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Advancing the integration of multi‐marker metabarcoding data in dietary analysis of trophic generalists
Author(s) -
Silva Luís P.,
Mata Vanessa A.,
Lopes Pedro B.,
Pereira Paulo,
Jarman Simon N.,
Lopes Ricardo J.,
Beja Pedro
Publication year - 2019
Publication title -
molecular ecology resources
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.96
H-Index - 136
eISSN - 1755-0998
pISSN - 1755-098X
DOI - 10.1111/1755-0998.13060
Subject(s) - biology , taxon , trophic level , molecular marker , genetic marker , evolutionary biology , identification (biology) , computational biology , ecology , genetics , gene
The application of DNA metabarcoding to dietary analysis of trophic generalists requires using multiple markers in order to overcome problems of primer specificity and bias. However, limited attention has been given to the integration of information from multiple markers, particularly when they partly overlap in the taxa amplified, and vary in taxonomic resolution and biases. Here, we test the use of a mix of universal and specific markers, provide criteria to integrate multi‐marker metabarcoding data and a python script to implement such criteria and produce a single list of taxa ingested per sample. We then compare the results of dietary analysis based on morphological methods, single markers, and the proposed combination of multiple markers. The study was based on the analysis of 115 faeces from a small passerine, the Black Wheatears ( Oenanthe leucura ). Morphological analysis detected far fewer plant taxa (12) than either a universal 18S marker (57) or the plant trn L marker (124). This may partly reflect the detection of secondary ingestion by molecular methods. Morphological identification also detected far fewer taxa (23) than when using 18S (91) or the arthropod markers IN16STK (244) and ZBJ (231), though each method missed or underestimated some prey items. Integration of multi‐marker data provided far more detailed dietary information than any single marker and estimated higher frequencies of occurrence of all taxa. Overall, our results show the value of integrating data from multiple, taxonomically overlapping markers in an example dietary data set.