Dietary DNA metabarcoding enables researchers to identify and characterize trophic interactions with a high degree of taxonomic precision. It is also sensitive to sources of bias and contamination in the field and lab. One of the earliest and most common strategies for dealing with such sensitivities has been to filter resulting sequence data to remove low-abundance sequences before conducting ecological analyses based on the presence or absence of food taxa. Although this step is now often perceived to be both necessary and sufficient for cleaning up datasets, evidence to support this perception is lacking and more attention needs to be paid to the related risk of introducing other undesirable errors. Using computer simulations, we demonstrate that common strategies to remove low-abundance sequences can erroneously eliminate true dietary sequences in ways that impact downstream dietary inferences. Using real data from well-studied wildlife populations in Yellowstone National Park, we further show how these strategies can markedly alter the composition of individual dietary profiles in ways that scale-up to obscure ecological interpretations about dietary generalism, specialism, and niche partitioning. Although the practice of removing low-abundance sequences may continue to be a useful strategy to address a subset of research questions that focus on a subset of relatively abundant food resources, its continued widespread use risks generating misleading perceptions about the structure of trophic networks. Researchers working with dietary DNA metabarcoding data—or similar data such as environmental DNA, microbiomes, or pathobiomes—should be aware of potential drawbacks and consider alternative bioinformatic, experimental, and statistical solutions.

We used fecal DNA metabarcoding to characterize the diets of bison and bighorn sheep in winter and summer. Our analyses are based on 35 samples (median per species per season = 10) analyzed using the P6 loop of the chloroplast trnL(UAA) intron together with publicly available plant reference data (Illumina sequence read data are available at NCBI (BioProject: PRJNA780500)). Obicut was used to trim reads with a minimum quality threshold of 30, and primers were removed from forward and reverse reads using cutadapt. All further sequence identifications were performed using obitools; forward and reverse sequences were aligned using the illuminapairedend command using a minimum alignment score of 40, and only joined sequences retained. We used the obiuniq command to group identical sequences and tally them within samples, enabling us to quantify the relative read abundance (RRA) of each sequence. Sequences that occurred ≤2 times overall or that were ≤8 bp were discarded. Sequences were considered to be likely PCR artifacts if they were highly similar to another sequence (1 bp difference) and had a much lower abundance (0.05%) in the majority of samples in which they occurred; we discarded these sequences using the obiclean command.

Overall, we characterized 357 plant sequences and a subset of 355 sequences were retained in the dataset after rarefying samples to equal sequencing depth. We then applied relative read abundance thresholds from 0% to 5% to the fecal samples. We compared differences in the inferred dietary richness within and between species based on individual samples, based on average richness across samples, and based on the total richness of each population after accounting for differences in sample size.

The readme file contains an explanation of each of the variables in the dataset. Information on the methodology can be found in the associated manuscript referenced above.