Hermoso, Virgilio, Januchowski-Hartley, Stephanie R., and Pressey, Robert L. (2013) When the suit does not fit biodiversity: loose surrogates compromise the achievement of conservation goals. Biological Conservation, 159. pp. 197-205.

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Abstract

The use of biodiversity surrogates is inevitable in conservation planning due to the frequent lack of consistent data on biodiversity patterns and processes. Top-down environmental classifications (coarse-filter surrogates) are the most common approach to defining surrogates. Their use relies on the assumption that priority areas identified using surrogates will adequately represent biodiversity. There remains no clear understanding about how the combination of different factors might affect the surrogacy value of these classifications. Here, we evaluate the role of three factors that could affect the effectiveness of coarse-filter surrogates: (a) thematic resolution (number of classes), (b) species' prevalence, and (c) the ability of classifications to portray homogeneous communities (classification strength). We explore the role of direct and indirect effects of these factors with a simulated dataset of 10,000 planning units and 96 species and structural equation modelling (SEM).

The surrogacy value of coarse-filter surrogates depended on the relative match between the extent of classes and species' distributions and the capacity of classifications to portray patterns in species composition (classification strength). Both determine the likelihood of erroneous selection of areas within a class where certain species do not occur. Common species were represented better than random only at high classification strength values (>0.5), while rare species never did. Finer classifications tended to be better surrogates although, when rare species were incorporated, the proportion of species that achieved the target level never exceeded 68%, even for the finest classification. This compromises the suitability of coarse-filter surrogates in areas where biodiversity is patchily distributed or with many rare species. We recommend using composite data sets containing environmental classes and biological data when a high effectiveness for all the species cannot be achieved.

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