A stochastic approach to marine reserve design: Incorporating data uncertainty

Marine reserves, or protected areas, are used to meet an array of biodiversity and conservation objectives. The design of regional networks of marine reserves is concerned with the problem of where to place the marine protected areas and how to spatially configure them. Quantitative methods for doing this provide important decision support tools for marine managers, The central problem is to balance the costs and benefits of the reserve network, whilst satisfying conservation objectives (hence solving a constrained optimization problem). Current optimization algorithms for reserve design are widely used, but none allow for the systematic incorporation of data uncertainty and its effect on the reserve design solutions. The central purpose of this study is to provide a framework for incorporating uncertain ecological input data into algorithms for designing networks of marine reserves. In order to do this, a simplified version of the marine reserve design optimization problem is considered. A Metropolis-Hastings random search procedure is introduced to systematically sample the model solution space and converge on an optimal reserve design. Incorporation of the uncertain input data builds on this process and relies on a parametric bootstrapping procedure. This allows for the solution (i.e, the marine reserve design) to be expressed as the probability of any planning unit being included in the marine reserve network. Spatial plots of this acceptance probability are easily interpretable for decision making under uncertainty. The bootstrapping methodology is also readily adapted to existing comprehensive reserve design algorithms. Here, a preliminary application of the algorithm is made to the Mesoamerican Barrier Reef System (in the Caribbean Sea) based on satellite-derived and mapped conservation features (from Landsat). (C) 2008 Elsevier B.V. All rights reserved.