Metabarcoding versus mapping unassembled shotgun reads for identification of prey consumed by arthropod epigeal predators

DCrossed D.Sign©bora Pires Paula, Suellen Karina Albertoni Barros, Rafael Major Pitta, Marliton Rocha Barreto, Roberto Coiti Togawa, David A. Andow

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Background: A central challenge of DNA gut content analysis is to identify prey in a highly degraded DNA community. In this study, we evaluated prey detection using metabarcoding and a method of mapping unassembled shotgun reads (Lazaro). Results: In a mock prey community, metabarcoding did not detect any prey, probably owing to primer choice and/or preferential predator DNA amplification, while Lazaro detected prey with accuracy 43-71%. Gut content analysis of field-collected arthropod epigeal predators (3 ants, 1 dermapteran, and 1 carabid) from agricultural habitats in Brazil (27 samples, 46-273 individuals per sample) revealed that 64% of the prey species detections by either method were not confirmed by melting curve analysis and 87% of the true prey were detected in common. We hypothesized that Lazaro would detect fewer true- and false-positive and more false-negative prey with greater taxonomic resolution than metabarcoding but found that the methods were similar in sensitivity, specificity, false discovery rate, false omission rate, and accuracy. There was a positive correlation between the relative prey DNA concentration in the samples and the number of prey reads detected by Lazaro, while this was inconsistent for metabarcoding. Conclusions: Metabarcoding and Lazaro had similar, but partially complementary, detection of prey in arthropod predator guts. However, while Lazaro was almost 2× more expensive, the number of reads was related to the amount of prey DNA, suggesting that Lazaro may provide quantitative prey information while metabarcoding did not.

Original languageEnglish (US)
Article numbergiac020
StatePublished - 2022

Bibliographical note

Funding Information:
This work was funded by the United States Department of Agriculture-National Institute of Food and Agriculture (USDANIFA) grant number 2016?67030-24950.

Publisher Copyright:
© 2022 The Author(s) 2022. Published by Oxford University Press GigaScience.


  • diet analysis
  • environmental DNA
  • generalist predators
  • gut content analysis

PubMed: MeSH publication types

  • Journal Article
  • Research Support, U.S. Gov't, Non-P.H.S.


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