To clean or not to clean: Cleaning open‐source data improves extinction risk assessments for threatened plant species

Connor T. Panter; Rosemary L. Clegg; Justin Moat; Steven P. Bachman; Bente B. Klitgård; Rachel L. White

doi:10.1111/csp2.311

journal article Open Access Nov 17, 2020

To clean or not to clean: Cleaning open‐source data improves extinction risk assessments for threatened plant species

Connor T. Panter

Rosemary L. Clegg Justin Moat

Steven P. Bachman

Bente B. Klitgård Rachel L. White

Conservation Science and Practice Vol. 2 No. 12 · Wiley

View at Publisher Save 10.1111/csp2.311

Abstract

Abstract
Plants are under‐represented in conservation efforts, with only 9% of described species published on the IUCN Red List. Biodiversity aggregators including the Global Biodiversity Information Facility (GBIF) and the more recent Botanical Information and Ecology Network (BIEN) contain a wealth of potentially useful occurrence data. We investigate the influence of these data in accelerating plant extinction risk assessments for 225 endemic, near‐endemic, and socioeconomic Bolivian plant species. Geo‐referenced herbarium voucher specimens verified by taxonomic experts comprised our control data set. Open‐source data for 77 species was subjected to a two‐stage cleaning protocol (using an automated R package followed by a manual clean) and threat categories were computed based on extent of occurrence thresholds. Accuracy was the highest using cleaned GBIF data (76%) and uncleaned BIEN data (79%). Sensitivity was the highest for cleaned GBIF (73%) and BIEN (80%) data suggesting our cleaning protocol was essential to maximize sensitivity rates. Comparisons between the control, GBIF and BIEN data sets revealed a paucity of occurrence data for 148 species (66%), 72% of which qualified for a threatened category. Balancing data quantity and accuracy must be considered when using open‐source data. Filling data gaps for threatened species is a conservation priority to improve the coverage of threatened species within biodiversity aggregators.

Topics

No keywords indexed for this article. Browse by subject →

References

57

[1]

10.1002/bes2.1336

[2]

10.3897/zookeys.150.2109

[3]

10.1111/cobi.13071

[4]

10.1111/cobi.12738

[5]

10.1016/j.ecoinf.2013.11.002

[6]

10.1111/1365-2664.12459

[7]

The taxonomic name resolution service: an online tool for automated standardization of plant names

Brad Boyle, Nicole Hopkins, Zhenyuan Lu et al.

BMC Bioinformatics 10.1186/1471-2105-14-16

[8]

10.1016/j.biocon.2010.02.015

[9]

10.1098/rstb.2014.0015

[10]

10.1017/s1367943003003044

[11]

10.1038/s41598-019-53104-6

[12]

10.1177/194008290900200304

[13]

CBD (2019)

[14]

Chamberlain S. "R python, and ruby clients for GBIF species occurrence data" PeerJ (2017)

[15]

10.1016/j.biocon.2016.09.004

[16]

10.1098/rsbl.2015.0843

[17]

10.1111/ddi.12532

[18]

10.1016/j.biocon.2013.12.015

[19]

10.1016/j.gecco.2017.01.008

[20]

10.1111/ecog.01587

[21]

Enquist B. J. "Cyberinfrastructure for an integrated botanical information network to investigate the ecological impacts of global climate change on plant biodiversity" PeerJ (2016)

[22]

ESRI (2011)

[23]

10.1111/geb.12260

[24]

10.1016/j.biocon.2012.01.020

[25]

10.1017/s0030605313001427

[26]

IUCN (2012)

[27]

IUCN. (2019).The IUCN red list of threatened species. Version 2019‐1. Available fromhttp://www.iucnredlist.org.

[28]

10.1111/cobi.12591

[29]

Jørgensen P. M. (2014)

[30]

10.1046/j.1523-1739.1998.97202.x

[31]

10.1111/j.1523-1739.2008.01044.x

[32]

10.1007/s12225-019-9813-6

[33]

The bien r package: A tool to access the Botanical Information and Ecology Network (BIEN) database

Brian S. Maitner, Brad Boyle, Nathan Casler et al.

Methods in Ecology and Evolution 10.1111/2041-210x.12861

[34]

10.1111/geb.12326

[35]

10.1002/ppp3.10053

[36]

Moat J.(2017).rCAT: Conservation assessment tools. R Package Version 0.1.5. Available fromhttps://CRAN.R-project.org/package=rCAT

[37]

10.1002/ppp3.10146

[38]

10.1098/rstb.2017.0402

[39]

10.1111/ddi.12489

[40]

Oliveira‐Filho A. T. (2017)

[41]

10.1016/j.ecoinf.2019.100991

[42]

R Core Team (2018)

[43]

10.1007/s10531-010-9826-9

[44]

10.1016/j.biocon.2011.07.014

[45]

10.1111/ddi.12418

[46]

10.13057/biodiv/d210831

[47]

10.1016/j.tree.2005.10.010

[48]

10.1046/j.1523-1739.1995.09030551.x

[49]

10.1371/journal.pbio.2005075

[50]

10.3897/zookeys.209.3125

Showing 50 of 57 references

Metrics

25

Citations

57

References

Details

Published: Nov 17, 2020
Vol/Issue: 2(12)
License: View

Authors

C

Connor T. Panter

Ecology, Conservation and Zoonosis Research and Enterprise, School of Pharmacy and Biomolecular Sciences University of Brighton Brighton East Sussex United Kingdom; Royal Botanic Gardens, Kew Richmond Surrey United Kingdom

R

Rosemary L. Clegg

Royal Botanic Gardens, Kew Richmond Surrey United Kingdom

J

Justin Moat

Royal Botanic Gardens, Kew Richmond Surrey United Kingdom

S

Steven P. Bachman

Royal Botanic Gardens, Kew Richmond Surrey United Kingdom

B

Bente B. Klitgård

Royal Botanic Gardens, Kew Richmond Surrey United Kingdom

R

Rachel L. White

Ecology, Conservation and Zoonosis Research and Enterprise, School of Pharmacy and Biomolecular Sciences University of Brighton Brighton East Sussex United Kingdom

Cite This Article

Connor T. Panter, Rosemary L. Clegg, Justin Moat, et al. (2020). To clean or not to clean: Cleaning open‐source data improves extinction risk assessments for threatened plant species. Conservation Science and Practice, 2(12). https://doi.org/10.1111/csp2.311

To clean or not to clean: Cleaning open‐source data improves extinction risk assessments for threatened plant species

You May Also Like