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Global Patterns and Drivers of Bee Distribution.

Michael C Orr | Alice C Hughes | Douglas Chesters | John Pickering | Chao-Dong Zhu | John S Ascher
Current biology : CB | 2021

Insects are the focus of many recent studies suggesting population declines, but even invaluable pollination service providers such as bees lack a modern distributional synthesis. Here, we combine a uniquely comprehensive checklist of bee species distributions and >5,800,000 public bee occurrence records to describe global patterns of bee biodiversity. Publicly accessible records are sparse, especially from developing countries, and are frequently inaccurate throughout much of the world, consequently suggesting different biodiversity patterns from checklist data. Global analyses reveal hotspots of species richness, together generating a rare bimodal latitudinal richness gradient, and further analyses suggest that xeric areas, solar radiation, and non-forest plant productivity are among the most important global drivers of bee biodiversity. Together, our results provide a new baseline and best practices for studies on bees and other understudied invertebrates.

Pubmed ID: 33217320

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Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.

This is a list of tools and resources that we have found mentioned in this publication.


Atlas of Living Australia (tool)

RRID:SCR_006467

Online repository of information about Australian plants, animals, and fungi. Development started in 2006. The Commonwealth Scientific and Industrial Research Organisation is organisation significantly involved in development of ALA.

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Integrated Digitized Biocollections (tool)

RRID:SCR_014336

iDigBio is the National Resource for Advancing Digitization of Biodiversity Collections (ADBC) funded by the National Science Foundation. Through ADBC, data and images for millions of biological specimens are being made available in electronic format for the research community, government agencies, students, educators, and the general public.

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Maxent (tool)

RRID:SCR_021830

Software tool for modeling species niches and distributions by applying machine learning technique called maximum entropy modeling. Used for maximum entropy modelling of species geographic distributions.

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