TY - JOUR
T1 - Metabarcoding versus mapping unassembled shotgun reads for identification of prey consumed by arthropod epigeal predators
AU - Paula, DCrossed D.Sign©bora Pires
AU - Barros, Suellen Karina Albertoni
AU - Pitta, Rafael Major
AU - Barreto, Marliton Rocha
AU - Togawa, Roberto Coiti
AU - Andow, David A.
N1 - 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.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - diet analysis
KW - environmental DNA
KW - generalist predators
KW - gut content analysis
UR - http://www.scopus.com/inward/record.url?scp=85128000757&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85128000757&partnerID=8YFLogxK
U2 - 10.1093/gigascience/giac020
DO - 10.1093/gigascience/giac020
M3 - Article
C2 - 35333301
AN - SCOPUS:85128000757
SN - 2047-217X
VL - 11
JO - GigaScience
JF - GigaScience
M1 - giac020
ER -