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The eastern Beaufort Sea (EBS) beluga whale Delphinapterus leucas population has experienced a 20 yr decline in inferred growth rates of individuals, which is hypothesized to have resulted from changes in prey availability. We used fatty acid signatures and stable isotope ratios to reconstruct the proportional contributions of 14 prey species to the diets of 178 beluga whales from 2011 to 2014. Prey estimates using quantitative fatty acid signature analysis suggest that EBS beluga whales primarily consume Arctic cod Boreogadus saida, a species highly sensitive to climate change. Prey estimates varied with year and sex and size class of the whales, with large males consuming the highest proportions of Arctic cod, and females consuming the highest proportions of capelin Mallotus villosus. Estimated proportional contributions of Arctic cod to beluga diet decreased from 2011 to 2014, coinciding with an increase in capelin. Belugas consumed the highest proportions of capelin and the lowest proportions of cod in 2014, the same year in which body condition indices were lowest in the whales. We hypothesize that changing conditions in the Beaufort Sea ecosystem may result in a decreased consumption of Arctic cod by belugas and increased consumption of capelin, which may result in a decline in condition. This may predominately affect females and juveniles since they consume the highest proportions of capelin; however, long-term monitoring is needed for confirmation. Understanding inter-annual variation in prey, and the longer-term nutritional implications of shifting from an Arctic cod- to a capelindominated diet should be a priority for monitoring EBS predators.

Changes in trophodynamics may affect trophic niche both at the individual and population levels. Using stable isotope ratios, we showed how contrasting oceanographic and trophic conditions in 1997–2003 and 2015–2020 have altered the diet and degree of individual specialization of St. Lawrence Estuary beluga ( Delphinapterus leucas ). The trophic niche of all sex and age classes changed over time, with beluga consuming more small pelagic prey during the first than the second period. Adult male diets differed from that of adult females and juveniles during the first period due to the other prey that were consumed. In 2015–2020, diet contributions by small pelagic prey decreased in all segments of the population and led to marginally significant differences in diet between adult males and females. These dietary changes were concomitant to a diversification of diet at the individual level and to an increase in diet heterogeneity among conspecifics and years within the 2015–2020 period. Whether these patterns emerged from an environment-driven reduction in prey biomass or from an increase in intra- and/or interspecific competition is unknown. Our findings illustrate the importance of considering individuals and not just the population when studying the foraging ecology of endangered species.

Quantitative fatty acid signature analysis (QFASA) is a well‐established diet estimation method that has been used extensively on a wide variety of marine mammal species. The method, along with its new refinements and extensions, requires the use of statistically intricate tools, many of which are computationally demanding. Recent developments in QFASA include a maximum likelihood framework for diet estimation, statistically valid inference procedures such as confidence intervals for the diet and hypothesis tests for comparing fatty acid signatures and/or diets, a measure of repeatability in the diet estimates, a prey species selection algorithm, as well as novel ways to estimate calibration coefficients, which are used to improve accuracy in the estimates. The QFASA R package was developed to facilitate access to the latest statistical QFASA tools and provide a means of efficiently disseminating new QFASA‐related research, often developed by statisticians in collaboration with biologists. Further, using up‐to‐date functions ensures that QFASA methods are being applied in a legitimate and consistent manner. In this work, we present the QFASA R package, highlighting key functions for diet estimation and demonstrating their use with sample data available in the package. The QFASA R package is user‐friendly, offers a broad range of functionality, and the vast majority of the functions are unique to this package. Quantitative fatty acid signature analysis (QFASA) is a well‐established diet estimation method that has been used extensively on a wide variety of marine mammal species. The method requires the use of statistically intricate tools, many of which are computationally demanding. The QFASA R package allows ecologists to access the latest statistical QFASA methodology and provides a means of efficiently disseminating new related research.

A key aspect in the use of fatty acids (FA) to estimate predator diets using quantitative FA signature analysis (QFASA) is the ability to account for FA assimilation through the use of calibration coefficients (CC). Here, we tested the assumption that CC are independent of dietary fat concentrations by feeding Atlantic pollock ( Pollachius virens ) three formulated diets with very similar FA proportions but different fat concentrations (5–9% of diet) for 20 weeks. CC calculated using FA profiles of diet and triacylglycerols in pollock liver were significantly different for the three diets. To test the robustness of diet estimates to these differences, we used the CC set derived from feeding the diet with the lowest fat concentration, published prey FA profiles and realistic diet estimates of pollock to construct ‘pseudo-predators'. Application of QFASA to each pseudo-predator using the three sets of CC and the same prey FA profiles resulted in diet estimate biases of twofold for major prey items and approximately fivefold for minor prey items. This work illustrates the importance of incorporating diets with fat concentrations that are similar to natural prey when conducting feeding experiments to calculate CC. This article is part of the theme ‘The next horizons for lipids as ‘trophic biomarkers': evidence and significance of consumer modification of dietary fatty acids'.

