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

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

Development and characterization of microsatellite loci for common raven (Corvus corax) and cross species amplification in other Corvidae

BACKGROUND: A priority for conservation is the identification of endemic populations. We developed microsatellite markers for common raven (Corvus corax), a bird species with a Holarctic distribution, to identify and assess endemic populations in Alaska. RESULTS: From a total of 50 microsatellite loci, we isolated and characterized 15 loci. Eight of these loci were polymorphic and readily scoreable. Eighteen to 20 common ravens from Fairbanks, Alaska were genotyped showing the following variability: 3–8 alleles per locus, 0.25–0.80 observed heterozygosity (H(o)), and 0.30–0.80 expected heterozygosity (H(e)). All loci were in Hardy–Weinberg equilibrium and linkage equilibrium and many loci amplified and were polymorphic in related taxa. CONCLUSIONS: These loci will be used to identify endemic populations of common raven and assess their genetic diversity and connectivity

Predicting the structure of soil communities from plant community taxonomy, phylogeny, and traits

There are numerous ways in which plants can influence the composition of soil communities. However, it remains unclear whether information on plant community attributes, including taxonomic, phylogenetic, or trait-based composition, can be used to predict the structure of soil communities. We tested, in both monocultures and field-grown mixed temperate grassland communities, whether plant attributes predict soil communities including taxonomic groups from across the tree of life (fungi, bacteria, protists, and metazoa). The composition of all soil community groups was affected by plant species identity, both in monocultures and in mixed communities. Moreover, plant community composition predicted additional variation in soil community composition beyond what could be predicted from soil abiotic characteristics. In addition, analysis of the field aboveground plant community composition and the composition of plant roots suggests that plant community attributes are better predictors of soil communities than root distributions. However, neither plant phylogeny nor plant traits were strong predictors of soil communities in either experiment. Our results demonstrate that grassland plant species form specific associations with soil community members and that information on plant species distributions can improve predictions of soil community composition. These results indicate that specific associations between plant species and complex soil communities are key determinants of biodiversity patterns in grassland soils