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. 2024 Jun 5;58(24):10504–10514. doi: 10.1021/acs.est.4c03302

Contaminant Biomagnification in Polar Bears: Interindividual Differences, Dietary Intake Rate, and the Gut Microbiome

Yuhao Chen †,‡, Terrence H Bell †, Sarra Gourlie §, Ying Duan Lei †, Frank Wania †,‡,*
PMCID: PMC11192032  PMID: 38838208

Abstract

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Some persistent hydrophobic pollutants biomagnify, i.e., achieve higher contaminant levels in a predator than in its prey (Cpredator/Cprey > 1). This ratio is called the biomagnification factor (BMF) and is traditionally determined using tissues from carcasses or biopsies. Using a noninvasive method that relies on equilibrium sampling in silicone-film-coated vessels and chemical analysis of paired diet and feces, we determined on three occasions the thermodynamic biomagnification limit (BMFlim) and feces-based biomagnification factor (BMFF) for three zoo-housed polar bears who experience seasonal periods of hyperphagia and hypophagia. All bears had high biomagnification capabilities (BMFlim was up to 200) owing to very efficient lipid assimilation (up to 99.5%). The bears differed up to a factor of 3 in their BMFlim. BMFlim and BMFF of a bear increased by up to a factor of 4 during the hypophagic period, when the ingestion rate was greatly reduced. Much of that variability can be explained by differences in the lipid assimilation efficiency, even though this efficiency ranged only from 98.1 to 99.5%. A high BMFlim was associated with a high abundance of Bacteroidales and Lachnospirales in the gut microbiome. Biomagnification varies to a surprisingly large extent between individuals and within the same individual over time. Future work should investigate whether this can be attributed to the influence of the gut microbiome on lipid assimilation by studying more individual bears at different key physiological stages.

Keywords: biomagnification, equilibrium sampling, noninvasive method, fugacity, fugacity capacity, gut microbiome, lipid assimilation, hypophagia

Short abstract

A noninvasive method based on equilibrium sampling reveals that biomagnification in polar bears varies to a surprisingly large extent between individuals and in the same individual over time.

Introduction

A persistent organic pollutant (POP) with an octanol–water equilibrium ratio ranging from 102–109 and an octanol–air equilibrium ratio > 106 can reach higher concentrations in the tissues of air-breathing predators (e.g., insectivorous bat; wolf; polar bear; humans) than are present in their prey.1,2 This is due to a process called biomagnification. The underlying mechanism is that the volume (V) and the fugacity capacity (Z) of food decrease during digestion in the gastrointestinal tract. This leads to a higher fugacity, or chemical activity, of some contaminants in the gut content compared to that of the gut tissues, i.e., the epithelial cells lining the intestinal tract, and creates a thermodynamic driving force for these contaminants to diffuse from food to tissues.3 When biomagnification increases tissue concentrations of POPs, the likelihood of an organism experiencing detrimental health effects (e.g., endocrine/immune system disruption; cell death) increases.4−6

Quantifying POP biomagnification is critical to environmental risk assessment (e.g., chemical screening)7 and is traditionally done by calculating the ratio between a contaminant’s concentrations in the tissue of a predator and its prey, also called the biomagnification factor (BMF). The BMF is calculated using either lipid-normalized concentrations8 or—a more thermodynamically rigorous approach—using fugacities in tissues and diet, fB and fD (in Pa).3 Most methods for quantifying biomagnification are invasive and require animal tissue from laboratory animals, hunted wildlife, or carcasses,9−11 and are therefore not suitable for studying biomagnification in many settings, including endangered species and humans. Furthermore, the traditional method for studying biomagnification is not suited to investigating changes in the BMF of an individual with age (i.e., juvenile and mature states) or feeding state (i.e., hyperphagic and hypophagic periods). It is even challenging to use it to study differences in the biomagnification capability between individuals.

Over the past few years, our group has developed an entirely noninvasive method for studying the process of biomagnification, which relies on equilibrium sampling in silicone-film coated vessels and chemical analysis of paired dietary and fecal samples.12,13 Specifically, the method determines the fugacities, fD and fF, and the fugacity capacities, ZD and ZF (in mol Pa–1 m–3), of the contaminants in diet and fecal matter.12,13 When combined with volume-based dietary intake and egestion rates, GD and GF, the approach yields the dimensionless thermodynamic limit to a chemical’s biomagnification in an organism (BMFlim).

