GBIF cleaning
GBIF data are useful for testing species distributions. To ensure
exclusion of inaccurate localities, we filtered them stepwise before
assessing ERMs. Firstly, oceanic records were removed with a terrestrial
mask. An ecoregion map (https://ecoregions2017.appspot.com/) was then
used to filter samples clearly in the wrong localities using the realms
species occupy according to IUCN data. Corrections were made when listed
IUCN realms were regularly inconsistent with distribution maps, and
further analysis to assay what realm the IUCN range maps were needed
assay genuine realms IUCN maps fell into, to develop matching filters
and using those realms to filter GBIF data from the same realms.
As GBIF data includes some synonyms, these were also corrected prior to
their use. Synonym lists were developed via IUCN-lists, for birds
Clements bird checklist
(https://www.worldbirdnames.org/ioc-lists/master-list-2/). As IUCN
lists sometimes gave species as both synonyms and true species, any
species listed as both was corrected during filtering. Given the slow
rate of taxonomic updates on the redlist e.g only 45% of amphibian
species described between 2004- 2016 were assessed by the IUCN (Tapley
et al., 2018) and GBIF efforts to update data filters, our approaches
are an appropriate resolution and accuracy to assess under-estimation in
species ranges from their polygon data. Following GBIF filtering,
percentages of points within appropriate polygons were calculated.
Analyses of relationships between species range boundaries and
administrative boundaries (coastal, non-coastal) were made in R.