# Dynamic behaviors of a discrete-time predator–prey bioeconomic system

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## Abstract

Bifurcation and chaotic behavior of a discrete-time singular bioeconomic system are investigated. First, the traditional catch equation is modified after accounting for the handling time of the catch in a singular bioeconomic system. To discover the richer dynamics compared with the continuous form, the proposed system is considered difference scheme. Specially, the tangent space local parameterization condensed method for DAEs is generalized. The new local parameterization method is sufficiently general to be applicable to this type of discrete singular system. Also the dynamic behaviors of the system are investigated, by using normal form theory, center manifold theorem and bifurcation theory, it is shown that the system undergoes a Neimark–Sacker bifurcation and a flip bifurcation, on varying step-size in some range. In addition, numerical simulations are presented not only to illustrate our results with the theoretical analysis, but also to exhibit the complex dynamical behaviors.

## Keywords

Generalized local parameterization method Discrete singular system Bioeconomic system Nonlinear harvesting rate Stability Codimension-one bifurcation Chaotic behavior## MSC

37G10 39A28 39A30## 1 Introduction

*x*is the biomass at time

*t*.

*E*is the harvesting effort, in the fishery setting, which is typically specified as the number of (standard) vessels actively fishing at time

*t*. The units of harvesting effort would then be Standard Vessel Units (SVU).

*c*is the cost of effort, and

*p*is the ex-vessel price of fish. The parameter

*q*is a constant, called catchability, units of

*q*are \(\mathrm{SVU}^{-1}\times (\mathrm{time}\ \mathrm{unit})^{-1}\). Thus,

*q*represents the proportion of the current stock

*x*caught by one standard vessel in one time unit (to normalize units, one could let \(q=1\)). To be specific we will now use time units of days (rather than years). Thus \(G(x)\) now represents the population growth per day and

*h*is the daily harvesting rate, both being measured in tonnes per day.

*x*and

*y*denote the prey and predator populations at time

*t*, respectively. In recent years, to analyze this model from an economic point of view, many researchers have studied a class of bioeconomic systems which combine system (1.3) with prey (or predator) harvesting and (1.1), and obtained some excellent results, such as the stability of equilibria, limit cycle, Hopf bifurcation, saddle-node bifurcation, singular induced bifurcation (see [4, 5, 6, 8, 9, 11, 12]). It is given by

*E*is the effort applied to harvesting the prey at time

*t*. The extended model is a system of differential-algebraic equations (DAEs) with differential variables

*x*,

*y*and algebraic variable

*E*. In these literatures, \(h(x,E)\) is traditionally taken as \(qEx\). However, the Schaefer catch–effort relation has some flaws as Ref. [13] points out:

- (i)
All processes affecting stock productivity (e.g., growth, mortality and recruitment) are subsumed in the effective relationship between effort and catch. This hypothesis has been proved to be not reasonable.

- (ii)
The catchability coefficient

*q*is not always constant. Improvements in technology and fishing power make*q*often vary through time. - (iii)
The harvesting function did not account for the handling time of the catch and the competition between standard vessels which are utilized for harvesting of resource. On the contrary, evidence for a nonlinear harvest function is quite strong for a number of stocks [14].

In economics there is a tradition of formulating the dynamical systems as processes in discrete time from the outset, as difference equations instead of as differential equations. The cobweb model, the Cournot duopoly model, and the Samuelson–Hicks business cycle model are all examples of this tradition. On the other hand, in population ecology many authors are more focused on discrete-time models for the following reasons: one is that, for nonoverlapping generations, births occur in regular, well-defined “breeding seasons”. This contradicts the assumption that births occur continuously [15]. The other is that the discrete-time models can also govern efficient computing models for continuous ones in order to achieve the numerical emulation. However, as far as we know, there are few studies of the difference scheme of system (1.4) which represents a class of discrete singular systems and no one has considered the general method to be applied to deal with it by now.

The main objective of this paper is to investigate the dynamic behaviors of a discrete-time economic predator–prey model. The model, featuring a more realistic nonlinear harvesting function, will give rise to some interesting dynamic behaviors, as we see in Sect. 4. The paper is organized as follows. We develop a local parameterization method based on the tangent space local parameterization of DAEs in [16] and use it to deal with the aforementioned discrete singular system in the next section. In Sect. 3, we analyze the stability of the fixed point. In addition, we also classify the fixed point based on the geometric properties of trajectories distribution near it. Surprisingly, little study has been done on this aspect for discrete systems. Section 4 is devoted to the main results of this paper, the proposed system is investigated as it undergoes a Neimark–Sacker bifurcation and a flip bifurcation. Section 5 contains a numerical verification of some key results. Conclusion and discussion are presented in Sect. 6.