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In a previous study,13 we assumed that biomagnifying chemicals achieve equilibrium between the gut content and the tissues of an organism, i.e., assumed that the fugacity in the feces fF can be used as a noninvasive means to obtain an estimate of the fugacity in the bear’s tissue fB. This assumption of fB being equal to fF was based on a study with rainbow trout.14 However, studies on humans showed that the fF of polychlorinated biphenyls (PCBs) can be more than 10 times lower than their fugacity in blood15,16 and attributed this observation to a “fat flush” effect, whereby the fugacity capacity of epithelial cells in the jejunum can increase significantly by rapid lipid absorption during a digestion event, thus decreasing the fugacity in the epithelial cells and encouraging contaminants to move toward the epithelial cells.17 Since the assumption of fB being equal to fF may thus not be appropriate in many instances, we correct the terminology used previously, now referring to the ratio of fF and fD as a feces-based BMFF

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and we emphasize that BMFF can only approximate the BMF, defined as the ratio of fB and fD. For the same reason, the “fat flush” effect may cause the real biomagnification potential to exceed the BMFlim as defined in eq 1.12

The feasibility of this approach to determining BMFlim and BMFF has been demonstrated for a domestic dog and zoo-housed Arctic wolves and polar bears (Ursus maritimus).12,13 It provided valuable insight about the major contributors to a high BMFlim: a high-fat diet and a high digestion efficiency.12,13 Polar bears, as one of the highest trophic level predators in the world, have a diet with abnormally high lipid content (i.e., consisting largely of seal blubber).18,19 They also have a very high lipid assimilation efficiency (∼99%), leading to uptake efficiencies for PCBs in zoo-housed polar bears as high as 98%.13 Combining a lipid-rich diet with high lipid assimilation efficiency, polar bears display some of the highest biomagnification capabilities in the animal kingdom, and despite their remote habitat, contaminant levels are often high enough to lead to adverse health effects, including hormone disruption, low bone mineral density, and small testes size.4,5,20

The dietary intake of wild polar bears is highly seasonal because their main prey, ringed seal, is only available when sea ice is present (from about October to the end of July).18,19 The body weight of polar bears can increase by more than 100 kg during this period due to hyperphagia. During the ice-free period, polar bears experience hypophagia and sustain themselves on stored fat and the occasional intake of plant matter (e.g., barriers and Lyme grass), birds (e.g., snow geese and Canada geese), and mammals (e.g., carrion and rodents) with a much lower lipid content compared to seal blubber.21−23

A change in available lipids in the diet can greatly alter the abundance and diversity of the gut microbiome, even in a short period of time.24−27 For example, Devkota et al. found that a fat-scarce diet can facilitate the growth of Bacillota.25 Polyunsaturated fat (e.g., safflower oil) and milk-derived saturated fat can suppress the growth of Bacillota but promote that of Bacteroidota.25 When the abundance of certain microbial taxa increases, the gut microbiome can play a crucial role in lipid absorption.28−30 Semova et al. pointed out that the gut microbiome can facilitate the uptake of fatty acids and increase the number and size of lipid droplets in the intestinal epithelium of zebrafish.28 A study on rats showed that antibiotics can reduce the intestinal permeability of dietary lipids, thus slowing down and reducing lipid absorption.29 The gut microbiome could also accelerate lipid absorption through regulation of diacylglycerol O-acyltransferases (DGAT2) in rats.24 Particularly, both Lactobacillus paracasei and Escherichia coli can regulate lipid metabolism by producing the chylomicron secretion inhibitor l-lactate (or acetate) and promoting lipid storage (or oxidation).30

Based on the hypothesis that an organism’s catabolic state may be influencing its biomagnification capabilities, by changing the gastrointestinal tract’s capability for lipid assimilation, we studied how biomagnification in polar bears responds to changes in dietary composition (i.e., changes in volume/calories and macronutrient densities of diet) and feeding state (i.e., hyperphagic and hypophagic periods). Specifically, using the noninvasive equilibrium sampling method by Chen et al.,12,13 we determined BMFlim and BMFF of PCBs in three zoo-housed bears on three occasions with different feeding states. Working with zoo-housed polar bears allowed us to accurately pair the undigested and digested food and distinguish the fecal samples from different bears. Considering the important role the gut microbiome plays in lipid assimilation, we surmised that differences in contaminant biomagnification between individuals and over time could also be related to changes in microbial composition; therefore, we also assessed bacterial composition in the fecal matter of the three bears in parallel at all three sampling points using 16S rRNA gene sequencing.

Materials and Methods

Lab Materials

Details about deuterated standards and other chemicals are given in Table S1 in the Supporting Information.

