Applying Pollen DNA Metabarcoding to the Study of Plant–Pollinator Interactions

Premise of the study: To study pollination networks in a changing environment, we need accurate, high-throughput methods. Previous studies have shown that more highly resolved networks can be constructed by studying pollen loads taken from bees, relative to field observations. DNA metabarcoding pote...

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Published in	Applications in plant sciences Vol. 5; no. 6
Main Authors	Bell, Karen L, Fowler, Julie, Burgess, Kevin S, Dobbs, Emily K, Gruenewald, David, Lawley, Brice, Morozumi, Connor, Brosi, Berry J
Format	Journal Article
Language	English
Published	United States Botanical Society of America 01.06.2017 John Wiley & Sons, Inc
Subjects	Application APPLICATION ARTICLES bees Contamination Deoxyribonucleic acid DNA DNA barcoding DNA metabarcoding Florida Forest management genes Identification ITS light microscopy Methods Nucleotide sequence palynology Plant reproduction Plant sciences plant–pollinator interactions Pollen Pollination pollination networks rbcL Studies Taxonomy Florida United States > US palynology ITS plant–pollinator interactions rbcL DNA metabarcoding pollination networks
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Abstract	Premise of the study: To study pollination networks in a changing environment, we need accurate, high-throughput methods. Previous studies have shown that more highly resolved networks can be constructed by studying pollen loads taken from bees, relative to field observations. DNA metabarcoding potentially allows for faster and finer-scale taxonomic resolution of pollen compared to traditional approaches (e.g., light microscopy), but has not been applied to pollination networks. Methods: We sampled pollen from 38 bee species collected in Florida from sites differing in forest management. We isolated DNA from pollen mixtures and sequenced rbcL and ITS2 gene regions from all mixtures in a single run on the Illumina MiSeq platform. We identified species from sequence data using comprehensive rbcL and ITS2 databases. Results: We successfully built a proof-of-concept quantitative pollination network using pollen metabarcoding. Discussion: Our work underscores that pollen metabarcoding is not quantitative but that quantitative networks can be constructed based on the number of interacting individuals. Due to the frequency of contamination and false positive reads, isolation and PCR negative controls should be used in every reaction. DNA metabarcoding has advantages in efficiency and resolution over microscopic identification of pollen, and we expect that it will have broad utility for future studies of plant–pollinator interactions.
AbstractList	Premise of the study: To study pollination networks in a changing environment, we need accurate, high-throughput methods. Previous studies have shown that more highly resolved networks can be constructed by studying pollen loads taken from bees, relative to field observations. DNA metabarcoding potentially allows for faster and finer-scale taxonomic resolution of pollen compared to traditional approaches (e.g., light microscopy), but has not been applied to pollination networks. Methods: We sampled pollen from 38 bee species collected in Florida from sites differing in forest management. We isolated DNA from pollen mixtures and sequenced rbcL and ITS2 gene regions from all mixtures in a single run on the Illumina MiSeq platform. We identified species from sequence data using comprehensive rbcL and ITS2 databases. Results: We successfully built a proof-of-concept quantitative pollination network using pollen metabarcoding. Discussion: Our work underscores that pollen metabarcoding is not quantitative but that quantitative networks can be constructed based on the number of interacting individuals. Due to the frequency of contamination and false positive reads, isolation and PCR negative controls should be used in every reaction. DNA metabarcoding has advantages in efficiency and resolution over microscopic identification of pollen, and we expect that it will have broad utility for future studies of plant–pollinator interactions. To study pollination networks in a changing environment, we need accurate, high-throughput methods. Previous studies have shown that more highly resolved networks can be constructed by studying pollen loads taken from bees, relative to field observations. DNA metabarcoding potentially allows for faster and finer-scale taxonomic resolution of pollen compared to traditional approaches (e.g., light microscopy), but has not been applied to pollination networks.PREMISE OF THE STUDYTo study pollination networks in a changing environment, we need accurate, high-throughput methods. Previous studies have shown that more highly resolved networks can be constructed by studying pollen loads taken from bees, relative to field