Evidence-based strategies to navigate complexity in dietary DNA metabarcoding: A reply.

It is clearly beneficial to eliminate low-abundance sequences that arise in error during dietary DNA metabarcoding studies, but to purge all low-abundance sequences is to risk eliminating real sequences and complicating ecological analyses. Our prior literature review noted that DNA sequence relative read abundance (RRA) thresholds can help ameliorate false-positive taxon occurrences, but that historical emphasis on this utility has fostered uncertainty about the associated risk of inflating the false-negative rate (Littleford-Colquhoun et al., 2022). To address this, we combined a simulation study and an empirical data set to both illustrate the issue and provide blueprints for simulation studies and sensitivity analyses that can help investigators avoid overcorrecting and thereby bolster confidence in ecological inferences. Awareness of both the costs and the benefits of abundance-filtering is needed because accurately characterizing dietary distributions can be critically important for understanding animal diets, nutrition and trophic networks. Highlighting the need to raise awareness, a critique of our paper emphasized the misleading notion that "false positive interactions between species can present fundamentally incorrect network structures in network ecology, whereas false negatives will provide a correct but incomplete version of the network" (Tercel & Cuff, 2022). Asserting that the reliability of results will be eroded by false positives but resilient to the omission of true positives is risky and runs counter to evidence. Unfortunately, abundance-filtering methods can introduce false negatives at higher rates than they eliminate false positives and thereby undermine the analysis of otherwise reliable sequencing data. Overcorrecting can qualitatively alter and ultimately undermine ecological interpretations.

The precautionary principle and dietary DNA metabarcoding: Commonly used abundance thresholds change ecological interpretation.

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 laboratory. One of the earliest and most common strategies for dealing with such sensitivities has been to remove all low-abundance sequences and conduct ecological analyses based on the presence or absence of food taxa. Although this step is now often perceived to be necessary, evidence of its sufficiency is lacking and more attention to the risk of introducing other errors is needed. Using computer simulations, we demonstrate that common strategies to remove low-abundance sequences can erroneously eliminate true dietary sequences in ways that impact downstream 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 dietary profiles in ways that scale-up to obscure ecological interpretations about dietary generalism, specialism, and composition. Although the practice of removing low-abundance sequences may continue to be a useful strategy to address research questions that focus on a subset of relatively abundant foods, 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 drawbacks and consider alternative bioinformatic, experimental, and statistical solutions.