Uncertainty surfaces and maps of ignorance: the possibility of spatially estimating dark diversity

In ecology, a number of studies have dealt with the prediction of species diversity over space and its changes over time based on a set of predictors related to environmental variability, productivity, spatial constraints, and climate drivers. However, the observed diversity is a portion of the actual pool which is strictly related to abiotic conditions and evolutionary history of species in the pool. In this study we aim to explicitly show uncertainty of the prediction of species distribution at a global scale. This is in line with the “dark diversity” concept extended to a global spatial scale. We will not deal with problems in the detectability of species but with hidden patterns in the probability of their distribution. Thus far, species distribution estimates based on field data sampling do not represent reality in a deterministic sense and are only estimates of potential presence. Therefore, the use of “maps of ignorance” representing the bias or the uncertainty deriving from species distribution modeling, along with predictive maps, is strongly encouraged. Uncertainty can derive from a number of input data sources, such as the definition or identification of a certain species, as well as location-based errors. The spatial distribution of uncertainty should explicitly be shown on maps to avoid ignoring overall accuracy or model errors. We propose methods mainly based on Bayesian logistic regression coupled with simulation-based Monte Carlo techniques and Cartograms applied to European and worldwide datasets for explicitly mapping uncertainty in the distribution of species in a Free and Open Source environment.