By a direct computation, it can be seen that
\frac{\sqrt{\sigma_1 \sigma_2}(\sqrt{\sigma_2}\alpha_2-\sqrt{\sigma_1}\alpha_1)}{(\sigma_1+\sigma_2)(\sqrt{\sigma_1}-\sqrt{\sigma_2})} \lt \frac{\sigma_{1}\sqrt{\sigma_{1}\sigma_{2}}+\sigma_{1}\sigma_{2}}{\sigma^{2}_{1}- \sigma^{2}_{2}}(\alpha_{2}-\alpha_{1}). |
Thus, for consistency with the ranges in the assertion of Theorem 2.1, we also write
\begin{align*} &\frac{\sigma_1 \sigma_2\alpha_{1}\alpha_{2}}{\sigma_2\alpha_{2}^2-\sigma_1\alpha_{1}^2 } \lt \bar{d}_{1} \lt \frac{\sigma_1\sigma_2\beta_{1}\beta_{2}}{\sigma_2\beta_{2}^2-\sigma_1\beta_{1}^2} \\ &\frac{\sigma_1\sigma_2}{\sigma_1- \sigma_2} \lt \bar{d}_{2} \lt \frac{\sigma_{1}\sqrt{\sigma_{1}\sigma_{2}}+\sigma_{1}\sigma_{2}}{\sigma^{2}_{1}- \sigma^{2}_{2}}(\alpha_{2}-\alpha_{1}). \end{align*} |
This completes the proof.
Remark 3. Assume that conditions (C) and \rm (P) hold. Under \sigma_2\beta_2 = \sigma_1\beta_1 and \beta_2-\beta_1 = \sigma_1+\sigma_1 , we do have g(d_{1} = d_{2} = d_{3} = d_{4}) = 0 and g'(d_{1} = d_{2} = d_{3} = d_{4}) = 0 , by Proposition 3.2(Ⅴ)(ⅲ) and the proof of Theorem 3.3, and hence \bar{d}_{1} = \bar{d}_{2} .
Next, let us focus on the boundary equilibrium (0, 0, \bar{v}_{1}, \bar{v}_{2}) . We shall discuss the stability of (0, 0, \bar{v}_{1}, \bar{v}_{2}) through analyzing the eigenvalues of the following submatrices in (3.7):
\begin{align} \begin{array}{ccc} \left[ \begin{array}{cc} \alpha_{1}-\bar{v}_{1}-d & d \\ d & \alpha_{2}-\bar{v}_{2}-d \end{array} \right] & {\rm and} & \left[ \begin{array}{cc} \beta_{1}-2\bar{v}_{1}-d & d \\ d & \beta_{2}-2\bar{v}_{2}-d \end{array} \right]. \end{array} \end{align} | (3.24) |
Theorem 3.4. Consider system (1.3) under condition (C). There exists a \bar{d}_{3} > 0 so that the boundary equilibrium (0, 0, \bar{v}_{1}, \bar{v}_{2}) is unstable if d < \bar{d}_{3} and asymptotically stable if d > \bar{d}_{3} . In addition,
\begin{align*} \frac{\sigma_1\sigma_2}{\sigma_1-\sigma_2} \lt \bar{d}_{3} \lt \frac{\sigma_1}{\sigma_1-\sigma_2}(\alpha_2-\alpha_1). \end{align*} |
Proof. Under condition (C), it can be shown that the two eigenvalues of the second matrix in (3.24) are negative, by Gerschgorin's Theorem and Proposition A.4 in Appendix Ⅱ. The stability of (0, 0, \bar{v}_{1}, \bar{v}_{2}) is thus determined by the two eigenvalues, denoted by \lambda_{\mp} , of the first matrix in (3.24). By a direct calculation, these two eigenvalues are
\begin{eqnarray*} \lambda_{\mp}: = \frac{1}{2} \left[(\alpha_1-\bar{v}_1+\alpha_2-\bar{v}_2-2d)\mp \sqrt{(\alpha_1-\bar{v}_1-\alpha_2+\bar{v}_2)^2+4d^2} \right]. \end{eqnarray*} |
From Proposition A.4, we have
\begin{eqnarray*} \alpha_1-\bar{v}_1+\alpha_2-\bar{v}_2-2d = (\beta_1-\bar{v}_1+\beta_2-\bar{v}_2)-(\sigma_1-\sigma_2)-2d \lt 0, \end{eqnarray*} |
and thus \lambda_{-} < 0 for all d > 0 . Next, let us identify the sign of \lambda_{+} = \lambda_{+}(d) . Note that \lambda_{+}(d)\geq 0 if and only if
\begin{eqnarray} &&\mid \alpha_1-\bar{v}_1+\alpha_2-\bar{v}_2-2d \mid \leq \sqrt{(\alpha_1-\bar{v}_1-\alpha_2+\bar{v}_2)^2+4d^2} \\ \Leftrightarrow &&(\alpha_1-\bar{v}_1)(\alpha_2-\bar{v}_2)-d(\alpha_1-\bar{v}_1+\alpha_2-\bar{v}_2)\leq 0. \end{eqnarray} | (3.25) |
By \alpha_{1} = \beta_{1}-\sigma_{1} , \alpha_{2} = \beta_{2}+\sigma_{2} , (3.5), and Proposition A.4, (3.25) is equivalent to
\left[d\left(1-\frac{\bar{v}_2}{\bar{v}_1}\right)- \sigma_{1}\right]\left[d\left(1-\frac{\bar{v}_1}{\bar{v}_2}\right)+ \sigma_{2}\right]-d^{2}\left[2-\left(\frac{\bar{v}_2}{\bar{v}_1}+\frac{\bar{v}_1}{\bar{v}_2} \right)\right]+d(\sigma_{1}-\sigma_{2}) \leq 0. |
This inequality can be simplified to
\begin{equation} d\left(\sigma_{1}\frac{\bar{v}_1}{\bar{v}_2}-\sigma_{2}\frac{\bar{v}_2}{\bar{v}_1} \right)- \sigma_{1}\sigma_{2}\leq 0. \end{equation} | (3.26) |
Now, we define
\begin{equation} h(d): = d\left(\sigma_{1}\frac{\bar{v}_1}{\bar{v}_2}(d)-\sigma_{2}\frac{\bar{v}_2}{\bar{v}_1}(d) \right)- \sigma_{1}\sigma_{2}. \end{equation} | (3.27) |
Then \lambda_{+}(d)\geq 0 if and only if h(d)\leq 0 . By Propositions A.4 and A.6, we have 1 < \frac{\bar{v}_2}{\bar{v}_1}(d) < \frac{\beta_2}{\beta_1} and \frac{\bar{v}_2}{\bar{v}_1}(d) decreases from \frac{\beta_2}{\beta_1} to 1 , as d increases from 0 to \infty . Hence, h(0) = -\sigma_{1}\sigma_{2} < 0 and h(d)\rightarrow \infty as d\rightarrow \infty due to \sigma_1 > \sigma_2 .
