Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data?

Area of Science:

• Ecology
• Genetics
• Bioinformatics

Background:

• DNA sequencing advancements enable molecular analysis of trophic interactions.
• Dietary metabarcoding recovers food DNA sequence counts from various samples.
• Interpreting these sequence counts for accurate diet estimation remains a challenge.

Purpose of the Study:

• To compare the accuracy of diet summaries derived from occurrence data versus relative read abundance (RRA).
• To investigate biases associated with each method in dietary metabarcoding.
• To provide guidance on interpreting DNA sequence count data in ecological studies.

Main Methods:

• Exploration of representative dietary metabarcoding datasets.
• Simulations incorporating taxa-specific recovery biases.
• Analysis of diet summaries based on frequency of occurrence and RRA.

Main Results:

• Occurrence data can overestimate low-quantity prey and are sensitive to count thresholds.
• RRA generally provides a more accurate population-level diet view, even with moderate biases.
• Both methods improve accuracy when the number of food taxa per sample is small.

Conclusions:

• Methodological choices significantly impact dietary metabarcoding interpretations.
• RRA may be more reliable than occurrence data, but both have limitations.
• Further research is needed to address biases and refine interpretation methods.