## 2 Statement of model and local parameterization

*m*being related to the handling or processing time [17, 19]. Though this nonlinear harvesting function is more realistic, not many researchers have used it in their studies. For \(m=0\) it reduces to the Schaefer catch–effort equation (1.2).

*δ*:

*M*) in \(R^{3}\) [16] whose number of independent coordinates is 2.

*Ė*is described through a derivation of the algebraic equation along the solution path with respect to time

*t*and it is not necessary for the concrete formula as we will see later. Accordingly, (2.2) becomes the following difference algebraic equations of the constrained form:

*M*in \(R^{3}\) as follows:

*Ỹ*might be locally represented by

*Y*. Indeed,

*D*is a derivation operator. Hence, there exists a smooth map: \(h: R^{2}\rightarrow R\) guaranteed by the implicit function theorem such that \(\widetilde{Y}=h(Y)\) locally holds. Putting it in (2.8) gives a local parameterization of the solution manifold with parameter

*Y*as follows:

*ψ*is related to

*h*.

*Y*.

*Ė*of the equation of (2.4). Hence using (2.10) and (2.11), we obtain the following parameterization system:

## Remark 2.2

Since the form of (2.6) or (2.7) has generality, the new parameterization method is also generic (\(X_{n}\) can be of arbitrary finite dimensions). Specifically, if we take the tangent space and normal vector space of solution manifold at \(X_{0}\) as \(U_{0}\) and \(V_{0}\), respectively, then the local parameterization (2.9) reduces to the tangent space local parameterization condensed method for DAEs in [16].

## 3 Fixed points classification and stability analysis

Our goal in this section is to examine the local stability of (2.3) based upon the standard linearization technique.

*y*and multiplying both sides by \(U_{0}^{T}\) gives

*h*, so we can give the concrete form of (2.12) by a Taylor formula.

## Definition 3.1

- (i)
a hyperbolic fixed point, if the moduli of all eigenvalues do not equal 1; and

- (ii)
a nonhyperbolic fixed point, if \(|\lambda_{1}|=1\) or \(|\lambda_{2}|=1\).

## Definition 3.2

- (i)
a sink, if \(|\lambda_{1}|<1\) and \(|\lambda_{2}|<1\);

- (ii)
a source, if \(|\lambda_{1}|>1\) and \(|\lambda_{2}|>1\); and

- (iii)
a saddle, if \(\lambda_{1,2}\) are real, with \(|\lambda_{1}|<1\) and \(|\lambda_{2}|>1\) (or \(|\lambda_{1}|>1\) and \(|\lambda_{2}|<1\)).

Moreover, we can classify fixed points based on the geometric properties of trajectory distribution near them.

## Definition 3.3

- (i)
a saddle, if \(\lambda_{1,2}\) are real, with \(|\lambda_{1}|<1\) and \(|\lambda_{2}|>1\) (or \(|\lambda_{1}|>1\) and \(|\lambda_{2}|<1\));

- (ii)
a node, if \(\lambda_{1,2}\) are real, with \(|\lambda_{1,2}|<1\) (or \(|\lambda_{1,2}|>1\));

- (iii)
a focus (sometimes called spiral point), if \(\lambda _{1,2}=r(\cos\theta\pm i \sin\theta)\), \(r\neq1\); and

- (iv)
a center, if \(\lambda_{1,2}=\cos\theta\pm i \sin\theta\).

Thus, we have the following lemma from the relations between roots and coefficients of the quadratic equation [21].

## Lemma 3.1

*Suppose that*\(F(1)>0\),

*then we have*

- (i)
\(|\lambda_{1}|<1\)

*and*\(|\lambda_{2}|<1\)*iff*\(F(-1)>0\)*and*\(C<1\); - (ii)
\(|\lambda_{1}|>1\)

*and*\(|\lambda_{2}|>1\)*iff*\(F(-1)>0\)*and*\(C>1\); - (iii)
\(|\lambda_{1}|<1\)

*and*\(|\lambda_{2}|>1\) (*or*\(|\lambda_{1}|>1\)*and*\(|\lambda_{2}|<1\))*iff*\(F(-1)<0\); - (iv)
\(\lambda_{1}\)

*and*\(\lambda_{2}\)*are complex and*\(|\lambda _{1}|=|\lambda_{2}|=1\)*iff*\(B^{2}-4C<0\)*and*\(C=1\);*and* - (v)
\(\lambda_{1}=-1\)

*and*\(|\lambda_{2}|\neq1\)*iff*\(F(-1)=0\)*and*\(B\neq0,2\).

