CoordinateCleaner: Standardized cleaning of occurrence records from biological collection databases

Abstract

1. Species occurrence records from online databases are an indispensable resource in ecological, biogeographical and palaeontological research. However, issues with data quality, especially incorrect geo-referencing or dating, can diminish their usefulness. Manual cleaning is time-consuming, error prone, difficult to reproduce and limited to known geographical areas and taxonomic groups, making it impractical for datasets with thousands or millions of records.2. Here, we present CoordinateCleaner, an r-package to scan datasets of species occurrence records for geo-referencing and dating imprecisions and data entry errors in a standardized and reproducible way. CoordinateCleaner is tailored to problems common in biological and palaeontological databases and can handle datasets with millions of records. The software includes (a) functions to flag potentially problematic coordinate records based on geographical gazetteers, (b) a global database of 9,691 geo-referenced biodiversity institutions to identify records that are likely from horticulture or captivity, (c) novel algorithms to identify datasets with rasterized data, conversion errors and strong decimal rounding and (d) spatio-temporal tests for fossils.3. We describe the individual functions available in CoordinateCleaner and demonstrate them on more than 90 million occurrences of flowering plants from the Global Biodiversity Information Facility (GBIF) and 19,000 fossil occurrences from the Palaeobiology Database (PBDB). We find that in GBIF more than 3.4 million records (3.7%) are potentially problematic and that 179 of the tested contributing This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Differentiation between wild and domesticated Ungulates based on ecological niches

“…Further research into the use of additional niche traits is needed. 24,33,8,34,7,21,41,31,39,35,3,22,40,19,9,16,29,20 Diceros bicornis 162 0.9237 22,12,2,36,18,35,29,32,10,20,40,14,34,19,5,21,15,41,39 Equus burchellii 740 0.894 35,19,16,18,28,40,15,32,25,5,22,37,41,21,20,…”

Crop Origins and Phylo Food: A database and a phylogenetic tree to stimulate comparative analyses on the origins of food crops

“…Then, qualitative information on the geographic distribution of each wild progenitor was searched for, and records were filtered manually by drawing one polygon per taxon that excluded occurrences outside its native range. Country centroids and capitals, coordinates with equal latitude/longitude, zero coordinates, coordinates in biodiversity institutions and GBIF headquarters, and records flagged as fossil specimens were also removed using function clean_coordinates of the ‘CoordinateCleaner’ package in R (Zizka et al, ). The cleaned set of coordinates included 149,847 occurrences, and was used to retrieve climate and ecoregion data at the locations of each crop wild progenitor.…”

Life history traits predict colonization and extinction lags of desert plant species since the Last Glacial Maximum

“…Species occurrence records were acquired from the Global Biodiversity Information Facility via the rgbif function in the dismo package in R (Hijmans et al 2015). Geographic outliers were removed (Zizka et al 2019), climatic outliers more than two standard deviations from the mean were removed (Butterfield et al 2017), as were duplicates within the same~1-km climate grid cell. Species with fewer than 20 records (30 of 385 species) were excluded from further analysis (Thuiller et al 2005).…”