
A linear dynamical model for the development of the turbulent energy cascade was introduced in Apolinário et al. (J. Stat. Phys., 186, 15 (2022)). This partial differential equation, randomly stirred by a forcing term which is smooth in space and delta-correlated in time, was shown to converge at infinite time towards a state of finite variance, without the aid of viscosity. Furthermore, the spatial profile of its solution gets rough, with the same regularity as a fractional Gaussian field. We here focus on the temporal behavior and derive explicit asymptotic predictions for the correlation function in time of this solution and observe that their regularity is not influenced by the spatial regularity of the problem, only by the correlation in time of the stirring contribution. We also show that the correlation in time of the solution depends on the position, contrary to its correlation in space at fixed times. We then investigate the influence of a forcing which is correlated in time on the spatial and time statistics of this equation. In this situation, while for small correlation times the homogeneous spatial statistics of the white-in-time case are recovered, for large correlation times homogeneity is broken, and a concentration around the origin of the system is observed in the velocity profiles. In other words, this fractional velocity field is a representation in one-dimension, through a linear dynamical model, of the self-similar velocity fields proposed by Kolmogorov in 1941, but only at fixed times, for a delta-correlated forcing, in which case the spatial statistics is homogeneous and rough, as expected of a turbulent velocity field. The regularity in time of turbulence, however, is not captured by this model.
Citation: Gabriel B. Apolinário, Laurent Chevillard. Space-time statistics of a linear dynamical energy cascade model[J]. Mathematics in Engineering, 2023, 5(2): 1-23. doi: 10.3934/mine.2023025
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A linear dynamical model for the development of the turbulent energy cascade was introduced in Apolinário et al. (J. Stat. Phys., 186, 15 (2022)). This partial differential equation, randomly stirred by a forcing term which is smooth in space and delta-correlated in time, was shown to converge at infinite time towards a state of finite variance, without the aid of viscosity. Furthermore, the spatial profile of its solution gets rough, with the same regularity as a fractional Gaussian field. We here focus on the temporal behavior and derive explicit asymptotic predictions for the correlation function in time of this solution and observe that their regularity is not influenced by the spatial regularity of the problem, only by the correlation in time of the stirring contribution. We also show that the correlation in time of the solution depends on the position, contrary to its correlation in space at fixed times. We then investigate the influence of a forcing which is correlated in time on the spatial and time statistics of this equation. In this situation, while for small correlation times the homogeneous spatial statistics of the white-in-time case are recovered, for large correlation times homogeneity is broken, and a concentration around the origin of the system is observed in the velocity profiles. In other words, this fractional velocity field is a representation in one-dimension, through a linear dynamical model, of the self-similar velocity fields proposed by Kolmogorov in 1941, but only at fixed times, for a delta-correlated forcing, in which case the spatial statistics is homogeneous and rough, as expected of a turbulent velocity field. The regularity in time of turbulence, however, is not captured by this model.
The predator-prey system is one of the most important systems for studying the interaction of two species in ecology. Predator-prey systems have important mathematical consequences because they describe ecosystem dynamics and the interactions of diverse species. These systems assist in the estimation of population dynamics by depicting the interaction between predators and prey using mathematical equations. Furthermore, they can inform ecologists by providing insights into the complex interactions between various species within an ecosystem. This knowledge can then be used to develop strategies aimed at the conservation of biodiversity and the effective management of resources [1]. Lotka [2] and Volterra [3] established a fundamental predator-prey system consisting of two species. Over time, several scholars have made modifications to this system to offer a more realistic explanation and improve understanding, as it fails to account for numerous real-world scenarios and complexities. To increase the predator-prey dynamic's authenticity, several ecological principles have been incorporated. These principles include the Allee effect, functional response, refuge-seeking behavior, cannibalism, harvesting impact, and interactions between predators and prey that are mediated by fear [4,5,6,7,8,9,10,11,12].
Numerous researchers have utilized the logistic map to illustrate the prey's growth [13,14,15,16]. Nonetheless, there is a lack of research on the stability analysis of a discrete predator-prey system that takes into account the growth of the prey using a Ricker map [17,18,19]. The logistic map in a one-dimensional population growth model is given by xn+1=rxn(1−xnk), while the Ricker map is defined as xn+1=rxne1−xnk. One evident unrealistic feature of the logistic map is that 1−xnk is negative for xn>k, implying that large populations become negative at the next time step. In contrast, the Ricker map is preferable, as large values of xn result in extremely small (but still positive) values of xn+1. Thus, if a population exceeds its carrying capacity, it will fall to extremely low levels, while some of the population survives. Another advantage of the Ricker map is that the exponential component e1−xnk provides a nonlinear response to population density changes, mimicking instances where prey populations might face abrupt declines due to predation pressure.
It is typical to represent dynamical systems in one of two ways when modeling them: i) either as continuous-time systems [20], which are described using differential equations, ii) or as discrete-time systems, which are described by difference equations. Throughout the years, scholars have conducted thorough investigations into the nonlinear dynamic properties exhibited by continuous systems. Recently, numerous researchers have paid significant attention to discrete-time systems [21,22,23,24,25,26,27]. This is because discrete systems are much more effective at facilitating nonoverlapping generations than continuous systems. Discrete-time systems have the advantage of making numerical solutions easy to obtain. The study in [28] describes a precise discrete-time analytical (DTA) signal processing method for estimating frequency and phasor that works well with real-time computing requirements. The study in [29] is primarily concerned with determining ways to compute state and output bounding sets for uncertain discrete-time systems with pointwise-bounded, persistent inputs. The authors in [30] proposed the k-symbol discrete-time fractional Lozi system (FLS). Several critical dynamics of these systems are examined. They also investigate the necessary and sufficient requirements for stable and asymptotically stable k-symbol fractional dynamical systems. Furthermore, substantial research suggests that discrete-time systems may display more complex dynamics than corresponding continuous-time systems [31,32,33,34,35,36,37,38,39,40].
