A workflow for standardising and integrating alien species distribution data

By

Seebens, Hanno
Clarke, David A.
Groom, Quentin
Wilson, John Ross
García-Berthou, Emili
Kühn, Ingolf
Roigé, Mariona
Pagad, Shyama
Essl, Franz
Vicente, Joana
Winter, Marten
McGeoch, Melodie

Type

Article

Date of Publication

2020

Original Publication

NeoBiota : advancing research on alien species and biological invasions

Volume

59

Pages

39--59

Subjects

Darwin Core , Databases , GBIF , invasive alien species , R software environment , reproducibility , Standardisation , taxonomy , workflow

Contributed by

Pensoft Publishers

Language

English

Published by

Sophia, Bulgaria Pensoft Publishers

Identifiers

DOI: https://doi.org/10.3897/neobiota.59.53578

 

Abstract

Biodiversity data are being collected at unprecedented rates. Such data often have significant value for purposes beyond the initial reason for which they were collected, particularly when they are combined and collated with other data sources. In the field of invasion ecology, however, integrating data represents a major challenge due to the notorious lack of standardisation of terminologies and categorisations, and the application of deviating concepts of biological invasions. Here, we introduce the SInAS workflow, short for Standardising and Integrating Alien Species data. The SInAS workflow standardises terminologies following Darwin Core, location names using a proposed translation table, taxon names based on the GBIF backbone taxonomy, and dates of first records based on a set of predefined rules. The output of the SInAS workflow provides various entry points that can be used both to improve coherence among the databases and to check and correct the original data. The workflow is flexible and can be easily adapted and extended to the needs of different users. We illustrate the workflow using a case-study integrating five widely used global databases of information on biological invasions. The comparison of the standardised databases revealed a surprisingly low degree of overlap, which indicates that the amount of data may currently not be fully exploited in the original databases. We highly recommend the use and development of publicly available workflows to ensure that the integration of databases is reproducible and transparent. Workflows, such as SInAS, ultimately increase trust in data, study results, and conclusions.