Authors: Kujala, Heini;

Spatial prioritisation methods, which identify priority areas for targeting conservation actions, are frequently used to guide conservation programs and land use decision-making. Uncertainties in input data and how these affect conservation solutions, have long been of central interest to both conservation scientists and practitioners. Because conservation priority maps are underpinned by biodiversity data, the greatest effort has traditionally focused on perfecting their accuracy. What is perhaps less well understood is that in spatial resource-allocation problems, some data influence the final solution far more strongly than others, and resolving uncertainty in the highly influential data layers is thus most important for the stability of the priority solution.
Using examples from different conservation case studies, I show that uncertainties in conservation costs, threats or vegetation condition often outweigh the impact of individual biodiversity data on conservation plans. In addition, I show that the decisions we make on which data to include and how we modify them before their use may have larger impacts on the prioritisation result than uncertainties in any individual species data. Understanding how different data types and their use affects spatial priorities is useful as it helps to focus our efforts in data improvement to those uncertainties that matter.