A direct calculation yields
\begin{equation} h'(d) = \left(\sigma_{1}\frac{\bar{v}_1}{\bar{v}_2}(d)-\sigma_{2}\frac{\bar{v}_2}{\bar{v}_1}(d) \right)+ d\left[\sigma_{1}(\frac{\bar{v}_1}{\bar{v}_2})'(d)-\sigma_{2}(\frac{\bar{v}_2}{\bar{v}_1})'(d) \right]. \end{equation} | (3.28) |
Notably, \sigma_{1}(\frac{\bar{v}_1}{\bar{v}_2})'-\sigma_{2}(\frac{\bar{v}_2}{\bar{v}_1})' > 0 for all d > 0 , \sigma_{1}\frac{\bar{v}_1}{\bar{v}_2}-\sigma_{2}\frac{\bar{v}_2}{\bar{v}_1} = - \frac{\sigma_2 \beta^{2}_2-\sigma_1 \beta^{2}_1}{\beta_1\beta_2} if d = 0 , and \sigma_{1}\frac{\bar{v}_1}{\bar{v}_2}-\sigma_{2}\frac{\bar{v}_2}{\bar{v}_1}\rightarrow \sigma_1-\sigma_2 > 0 as d\rightarrow \infty ; namely, \sigma_{1}\frac{\bar{v}_1}{\bar{v}_2}-\sigma_{2}\frac{\bar{v}_2}{\bar{v}_1} increases from - \frac{\sigma_2 \beta^{2}_2-\sigma_1 \beta^{2}_1}{\beta_1\beta_2} to \sigma_1-\sigma_2 as d increases from 0 to \infty . Moreover, from (3.5), the equations for \bar{v}_i , we have
\begin{eqnarray} \left\{ \begin{array}{ll} d\left(\frac{\bar{v}_{2}}{\bar{v}_{1}} \right)' = 1-\frac{\bar{v}_{2}}{\bar{v}_{1}}+\bar{v}'_{1} &\\ d\left(\frac{\bar{v}_{1}}{\bar{v}_{2}} \right)' = 1-\frac{\bar{v}_{1}}{\bar{v}_{2}}+\bar{v}'_{2}.&\\ \end{array} \right. \end{eqnarray} | (3.29) |
With (3.28) and (3.29), we obtain
\begin{equation} h'(d) = (\sigma_1-\sigma_2)+\sigma_1\bar{v}'_{2}-\sigma_2\bar{v}'_{1}, \end{equation} | (3.30) |
and then
\begin{equation*} h''(d) = \sigma_1\bar{v}''_{2}-\sigma_2\bar{v}''_{1} \gt 0, \end{equation*} |
by Proposition 3.1. Thus, the graph of h(d) is concave upward. Therefore, from the above discussions, there is a unique \bar{d}_{3} > 0 such that
\begin{equation*} \left\{ \begin{array}{lll} h(d) \lt 0 \;{\rm if } \; d \lt \bar{d}_{3}& \\ h(d) = 0 \; {\rm if } \; d = \bar{d}_{3}& \\ h(d) \gt 0 \; {\rm if } \;d \gt \bar{d}_{3} & \\ \end{array} \right. \end{equation*} |
and accordingly,
\begin{equation} \left\{ \begin{array}{lll} \lambda_{+}(d) \gt 0 \;{\rm if } \;d \lt \bar{d}_{3}& \\ \lambda_{+}(d) = 0 \; {\rm if } \; d = \bar{d}_{3} & \\ \lambda_{+}(d) \lt 0 \; {\rm if } \; d \gt \bar{d}_{3}. & \\ \end{array} \right. \end{equation} | (3.31) |
The graph of h(d) is illustrated in Figure 4.
Now, we estimate the range for the values of \bar{d}_{3} . Function h in (3.30) can be expressed by
\begin{equation} h(d) = \sigma_2(\beta_1-\bar{v}_1)-\sigma_1(\beta_2-\bar{v}_2)+d(\sigma_1-\sigma_2)-\sigma_1\sigma_2, \end{equation} | (3.32) |
via (3.5). Thus, inequality (3.26) is equivalent to
\begin{equation} \sigma_2(\beta_1-\bar{v}_1)-\sigma_1(\beta_2-\bar{v}_2)+d(\sigma_1-\sigma_2)-\sigma_1\sigma_2 \leq 0. \end{equation} | (3.33) |
According to Proposition A.4(ⅰ), we have
-\sigma_1(\beta_2-\beta_1) \lt \sigma_2(\beta_1-\bar{v}_1)-\sigma_1(\beta_2-\bar{v}_2) \lt 0. |
Then,
\begin{equation*} h\left(\frac{\sigma_1\sigma_2}{\sigma_1-\sigma_2}\right) \lt \sigma_1\sigma_2-\sigma_1\sigma_2 = 0, \end{equation*} |
and
\begin{eqnarray*} h\left(\frac{\sigma_1}{\sigma_1-\sigma_2}(\alpha_2-\alpha_1)\right)& \gt &-\sigma_1(\beta_2-\beta_1)+\sigma_1(\alpha_2-\alpha_1)-\sigma_1\sigma_2\\ & = & -\sigma_1(\beta_2-\beta_1)+\sigma_1(\beta_2-\beta_1+\sigma_1+\sigma_2)-\sigma_1\sigma_2\\ & = & \sigma^{2}_1 \gt 0. \end{eqnarray*} |
Consequently, (3.31) holds with
\begin{align*} \frac{\sigma_1\sigma_2}{\sigma_1-\sigma_2} \lt \bar{d}_{3} \lt \frac{\sigma_1}{\sigma_1-\sigma_2}(\alpha_2-\alpha_1). \end{align*} |
This completes the proof.
Let us summarize the main results in Sections 2 and 3:
(ⅰ) Under \sigma_2\beta_{2} < \sigma_1\beta_{1} and \beta_2-\beta_1\geq \sigma_1+\sigma_2 , the positive equilibrium (u^{*}_{1}, u^{*}_{2}, v^{*}_{1}, v^{*}_{2}) exists if d < d^{*}_{3} (Theorem 2.1); the semitrivial equilibrium (\bar{u}_{1}, \bar{u}_{2}, 0, 0) is unstable for all d > 0 (Theorem 3.3) and the semitrivial equilibrium (0, 0, \bar{v}_{1}, \bar{v}_{2}) is unstable if d < \bar{d}_{3} and asymptotically stable if d > \bar{d}_{3} (Theorem 3.4). Besides, the estimated range of d^{*}_{3} in Theorem 2.1 coincides with the one of \bar{d}_{3} in Theorem 3.4.
