Abstract
AbstractPlants 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.
Highlights
Despite their important role as the foundation of ecosystems, plants are often under-represented in conservation research (Di Marco et al, 2017)
Target 2 serves as an underpinning target that must be delivered in order to make quantitative responses to other Global Strategy for Plant Conservation (GSPC) targets referring to extinction risk, for example, Target 7 “threatened species conserved in situ” and Target 8 “threatened species in ex situ collections”
These specimens are of particular scientific interest as they have not been digitized prior to the Tropical Important Plant Areas (TIPAs) project and not used in any other large-scale study and our control records for these remained unavailable from open-source aggregators such as Global Biodiversity Information Facility (GBIF) and Botanical Information and Ecology Network (BIEN)
Summary
We applied a two-stage cleaning protocol using a selected group of plants from our study area (Figure 1). The associated herbarium specimen data for these species and their synonyms were collated from eight herbaria (see Appendix S1) These specimens are of particular scientific interest as they have not been digitized prior to the TIPAs. project and not used in any other large-scale study and our control records for these remained unavailable from open-source aggregators such as GBIF and BIEN. We standardized the species names of the control data set according to the accepted names, and noted their synonyms, gleaned from the NeoTropTree (Oliveira-Filho, 2017; http://www.neotroptree.info) and The Plant List databases (http://www.theplantlist.org) (Figure 2). The R package “rCAT” (Moat, 2017) was used to compute EOOs based on a convex hull for each species from (a) the control, GBIF, and BIEN data sets and (b) for each cleaning stage (uncleaned, automated and manual). Similar to Nic Lughadha et al (2018), we adopted a confusion matrix to examine differences and similarities between threat categories comparing (a) accuracy rates (overall, how often was the classifier correct when predicting threat categories), (b) sensitivity rates (rate of correctly predicting threatened categories [VU, EN and CR]) and (c) specificity rates (rate of correctly predicting nonthreatened categories (LC, NT, and DD)
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