Skip to main content

Leopard seal diets in a rapidly warming polar region vary by year, season, sex, and body size



Resolving the preferred prey items and dietary proportions of leopard seals is central to understanding food-web dynamics in the rapidly-warming Antarctic Peninsula region. Previous studies have identified a wide range of prey items; however, due to anecdotal or otherwise limited information, leopard seal diets remain unresolved by seal sex, individual, body size, region, and season. Over the 2013, 2014, and 2017 field seasons we collected scat, tissue samples (red blood cells and plasma; n = 23) for stable isotope analyses, and previously-reported animal-borne video from 19 adult leopard seals foraging near mesopredator breeding colonies at Cape Shirreff, Livingston Island. We summarized a priori diet information from scat and video analysis and applied a three-isotope (δ13C, δ15N, δ34S), four-source (fish, fur seal, krill, penguin) Bayesian mixing model to examine temporal variability in both prey sources and leopard seal tissues.


The austral spring diets of males and females focused on Antarctic krill (31.7–38.0%), notothen fish (31.6–36.5%), and penguin (24.4–26.9%) and were consistent across all 3 years. Several lines of evidence suggest the transition to summer foraging was distinct for males and females. Female diets transitioned rapidly to higher δ15N values (+2.1‰), indicating increased consumption of penguin (29.5–46.2%) and energy-dense Antarctic fur seal pup (21.3–37.6%).


The seasonal increase in leopard seal δ15N values, and thus fur seal in their diet, was predictably related to larger body size; it may also be forcing reductions to the largest Antarctic fur seal colony in the Antarctic Peninsula. Our ensemble sampling approach reduces historical biases in monitoring marine apex predator diets. Further, our results are necessary to best inform regional fisheries management planning.


Changes in the foraging behavior of large marine predators can fundamentally transform ecosystems through cascading predator–prey interactions [1,2,3]. The Southern Ocean supports one of the largest communities of endothermic predators in the world [4, 5], and many of those populations are fluctuating in a swiftly warming climate [6,7,8]. Leopard seals (Hydrurga leptonyx) are apex Antarctic predators capable of foraging across a range of trophic levels from mesopredators (e.g., penguins and seals) to fish and krill [9, 10]. Quantifying their effect on coastal ecosystems is dependent upon measuring their diet variability across demographic categories and over a variety of time scales. Focal studies of leopard seals have been difficult to conduct, however, leading to a poor understanding of their foraging ecology.

Reports on leopard seal diets using anecdotal data (e.g., reviewed by 11), and stomach contents [9, 12], scats [11, 13], and fatty acid analysis [14] indicate that leopard seals eat planktivorous krill and fish, as well as squid, seabirds, and seals. Krill stands out as a potentially key diet component, but is not consistently observed (e.g., [15]), and these studies generally employed biased techniques. For example, stomach-content and scat data from pinnipeds differentially represent recently consumed, and hard-shelled prey [16, 17] and are further biased by varying prey digestion rates [18]. Therefore, while leopard seal prey items have been identified, diets remain unresolved in terms of variation between seal sexes, and among individuals, age classes, regions, and seasons.

Multiyear behavioral studies utilizing focal observations, bio-loggers, and animal-borne cameras have shown evidence of specialization by individual leopard seals in prey selection, area use [19,20,21], and temporal foraging activity [22]. Individual foraging specialization is both taxonomically widespread and ecologically important [23]; however short observation periods (e.g., video, scat analysis) tend to overestimate individual specialization [24].

Stable isotope analyses (SIA) avoid some of these biases and are commonly used to study the trophic ecology of free ranging pinnipeds (e.g., [25,26,27,28]). Stable carbon (δ13C) and nitrogen (δ15N) isotopes are typically used in diet studies because they reflect the corresponding isotope values of the consumer’s prey field plus tissue-specific trophic discrimination factors which increase consumer isotope values through the processes of diet assimilation and excretion [29]. Stable sulfur (δ34S) isotopes also illustrate consumer diet shifts [30, 31], particularly for carnivores with high-protein diets [32].

Additionally, if isotope values for the consumer and prey items are known, stable isotope mixing models can quantitatively estimate the relative proportions of prey within consumer diets (e.g., [33, 34]). For example, an early isotope mixing model [35] illustrated seasonal variation and individual dietary separation for three leopard seals in East Antarctica [36]. However, these earlier models were unable to incorporate the variance of isotopic measurements which can have dramatic effects on dietary estimates [37]. Recently, Bayesian stable isotope mixing models explicitly characterize uncertainties around the isotopic measurements of consumer tissues, the trophic discrimination factors [38], and prey sources [39,40,41] to ensure that dietary proportions are reported with associated uncertainty.

A further advantage of SIA over conventional diet observations is that consumer tissues assimilate digested diet components over time scales that vary with tissue type. The corresponding time frame depends on the protein turnover rate of the sampled tissue. Therefore, temporal changes in diet can be detected by aligning the time scale of consumer tissue turnover rates with potential diet shifts [42,43,44,45,46]. Several studies have established turnover rates for various pinniped tissue types (e.g., [47, 48]). For example, isotope values from blood plasma and red blood cells provide dietary information on the order of approximately 1 week to 1 month, respectively, prior to pinniped tissue collection [49, 50].

There are limitations to using SIA for quantifying consumer diets, particularly when there is overlap in the stable isotope values from prey resources [51]. Historically, the greatest dietary resolution has been derived from studies which combined SIA and more traditional field data collection (e.g., observations, gut contents, and fecal samples) [52,53,54] as these methods can add a priori or a posteriori information to models to help ensure all prey sources are identified, which is a basic assumption of stable isotope mixing models [37]. Finally, the incorporation of additional stable isotopes (e.g., δ34S) to the more traditionally utilized δ13C and δ15N values can provide greater resolution for Bayesian mixing models when isotope values from potential prey overlap [55].

Describing leopard seal diet variability by individual, sex, and mass over relevant time scales is central to understanding their responses to environmental change [56], and the potential top-down effects they may impose on Antarctic ecosystems in a rapidly changing region [57, 58]. Therefore, the objectives of our study were to: (1) identify leopard seal prey sources (a priori) via analysis of beach-collected seal scats and animal-borne video of seal foraging behavior, (2) estimate their trophic position and dietary proportions using the δ13C, δ15N, and δ34S values from prey sources and leopard seal tissues within a Bayesian stable isotope mixing model, and (3) assess temporal and individual variation in seal diets by comparing red blood cell (RBCs) and plasma SI signatures sampled within and between years.


Study site

We conducted field studies within the U.S. Antarctic Marine Living Resources (AMLR) Program research area at Cape Shirreff on the north shore of Livingston Island, Antarctic Peninsula (Fig. 1, 62.47° S, 60.77° W). Since the late 1990s, adult female leopard seals have been hauling-out on land annually between late-December and May, with peak numbers in January and February, near mesopredator breeding colonies at Cape Shirreff. Reduced sea ice tends to concentrate leopard seals [59, 60]; therefore, the loss of sea ice near Cape Shirreff may have further increased leopard seal density by limiting available haul-outs to coastal beaches. Since 2010, leopard seal densities have increased by two orders of magnitude (> 20 seals/nautical mile2, [21]) over those reported by previous surveys in the region [58, 61]. Predation by leopard seals on breeding penguins (gentoo (Pygoscelis papua) and chinstrap (Pygoscelis antarcticus)) and Antarctic fur seal pups (Arctocephalus gazella) is common between December and March [21, 62], but the contributions of these mesopredators to leopard seal diets has been difficult to quantify.

Fig. 1

The U.S. AMLR long-term ecosystem monitoring site at Cape Shirreff, Livingston Island, Antarctic Peninsula

Stable isotope sampling: consumer

We selected and chemically immobilized [22, 63] healthy adult female (n = 17) and male (n = 2) leopard seals for blood collection during the course of the 2013 (n = 9), 2014 (n = 10), and 2017 (n = 4) field seasons. Field seasons were conducted between October 10 and March 16 each year. We sampled four of those same individuals in different years, providing 23 blood samples from 19 individuals (Table 1). Once sedated, we drew blood samples via a hypodermic needle into vacuumed, additive-free, blood-collection vials. We centrifuged the blood samples to separate plasma and RBC components, then stored them at − 20 °C. Over the three field seasons we also collected 46 scats and 25 visual prey-consumption observations of our study animals (Table 1).

