Spatial prioritization, where priority areas for conservation actions are identified from a set of candidate locations, is a critical step in many conservation planning problems. How priorities are formed depend on multiple factors, including: i) the attributes of candidate locations, such as the species and other biodiversity values present, cost of acting, current condition and risk of loss; ii) how information of different types are combined across species and habitats, costs etc., and; iii) what is the general objective against which priority of a site is evaluated.
Uncertainties in input data and how these affect conservation solutions have long been of central interest to both conservation scientists and practitioners. Particularly, gaps and uncertainties in ecological data, but increasingly also in the economic and socio-political data, have been studied. Yet, there remains notably ambiguity on how these data gaps influence solutions that are optimized across multiple biodiversity features and locations, and what is the relative importance of gaps in different data.
In this presentation, I will explain how basic mathematical restrictions can be used to understand the ways different data types, and hence uncertainties in them, influence outcomes in common conservation prioritization approaches. I will also show how, when looking for a solution that maximizes benefits for all included species (maximum-coverage problem), the spatial characteristics of species distributions play a role in the way priorities are distributed in space.
These studies show that in simple scoring based approaches, information on costs, threats, and habitat condition quickly dominate prioritization solutions, particularly when done for large number of species and other biodiversity features. When more sophisticated, complementarity based prioritization approaches are used, the solutions become more sensitive to the number and type of included species . When only few species are used, the location of priority areas are driven by the distribution patterns of intermediately rare species that occupy species poor areas. The most important and least important areas also behave differently to changes in ecological data.
Understanding how spatial priorities are driven by different data types is useful as it improves the transparency of prioritization results, provides clarity to species weighting in multi-species prioritization problems, and helps to focus our efforts in improving data.
 Kujala et al. (in press) Spatial characteristics of species distributions as drivers in conservation prioritization. Methods in Ecology and Evolution. DOI: 10.1111/2041-210X.12939