observations. DNA metabarcoding potentially allows for faster and finer-scale taxonomic resolution of pollen compared to traditional approaches (e.g., light microscopy), but has not been applied to pollination networks.We sampled pollen from 38 bee species collected in Florida from sites differing in forest management. We isolated DNA from pollen mixtures and sequenced rbcL and ITS2 gene regions from all mixtures in a single run on the Illumina MiSeq platform. We identified species from sequence data using comprehensive rbcL and ITS2 databases.METHODSWe sampled pollen from 38 bee species collected in Florida from sites differing in forest management. We isolated DNA from pollen mixtures and sequenced rbcL and ITS2 gene regions from all mixtures in a single run on the Illumina MiSeq platform. We identified species from sequence data using comprehensive rbcL and ITS2 databases.We successfully built a proof-of-concept quantitative pollination network using pollen metabarcoding.RESULTSWe successfully built a proof-of-concept quantitative pollination network using pollen metabarcoding.Our work underscores that pollen metabarcoding is not quantitative but that quantitative networks can be constructed based on the number of interacting individuals. Due to the frequency of contamination and false positive reads, isolation and PCR negative controls should be used in every reaction. DNA metabarcoding has advantages in efficiency and resolution over microscopic identification of pollen, and we expect that it will have broad utility for future studies of plant-pollinator interactions.DISCUSSIONOur work underscores that pollen metabarcoding is not quantitative but that quantitative networks can be constructed based on the number of interacting individuals. Due to the frequency of contamination and false positive reads, isolation and PCR negative controls should be used in every reaction. DNA metabarcoding has advantages in efficiency and resolution over microscopic identification of pollen, and we expect that it will have broad utility for future studies of plant-pollinator interactions. To study pollination networks in a changing environment, we need accurate, high-throughput methods. Previous studies have shown that more highly resolved networks can be constructed by studying pollen loads taken from bees, relative to field observations. DNA metabarcoding potentially allows for faster and finer-scale taxonomic resolution of pollen compared to traditional approaches (e.g., light microscopy), but has not been applied to pollination networks. We sampled pollen from 38 bee species collected in Florida from sites differing in forest management. We isolated DNA from pollen mixtures and sequenced and ITS2 gene regions from all mixtures in a single run on the Illumina MiSeq platform. We identified species from sequence data using comprehensive and ITS2 databases. We successfully built a proof-of-concept quantitative pollination network using pollen metabarcoding. Our work underscores that pollen metabarcoding is not quantitative but that quantitative networks can be constructed based on the number of interacting individuals. Due to the frequency of contamination and false positive reads, isolation and PCR negative controls should be used in every reaction. DNA metabarcoding has advantages in efficiency and resolution over microscopic identification of pollen, and we expect that it will have broad utility for future studies of plant-pollinator interactions. Premise of the study: To study pollination networks in a changing environment, we need accurate, high‐throughput methods. Previous studies have shown that more highly resolved networks can be constructed by studying pollen loads taken from bees, relative to field observations. DNA metabarcoding potentially allows for faster and finer‐scale taxonomic resolution of pollen compared to traditional approaches (e.g., light microscopy), but has not been applied to pollination networks. Methods: We sampled pollen from 38 bee species collected in Florida from sites differing in forest management. We isolated DNA from pollen mixtures and sequenced rbcL and ITS2 gene regions from all mixtures in a single run on the Illumina MiSeq platform. We identified species from sequence data using comprehensive rbcL and ITS2 databases. Results: We successfully built a proof‐of‐concept quantitative pollination network using pollen metabarcoding. Discussion: Our work underscores that pollen metabarcoding is not quantitative but that quantitative networks can be constructed based on the number of interacting individuals. Due to the frequency of contamination and false positive reads, isolation and PCR negative controls should be used in every reaction. DNA metabarcoding has advantages in efficiency and resolution over microscopic identification of pollen, and we expect that it will have broad utility for future studies of plant–pollinator interactions.
