Document Type

Article

Publication Version

Version of Record

Publication Date

1-2021

Abstract

Premise
The tallgrass prairies of North America are one of the most threatened ecosystems in the world, making efficient species identification essential for understanding and managing diversity. Here, we assess DNA barcoding with high‐throughput sequencing as a method for rapid plant species identification.
Methods
Using herbarium collections representing the tallgrass prairie flora of Oak Lake Field Station, South Dakota, USA, we amplified and examined four common nuclear and plastid barcode regions (ITS, matK, psbA‐trnH, and rbcL), individually and in combination, to test their success in identifying samples to family, genus, and species levels using BLAST searches of three databases of varying size.
Results
Concatenated barcodes increased performance, although none were significantly different than single‐region barcodes. The plastid region psbA‐trnH performed significantly more poorly than the others, while barcodes containing ITS performed best. Database size significantly affected identification success at all three taxonomic levels. Confident species‐level identification ranged from 8–44% for the global database, 13–56% for the regional database, and 21–80% for the sampled species database, depending on the barcode used.
Discussion
Barcoding was generally successful in identifying tallgrass prairie genera and families, but was of limited use in species‐level identifications. Database size was an important factor in successful plant identification. We discuss future directions and considerations for improving the performance of DNA barcoding in tallgrass prairies.

Publication Title

Applications in Plant Science

Volume

9

Issue

1

First Page

e11405

DOI of Published Version

10.1002/aps3.11405

Publisher

Wiley on behalf of the Botanical Society of AMerica

Rights

Copyright © 2021 Herzog and Latvis

Creative Commons License

Creative Commons Attribution-NonCommercial 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

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