IDEAS home Printed from https://ideas.repec.org/p/hhs/nhhfms/2007_029.html

Optimal Fishing Policy for Two Species in a Three-Species Predator-Prey Model The case of Capelin, Cod and Juvenile Herring in the Barents Sea

Author

Listed:
  • Aanestad, Sigurd

    (Dept. of Economics and Management, Norwegian College of Fishery Science, University of Tromsø)

  • Sandal, Leif K.

    (Dept. of Finance and Management Science, Norwegian School of Economics and Business Administration)

  • Eide, Arne

    (Dept. of Economics and Management, Norwegian College of Fishery Science, University of Tromsø)

Abstract

This paper presents a management model for the Barents Sea capelin and cod fisheries including juvenile herring in the biological model as the young herring influences the cod-capelin system. The objective of the study is to balance model-complexity of biology and economics when investigating possible optimal catch strategies given that one aims to maximize economic rent in the fishery. The three species constitute a highly dynamic system, also because prey-predation relations are functions of ages within each stock. A top-down approach is employed and the biological growth equations relate to stock biomass estimates. Economic relations are based on empirical data and previous studies. Optimal fishing strategies are identified by employing a numerical feedback rule for optimal fishing through dynamic programming. The feedback rule suggests that previous TAC (total allowable catch) levels on average have been too large for both capelin and cod over the past 30 years, according to the management objectives assumed in the study. Moreover, presence of some herring in the system is important for the economic yield although the herring fishery is closed. This indicates that a focus only on the capelin-predator role of herring is too narrow, as herring is also an important prey for cod.

Suggested Citation

  • Aanestad, Sigurd & Sandal, Leif K. & Eide, Arne, 2007. "Optimal Fishing Policy for Two Species in a Three-Species Predator-Prey Model The case of Capelin, Cod and Juvenile Herring in the Barents Sea," Discussion Papers 2007/29, Norwegian School of Economics, Department of Business and Management Science.
    • Handle: RePEc:hhs:nhhfms:2007_029
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/11250/163940
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Grune, Lars & Semmler, Willi, 2004. "Using dynamic programming with adaptive grid scheme for optimal control problems in economics," Journal of Economic Dynamics and Control, Elsevier, vol. 28(12), pages 2427-2456, December.
    2. C. Tara Marshall & Nathalia A. Yaragina & Yvan Lambert & Olav S. Kjesbu, 1999. "Total lipid energy as a proxy for total egg production by fish stocks," Nature, Nature, vol. 402(6759), pages 288-290, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Poudel, Diwakar & Sandal, Leif K. & Steinshamn, Stein I. & Kvamsdal, Sturla F., 2012. "Do Species Interactions and Stochasticity Matter to Optimal Management of Multispecies Fisheries?," Discussion Papers 2012/1, Norwegian School of Economics, Department of Business and Management Science.
    2. Poudel, Diwakar & Sandal, Leif K., 2014. "Stochastic Optimization for Multispecies Fisheries in the Barents Sea," Discussion Papers 2014/2, Norwegian School of Economics, Department of Business and Management Science.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andreas Lanz & Gregor Reich & Ole Wilms, 2022. "Adaptive grids for the estimation of dynamic models," Quantitative Marketing and Economics (QME), Springer, vol. 20(2), pages 179-238, June.
    2. Brumm, Johannes & Grill, Michael, 2014. "Computing equilibria in dynamic models with occasionally binding constraints," Journal of Economic Dynamics and Control, Elsevier, vol. 38(C), pages 142-160.
    3. Enrico Saltari & Willi Semmler & Giovanni Di Bartolomeo, 2022. "A Nash Equilibrium for Differential Games with Moving-Horizon Strategies," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 1041-1054, October.
    4. Giovanni, Domenico De & Lamantia, Fabio & Pezzino, Mario, 2019. "A behavioral model of evolutionary dynamics and optimal regulation of tax evasion," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 79-89.
    5. Lucidi, Francesco Simone & Semmler, Willi, 2023. "Long-run scarring effects of meltdowns in a small-scale nonlinear quadratic model," Journal of Macroeconomics, Elsevier, vol. 75(C).
    6. John Stachurski, 2009. "Economic Dynamics: Theory and Computation," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262012774, December.
    7. Grüne, Lars & Semmler, Willi & Stieler, Marleen, 2015. "Using nonlinear model predictive control for dynamic decision problems in economics," Journal of Economic Dynamics and Control, Elsevier, vol. 60(C), pages 112-133.
    8. David Brasington & Mika Kato & Willi Semmler, 2010. "Transitioning Out Of Poverty," Metroeconomica, Wiley Blackwell, vol. 61(1), pages 68-95, February.
    9. Stephanie Becker & Lars Grüne & Willi Semmler, 2007. "Comparing accuracy of second-order approximation and dynamic programming," Computational Economics, Springer;Society for Computational Economics, vol. 30(1), pages 65-91, August.
    10. Willi Semmler & Mika Kato, 2005. "Dominant Firms, Barriers to Entry Capital and Entry Dynamics," Computing in Economics and Finance 2005 194, Society for Computational Economics.
    11. Alexandre Jacquillat & Michael Lingzhi Li & Martin Ramé & Kai Wang, 2025. "Branch-and-Price for Prescriptive Contagion Analytics," Operations Research, INFORMS, vol. 73(3), pages 1558-1580, May.
    12. Andries Richter & Anne Maria Eikeset & Daan Soest & Florian Klaus Diekert & Nils Chr. Stenseth, 2018. "Optimal Management Under Institutional Constraints: Determining a Total Allowable Catch for Different Fleet Segments in the Northeast Arctic Cod Fishery," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 69(4), pages 811-835, April.
    13. Unami, Koichi & Mohawesh, Osama & Fadhil, Rasha M., 2019. "Time periodic optimal policy for operation of a water storage tank using the dynamic programming approach," Applied Mathematics and Computation, Elsevier, vol. 353(C), pages 418-431.
    14. F. O. O. Wagener, 2006. "Skiba Points for Small Discount Rates," Journal of Optimization Theory and Applications, Springer, vol. 128(2), pages 261-277, February.
    15. Domenico De Giovanni & Fabio Lamantia & Mario Pezzino, 2018. "Evolutionary Tax Evasion and Optimal Regulation," Economics Discussion Paper Series 1814, Economics, The University of Manchester.
    16. Moghayer, S. & Wagener, F.O.O., 2009. "Genesis of indifference thresholds and infinitely many indifference points in discrete time infinite horizon optimisation problems," CeNDEF Working Papers 09-14, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    17. Alfred Greiner & Willi Semmler & Tobias Mette, 2012. "An Economic Model of Oil Exploration and Extraction," Computational Economics, Springer;Society for Computational Economics, vol. 40(4), pages 387-399, December.
    18. Schleer, Frauke & Semmler, Willi, 2013. "Financial sector-output dynamics in the euro area: Non-linearities reconsidered," ZEW Discussion Papers 13-068, ZEW - Leibniz Centre for European Economic Research.
    19. Dawid, Herbert & Greiner, Alfred & Zou, Benteng, 2010. "Optimal foreign investment dynamics in the presence of technological spillovers," Journal of Economic Dynamics and Control, Elsevier, vol. 34(3), pages 296-313, March.
    20. Ernst, Ekkehard & Semmler, Willi, 2010. "Global dynamics in a model with search and matching in labor and capital markets," Journal of Economic Dynamics and Control, Elsevier, vol. 34(9), pages 1651-1679, September.

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hhs:nhhfms:2007_029. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Stein Fossen (email available below). General contact details of provider: https://edirc.repec.org/data/dfnhhno.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.