The power, potential, and pitfalls of open access biodiversity data in range size assessments: Lessons from the fishes

Geographic rarity is a driver of a species’ intrinsic risk of extinction. It encompasses multiple key components including range size, which is one of the most commonly measured estimates of geographic rarity. Range size estimates are often used to prioritize conservation efforts when there are multiple candidate species, because data for other components of rarity such as population size are sparse, or do not exist for species of interest. Range size estimates can provide rankings of species vulnerability to changing environments or threats, identifying rare species for future study or conservation initiatives. However, range sizes can be estimated by several different metrics, and the degree of overlap in the identification of the rarest or most common species across methodologies is not well understood. This knowledge gap compromises our ability to prioritize correctly rare species, and presents a particularly difficult challenge for stream-dwelling organisms with distributions constrained to river networks. We evaluated the relationship of multiple range size estimates of a subset of freshwater fishes native to the United States to determine the degree of overlap in rarity rankings using different data sources and grain sizes. We used publicly available, open access data from the Global Biodiversity Information Facility (GBIF) to calculate extent of occurrence (minimum convex polygons) and area of occupancy (total area occupied, measured across various grain sizes). We compared range sizes estimated using GBIF data with the best available estimates of current distributions described by publicly available digital maps (NatureServe) to evaluate the efficacy of GBIF data in assessments of range size. We found strong correlations between range size estimates across analytical approaches and data sources with no detectable bias of taxonomy. We found that variation among rarity rankings was highest for species with intermediate range sizes indicating that the approaches considered here generally converge when used to identify the rarest or the most common species. Importantly, our results show that the rarest, and perhaps the most vulnerable, species are consistently identified across common methodological approaches. More broadly, our results support the use of open access biodiversity data that include opportunistically collated and collected point occurrence records as a complement to coarse-grain (e.g., whole range map) approaches, as we observed no systematic bias or deviation across data sources in our analyses. This indicates databases such as the GBIF may help fill important fundamental and applied knowledge gaps for many poorly understood species, particularly in a broad-scale, multispecies framework.