Sample Collection

We collected paired diets and feces of three female polar bears (two 22 year old twins called Aurora and Nikita, and a 7 year old offspring of Aurora’s called Juno) housed in the Toronto Zoo. Like polar bears in the wild, zoo-housed polar bears experience hyperphagic or hypophagic periods due to sunlight cues and hormonal cascades. Following the bears’ intake cues, the Toronto Zoo varies the feeding rate of polar bears throughout the year to mimic the natural cycle observed in the wild. The dietary intake rates, GD (in units of mL/day), are known because each bear is fed a diet of prescribed size and composition. The two female adult twins had a dietary ingestion rate of 8400 mL/day during the hyperphagic period, which was reduced by 71% during the hypophagic period. Due to health and behavior, the subadult experienced a much smaller reduction in GD (only by 29%).

During the hyperphagic period (from December to June), the bears were fed mainly horse meat and beef (i.e., TZ polar bear meat diet and TZ Feline meat diet, 46% wet weight w.w.) and fish (i.e., smelt, 46% of total mass of foods, 34% w.w.) to facilitate weight gain.31 Seal oil and dry kibble (i.e., Mazuri Marine Bear) help to increase total fat content and lower protein concentration in the bear’s diet.31,32 Vegetables and fruits (e.g., carrot, romaine, apple, pear and honeydew) were provided as a source of enrichment but contribute minimally to the caloric density of the diet.33 During the hypophagic period (from July to November), the amount of commercial meat mix and fish was greatly reduced (by 71% w.w.), and seal oil was removed from the diet. Herring became the main source of lipids, while the amounts of vegetables and fruits remained unchanged.

Zookeepers separately collected fecal samples from each polar bear three times in March, June, and November 2021, i.e., during the hyperphagic period, and at the beginning and the end of the hypophagic period. Because it was not feasible to collect the feces egested by each bear in its entirety, the egestion rate GF was estimated by first using an empirical relationship34 with body weight and then scaling this value with GD. Details are given in Text S1 in the Supporting Information. Densities of dietary and fecal samples (ρD or F, in units of g mL–1), quantified by immersing a preweighted sample into a water-filled vial,12 were used to convert the GD and GF from units of g day–1 to mL day–1 (Table S2). The sampling of the feces of a domestic dog (both juvenile and adult stages) and Arctic wolves had been described previously.12

Sample Preparation

Details of the sample preparation are given in Text S2. Briefly, 400 g of dietary samples (or around 1500 g of fecal samples) were homogenized into a smooth puree using a blender. An amount of the biocide sodium azide equal to 1% of the w.w. of the puree was added to prevent samples from decomposing during the experimental period.35

Passive Equilibrium Sampling

Homogenized diet and fecal matter were equilibrated with silicone films coated on the inside of glass vessels on a roller mixer at 10 rpm for 5 days and 2 weeks, respectively. Larger sample mass (∼350 to 400 g versus ∼35 g) and therefore larger equilibration vessels (500 mL jars vs 40 mL vials) are required for the fecal samples, which contain relatively small quantities of PCBs. This assured collection of a PCB amount in the silicone that is sufficient for reliable quantification but also smaller than 5% of the initial PCB amount present in the sample, i.e., assures negligible sample depletion. Details of preparation of silicone-coated vessels and equilibrium passive sampling are given in Texts S3 and S4. Briefly, for each individual bear and each of the three sampling events, 18 silicone-coated vials and 9 silicone-coated jars were prepared for a total of 162 silicone-coated vials and 81 silicone-coated jars. The thicknesses of silicone films were 8, 10, 15, 18, 20, and 23 μm in vials and 0.6, 0.9, and 1.5 μm in jars. After equilibration, samples were removed from the vessels. The chemicals absorbed by the silicone polymer were extracted three times with 8 mL of hexane on a roller mixer at 30 rpm for 90 min. After the volume of extracts had been reduced to around 1 mL, 13C12-PCB105 was added as internal standard to account for any instrument fluctuations.

Quantification of Lipid Content and PCBs

Approximately 30 g of homogenized dietary and fecal sample puree was collected for the determination of native polychlorinated biphenyl (PCBs) concentrations (Table S3). The sample puree went through accelerated solvent extraction (Dionex ASE 350), purification (i.e., Na2SO4 column, gel permeation column and silica gel column), N2 blowdown and addition of injection standard. The lipid content of dietary or fecal samples (Plipids D or F) was determined by the ratio of the mass of lipid residue collected in fraction A eluted from the gel permeation column and the total mass of sample used in ASE. The assimilation efficiency of lipids (AElipids) can be calculated by using

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The concentrations of the native PCB congeners in different biological samples (i.e., CD and CF) and in polymer (i.e., CsiliconeD and CsiliconeF) were quantified using the instrumental analytical method described in Chen et al.13 For CsiliconeD and CsiliconeF, this involved plotting the quantified mass of a PCB congener against the volume of silicone in a vial (Figures S2 and S3). The slope of a linear regression through these data yields the concentration in the silicone (in units of ng/mL) (Table S4). The linearity of the plots further serves as confirmation that equilibrium between sample and silicone had indeed been achieved.