(ⅱ) Under \sigma_2\beta_{2} > \sigma_1\beta_{1} , the positive equilibrium (u^{*}_{1}, u^{*}_{2}, v^{*}_{1}, v^{*}_{2}) exists if d < d^{*}_{1} or d^{*}_{2} < d < d^{*}_{3} (Theorem 2.1); the semitrivial equilibrium (\bar{u}_{1}, \bar{u}_{2}, 0, 0) is unstable if d < \bar{d}_{1} or d > \bar{d}_{2} , and asymptotically stable if \bar{d}_{1} < d < \bar{d}_{2} (Theorem 3.3); the semitrivial equilibrium (0, 0, \bar{v}_{1}, \bar{v}_{2}) is unstable if d < \bar{d}_{3} and asymptotically stable if d > \bar{d}_{3} (Theorem 3.2). In addition, the estimated ranges of d^{*}_{1} and d^{*}_{2} in Theorem 2.1 respectively coincide with those of \bar{d}_{1} and \bar{d}_{2} in Theorem 3.3.
In fact, the following theorem reveals that these critical values of d are consistent in determining the existence of the positive equilibrium and the stability of semitrivial equilibria, namely, d^{*}_{1} = \bar{d}_{1} , d^{*}_{2} = \bar{d}_{2} and d^{*}_{3} = \bar{d}_{3} . Such interesting consistency makes precise the global dynamics of this competitive species model (1.3), under the framework of monotone dynamics. Let us elaborate.
Theorem 4.1. d^{*}_{1} = \bar{d}_{1} , d^{*}_{2} = \bar{d}_{2} and d^{*}_{3} = \bar{d}_{3} .
Proof. From (3.26), we see that h(d) = 0 if and only if
\begin{equation*} d\left(\sigma_{1}\frac{\bar{v}_1}{\bar{v}_2}(d)-\sigma_{2}\frac{\bar{v}_2}{\bar{v}_1}(d) \right) = \sigma_{1}\sigma_{2}. \end{equation*} |
That is, \bar{d}_{3} satisfies
\begin{equation*} \bar{d}_{3}\left(\sigma_{1}\frac{\bar{v}_1}{\bar{v}_2}(\bar{d}_{3})-\sigma_{2}\frac{\bar{v}_2}{\bar{v}_1}(\bar{d}_{3}) \right) = \sigma_{1}\sigma_{2}. \end{equation*} |
Let \bar{b}: = \frac{\bar{v}_2}{\bar{v}_1}(\bar{d}_{3}) , then
\begin{equation*} \bar{d}_{3}\left(\sigma_{1}\frac{1}{\bar{b}}-\sigma_{2}\bar{b} \right) = \sigma_{1}\sigma_{2}. \end{equation*} |
Thus,
\begin{equation} \bar{b} = \frac{-\sigma_1+\sqrt{\sigma^{2}_1+4k\bar{d}_{3}^{2}}}{2\bar{d}_{3}}, \end{equation} | (4.1) |
where k = \frac{\sigma_1}{\sigma_2} . Recall the definition of b in (2.7). From Remark 2(Ⅰ), we see that (u^{*}_{1}, u^{*}_{2}, v^{*}_{1}, v^{*}_{2})\rightarrow (0, 0, \bar{v}_{1}, \bar{v}_{2}) , as d\rightarrow (d^{*}_{3})^{-} . Therefore, recalling (2.10), we obtain
\begin{equation*} \frac{-\sigma_1+\sqrt{\sigma_1^2+4k (d^{*}_{3})^{ 2}}}{2 d^{*}_{3}} = \lim\limits_{d\rightarrow (d^{*}_{3})^-}b(d) = \lim\limits_{d\rightarrow (d^{*}_{3})^-} \frac{v_2^*}{v_1^*}(d) = \frac{\bar{v}_2}{\bar{v}_1}(d^{*}_{3}). \end{equation*} |
Noting that b = b(d) in (2.10) is monotone in d (shown in (2.18)), with (4.1), we thus conclude that d^{*}_{3} = \bar{d}_{3} .
If \sigma_2\beta_{2} > \sigma_1\beta_{1} , from (3.11), we have g(\bar{d}_{1}) = 0 and g(\bar{d}_{2}) = 0 . Let \bar{a}_1: = \frac{\bar{u}_2}{\bar{u}_1}(\bar{d}_{1}) , \bar{a}_2: = \frac{\bar{u}_2}{\bar{u}_1}(\bar{d}_{2}) . Then
\begin{equation} \bar{a}_{1} = \frac{\sigma_1+\sqrt{\sigma_1^2+4k \bar{d}_{1}^{2}}}{2 \bar{d}_{1}}, \bar{a}_{2} = \frac{\sigma_1+\sqrt{\sigma_1^2+4k \bar{d}_{2}^{2}}}{2 \bar{d}_{2}}. \end{equation} | (4.2) |
It follows from Remark 2(Ⅰ) that (u^{*}_{1}, u^{*}_{2}, v^{*}_{1}, v^{*}_{2})\rightarrow (\bar{u}_{1}, \bar{u}_{2}, 0, 0) , as d \rightarrow (d^{*}_{1})^- . Notice that a = a(d) in (2.10) is monotone in d , shown in (2.25). Therefore, recalling (2.10), we have
\frac{\sigma_1+\sqrt{\sigma_1^2+4k (d^{*}_{1})^{ 2}}}{2 d^{*}_{1}} = \lim\limits_{d\rightarrow (d^{*}_{1})^-}a(d) = \lim\limits_{d\rightarrow (d^{*}_{1})^-} \frac{u_2^*}{u_1^*}(d) = \frac{\bar{u}_2}{\bar{u}_1}(d^{*}_{1}). |
With (4.2), we thus conclude that d^{*}_{1} = \bar{d}_{1} . In addition, by Remark 2(Ⅰ), we see that (\bar{u}_{1}, \bar{u}_{2}, 0, 0)\rightarrow (u^{*}_{1}, u^{*}_{2}, v^{*}_{1}, v^{*}_{2}) as d\rightarrow (d^{*}_{2})^{+} . Consequently,
\lim\limits_{d\rightarrow (d^{*}_{2})^+}\bar{a}_{2}(d) = \lim\limits_{d\rightarrow (d^{*}_{2})^+} \frac{\bar{u}_2}{\bar{u}_1}(d) = \frac{u^{*}_{2}}{u^{*}_{1}}(d^{*}_{2}) = \frac{\sigma_1+\sqrt{\sigma_1^2+4k (d^{*}_{2})^{2}}}{2 d^{*}_{2}}. |
With (4.2), we conclude that d^{*}_{2} = \bar{d}_{2} . This completes the proof.
