Widespread sampling biases in herbaria revealed from large-scale digitization
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,...
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Veröffentlicht in: | The New phytologist 2018-01, Vol.217 (2), p.939-955 |
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creator | 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. |
description | 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. |
doi_str_mv | 10.1111/nph.14855 |
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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.</description><identifier>ISSN: 0028-646X</identifier><identifier>EISSN: 1469-8137</identifier><identifier>DOI: 10.1111/nph.14855</identifier><identifier>PMID: 29083043</identifier><language>eng</language><publisher>England: New Phytologist Trust</publisher><subject>Accumulators ; Australia ; Bias ; Collection ; Collections ; collector bias ; Digitization ; Endangered & extinct species ; geographic bias ; Geography ; herbarium ; Models, Theoretical ; New records ; Phylogeny ; Plants - anatomy & histology ; Quantitative Trait, Heritable ; Rare species ; Records ; regional flora ; Regions ; Regression Analysis ; Sampling ; sampling bias ; Selection Bias ; temporal bias ; Threatened species ; Time Factors ; trait bias</subject><ispartof>The New phytologist, 2018-01, Vol.217 (2), p.939-955</ispartof><rights>2017 New Phytologist Trust</rights><rights>2017 The Authors. New Phytologist © 2017 New Phytologist Trust</rights><rights>2017 The Authors. New Phytologist © 2017 New Phytologist Trust.</rights><rights>Copyright © 2018 New Phytologist Trust</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4765-709dd75742439cc71e9b00ffe9323dbe1c8cf2143710b66e681edba71d306b7a3</citedby><cites>FETCH-LOGICAL-c4765-709dd75742439cc71e9b00ffe9323dbe1c8cf2143710b66e681edba71d306b7a3</cites><orcidid>0000-0003-4151-6081 ; 0000-0002-2115-0257</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/90016953$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/90016953$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,776,780,799,1411,1427,27903,27904,45553,45554,46388,46812,57996,58229</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29083043$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Daru, Barnabas H.</creatorcontrib><creatorcontrib>Park, Daniel S.</creatorcontrib><creatorcontrib>Primack, Richard B.</creatorcontrib><creatorcontrib>Willis, Charles G.</creatorcontrib><creatorcontrib>Barrington, David S.</creatorcontrib><creatorcontrib>Whitfeld, Timothy J. S.</creatorcontrib><creatorcontrib>Seidler, Tristram G.</creatorcontrib><creatorcontrib>Sweeney, Patrick W.</creatorcontrib><creatorcontrib>Foster, David R.</creatorcontrib><creatorcontrib>Ellison, Aaron M.</creatorcontrib><creatorcontrib>Davis, Charles C.</creatorcontrib><title>Widespread sampling biases in herbaria revealed from large-scale digitization</title><title>The New phytologist</title><addtitle>New Phytol</addtitle><description>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.</description><subject>Accumulators</subject><subject>Australia</subject><subject>Bias</subject><subject>Collection</subject><subject>Collections</subject><subject>collector bias</subject><subject>Digitization</subject><subject>Endangered & extinct species</subject><subject>geographic bias</subject><subject>Geography</subject><subject>herbarium</subject><subject>Models, Theoretical</subject><subject>New records</subject><subject>Phylogeny</subject><subject>Plants - anatomy & histology</subject><subject>Quantitative Trait, Heritable</subject><subject>Rare species</subject><subject>Records</subject><subject>regional flora</subject><subject>Regions</subject><subject>Regression Analysis</subject><subject>Sampling</subject><subject>sampling bias</subject><subject>Selection Bias</subject><subject>temporal bias</subject><subject>Threatened species</subject><subject>Time Factors</subject><subject>trait bias</subject><issn>0028-646X</issn><issn>1469-8137</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp10E1LwzAYB_AgipvTgx9AKXjRQ7ekaZLmKEOdMF8Oit5K2jzdMvpm0inz01vXbQfBXB4Iv-fPwx-hU4KHpH2jsp4PSRgxtof6JOTSjwgV-6iPcRD5POTvPXTk3AJjLBkPDlEvkDiiOKR99PBmNLjagtKeU0Wdm3LmJUY5cJ4pvTnYRFmjPAufoHLQXmarwsuVnYHv0vbH02ZmGvOtGlOVx-ggU7mDk80coNfbm5fxxJ8-3d2Pr6d-GgrOfIGl1oKJMAipTFNBQCYYZxlIGlCdAEmjNAtISAXBCefAIwI6UYJoinkiFB2gyy63ttXHElwTF8alkOeqhGrpYiKZEFwwSVp68YcuqqUt2-taFUlOWbBWV51KbeWchSyurSmUXcUEx78dx23H8brj1p5vEpdJAXont6W2YNSBL5PD6v-k-PF5so086zYWrqnsbkNiTLhklP4AvYSOvQ</recordid><startdate>201801</startdate><enddate>201801</enddate><creator>Daru, Barnabas H.