Table 1 Individual identifications and summary foraging statistics for leopard seals sampled for red blood cells (RBC), plasma, and scat in 2013, 2014, and 2017 at Cape Shirreff, Livingston Island (23 samples from 19 individuals)

We measured each animal for standard length and girth [64], and weighed each using a sling, tripod, hand winch, and a tensionometer (MSI-7300 Dyna-Link 2, capacity 1000 ± 0.5 kg). We assigned adult life stage based on standard lengths of > 2.5 m for males and > 2.7 m for females [65]. We collected scats deposited during the capture event in two-gallon plastic bags and froze them for later analysis. Of the 23 animals in our study, we deployed animal-borne cameras on seven (CRITTERCAM, [21]), and we recaptured 20 after ~ 1 week (7.39 ± 4.29 days) to recover instruments, re-sample the seals for blood (RBC/plasma), and re-weigh each individual. Upon capture completion, sedative-reversal pharmaceuticals were administered [63]. Each animal’s recovery was visually monitored until it reached a mobile state. After handling, all animals in this study were re-sighted in a healthy state at least once within 2 weeks of capture.

We thawed scats and rinsed them with fresh water through a series of stainless-steel sieves (range: 2.8–710 mm). We sorted hard prey parts (fish bone, otolith, fur seal bone, etc.) and identified them to species when possible. Additionally, field personnel patrolled study beaches daily and collected fresh scats (warm, no evidence of scavenging by shorebirds) from study animals. These were sorted without sieves, and components were identified to species when possible. In addition to visual observations from animal-borne cameras [21], several observers witnessed one study animal (36 OR) ambush hunting and consuming Antarctic fur seal pups on several occasions.

Stable isotope sampling: prey sources

Based on a literature search from regional sites, scat analysis, and visual observations, we determined the potential prey field of leopard seals at Cape Shirreff to contain demersal fish (notothen spp.), Antarctic fur seal pups, gentoo (Pygoscelis papua) and chinstrap (Pygoscelis antarcticus) penguins, and Antarctic krill (Euphausia superba). Therefore, we collected samples of each prey type from Cape Shirreff during each of the 2013, 2014, and 2017 field seasons when possible. Because baseline stable isotope signals of potential prey sources can vary greatly over small spatial and temporal scales [37, 46, 66], we collected prey samples from Cape Shirreff concurrent with field sampling of leopard seal tissues.

We extracted Antarctic fur seal pup and penguin muscle tissue samples (~ 2 cm × 2 cm) with a scalpel from recently deceased animals that did not appear to be emaciated or obviously diseased. We opportunistically collected whole krill from the shoreline following large storms, and fish heads and bodies discarded by predatory shorebirds (Additional file 1: Table S1). Both fish and krill were the same species and body size of those identified in scats and video. Field personnel cleaned penguin muscle tissue with deionized water and dried the tissue at 60 °C for ≥ 24 h. All other prey samples were packed in plastic bags and frozen at − 20 °C until prepared for stable isotope analysis.

Stable isotope sample preparation

We conducted all stable isotope sample preparation in the Kurle Lab at the University of California San Diego. We thawed all samples, rinsed with deionized water, freeze-dried for ≥ 24 h, and homogenized them with a metal spatula. We extracted lipids from animal tissues with C:N ratios ≥ 3.5 (fur seal and penguin muscle) according to Folch et al. [67] as modified by Sweeting et al. [68] and Post, Layman [69]. We placed each sample in an 18 ml glass tube, added 10 ml of petroleum ether, and sonicated the samples at 40 kHz for 10 min in a water bath warmed to 60 °C. We centrifuged the samples at 12,000g for 5 min, poured or pipetted off the petroleum ether, rinsed the samples with nanopure water, then sonicated the samples again with micro-pure water for 10 min. Sample vials were centrifuged again for 10 min and excess water removed. Finally, we transferred tissues to cryovials and dried them at 43 °C for 24–48 h.

Stable isotope analysis

We packaged subsamples of dried, homogenized tissue (1.0 ± 0.5 mg for δ13C and δ15N, 4.0 ± 0.5 mg for δ34S) into 5 mm × 9 mm tin capsules for analysis. Personnel at the University of California Davis Stable Isotope Facility combusted the samples in a PDZ Europa ANCA-GSL elemental analyzer interfaced with a PDZ Europa 20–20 (δ13C and δ15N) or SerCon 20–22 (δ34S) isotope ratio mass spectrometer (Sercon Ltd., Cheshire, UK). They normalized the raw stable isotope values using laboratory standards calibrated against NIST Standard Reference Materials and calculated sample precision at 0.2‰, 0.3‰ and 0.4‰ for δ13C, δ15N, and δ34S respectively. The abundance of stable isotopes is expressed in notation according to the following equation:

$$\delta {\text{X }} = \frac{{{\text{R}}_{\text{sample}} - {\text{R}}_{\text{standard}} }}{{{\text{R}}_{\text{standard}} }}*1000$$

where X is 13C, 15N or 34S and R is the corresponding ratio of 13C/12C, 15N/14N or 34S/32S. The Rstandard value is set by PeeDee Belemnite for δ13C, atmospheric N2 for δ15N, or Vienna-Canyon Diablo Troilite for δ34S.

Data analysis

We conducted statistical analyses using R [70]. Uncertainty in SIA analyses can be strongly related to the degree to which the stable isotope values from prey or prey categories are distinct from one another in multi-dimensional space [71]. We treated all δ13C–δ15N–δ34S three-dimensional (3D) “isospace” data as spatial [72] and tested for differences between groups of isotopic data using either a K nearest-neighbors randomization test (KNN) [73, 74] for 3D data, or Welch’s two-sample t tests for one-dimensional data. We tested the explanatory power of our model covariates (mass, sex, year) for each set of consumer isotope values using analysis of variance (ANOVA) and linear regression tests. All values are listed as mean (\(\bar{X}\)) ± standard deviation (SD), and all inferences were based on a significance level of P ≤ 0.05 unless otherwise indicated.

A priori prey determinations

We summarized the relative proportions of prey consumed by leopard seals, as estimated from seal scats, in two ways: (1) we calculated the frequency of occurrence (F%) for each prey taxon as the percentage that a prey item was seen in all scats during that season as per Casaux et al. [13]. (2) Because of the partial and inconsistent consumption of prey items consumed by leopard seals, we lacked reliable prey hard parts (e.g., otoliths, jaw bones) needed to quantify the number or original volume of prey consumed. Therefore, as an alternative metric of prey importance, we estimated the volumetric proportion of each diet component in the overall scat composition. We used these scat data to support our a priori choice of prey sources for SIA analysis. We categorized results into “Scat Observations” and “Visual Observations” to reflect the fundamental differences in data collection. A detailed description of animal-borne video deployment and analysis can be found in Krause et al. [21].

Grouping stable isotope data

Reducing the number of prey sources to the fewest ecologically-relevant groups is likely to improve the explanatory power of stable isotope mixing models [51]. Therefore, we grouped each prey type (a.k.a., “source”) into taxonomic categories, including only prey species for which we had evidence of consumption by leopard seals (see above). The “krill” and “fur seal” groups contained only a single species each, and we included the isotope values from all krill and fur seal samples in each group. We combined the stable isotope values from two species of demersal fishes, Notothenia coriiceps and Trematomus newnesi, and categorized them as “fish.” There was no support for isospace separation between gentoo and chinstrap penguins (KNN, P = 0.89), so we combined the isotope values from both species into one “penguin” prey category.

We collected representative prey samples from the “krill” and “fur seal” categories during all three seasons. We were only able to collect “fish” tissue during the 2014 and 2017 seasons, and “penguin” tissue during the 2013 and 2014 seasons. However, the δ15N values from “fish” and “penguin” corresponded closely with published values from locally conducted studies, Zamzow et al. [66] and Polito et al. [54] respectively, and there was no support for differences in the δ13C and δ15N values between years (KNN, P = 0.69). Therefore, we used source stable isotope values unique to the year of their collection for “krill” and “fur seal”, and we used the mean values from collected years for missing years for “fish” and “penguin.”

