Abstract
There are insufficient resources available to manage the world's existing protected area portfolio effectively, so the most important sites should be prioritised in investment decision-making. Sophisticated conservation planning and assessment tools developed to identify locations for new protected areas can provide an evidence base for such prioritisations, yet decision-makers in many countries lack the institutional support and necessary capacity to use the associated software. As such, simple heuristic approaches such as species richness or number of threatened species are generally adopted to inform prioritisation decisions. However, their performance has never been tested. Using the reptile fauna of Madagascar's dry forests as a case study, we evaluate the performance of four site prioritisation protocols used to rank the conservation value of 22 established and candidate protected areas. We compare the results to a benchmark produced by the widely-used systematic conservation planning software Zonation. The four indices scored sites on the basis of: i) species richness; ii) an index based on species' Red List status; iii) irreplaceability (a key metric in systematic conservation planning); and, iv) a novel Conservation Value Index (CVI), which incorporates species-level information on endemism, representation in the protected area system, tolerance of habitat degradation and hunting/collection pressure. Rankings produced by the four protocols were positively correlated to the results of Zonation, particularly amongst high-scoring sites, but CVI and Irreplaceability performed better than Species Richness and the Red List Index. Given the technological capacity constraints experienced by decision-makers in the developing world, our findings suggest that heuristic metrics can represent a useful alternative to more sophisticated analyses, especially when they integrate species-specific information related to extinction risk. However, this can require access to, and understanding of, more complex species data.