ABSTRACT
Spatially explicit information on species distributions for conservation planning is invariably incomplete; therefore, the use of surrogates is required to represent broad-scale patterns of biodiversity. Despite significant interest in the effectiveness of surrogates for predicting spatial distributions of biodiversity, few researchers have explored questions involving the ability of surrogates to incidentally represent unknown features of conservation interest. We used the Great Barrier Reef marine reserve network to examine factors affecting incidental representation of conservation features that were unknown at the time the reserve network was established. We used spatially explicit information on the distribution of 39 seabed habitats and biological assemblages and the conservation planning software Marxan to examine how incidental representation was affected by the spatial characteristics of the features; the conservation objectives (the minimum proportion of each feature included in no-take areas); the spatial configuration of no-take areas; and the opportunity cost of conservation. Cost was closely and inversely correlated to incidental representation. However, incidental representation was achieved, even in a region with only coarse-scale environmental data, by adopting a precautionary approach that explicitly considered the potential for unknown features. Our results indicate that incidental representation is enhanced by partitioning selection units along biophysical gradients to account for unknown within-feature variability and ensuring that no-take areas are well distributed throughout the region; by setting high conservation objectives that (in this case >33%) maximize the chances of capturing unknown features incidentally; and by carefully considering the designation of cost to planning units when using decision-support tools for reserve design. The lessons learned from incidental representation in the Great Barrier Reef have implications for conservation planning in other regions, particularly those that lack detailed environmental and ecological data.