SIA mixing model theory dictates that each of the source groupings should be distinct in isospace [51, 73]. Therefore, we tested the prey source groupings within each field season using KNN analysis. The null hypothesis was that there was no spatial overlap between groups.

We grouped all leopard seal (a.k.a., “consumer”) isotopic data by tissue type, capture order (1st or 2nd capture), and year. During subsequent analyses, we matched each group with the corresponding source data (by year) and tissue-specific discrimination factor. For inter-annual or inter-tissue comparisons, we used only capture 1 data to maintain independence.

Stable isotope trophic discrimination factors

Stable isotopes fractionate as prey tissues are broken down and assimilated into a consumer, generally leading to higher δ13C and δ15N values in consumer tissues compared to their prey [75]. Such differences are called trophic discrimination factors (TDFs), and are written with the notation Δ13C (and similarly for other stable isotopes). TDFs can vary by species and tissue type [75, 76] and are affected by a host of environmental and physiological variables ([37, 76] and references therein). To account for these variations, it is important to include TDFs that most closely reflect those of the consumer and tissues of interest as dietary estimates generated by the Bayesian stable isotope mixing models can vary widely with different TDFs [34, 77].

We used species and tissue specific TDFs for plasma and RBCs reported in the literature. We chose TDFs determined from captive feeding studies by taking the mean values of the seals most closely related to leopard seals, including: ringed seals (Pusa hispida), spotted seals (Phoca largha), and Hawaiian monk seals (Monachus schauinslandi). The resulting mean (± SD) TDF values for all prey sources in all seasons were: plasma Δ13C = 1.20 ± 0.14‰, Δ15N = 3.85 ± 0.49‰, and Δ34S = − 0.6 ± 0.15‰ and RBC Δ13C = 1.53 ± 0.10‰, Δ15N = 2.75 ± 0.44‰ [78], and Δ34S = − 0.6 ± 0.15‰ [32].

Bayesian stable isotope mixing model (MixSIAR)

We used the Bayesian stable isotope mixing model MixSIAR [79, 80] to estimate leopard seal diet composition. We tested four-source (fish, fur seal, krill, penguin) models with combinations of two isotope (δ13C, δ15N=CN, δ13C, δ34S=CS, and δ15N, δ34S=NS), and three isotope (δ13C, δ15N, and δ34S=CNS) models. Candidate hierarchical models were created to evaluate each of our objective covariates: year, sex, mass, and individual (Seal ID). We applied year and seal ID as random effects, sex as a fixed effect, and mass as a continuous effect. To account for inter-annual shifts in δ13C values at the base of the food web, and because source data were indexed per year, we nested all model effects within year. For example, CNS ~ (1|Year/Seal ID) represents a three-isotope hierarchical model with seal ID nested within year.

We incorporated the stable isotope values (± SD) from the consumers (leopard seals), prey sources, and TDFs into MixSIAR for each year and tissue (plasma or RBC). Due to our low sample sizes (< 20), we modeled the relationship between source and consumer data using a “fully Bayesian” implementation [39]. Prior distributions for relative contributions of source data were Dirichlet, or “generalist,” distributions which are uninformative on the simplex, making all combinations of source data equally likely [80]. The models ran three Markov Chain Monte Carlo (MCMC) chains with 3 million iterations, a burn-in of 1.5 million, and were thinned by 500. MixSIAR reported results as mean, 1 SD, and credible intervals (CI) for each posterior density distribution per prey source. We tested model convergence using Gelman–Rubin and Geweke diagnostics as well as posterior density, trace, running means, and autocorrelation plots. We evaluated model performance using the Deviance Information Criterion (DIC) [81].

Antarctic fur seal availability as potential prey

To provide context for model outputs, we calculated an index of Antarctic fur seal pup availability (as potential leopard seal prey) based on pup census and growth data. We tracked the pup survival of all known (identification-tagged) Antarctic fur seal females daily throughout the season because the survival rate of their pups is an index of survival for the entire pup population. We collected data on pup growth rates starting in early January (30 days after the date of median pupping). Over 4 intervals of 15 days, we randomly selected and weighed 100 fur seal pups. We multiplied the number of live pups by the average weekly pup mass for each of the first eight weeks of the year for 2013, 2014, and 2017.

Additionally, we established the number of locally foraging leopard seals by conducting a Cape Shirreff-wide, weekly phocid census during which we counted and reported all seals by species and life stage.


A priori prey estimates from scat analysis and visual observations

The proportions of prey present in leopard seal diets based on analysis of seal scats and visual observations for 2013, 2014, and 2017 are summarized in Table 2. Fur seal, penguin, and krill remains were identifiable to species in scats, notably, fish were not. Leopard seals typically remove notothen fish heads during prey processing [21]; hence we found no otoliths or diagnostic fish parts in any scat. Therefore, we used identifications from video observations of fish consumption [21], all of which were notothen species (including Notothenia coriiceps) in concurrence with a previous regional report [13]. In 2013 and 2014 Antarctic fur seal was by far the largest prey source identified by frequency of occurrence (2013 = 100%; 2014 = 100%) followed by penguin (2013 = 30.8%; 2014—48.4%), fish (2013 = 23.1%; 2014 = 50.0%), and krill (2013 = 23.1%; 2014 = 0.0%). During 2017 the only scat collected from a study animal was from a male (120OR) and contained 100% krill.

Table 2 Frequency of occurrence (F%) and estimated percentage of total volume (V%) of prey items in leopard seal diets for 2013, 2014, and 2017 based on scat and visual observation data

Stable isotope data

The δ13C, δ15N, and δ34S values for the four prey sources (Additional file 1: Table S1) were distinct in isospace, except for fur seal and fish (Table 3, Fig. 2a, b). Fish and fur seal overlap probabilities were mostly low (Table 3), so we elected to keep them as separate groups in the stable isotope mixing model. Isospace plots showing source (\(\bar{X}\) ± SD) and individual leopard seal δ13C, δ15N, and δ34S values for each category of leopard seal tissue (plasma and RBC) from each year (2013, 2014, 2017) demonstrate that all consumer values fall within the range of the three isotope values from the prey sources (Fig. 2a–f), satisfying mixing model assumptions [37].

Table 3 P-values of K nearest-neighbors randomization tests (KNN) of proximity in isospace for the four leopard seal prey source groups from 2013, 2014, and 2017
Fig. 2

Paired isospace plots of the δ13C, δ15N, and δ34S values from leopard seal plasma (a, c, e) and RBCs (b, d, f) (n = 23, colored dots) plotted with concurrent prey stable isotope values (colored crosses: dotted = krill, dot-dash = penguin, dashed = fur seal, solid = fish) from 2013, 2014, and 2017. The prey sources were corrected using tissue specific (plasma or RBC) trophic discrimination factors

K nearest-neighbor randomization tests showed no statistically significant spatial separation between the 1st and 2nd captures within those groups (KNN, P ≥ 0.99; Additional file 1: Table S2). We detected no trends or differences in the δ13C and δ34S values from leopard seal samples across years and tissues; however, there were some differences in their δ15N values. The δ15N values for all tissue samples across all years were slightly higher (≤ 0.8‰) for those animals sampled in capture 2 vs. 1, although this difference was not significant (Welch’s two-sample t-test; P = 0.23, t = − 1.2, df = 31).

Plasma tissues had higher δ15N values by 0.7 to 2.3‰ compared with those from RBCs for all seasons (n = 23, P < 0.001, t = 7.42, df = 22). Females exhibited a larger average difference than males (+2.1‰ and +0.5‰ respectively, Fig. 3). Finally, the δ15N values from leopard seal plasma had a positive relationship with seal body mass both with (Fig. 4; P < 0.001, Adjusted R2: 0.55) and without (P = 0.02, Adjusted R2: 0.31) males included; whereas the δ13C and δ34S values did not (P = 0.38, Adjusted R2: 0.05; and P = 0.94, Adjusted R2 < 0.01, respectively).