Using Lemma 3.1, we obtain the following results.

## Theorem 3.1

*Assuming parameters*\((a,k,s,p,m,c,v)\in\pi\),

*system*(2.3)

*has a unique positive fixed point*\(X_{0}\)

*and*

- (i)
*it is a sink iff*$$4+2\delta\Lambda_{1}-2\delta\Lambda_{2}+ \delta^{2}\Lambda_{3}>0 \quad \textit{and}\quad \Lambda _{1}-\Lambda_{2}+\delta\Lambda_{3}< 0; $$ - (ii)
*it is a source iff*$$4+2\delta\Lambda_{1}-2\delta\Lambda_{2}+ \delta^{2}\Lambda_{3}>0\quad \textit{and} \quad \Lambda _{1}-\Lambda_{2}+\delta\Lambda_{3}>0; $$ - (iii)
*it is a saddle iff*$$4+2\delta\Lambda_{1}-2\delta\Lambda_{2}+ \delta^{2}\Lambda_{3}< 0;\quad \textit{and} $$ - (iv)
*it is nonhyperbolic if one of the following conditions is satisfied*:- (iv1)
\(\delta\Lambda_{3}=\Lambda_{2}-\Lambda_{1}\)

*and*\((\Lambda _{2}-\Lambda_{1})^{2}-4\Lambda_{3}<0\); - (iv2)
\(4+2\delta\Lambda_{1}-2\delta\Lambda_{2}+\delta^{2}\Lambda _{3}=0\)

*and*\(\delta\Lambda_{2}-\delta\Lambda_{1}\neq2,4\).

- (iv1)

## Theorem 3.2

*With the assumption of Theorem*3.1, \(X_{0}\)

*is*

- (i)
*a stable node iff*$$\delta(\Lambda_{2}-\Lambda_{1})< 2 \quad \textit{and}\quad 1+\delta\Lambda_{1}-\delta\Lambda _{2}+\delta^{2} \Lambda_{3}>0; $$ - (ii)
*an unstable node iff*$$\Lambda_{2}-\Lambda_{1}< 0; $$ - (iii)
*a stable focus iff*$$(\Lambda_{2}-\Lambda_{1})^{2}-4 \Lambda_{3}< 0 \quad \textit{and}\quad \delta\Lambda_{3}< \Lambda _{2}-\Lambda_{1}; $$ - (iv)
*an unstable focus iff*$$(\Lambda_{2}-\Lambda_{1})^{2}-4 \Lambda_{3}< 0 \quad \textit{and}\quad \delta\Lambda_{3}> \Lambda _{2}-\Lambda_{1}, $$*where*$$\begin{aligned}& \Lambda_{1}=\frac{psv}{((p-cm)s-c)^{2}(1+ms)}, \\& \Lambda_{2}=s \biggl(k-\frac{vm}{((p-cm)s-c)(1+ms)} \biggr), \\& \Lambda_{3}=s \biggl(a-ks-\frac{v}{(p-cm)s-c} \biggr). \end{aligned}$$

## 4 Bifurcation analysis

Based on the analysis of Sect. 3, we discuss a codimension-one bifurcation (Neimark–Sacker bifurcation and flip bifurcation) of system (2.3) at the fixed point.

### 4.1 Neimark–Sacker bifurcation and invariant curve

We choose *δ* as a bifurcation parameter to discuss the Neimark–Sacker bifurcation when *δ* varies in a small neighborhood of \(\delta_{1}\). If we consider \(\delta^{*}=\delta-\delta_{1}\), then \(\delta=\delta_{1}\) is equivalent to \(\delta^{*}=0\).

