Abstract
The fundamental goal of conservation planning is biodiversity persistence, yet most reserve selection methods prioritize sites using occurrence data. We describe a method that integrates correlates of persistence for multiple species into a single currency – site quality. Site quality is, in turn, an explicit measure of performance used in optimization. We develop a Bayesian network to assess site quality, which assigns an expected value to a property based on criteria arrayed into a causal diagram. We then use stochastic dynamic programming to determine whether an organization should acquire or reject a site placed on the public market. Our framework for assessing sites and making land acquisition decisions represents a compromise between the use of generic spatial design criteria and more intensive computational tools, like spatially-explicit population models. There is certainly a loss of precision by using site quality as a surrogate for more direct measures of persistence. However, we believe this simplification is defensible when sufficient data, expertise, or other resources are lacking.
Published Version
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