Combining the discussions in Sections 2 and 3 with the assertion in Theorem 4.1, we conclude that for system (1.3), either there exists a positive equilibrium representing the coexistence of two species or one species drives the other to extinction, depending on the magnitude of the dispersal rate d .
Theorem 4.2. Consider system (1.3) under conditions (C), and ({\rm P}) .
(Ⅰ) Assume that \sigma_2\beta_{2} < \sigma_1\beta_{1} and \beta_2-\beta_1\geq \sigma_1+\sigma_2 hold.
(ⅰ) If d < d^{*}_{3} , then the positive equilibrium (u^{*}_{1}, u^{*}_{2}, v^{*}_{1}, v^{*}_{2}) is stable, and
\lim_{t \rightarrow \infty}(u_{1}(t), u_{2}(t) , v_{1}(t), v_{2}(t)) = (u^{*}_{1}, u^{*}_{2}, v^{*}_{1}, v^{*}_{2}) , for all (u_{1}(0), u_{2}(0), v_{1}(0), v_{2}(0)) \in {\mathbb R}^4_+ with u_{1}(0)+u_{2}(0) > 0 and v_{1}(0)+v_{2}(0) > 0 .
(ⅱ) If d \geq d^{*}_{3} , then \lim_{t \rightarrow \infty}(u_{1}(t), u_{2}(t), v_{1}(t), v_{2}(t)) = (0, 0, \bar{v}_{1}, \bar{v}_{2}) , for all (u_{1}(0), u_{2}(0) , v_{1}(0), v_{2}(0)) \in {\mathbb R}^4_+ with v_1(0) +v_2(0) > 0 .
(Ⅱ) Assume that \sigma_2\beta_{2} > \sigma_1\beta_{1} holds.
(ⅰ) If d < d^{*}_{1} or d^{*}_{2} < d < d^{*}_{3} , then the positive equilibrium (u^{*}_{1}, u^{*}_{2}, v^{*}_{1}, v^{*}_{2}) is stable, and \lim_{t \rightarrow \infty}(u_{1}(t), u_{2}(t) , v_{1}(t), v_{2}(t)) = (u^{*}_{1}, u^{*}_{2}, v^{*}_{1}, v^{*}_{2}) , for all (u_{1}(0), u_{2}(0), v_{1}(0), v_{2}(0)) \in {\mathbb R}^4_+ with u_{1}(0)+u_{2}(0) > 0 and v_{1}(0)+v_{2}(0) > 0 .
(ⅱ) = If d^{*}_{1}\leq d \leq d^{*}_{2} , then \lim_{t \rightarrow \infty}(u_{1}(t), u_{2}(t), v_{1}(t), v_{2}(t)) = (\bar{u}_{1}, \bar{u}_{2}, 0, 0) , for all (u_{1}(0), u_{2}(0) , v_{1}(0), v_{2}(0)) \in {\mathbb R}^4_+ with u_1(0) +u_2(0) > 0 .
(ⅲ) If d \geq d^{*}_{3} , then \lim_{t \rightarrow \infty}(u_{1}(t), u_{2}(t), v_{1}(t), v_{2}(t)) = (0, 0, \bar{v}_{1}, \bar{v}_{2}) , for all (u_{1}(0), u_{2}(0) , v_{1}(0), v_{2}(0)) \in {\mathbb R}^4_+ with v_1(0) +v_2(0) > 0 .
Proof. The assertions are based on the monotone dynamics theory which is reviewed in Appendix I. (I) Assume that \sigma_2\beta_{2} < \sigma_1\beta_{1} and \beta_2-\beta_1\geq \sigma_1+\sigma_2 hold. (ⅰ) If d < d^{*}_{3} , then the positive equilibrium (u^{*}_{1}, u^{*}_{2}, v^{*}_{1}, v^{*}_{2}) is unique, by Theorem 2.1, the semitrivial equilibrium (\bar{u}_{1}, \bar{u}_{2}, 0, 0) is unstable, by Theorem 3.3, and the semitrivial equilibrium (0, 0, \bar{v}_{1}, \bar{v}_{2}) is unstable, by Theorem 3.4. Therefore, the assertion follows from Theorem A.1. (ⅱ) If d > d^{*}_{3} , then case (a) of the trichotomy in Theorem A.2 does not hold, since the positive steady state does not exist, by Theorem 2.1; case (b) does not hold since (\bar{u}_{1}, \bar{u}_{2}, 0, 0) is unstable, by Theorem 3.3, and (0, 0, \bar{v}_{1}, \bar{v}_{2}) is asymptotically stable, by Theorem 3.4. Therefore, the assertion follows from case (c) of Theorem A.2.
(Ⅱ) Assume that \sigma_2\beta_{2} > \sigma_1\beta_{1} . (ⅰ) If d < d^{*}_{1} or d^{*}_{2} < d < d^{*}_{3} , the argument is similar to the one in (Ⅰ)(ⅰ). (ⅱ) If d^{*}_{1} < d < d^{*}_{2} , then case (a) of the trichotomy in Theorem A.2 does not hold, since the positive equilibrium does not exist, by Theorem 2.1; case (c) does not hold since (0, 0, \bar{v}_{1}, \bar{v}_{2}) is unstable, by Theorem 3.2, and (\bar{u}_{1}, \bar{u}_{2}, 0, 0) is asymptotically stable, by Theorem 3.1. Therefore, the assertion follows from case (b) of Theorem A.2. (ⅲ) If d > d^{*}_{3} , the argument is similar to the one in (Ⅰ)(ⅱ). This completes the proof.
Remark 4 That the equilibrium (0, 0, \bar{v}_{1}, \bar{v}_{2}) is globally asymptotically stable for d > d^{*}_{3} now follows from Theorem 4.2. In fact it also holds true for d = d^{*}_{3} . In this case, the stability for (0, 0, \bar{v}_{1}, \bar{v}_{2}) can be concluded by some comparison argument. In addition, there is no positive equilibrium and the equilibrium (\bar{u}_{1}, \bar{u}_{2}, 0, 0) is unstable in both cases (Ⅰ) and (Ⅱ), by Theorem 2.1 and Theorem 3.3. Hence the trichotomy in Theorem A.2 implies the global convergence to (0, 0, \bar{v}_{1}, \bar{v}_{2}) . Similarly, we see that the equilibrium (\bar{u}_{1}, \bar{u}_{2}, 0, 0) is globally asymptotically stable for d = d^{*}_{1} or d^{*}_{2} .
We arrange two examples to illustrate the global dynamics of system (1.3), and the bifurcation with respect to the dispersal rate d , which are concluded in Theorem 4.2. We also present two more examples to demonstrate that the established scenarios still hold without satisfying condition (P).
