Medical, Pharma, Engineering, Science, Technology and Business

**Mohammad Ali Khan ^{1*}, Joydev Ghosh^{2} and Banshidhar Sahoo^{2}**

^{1}Department of Mathematics, Ramananda College, Bishnupur, Bankura, West Bengal, India

^{2}Department of Applied Mathematics, University of Calcutta, Kolkata, West Bengal, India

- Corresponding Author:
- Mohammad Ali Khan

Department of Mathematics

Ramananda College, Bishnupur

Bankura, West Bengal, India

**Tel:**03244-252059

**E-mail:**[email protected]

**Received date:** June 22, 2015; **Accepted date:** August 19, 2015; **Published date:** August 26, 2015

**Citation:** Khan MA, Ghosh J, Sahoo B (2015) Controlling Chaos in a Food Chain Model through Threshold Harvesting. Fish Aquac J 6:142. doi:10.4172/2150-3508.1000142

**Copyright:** © 2015 Sogbesan OA, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

**Visit for more related articles at** Fisheries and Aquaculture Journal

In this paper, we propose a new harvesting strategy namely the harvesting for controlling chaotic population in a food chain model. In particular, we have taken the three species Hastings and Powell food chain model for demonstration. We have shown threshold harvesting strategy can be effectively employed to obtain a steady or cyclic behaviour from chaotic fish population by varying either the frequency of harvesting or the amount of harvesting of fish population. Numerical simulation results are presented to show the effectiveness of the scheme. We obtain steady state; limit cycle, period-2 and period-4 behaviour from chaotic Hastings and Powell model. This threshold harvesting strategy will be very useful for species conservation and fishery management

Chaos; Chaos control; Threshold mechanism; Hastings and Powell model; Maximum sustainable yield

An extremely common phenomenon in nonlinear dynamical systems arising from a variety of disciplines is chaos. Chaotic dynamics is interesting to analyse but for technological processes like optimization of the production in a production farm chaos is highly unwanted or even harmful. Therefore, strategies are required to devise control algorithms capable of achieving the desired type of behaviour from a chaotic system. There has been many techniques for designing effective control of chaotic systems but very few of these methods are applicable to control a biological systems like food chain models [1-4]. Many authors proposed harvesting model for ecological systems [5-8]. But none of these are very useful from applied point of view because harvesting at every time is not realistic and meaningful for many biological systems. Those methods also required knowledge of functional response and knowledge of system parameters.

Now, we shall discuss a new harvesting mechanism. Consider a general N-dimensional food chain model

Where X=(x_{1}, x_{2},…….., x_{N}) are the state variables. Let the variable xi is chosen for harvesting. The threshold harvesting strategy is as follows. We shall check the population size represented by the state variable x_{i} at regular interval of time. At the time of checking if the population of variable xiexceeds a critical population x_{i}^{*}, then harvesting will be done and collect (x_{i}-x_{i}^{*}) number of fishes otherwise do not collect any fish. This assumption is natural because in fishery because harvesting of fish takes place at regular interval of time. Hastings and Powell [1] introduced a continuous time model of a food chain incorporating nonlinear functional responses and shown that model exhibits chaotic dynamics in long term behaviour when biologically reasonable parameter values are chosen. Wilson et al. [4] had obtained chaotic dynamics in a multi-species fishery. Managing such a chaotic fishery system, demands different approach for controlling **chaos**. Chattopadhyay et al. [5] interpret the population of first, second and third species of the Hastings and Powell [1] model as the population of the toxin producing phytoplankton (TPP), **zooplankton** and fish respectively. Then according to Hastings and Powell [1], the fish population will vary chaotically for some biologically significant parameter region. In this work, we have shown that threshold harvesting strategy can be applied for controlling chaotic fish population and to obtain regular fish population dynamics e.g., steady state, limit cycle, period-2, period-4 etc. Here the thresholding variable is chosen as the fish population variable of the system. In section-2, the threshold harvesting mechanism is discussed. In section-3, application of threshold harvesting for controlling chaotic dynamics of fish population is demonstrated for chaotic Hastings and Powell [1] model. In section-4, numerical simulation results are discussed. Finally a conclusion is drawn in section-5.

Consider a general N dimensional dynamical system, described by the following evolution equations

Where X=(*x _{1}, x_{2}, x_{3},……, x_{N}*)are the state variables. Let the variable x

if then no harvesting

if *x _{i }> x^{*}* then

The dynamics continues undisturbed until xi exceeding the **threshold value**. When it exceeds then control resets its value to x^{*} again. As the system parameters are left invariant by this method therefore it acts only on state variable. In fact the method requires no knowledge of the parameters, which is advantageous for controlling chaos in biological systems. The moment thresholding is removed the system is back to its original dynamics. The threshold action is necessarily stroboscopic, as the threshold condition can be checked only at finite intervals. Here we will study the interesting effects of implementing the threshold action at varying intervals. We will show that changing the frequency of thresholding leads to many different regular temporal patterns. In fact very infrequent thresholding is capable of yielding amazingly simple and regular behaviour of a chaotic system.