</creator><creator>Park, Daniel S.</creator><creator>Primack, Richard B.</creator><creator>Willis, Charles G.</creator><creator>Barrington, David S.</creator><creator>Whitfeld, Timothy J. S.</creator><creator>Seidler, Tristram G.</creator><creator>Sweeney, Patrick W.</creator><creator>Foster, David R.</creator><creator>Ellison, Aaron M.</creator><creator>Davis, Charles C.</creator><general>New Phytologist Trust</general><general>Wiley Subscription Services, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7SN</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H95</scope><scope>L.G</scope><scope>M7N</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-4151-6081</orcidid><orcidid>https://orcid.org/0000-0002-2115-0257</orcidid></search><sort><creationdate>201801</creationdate><title>Widespread sampling biases in herbaria revealed from large-scale digitization</title><author>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.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4765-709dd75742439cc71e9b00ffe9323dbe1c8cf2143710b66e681edba71d306b7a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Accumulators</topic><topic>Australia</topic><topic>Bias</topic><topic>Collection</topic><topic>Collections</topic><topic>collector bias</topic><topic>Digitization</topic><topic>Endangered & extinct species</topic><topic>geographic bias</topic><topic>Geography</topic><topic>herbarium</topic><topic>Models, Theoretical</topic><topic>New records</topic><topic>Phylogeny</topic><topic>Plants - anatomy & histology</topic><topic>Quantitative Trait, Heritable</topic><topic>Rare species</topic><topic>Records</topic><topic>regional flora</topic><topic>Regions</topic><topic>Regression Analysis</topic><topic>Sampling</topic><topic>sampling bias</topic><topic>Selection Bias</topic><topic>temporal bias</topic><topic>Threatened species</topic><topic>Time Factors</topic><topic>trait bias</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Daru, Barnabas H.</creatorcontrib><creatorcontrib>Park, Daniel S.</creatorcontrib><creatorcontrib>Primack, Richard B.</creatorcontrib><creatorcontrib>Willis, Charles G.</creatorcontrib><creatorcontrib>Barrington, David S.</creatorcontrib><creatorcontrib>Whitfeld, Timothy J. S.</creatorcontrib><creatorcontrib>Seidler, Tristram G.</creatorcontrib><creatorcontrib>Sweeney, Patrick W.</creatorcontrib><creatorcontrib>Foster, David R.</creatorcontrib><creatorcontrib>Ellison, Aaron M.</creatorcontrib><creatorcontrib>Davis, Charles C.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Ecology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 1: Biological Sciences & Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>The New phytologist</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Daru, Barnabas H.</au><au>Park, Daniel S.</au><au>Primack, Richard B.</au><au>Willis, Charles G.</au><au>Barrington, David S.</au><au>Whitfeld, Timothy J. S.</au><au>Seidler, Tristram G.</au><au>Sweeney, Patrick W.</au><au>Foster, David R.</au><au>Ellison, Aaron M.</au><au>Davis, Charles C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Widespread sampling biases in herbaria revealed from large-scale digitization</atitle><jtitle>The New phytologist</jtitle><addtitle>New Phytol</addtitle><date>2018-01</date><risdate>2018</risdate><volume>217</volume><issue>2</issue><spage>939</spage><epage>955</epage><pages>939-955</pages><issn>0028-646X</issn><eissn>1469-8137</eissn><abstract>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.</abstract><cop>England</cop><pub>New Phytologist Trust</pub><pmid>29083043</pmid><doi>10.1111/nph.14855</doi><tpages>17</tpages><orcidid>https://orcid.org/0000-0003-4151-6081</orcidid><orcidid>https://orcid.org/0000-0002-2115-0257</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Accumulators Australia Bias Collection Collections collector bias Digitization Endangered & extinct species geographic bias Geography herbarium Models, Theoretical New records Phylogeny Plants - anatomy & histology Quantitative Trait, Heritable Rare species Records regional flora Regions Regression Analysis Sampling sampling bias Selection Bias temporal bias Threatened species Time Factors trait bias |
title | Widespread sampling biases in herbaria revealed from large-scale digitization |
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