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Aim: Spatial and temporal biases in species-occurrence data can compromise broad-scale biogeographical research and conservation planning. Although spatial biases have been frequently scrutinized, temporal biases and the overall quality of species-occurrence data have received far less attention. This study aims to answer three questions: (1) How reliable are species-occurrence data for flowering plants in Africa? (2) Where and when did botanical sampling occur in the past 300 years? (3) How complete are plant inventories for Africa?. Location: Africa. Methods: By filtering a publicly available dataset containing 3.5 million records of flowering plants, we obtained 934,676 herbarium specimens with complete information regarding species name, date and location of collection. Based on these specimens, we estimated inventory completeness for sampling units (SUs) of 25 km × 25 km. We then tested whether the spatial distribution of well-sampled SUs was correlated with temporal parameters of botanical sampling. Finally, we determined whether inventory completeness in individual countries was related to old or recently collected specimens. Results: Thirty-one per cent of SUs contained at least one specimen, whereas only 2.4% of SUs contained a sufficient number of specimens to reliably estimate inventory completeness. We found that the location of poorly sampled areas remained almost unchanged for half a century. Moreover, there was pronounced temporal bias towards old specimens in South Africa, the country that holds half of the available data for the continent. There, high inventory completeness stems from specimens collected several decades ago. Main conclusions: Despite the increasing availability of species occurrence data for Africa, broad-scale biogeographical research is still compromised by the uncertain quality and spatial and temporal biases of such data. To avoid erroneous inferences, the quality and biases in species-occurrence data should be critically evaluated and quantified prior to use. To this end, we propose a quantification method based on inventory completeness using easily accessible species-occurrence data., J.H. was funded by a Spanish DGCyT Ramón y Cajal grant and by a CNPq Special Visiting Researcher grant (no. 401471/2014-4).

Here we present the code to generate MoBIs (Maps of Biogeographical Ignorance) for single species. The method is based on the calculation of different dimensions of biodiversity data (spatial, temporal and taxonomic). Specifically, in this method, we calculated completeness of each cell by generatingaccumulation curves with records of all individuals of a group. We also set taxonomy quality values for each identifier considering their experience with the group. For the temporal dimension, we adjust adecay curveto quantify temporal decay of information with time so that older records of the species are least informativeand recent ones are the most informative. These components are summarized to generate an index varying from 0 to 1 (lower and higher ignorance, respectively) for each cell for a species. Finally, for cells without data we performed an interpolation, based on the BI calculated for neighbor cells.

Here we present the code to generate MoBIs (Maps of Biogeographical Ignorance) for single species. The method is based on the calculation of different dimensions of biodiversity data (spatial, temporal and taxonomic). Specifically, in this method, we calculated completeness of each cell by generating accumulation curves with records of all individuals of a group. We also set taxonomy quality values for each identifier considering their experience with the group. For the temporal dimension, we adjust a decay curve to quantify temporal decay of information with time so that older records of the species are least informative and recent ones are the most informative. These components are summarized to generate an index varying from 0 to 1 (lower and higher ignorance, respectively) for each cell for a species. Finally, for cells without data we performed an interpolation, based on the BI calculated for neighbor cells., Funding – This work has been funded by the Brazilian CNPq PVE grant 314523/2014-6. GT was supported by CAPES REUNI doctorate fellowship and PDSE grant no. 11842121. RJL is supported by the European Union's Horizon 2020 research and innovation programme under grant agreement no. 854248.