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Current GBIF occurrence data demonstrates both promise and limitations for potential red listing of spiders.

Vaughn ShireySini SeppäläVasco Veiga BrancoPedro Cardoso
Published in: Biodiversity data journal (2019)
Conservation assessments of hyperdiverse groups of organisms are often challenging and limited by the availability of occurrence data needed to calculate assessment metrics such as extent of occurrence (EOO). Spiders represent one such diverse group and have historically been assessed using primary literature with retrospective georeferencing. Here we demonstrate the differences in estimations of EOO and hypothetical IUCN Red List classifications for two extensive spider datasets comprising 479 species in total. The EOO were estimated and compared using literature-based assessments, Global Biodiversity Information Facility (GBIF)-based assessments and combined data assessments. We found that although few changes to hypothetical IUCN Red List classifications occurred with the addition of GBIF data, some species (3.3%) which could previously not be classified could now be assessed with the addition of GBIF data. In addition, the hypothetical classification changed for others (1.5%). On the other hand, GBIF data alone did not provide enough data for 88.7% of species. These results demonstrate the potential of GBIF data to serve as an additional source of information for conservation assessments, complementing literature data, but not particularly useful on its own as it stands right now for spiders.
Keyphrases
  • electronic health record
  • big data
  • systematic review
  • risk assessment
  • healthcare
  • artificial intelligence
  • deep learning
  • health information
  • rna seq
  • gram negative