Fig. 3

The δ15N values from all leopard seal plasma (n = 23) and RBCs (n = 23) collected in 2013, 2014, and 2017 and separated by sex, F = female (n = 21), M = male (n = 2). Boxes indicate median and upper and lower 75th and 25th percentiles; whiskers represent 10th and 90th percentiles, and dots represent values outside the 10th and 90th percentiles

Fig. 4

Linear relationship between the δ15N values from leopard seal plasma (n = 23) and leopard seal mass. Dotted lines indicate the 95% confidence intervals

Bayesian stable isotope mixing model (MixSIAR)

The stable isotope mixing models using only two isotopes (δ13C and δ15N, δ13C and δ34S, and δ15N and δ34S) failed to converge, even with extreme MCMC chain lengths (> 3 million) and yielded biologically unreasonable posterior estimates of diet due to a linear distribution of source values in isospace (e.g., Fig. 2a).

Conversely, the MCMC chains for the CNS models likely converged. The Gelman diagnostic for our isotope models incorporating three stable isotopes was < 1.05 for all variables, and the Geweke scores for all chains showed ≤ 5% of variables outside of ± 1.96. Additionally, posterior density plots by chain showed high correspondence, traceplots showed broad mixing through parameter space, running means converged over time, and autocorrelation plots showed a decrease with increasing iterations. For stable isotope values from both plasma and RBC tissue, the models with year alone as a random effect were the most parsimonious, and models with sex nested within year were comparably informative (Table 4).

Table 4 DIC model selection results for the δ13C, δ15N, δ34S stable isotope (CNS) Bayesian mixing models (MixSIAR) used to estimate diets of leopard seals based on stable isotope values from plasma and red blood cells (RBC) sampled from seals at Cape Shirreff, Livingston Island in 2013, 2014, and 2017 (n = 23)

Model posterior distributions provide an estimate, with associated uncertainty, for the contributions of each prey type to the consumer diet [34]. Our posterior distributions of stable isotope values from RBCs were consistent across years (Fig. 5) and sex (Additional file 1: Table S3), but this was not the case from plasma between years (Fig. 6). Due to sex-based differences in model results, final posterior dietary estimates for plasma were conducted with males excluded (n = 21, Fig. 5).

Fig. 5

Estimates of prey contribution to the diet of leopard sealsfrom the Bayesian stable isotope mixing model posterior densities incorporating the δ13C, δ15N, and δ34S data from four prey sources and leopard seal RBCs collected in a) 2013 (n = 9), b) 2014 (n = 10), and c) 2017 (n = 4)

Fig. 6

Estimates of prey contribution to the diet of leopard seals from the Bayesian stable isotope mixing model posterior densities incorporating the δ13C, δ15N, and δ34S data from four prey sources and leopard seal plasma collected in a) 2013 (n = 9), b) 2014 (n = 10) and c) 2017 (n = 2)

Antarctic fur seal availability as prey

When pup growth and weekly counts are taken into account, fur seal pups were less available as a prey resource to leopard seals in each successive year of the study (Fig. 7a). The drop in pup availability during the 2014 season was particularly steep. The number of adult female leopard seals counted in the weekly census during January through March were similar between 2013 and 2014, but markedly lower in 2017 (Fig. 7b).

Fig. 7

a Indices of Antarctic fur seal pup availability for leopard seal predation. Week 1 is the first week of January. b Weekly census counts of adult female leopard seals hauled out at Cape Shirreff during January to March for 2013, 2014, and 2017. Boxes indicate median and upper and lower 75th and 25th percentiles; whiskers represent 10th and 90th percentiles, and open diamonds indicate mean values


Our a priori observations identified four groups of leopard seal prey items (fish, fur seal, krill, and penguin) which were well supported by our stable isotope data (Fig. 2).

Weekly diet variability

As the KNN comparison results for the δ13C/δ15N/δ34S values from plasma indicated no differences between capture dates, we conclude there were no substantial differences in leopard seal diets over weekly time scales during summer. This inference is supported by previous, land-based, opportunistic observations of individuals repeatedly foraging at one location on particular prey over these time scales [15, 20].

Seasonal diet variability

Leopard seals were typically sampled within 1 week of arrival to Cape Shirreff (December through February), and RBCs reflect seal diets from about 1 month prior and up to collection. Therefore, the dietary estimations from MixSIAR using stable isotope data from the seals’ RBCs reflect foraging behavior in the early austral spring/summer months (~ November through January). These pre-arrival diets were consistent across all years (Fig. 5), and between sexes (Additional file 1: Table S3). They were characterized by krill (31.7–38.0%), fish (31.6–36.5%), and penguin (24.4–26.9%). Such consistency suggests that, irrespective of sex, leopard seals in the western Antarctic Peninsula (WAP) depend largely upon krill and fish during the spring months when mesopredator prey are more broadly distributed and, therefore, less available.

Leopard seal diets changed quickly, however, after arriving to Cape Shirreff. The δ15N values from plasma, which reflect seal diet from about 1 week prior to collection, from females were significantly higher (average of +2.1‰) than those from RBCs, representing a potential increase of about half a trophic level within a week of arrival. Previous work demonstrated that the δ15N values from plasma from captive northern fur seals (Callorhinus urinus) consuming known diets were an average of 1.1‰ higher than those from RBCs, likely due to differences in their amino acid composition [50]. However, the trend of higher δ15N values from plasma collected from female seals we observed is larger than 1.1‰ and consistent across all observations, suggesting a diet change likely driven by increased consumption of fur seal pups by females. The δ15N values from plasma for males only differed from those of their RBCs by a mean of 0.5‰, which may or may not be indicative of a diet switch.

This differential prey selection by sex is also evident from the MixSIAR results for the CNS model using isotope values from plasma (Additional file 1: Table S3). However, inference based solely upon CNS model results should be made with caution given our moderate sample sizes. While much of the sampling-associated uncertainty is explicitly propagated through the Bayesian framework, results based on small samples, like male leopard seal tissues (N = 2), require additional context. In this case, several other lines of evidence support sex-based differences in leopard seal diets during early austral summer. Leopard seals are sexually dimorphic and the larger, territorial females exclude males and less dominant females from foraging near fur seal colonies [21, 22]. While we record female leopard seals daily, even hourly, hunting near fur seal beaches at Cape Shirreff during the summer, since 2011 we observed only a single male doing so (U.S. AMLR unpublished data). During 9 seasons of observing leopard seal kills at Seal Island, when sex was observable, 100% of fur seal pups were taken by adult females [20]. Finally, records spanning the Antarctic and sub-Antarctic across decades, indicate sex-based differences in foraging and migration patterns likely result in differential diet (summarized by [15]).

The trend of female leopard seals consuming more fur seals than males may be related to the energetic demands of breeding. Cape Shirreff is the largest fur seal breeding colony in the WAP [82]. Leopard seals are capital breeders that likely wean their pups in December [10] and reach peak molt in early February (U.S. AMLR unpublished data). Therefore, December-February is a brief window during which leopard seals must consume adequate resources to compensate for these two energetically demanding events [83]. The unusually-dense summer aggregations of leopard seals at Cape Shirreff, then, are possibly driven by the availability of easily-consumed, energy-rich Antarctic fur seal pups.

In addition to such sex-based differences in life history, the foraging behavior of leopard seals is influenced by their body size. In high density foraging environments, like Cape Shirreff, adult female leopard seals compete intraspecifically in ways beyond excluding males [22, 84]. For example, larger females regularly kleptoparasitize fur seal pups from their smaller conspecifics [21]. Additionally, leopard seals maximize fur seal and penguin harvesting by caching carcasses, potentially adjusting their caching strategy based on their body size [85]. Likely due to such size-based trophic interactions, larger animals have higher δ15N values at a predictable rate (Fig. 4) that reflect their increased consumption of prey with higher δ15N values (fur seals or fish). This diet-mass relationship is present either with or without males included in the analyses. Therefore, although mass and sex effects are confounded to some degree, both appear integral to understanding leopard seal diet.

Interannual diet variability

Summer dietary estimates from leopard seal plasma showed pronounced interannual differences (Fig. 6), and sex and year emerged as the most informative factors for mixing models. While seal ID and mass were informative co-variates, median posterior variances suggest that year and sex explained much more of the model variation (Table 4). The summer diets of adult female leopard seals were dominated by fur seal (21.3–37.5%) and penguin (29.5–46.2%) in all years, with substantial proportions of krill (21.9–26.8%) in 2013 and 2014.