In this section, we shall first discuss briefly the Hastings and Powell [1] three species **food chain** model. Hastings and Powell [1] assume X as the number of species at the lowest level of the food chain, Y the number of species that preys upon X, and Z the number of the species that preys upon Y. The model takes the form

With

representing the functional response. Here T is time. The constant R_{0} is the ‘intrinsic growth rate’ and the constant K_{0} is the ‘carrying capacity’ of species X. The constants C_{1}^{-1} and C_{2} are conversion rates of prey to predator for species Y and Z respectively, D_{1} and D_{2} are constant death rates for species Y and Z respectively. The constants A_{i} and B_{i} for i = 1, 2 parametrize the saturating functional response, Bi is the prey population level where the predation rate per unit prey is half its maximum value. With the following dimensionless variables

the model takes the form

where d_{2}=d′_{2}+ h. where h represents the rate of **harvesting**. Chattopadhyay et al. [5] interpret the variables x, y, z as the toxin producing plankton (TPP), zooplankton and fish population respectively. They have interpreted the parameters a_{1}, a_{2}, b_{1}, b_{2}, d_{1} and d′_{2} as intrinsic birth rate of prey, intrinsic death rate of predator population and h is the rate of harvesting. In this work we interpret the variables x, y, z as x as TPP, y as Zooplankton and z as **fish population**.

We apply threshold harvesting technique on Hastings and Powell model. We impose threshold condition on z variable here. Because in fish population model it is possible to threshold fish population by harvesting fish at a regular interval. In the Hastings and Powell model we interpret the z variable as fish population. The parameters of the model are chosen a_{1}=5.0, a_{2}=0.1, b_{1}=2.8, b_{2}=2.0, d_{1}=0.4, d′_{2}=0.01. For these set of parameter values the model have chaotic behaviour. We use Runge Kutta 4^{th} order scheme for solving the system with time step 0.005. Since thresholding, the variable z is biologically meaningful; we choose different threshold values for z together with different time interval of control. We start with choosing threshold value (MSY effort)h=h_{MSY} =0.5 with control acts at interval δt=0.10, and obtain the time evolution of Hastings and Powell model is shown in **figure 1**. And phase diagram in y-z plane is depicted in **figure 2**. From **figures 1 and 2** it is clear that Hastings and Powell model is chaotic for the threshold value (MSY effort) h=h_{MSY}=0.5 with control acts at interval δt=0.10. Therefore MSY is not a stable for the effort h=h_{MSY}=0.5. The chaotic Hastings and Powell model under threshold control of variable z, with threshold value (MSY effort) h=h_{MSY}=0.13, the control acts at interval δt=0.275, the controlled steady state behaviour which is shown in **figure 3**. From **figure 3**, it is clear that chaotic behaviour of fish population is controlled and steady state dynamics is obtained under the threshold harvesting. In this case MSY is stable steady state behaviour for the effort h=h_{MSY}=0.13. The time evolution of chaotic Hastings and Powell model under threshold control of variable z, with threshold value (MSY effort) h=h_{MSY}=0.175, the control acts at interval δt=0.15 is shown in **figure 4**, From **figure 4** it is clear that chaotic MSY is replaced by limit cycle MSY under the threshold control. The same chaotic model under threshold control of variable z, with threshold value (MSY effort) h=h_{MSY}=0.18, the control acts at an interval δt=0.075, obtain period-2 behaviour of the threshold control system which is shown in **figure 5**. The model under threshold control of variable z, with threshold value (MSY effort) h=h_{MSY}=0.2, the control acts at an interval δt =0.095, obtain period-4 behaviour of the threshold control system which is shown in **figure 5**. Therefore by different choice of MSY effort we can obtain any periodic behaviour. We have employed successfully the threshold mechanism to Hastings and Powell model **figure 6** and obtain steady state limit cycle, period-2 and period-4 behaviour of the system.

We define the threshold harvesting as the catching of fish after an interval of time provided fish population is above some critical value. The knowledge of functional response is required for applying almost all existing chaos control methods. However, in threshold harvesting strategy no knowledge of system parameters and functional responses are required. Therefore this method is suitable for controlling chaos of many biological or real world systems. A hyper chaotic food chain can also be controlled to obtain steady state or low periodic behaviour choosing suitable threshold value. The threshold harvesting method of chaos control is suitable for any controlling chaotic dynamics of a particular state variable of any biological model. Therefore by choice of threshold value (MSY effort) we may harvests the fish population to MSY. This control strategy is really very useful for fishery management of marine ecological systems. This method is also applicable for biological conservation of **species** in real world **biological systems**.