Example 1. Consider system (1.3) with \alpha_1 = 1 , \alpha_2 = 3 , \beta_1 = 1.5 and \beta_2 = 2.8 , i.e., \sigma_1 = 0.5 and \sigma_2 = 0.2 . Let us examine the conditions in Theorem 4.2(Ⅰ): condition (C): \alpha_1 = 1 < \beta_1 = 1.5 < \beta_2 = 2.8 < \alpha_2 = 3 with \sigma_2 = 0.2 < \sigma_1 = 0.5 ; condition ({\rm P}) : \frac{\sigma_{2}\beta_{2}}{\sigma_{1}+\sigma_{2}} = 0.8 < \frac{\sigma_1\sigma_2\beta_{1}\beta_{2}}{\sigma_2\beta_{2}^2-\sigma_1\beta_{1}^2} = 0.948 ; \sigma_2 \beta_2 = 0.56 < \sigma_1 \beta_1 = 0.75 , and \beta_2-\beta_1 = 1.3\geq \sigma_1+\sigma_2 = 0.7 . We depict in Figure 5 the bifurcation diagram with respect to the dispersal rate d . It appears that d^{*}_{3}\cong 1.22 , which is consistent with Theorems 2.1(ⅰ) and 4.2(Ⅰ): \frac{\sigma_1 \sigma_2}{\sigma_1- \sigma_2} = 0.333 < d^{*}_{3} < \frac{\sigma_1}{\sigma_1-\sigma_2}(\alpha_2-\alpha_1) = 3.33 . The globally stable positive equilibrium (u^{*}_{1}, u^{*}_{2}, v^{*}_{1}, v^{*}_{2}) exists for d < d^{*}_{3} and collides with the semitrivial equilibrium (0, 0, \bar{v}_{1}, \bar{v}_{2}) at d = d^{*}_{3} . For d\geq d^{*}_{3} , the semitrivial equilibrium (0, 0, \bar{v}_{1}, \bar{v}_{2}) becomes globally attractive.
Example 2. Consider system (1.3) with \alpha_1 = 1 , \alpha_2 = 3 , \beta_1 = 1.7 and \beta_2 = 2.5 , i.e., \sigma_1 = 0.7 and \sigma_2 = 0.5 . Let us examine the conditions in Theorem 4.2(Ⅱ): condition (C): \alpha_1 = 1 < \beta_1 = 1.7 < \beta_2 = 2.5 < \alpha_2 = 3 with \sigma_2 = 0.5 < \sigma_1 = 0.7 ; condition ({\rm P}) : \frac{\sigma_{2}\beta_{2}}{\sigma_{1}+\sigma_{2}} = 1.042 < \frac{\sigma_1\sigma_2\beta_{1}\beta_{2}}{\sigma_2\beta_{2}^2-\sigma_1\beta_{1}^2} = 1.35 ; \sigma_2 \beta_2 = 1.25 > \sigma_1 \beta_1 = 1.19 . The bifurcation diagram with respect to the dispersal rate d is depicted in Figure 6. It appears that d^{*}_{1}\cong 0.91 , d^{*}_{2} \cong 1.92 , d^{*}_{3}\cong 4.15 , which are consistent with Theorem 2.1(ⅱ) and 4.2(Ⅱ): \frac{\sigma_1 \sigma_2\alpha_{1}\alpha_{2}}{\sigma_2\alpha_{2}^2-\sigma_1\alpha_{1}^2 } = 0.276 < d^{*}_{1} < \frac{\sigma_1\sigma_2\beta_{1}\beta_{2}}{\sigma_2\beta_{2}^2-\sigma_1\beta_{1}^2} = 1.35 , \frac{\sigma_{1}\sigma_{2}}{\sigma_{1}-\sigma_{2}} = 1.75 < d^{*}_{2} < \frac{\sigma_{1}\sqrt{\sigma_{1}\sigma_{2}}+\sigma_{1}\sigma_{2}}{\sigma_{1}^{2}-\sigma_{2}^{2}}(\alpha_{2}-\alpha_{1}) = 6.36 , and \frac{\sigma_{1}\sigma_{2}}{\sigma_{1}-\sigma_{2}} = 1.75 < d^{*}_{3} < \frac{\sigma_{1}}{\sigma_{1}-\sigma_{2}}(\alpha_{2}-\alpha_{1}) = 7 . The globally stable positive equilibrium (u^{*}_{1}, u^{*}_{2}, v^{*}_{1}, v^{*}_{2}) exists for d < d^{*}_{1} and collides with the semitrivial equilibrium (\bar{u}_{1}, \bar{u}_{2}, 0, 0) at d = d^{*}_{1} . For d^{*}_{1}\leq d \leq d^{*}_{2} , the equilibrium (\bar{u}_{1}, \bar{u}_{2}, 0, 0) becomes globally attractive and for d^{*}_{2} < d < d^{*}_{3} , the globally stable positive equilibrium (u^{*}_{1}, u^{*}_{2}, v^{*}_{1}, v^{*}_{2}) exists. For d \geq d^{*}_{3} , the semitrivial equilibrium (0, 0, \bar{v}_{1}, \bar{v}_{2}) becomes globally attractive.
Example 3. Consider system (1.3) with \alpha_1 = 1 , \alpha_2 = 3 , \beta_1 = 1.4 and \beta_2 = 2.85 , i.e., \sigma_1 = 0.4 and \sigma_2 = 0.15 . For such parameter values, condition (C) holds: \alpha_1 = 1 < \beta_1 = 1.4 < \beta_2 = 2.85 < \alpha_2 = 3 with \sigma_2 = 0.15 < \sigma_1 = 0.4 . In addition, \sigma_2 \beta_2 = 0.4275 < \sigma_1 \beta_1 = 0.56 . Such parameter values violate condition ({\rm P}) , as \frac{\sigma_{2}\beta_{2}}{\sigma_{1}+\sigma_{2}} = 0.777 > \frac{\sigma_1\sigma_2\beta_{1}\beta_{2}}{\sigma_2\beta_{2}^2-\sigma_1\beta_{1}^2} = 0.551 . Nevertheless, the same dynamical scenario as Example 1 takes place, as seen in Figure 7.
Example 4. Consider system (1.3) with \alpha_1 = 1 , \alpha_2 = 3 , \beta_1 = 1.35 and \beta_2 = 2.8 , i.e., \sigma_1 = 0.35 and \sigma_2 = 0.2 . With such parameter values, condition (C) holds: \alpha_1 = 1 < \beta_1 = 1.35 < \beta_2 = 2.8 < \alpha_2 = 3 with \sigma_2 = 0.2 < \sigma_1 = 0.35 ; \sigma_2 \beta_2 = 0.56 > \sigma_1 \beta_1 = 0.4725 . These parameter values violate condition ({\rm P}) , as \frac{\sigma_{2}\beta_{2}}{\sigma_{1}+\sigma_{2}} = 1.0182 > \frac{\sigma_1\sigma_2\beta_{1}\beta_{2}}{\sigma_2\beta_{2}^2-\sigma_1\beta_{1}^2} = 0.2845 . Nevertheless, the dynamical scenario shown in Figure 8 remains identical to Example 2.