Such interannual variation in the selection of fur seal pups suggests that leopard seals adjust foraging strategies on seasonal and interannual time scales in response to prey availability. Historical observations indicate that adult female leopard seals in the WAP met their December-February energetic needs by consuming recently-weaned crabeater seal (Lobodon carcinophagus) pups [86]. However, rapid regional warming in recent decades has removed sea-ice habitat for breeding crabeater seals [58], thereby disrupting the summer foraging patterns of leopard seals. Such disruptions can drive marine apex predators to switch to the next most energy-dense prey available [87, 88]. Hundreds of female leopard seals in the WAP have likely switched their diet focus to Antarctic fur seals at Cape Shirreff during the last two decades due to decreased availability of sea-ice dependent crabeater seals.

Before 1996 no more than two leopard seals were seen foraging concurrently at Cape Shirreff ([89, 90], D. Torres Pers. Comm. in [91]), but their numbers rose rapidly between 1998 and 2011 [92]. Concomitantly, after over 50 years of strong population growth, the Antarctic fur seal population at Cape Shirreff has decreased since 2007 on average 16.3% per year as measured by pup production. This significant decrease may be driven by consumption of fur seal pups by leopard seals given the number of animals present (Fig. 7b) and the proportion of fur seal in their diet (21.3 to 37.6%). Fur seal pups have become less available to leopard seals each successive year (Fig. 7a), and the proportion of fur seal in leopard seal diets decreased from 2013 to 2014 (Fig. 6). However, that proportion rebounded in 2017, coincident with a ˃ 40% decrease in the number of adult female leopard seals (Fig. 7b). The availability of Antarctic fur seals as a prey resource potentially drew leopard seals to Cape Shirreff, and as that resource has declined, it appears to have driven a decrease in leopard seal abundance as well. This interaction underscores the important role of top predators in shaping community structure and driving population trajectories of other species within their food web [1, 93, 94]. In addition, as seen in this example, anthropogenic or other perturbations to ecosystems, such as those from sea ice reduction due to climate change, can have unexpected and often drastic indirect effects across multiple levels within an ecosystem.

Diets of leopard seals estimated across methods

Our a priori diet observations indicated that fur seal, penguin, fish, and krill, in order of highest to lowest occurrence, were the primary components of leopard seal diets (Table 2) at Cape Shirreff during the austral summer. Behavioral reports from Cape Shirreff also emphasized fur seal as a main diet component, along with penguin and fish [21, 62]. However, the dietary estimates derived from the stable isotope mixing models identify krill as a key diet component of leopard seal diets in spring and summer for both sexes. This finding supports the designation of leopard seals as a krill dependent predator [12], and emphasizes the potential bias in diet studies based solely upon scat data [17].

Krill is under-represented in leopard seal scats which may be the result of foraging patterns coupled with an overlap in their average foraging trip durations (10.3–46.6 h, [22]) and gut passage times (15.3–164 h, [95]). A recent summary of leopard seal diving behavior at Cape Shirreff reported consistent foraging patterns with high activity at night, peaks during the crepuscular hours, and resting haul-outs centered around local noon [22]. Leopard seals likely feed on krill at night, when zooplankton typically migrate toward the surface, whereas pup and penguin hunting happens mostly in daylight [21]. Different prey types move at different speeds through the intestines of leopard seals [95]. Therefore, this foraging pattern may result in the krill consumed at night being evacuated at sea, whereas mesopredator prey captured during the day are defecated on study beaches when the seals haul-out. The successful management of an increasing krill fishery in Antarctic waters relies on accurate estimates of krill occurrence in the diets of Antarctic predators which are likely impossible using estimates from scat data alone. This is especially important as over-fishing krill could result in further decreases in prey availability for leopard seals already coping with reductions in important prey from sea-ice loss and declining fur seal numbers.


We integrated scat, visual observation, and stable isotope data to identify Antarctic fur seal pups, penguins, krill, and demersal notothen fishes as key prey items for leopard seals foraging in the South Shetland Islands. We aligned sample tissue turnover rates with seasonal transition periods, to illustrate leopard seal diet variability on weekly, monthly, and inter-annual time scales. The austral spring diets of males and females were consistent across all 3 years and focused on krill, fish, and to a lesser extent, penguin. The transition to summer foraging was distinct for males and females. Male diets were likely unchanged, whereas female diets transitioned rapidly to include more energy-dense Antarctic fur seal pups along with penguins. The extent of trophic enrichment, and fur seal in the diet, appears governed by a combination of leopard seal sex and body size.

Resolving the preferred prey items of leopard seals is central to understanding food web dynamics in the rapidly warming Antarctic Peninsula region [60]. This is particularly true for large adult females, which are important predators of fur seals and penguins [19,20,21, 62], and therefore may be affecting population dynamics of these mesopredators [88] or ecosystem function through top-down forcing [3]. In addition, climatic variability can lead to high variance in predator diets (e.g., [96]), and displaced predators may destabilize prey populations [1, 97]. Leopard seal diets and the associated predator–prey population dynamics appear to illustrate both. The rapid increase [21, 81] and subsequent decrease of leopard seals at Cape Shirreff appear to be moderated by the availability of sea ice as habitat for pagophilic crabeater seals resulting in a prey shift to fur seal pups.

Finally, as the krill fishery in the Antarctic expands [98], a full understanding of krill as a prey resource for multiple consumers in the region is essential for effective fishery management. The sex- and season-specific dietary proportions reported here will improve spatio-temporal ecosystem models that inform management of Antarctic resources (e.g., [99]). They will also increase the accuracy of bioenergetic models and deepen our understanding of Antarctic ecosystem function. Future studies should consider addressing longer time scales by analyzing the stable isotope values from slow-growing tissues like vibrissae or teeth.

Availability of data and materials

All data generated or analysed during this study are included in this published article and its Additional files.


  1. 1.

    Estes J, Tinker M, Williams T, Doak D. Killer whale predation on sea otters linking oceanic and nearshore ecosystems. Science. 1998;282(5388):473–6.

    CAS  PubMed  Article  Google Scholar 

  2. 2.

    Ballance LT, Pitman RL, Hewitt RP, Siniff DB, Trivelpiece WZ, Clapham P, et al. The removal of large whales from the Southern Ocean: evidence for long-term ecosytem effects? In: James AE, Daniel FD, Terrie MW, Robert Jr LB, editors. Whales, whaling, and ocean ecosystems. Berkeley: University of California Press; 2006. p. 215–30.

    Google Scholar 

  3. 3.

    Estes JA, Terborgh J, Brashares JS, Power ME, Berger J, Bond WJ, et al. Trophic downgrading of planet earth. Science. 2011;333(6040):301–6.

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Ducklow HW, Baker K, Martinson DG, Quetin LB, Ross RM, Smith RC, et al. Marine pelagic ecosystems: the West Antarctic Peninsula. Philos Trans Royal Soc B Biol Sci. 2007;362(1477):67–94.

    Article  Google Scholar 

  5. 5.

    Croxall JP, Callaghan T, Cervellati R, Walton DWH, Drewry DJ, Laws RM, et al. Southern Ocean environmental changes: effects on seabird, seal and whale populations. Philos Trans R Soc Lond B Biol Sci. 1992;338(1285):319–28.

    Article  Google Scholar 

  6. 6.

    Trivelpiece WZ, Hinke JT, Miller AK, Reiss CS, Trivelpiece SG, Watters GM. Variability in krill biomass links harvesting and climate warming to penguin population changes in Antarctica. Proc Natl Acad Sci. 2011;11:2011.

    Google Scholar 

  7. 7.

    Ducklow HW, Fraser WR, Meredith MP, Stammerjohn SE, Doney SC, Martinson DG, et al. West Antarctic Peninsula: an ice-dependent coastal marine ecosystem in transition. Oceanography. 2013;26(3):190–203.

    Article  Google Scholar 

  8. 8.

    Hinke JT, Salwicka K, Trivelpiece SG, Watters GM, Trivelpiece WZ. Divergent responses of Pygoscelis penguins reveal a common environmental driver. Oecologia. 2007;153:853.

    Article  Google Scholar 

  9. 9.

