No one-size-fits-all solution to clean GBIF.
Name
peerj-9916.pdf
Description
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Size
22.95 MB
Format
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Resource type
Journal article
Creator (person)
Zizka, Alexander
Antunes Carvalho, Fernanda
Calvente, Alice
Baez-Lizarazo, Mabel Rocio
Cabral, Andressa
Coelho, Jéssica Fernanda Ramos
Colli-Silva, Matheus
Ramos Fantinati, Mariana
Fernandes, Moabe F.
Ferreira-Araújo, Thais
Gondim Lambert Moreira, Fernanda
Cunha Santos, Nathália Michellyda
Andrade Borges Santos, Tiago
dos Santos-Costa, Renata Clicia
Serrano, Filipe C.
Alves da Silva, Ana Paula
de Souza Soares, Arthur
Cavalcante de Souza, Paolla Gabryelle
Calisto Tomaz, Eduardo
Fonseca Vale, Valéria
Vieira, Tiago Luiz
Antonelli, Alexandre
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 name | Awards |
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
Rights statement
In Copyright