We have exhibited the global dynamics for a model on two-species competition in a two-patch environment, under certain conditions. The main condition (C): \alpha_{1} < \beta_{1} < \beta_{2} < \alpha_{2} , (\beta_{1}+ \beta_{2})-(\alpha_{1}+\alpha_{2}) = \sigma_{1}-\sigma_{2} > 0 , indicates that the birth rate of u -species in the second patch is the largest among all birth rates of two species on two patches, yet the average birth rate of v -species is larger than u -species. This means that the birth rate for v -species is larger than u -species in the first patch. The present investigation exploited analytically two dynamical scenarios for such competition, as demonstrated in Examples 1 and 2, respectively. The first scenario takes place under \sigma_{2}\beta_{2} < \sigma_{1}\beta_{1} . As expressed by \frac{\sigma_{1}}{\sigma_2} > \frac{\beta_{2}}{\beta_{1}} > 1 , it indicates that the value of \sigma_1 is larger than the value of \sigma_2 in a way that its ratio exceeds the ratio of \beta_2 over \beta_1 . This includes the situation that \sigma_1 is much bigger than \sigma_2 , which is denoted by (\beta_{1}+ \beta_{2})\gg (\alpha_{1}+\alpha_{2}) . The second scenario comes about under \sigma_{2}\beta_{2} > \sigma_{1}\beta_{1} . On the contrary, as expressed by 1 < \frac{\sigma_{1}}{\sigma_2} < \frac{\beta_{2}}{\beta_{1}} , it indicates that the value of \sigma_1 may be merely a little over the value of \sigma_2 , depending on the ratio of \beta_2 over \beta_1 . In this case, the average birth rate of v -species may be merely a little more than and close to the average birth rate of u -species; we denote this situation by (\beta_{1}+ \beta_{2})\approx (\alpha_{1}+\alpha_{2}) .
In the first case, including the sense (\beta_{1}+ \beta_{2})\gg (\alpha_{1}+\alpha_{2}) , coexistence of two species occurs for dispersal rate d < d^{*}_{3} , and (0, 0, \bar{v}_{1}, \bar{v}_{2}) is globally attractive for d \geq d^{*}_{3} , where d^{*}_{3} has been estimated by system parameters. In this situation, (\bar{u}_{1}, \bar{u}_{2}, 0, 0) is unstable for any d > 0 and an eigenvalue of the linearized system at (0, 0, \bar{v}_{1}, \bar{v}_{2}) changes from positive to negative as d , being increasing from 0 , exceeds d^{*}_{3} , and (0, 0, \bar{v}_{1}, \bar{v}_{2}) becomes stable for d \geq d^{*}_{3} .
In the second case, including the sense (\beta_{1}+ \beta_{2})\approx (\alpha_{1}+\alpha_{2}) , the coexistence of two species takes place for d < d^{*}_{1} or d^{*}_{2} < d < d^{*}_{3} , (\bar{u}_{1}, \bar{u}_{2}, 0, 0) is globally attractive for d^{*}_{1}\leq d \leq d^{*}_{2} , and (0, 0, \bar{v}_{1}, \bar{v}_{2}) becomes globally attractive for d \geq d^{*}_{3} , where d^{*}_{1}, d^{*}_{2}, d^{*}_{3} have been estimated. An eigenvalue of the linearized system at (\bar{u}_{1}, \bar{u}_{2}, 0, 0) changes from positive to negative at d^{*}_{1} , and then back to positive at d^{*}_{2} . In addition, an eigenvalue of the linearized system at (0, 0, \bar{v}_{1}, \bar{v}_{2}) changes from positive to negative at d^{*}_{3} , and d^{*}_{2} < d^{*}_{3} .
Our analytical investigation on this model strongly suggests that, in high-dispersal situations, one species will prevail if its average birth rate is larger than the other competing species, whereas in low-dispersal situations, the two species can coexist or one species that has the greatest birth rate in one patch among all species and patches will be able to persist and drive the other species to extinction, even though its average birth rate is lower. Such findings may illuminate some insights into how species learn to compete and point out the evolution directions.
Condition (C) is a basic assumption for the present results. Although there are additional conditions ({\rm P}) and \beta_2 - \beta_1 \geq \sigma_1+\sigma_2 , due to mathematical technicality, it is believed that such scenarios remain true under condition (C) only. However, it is very difficult to remove these additional conditions, as the algebraic operations involving five parameters are rather involved. In Examples 3, 4, we have demonstrated exactly the same dynamical scenarios for parameter values which do not satisfy condition ({\rm P}) .
To compare our results with those in [19], we set \sigma_1 = \xi\sigma_2 , \xi > 1 , according to condition (C). The resource difference between two species can be depicted as (\sigma_1, -\sigma_2) among two patches, where \beta_1-\alpha_1 = \sigma_1 > 0 means that v -species has an advantage over v -species in competing the resource in patch-1, while \beta_2-\alpha_2 = -\sigma_2 < 0 means that it is disadvantageous for v -species to compete with u -species for the resource in patch-2. We rewrite it as \sigma_2(\xi, -1) with fixed \xi > 1 , and now the value of \sigma_2 measures the difference between two species and resembles the value of \tau in [19]. We accordingly rewrite the conditions in Theorem 4.2 to explore how the magnitude of resource difference affects the invasion of mutant species:
\begin{eqnarray*} & & ({\rm P}) \Leftrightarrow \sigma_2 \gt \sigma_2^{*}: = \frac{\beta_2^2-\xi\beta_1^2}{\xi(\xi+1)\beta_1}, \\ & & \beta_2-\beta_1\geq ( \lt ) \sigma_1+\sigma_2 \Leftrightarrow \sigma_2\leq ( \gt ) \sigma_2^{**}: = \frac{\beta_2-\beta_1}{\xi+1}, \\ & & \sigma_2\beta_2 \lt ( \gt ) \sigma_1\beta_1 \Leftrightarrow \xi \gt ( \lt ) \frac{\beta_2}{\beta_1}. \end{eqnarray*} |
Note that the criteria in Theorem 4.2(Ⅱ) imply \beta_2-\beta_1 < \sigma_1+\sigma_2 . Therefore, by increasing the dispersal rate d , the global convergence of system (1.1) switches in case (Ⅰ) of Theorem 4.2 from the coexistence to extinction of u -species when \sigma_2^{*} < \sigma_2\leq\sigma_2^{**} and \xi > \frac{\beta_2}{\beta_1} ; on the other hand, in case (Ⅱ), the dynamics switches three times from global convergence to the coexistence to extinction of (mutant) v -species, again to the coexistence and then the persistence of v -species, when \sigma_2 > \sigma_2^{**} and \xi < \frac{\beta_2}{\beta_1} . This result enhances the understanding on the dynamics of competitive species from the viewpoint of patchy habitat in the following aspects: Compared to concluding global convergence under small magnitude of spatial heterogeneity ( \tau ) in [19,Theorem 1.2], our result in Theorem 4.2 admits a large range of magnitudes ( \sigma_2 ) depicting spatial heterogeneity. The multiple stability switches in Theorem 4.2 are global dynamics, as compared to local dynamics in [19,Theorem 1.1].