    Laws RM. Seals. In: Laws RM, editor. Antarctic ecology. Cambridge: Academic Press; 1984. p. 621–716.

    Google Scholar 

  10. 10.

    Rogers T. Leopard seal, Hydrurga leptonyx. In: Würsig B, Thewissen JGM, Kovacs KM, editors. Encyclopedia of marine mammals. San Diego: Academic Press; 2017. p. 550–1.

    Google Scholar 

  11. 11.

    Hall-Aspland SA, Rogers TL. Summer diet of leopard seals (Hydrurga leptonyx) in Prydz Bay, Eastern Antarctica. Polar Biol. 2004;27(12):729–34.

    Article  Google Scholar 

  12. 12.

    Siniff DB, Stone S. The role of the leopard seal in the tropho-dynamics of the Antarctic marine ecosystem. In: Siegfried WR, Condy PR, Laws RM, editors. Antarctic nutrient cycles and food webs. Berlin: Springer Verlag; 1985. p. 555–60.

    Google Scholar 

  13. 13.

    Casaux R, Baroni A, Ramón A, Carlini A, Bertolin M, DiPrinzio C. Diet of the leopard seal (Hydrurga leptonyx) at the Danco Coast, Antarctic Peninsula. Polar Biol. 2009;32(2):307–10.

    Article  Google Scholar 

  14. 14.

    Guerrero AI, Negrete J, Marquez MEI, Mennucci J, Zaman K, Rogers TL. Vertical fatty acid composition in the blubber of leopard seals and the implications for dietary analysis. J Exp Mar Biol Ecol. 2016;478:54–61.

    CAS  Article  Google Scholar 

  15. 15.

    Walker T, Boyd I, McCafferty D, Taylor R, Reid K. Seasonal occurrence and diet of leopard seals (Hydrurga leptonyx) at Bird Island, South Georgia. Antarct Sci. 1998;10(1):75–81.

    Article  Google Scholar 

  16. 16.

    Staniland IJ. Investigating the biases in the use of hard prey remains to identify diet composition using Antarctic fur seals (Arctocephalus gazella) in captive feeding trials. Mar Mamm Sci. 2002;18(1):223–43.

    Article  Google Scholar 

  17. 17.

    Bowen WD, Iverson SJ. Methods of estimating marine mammal diets: a review of validation experiments and sources of bias and uncertainty. Mar Mamm Sci. 2012;29:1–35.

    Google Scholar 

  18. 18.

    Orr AJ, Harvey JT. Quantifying errors associated with using fecal samples to determine the diet of the California sea lion (Zalophus californianus). Can J Zool. 2001;79(6):1080–7.

    Article  Google Scholar 

  19. 19.

    Rogers T, Bryden MM. Predation of Adélie penguins (Pygoscelis adeliae) by leopard seals (Hydrurga leptonyx) in Prydz Bay, Antarctica. Can J Zool. 1995;73(5):1001–4.

    Article  Google Scholar 

  20. 20.

    Hiruki L, Schwartz M, Boveng P. Hunting and social behaviour of leopard seals (Hydrurga leptonyx) at Seal Island, South Shetland Islands, Antarctica. J Zool. 1999;249(1):97–109.

    Article  Google Scholar 

  21. 21.

    Krause DJ, Goebel ME, Marshall GJ, Abernathy K. Novel foraging strategies observed in a growing leopard seal (Hydrurga leptonyx) population at Livingston Island, Antarctic Peninsula. Anim Biotelemetry. 2015;3(24):1–14.

    Google Scholar 

  22. 22.

    Krause DJ, Goebel ME, Marshall GJ, Abernathy K. Summer diving and haul-out behavior of leopard seals (Hydrurga leptonyx) near mesopredator breeding colonies at Livingston Island, Antarctic Peninsula. Mar Mamm Sci. 2016;32(3):839–67.

    Article  Google Scholar 

  23. 23.

    Bolnick DI, Svanbäck R, Fordyce JA, Yang LH, Davis JM, Hulsey CD, et al. The ecology of individuals: incidence and implications of individual specialization. Am Nat. 2003;161(1):1–28.

    PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

    Kernaléguen L, Dorville N, Ierodiaconou D, Hoskins AJ, Baylis AMM, Hindell MA, et al. From video recordings to whisker stable isotopes: a critical evaluation of timescale in assessing individual foraging specialisation in Australian fur seals. Oecologia. 2015;180(3):657–70.

    PubMed  Article  Google Scholar 

  25. 25.

    Kurle CM, Gudmundson CJ. Regional differences in foraging of young-of-the-year Steller sea lions Eumetopias jubatus in Alaska: stable carbon and nitrogen isotope ratios in blood. Mar Ecol Prog Ser. 2007;342:303–10.

    Article  Google Scholar 

  26. 26.

    Cherel Y, Ducatez S, Fontaine C, Richard P, Guinet C. Stable isotopes reveal the trophic position and mesopelagic fish diet of female southern elephant seals breeding on the Kerguelen Islands. Mar Ecol Prog Ser. 2008;370:239–47.

    Article  Google Scholar 

  27. 27.

    Polito MJ, Goebel ME. Investigating the use of stable isotope analysis of milk to infer seasonal trends in the diets and foraging habitats of female Antarctic fur seals. J Exp Mar Biol Ecol. 2010;395(1–2):1–9.

    CAS  Article  Google Scholar 

  28. 28.

    Hückstädt LA, Burns JM, Koch PL, McDonald BI, Crocker DE, Costa DP. Diet of a specialist in a changing environment: the crabeater seal along the western Antarctic Peninsula. Mar Ecol Prog Ser. 2012;455:287–301.

    Article  CAS  Google Scholar 

  29. 29.

    Minagawa M, Wada E. Stepwise enrichment of 15 N along food chains: further evidence and the relation between δ15 N and animal age. Geochim Cosmochim Acta. 1984;48(5):1135–40.

    CAS  Article  Google Scholar 

  30. 30.

    Felicetti LA, Schwartz CC, Rye RO, Haroldson MA, Gunther KA, Phillips DL, et al. Use of sulfur and nitrogen stable isotopes to determine the importance of whitebark pine nuts to Yellowstone grizzly bears. Can J Zool. 2003;81(5):763–70.

    Article  Google Scholar 

  31. 31.

    Hopkins JB, Whittington J, Clevenger AP, Sawaya MA, St Clair CC. Stable isotopes reveal rail-associated behavior in a threatened carnivore. Isot Environ Health Stud. 2014;50(3):322–31.

    CAS  Article  Google Scholar 

  32. 32.

    McCutchan JH Jr, Lewis WM Jr, Kendall C, McGrath CC. Variation in trophic shift for stable isotope ratios of carbon, nitrogen, and sulfur. Oikos. 2003;102(2):378–90.

    CAS  Article  Google Scholar 

  33. 33.

    Fry B. Stable isotope ecology. New York: Springer; 2006.

    Google Scholar 

  34. 34.

    Phillips DL. Converting isotope values to diet composition: the use of mixing models. J Mammal. 2012;93(2):342–52.

    Article  Google Scholar 

  35. 35.

    Hall-Aspland SA, Hall AP, Rogers TL. A new approach to the solution of the linear mixing model for a single isotope: application to the case of an opportunistic predator. Oecologia. 2005;143(1):143–7.

    CAS  PubMed  Article  Google Scholar 

  36. 36.

    Hall-Aspland SA, Rogers TL, Canfield RB. Stable carbon and nitrogen isotope analysis reveals seasonal variation in the diet of leopard seals. Mar Ecol Prog Ser. 2005;305:249–59.

    CAS  Article  Google Scholar 

  37. 37.

    Phillips DL, Inger R, Bearhop S, Jackson AL, Moore JW, Parnell AC, et al. Best practices for use of stable isotope mixing models in food-web studies. Can J Zool. 2014;92(10):823–35.

    Article  Google Scholar 

  38. 38.

    Parnell AC, Inger R, Bearhop S, Jackson AL. Source partitioning using stable isotopes: coping with too much variation. PLoS ONE. 2010;5(3):e9672.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  39. 39.

    Ward EJ, Semmens BX, Schindler DE. Including source uncertainty and prior information in the analysis of stable isotope mixing models. Environ Sci Technol. 2010;44(12):4645–50.

