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

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

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Resource type
Journal article
Creator (person)
Zizka, Alexander
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Antunes Carvalho, Fernanda
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Calvente, Alice
Baez-Lizarazo, Mabel Rocio
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Cabral, Andressa
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Coelho, Jéssica Fernanda Ramos
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Colli-Silva, Matheus
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Ramos Fantinati, Mariana
Fernandes, Moabe F.
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Ferreira-Araújo, Thais
Gondim Lambert Moreira, Fernanda
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Cunha Santos, Nathália Michellyda
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Andrade Borges Santos, Tiago
dos Santos-Costa, Renata Clicia
Serrano, Filipe C.
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Alves da Silva, Ana Paula
de Souza Soares, Arthur
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Cavalcante de Souza, Paolla Gabryelle
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Calisto Tomaz, Eduardo
Fonseca Vale, Valéria
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Vieira, Tiago Luiz
Antonelli, Alexandre
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Date published
September 28, 2020
Abstract
Species occurrence records provide the basis for many biodiversity studies. They derive from georeferenced specimens deposited in natural history collections and visual observations, such as those obtained through various mobile applications. Given the rapid increase in availability of such data, the control of quality and accuracy constitutes a particular concern. Automatic filtering is a scalable and reproducible means to identify potentially problematic records and tailor datasets from public databases such as the Global Biodiversity Information Facility (GBIF; http://www.gbif.org), for biodiversity analyses. However, it is unclear how much data may be lost by filtering, whether the same filters should be applied across all taxonomic groups, and what the effect of filtering is on common downstream analyses. Here, we evaluate the effect of 13 recently proposed filters on the inference of species richness patterns and automated conservation assessments for 18 Neotropical taxa, including terrestrial and marine animals, fungi, and plants downloaded from GBIF. We find that a total of 44.3% of the records are potentially problematic, with large variation across taxonomic groups (25–90%). A small fraction of records was identified as erroneous in the strict sense (4.2%), and a much larger proportion as unfit for most downstream analyses (41.7%). Filters of duplicated information, collection year, and basis of record, as well as coordinates in urban areas, or for terrestrial taxa in the sea or marine taxa on land, have the greatest effect. Automated filtering can help in identifying problematic records, but requires customization of which tests and thresholds should be applied to the taxonomic group and geographic area under focus. Our results stress the importance of thorough recording and exploration of the meta-data associated with species records for biodiversity research.
Funder
Funder nameAwards
Deutsche Forschungsgemeinschaft, Germany
DFG FZT 118
Universidade Federal do Rio Grande do Norte, Brazil
Pró-reitoria de Pesquisa and the Pró-reitoria de Pós-graduação edital 02/2016 –internacionalização
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brazil
Vetenskapsrådet, Sweden
Stiftelsen för Strategisk Forskning, Sweden
Fundação de Amparo à Pesquisa do Estado de São Paulo, Brazil
Process 2015/20215-7
Knut och Alice Wallenbergs Stiftelse, Sweden
Royal Botanic Gardens, Kew, United Kingdom
Journal title
PeerJ
Volume
8
Publisher
PeerJ
Place of publication
Corte Madera, Calif., US
eISSN
2167-8359
Date accepted
August 20, 2020
Official URL
http://dx.doi.org/10.7717/peerj.9916
Rights statement
In Copyright
Licence
https://creativecommons.org/licenses/by/4.0/
DOI
10.7717/peerj.9916
Keywords
Georeferenced specimens
Metadata
Filters
Species occurrence records
Quality control
Natural history collections
Conservation assessments
Global Biodiversity Information Facility (GBIF)
Species richness
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