There are two distinct approaches to obtaining a discrete system. One way is to start with a continuous system and then use different techniques, such as the Euler technique [41,42,43,44,45] and the piecewise constant argument method [46,47,48,49,50,51], to turn it into a discrete system. On the other hand, we begin the analysis directly with the discrete system. Hamada et al. [52] studied the following discrete predator-prey system with the Ricker-type growth function:
{xn+1=rxne1−xnk−bxnyn,yn+1=dxnyn, | (1.1) |
where xn denotes prey density, yn is predator density, r is the intrinsic growth rate of the prey, k is the environmental carrying capacity of prey, and bxnyn and dxnyn represent the predator-prey confrontation, respectively, which are useful for predators and harmful for prey. The parameters r,k,b, and d are positive constants.
To enhance their chances of survival and minimize predation risks, prey species often engage in active refuge-seeking behaviors. This phenomenon has a significant impact on the dynamics of predator-prey interactions because it acts as a crucial mechanism in the preservation and mitigation of the extinction risk that prey species face. Gonzalez-Olivares and Ramos-Jiliberto [53] presented prey refuges in a simple predator-prey system. Ma et al. [54] investigated the dynamic behaviors of a predator-prey system, considering the mutual interference of a predator and a prey refuge. Chen et al. [55] investigated the prey refuge in a Leslie-Gower predator-prey model. Molla et al. [56] investigated the stability and Hopf bifurcation of the predator-prey system with refuge on prey. Numerous researchers have conducted extensive research on the refuge effect and acquired some fascinating results [57,58,59,60,61,62,63]. According to literature studies, the change in prey refuge positively affects prey density and negatively influences predator density. For instance, increasing prey refuge leads to a rise in the prey population, while decreasing prey refuge results in a decrease in the prey population. Negative influence signifies an inverse correlation, with one quantity increasing as the other decreases. Moreover, it has both stabilizing and destabilizing effects. Our work supports previous studies [64,65,66] by demonstrating that both prey and predators benefit from a moderate refuge level.
Thus, motivated by the above discussion, we naturally want to know: When a refuge effect is added to the prey population in system (1.1), what will happen to the dynamical properties? Hence, we extend the system (1.1) by adding the refuge effect to the prey population. Thus, the following modified system is obtained:
{xn+1=rxne1−xnk−b(1−m)xnyn,yn+1=d(1−m)xnyn. | (1.2) |
Here (1−m)xn represents the quantity of prey available for predation, where 0<m<1 is the protection rate of the prey refuge for prey.
The remainder of the paper is formatted as follows: Section 2 investigates the presence and topological classification of fixed points. Section 3 explores the period-doubling (PD) and Neimark-Sacker (NS) bifurcation analysis at the positive fixed point. Section 4 applies two control methods to regulate bifurcations and chaos. To verify and describe the theoretical results, Section 5 presents some numerical examples. Section 6 discusses the influence of refuge on system (1.2). Lastly, our analysis is summarized in Section 7.
Understanding the stability of fixed points is critical in a predator-prey system. These fixed points depict equilibrium states in which predator and prey populations have reached a balance. Analyzing their stability allows us to forecast the long-term behavior of these ecological systems and provide insight into how different elements influence the overall dynamics of the ecosystem.
Proposition 2.1. For system (1.2), we have three types of fixed points:
1) The trivial fixed point E0=(0,0) always exists.
2) The predator-free fixed point E1=(k(ln(r)+1),0) exists if r>1e.
3) The coexistence fixed point E2=(1d(1−m),re1−1kd(1−m)−1b(1−m)) exists if r>e−1+1kd(1−m).
Proof. To determine the fixed points of system (1.2), we need to solve
x=rxe1−xk−b(1−m)xy, | (2.1) |
y=d(1−m)xy. | (2.2) |
From Eq (2.2), it follows that either y=0 or x=1d(1−m). Substituting y=0 into Eq (2.1), we obtain
x=rxe1−xk. | (2.3) |
From Eq (2.3), it follows that either x=0 or x=k(ln(r)+1). Next, substituting x=1d(1−m) into Eq (2.1), we obtain
y=re1−1kd(1−m)−1b(1−m). |
The eigenvalues of the Jacobian matrix help determine the stability of fixed points. If ξ1,ξ2 are eigenvalues of the Jacobian matrix, then (x,y) is a sink (locally asymptotically stable (LAS)) when |ξ1|<1 along with |ξ2|<1. The fixed point (x,y) is a source when |ξ1|>1 along with |ξ2|>1. The fixed point (x,y) is a saddle point (SP) when |ξ1|<1∧|ξ2|>1 (or |ξ1|>1∧|ξ2|<1). Moreover, the fixed point (x,y) is a non-hyperbolic point (NHP) when the absolute value of either ξ1 and ξ2 is one. Classifying the positive fixed point directly using eigenvalues is not easy. Thus, we employ the following result:
Lemma 2.2. [67]
Consider the quadratic function Λ(ξ)=ξ2+K1ξ+K0. Suppose that Λ(1)>0. If ξ1 and ξ2 both satisfy the equation Λ(ξ)=0, then
1) |ξ1|<1 along with |ξ2|<1 if Λ(−1)>0∧K0<1,
2) |ξ1|<1∧|ξ2|>1 (or |ξ1|>1∧|ξ2|<1) if Λ(−1)<0,
3) |ξ1,2|>1 if Λ(−1)>0∧K0>1,
4) |ξ2|≠1∧ξ1=−1 if Λ(−1)=0∧K1≠0,2,
5) ξ1,ξ2∈C along with |ξ1,2|=1 if K21−4K0<0∧K0=1.
Through simple computations, one can obtain that:
J(x,y)=[e1−xkr(k−x)k+b(−1+m)yb(−1+m)x−d(−1+m)y−d(−1+m)x]. |
Proposition 2.3. The trivial fixed point E0 is a
1) LAS if 0<r<1e,
2) SP if r>1e,
3) NHP if r=1e.
