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

Abstract

Advances in DNA sequencing technology have revolutionized the field of molecular analysis of trophic interactions, and it is now possible to recover counts of food DNA sequences from a wide range of dietary samples. But what do these counts mean? To obtain an accurate estimate of a consumer's diet should we work strictly with data sets summarizing frequency of occurrence of different food taxa, or is it possible to use relative number of sequences? Both approaches are applied to obtain semi-quantitative diet summaries, but occurrence data are often promoted as a more conservative and reliable option due to taxa-specific biases in recovery of sequences. We explore representative dietary metabarcoding data sets and point out that diet summaries based on occurrence data often overestimate the importance of food consumed in small quantities (potentially including low-level contaminants) and are sensitive to the count threshold used to define an occurrence. Our simulations indicate that using relative read abundance (RRA) information often provides a more accurate view of population-level diet even with moderate recovery biases incorporated; however, RRA summaries are sensitive to recovery biases impacting common diet taxa. Both approaches are more accurate when the mean number of food taxa in samples is small. The ideas presented here highlight the need to consider all sources of bias and to justify the methods used to interpret count data in dietary metabarcoding studies. We encourage researchers to continue addressing methodological challenges and acknowledge unanswered questions to help spur future investigations in this rapidly developing area of research.

DNA metabarcoding assays reveal a diverse prey assemblage for Mobula rays in the Bohol Sea, Philippines

“…Deagle et al, 2018). In our case, where we study the diet of four closely related sympatric species, biases are very likely to be consistent among the four ray species, so it is reasonable to infer that all of them eat a similar proportion of Euphausia and have a similarly low level of dependence on nonkrill items.…”

Messy eaters: Swabbing prey DNA from the exterior of inconspicuous predators when foraging cannot be observed

“…We determined that beaks and talons can be swabbed together to increase likelihood of collecting prey DNA, as both sample types contained prey DNA that was able to be amplified and se- (Vo & Jedlicka, 2014). Lastly, all biases associated with using DNA metabarcoding for dietary analyses should be considered (Deagle et al, 2018).…”

DNA metabarcoding in diet studies: Unveiling ecological aspects in aquatic and terrestrial ecosystems

“…Although metabarcoding applied to dietary studies is now well established, there are still some important limitations to the method, the most important being the lack of a standardized method for quantitative assessment of prey items, as the use of the number of reads belonging to a certain prey as a proxy for its abundance or biomass is not straightforward, nor are the simple counts of sequence reads to estimate the relative levels of prey diversity available (reviewed by Deagle et al, 2019). This is due to many factors, which include biological issues, such as the number of target genes present in each prey cell, body size, and prey digestibility, and also technical factors such as primer annealing bias and PCR random effects (Corse et al, 2019;Darby, Todd, & Herman, 2013;Deagle, Thomas, Shaffer, Trites, & Jarman, 2013;Mumma et al, 2016).…”

“…This is due to many factors, which include biological issues, such as the number of target genes present in each prey cell, body size, and prey digestibility, and also technical factors such as primer annealing bias and PCR random effects (Corse et al, 2019;Darby, Todd, & Herman, 2013;Deagle, Thomas, Shaffer, Trites, & Jarman, 2013;Mumma et al, 2016). The inclusion of mock communities of known composition, sequenced alongside the samples of interest, might allow establishing a direct correlation between the number of reads and the biomass/number of individuals or estimating a correction factor for both biological and technical biases (Thomas et al, 2016), but different outcomes have been obtained from different experiments and the feasibility of such trials varies (reviewed by Deagle et al, 2019). For a successful quantification of prey items from sequence reads, it is necessary to (a) design a good sampling scheme, covering both prey and predator putative spatiotemporal diversities while ensuring the distinction between rare and frequent prey taxa; (b) delimit and describe bioinformatic thresholds, from quality control of raw reads to counting of sequence reads, increasing both accuracy of taxonomic assignments and allowing reproducibility; (c) choose the most appropriate metrics (i.e., using an oversimplification, estimates based on relative abundances for generalist predators or per cent of occurrence for less generalist feeders); and d) test new methodologies (from species sampling and laboratory procedures to description of better-suited mathematical models and tools for their application; Deagle et al, 2019).…”

“…The inclusion of mock communities of known composition, sequenced alongside the samples of interest, might allow establishing a direct correlation between the number of reads and the biomass/number of individuals or estimating a correction factor for both biological and technical biases (Thomas et al, 2016), but different outcomes have been obtained from different experiments and the feasibility of such trials varies (reviewed by Deagle et al, 2019). For a successful quantification of prey items from sequence reads, it is necessary to (a) design a good sampling scheme, covering both prey and predator putative spatiotemporal diversities while ensuring the distinction between rare and frequent prey taxa; (b) delimit and describe bioinformatic thresholds, from quality control of raw reads to counting of sequence reads, increasing both accuracy of taxonomic assignments and allowing reproducibility; (c) choose the most appropriate metrics (i.e., using an oversimplification, estimates based on relative abundances for generalist predators or per cent of occurrence for less generalist feeders); and d) test new methodologies (from species sampling and laboratory procedures to description of better-suited mathematical models and tools for their application; Deagle et al, 2019). For aquatic organisms, the search for standardized methodologies accounting for differential digestibility of prey items is particularly important and challenging, since mainly stomach and intestinal contents can be assessed (but see Jarman et al, 2002;Jarman & Wilson, 2004), with prey items at different stages of digestibility significantly influencing the amount of DNA recovered for each prey.…”