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The effect of spatial heterogeneity and mobility on the performance of social–ecological systems

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  • Pérez, Irene
  • Janssen, Marco A.

Abstract

We use an agent-based model to analyze the effects of spatial heterogeneity and agents’ mobility on social–ecological outcomes. Our model is a stylized representation of a dynamic population of agents moving and harvesting a renewable resource. Cooperators (agents who harvest an amount close to the maximum sustainable yield) and selfish agents (those who harvest an amount greater than the sustainable yield) are simulated in the model. Three indicators of the outcomes of the system are analyzed: the number of settlements, the resource level, and the proportion of cooperators in the population. Our paper adds a more realistic approach to previous studies on the evolution of cooperation by considering a social–ecological system in which agents move in a landscape to harvest a renewable resource. Our results conclude that resource dynamics play an important role when studying levels of cooperation and resource use. Our simulations show that the agents’ mobility significantly affects the outcomes of the system. This response is nonlinear and very sensible to the type of spatial distribution of the resource richness. In our simulations, better outcomes of long-term sustainability of the resource are obtained with moderate agent mobility and cooperation is enhanced in harsh environments with low resource level in which cooperative groups have natural boundaries fostered by agents’ low mobility.

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  • Pérez, Irene & Janssen, Marco A., 2015. "The effect of spatial heterogeneity and mobility on the performance of social–ecological systems," Ecological Modelling, Elsevier, vol. 296(C), pages 1-11.
    • Handle: RePEc:eee:ecomod:v:296:y:2015:i:c:p:1-11
    DOI: 10.1016/j.ecolmodel.2014.10.014
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    References listed on IDEAS

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    1. Ádám Kun & Ulf Dieckmann, 2013. "Resource heterogeneity can facilitate cooperation," Nature Communications, Nature, vol. 4(1), pages 1-8, December.
    2. Grimm, Volker & Berger, Uta & DeAngelis, Donald L. & Polhill, J. Gary & Giske, Jarl & Railsback, Steven F., 2010. "The ODD protocol: A review and first update," Ecological Modelling, Elsevier, vol. 221(23), pages 2760-2768.
    3. Smaldino, Paul E., 2013. "Cooperation in harsh environments and the emergence of spatial patterns," Chaos, Solitons & Fractals, Elsevier, vol. 56(C), pages 6-12.
    4. Christoph Hauert & Michael Doebeli, 2004. "Spatial structure often inhibits the evolution of cooperation in the snowdrift game," Nature, Nature, vol. 428(6983), pages 643-646, April.
    5. Smaldino, Paul E. & Schank, Jeffrey C., 2012. "Movement patterns, social dynamics, and the evolution of cooperation," Theoretical Population Biology, Elsevier, vol. 82(1), pages 48-58.
    6. Salau, Kehinde & Schoon, Michael L. & Baggio, Jacopo A. & Janssen, Marco A., 2012. "Varying effects of connectivity and dispersal on interacting species dynamics," Ecological Modelling, Elsevier, vol. 242(C), pages 81-91.
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    Cited by:

    1. Waring, Timothy M. & Goff, Sandra H. & Smaldino, Paul E., 2017. "The coevolution of economic institutions and sustainable consumption via cultural group selection," Ecological Economics, Elsevier, vol. 131(C), pages 524-532.
    2. Akopov, Andranik S. & Beklaryan, Levon A. & Saghatelyan, Armen K., 2017. "Agent-based modelling for ecological economics: A case study of the Republic of Armenia," Ecological Modelling, Elsevier, vol. 346(C), pages 99-118.
    3. Drechsler, Martin & Wätzold, Frank & Grimm, Volker, 2022. "The hitchhiker's guide to generic ecological-economic modelling of land-use-based biodiversity conservation policies," Ecological Modelling, Elsevier, vol. 465(C).
    4. Lapp, Maya & Long, Colby, 2022. "A new approach to agent-based models of Community Resource Management based on the analysis of cheating, monitoring, and sanctioning," Ecological Modelling, Elsevier, vol. 468(C).

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