Proof. We obtain
J(E0)=[er000]. | (2.4) |
The diagonal entries ξ1=0 and ξ2=er>0 are the eigenvalues J(E0). Clearly |ξ1|<1 and
er{<1 if 0<r<1e,=1 if r=1e,>1 if r>1e. |
Proposition 2.4. The fixed point E1 is
1) LAS if 1e<r<min{e,e1dk(1−m)−1},
2) source if r>max{e,e1dk(1−m)−1},
3) SP if mix{e,e1dk(1−m)−1}<r<max{e,e1dk(1−m)−1},
4) NHP if any one of the following satisfies:
(i) r=e,
(ii) r=e1dk(1−m)−1.
Proof. We obtain
J(E1)=[−ln(r)bk(−1+m)(1+ln(r))0dk(1−m)(1+ln(r))]. | (2.5) |
The eigenvalues of J(E0) are ξ1=−ln(r) and ξ2=dk(1−m)(1+ln(r))>0. One can see that
|−ln(r)|{<1 if 1e<r<e,=1 if r=e,>1 if r>e. |
Similarly, we obtain
dk(1−m)(1+ln(r)){<1 if 1e<r<e1dk(1−m)−1,=1 if r=e1dk(1−m)−1,>1 if r>e1dk(1−m)−1. |
Next, we classify the positive fixed point E2 of system (1.2) using the Jacobian matrix J(x,y) and Lemma 2.2.
Theorem 2.5. The positive fixed point
1) E2 is LAS if any one of the following satisfies:
(i) d<1k(1−m) and
e−1−1dk(−1+m)(dk(1−m)−1+dk(1−m))<r<−3e−1−1dk(−1+m)(dk(1−m)−2+dk(1−m)),
(ii) 1k(1−m)<d<2k(1−m) and
r<min{e−1−1dk(−1+m)(dk(1−m)−1+dk(1−m)),−3e−1−1dk(−1+m)(dk(1−m)−2+dk(1−m))},
(iii) d>2k(1−m) and
−3e−1−1dk(−1+m)(dk(1−m)−2+dk(1−m))<r<e−1−1dk(−1+m)(dk(1−m)−1+dk(1−m)),
2) E2 is an SP if one of the following satisfies:
(i) d<2k(1−m) and r>−3e−1−1dk(−1+m)(dk(1−m)−2+dk(1−m)),
(ii) d>2k(1−m) and r<−3e−1−1dk(−1+m)(dk(1−m)−2+dk(1−m)),
3) E2 is a source if any one of the following satisfies:
(i) d>2k(1−m) and
r>max{e−1−1dk(−1+m)(dk(1−m)−1+dk(1−m)),−3e−1−1dk(−1+m)(dk(1−m)−2+dk(1−m))},
(ii) 1k(1−m)<d<2k(1−m) and
e−1−1dk(−1+m)(dk(1−m)−1+dk(1−m))<r<−3e−1−1dk(−1+m)(dk(1−m)−2+dk(1−m)),
(iii) d<1k(1−m) and
r<min{e−1−1dk(−1+m)(dk(1−m)−1+dk(1−m)),−3e−1−1dk(−1+m)(dk(1−m)−2+dk(1−m))},
4) E2 is NHP and experiences PD bifurcation if
r=−3e−1−1dk(−1+m)(dk(1−m)−2+dk(1−m)) and
d≠2k(1−m), r≠2dk(1−m)e−1−1dk(−1+m), 4dk(1−m)e−1−1dk(−1+m).
5) E2 is NHP and experiences NS bifurcation if
r=e−1−1dk(−1+m)(dk(1−m)−1+dk(1−m)),d≠1k(1−m) and 0<r<4dk(1−m)e−1−1dk(−1+m).
Proof. We obtain
J(E2)=[1+e1+1dk(−1+m)rdk(−1+m)−bdd(−1+e1+1dk(−1+m)r)b1]. | (2.6) |
The corresponding characteristic polynomial is
Λ(ξ)=ξ2+K1ξ+K0, |
where
K1=−2−e1+1dk(−1+m)rdk(−1+m), K0=e1+1dk(−1+m)(1+dk(−1+m))rdk(−1+m). |
It can be obtained through calculations that
Λ(0)=e1+1dk(−1+m)(1+dk(−1+m))rdk(−1+m),Λ(−1)=3+e1+1dk(−1+m)(1+2dk(−1+m))r,Λ(1)=−1+e1+1dk(−1+m)r. |
It is easy to see that the positivity of the y-coordinate of E2 implies that Λ(1)>0. By setting Λ(−1)=0, one can obtain that:
e1+1dk(−1+m)(1+2dk(−1+m))r=−3,(−2+dk(1−m)dk(1−m))r=−3e−1−1dk(−1+m),r=−3e−1−1dk(−1+m)(dk(1−m)−2+dk(1−m)), d≠2k(1−m). |
By setting Λ(0)=1, one can obtain that:
e1+1dk(−1+m)(1+dk(−1+m))rdk(−1+m)=1,(−1+dk(1−m))rdk(1−m)=e−1−1dk(−1+m),r=e−1−1dk(−1+m)(dk(1−m)−1+dk(1−m)), d≠1k(1−m). |
By setting K1≠0,2, we obtain that:
r≠2dk(1−m)e−1−1dk(−1+m), 4dk(1−m)e−1−1dk(−1+m). |
Next, by setting K21−4K0<0 and K0=1, we obtain that:
0<r<4dk(1−m)e−1−1dk(−1+m). |
The fixed point categorizations in a discrete-time predator-prey model possess distinct ecological interpretations. A sink represents a state of steady coexistence, a saddle shows a state of intermittent stability, an unstable source implies unexpected shifts in population, and non-hyperbolic points hint at complicated and difficult-to-predict interactions. Understanding these categorizations assists ecologists in comprehending the stability and dynamics of predator-prey interactions, which are vital for efficient ecosystem management and conservation.
