Abstract

Conservation assessments of hyperdiverse groups of organisms are often challenging and limited by the availability of occurrence data needed to calculate assessment metrics such as extent of occurrence (EOO). Spiders represent one such diverse group and have historically been assessed using primary literature with retrospective georeferencing. Here we demonstrate the differences in estimations of EOO and hypothetical IUCN Red List classifications for two extensive spider datasets comprising 479 species in total. The EOO were estimated and compared using literature-based assessments, Global Biodiversity Information Facility (GBIF)-based assessments and combined data assessments. We found that although few changes to hypothetical IUCN Red List classifications occurred with the addition of GBIF data, some species (3.3%) which could previously not be classified could now be assessed with the addition of GBIF data. In addition, the hypothetical classification changed for others (1.5%). On the other hand, GBIF data alone did not provide enough data for 88.7% of species. These results demonstrate the potential of GBIF data to serve as an additional source of information for conservation assessments, complementing literature data, but not particularly useful on its own as it stands right now for spiders.

Highlights

  • The mobilisation of biodiversity data through aggregating platforms such as the Global Biodiversity Information Facility (GBIF) has generated excitement about the potential for applying such publicly available data towards filling gaps in biological knowledge (Edwards 2004)

  • Conservation assessments are conducted through the International Union for Conservation of Nature’s (IUCN) Red List framework, which provides information about species threat levels

  • Using GBIF data alone, 17.5% of species from our global taxon list could be classified into a hypothetical IUCN category

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Summary

Introduction

The mobilisation of biodiversity data through aggregating platforms such as the Global Biodiversity Information Facility (GBIF) has generated excitement about the potential for applying such publicly available data towards filling gaps in biological knowledge (Edwards 2004). To this end, the ability to predict species distributions more accurately using aggregated occurrence data may have broad implications for land management, environmental policy, ecosystem monitoring and conservation. A comprehensive, or at least representative, set of georeferenced occurrence data is needed to assess the potential threat to a species

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