The choice of universal primers and the characteristics of the species mixture determine when DNA metabarcoding can be quantitative

Josep Piñol, Miquel A. Senar, William O.C. Symondson

Research output: Contribution to journalArticleResearch

51 Citations (Scopus)

Abstract

DNA metabarcoding is a technique used to survey biodiversity in many ecological settings, but there are doubts about whether it can provide quantitative results, that is, the proportions of each species in the mixture as opposed to a species list. While there are several experimental studies that report quantitative metabarcoding results, there are a similar number that fail to do so. Here, we provide the rationale to understand under what circumstances the technique can be quantitative. In essence, we simulate a mixture of DNA of S species with a defined initial abundance distribution. In the simulated PCR, each species increases its concentration following a certain amplification efficiency. The final DNA concentration will reflect the initial one when the efficiency is similar for all species; otherwise, the initial and final DNA concentrations would be poorly related. Although there are many known factors that modulate amplification efficiency, we focused on the number of primer–template mismatches, arguably the most important one. We used 15 common primers pairs targeting the mitochondrial COI region and the mitogenomes of ca. 1,200 insect species. The results showed that some primers pairs produced quantitative results under most circumstances, whereas some other primers failed to do so. In conclusion, depending on the primer pair used in the PCR amplification and on the characteristics of the mixture analysed (i.e., high species richness, low evenness), DNA metabarcoding can provide a quantitative estimate of the relative abundances of different species.
Original languageEnglish
Pages (from-to)407-419
Number of pages13
JournalMolecular Ecology
Volume28
Issue number2
DOIs
Publication statusPublished - 1 Jan 2019

Keywords

  • COI
  • diet analysis
  • environmental DNA
  • in silico PCR
  • insects
  • primer bias

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