This section is dedicated to conducting a thorough investigation of PD and NS bifurcation in system (1.2) at E2. To get a comprehensive examination of bifurcation analysis, we suggest the readers to [68,69,70,71,72,73,74,75,76,77,78,79,80,81]. These bifurcations signify important changes in the dynamics of the system, providing insights into situations in which minor changes to parameters result in major changes in the dynamics of predator-prey interactions. Additionally to enhance our understanding of ecosystem dynamics, knowing the roles of PD and NS bifurcations also makes it easier to develop efficient conservation and management methods to maintain the long-term coexistence of predator and prey populations.
In this section, we investigate the PD bifurcation at E2 under condition 4) stated in Theorem 2.5. By introducing a minimal perturbation δ (|δ|⋘1) to the bifurcation parameter r in system (1.2), the resulting system is obtained:
{xn+1=(r+δ)xne1−xnk−b(1−m)xnyn,yn+1=d(1−m)xnyn. | (3.1) |
Assume that un=xn−1d(1−m), vn=yn−(r+δ)e1−1kd(1−m)−1b(1−m). After substituting the value of r=−3e−1−1dk(−1+m)(dk(1−m)−2+dk(1−m)), the system (3.1) is simplified to
[un+1vn+1]=[−1+dk(−1+m)2+dk(−1+m)−bd−2d(1+2dk(−1+m))b(2+dk(−1+m))1][unvn]+[F(un,vn,δ)G(un,vn,δ)], | (3.2) |
where
F(un,vn,δ)=a1u2n+a2u3n+a3unvn+a4unδ+a5u2nδ+O((|un|+|vn|+|δ|)4),G(un,vn,δ)=b1unvn+b2unδ, |
a1=(3+6dk(−1+m))2k(2+dk(−1+m)), a2=−(1+3dk(−1+m))2k2(2+dk(−1+m)), a3=b(−1+m), a4=e1−1dk−dkmdk(−1+m), |
a5=−e1−1dk−dkm(1+2dk(−1+m))2dk2(−1+m), b1=d(1−m), b2=de1+1dk(−1+m)b. |
Next, the system (3.2) is diagonalized through the consideration of the following transformation:
[unvn]=[−−2b+bdk−bdkmd(1−2dk+2dkm)−b2d11][enfn], | (3.3) |
Upon applying the mapping (3.3), the system (3.2) undergoes the alteration as follows:
[en+1fn+1]=[−1003+3dk(−1+m)2+dk(−1+m)][enfn]+[Γ(en,fn,δ)Υ(en,fn,δ)], | (3.4) |
where
Γ(en,fn,δ)=c1e2n+c2enf2n+c3e3n+c4e2nfn+c5f3n+c6enfn+c7f2n+c8e2nδ+c9enδ+c10enfnδ+c11fnδ+c12f2nδ+O((|en|+|fn|+|δ|)4),Υ(en,fn,δ)=d1enf2n+d2e3n+d3e2nfn+d4f3n+d5enfn+d6e2n+d7f2n+d8e2nδ+d9enfnδ+d10f2nδ+d11fnδ+d12enδ+O((|en|+|fn|+|δ|)4), |
c1=b(2+dk(−1+m))(3+dk(−1+m))dk(5+4dk(−1+m)), c2=−3b2(1+3dk(−1+m))4d2k2(5+4dk(−1+m)), |
c3=−b2(2+dk(−1+m))2(1+3dk(−1+m))d2k2(1+2dk(−1+m))2(5+4dk(−1+m)), |
c4=3b2(2+dk(−1+m))(1+3dk(−1+m))2d2k2(1+2dk(−1+m))(5+4dk(−1+m)), |
c5=b2 (1+2dk(−1+m))(1+3dk(−1+m))8d2k2(2+dk(−1+m))(5+4dk(−1+m)), c6=−3b(2+3dk(−1+m))2dk(5+4dk(−1+m)), |
c7=−b(1+2dk(−1+m))(−3−2dk(−1+m)+2d2k2(−1+m)2)4dk(2+dk(−1+m))(5+4dk(−1+m)), |
c8=−b e1−1dk−dkm(2+dk(−1+m))2d2k2(5+4dk(−1+m))(−1+m), c9=e1+1dk(−1+m)(2+dk(−1+m))2dk(5+4dk(−1+m))(−1+m), |
c10=be1−1dk−dkm(2+dk(−1+m))(1+2dk(−1+m))d2k2(5+4dk(−1+m))(−1+m), |
c11=−e1+1dk(−1+m)(2+dk(−1+m))(1+2dk(−1+m))2dk(5+4dk(−1+m))(−1+m), |
c12=−be1−1dk−dkm(1+2dk(−1+m))24d2k2(5+4dk(−1+m))(−1+m), |
d1=3b2(1+3dk(−1+m))4d2k2(5+4dk(−1+m)), d2=b2(2+dk(−1+m))2(1+3dk(−1+m))d2k2(1+2dk(−1+m))2(5+4dk(−1+m)), |
d3=−3b2 (2+dk(−1+m))(1+3dk(−1+m))2d2k2(1+2dk(−1+m))(5+4dk(−1+m)), |
d4=−b2(1+2dk(−1+m))(1+3dk(−1+m))8d2k2(2+dk(−1+m))(5+4dk(−1+m)), |
d5=3b(1+dk(−1+m)+d2k2(−1+m)2)dk(1+2dk(−1+m))(5+4dk(−1+m)), |
d6=−3b(2+dk(−1+m))(1+4dk(−1+m)+2d2k2(−1+m)2)dk(1+2dk(−1+m))(5+4dk(−1+m)), |
d7=3b(−1+4dk(−1+m)+8d2k2(−1+m)2+4d3k3(−1+m)3)4dk(2+dk(−1+m))(5+4dk(−1+m)), |
d8=b e1−1dk−dkm(2+dk(−1+m))2d2k2(5+4dk(−1+m))(−1+m), |
d9=−be1−1dk−dkm(2+dk(−1+m))(1+2dk(−1+m))d2k2(5+4dk(−1+m))(−1+m), |
d10=be1−1dk−dkm(1+2dk(−1+m))24d2k2(5+4dk(−1+m))(−1+m), d11=e1−1dk−dkm(1−d2k2(−1+m)2)dk(5+4dk(−1+m))(−1+m), |
d12=2e1−1dk−dkm(2+dk(−1+m))(−1+d2k2(−1+m)2)dk(1+2dk(−1+m))(5+4dk(−1+m))(−1+m). |
