CoordinateCleaner: Standardized cleaning of occurrence records from biological collection databases

• Published in: Methods in ecology and evolution Vol. 10; no. 5; pp. 744 - 751
• Main Authors: Zizka, Alexander, Silvestro, Daniele, Andermann, Tobias, Azevedo, Josué, Duarte Ritter, Camila, Edler, Daniel, Farooq, Harith, Herdean, Andrei, Ariza, María, Scharn, Ruud, Svantesson, Sten, Wengström, Niklas, Zizka, Vera, Antonelli, Alexandre, Quental, Tiago
• Format: Journal Article
• Language: English
• Published: London John Wiley & Sons, Inc 01-05-2019
• Subjects: Algorithms; Annan fysik; big data; Biodiversity; biodiversity institutions; Bioinformatics and Systems Biology; Bioinformatik och systembiologi; Biological Sciences; Biologiska vetenskaper; Captivity; Chronology; Cleaning; data quality; Datasets; Dating; diversity; Environmental Sciences; Environmental Sciences & Ecology; Flowering; Fossils; Fysik; GBIF; geo-referencing; Horticulture; Miljövetenskap; Natural Sciences; Naturvetenskap; Other Physics Topics; palaeobiology database (PBDB); Physical Sciences; Plants (botany); r package; Raster; Rounding; species distribution modelling
• ISSN: 2041-210X; 2041-210X
• DOI: 10.1111/2041-210X.13152

Species occurrence records from online databases are an indispensable resource in ecological, biogeographical and palaeontological research. However, issues with data quality, especially incorrect geo‐referencing or dating, can diminish their usefulness. Manual cleaning is time‐consuming, error prone, difficult to reproduce and limited to known geographical areas and taxonomic groups, making it impractical for datasets with thousands or millions of records.
Here, we present CoordinateCleaner, an r‐package to scan datasets of species occurrence records for geo‐referencing and dating imprecisions and data entry errors in a standardized and reproducible way. CoordinateCleaner is tailored to problems common in biological and palaeontological databases and can handle datasets with millions of records. The software includes (a) functions to flag potentially problematic coordinate records based on geographical gazetteers, (b) a global database of 9,691 geo‐referenced biodiversity institutions to identify records that are likely from horticulture or captivity, (c) novel algorithms to identify datasets with rasterized data, conversion errors and strong decimal rounding and (d) spatio‐temporal tests for fossils.
We describe the individual functions available in CoordinateCleaner and demonstrate them on more than 90 million occurrences of flowering plants from the Global Biodiversity Information Facility (GBIF) and 19,000 fossil occurrences from the Palaeobiology Database (PBDB). We find that in GBIF more than 3.4 million records (3.7%) are potentially problematic and that 179 of the tested contributing datasets (18.5%) might be biased by rasterized coordinates. In PBDB, 1205 records (6.3%) are potentially problematic.
All cleaning functions and the biodiversity institution database are open‐source and available within the CoordinateCleaner r‐package.