CoordinateCleaner: Standardized cleaning of occurrence records from biological collection databases

By Alexander Zizka, Daniele Silvestro, Tobias Andermann, J. Azevedo, Camila Duarte Ritter, Daniel Edler, Harith Farooq, Andrei Herdean, M. Ariza, Ruud Scharn and 4 more. Methods in Ecology and Evolution, 2019

894 citations recorded by the source corpus.

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.

Subfields: Ecological Modeling, Molecular Biology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/cc15144bf41900588a6f62dc8aeca53a01c5135f

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In Ecological Modeling

In Molecular Biology

In Nature and Landscape Conservation

Citations in the corpus

Outstanding plant endemism levels strongly support the recognition of campo rupestre provinces in mountaintops of eastern South America (2019, methodology): “…outlier records that did not correspond to the overall distribution pattern of the species as informed by the data retrieved from the BFG. Data cleaning proce‐ dures and maps were performed in the “CoordinateCleaner” pack‐ age in R (Zizka et al., 2019) and using the QGIS software and its tools (”
Sampling biases shape our view of the natural world (2020, methodology): “Furthermore, whilst approaches have been developed to ‘clean data’ (Zizka et al. 2019, 2021, Jin and Yang 2020) these do not provide mechanisms to use existing data better to assess global diversity patterns or their completeness.”
Mountains, climate and niche heterogeneity explain global patterns of fern diversity (2021, methodology): “CoordinateCleaner: Standardized cleaning of occurrence records from biological collection databases.”