Simulating the effects of using different types of species distribution data in reserve selection

In a perfect world, systematic conservation planning would use complete information on the distribution of biodiversity. However, information on most species is grossly incomplete. Two main types of distribution data are frequently used in conservation planning: observed and predicted distribution data. A fundamental question that planners face is – which kind of data is better under what circumstances? We used simulation procedures to analyse the effects of using different types of distribution data on the performance of reserve selection algorithms in scenarios using different reserve selection problems, amounts of species distribution known, conservation targets and costs. To compare these scenarios we used occurrence data from 25 amphibian and 41 reptile species of the Iberian Peninsula and assumed the available data represented the whole truth. We then sampled fractions of these data and either used them as they were, or converted them to modelled predicted distributions. This enabled us to build three other types of species distribution data sets commonly used in conservation planning: “predicted”, “transformed predicted” and “mixed”. Our results suggest that reserve selection performance is sensitive to the type of species distribution data used and that the most cost-efficient decision depends most on the reserve selection problem and on how much we have of the species distribution data. Choosing the most appropriate type of distribution data should start by evaluating the scenario circumstances. While there is no one best approach for every scenario, we discovered that using a mixed approach usually provides an acceptable compromise between species representation and cost.