Analysing botanical collecting effort in Amazonia and correcting for it in species range estimation

The aim of this study is to analyse the distribution pattern of the botanical collecting effort in Amazonia so that it can be accounted for when interpreting phytogeographical patterns such as inferred species ranges. We also develop a mechanistic and transparent method for taking into account the bias in collecting effort when estimating likelihoods of species occurrences. Amazonia, Neotropics. We utilized electronic data sets of georeferenced herbarium collections (1,063,530 in total). We plotted collecting localities (68,246 in total) on maps overlaid with 1° and 0.5° square grids, and analysed collecting effort using a geographical information system (GIS). We also drew a map of Thiessen polygons, using collecting localities as polygon centres, to visualize collecting density in a scale-independent way. We then created a 'collecting activity landscape' in which well-collected areas appear as peaks and poorly studied areas as valleys. We demonstrate how this surface can be utilized when estimating species distributions. The data available to us confirm that botanical collecting activity is still severely biased in Amazonia. The uncollected area represents 43% of the total area of Amazonia, while another 28% is poorly collected and only 2% can be considered relatively well collected. The Thiessen polygon network represents an improvement in the presentation of collecting intensity compared with square grids. The maps of botanical collecting effort in the Neotropics should be used for visually correcting phytogeographical interpretations. With the help of GIS applications the observed spatial bias in collecting effort can be utilized in estimating the likelihood of occurrence of species in a repeatable manner. These estimates, in turn, can be used for various purposes in basic and applied science as well as in decision-making. The biased collecting effort should, in the long run, be corrected by further field work in unexplored areas, which can be identified with the maps presented here.