Herbivorous primary consumers are a key intermediate trophic linkage between primary production from microalgae, macrophytes, and vascular plants and higher-level consumers. Fatty acid (FA) biomarkers are useful for evaluating trophic interactions in aquatic ecosystems because of clear phylogenetic separation of algal group FA signatures. We used a FA-based Bayesian mixing model (FASTAR) to generate quantitative diet estimates of 3 algal phyla for an intertidal herbivorous isopod, Idotea wosnesenskii, at 6 sites in Puget Sound, Washington, USA. We generated a ‘resource library’ of FA signatures of isopods fed diverse algal diets in 10-wk feeding trials and used these empirical data to parameterize FASTAR, thus accounting for isopod modification of dietary FA. The FA profiles of fast-growing juvenile Idotea were closely related to the signatures of their diets, and several polyunsaturated FA (PUFA) were highly correlated between diet and consumer (e.g. ΣC18ω6 + C18ω3, 20:4ω6, and 20:5ω3). We used the model to characterize individual isopod diet variability within sites and to test whether isopods utilize specific algal phyla preferentially or in similar proportions to algae available in the field. The results identified both variation in resource utilization among individual isopods within certain sites, and site level similarities with total available algal cover. Body mass index of wild isopods was highest at sites where the model indicated high utilization (e.g. >30%) of both green and brown algae and low support from red algae. This novel FA-based mixing model approach demonstrated the potential for quantitative diet estimations of fast-growing aquatic herbivorous consumers.

Understanding diet composition is fundamental to making conservation and management decisions about depleted species, particularly when nutritional stress is a potential threat hindering recovery. Diet in free-ranging marine mammals is challenging to study, but stable isotope mixing models are a powerful means of estimating the contribution of prey species to diet and can improve precision by leveraging information from multiple data sources. We evaluated diet composition of a fish-eating killer whale population (Southern Resident killer whales, Orcinus orca) using 2 approaches. First, we fit generalized additive models to evaluate seasonal and interannual patterns in isotopic values across age, sex, and pod, which revealed seasonal carbon enrichment for certain pods and a recent increased nitrogen enrichment that could suggest increased Chinook salmon consumption, changing isotopic values of prey, or nutritional stress. Second, we developed a Bayesian stable isotope mixing model that accounts for the different integration times represented by bulk stable isotopes and fecal samples. Results showed that estimated prey contributions are similar between prey data sources, though the precision of estimates from periods with smaller sample sizes was improved by using an informative prior to account for the different consumption windows of the data. This study illustrates the importance of improving our understanding of how killer whale diets vary over time (both seasonally and across years) and uses a novel approach to resolve 2 sources of diet information (stable isotope, fecal samples) with different consumption windows.

Assessing the diet of wild animals reveals valuable information about their ecology and trophic relationships that may help elucidate dynamic interactions in ecosystems and forecast responses to environmental changes. Advances in molecular biology provide valuable research tools in this field. However, comparative empirical research is still required to highlight strengths and potential biases of different approaches. Therefore, this study compares environmental DNA and observational methods for the same study population and sampling duration. We employed DNA metabarcoding assays targeting plant and arthropod diet items in 823 fecal samples collected over 12 months in a wild population of an omnivorous primate, the vervet monkey (Chlorocebus pygerythrus). DNA metabarcoding data were subsequently compared to direct observations. We observed the same seasonal patterns of plant consumption with both methods; however, DNA metabarcoding showed considerably greater taxonomic coverage and resolution compared to observations, mostly due to the construction of a local plant DNA database. We found a strong effect of season on variation in plant consumption largely shaped by the dry and wet seasons. The seasonal effect on arthropod consumption was weaker, but feeding on arthropods was more frequent in spring and summer, showing overall that vervets adapt their diet according to available resources. The DNA metabarcoding assay outperformed also direct observations of arthropod consumption in both taxonomic coverage and resolution. Combining traditional techniques and DNA metabarcoding data can therefore not only provide enhanced assessments of complex diets and trophic interactions to the benefit of wildlife conservationists and managers but also opens new perspectives for behavioral ecologists studying whether diet variation in social species is induced by environmental differences or might reflect selective foraging behaviors. In this study, we compare observational and eDNA methodologies for studying plant and arthropod diet items of wild vervet monkeys (Chlorocebus pygerythrus). We observed the same seasonal patterns with both methods, however, DNA metabarcoding showed considerably greater taxonomic coverage and resolution compared to observations. The application of a DNA metabarcoding approach can be useful not only for conservation studies aimed at disentangling complex diets or reveal trophic interactions, but also opens new perspectives for behavioural ecologists studying social species in the wild.

