Geographic And Taxonomic Occurrence R-based Scrubbing (gatoRs): An R package and workflow for processing biodiversity data

Natalie N Patten; Michelle L Gaynor; Douglas E Soltis; Pamela S Soltis

doi:10.1002/aps3.11575

Geographic And Taxonomic Occurrence R-based Scrubbing (gatoRs): An R package and workflow for processing biodiversity data

Appl Plant Sci. 2024 Mar 21;12(2):e11575. doi: 10.1002/aps3.11575. eCollection 2024 Mar-Apr.

Authors

Natalie N Patten^{1

2}, Michelle L Gaynor^{3

4}, Douglas E Soltis^{3

4}, Pamela S Soltis³

Affiliations

¹ Department of Mathematics University of Florida Gainesville 32611 Florida USA.
² Present address: Department of Mathematics The Ohio State University Columbus 43210 Ohio USA.
³ Florida Museum of Natural History University of Florida Gainesville 32611 Florida USA.
⁴ Department of Biology University of Florida Gainesville 32611 Florida USA.

Abstract

Premise: Digitized biodiversity data offer extensive information; however, obtaining and processing biodiversity data can be daunting. Complexities arise during data cleaning, such as identifying and removing problematic records. To address these issues, we created the R package Geographic And Taxonomic Occurrence R-based Scrubbing (gatoRs).

Methods and results: The gatoRs workflow includes functions that streamline downloading records from the Global Biodiversity Information Facility (GBIF) and Integrated Digitized Biocollections (iDigBio). We also created functions to clean downloaded specimen records. Unlike previous R packages, gatoRs accounts for differences in download structure between GBIF and iDigBio and allows for user control via interactive cleaning steps.

Conclusions: Our pipeline enables the scientific community to process biodiversity data efficiently and is accessible to the R coding novice. We anticipate that gatoRs will be useful for both established and beginning users. Furthermore, we expect our package will facilitate the introduction of biodiversity-related concepts into the classroom via the use of herbarium specimens.

Keywords: GBIF; basis cleaning; biodiversity data download; herbaria; iDigBio; locality cleaning; spatial correction; taxonomic harmonization.