DNA barcode trnH-psbA is a promising candidate for efficient identification of forage legumes and grasses

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Loera-Sánchez, Miguel;Studer, Bruno;Kölliker, Roland

Description

Objective Grasslands are widespread ecosystems that fulfil many functions. Plant species richness (PSR) is known to have beneficial effects on such functions and monitoring PSR is crucial for tracking the effects of land use and agricultural management on these ecosystems. Unfortunately, traditional morphology-based methods are labor-intensive and cannot be adapted for high-throughput assessments. DNA barcoding could aid increasing the throughput of PSR assessments in grasslands. In this proof-of-concept work, we aimed at determining which of three plant DNA barcodes (rbcLa, matK and trnH-psbA) best discriminates 16 key grass and legume species common in temperate sub-alpine grasslands. Results Barcode trnH-psbA had a 100% correct assignment rate (CAR) in the five analyzed legumes, followed by rbcLa (93.3%) and matK (55.6%). Barcode trnH-psbA had a 100% CAR in the grasses Cynosurus cristatus, Dactylis glomerata and Trisetum flavescens. However, the closely related Festuca, Lolium and Poa species were not always correctly identified, which led to an overall CAR in grasses of 66.7 %, 50.0% and 46.4% for trnH-psbA, matK and rbcLa, respectively. Barcode trnH-psbA is thus the most promising candidate for PSR assessments in permanent grasslands and could greatly support plant biodiversity monitoring on a larger scale. Content of data file This data file contains all raw data obtained during the study. The full information on the project can be found on the BOLD database (http://www.boldsystems.org/index.php/Public_SearchTerms) using the search term SWFRG

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Metrics

Dataset Index

0.3

FAIR Score

77%

Citations

0

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

Zenodo

License

Creative Commons Attribution 4.0 International

Open Access

Assigned Domain

Subfield

Agronomy and Crop Science

Field

Agricultural and Biological Sciences

Domain

Life Sciences

Confidence Score

94%

Source

Open Alex

Normalization Factors

FT

76.92

CTw

1.00

MTw

1.00