The authors are grateful to Chao-Nien Chen for suggesting to study the case with average birth rates of opposite order, and to Yuan Lou for very helpful discussions. The authors are supported, in part, by the Ministry of Science and Technology, Taiwan.
All authors declare no conflicts of interest in this paper.
For reader's convenience, we review some theory on monotone dynamical systems from [17] and [28]. Denote by \mathbb{R}^{n}_{+} = \{\mathbf{x} = (x_1, \ldots, x_n)\in \mathbb{R}^{n}: x_{i}\geq 0, 1\leq i\leq n\} the first octant of \mathbb{R}^{n} . For \mathbf{x}, \mathbf{y}\in \mathbb{R}^{n}_{+} , define the following order: \mathbf{x} \leq_{m} \mathbf{y} if \mathbf{y}-\mathbf{x}\in K_m , and \mathbf{x} \ll_m \mathbf{y} whenever \mathbf{y}-\mathbf{x}\in {\rm Int}K_m , where
\begin{equation*} K_m = \{ \mathbf{x} \in \mathbb{R}^{n}: x_{i}\geq 0, 1\leq i \leq k, {\rm and} x_{j}\leq 0, k+1\leq j \leq n \} = \mathbb{R}^{k}_{+}\times (-\mathbb{R}^{n-k}_{+}). \end{equation*} |
If \mathbf{x} \leq_m \mathbf{y} , we define [\mathbf{x}, \mathbf{y}]_m = \{\mathbf{z}\in \mathbb{R}^{n}_{+}: \mathbf{x} \leq_m \mathbf{z} \leq_m \mathbf{y} \} and (\mathbf{x}, \mathbf{y})_m = \{\mathbf{z}\in \mathbb{R}^{n}_{+}: \mathbf{x} \ll_m \mathbf{z} \ll_m \mathbf{y} \} .
A semiflow \phi is said to be of type- K monotone with respect to K_m , provided
\begin{equation*} \phi_{t}(\mathbf{x})\leq_{m}\phi_{t}(\mathbf{y}) {\rm whenever} \mathbf{x} \leq_{m} \mathbf{y}, t\geq 0. \end{equation*} |
A system of ODEs \dot{\mathbf{x}} = {\bf f}(\mathbf{x}) is called a type- K monotone system with respect to K_m if the Jacobian matrix of {\bf f} takes the form
\begin{equation*} \left[ \begin{array}{cc} A_{1} & -A_{2} \\ -A_{3} & A_{4} \end{array} \right], \end{equation*} |
where A_{1} is an k\times k matrix, A_{4} an (n-k)\times (n-k) matrix, A_{2} an k\times (n-k) matrix, A_{3} an (n-k)\times k matrix, every off-diagonal entry of A_{1} and A_{4} is nonnegative, and A_{2} and A_{3} are nonnegative matrices, for some k with 1 \leq k\leq n . It was shown in [27] that the flow \phi_{t}(\mathbf{x}) generated by the type- K monotone system is type- K monotone with respect to the cone K_m , i.e., if \mathbf{x}, \mathbf{y}\in \mathbb{R}^{n}_{+} with x_{i} \leq y_{i} for 1 \leq i \leq k and x_{j} \geq y_{j} for k+1 \leq j \leq n , then for any t > 0 , (\phi_{t}(\mathbf{x}))_{i} \leq (\phi_{t}(\mathbf{y}))_{i} for 1 \leq i \leq k and (\phi_{t}(\mathbf{x}))_{j} \geq (\phi_{t}(\mathbf{y}))_{j} for k+1 \leq j \leq n .
System (1.3) is a type- K monotone system with respect to
\begin{equation*} K_m = \{ (u_1, u_2, v_1, v_2): u_{i}\geq 0, v_{i}\leq 0, i = 1, 2 \}, \end{equation*} |
since its Jacobian matrix is
\begin{align*} \left[ \begin{array}{cccc} \alpha_{1}-2u_{1}-v_{1}-d & d & -u_{1} & 0\\ d & \alpha_{2}-2u_{2}-v_{2}-d & 0 & -u_{2} \\ -v_{1} & 0 & \beta_{1}-2v_{1}-u_{1}-d & d\\ 0 & -v_{2} & d & \beta_{2}-2v_{2}-u_{2}-d \end{array} \right]. \end{align*} |
For system (1.3), let us denote by e_{\mathbf{0}}: = (0, 0, 0, 0) the trivial equilibrium, by e_{\bar{\mathbf{u}}}: = (\bar{u}_{1}, \bar{u}_{2}, 0, 0) , and e_{\bar{\mathbf{v}}}: = (0, 0, \bar{v}_{1}, \bar{v}_{2}) , \bar{u}_{i} , \bar{v}_{i} > 0 , i = 1, 2 , the semitrivial equilibria. If (u_{1}, u_{2}, v_{1}, v_{2}) \in \mathbb{R}^{4}_{+} , then (0, 0, v_{1}, v_{2})\leq_{m} (u_{1}, u_{2}, v_{1}, v_{2}) \leq_{m} (u_{1}, u_{2}, 0, 0) , and therefore,
\phi_{t}((0, 0, v_{1}, v_{2}))\leq_{m} \phi_{t}((u_{1}, u_{2}, v_{1}, v_{2})) \leq_{m} \phi_{t}((u_{1}, u_{2}, 0, 0)), |
for all t \geq 0 . Since \phi_{t}((0, 0, v_{1}, v_{2}))\rightarrow e_{\bar{\mathbf{v}}} and \phi_{t}((u_{1}, u_{2}, 0, 0))\rightarrow e_{\bar{\mathbf{u}}} as t \rightarrow \infty , for (u_{1}, u_{2}, v_{1}, v_{2}) \in \mathbb{R}^{4}_{+} , and u_{1}+ u_{2} > 0 , v_{1}+ v_{2} > 0 , it follows that all points in \mathbb{R}^{4}_{+} are attracted to the set
\begin{eqnarray*} \Gamma : = [0, \bar{u}_{1}] \times [0, \bar{u}_{2}] \times [0, \bar{v}_{1}] \times [0, \bar{v}_{2}] = [e_{\bar{\mathbf{v}}},e_{\bar{\mathbf{u}}}]_{m} = \{ \mathbf{w} \in \mathbb{R}^{4}_{+}: e_{\bar{\mathbf{v}}} \leq_{m} \mathbf{w} \leq_{m} e_{\bar{\mathbf{u}}}\}. \end{eqnarray*} |
If \mathbf{w} = (u_{1}, u_{2}, v_{1}, v_{2}) with u_{1}, u_{2}, v_{1}, v_{2} > 0 , then \phi_{t}(\mathbf{w})\gg 0 for t > 0 . Define E and E^{+} the sets of all nonnegative equilibria and all positive equilibria for \phi_{t} , respectively. Obviously, [e_{\bar{\mathbf{v}}}, e_{\bar{\mathbf{u}}}]_{m} contains E and e_{*}\in (e_{\bar{\mathbf{v}}}, e_{\bar{\mathbf{u}}})_{m} for any e_{*}\in E^{+} . The following theorem restates Corollary 4.4.3 in [28] for system (1.3), see also [27,31].