Data analysis

According to eq 1, the thermodynamic limit to an organism’s gastrointestinal biomagnification capability BMFlim can be determined noninvasively by the ratio of the products of the volume-based flow rates (G) and fugacity capacities (Z) of food and feces.12,13 The fugacity f of a chemical in two phases in equilibrium are the same

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and f is the ratio between the concentration and the Z value of a chemical in a phase. Therefore

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With a minor rearrangement, we obtain

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By combining eqs 1 & 6, the BMFlim can be calculated using the four measured concentrations (in both biological samples and in polymers equilibrated with both biological samples) and the volume-based ingestion and egestion rates

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We further estimate the feces-based BMFF using

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Gut Microbiome Analysis

We extracted DNA from fecal samples using a Qiagen PowerSoil DNA Extraction Kit following the manufacturer’s instructions. This includes feces from polar bears collected in this study and in previous studies on polar bears13 and canines.12,13 DNA extracts were sent to Genome Quebec (Montreal, Quebec), where 16S rRNA gene amplicon libraries were prepared using the V4-targeting 515F/806R primer set36 and sequenced on an Illumina NextSeq (PE300). All raw reads were submitted to NCBI and are available under BioProject ID PRJNA1087346.

Preprocessing of amplicon sequence data and downstream graphing and statistical analyses were performed in R v.4.3.1. (R Core Team 2023). Preprocessing followed the DADA2 pipeline37 to generate amplicon sequence variants (ASVs) and assign taxonomy using the SILVA database38 (v.128). We removed taxa that could not be identified as Bacteria or that classified as Archaea, mitochondria, or chloroplasts. All samples were rarefied to a depth of 5477 reads for downstream analysis. Although there are mixed views on sample rarefaction,39 multiple studies suggest that rarefaction is the more robust approach when comparing microbiome composition across samples.40,41

After preprocessing, we compared global fecal bacterial composition across the three bears and across time points and benchmarked this against three canine subjects by generating a principal coordinates analysis (PCoA; cmdscale, “vegan” package) based on a Bray–Curtis dissimilarity matrix (vegdist, “vegan” package). We also generated 100% plots of bacterial composition at the Order level (subsetting through the “phyloseq” package, plotting through barplot, “graphics” package). Finally, we agglomerated taxa to the Order level (tax_glom, “phyloseq”), removed Orders not representing >0.1% of reads/sample on average, and performed a redundancy analysis on a hellinger-transformed Order occurrence matrix, constrained by BMFLim and BMFF. This aimed to identify relationships in our data between these metrics and moderately or highly abundant Orders of fecal bacteria.

Results

Polar Bear Weight and Egestion Rates during Hyperphagic and Hypophagic Periods

The body weight of the two adult bears fluctuated in response to the variable caloric intake (Figure 1 and Tables S2 and S5). Aurora and Nikita gained weight by 13 and 8%, respectively, during the hyperphagic period. Their weight reached a peak at the beginning of the hypophagic period and dropped by ∼10% upon approaching the end of that period. Aurora’s body weight was up to 17% higher than Nikita’s, even though the twins share the same diet and living environment, suggesting that Aurora has a higher dietary assimilation efficiency and therefore higher growth rate.42 Juno’s body weight kept increasing throughout the year of the study, from 290 to 375 kg. Aurora and Nikita’s fecal egestion rate GF was estimated to drop to around 300 mL/day and that of Juno was reduced from 920 to 650 mL/day during the hypophagic period (Table S2).

Figure 1.

Figure 1

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Dietary ingestion rate, body weight, and adjusted fecal egestion rate of three zoo-housed polar bears (i.e., Aurora, Nikita, and Juno) at three different times. The times are the middle of the hyperphagic period (dark shading), the beginning of the hypophagic period (intermediate shading), and the end of the hypophagic period (light shading). The egestion rates in March were estimated according to the equation from Yang et al.34; while the egestion rates in July and November were adjusted according to the reduction of dietary ingestion rate. The green dashed line shows where BMFF equals to 1.