Author	Gruenewald, David Morozumi, Connor Brosi, Berry J Bell, Karen L Burgess, Kevin S Lawley, Brice Dobbs, Emily K Fowler, Julie
Author_xml	– sequence: 1 givenname: Karen L surname: Bell fullname: Bell, Karen L organization: CSIRO Land and Water and CSIRO Health and Biosecurity, 147 Underwood Avenue, Floreat, Western Australia 6014, Australia – sequence: 2 givenname: Julie surname: Fowler fullname: Fowler, Julie organization: Department of Environmental Sciences, Emory University, 400 Dowman Drive, Atlanta, Georgia 30322 USA – sequence: 3 givenname: Kevin S surname: Burgess fullname: Burgess, Kevin S organization: Department of Biology, Columbus State University, Columbus, Georgia 31907-5645 USA – sequence: 4 givenname: Emily K surname: Dobbs fullname: Dobbs, Emily K organization: Department of Environmental Sciences, Emory University, 400 Dowman Drive, Atlanta, Georgia 30322 USA – sequence: 5 givenname: David surname: Gruenewald fullname: Gruenewald, David organization: Department of Environmental Sciences, Emory University, 400 Dowman Drive, Atlanta, Georgia 30322 USA – sequence: 6 givenname: Brice surname: Lawley fullname: Lawley, Brice organization: Department of Environmental Sciences, Emory University, 400 Dowman Drive, Atlanta, Georgia 30322 USA – sequence: 7 givenname: Connor surname: Morozumi fullname: Morozumi, Connor organization: Department of Environmental Sciences, Emory University, 400 Dowman Drive, Atlanta, Georgia 30322 USA – sequence: 8 givenname: Berry J surname: Brosi fullname: Brosi, Berry J organization: Department of Environmental Sciences, Emory University, 400 Dowman Drive, Atlanta, Georgia 30322 USA
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Copyright	2017 The Author(s) 2017. This work is published under http://creativecommons.org/licenses/by-nc-sa/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2017 Bell et al. Published by the Botanical Society of America 2017
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Keywords	palynology ITS plant–pollinator interactions rbcL DNA metabarcoding pollination networks
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Notes	The authors thank the U.S. Army Research Office (grants W911NF‐13‐1‐0247 and W911NF‐13‐1‐0100) and the U.S. Department of Agriculture (USDA‐NIFA‐2012‐67009‐20090) for funding. The authors thank Rachel Gardner for fieldwork assistance and Isabel Gottleib and Robert Fletcher for assistance with site selection and project logistics. Sam Droege provided extensive assistance with identification of difficult bee specimens. The authors thank Alexander Keller and Markus Ankenbrand (University of Würzburg) for providing advice on adapting their bioinformatics pipeline to our rbcL reference library. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 The authors thank the U.S. Army Research Office (grants W911NF-13-1-0247 and W911NF-13-1-0100) and the U.S. Department of Agriculture (USDA-NIFA-2012-67009-20090) for funding. The authors thank Rachel Gardner for fieldwork assistance and Isabel Gottleib and Robert Fletcher for assistance with site selection and project logistics. Sam Droege provided extensive assistance with identification of difficult bee specimens. The authors thank Alexander Keller and Markus Ankenbrand (University of Würzburg) for providing advice on adapting their bioinformatics pipeline to our rbcL reference library.
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Snippet	Premise of the study: To study pollination networks in a changing environment, we need accurate, high-throughput methods. Previous studies have shown that more... Premise of the study: To study pollination networks in a changing environment, we need accurate, high‐throughput methods. Previous studies have shown that more... To study pollination networks in a changing environment, we need accurate, high-throughput methods. Previous studies have shown that more highly resolved... Premise of the study:To study pollination networks in a changing environment, we need accurate, high‐throughput methods. Previous studies have shown that more... PREMISE OF THE STUDY: To study pollination networks in a changing environment, we need accurate, high‐throughput methods. Previous studies have shown that more...
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SubjectTerms	Application APPLICATION ARTICLES bees Contamination Deoxyribonucleic acid DNA DNA barcoding DNA metabarcoding Florida Forest management genes Identification ITS light microscopy Methods Nucleotide sequence palynology Plant reproduction Plant sciences plant–pollinator interactions Pollen Pollination pollination networks rbcL Studies Taxonomy
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Title	Applying Pollen DNA Metabarcoding to the Study of Plant–Pollinator Interactions
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