    CAS  PubMed  Article  Google Scholar 

  40. 40.

    Hopkins JBI, Ferguson JM. Estimating the diets of animals using stable isotopes and a comprehensive Bayesian mixing model. PLoS ONE. 2012;7(1):e28478.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. 41.

    Parnell AC, Phillips DL, Bearhop S, Semmens BX, Ward EJ, Moore JW, et al. Bayesian stable isotope mixing models. Environmetrics. 2013;24(6):387–99.

    Google Scholar 

  42. 42.

    Hobson KA, Schell DM, Renouf D, Noseworthy E. Stable carbon and nitrogen isotopic fractionation between diet and tissues of captive seals: implications for dietary reconstructions involving marine mammals. Can J Fish Aquat Sci. 1996;53:528–33.

    Article  Google Scholar 

  43. 43.

    Dalerum F, Angerbjörn A. Resolving temporal variation in vertebrate diets using naturally occurring stable isotopes. Oecologia. 2005;144(4):647–58.

    CAS  PubMed  Article  Google Scholar 

  44. 44.

    Phillips DL, Eldridge PM. Estimating the timing of diet shifts using stable isotopes. Oecologia. 2006;147(2):195–203.

    PubMed  Article  Google Scholar 

  45. 45.

    Kurle CM. Interpreting temporal variation in omnivore foraging ecology via stable isotope modelling. Funct Ecol. 2009;23(4):733–44.

    Article  Google Scholar 

  46. 46.

    Kurle CM, Sinclair EH, Edwards AE, Gudmundson CJ. Temporal and spatial variation in the δ15 N and δ13C values of fish and squid from Alaskan waters. Mar Biol. 2011;158(11):2389–404.

    Article  Google Scholar 

  47. 47.

    Kurle CM, Worthy G. Stable nitrogen and carbon isotope ratios in multiple tissues of the northern fur seal Callorhinus ursinus: implications for dietary and migratory reconstructions. Mar Ecol Prog Ser. 2002;2002(236):289–300.

    Article  Google Scholar 

  48. 48.

    Rogers TL, Fung J, Slip D, Steindler L, O’Connell TC. Calibrating the time span of longitudinal biomarkers in vertebrate tissues when fine-scale growth records are unavailable. Ecosphere. 2016;7(12):e01449.

    Article  Google Scholar 

  49. 49.

    Hobson KA, Clark RG. Turnover of 13C in cellular and plasma fractions of blood: implications for nondestructive sampling in avian dietary studies. Auk. 1993;110(3):638–41.

    Article  Google Scholar 

  50. 50.

    Kurle CM. Stable-isotope ratios of blood components from captive northern fur seals (Callorhinus ursinus) and their diet: applications for studying the foraging ecology of wild otariids. Can J Zool. 2002;80(5):902.

    Article  Google Scholar 

  51. 51.

    Phillips DL, Newsome SD, Gregg JW. Combining sources in stable isotope mixing models: alternative methods. Oecologia. 2005;144(4):520–7.

    PubMed  Article  Google Scholar 

  52. 52.

    Burns MJ, Trumble JS, Castellini AM, Testa WJ. The diet of weddell seals in McMurdo Sound, Antarctica as determined from scat collections and stable isotope analysis. Polar Biol. 1998;19(4):272–82.

    Article  Google Scholar 

  53. 53.

    Moore JW, Semmens BX. Incorporating uncertainty and prior information into stable isotope mixing models. Ecol Lett. 2008;11(5):470–80.

    Article  PubMed  Google Scholar 

  54. 54.

    Polito MJ, Trivelpiece WZ, Karnovsky NJ, Ng E, Patterson WP, Emslie SD. Integrating stomach content and stable isotope analyses to quantify the diets of Pygoscelid Penguins. PLoS ONE. 2011;6(10):e26642.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    Hopkins JBI, Ferguson JM, Tyers DB, Kurle CM. Selecting the best stable isotope mixing model to estimate grizzly bear diets in the Greater Yellowstone Ecosystem. PLoS ONE. 2017;12(5):e0174903.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  56. 56.

    Lomnicki A. Individual differences between animals and the natural regulation of their numbers. J Anim Ecol. 1978;47(2):461–75.

    Article  Google Scholar 

  57. 57.

    Massom RA, Stammerjohn SE. Antarctic sea ice change and variability—physical and ecological implications. Polar Sci. 2010;4(2):149–86.

    Article  Google Scholar 

  58. 58.

    Forcada J, Trathan PN, Boveng PL, Boyd IL, Burns JM, Costa DP, et al. Responses of Antarctic pack-ice seals to environmental change and increasing krill fishing. Biol Cons. 2012;149(1):40–50.

    Article  Google Scholar 

  59. 59.

    Bester M, Erickson AW, Ferguson JWH. Seasonal change in the distribution and density of seals in the pack ice off Princess Martha Coast,Antarctica. Antarct Sci. 1995;7(04):357–64.

    Article  Google Scholar 

  60. 60.

    Meade J, Ciaglia M, Slip D, Negrete J, Marquez M, Mennucci J, et al. Spatial patterns in activity of leopard seals Hydrurga leptonyx in relation to sea ice. Mar Ecol Prog Ser. 2015;2015(521):265–75.

    Article  Google Scholar 

  61. 61.

    Erickson AW, Hofman RJ. Antarctic Seals. Antarctic Map Folio Series. 1974;Folio 18:4-13.

  62. 62.

    Vera C, Vargas R, Torres DN. Estrategias depredatorias del la foca leopardo sobre cachorros de lobo fino Antarctico. Boletin Antarctico Chileno. 2005;24(2):12–7.

    Google Scholar 

  63. 63.

    Pussini N, Goebel ME. A safer protocol for field immobilization of leopard seals (Hydrurga leptonyx). Mar Mamm Sci. 2015;31:1549–58.

    Article  Google Scholar 

  64. 64.

    Scheffer VB. Standard measurements of seals. J Mammal. 1967;48(3):459–62.

    Article  Google Scholar 

  65. 65.

    Laws RM. On the growth rates of the leopard seal, Hydrurga leptonyx (De Blainville, 1820). Säugetierkundliche Mitteilungen. 1957;5:49–55.

    Google Scholar 

  66. 66.

    Zamzow JP, Aumack CF, Amsler CD, McClintock JB, Amsler MO, Baker BJ. Gut contents and stable isotope analyses of the Antarctic fish, Notothenia coriiceps (Richardson), from two macroalgal communities. Antarct Sci. 2011;23(02):107–16.

    Article  Google Scholar 

  67. 67.

    Folch J, Lees M, Stanley GHS. A simple method for the isolation and purification of total lipids from animal tissues. J Biol Chem. 1957;226:497–509.

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Sweeting CJ, Polunin NVC, Jennings S. Effects of chemical lipid extraction and arithmetic lipid correction on stable isotope ratios of fish tissues. Rapid Commun Mass Spectrom. 2006;20(4):595–601.

    CAS  PubMed  Article  Google Scholar 

  69. 69.

    Post DM, Layman CA, Arrington DA, Takimoto G, Quattrochi J, Montaña CG. Getting to the fat of the matter: models, methods and assumptions for dealing with lipids in stable isotope analyses. Oecologia. 2007;152(1):179–89.

    PubMed  Article  Google Scholar 

  70. 70.

    R-Core-Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing. 2018;3.5.0.

  71. 71.

    Phillips LD, Gregg WJ. Uncertainty in source partitioning using stable isotopes. Oecologia. 2001;127(2):171–9.

    Article  PubMed  Google Scholar 

  72. 72.

    Cressie NAC. Statistics for spatial data. New York: Wiley; 1993.

    Google Scholar 

  73. 73.

    Ben-David M, Flynn WR, Schell MD. Annual and seasonal changes in diets of martens: evidence from stable isotope analysis. Oecologia. 1997;111(2):280–91.

    CAS  PubMed  Article  Google Scholar 

  74. 74.

    Rosing MN, Ben-David M, Barry RP. Analysis of stable isotope data: a K nearest-neighbors randomization test. J Wildl Manag. 1998;62(1):380–8.

    Article  Google Scholar 

  75. 75.