Next, we determine the center manifold denoted by QC for the system (3.4) at the origin, in a close neighborhood to δ=0. Using the center manifold theorem, we can derive the following approximate expression for the center manifold QC:
QC={(en,fn,δ)∈R3+|fn=p1e2n+p2enδ+p3δ2+O((|en|+|δ|)3)}, |
where
p1=d61−ξ, p2=−d121+ξ, p3=0, |
where ξ=3+3dk(−1+m)2+dk(−1+m). As a result, the system (3.4) is limited to QC in the manner as follows:
˜F:=en+1=−en+c1e2n+c9enδ+(c3−c6d6−1+ξ)e3n−(c11d121+ξ)enδ2+(c8−c11d6−1+ξ−c6d121+ξ)e2nδ+O((|en|+|δ|)4). | (3.5) |
For the function (3.5) to go through PD bifurcation, the following two quantities must possess nonzero values:
l1=˜Fδ˜Fenen+2˜Fenδ|(0,0)=2c9, | (3.6) |
l2=12(˜Fenen)2+13˜Fenenen|(0,0)=2(c3+c21+c6d61−ξ). | (3.7) |
Based on the aforementioned study, the following result is obtained:
Theorem 3.1. Assume that condition 4) of Theorem 2.5 is satisfied. The system (1.2) experiences PD bifurcation at E2 if l1,l2 given in (3.6) and (3.7) are nonzero and r changes in a close neighborhood of r=−3e−1−1dk(−1+m)(dk(1−m)−2+dk(1−m)). Moreover, if l2>0 (respectively l2<0), then a period-2 orbit of the system (1.2) emerges and it is stable (respectively, unstable).
The above result demonstrates how small changes may produce a significant change in the system's behavior, resulting in a doubling of population oscillation periods. This result discloses an important component of the predator-prey relationship, revealing a transition point in the ecosystem from orderly and predictable cycles to chaotic and unpredictable dynamics.
In this section, we investigate the NS bifurcation at E2 under condition (5) stated in Theorem 2.5. By introducing a minimal perturbation δ (|δ|⋘1) to the bifurcation parameter r in system (1.2), the resulting system is obtained:
{xn+1=(r+δ)xne1−xnk−b(1−m)xnyn,yn+1=d(1−m)xnyn. | (3.8) |
Assume that un=xn−1d(1−m), vn=yn−(r+δ)e1−1kd(1−m)−1b(1−m). After substituting the value of r=e−1−1dk(−1+m)(dk(1−m)−1+dk(1−m)), the system (3.8) is simplified to
[un+1vn+1]=[b11−bdd(−1+e1+1dk(−1+m)(1+dk(−1+m))δ)b+bdk(−1+m)1][unvn]+[F(un,vn)G(un,vn)], | (3.9) |
where
b11=e−1dk(1−m)(d2e1dk(1−m)k2(−1+m)2+eδ+dk(−1+m)(2e1dk(1−m)+eδ))dk(1+dk(−1+m))(−1+m), |
F(un,vn)=b(−1+m)unvn−e−1dk(1−m)(1+2dk(−1+m))(eδ+dk(−1+m)(e1dk(1−m)+eδ))2dk2(1+dk(−1+m))(−1+m)u2n+e−1dk(1−m)(1+3dk(−1+m))(eδ+dk(−1+m)(e1dk(1−m)+eδ))6dk3(1+dk(−1+m))(−1+m)u3n+O((|un|+|vn|)4),G(un,vn)=d(1−m)unvn, |
The characteristic equation of the linearized system (3.9) is
ξ2−α(δ)ξ+β(δ)=0, | (3.10) |
where
α(δ)=−e−1dk−dkm(−2d2e1dk−dkmk2(−1+m)2−eδ−dk(−1+m)(3e1dk−dkm+eδ))dk(1+dk(−1+m))(−1+m),β(δ)=1+e1+1dk(−1+m)(δ+δdk(−1+m)). |
The solutions of (3.10) are
ξ1,2=α(δ)2±i2√4β(δ)−α2(δ). | (3.11) |
Moreover, we obtain
(d|ξ1|dδ)δ=0=(d|ξ2|dδ)δ=0=12e1+1dk(−1+m)(dk(1−m)−1dk(1−m))>0. |
Additionally, it is required that ξi1,2≠1 when δ=0 for i=1,2,3,4, which corresponds to α(0)≠−2,2,0,1. We obtain
α(0)=3+2dk(−1+m)1+dk(−1+m)=2−1−1+dk(1−m)<2. |
Moreover, α(0)≠−2,0,1 is equivalent to
d≠54k(1−m),32k(1−m),2k(1−m). | (3.12) |
Next, to change (3.9) into normal form at δ=0, we use the following similarity transformation:
[unvn]=[−bd0−12+2dk(−1+m)−√−5+4dk(1−m)2+2dk(−1+m)][enfn]. | (3.13) |
Upon application of the mapping (3.13), the system (3.9) takes the following form:
[en+1fn+1]=[3+2dk(−1+m)2+2dk(−1+m)−√−5+4dk(1−m)2+2dk(−1+m)√−5+4dk(1−m)2+2dk(−1+m)3+2dk(−1+m)2+2dk(−1+m)][enfn]+[Γ(en,fn)Υ(en,fn)], | (3.14) |
where
Γ(en,fn)=b2dke2n+b2(1+3dk(−1+m))6d2k2(1+dk(−1+m))e3n−b√−5+4dk(1−m)(−1+m)2+2dk(−1+m)enfn+O((|en|+|fn|)4),Υ(en,fn)=b(−1+2dk(−1+m))2dk√−5+4dk(1−m)e2n−b2(1+3dk(−1+m))6d2k2√−5+4dk(1−m)(1+dk(−1+m))e3n+b(3+2dk(−1+m))(−1+m)2+2dk(−1+m)enfn+O((|en|+|fn|)4). |
Next, we need the following discriminatory value L to be not zero to make sure that system (1.2) undergoes NS bifurcation.
