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

PeerJ

article

Bjørn Tore Kopperud¹  Scott Lidgard⁴  Lee Hsiang Liow¹ 

[1] Natural History Museum, University of Oslo;GeoBio-Center, Ludwig-Maximilians-Universität München;Department of Earth and Environmental Sciences, Ludwig-Maximilians-Universität München;Negaunee Integrative Research Center;Centre for Ecological and Evolutionary Synthesis, University of Oslo

关键词: Marine invertebrates;    Bryozoa;    Geographic distribution;    Latitudinal diversity gradient (LDG);    Public data repositories;    Natural langauge processing (NLP);    Text-mining;    Bimodality;    Species richness;   

DOI  :  10.7717/peerj.13921

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

We 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 combined from two major public databases (DB): the Ocean Biodiversity Information System (OBIS), and the Global Biodiversity Information Facility (GBIF). Using DB 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 DB and TMO species richness curves quantitatively document a bimodal global latitudinal diversity gradient for extant cheilostomes for the first time, with peaks in the temperate zones. A total of 79% of the georeferenced species we retrieved from TMO (N = 1,408) and DB (N = 4,549) are non-overlapping. Despite clear indications that global location data compiled for cheilostomes should be improved with concerted effort, our study supports the view that many marine latitudinal species richness patterns deviate from the canonical latitudinal diversity gradient (LDG). Moreover, combining online biodiversity databases with automated information retrieval from the published literature is a promising avenue for expanding taxon-location datasets.

【 授权许可】

CC BY