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Evaluating the effects of area closure for recreational fishing in a coral reef ecosystem: The benefits of an integrated economic and biophysical modeling

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  • Gao, Lei
  • Hailu, Atakelty

Abstract

This paper presents an integrated agent-based model of recreational fishing behavior within a reef ecosystem as a platform for the evaluation of recreational fishing management strategies. Angler behavior is described using econometrically estimated site choice models, with site choice among anglers driven by site attributes and angler characteristics. The biophysical model represents the marine reef environment as a system with different trophic levels identifying algal and coral growth as well as two types of fish (piscivores and herbivores). Ecosystem dynamics are driven by interactions within the trophic levels and interaction between fish populations and fishing activities. The model is used to simulate recreational fishing activities and their interactions with the environment. Recreational fishing sites from the Ningaloo Marine Park, an iconic coral reef system in Western Australia, are used as a case study. A set of management strategies, including "business-as-usual" and different site closure durations, are assessed for two different levels of fishing pressures. The results show that not only the effectiveness but also the distribution of management impacts across space and over time can be very different from what one would expect without the benefit of integrated modeling.

Suggested Citation

  • Gao, Lei & Hailu, Atakelty, 2011. "Evaluating the effects of area closure for recreational fishing in a coral reef ecosystem: The benefits of an integrated economic and biophysical modeling," Ecological Economics, Elsevier, vol. 70(10), pages 1735-1745, August.
    • Handle: RePEc:eee:ecolec:v:70:y:2011:i:10:p:1735-1745
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    References listed on IDEAS

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    1. Peter Schuhmann & Kurt Schwabe, 2004. "An Analysis of Congestion Measures and Heterogeneous Angler Preferences in a Random Utility Model of Recreational Fishing," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 27(4), pages 429-450, April.
    2. J. Doyne Farmer & Duncan Foley, 2009. "The economy needs agent-based modelling," Nature, Nature, vol. 460(7256), pages 685-686, August.
    3. McFadden, Daniel, 1974. "The measurement of urban travel demand," Journal of Public Economics, Elsevier, vol. 3(4), pages 303-328, November.
    4. Little, L. Richard & Punt, André E. & Mapstone, Bruce D. & Begg, Gavin A. & Goldman, Barry & Williams, Ashley J., 2009. "An agent-based model for simulating trading of multi-species fisheries quota," Ecological Modelling, Elsevier, vol. 220(23), pages 3404-3412.
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    Cited by:

    1. Carrella, Ernesto & Saul, Steven & Marshall, Kristin & Burgess, Matthew G. & Cabral, Reniel B. & Bailey, Richard M. & Dorsett, Chris & Drexler, Michael & Madsen, Jens Koed & Merkl, Andreas, 2020. "Simple Adaptive Rules Describe Fishing Behaviour Better than Perfect Rationality in the US West Coast Groundfish Fishery," Ecological Economics, Elsevier, vol. 169(C).
    2. Atakelty Hailu & Lei Gao, 2012. "Research Note: Recreational Trip Timing and Duration Prediction," Tourism Economics, , vol. 18(1), pages 243-251, February.
    3. Raguragavan, Jananee & Hailu, Atakelty & Burton, Michael, 2013. "Economic Valuation of Recreational Fishing in Western Australia: Statewide Random Utility Modelling of Fishing Site Choice Behaviour," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 57(4), pages 1-20.
    4. Marre, Jean-Baptiste & Thebaud, Olivier & Pascoe, Sean & Jennings, Sarah & Boncoeur, Jean & Coglan, Louisa, 2015. "The use of ecosystem services valuation in Australian coastal zone management," Marine Policy, Elsevier, vol. 56(C), pages 117-124.
    5. Md. Sayed Iftekhar & John G. Tisdell, 2016. "An Agent Based Analysis of Combinatorial Bidding for Spatially Targeted Multi-Objective Environmental Programs," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 64(4), pages 537-558, August.
    6. Gao, Lei & Hailu, Atakelty, 2013. "Identifying preferred management options: An integrated agent-based recreational fishing simulation model with an AHP-TOPSIS evaluation method," Ecological Modelling, Elsevier, vol. 249(C), pages 75-83.
    7. Baulcomb, Corinne & Fletcher, Ruth & Lewis, Amy & Akoglu, Ekin & Robinson, Leonie & von Almen, Amanda & Hussain, Salman & Glenk, Klaus, 2015. "A pathway to identifying and valuing cultural ecosystem services: An application to marine food webs," Ecosystem Services, Elsevier, vol. 11(C), pages 128-139.
    8. Thanassekos, Stéphane & Scheld, Andrew M., 2020. "Simulating the effects of environmental and market variability on fishing industry structure," Ecological Economics, Elsevier, vol. 174(C).
    9. Jegnie, Alemken & Hailu, Atakelty & Burton, Michael P., 2017. "Boat-based and other recreational fishing in Western Australia: Analysis of site choice, access values and bag limit effects," Working Papers 257167, University of Western Australia, School of Agricultural and Resource Economics.

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