Tradeoffs in marine reserve design: habitat condition, representation, and socioeconomic costs

  • Carissa J. Klein
    Australian Research Council Centre of Excellence for Environmental Decisions National Environmental Research Program funded Environmental Decisions Hub, School of Biological Sciences, University of Queensland St. Lucia Queensland Australia
  • Vivitskaia J. Tulloch
    Australian Research Council Centre of Excellence for Environmental Decisions National Environmental Research Program funded Environmental Decisions Hub, School of Biological Sciences, University of Queensland St. Lucia Queensland Australia
  • Benjamin S. Halpern
    National Center for Ecological Analysis and Synthesis Santa Barbara California USA
  • Kimberly A. Selkoe
    National Center for Ecological Analysis and Synthesis Santa Barbara California USA
  • Matthew E. Watts
    Australian Research Council Centre of Excellence for Environmental Decisions National Environmental Research Program funded Environmental Decisions Hub, School of Biological Sciences, University of Queensland St. Lucia Queensland Australia
  • Charles Steinback
    Ecotrust Portland Oregon USA
  • Astrid Scholz
    Ecotrust Portland Oregon USA
  • Hugh P. Possingham
    Australian Research Council Centre of Excellence for Environmental Decisions National Environmental Research Program funded Environmental Decisions Hub, School of Biological Sciences, University of Queensland St. Lucia Queensland Australia

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<jats:title>Abstract</jats:title><jats:p>We present a novel method for designing marine reserves that trades off three important attributes of a conservation plan: habitat condition, habitat representation, and socioeconomic costs. We calculated habitat condition in four ways, using different human impacts as a proxy for condition: all impacts; impacts that cannot be managed with a reserve; land‐based impacts; and climate change impacts. We demonstrate our approach in California, where three important tradeoffs emerged. First, reserve systems that have a high chance of protecting good condition habitats cost fishers less than 3.1% of their income. Second, cost to fishers can be reduced by 1/2–2/3 by triaging less than 1/3 of habitats. Finally, increasing the probability of protecting good condition habitats from 50% to 99% costs fishers an additional 1.7% of their income, with roughly 0.3% added costs for each additional 10% confidence. Knowing exactly what the cost of these tradeoffs are informs discussion and potential compromise among stakeholders involved in protected area planning worldwide.</jats:p>

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