Geology topics

Suzanne M. Budge

Publications and source records attributed to Suzanne M. Budge.

8 recordsLinked to original sources

USGS2020

Dietary fat concentrations influence fatty acid assimilation patterns in Atlantic pollock (Pollachius virens)

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 2-fold for major prey items and ~ 5-fold 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.

Philosophical Transactions of the Royal Society B:↗

USGS2018

Energy-rich mesopelagic fishes revealed as a critical prey resource for a deep-diving predator using quantitative fatty acid signature analysis

Understanding the diet of deep-diving predators can provide essential insight to the trophic structure of the mesopelagic ecosystem. Comprehensive population-level diet estimates are exceptionally difficult to obtain for elusive marine predators due to the logistical challenges involved in observing their feeding behavior and collecting samples for traditional stomach content or fecal analyses. We used quantitative fatty acid signature analysis (QFASA) to estimate the diet composition of a wide-ranging mesopelagic predator, the northern elephant seal ( Mirounga angustirostris ), across five years. To implement QFASA, we first compiled a library of prey fatty acid (FA) profiles from the mesopelagic eastern North Pacific. Given the scarcity of a priori diet data for northern elephant seals, our prey library was necessarily large to encompass the range of potential prey in their foraging habitat. However, statistical constraints limit the number of prey species that can be included in the prey library to the number of dietary FAs in the analysis. Exceeding that limit could produce non-unique diet estimates (i.e., multiple diet estimates fit the data equally well). Consequently, we developed a novel ad-hoc method to identify which prey were unlikely to contribute to diet and could, therefore, be excluded from the final QFASA model. The model results suggest that seals predominantly consumed small mesopelagic fishes, including myctophids (lanternfishes) and bathylagids (deep sea smelts), while non-migrating mesopelagic squids comprised a third of their diet, substantially less than suggested by previous studies. Our results revealed that mesopelagic fishes, particularly energy-rich myctophids, were a critical prey resource, refuting the long-held view that elephant seals are squid specialists.

Frontiers in Marine Science↗

USGS2017

Detect and exploit hidden structure in fatty acid signature data

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.

Ecosphere↗

USGS2017

Simultaneous estimation of diet composition and calibration coefficients with fatty acid signature data

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.

Ecology and Evolution↗

USGS2016

Should fatty acid signature proportions sum to 1 for diet estimation?

Knowledge of predator diets, including how diets might change through time or differ among predators, provides essential insights into their ecology. Diet estimation therefore remains an active area of research within quantitative ecology. Quantitative fatty acid signature analysis (QFASA) is an increasingly common method of diet estimation. QFASA is based on a data library of prey signatures, which are vectors of proportions summarizing the fatty acid composition of lipids, and diet is estimated as the mixture of prey signatures that most closely approximates a predator’s signature. Diets are typically estimated using proportions from a subset of all fatty acids that are known to be solely or largely influenced by diet. Given the subset of fatty acids selected, the current practice is to scale their proportions to sum to 1.0. However, scaling signature proportions has the potential to distort the structural relationships within a prey library and between predators and prey. To investigate that possibility, we compared the practice of scaling proportions with two alternatives and found that the traditional scaling can meaningfully bias diet estimators under some conditions. Two aspects of the prey types that contributed to a predator’s diet influenced the magnitude of the bias: the degree to which the sums of unscaled proportions differed among prey types and the identifiability of prey types within the prey library. We caution investigators against the routine scaling of signature proportions in QFASA.

Ecological Research↗

USGS2015

Assessing the robustness of quantitative fatty acid signature analysis to assumption violations

