Complementarity analysis: Mapping the performance of surrogates for biodiversity

Abstract

Efficient planning for biodiversity conservation requires a consideration of complementarity when assessing the value of adding new areas for management. Unfortunately, complementarity in biodiversity across all groups cannot usually be measured directly, so methods are needed to choose good surrogates (or ‘indicators’) for predicting this overall complementarity. Previous attempts at assessment of biological surrogates have measured dissimilarity among biotas, or congruence between sets of selected areas, or species representation within a set of selected areas, all of which can seriously misrepresent the strength of a surrogate relationship across all areas. Therefore, we propose a new approach to complementarity analysis. We show that the pattern of complementarity among all biotas can be assessed in terms of the frequency of false high and false low predictions by the surrogates. We also show how the spatial pattern in these false predictions can be mapped and discuss their usefulness. On the one hand, areas on these maps associated with many false high predictions are overvalued and would be an inefficient investment for scarce conservation resources. On the other hand, areas associated with many false low predictions are undervalued and unlikely to attract conservation action, so we need to know whether they are particularly likely to be highly threatened. These geographical patterns can be used to identify habitat-associated biases in the performance of surrogate groups.