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.

Background: Poorly preserved biological tissues have become an important source of DNA for a wide range of zoological studies. Measuring the quality of DNA obtained from these samples is often desired; however, there are no widely used techniques available for quantifying damage in highly degraded DNA samples. We present a general method that can be used to determine the frequency of polymerase blocking DNA damage in specific gene-regions in such samples. The approach uses quantitative PCR to measure the amount of DNA present at several fragment sizes within a sample. According to a model of random degradation the amount of available template will decline exponentially with increasing fragment size in damaged samples, and the frequency of DNA damage (λ) can be estimated by determining the rate of decline.

DNA metabarcoding is a powerful new tool allowing characterization of species assemblages using high-throughput amplicon sequencing. The utility of DNA metabarcoding for quantifying relative species abundances is currently limited by both biological and technical biases which influence sequence read counts. We tested the idea of sequencing 50/50 mixtures of target species and a control species in order to generate relative correction factors (RCFs) that account for multiple sources of bias and are applicable to field studies. RCFs will be most effective if they are not affected by input mass ratio or co-occurring species. In a model experiment involving three target fish species and a fixed control, we found RCFs did vary with input ratio but in a consistent fashion, and that 50/50 RCFs applied to DNA sequence counts from various mixtures of the target species still greatly improved relative abundance estimates (e.g. average per species error of 19 ± 8% for uncorrected vs. 3 ± 1% for corrected estimates). To demonstrate the use of correction factors in a field setting, we calculated 50/50 RCFs for 18 harbour seal (Phoca vitulina) prey species (RCFs ranging from 0.68 to 3.68). Applying these corrections to field-collected seal scats affected species percentages from individual samples (Δ 6.7 ± 6.6%) more than population-level species estimates (Δ 1.7 ± 1.2%). Our results indicate that the 50/50 RCF approach is an effective tool for evaluating and correcting biases in DNA metabarcoding studies. The decision to apply correction factors will be influenced by the feasibility of creating tissue mixtures for the target species, and the level of accuracy needed to meet research objectives.

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The production of marine protein from fishing and aquaculture is influenced by environmental conditions. Ocean temperature, for example, can change the growth rate of cultured animals, or the distribution of wild stocks. In turn these impacts may require changes in fishing or farming practices. In addition to short‐term environmental fluctuations, long‐term climate‐related trends are also resulting in new conditions, necessitating adjustment in fishing, farming and management approaches. Longer‐term climate forecasts, however, are seen as less relevant by many in the seafood sector owing to more immediate concerns. Seasonal forecasts provide insight into upcoming environmental conditions, and thus allow improved decision making. Forecasts based on dynamic ocean models are now possible and offer improved performance relative to statistical forecasts, particularly given baseline shifts in the environment as a result of climate change. Seasonal forecasting is being used in marine farming and fishing operations in Australia, including wild tuna and farmed salmon and prawns, to reduce uncertainty and manage business risks. Forecast variables include water temperature, rainfall and air temperature, and are considered useful up to approximately 4 months into the future, depending on the region and season of interest. Species‐specific habitat forecasts can also be made by combining these environment forecasts with biological habitat preference data. Seasonal forecasts are useful when a range of options are available for implementation in response to the forecasts. The use of seasonal forecasts in supporting effective marine management may also represent a useful stepping stone to improved decision making and industry resilience at longer timescales.