Knowledge of animal diets can provide important insights into life history and ecology, relationships among species in a community and potential response to ecosystem change or perturbation. Quantitative fatty acid signature analysis (QFASA) is a method of estimating diets from data on the composition, or signature, of fatty acids stored in adipose tissue. Given data on signatures of potential prey, a predator diet is estimated by minimizing the distance between its signature and a mixture of prey signatures. Calibration coefficients, constants derived from feeding trials, are used to account for differential metabolism of individual fatty acids. QFASA has been widely applied since its introduction and several variants of the original estimator have appeared in the literature. However, work to compare the statistical properties of QFASA estimators has been limited. One important characteristic of an estimator is its robustness to violations of model assumptions. The primary assumptions of QFASA are that prey signature data contain representatives of all prey types consumed and the calibration coefficients are known without error. We investigated the robustness of two QFASA estimators to a range of violations of these assumptions using computer simulation and recorded the resulting bias in diet estimates. We found that the Aitchison distance measure was most robust to errors in the calibration coefficients. Conversely, the Kullback–Leibler distance measure was most robust to the consumption of prey without representation in the prey signature data. In most QFASA applications, investigators will generally have some knowledge of the prey available to predators and be able to assess the completeness of prey signature data and sample additional prey as necessary. Conversely, because calibration coefficients are derived from feeding trials with captive animals and their values may be sensitive to consumer physiology and nutritional status, their applicability to free-ranging animals is difficult to establish. We therefore recommend that investigators first make any improvements to the prey signature data that seem warranted and then base estimation on the Aitchison distance measure, as it appears to minimize risk from violations of the assumption that is most difficult to verify.

Methods in Ecology and Evolution↗

USGS2015

Distance measures and optimization spaces in quantitative fatty acid signature analysis

Quantitative fatty acid signature analysis has become an important method of diet estimation in ecology, especially marine ecology. Controlled feeding trials to validate the method and estimate the calibration coefficients necessary to account for differential metabolism of individual fatty acids have been conducted with several species from diverse taxa. However, research into potential refinements of the estimation method has been limited. We compared the performance of the original method of estimating diet composition with that of five variants based on different combinations of distance measures and calibration-coefficient transformations between prey and predator fatty acid signature spaces. Fatty acid signatures of pseudopredators were constructed using known diet mixtures of two prey data sets previously used to estimate the diets of polar bears Ursus maritimus and gray seals Halichoerus grypus, and their diets were then estimated using all six variants. In addition, previously published diets of Chukchi Sea polar bears were re-estimated using all six methods. Our findings reveal that the selection of an estimation method can meaningfully influence estimates of diet composition. Among the pseudopredator results, which allowed evaluation of bias and precision, differences in estimator performance were rarely large, and no one estimator was universally preferred, although estimators based on the Aitchison distance measure tended to have modestly superior properties compared to estimators based on the Kullback-Leibler distance measure. However, greater differences were observed among estimated polar bear diets, most likely due to differential estimator sensitivity to assumption violations. Our results, particularly the polar bear example, suggest that additional research into estimator performance and model diagnostics is warranted.

Ecology and Evolution↗

USGS2013

Variation in the response of an Arctic top predator experiencing habitat loss: Feeding and reproductive ecology of two polar bear populations

Polar bears ( Ursus maritimus ) have experienced substantial changes in the seasonal availability of sea ice habitat in parts of their range, including the Beaufort, Chukchi, and Bering Seas. In this study, we compared the body size, condition, and recruitment of polar bears captured in the Chukchi and Bering Seas (CS) between two periods (1986–1994 and 2008–2011) when declines in sea ice habitat occurred. In addition, we compared metrics for the CS population 2008–2011 with those of the adjacent southern Beaufort Sea (SB) population where loss in sea ice habitat has been associated with declines in body condition, size, recruitment, and survival. We evaluated how variation in body condition and recruitment were related to feeding ecology. Comparing habitat conditions between populations, there were twice as many reduced ice days over continental shelf waters per year during 2008–2011 in the SB than in the CS. CS polar bears were larger and in better condition, and appeared to have higher reproduction than SB bears. Although SB and CS bears had similar diets, twice as many bears were fasting in spring in the SB than in the CS. Between 1986–1994 and 2008–2011, body size, condition, and recruitment indices in the CS were not reduced despite a 44-day increase in the number of reduced ice days. Bears in the CS exhibited large body size, good body condition, and high indices of recruitment compared to most other populations measured to date. Higher biological productivity and prey availability in the CS relative to the SB, and a shorter recent history of reduced sea ice habitat, may explain the maintenance of condition and recruitment of CS bears. Geographic differences in the response of polar bears to climate change are relevant to range-wide forecasts for this and other ice-dependent species.

Beaufort Sea; Bering Sea; Chukchi Sea↗