Artificial Hotspot Occurrence Inventory (AHOI)

Aim: Species occurrence records are essential to understanding Earth's biodiversity and addressing global environmental issues, but do not always reflect actual locations of occurrence. Certain geographic coordinates are assigned repeatedly to thousands of observation/collection records. This may result from imperfect data management and georeferencing practices, and can greatly bias the inferred distribution of biodiversity and associated environmental conditions. Nonetheless, these 'biodiverse' coordinates are often overlooked in taxon-centric studies, as they are identifiable only in aggregate across taxa and datasets, and it is difficult to determine their true circumstance without in-depth, focused investigation. Here we assess highly recurring coordinates in biodiversity data to determine artificial hotspots of occurrences.

Location: Global

Taxon: Land plants, birds, mammals, insects

Methods: We identified highly recurring coordinates across plant, bird, insect, and mammal records in the Global Biodiversity Information Facility, the largest aggregator of biodiversity data. We determined which are likely artificial hotspots by examining metadata from over 40 million records; assessing spatial distributions of associated datasets; contacting data managers; and reviewing literature. These results were compiled into the Artificial Hotspot Occurrence Inventory (AHOI). Results: Artificial biodiversity hotspots generally comprised geopolitical and grid centroids. The associated uncertainty ranged from several square kilometers to millions. Such artificial biodiversity hotspots were most prevalent in plant records. For instance, over 100,000 plant occurrence records were assigned the centroid coordinates of Brazil, and points that have at least 1,000 associated occurrences comprised over 9 million records. In contrast, highly recurring coordinates in animal data more often reflected actual sites of observation.

Main Conclusions: AHOI can be used to i) improve accuracy of biodiversity assessments; ii) estimate uncertainty associated with records from artificial hotspots and make informed decisions on whether to include them in scientific studies; and iii) identify problems in biodiversity informatics workflows and priorities for improvement.