Maps of relative floristic ignorance and virtual floristic lists: An R package to incorporate uncertainty in mapping and analysing biodiversity data

The vast amount of occurrence records currently available offers increasing opportunities for biodiversity data analyses. This amount of data poses new challenges for the reliability and correct interpretation of the results. Indeed, to safely deal with occurrence records, their uncertainty and associated biases should be taken into account. We developed an R package to explicitly include spatial and temporal uncertainties during the mapping and listing of plant occurrence records for a given study area. Our workflow returns two objects: (a) a 'Map of Relative Floristic Ignorance' (MRFI), which represents the spatial distribution of the lack of floristic knowledge; (b) a 'Virtual Floristic List' (VFL), i.e. a list of taxa potentially occurring in the area with an associated probability of occurrence. The method implemented in the package can manage a large amount of occurrence data and represents relative floristic ignorance across a study area with a sustainable computational effort. Several parameters can be set by the user, conferring high flexibility to the method. Uncertainty is not avoided, but incorporated into biodiversity analyses through appropriate methodological approaches and innovative spatial representations. Our study introduces a workflow that pushes forward the analytical capacities to deal with uncertainty in biological occurrence records, allowing to produce more accurate outputs.

Automated summary

The vast amount of occurrence records available for biodiversity data analyses poses challenges in terms of reliability and interpretation. To address this, we developed an R package that incorporates spatial and temporal uncertainties, providing a map of floristic ignorance and a virtual floristic list for a study area. The method can handle large amounts of occurrence data and represents relative floristic ignorance in a computationally sustainable way. The approach integrates uncertainty into biodiversity analyses through methodological approaches and spatial representations. This workflow improves the accuracy of outputs.