*λ*and

*λ̅*with \(|\lambda|=|\overline{\lambda}|=1\), where

## Theorem 4.1

*Assume that*\((a,k,s,p,m,c,v,\delta)\in\Omega _{1}\)*and the condition* (4.1) *holds*. *If*\(l\neq0\), *then the system* (2.3) *undergoes a Neimark–Sacker bifurcation at the fixed point*\((x_{0},y_{0},E_{0})\)*when the bifurcation parameter**δ**varies in a small neighborhood of*\(\delta_{1}\). *Moreover*, *if*\(l<0\) (*resp*. \(l>0\)), *then an attracting* (*resp*. *repelling*) *invariant closed curve bifurcates from the fixed point for*\(\delta>\delta_{1}\) (*resp*. \(\delta<\delta_{1}\)).

### 4.2 Flip bifurcation and chaos

*δ*as a bifurcation parameter to discuss the flip bifurcation when

*δ*varies in a small neighborhood of \(\delta _{2}\). In the same way as above, we consider \(\delta^{*}=\delta-\delta_{2}\), then \(\delta=\delta_{2}\) is equivalent to \(\delta^{*}=0\). Since we would like to study the system as we vary \(\delta^{*}\) near 0, we treat \(\delta ^{*}\) as a variable (which has no dependence on time) and hence the parameter-free center manifold theorem can easily be extended for a parameter-dependent system.

## Theorem 4.2

*Assuming that*\((a,k,s,p,m,c,v,\delta)\in\Omega_{2}\). *If*\(\tilde{\alpha}_{2}\neq0\), *then the system* (2.3) *undergoes a flip bifurcation at the fixed point*\((x_{0},y_{0},E_{0})\)*when the bifurcation parameter**δ**varies in a small neighborhood of*\(\delta _{2}\). *Moreover*, *if*\(\tilde{\alpha}_{2}>0\) (*resp*., \(\tilde{\alpha}_{2}<0\)), *then the period*-2 *orbits that bifurcate from*\((x_{0},y_{0},E_{0})\)*are stable* (*resp*., *unstable*).

## 5 Numerical simulations

In this section, we give two examples to illustrate our theoretical analysis.

## Example 5.1

(Neimark–Sacker bifurcation)

Here we present a numerical analysis of the proposed system (2.3) with the following artificially chosen data: \(a=4\), \(k=1\), \(s=2\), \(p=1\), \(m=0.1\), \(c=1\) and \(v=0.6\) with \((a,k,s,p,m,c,v)\in\pi\). It is easy to verify that the system (2.3) has a unique fixed point \((2,1.25,0.9)\) and \(\delta_{1}=0.125\).

The multipliers of the positive fixed point are \(\lambda_{\pm}\approx 0.9805\pm0.1967i\) with \(|\lambda_{\pm}|=1\) and \(\alpha=0.1563>0\). Since \(H=0.0391\neq2,3,4\), the condition (4.1) holds. Then, using Theorem 4.1, the system (2.3) undergoes a Neimark–Sacker bifurcation at the fixed point \((2,1.25,0.9)\) with \(l=0.0017>0\).

## Example 5.2

(Flip bifurcation)

## 6 Conclusion

The present paper is concerned with the dynamics of a discrete-time economic predator–prey system in the presence of a type of nonlinear harvesting function. We find the fixed point and its stability. Most interestingly, we have seen that our results reveal a far richer dynamics of the discrete model compared with the continuous one proposed in [9], including an invariant circle, cascades of period-doubling bifurcation and chaotic sets. We confirm the complexity of the dynamic behavior by computing the largest Lyapunov exponents. This paper extends our previous works (see [8, 9]) and provides a sufficiently general parameterization method for a wide range of discrete singular systems.

However, in the presented harvesting function, we have no concern about the effects of competition between boats which will increase the complexity of the normal form and the amount of calculation. Moreover, for discrete-time systems, Marotto’s theorem [23, 24] is a sufficient criterion for the existence of chaos. If the fixed point of system (2.3) is a snap-back repeller under certain parameter conditions, then one can conclude that the system is chaotic in the Marotto sense. Furthermore, various feedback controls can be implemented for controlling the bifurcation and chaos in the system (2.3), which should be useful for fishery management control and biological conversion. Space prevents that discussion here, but these issues will be the topics of our future research.

## Notes

### Acknowledgements

The authors wish to express their gratitude to the editors and reviewers for the helpful comments. This research is supported by National Natural Science Foundation of China under grant No. 71271158.

### Authors’ contributions

The study presented here was carried out in collaboration between all authors. All authors read and approved the final manuscript.

## Competing interests

The authors declare that they have no competing interests.

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