Ecological Applications · 2003 · 215 citations · 23 references
Natural ResourcesBiodiversity ConservationConservation BiologyConservation PlanningMarine ConservationSpatial AnalysisHabitat Conservation PlanningHabitat Conservation PlansHabitat ManagementMarine Habitat ConservationReserve DesignSpatial Conservation PlanningBenthic Habitat ClassificationIrreplaceability AnalysisMarine Conservation PlanningMarine Reserve DesignOptimization Models For ConservationReserve Network PlanningSimulated AnnealingSimulated Annealing Optimization
The study demonstrates how siting algorithms can identify marine reserve networks that comprehensively represent target habitat types using benthic habitat data from the Florida Keys. The authors used simulated annealing to optimize reserve networks, exploring how spatial data, planning‑unit size, habitat classification detail, and conservation goals affect the resulting scenarios. The algorithm identified numerous reserve systems meeting conservation targets, revealed that many site combinations were viable, highlighted consistently chosen larger areas via irreplaceability analysis, and showed that highly clustered designs reduce perimeter while increasing area, demonstrating the method’s efficiency and flexibility for decision makers.
Using benthic habitat data from the Florida Keys (USA), we demonstrate how siting algorithms can help identify potential networks of marine reserves that comprehensively represent target habitat types. We applied a flexible optimization tool—simulated annealing—to represent a fixed proportion of different marine habitat types within a geographic area. We investigated the relative influence of spatial information, planning-unit size, detail of habitat classification, and magnitude of the overall conservation goal on the resulting network scenarios. With this method, we were able to identify many adequate reserve systems that met the conservation goals, e.g., representing at least 20% of each conservation target (i.e., habitat type) while fulfilling the overall aim of minimizing the system area and perimeter. One of the most useful types of information provided by this siting algorithm comes from an "irreplaceability analysis," which is a count of the number of times unique planning units were included in reserve system scenarios. This analysis indicated that many different combinations of sites produced networks that met the conservation goals. While individual 1-km2 areas were fairly interchangeable, the irreplaceability analysis highlighted larger areas within the planning region that were chosen consistently to meet the goals incorporated into the algorithm. Additionally, we found that reserve systems designed with a high degree of spatial clustering tended to have considerably less perimeter and larger overall areas in reserve—a configuration that may be preferable particularly for sociopolitical reasons. This exercise illustrates the value of using the simulated annealing algorithm to help site marine reserves: the approach makes efficient use of available resources, can be used interactively by conservation decision makers, and offers biologically suitable alternative networks from which an effective system of marine reserves can be crafted.
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Optimization by Simulated Annealing
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