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Keywords:

  • Conservation planning;
  • boosted regression trees;
  • data mining;
  • demersal fish;
  • species distribution model;
  • marine protected area;
  • oceans;
  • Zonation

Abstract

There is strong international agreement on the need for marine protected areas to reverse pervasive human impacts on the oceans' biodiversity. However, their implementation is often hampered both by legal difficulties in defining reserves in international waters and the patchy nature of data in many offshore waters. We demonstrate the use of recent advances in statistical learning and conservation prioritization to produce MPA scenarios with varying costs and benefits for New Zealand's Exclusive Economic Zone, based on the analyses of distributions of 96 demersal fish species. MPAs based on our most cost-effective scenario would deliver conservation benefits nearly 2.5 times greater than those from equivalent-sized areas recently implemented at the request of fishers, and at lower cost to them. Such results demonstrate the power of quantitative, knowledge-based prioritization approaches, which can be applied at high resolution and at oceanic scales.