Knowledge of animal diets provides essential insights into their life history and ecology, although diet estimation is challenging and remains an active area of research. Quantitative fatty acid signature analysis (QFASA) has become a popular method of estimating diet composition, especially for marine species. A primary assumption of QFASA is that constants called calibration coefficients, which account for the differential metabolism of individual fatty acids, are known. In practice, however, calibration coefficients are not known, but rather have been estimated in feeding trials with captive animals of a limited number of model species. The impossibility of verifying the accuracy of feeding trial derived calibration coefficients to estimate the diets of wild animals is a foundational problem with QFASA that has generated considerable criticism. We present a new model that allows simultaneous estimation of diet composition and calibration coefficients based only on fatty acid signature samples from wild predators and potential prey. Our model performed almost flawlessly in four tests with constructed examples, estimating both diet proportions and calibration coefficients with essentially no error. We also applied the model to data from Chukchi Sea polar bears, obtaining diet estimates that were more diverse than estimates conditioned on feeding trial calibration coefficients. Our model avoids bias in diet estimates caused by conditioning on inaccurate calibration coefficients, invalidates the primary criticism of QFASA, eliminates the need to conduct feeding trials solely for diet estimation, and consequently expands the utility of fatty acid data to investigate aspects of ecology linked to animal diets. We present a new model for diet estimation with fatty acid data. The model simultaneously estimates both diet composition and calibration coefficients using fatty acid signature samples from wild predators and potential prey. This breakthrough eliminates bias caused by conditioning on inaccurate feeding trial calibration coefficients, nullifies the primary criticism of quantitative fatty acid signature analysis, and substantially increases the utility of fatty acid data to investigate aspects of predator ecology linked to their diets.

Understanding the dietary habits of yellow-billed loons ( Gavia adamsii ) can give important insights into their ecology, however, studying the diet of loons is difficult when direct observation or specimen collection is impractical. We investigate the diet of yellow-billed loons nesting on the Arctic Coastal Plain of Alaska using quantitative fatty acid signature analysis. Tissue analysis from 26 yellow-billed loons and eleven prey groups (nine fish species and two invertebrate groups) from Arctic lakes suggests that yellow-billed loons are eating high proportions of Alaska blackfish ( Dallia pectoralis ), broad whitefish ( Coregonus nasus ) and three-spined stickleback ( Gasterosteus aculeatus ) during late spring and early summer. The prominence of blackfish in diets highlights the widespread availability of blackfish during the early stages of loon nesting, soon after spring thaw. The high proportions of broad whitefish and three-spined stickleback may reflect a residual signal from the coastal staging period prior to establishing nesting territories on lakes, when loons are more likely to encounter these species. Our analyses were sensitive to the choice of calibration coefficient based on data from three different species, indicating the need for development of loon-specific coefficients for future study and confirmation of our results. Regardless, fish that are coastally distributed and that successfully overwinter in lakes are likely key food items for yellow-billed loons early in the nesting season.

Estimates of predator diet composition are essential to our understanding of their ecology. Although several methods of estimating diet are practiced, methods based on biomarkers have become increasingly common. Quantitative fatty acid signature analysis (QFASA) is a popular method that continues to be refined and extended. Quantitative fatty acid signature analysis is based on differences in the signatures of prey types, often species, which are recognized and designated by investigators. Similarly, predator signatures may be structured by known factors such as sex or age class, and the season or region of sample collection. The recognized structure in signature data inherently influences QFASA results in important and typically beneficial ways. However, predator and prey signatures may contain additional, hidden structure that investigators either choose not to incorporate into an analysis or of which they are unaware, being caused by unknown ecological mechanisms. Hidden structure also influences QFASA results, most often negatively. We developed a new method to explore signature data for hidden structure, called divisive magnetic clustering (DIMAC). Our DIMAC approach is based on the same distance measure used in diet estimation, closely linking methods of data exploration and parameter estimation, and it does not require data transformation or distributional assumptions, as do many multivariate ordination methods in common use. We investigated the potential benefits of the DIMAC method to detect and subsequently exploit hidden structure in signature data using two prey signature libraries with quite different characteristics. We found that the existence of hidden structure in prey signatures can increase the confusion between prey types and thereby reduce the accuracy and precision of QFASA diet estimates. Conversely, the detection and exploitation of hidden structure represent a potential opportunity to improve predator diet estimates and may lead to new insights into the ecology of either predator or prey. The DIMAC algorithm is implemented in the R diet estimation package qfasar.