Enhancing georeferenced biodiversity inventories: automated information extraction from literature records reveal the gaps

Abstract

AbstractWe use natural language processing (NLP) to retrieve location data for cheilostome bryozoan species (text-mined occurrences [TMO]) in an automated procedure. We compare these results with data from the Ocean Biogeographic Information System (OBIS). Using OBIS and TMO data separately and in combination, we present latitudinal species richness curves using standard estimators (Chao2 and the Jackknife) and range-through approaches. Our combined OBIS and TMO species richness curves quantitatively document a bimodal global latitudinal diversity gradient for cheilostomes for the first time, with peaks in the temperate zones. 79% of the georeferenced species we retrieved from TMO (N = 1780) and OBIS (N = 2453) are non-overlapping and underestimate known species richness, even in combination. Despite clear indications that global location data compiled for cheilostomes should be improved with concerted effort, our study supports the view that latitudinal species richness patterns deviate from the canonical LDG. Moreover, combining online biodiversity databases with automated information retrieval from the published literature is a promising avenue for expanding taxon-location datasets.