- Hastings A, Powell T (1991) Chaos in a three species food chain. Ecology 72: 896-903.
- Murali K, Sinha S (2003) Experimental realization of chaos control by thresholding. Physical Review E 68: 016210.
- Sinha S (2001) Using thresholding at varying intervals to obtain different temporal patterns. Physical Review E 63: 036212.
- Wilson, French JA, Kleban J, Mckay P, Townsend SR(1991) Chaotic dynamics in a multiple species fishery: A model of community predation. Ecological Modelling 58: 303-322.
- Chattopadhyay J, Sarkar RR (2003) Chaos to order: preliminary experiments with a population dynamics models of three trophic levels. Ecological Modelling 163:45-50.
- Sahoo B, Poria S (2014) Diseased Prey predator model with general Holling type-interactions. Applied Mathematics and Computation 226: 83-100.
- Bhattacharya R, Mukhopadhyay B (2010) On an eco-epidemiological model with prey harvesting and predator switching-Local and global perspectives. Nonlinear Analysis: Real world Application 11: 3824-3833.
- Chakraborty K, Jana S, Kar TK (2012) Global dynamics and bifurcation in a stage structured Prey-predator fishery model with harvesting. Applied Mathematics and Computation 218: 9271-9290

Select your language of interest to view the total content in your interested language

- Acoustic Survey
- Animal Husbandry
- Animal Viral diseases
- Animal-Welfare
- Aquaculture
- Aquaculture Developement
- Aquaculture Nutrition
- Aquaculture Production
- Aquaculture Supplies
- Aquaculture Systems
- Aquaculture Tanks
- Aquaponics
- Aquatic Physiology
- Beach Evolution
- Beach Nourishment
- Beach Placers
- Benefits of Aquaculture
- Best Aquaculture Practices
- Bioacoustics
- Biological Diversity
- Biological Sciences
- Biosciences
- Captive Fishing
- Coastal Anthropogenic Pressure
- Coastal Aquifer
- Coastal Bar/ Coastal Spit
- Coastal Deposition
- Coastal Development
- Coastal Dunes
- Coastal Engineering
- Coastal Erosion
- Coastal Geomorphology
- Coastal Irrigation
- Coastal Lagoon
- Coastal Mining
- Coastal Plain
- Coastal Ports
- Coastal Region/ Coastal Area
- Coastal Studies
- Coastal Upwelling
- Coastal Waters
- Coastal Wetlands
- Cryobiology
- Dropline
- Dynamical Oceanography
- Environmental Oceanography
- Experimental Oceanography
- Fish Feed Technologies
- Fish Pathology
- Fish Production
- Fisheries
- Fisheries Management
- Fisheries Oceanography
- Fisheries Sciences
- Fisheries-Research
- Fishery
- Fishing Technology
- Fishing Vessel
- Gillnet
- Global Aquaculture Alliance
- Home Aquaculture
- Hybrid poultry
- Hydrologic
- Integrated Coastal Zone Management
- Jigging
- Life Sciences
- Limnology and Oceanography
- Livestock Nutrition
- Livestock Production
- Marine
- Marine Biology
- Marine Biotechnology
- Marine Chemistry
- Marine Ecosystem
- Marine Fish
- Marine Food
- Marine Isotope Stage
- Marine Sediment
- Marine Spatial Planning
- Marine and Coastal Biodiversity
- Marine-Engineering
- Maritime Policy
- Meridional Overturning Circulation
- Microbial Oceanography
- Natural Resource Management
- Oceanography
- Oceanography and Marine Biology
- Operational Oceanography
- Oyster Aquaculture
- Pelagic Fish
- Physical Oceanography
- Poultry
- Poultry Diseases
- Poultry Management and Processing
- Poultry Physiology and Diseases
- Poultry Vaccine
- Poultry-Farming
- Recirculating Aquaculture Systems
- Salmon Aquaculture
- Sea Surface Salinity
- Shallow Water Equation
- Shrimp Aquaculture
- Stratified Flow
- Sustainable Aquaculture
- Sustainable Fishing
- Sustainable fishery
- Tilapia Aquaculture
- Trawling
- Tropical Aquaculture and Fisheries
- Veterinary Medicine
- Wild-life overkill
- Wildlife Conservation
- Wildlife Ecology
- Wildlife Research
- Wildlife-Demography
- Wildlife-Diseases
- Zoonotic Disease

- Total views:
**11896** - [From(publication date):

December-2015 - Dec 14, 2017] - Breakdown by view type
- HTML page views :
**7953** - PDF downloads :
**3943**

Peer Reviewed Journals

International Conferences
2017-18