28Species occurrence records provide the basis for many biodiversity studies. They derive from geo-referenced specimens deposited in natural history collections and visual observations, such as those obtained through various mobile applications. Given the rapid increase in availability of such data, the control of quality and accuracy constitutes a particular concern. Automatic flagging and filtering are a scalable and reproducible means to identify potentially problematic records in datasets from public databases such as the Global Biodiversity Information Facility (GBIF; www.gbif.org). However, it is unclear how much data may be lost by filtering, whether the same tests should be applied across all taxonomic groups, and what is the effect of filtering for common downstream analyses. Here, we evaluate the effect of 13 recently proposed filters on the inference of species richness patterns and automated conservation assessments for 18 Neotropical taxa including animals, fungi, and plants, terrestrial and marine, downloaded from GBIF. We find that 29-90% of the records are potentially erroneous, with large variation across taxonomic groups. Tests for duplicated information, collection year, basis of record as well as urban areas and coordinates for terrestrial taxa in the sea or marine taxa on land have the greatest effect. While many flagged records might not be de facto erroneous, they could be overly imprecise and increase uncertainty in downstream analyses. Automated flagging can help in identifying problematic records, but requires customization of which tests and thresholds should be applied to the taxonomic group and geographic area under focus. Our results stress the importance of thorough exploration of the meta-data associated with species records for biodiversity research. 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44Publicly available species distribution data have become a crucial resource in biodiversity research, including studies in 46 ecology, biogeography, systematics and conservation biology. In particular, the availability of digitized collections from 47 museums and herbaria, and citizen science observations has increased drastically over the last few years. As of today, 48 the largest public aggregator for geo-referenced species occurrences data, the Global Biodiversity Information Facility 49 (www.gbif.org), provides access to more than 1.3 billion geo-referenced occurrence records for species from across the 50 globe and the tree of life.
51A central challenge to the use of these publicly available species occurrence data in research are erroneous geographic 52 coordinates (Anderson et al. 2016). Errors mostly arise because public databases integrate records collected with 53 different methodologies in different places, at different times; often without centralized curation and only rudimentary 54 meta-data. For instance, erroneous coordinates caused by data-entry errors or automated geo-referencing from vague 55 locality descriptions are common (Maldonado et al. 2015; Yesson et al. 2007)...
