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Self-feedbacks determine the sustainability of human interventions in eco-social complex systems: Impacts on biodiversity and ecosystem health

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  • Marco Ortiz
  • Richard Levins

Abstract

Several administrative polices have been implemented in order to reduce the negative impacts of fishing on natural ecosystems. Four eco-social models with different levels of complexity were constructed, which represent the seaweed harvest in central-northern Chile under two different regimes, Management and Exploitation Areas for Benthic Resources (MAEBRs) and Open Access Areas (OAAs). The dynamics of both regimes were analyzed using the following theoretical frameworks: (1) Loop Analysis, which allows the local stability or sustainability of the models and scenarios to be assessed; and (2) Hessian´s optimization procedure of a global fishery function (GFF) that represents each dynamics of each harvest. The results suggest that the current fishing dynamics in MAEBRs are not sustainable unless the market demand presents some type of control (i.e. taxes). Further, the results indicated that if the demand changes to a self-negative feedback (self-control) in MAEBRs, the stability is increased and, simultaneously, a relative maximum for the GFF is reached. Contrarily, the sustainability of the model/system representing the harvest (principally by cutting plants) in OAAs is not reached. The implementation of an “ecological” tax for intensive artisanal fisheries with low operational cost is proposed. The network analysis developed here is proposed as a general strategy for studying the effects of human interventions in marine coastal ecosystems under transient (short-term) dynamics.

Suggested Citation

  • Marco Ortiz & Richard Levins, 2017. "Self-feedbacks determine the sustainability of human interventions in eco-social complex systems: Impacts on biodiversity and ecosystem health," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-18, April.
  • Handle: RePEc:plo:pone00:0176163
    DOI: 10.1371/journal.pone.0176163
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    Cited by:

    1. Ferreira, Paulo, 2018. "What detrended fluctuation analysis can tell us about NBA results," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 500(C), pages 92-96.

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