L=(−Re((1−2ξ1)ξ221−ξ1τ20τ11)−12|τ11|2−|τ02|2+Re(ξ2τ21))δ=0, | (3.15) |
where
τ20=18(Γee−Γff+2Υef+i(Υee−Υff−2Γef)), τ11=14(Γee+Γff+i(Υee+Υff)),τ02=18(Γee−Γff−2Υef+i(Υee−Υff+2Γef)),τ21=116(Γeee+Γeff+Υeef+Υfff+i(Υeee+Υeff−Γeef−Γfff)). |
Therefore, the result derived from the above analysis is as follows:
Theorem 3.2. Suppose that condition 5) of Theorem 2.5 is satisfied. If the condition (3.12) is satisfied and L given in (3.15) holds a nonzero value, then system (1.2) experiences NS bifurcation at E2 as long as r varies in a close neighbourhood of r=e−1−1dk(−1+m)(dk(1−m)−1+dk(1−m)). Furthermore, in instances where L is negative (alternatively, positive), the NS bifurcation encountered in system (1.2) at E2 is categorized as supercritical (subcritical), giving rise to the presence of a unique closed invariant curve originating from E2 that is attracting (repelling).
The above result illustrates that, under certain conditions, the predator-prey system experiences an NS bifurcation at point E2. This finding indicates a transition in the ecosystem from simple to more complex patterns, resulting in the presence of consistent, non-repeating cycles. Understanding the NS bifurcation enables ecologists to identify the start of enduring, nonlinear fluctuations in the ecosystem, hence facilitating the assessment of long-term population dynamics and ecological stability.
In a predator-prey model, real-world factors serve as control parameters, influencing population dynamics. Environmental changes, such as changes in vegetation or landscape, have an impact on both predators and prey by influencing shelter, food availability, and reproductive success. Introducing a competitor species influences both populations, whether it be new prey for the predator or a competing predator for the prey. Human activities such as hunting rules, conservation initiatives, and harvesting have a direct impact on population size and relationships. Climate elements, such as temperature and precipitation, operate as control variables, influencing birth, mortality, and migration patterns.
Control theory may be employed to control population dynamics in a predator-prey model. It is possible to avoid overpopulation and the extinction of species by maintaining a sustainable and balanced ecosystem by the adjustment of factors such as hunting limits or habitat protection. White-tailed deer populations in the US are managed by hunting limitations to minimize overpopulation and habitat destruction [82]. The Great Barrier Reef Marine Park Authority in Australia prioritizes coral ecosystem maintenance and habitat protection for marine biodiversity [83]. Community-based natural resource management in Namibia promotes sustainable activities like controlled hunting, benefitting wildlife and livelihoods [84]. These examples demonstrate how hunting limitations and habitat conservation affect ecological balance and biodiversity globally.
The objective of control theory is to create management plans that guarantee the populations of prey and predators will coexist in the long run. Bifurcations and unstable oscillations have historically been thought of negatively in mathematical biology since they harm the biological population's ability to reproduce. One can create a controller that may alter the bifurcation characteristics for some non-linear systems to obtain certain desired dynamical properties and manage chaos under the impact of PD and NS bifurcations. There are several strategies for chaos control in a discrete-time system. This section focuses on two different types of control strategies: state feedback control and hybrid control approaches. Both methods are effective in controlling bifurcation and chaos. The hybrid control method is easy to implement. The controlled system in the hybrid control method preserves the fixed points of the original system, while in feedback control, the controlled system may preserve only one fixed point at which we want to control bifurcation and chaos. There is only one control parameter ρ∈(0,1) in the hybrid control method, while there are two control parameters (κ1,κ2∈R) in the feedback control method.
The feedback control technique [85,86] involves transforming the chaotic system into a piecewise linear system to derive an optimal controller that reduces the upper limit. Subsequently, the optimization issue is performed subject to specified constraints. The aforementioned technique is employed to achieve stabilization of chaotic orbits located at an unstable fixed point inside the system (1.2). The controlled system under consideration for this purpose is as follows:
{xn+1=rxne1−xnk−b(1−m)xnyn−Un,yn+1=d(1−m)xnyn, | (4.1) |
where Un=κ1(xn−1d(1−m))+κ2(yn−re1−1kd(1−m)−1b(1−m)) is the feedback controlling force, κ1 and κ2 are feedback gains. Through simple calculations, it is obtained that for system (4.1), we have
J(E2)=[1−κ1+e1−1dk(1−m)rdk(−1+m)−b+dκ2dd(−1+e1+1dk(−1+m)r)b1]. | (4.2) |
The matrix J(E2) has the following characteristic equation:
ξ2+K1ξ+K0=0, | (4.3) |
where
K1=−2+κ1−e1+1dk(−1+m)rdk(−1+m),K0=−κ1+e1+1dk(−1+m)(1+dk(−1+m))rdk(−1+m)+dκ2(−1+e1+1dk(−1+m)r)b. |
Let ξ1 and ξ2 are the roots of (4.3), then we have
ξ1+ξ2=2−κ1+e1+1dk(−1+m)rdk(−1+m), | (4.4) |
ξ1ξ2=−κ1+e1+1dk(−1+m)(1+dk(−1+m))rdk(−1+m)+dκ2(−1+e1+1dk(−1+m)r)b. | (4.5) |
The marginal stability lines may be found by solving the systems of equations ξ1=±1 and ξ1ξ2=1. These conditions ensure that |ξ1,2|<1. Assume that ξ1ξ2=1, then Eq (4.5) implies that
L1:−κ1+(d(−1+e1+1dk(−1+m)r)b)κ2−1+e1+1dk(−1+m)(1+dk(−1+m))rdk(−1+m)=0. | (4.6) |
Next, we take ξ1=1 and utilizing Eqs (4.4) and (4.5), we obtain
L2:(d−de1+1dk(−1+m)rb)κ2+1−e1+1dk(−1+m)r=0. | (4.7) |
Next, we take ξ1=−1 and utilizing Eqs (4.4) and (4.5), we obtain
L3:−2κ1+(d(−1+e1+1dk(−1+m)r)b)κ2+3+e1+1dk(−1+m)(r−2rdk(1−m))=0. | (4.8) |
The stable eigenvalues are enclosed within the triangular region bounded by L1,L2, and L3.