    Kurle CM, Koch PL, Tershy BR, Croll DA. The effects of sex, tissue type, and dietary components on stable isotope discrimination factors (Δ13C and Δ15 N) in mammalian omnivores. Isot Environ Health Stud. 2014;50(3):307–21.

    CAS  Article  Google Scholar 

  76. 76.

    Vanderklift MA, Ponsard S. Sources of variation in consumer-diet δ15 N enrichment: a meta-analysis. Oecologia. 2003;136(2):169–82.

    PubMed  PubMed Central  Article  Google Scholar 

  77. 77.

    Bond AL, Diamond AW. Recent Bayesian stable-isotope mixing models are highly sensitive to variation in discrimination factors. Ecol Appl. 2011;21(4):1017–23.

    Article  PubMed  Google Scholar 

  78. 78.

    Beltran RS, Peterson SH, McHuron EA, Reichmuth C, Hückstädt LA, Costa DP. Seals and sea lions are what they eat, plus what? Determination of trophic discrimination factors for seven pinniped species. Rapid Commun Mass Spectrom. 2016;30(9):1115–22.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  79. 79.

    Stock BC, Semmens BX. Unifying error structures in commonly used biotracer mixing models. Ecology. 2016;97(10):2562–9.

    PubMed  Article  Google Scholar 

  80. 80.

    Stock BC, Jackson AL, Ward EJ, Parnell AC, Phillips DL, Semmens BX. Analyzing mixing systems using a new generation of Bayesian tracer mixing models. PeerJ. 2018;6:e5096.

    PubMed  PubMed Central  Article  Google Scholar 

  81. 81.

    Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A. Bayesian measures of model complexity and fit. J Royal Stat Soc Ser B. 2002;64(4):583–639.

    Article  Google Scholar 

  82. 82.

    Goebel ME, Vallejos VI, Trivelpiece WZ, Holt RS, Acevedo J. AMLR 2001/02 Field Season Report. Capter 7: Antarctic fur seal pup production. US Department of Commerce, NOAA Technical Memorandum NMFS-SWFSC-350. 2003:139–152.

    Google Scholar 

  83. 83.

    Costa DP, Crocker DE. Marine Mammals of the Southern Ocean. In: Ross RM, Hofmann EE, Quetin LB, editors. Foundations for Ecological Research West of the Antarctic Peninsula. Washington: American Geophysical Union; 1996. p. 287–301.

    Google Scholar 

  84. 84.

    Robbins JR, Poncet D, Evans AR, Hocking DP. A rare observation of group prey processing in wild leopard seals (Hydrurga leptonyx). Polar Biol. 2019;42(8):1625–30.

    Article  Google Scholar 

  85. 85.

    Krause DJ, Rogers TL. Food caching by a marine apex predator, the leopard seal (Hydrurga leptonyx). Can J Zool. 2019;97(6):573–8.

    Article  Google Scholar 

  86. 86.

    Siniff DB, Bengtson JL. Observations and hypotheses concerning the interactions among crabeater seals, leopard seals, and killer whales. J Mammal. 1977;58(3):414–6.

    Article  Google Scholar 

  87. 87.

    Springer A, Estes J, Van Vliet G, Williams T, Doak D, Danner E, et al. Sequential megafaunal collapse in the North Pacific Ocean: an ongoing legacy of industrial whaling? Proc Natl Acad Sci USA. 2003;100(21):12223.

    CAS  PubMed  Article  Google Scholar 

  88. 88.

    Williams T, Estes J, Doak D, Springer A. Killer appetites: assessing the role of predators in ecological communities. Ecology. 2004;85(12):3373–84.

    Article  Google Scholar 

  89. 89.

    Weddell J. A voyage towards the South Pole, performed in the years 1822–24: containing an examination of the Antarctic Sea (1827). 2nd ed. Annapolis: United States Naval Institue; 1970. p. 324.

    Google Scholar 

  90. 90.

    Aguayo LA. The present status of the Antarctic Fur Seal Arctocephalus gazella at South Shetland Islands. Polar Record. 1978;19(119):167–73.

    Article  Google Scholar 

  91. 91.

    Boveng P, Hiruki L, Schwartz M, Bengtson J. Population growth of Antarctic fur seals: limitation by a top predator, the leopard seal? Ecology. 1998;79(8):2863–77.

    Article  Google Scholar 

  92. 92.

    Goebel ME, Pussini N, Buchheit R, Pietrzak KW, Krause DJ, Van Cise AM, et al. AMLR 2010/11 Field Season Report. Chapter 8: Pinniped research at Cape Shirreff, Livingston Island, Antarctica. US Department of Commerce, NOAA Technical Memorandum NMFS-SWFSC-524. 2014:50-7.

  93. 93.

    Paine RT. Food Web Complexity and Species Diversity. Am Nat. 1966;100(910):65–75.

    Article  Google Scholar 

  94. 94.

    Kurle CM, Croll DA, Tershy BR. Introduced rats indirectly change marine rocky intertidal communities from algae- to invertebrate-dominated. Proc Natl Acad Sci. 2008;105(10):3800–4.

    CAS  PubMed  Article  Google Scholar 

  95. 95.

    Hall-Aspland SA, Rogers T, Canfield RB, Tripovich J. Food transit times in captive leopard seals (Hydrurga leptonyx). Polar Biol. 2011;34:95–9.

    Article  Google Scholar 

  96. 96.

    Robinson H, Thayer J, Sydeman WJ, Weise M. Changes in California sea lion diet during a period of substantial climate variability. Mar Biol. 2018;165(10):165.

    Article  Google Scholar 

  97. 97.

    Layman CA, Quattrochi JP, Peyer CM, Allgeier JE. Niche width collapse in a resilient top predator following ecosystem fragmentation. Ecol Lett. 2007;10(10):937–44.

    PubMed  PubMed Central  Article  Google Scholar 

  98. 98.

    Nicol S, Foster J, Kawaguchi S. The fishery for Antarctic krill—recent developments. Fish Fish. 2012;13(1):30–40.

    Article  Google Scholar 

  99. 99.

    Dahood A, Watters GM, de Mutsert K. Using sea-ice to calibrate a dynamic trophic model for the Western Antarctic Peninsula. PLoS ONE. 2019;14(4):e0214814.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

Download references


This paper was improved by comments from G. Watters, C. Turner Tomaszewicz, A. Curtis, and analytical advice from B. Stock, J. Hopkins, and M. Ben-David. We are grateful to K. Pietrzak, M. Mudge, J. Wright, N. Cook, M. Zimmerman, M. Goh, T. Joyce, G. Marshall, K. Abernathy, S. Woodman, W. Taylor, N. de Gracia, M. Klostermann, R. Borras, and J. Hinke for assistance in the field, and R. Steinitz and K. Reynolds for support in the lab. We thank M. Polito and S. Emslie for help with sample collection and transportation, and for editorial insight from the Borrego Springs Writer's Workshop. A heartfelt thank you to a beloved and deeply-missed colleague, A. Dahood for Fig. 1.


Financial, infrastructure and logistical support was provided by the U.S. AMLR Program, National Marine Fisheries Service, National Oceanic and Atmospheric Administration.

Author information




DJK and CK designed the study. DJK conducted the laboratory analysis and analyzed the data with substantial advice and direction from CK. DJK wrote the manuscript with extensive editing from CK and MEG. DJK conducted all field work and collected all samples with assistance from MEG. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Douglas J. Krause.

Ethics declarations

Ethics approval and consent to participate

Leopard seal interactions and captures were conducted in accordance with Marine Mammal Protection Act Permit Nos. 16472 and 20599 granted by the Office of Protected Resources, National Marine Fisheries Service, the Antarctic Conservation Act Permit Nos. 2012-005, 2013-003 and 2017-012, USDA Permit No. 42994, and the NMFS-SWFSC Institutional Animal Care and Use Committee Permit Nos. SWPI2011-02 and SWPI 2014-03R.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Krause, D.J., Goebel, M.E. & Kurle, C.M. Leopard seal diets in a rapidly warming polar region vary by year, season, sex, and body size. BMC Ecol 20, 32 (2020).

Download citation


  • Stable isotope mixing model
  • Apex predator
  • Top down
  • Prey shift
  • Hydrurga leptonyx
  • Climate change
  • Animal-borne video