Theorem A.1. If e_{\bar{\mathbf{u}}} and e_{\bar{\mathbf{v}}} are both linearly unstable, then system (1.3) is permanent. More precisely, there exist positive equilibria e_{*} and e_{**} , not necessarily distinct, satisfying
e_{\bar{\mathbf{v}}} \ll_{m} e_{**} \leq_{m} e_{*} \ll_{m} e_{\bar{\mathbf{u}}}. |
The order interval
[e_{**}, e_{*}]_{m}: = \{\mathbf{w}: e_{**} \leq_{m}\mathbf{w} \leq_{m}e_{*}\} |
attracts all solutions evolved from \mathbf{w} = (u_{1}, u_{2}, v_{1}, v_{2}) \in {\mathbb R}^4_+ , with u_{1}+ u_{2} > 0 and v_{1}+ v_{2} > 0 . In particular, if e_{**} = e_{*} , then e_{*} attracts all such solutions.
It was shown in [17] that, for models of two competing species, either there is a positive equilibrium representing coexistence of two species, or one species drives the other to extinction. Note that system (1.3) satisfies conditions (H1)-(H4) in [17], and thus Theorem B in [17] can be restated as follows.
Theorem A.2. Consider system (1.3). The \omega -limit set of every orbit evolved from a point in \mathbb{R}^{4}_{+} is contained in \Gamma and exactly one of the following holds:
(a) There exists a positive equilibrium e_{*} of in \Gamma .
(b) \phi_{t}(\mathbf{w})\rightarrow e_{\bar{\mathbf{u}}} as t\rightarrow \infty , for every \mathbf{w} = (u_{1}, u_{2}, v_{1}, v_{2})\in \Gamma with u_{1}+ u_{2} > 0 .
(c) \phi_{t}(\mathbf{w})\rightarrow e_{\bar{\mathbf{v}}} as t\rightarrow \infty , for every \mathbf{w} = (u_{1}, u_{2}, v_{1}, v_{2})\in \Gamma with v_{1}+ v_{2} > 0 .
In addition, if (b) or (c) holds, then either \phi_{t}(\mathbf{w})\rightarrow e_{\bar{\mathbf{u}}} or \phi_{t}(\mathbf{w})\rightarrow e_{\bar{\mathbf{v}}} , as t\rightarrow \infty , for \mathbf{w} = (u_{1}, u_{2}, v_{1}, v_{2})\in \mathbb{R}^{4}_{+}\setminus \Gamma .
We recall some qualitative properties of the semitrivial equilibria for system (1.3) in [24]. The following results are independent of the order between \sigma_1 and \sigma_2 .
Proposition A.3 (Proposition 3.7 [24]). If \alpha_1 < \alpha_2 , the following hold for all d > 0 .
(ⅰ) \alpha_{1} < \bar{u}_{1} < \bar{u}_{2} < \alpha_{2} .
(ⅱ) (\alpha_{1}-\bar{u}_{1})- (\alpha_{2}-\bar{u}_{2}) = \frac{d(\bar{u}_1^2-\bar{u}_2^2)}{\bar{u}_1\bar{u}_2} < 0 , (\alpha_{1}-\bar{u}_{1})+ (\alpha_{2}-\bar{u}_{2}) = d\left[2-\left(\frac{\bar{u}_2}{\bar{u}_1}+\frac{\bar{u}_1}{\bar{u}_2}\right)\right] < 0 .
(ⅲ) \alpha_{1} < \bar{u}_{1} < \frac{\alpha_{1}+\alpha_{2}}{2} < \bar{u}_{2} < \alpha_{2} .
Proposition A.4 (Proposition 3.8 [24]). If \beta_1 < \beta_2 , the following hold for all d > 0 .
(ⅰ) \beta_{1} < \bar{v}_{1} < \bar{v}_{2} < \beta_{2} .
(ⅱ) (\beta_{1}-\bar{v}_{1})- (\beta_{2}-\bar{v}_{2}) = \frac{d(\bar{v}_1^2-\bar{v}_2^2)}{\bar{v}_1\bar{v}_2} < 0 , (\beta_{1}-\bar{v}_{1})+ (\beta_{2}-\bar{v}_{2}) = d\left[2-\left(\frac{\bar{v}_2}{\bar{v}_1}+\frac{\bar{v}_1}{\bar{v}_2}\right)\right] < 0 .
(ⅲ) \beta_{1} < \bar{v}_{1} < \frac{\beta_{1}+\beta_{2}}{2} < \bar{v}_{2} < \beta_{2} .
Proposition A.5 (Proposition 3.9 [24]). If \alpha_1 < \alpha_2 , the following hold:
(ⅰ) \bar{u}_{1}, \bar{u}_{2}\rightarrow \frac{\alpha_{1}+\alpha_{2}}{2} as d\rightarrow\infty .
(ⅱ) d is strictly decreasing with respect to \bar{u}_{2} on (\frac{\alpha_{1}+\alpha_{2}}{2}, \alpha_{2}) , and d is strictly increasing with respect to \bar{u}_{1} on (\alpha_{1}, \frac{\alpha_{1}+\alpha_{2}}{2}) .
Proposition A.6 (Proposition 3.10 [24]). If \beta_1 < \beta_2 , the following hold:
(ⅰ) \bar{v}_{1}, \bar{v}_{2}\rightarrow \frac{\beta_{1}+\beta_{2}}{2} as d\rightarrow\infty .
(ⅱ) d is strictly decreasing with respect to \bar{v}_{2} on (\frac{\beta_{1}+\beta_{2}}{2}, \beta_{2}) , and d is strictly increasing with respect to \bar{v}_{1} on (\beta_{1}, \frac{\beta_{1}+\beta_{2}}{2}) .
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