Widespread sampling biases in herbaria revealed from large‐scale digitization. Issue 2 (30th October 2017)

• Record Type: Journal Article
• Authors: Daru, Barnabas H.; Park, Daniel S.; Primack, Richard B.; Willis, Charles G.; Barrington, David S.; Whitfeld, Timothy J. S.; Seidler, Tristram G.; Sweeney, Patrick W.; Foster, David R.; Ellison, Aaron M.; Davis, Charles C.
• Is Part Of: New phytologist. Volume 217:Issue 2(2018)
• Page Start: 939
• Page End: 955
• Publication Date: 2017-10-30
• Subjects: collector bias -- geographic bias -- herbarium -- regional flora -- sampling bias -- temporal bias -- trait bias; Botany -- Periodicals; 580
• DOI: 10.1111/nph.14855
• Languages: English
• ISSNs: 0028-646X

Abstract

Summary: Nonrandom collecting practices may bias conclusions drawn from analyses of herbarium records. Recent efforts to fully digitize and mobilize regional floras online offer a timely opportunity to assess commonalities and differences in herbarium sampling biases. We determined spatial, temporal, trait, phylogenetic, and collector biases in c . 5 million herbarium records, representing three of the most complete digitized floras of the world: Australia (AU), South Africa (SA), and New England, USA (NE). We identified numerous shared and unique biases among these regions. Shared biases included specimens collected close to roads and herbaria; specimens collected more frequently during biological spring and summer; specimens of threatened species collected less frequently; and specimens of close relatives collected in similar numbers. Regional differences included overrepresentation of graminoids in SA and AU and of annuals in AU; and peak collection during the 1910s in NE, 1980s in SA, and 1990s in AU. Finally, in all regions, a disproportionately large percentage of specimens were collected by very few individuals. We hypothesize that these mega‐collectors, with their associated preferences and idiosyncrasies, shaped patterns of collection bias via 'founder effects'. Studies using herbarium collections should account for sampling biases, and future collecting efforts should avoid compounding these biases to the extent possible.