The hybrid control technique [87] is a method that combines state feedback and parameter modification to stabilize unstable periodic orbits contained in the system's chaotic attractor. As a result, the regulated system retains its stability over a wide variety of parameters. We take the following controlled system:
{xn+1=ρ(rxne1−xnk−b(1−m)xnyn)+(1−ρ)xn,yn+1=ρd(1−m)xnyn+(1−ρ)yn, | (4.9) |
where ρ∈(0,1). The parameter ρ, acting like a control parameter, balances the impact of the original system (1.2) with the modified system (4.9). If the value of ρ becomes negative, it might indicate the reverse impact of the original system (1.2). Conversely, if ρ exceeds 1, it could indicate an amplified effect of the original system (1.2) beyond its natural influence, perhaps leading to unrealistic or unworkable consequences in the modified system (4.9). The same fixed points are shared by systems (4.9) and (1.2). We obtain
J(E2)=[−1+m+e1+1dk(−1+m)rρdk−1+m−bρdd(−1+e1+1dk(−1+m)r)ρb1], | (4.10) |
with corresponding characteristic polynomial
Λ(ξ)=ξ2+K1ξ+K0, | (4.11) |
where
K1=2−2m−e1+1dk(−1+m)rρdk−1+m,K0=1+e1+1dk(−1+m)rρdk(−1+m)+(−1+e1+1dk(−1+m)r)ρ2. |
Theorem 4.1. The fixed point E2 of the system (4.9) is LAS if
|K1|<1+K0<2. |
Remark 4.2. These control strategies aim to mitigate bifurcation and chaos in the system (1.2). The mathematical equations in systems (4.1) and (4.9) define parameters κ1,κ2 and ρ in the context of control techniques. It is important to note that these specific control methods may not have direct, established parallels in current ecological models or practices. Our approach introduces theoretical modifications, and we acknowledge the need for further research and practical applications within the field of mathematical ecology to fully validate these methods.
In this section, we will corroborate our theoretical findings for system (1.2) by numerical simulations. These numerical simulations will include bifurcation diagrams, phase portraits, time series plots, and maximum Lyapunov exponent (MLE) graphs. We have used MATHEMATICA for computations and MATLAB for graphs.
We assume that k=2.5,b=1.3,m=0.5,d=0.9,x0=2.25,y0=4.45,r∈[3.38,3.68], then, system (1.2) goes through PD bifurcation when r≈3.451523. The positive fixed point is obtained as E2=(2.222222,4.395604). The eigenvalues of J(E2) are ξ1=−1 and ξ2=−0.428571 with |ξ2|≠1. For these parametric values, we obtain
Γ(en,fn,δ)=23.1742e2n−1.39513e3n−55.4374enfn+5.08261e2nfn+33.1465f2n−6.17217enf2n+2.49844f3n+6.23407×10−15δ+31.3355enδ+2.72516e2nδ−38.053fnδ−6.6187enfnδ+4.01878f2nδ+O((|en|+|fn|+|δ|)4),Υ(en,fn,δ)=19.873e2n−1.21015e3n−47.5458enfn+4.4087e2nfn+28.4316f2n−5.35379enf2n+2.16716f3n+5.13358×10−15δ+26.6219enδ+2.36382e2nδ−32.3289fnδ−5.74111enfnδ+3.48592f2nδ+O((|en|+|fn|+|δ|)4). |
Thus, we obtain
l1=2c9=2×31.3355=62.671>0,l2=2(c3+c21+c6d61−ξ)=2(−1.39513+(23.1742)2+−55.4374×19.8731+0.428571)=−471.0941<0. |
The bifurcation diagrams of system (1.2) are given in Figure 1(a), (b), while the MLE is plotted in Figure 1(c).
Next, consider k=2.5,b=1.3,m=0.5,d=1.5,x0=1.3,y0=1.7 and varying r∈[1.1,2.1]. The system (1.2) goes through NS bifurcation at r≈1.343762 and has the positive fixed point E2=(1.333333,1.758242). The eigenvalues of J(E2) are ξ1,2=0.428571±0.903508i with |ξ1,2|=1. Moreover, some careful calculations give
τ20=−0.026310−0.016640i,τ11=0.086667+0.260361i,τ02=0.112976+0.277i,τ21=0.039702+0.02511. |
Thus, it is obtained that L=−0.078752<0, which proves the correctness of Theorem 3.2. Bifurcation diagrams are depicted in Figure 2(a), (b), while the MLE is plotted in Figure 2(c). The presence of negative MLEs indicates the presence of chaotic areas.
Next, Figure 3(a)–(h) shows phase portraits of system (1.2) for various various values of r. One can observe that E2 is LAS for r<1.343762 but loses stability at r≈1.343762 when the system (1.2) goes through NS bifurcation. For r≥1.343762, an invariant curve emerges from E2, the radius of which grows as r grows. Some 5−,10− periodic orbits are also plotted in Figure 3(d), (f), Finally, we obtain a strange chaotic attractor given in Figure 3(h).
