Complementarity, biodiversity viability analysis, and policy-based algorithms for conservation

Biodiversity conservation area-selection strategies include not only trade-offs among society's needs in land-use allocation, but also allocation of economic instruments such as incentives, levies, and biodiversity credits. For these applications, the key property of an area is its complementarity-the context-dependent, marginal gain in biodiversity provided by the area. Given that there has been little implementation of whole-sets of areas generated by the popular computer-based selection methods, we suggest that analogous policy-based algorithms would be a more effective real-world application of complementarity. Areas would be selected for conservation over time as a consequence of policies in which dynamic complementarity values influence application of economic instruments. These integrated biodiversity/economic strategies can use an extended form of complementarity reflecting marginal changes in regional probability of persistence of biodiversity. While probabilistic measures of biodiversity viability have been explored in area-selection for some time, it remains difficult to make viability statements about all of biodiversity. New approaches that use biodiversity surrogate information for biodiversity viability analysis (BVA) can take advantage of a general quantitative biodiversity framework in which pattern-based relationships among areas allow predictions at the species level. A standard assumption of unimodal species responses to environmental gradients yields an expected distribution of species in an ordination pattern, and allows sampling of inferred species. Based on environmental correlates, inferred species can be mapped in geographic space, forming distribution fragments. This information, when linked to species persistence models, may allow ongoing calculation of areas' complementarity values. An example illustrates application of these ordination models to museum collection data for lizards from New South Wales (NSW), Australia. (C) 2003 Published by Elsevier Science Ltd.