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No one-size-fits-all solution to clean GBIF.

Alexander Zizka1,2, Fernanda Antunes Carvalho3, Alice Calvente4

  • 1sDiv, German Centre for Integrative Biodiversity Research Halle-Jena-Leipzig (iDiv), Leipzig, Germany.

Peerj
|October 16, 2020
PubMed
Summary

Automatic filtering of species occurrence data identifies 44.3% of records as potentially problematic, varying by taxonomic group. Customization is crucial for accurate biodiversity analyses and conservation assessments.

Keywords:
Automated cleaningAutomated conservation assessmentData qualityGBIFNeotropicsSpecies distributions

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Area of Science:

  • Biodiversity informatics
  • Data science
  • Conservation biology

Background:

  • Species occurrence records are vital for biodiversity studies, sourced from collections and mobile apps.
  • Increasing data availability necessitates robust quality control for accuracy and reliability.
  • Automatic filtering offers a scalable solution for identifying and managing problematic records from databases like GBIF.

Purpose of the Study:

  • To evaluate the impact of 13 proposed filters on biodiversity analyses using Neotropical taxa data from GBIF.
  • To assess data loss and the effect of filtering on species richness patterns and conservation assessments.
  • To determine if filters should be universally applied across all taxonomic groups.

Main Methods:

  • Downloaded 18 Neotropical taxa datasets from the Global Biodiversity Information Facility (GBIF).
  • Applied 13 recently proposed automatic filtering methods to the occurrence records.
  • Analyzed the effect of filters on species richness patterns and automated conservation assessments.

Main Results:

  • 44.3% of records were identified as potentially problematic, with significant variation (25-90%) across taxa.
  • A small percentage (4.2%) were strictly erroneous, while 41.7% were deemed unfit for most analyses.
  • Filters for duplicate information, collection year, basis of record, and coordinate accuracy had the greatest impact.

Conclusions:

  • Automated filtering effectively identifies problematic species occurrence records but requires taxonomic and geographic customization.
  • Significant data loss can occur, highlighting the need for careful filter selection and threshold adjustment.
  • Thorough metadata recording and exploration are essential for reliable biodiversity research and conservation efforts.