We assume that r=3.5,k=2.5,b=1.3,d=0.9,x0=1.95,y0=4.50,m∈[0.42,0.52], then, system (1.2) experiences both NS bifurcation and PD bifurcation as m varies in small neighborhoods of m1≈0.424620 and m2≈0.495050, respectively. The bifurcation diagrams of system (1.2) are given in Figure 4(a), (b), while the MLE is plotted in Figure 4(c). The presence of negative MLEs indicates the existence of stable fixed points or stable periodic windows, whereas positive MLEs indicate the presence of chaotic areas. Furthermore, the phase portraits of system (1.2) are given in Figure 5(a)–(h) for various values of the parameter m. One can observe that system (1.2) experiences NS bifurcation for small values of refuge. At the NS bifurcation point, an invariant closed curve emerges, representing a repeating pattern in predator-prey populations. Moreover, the system experiences PD bifurcation for large values of refuge. At the PD bifurcation point, the system (1.2) transitions from stable behavior to periodic oscillations, and subsequently, the period of these oscillations doubles.
The existence of two critical values, m1 and m2, suggests a threshold behavior in the system (1.2). When m is less than m1, the positive fixed point E2 is unstable, implying that predator-prey interaction is too skewed in favor of the predators, and the prey population cannot sustain itself. Similarly, when m is greater than m2, then E2 is also unstable, indicating that too much refuge availability disrupts the predator-prey balance. This suggests that a moderate level of refuge is beneficial for both predator and prey populations.
The efficacy of the hybrid control approach will next be evaluated. We assume ρ=0.96,r=3.5,k=2.5,b=1.3,d=0.9,x0=1.95,y0=4.50 and vary m for the controlled system (4.9). If 0.405045<m<0.500983, the positive fixed point E2 is LAS. The controlled system's bifurcation diagrams, and Figure 6(a), (b) show that the bifurcation has been postponed in the controlled system (4.9).
Next, we aim to evaluate the efficacy of the feedback control technique. Considering r=3.5,k=2.5,b=1.3,d=0.9, and m=0.515, as well as the initial conditions x0=1.95 and y0=4.50 for the controlled system (4.1), the marginal stability lines are as follows:
L1:κ2=0.351067+0.514907κ1, |
L2:κ2=−1.444444, |
and
L1:κ2=0.086950+1.02981κ1. |
Figure 7(a) depicts the stability region bounded by lines L1,L2, and L3 for system (4.1). The fixed point E2 of system (1.2) is shown to be unstable for the given parametric values. The controlled system (4.1) is examined with feedback gains κ1=−2.95 and κ2=−1.20. Figure 7 illustrates the graph of xn as shown in Figure 7(c), yn as shown in Figure 7(d), and the phase portrait as presented in Figure 7(b) for the system (4.1). Therefore, it may be deduced that the use of the feedback control methodology seems to be effective in controlling bifurcation and chaos.
Trivial fixed point E0=(0,0) and boundary fixed point E1=(k(ln(r)+1),0) are independent of refuge effect m. The positive fixed point E2=(1d(1−m),re1−1kd(1−m)−1b(1−m)) is dependent on refuge effect m. We assume that r=3.5,k=2.5,b=1.3,d=0.9. Then, in Table 1, we observe that the density of prey population increases as refuges used by prey increases, while that of predators first increases and then decreases with prey refuges. The same is observed in Figure 8. When the number of prey using refuges is sufficiently large, our findings indicate that the prey population will exceed its maximum environmental carrying capacity, leading to the extinction of predators. Furthermore, the examination of stability and bifurcation demonstrates that an appropriate amount of refuge is advantageous for the populations of both predators and prey.
value of m | prey population in E2 | predator population in E2 |
0.1 | 1.23457 | 4.10791 |
0.3 | 1.5873 | 4.44198 |
0.5 | 2.22222 | 4.47895 |
0.7 | 3.7037 | 2.98086 |
0.9 | 11.1111 | −6.83286 |
The refuge effect plays a crucial role in determining the stability of predator-prey interactions in an ecosystem. In a predator-prey system, such a refuge could be a physical space, a habitat, or any resource that protects the prey. In this paper, we present and study the complex dynamics of a discrete-time predator-prey system with the refuge effect. The presence and stability of fixed points are investigated. Moreover, a thorough analysis of local bifurcations at the positive fixed point is conducted. The study illustrates that the system (1.2) goes through both PD and NS bifurcation. Moreover, the presence of a positive MLE guarantees the existence of chaos in the system (1.2). Feedback control and hybrid control approaches are used to control bifurcation and chaos. Consequently, effective control is achieved for both types of bifurcation across an extensive range of parameters. Furthermore, numerical simulations are executed to demonstrate the theoretical results that were previously presented. These simulations use several visual representations, including bifurcation diagrams, MLE graphs, phase portraits, and time series plots.
It is observed that a positive fixed point is stable if the refuge parameter m lies in an optimal range m1<m<m2. When m is less than m1, the positive fixed point E2 is unstable, implying that predator-prey interaction is too skewed in favor of the predators, and the prey population cannot sustain itself. Similarly, when m is greater than m2, then E2 is also unstable, indicating that too much refuge availability disrupts the predator-prey balance. This suggests that a moderate level of refuge is beneficial for both predator and prey populations.
The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.
The authors declare there is no conflict of interest.
This work was supported by Research Funding from Youjiang Medical University for Nationalities, Baise, China under Grant numbers yy2020bsky050 & yy2023rcky002, the National Natural Science Foundation of China under Grant number 62162063, and the Scientific Research and Technology Development Program of Guangxi, China under Grant number 2021AC19308. The funding bodies did not play any role in the design of the study and in writing the manuscript.
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value of m | prey population in E2 | predator population in E2 |
0.1 | 1.23457 | 4.10791 |
0.3 | 1.5873 | 4.44198 |
0.5 | 2.22222 | 4.47895 |
0.7 | 3.7037 | 2.98086 |
0.9 | 11.1111 | −6.83286 |