Biomagnification Parameters

The measured values for the concentrations of selected PCBs in diet (CD) and feces (CF), and in silicone film equilibrated with diet (CsiliconeD) and feces (CsiliconeF) are given in Tables S3 and S4 in the Supporting Information. The BMFlim, calculated using eq 7, range from 28 to 357 (Table S6 and upper panel of Figure 2) and thus confirm our earlier findings.13 The BMFF values of PCBs (Table S7 and the bottom panel of Figure 2) were all above 1, indicative of gastrointestinal biomagnification in all three polar bears at all sampling times. Because BMFlim and BMFF of different PCB congeners were not significantly different, we discuss largely the average values of the targeted PCBs.12,13 Efficient biotransformation of a PCB congener could lower its fecal fugacity and therefore BMFF. While the measured BMFFs are generally similar for different PCBs (Figure 2), the slightly lower values for congener 28 may be related to the elimination by biotransformation.

Figure 2.

Figure 2

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Thermodynamic biomagnification limit (BMFlim) and feces-based biomagnification factors (BMFF) in three zoo-housed polar bears at different feeding states (i.e., hyperphagic or hypophagic period). The green dashed line shows where BMFF equals to 1.

Individual Bears Differ in Their Biomagnification Capabilities

The lipid assimilation efficiency, when averaged over the three sampling occasions, differed among the three bears. Consistent with her higher body mass and growth rate, Aurora had a consistently higher lipid digestion efficiency (99.5%) compared to that of Juno and Nikita (around 98.6%). While this difference may seem numerically small, it has a large impact on ZD/ZF, and in turn, on BMFlim. With the highest lipid digestion efficiency among the three bears, Aurora was capable of lowering the lipid content in feces to a minimum of 0.32%, leading to a low fugacity capacity ZF (ranging for different PCB congeners from 4.2 × 102 to 5.1 × 104 mol Pa–1 m–3, when averaged over the three sampling events) and further a high average ZD/ZF of 28 (Tables S6, S8 and Figure S4). With lower lipid digestion efficiencies, the average lipid contents in Nikita’s and Juno’s feces were 1% and 1.6%, respectively. As a result, the average ZF for different PCB congeners ranged from 1.8 × 103 to 2.0 × 105 for Nikita and had even higher values for Juno (from 2.8 × 103 to 5.0 × 105). On average, lipids contributed ∼74% of the bulk ZF (Table S10), i.e., significantly influence the ZF.

The ZD/ZF values of Nikita and Juno were at least two times lower than that of Aurora (11 and 5 on average, respectively). The ratios of ingestion and egestion rates GD/GF for Aurora, Nikita, and Juno were 7, 8, and 11, respectively. Accordingly, Aurora had the highest BMFlim, ranging (when averaged across the three sampling events) for different PCB congeners from 167 to 268 (202 overall), followed by Nikita (65 to 121; 90 overall) and Juno (35 to 86; 60 overall) (Figure 2 and Table S6). The same pattern holds for the BMFF, with Aurora having the highest BMFF ranging from 2 to 9 (7 overall), while Nikita and Juno had similar BMFF (∼1 to 4; 3 overall) (Figure 2 and Table S7). In short, we found differences in BMFlim and BMFF as high as a factor of 3 between the three bears, even though they ate the same food, were all females, shared the same habitat, and were genetically related.

Biomagnification Capability of an Individual Bear can Change during a Year

Both Aurora and Nikita experienced hyperphagic and hypophagic periods, while for Juno, GD varied little between seasons. There are indications that a period of fasting may increase the lipid assimilation efficiency. This was most apparent for Nikita, as at the end of the hypophagic period, Nikita’s lipid assimilation efficiency increased from 98.1 to 99.4%, leaving only 0.42% of lipids in feces. For Aurora, the lipid assimilation efficiency also increased during the hypophagic period but less so (from 99.3 to 99.5%), presumably because it was already high to begin with. Juno’s assimilation efficiency changed little over time (i.e., 98.6%), consistent with the absence of hypophagia. The change in diet over time caused a decrease in ZF by a factor of 1.3 and 4 for Aurora and Nikita, respectively. While the dietary lipid assimilation efficiency influences ZF, the dietary lipid content can also contribute to differences in the biomagnification capability between hyperphagic and hypophagic periods through its influence on ZD. During the hyperphagic period, the polar bears were fed lipid-rich food (including seal oil) with a lipid content of ∼12% w.w., leading to a high ZD. During the hypophagic period, the dietary lipid content was reduced to ∼9%, leading to an average reduction in ZD of 28%.

These seasonal changes in lipid assimilation efficiency and dietary lipid content are reflected in changes in ZD/ZF (e.g., Nikita’s ZD/ZF increased from 6 to 21 during the hypophagic period) and finally in the BMFlim. The average BMFlim increased from 46 to 168 and from 150 to 215 for Nikita and Aurora, respectively (Figure 2). These differences in the BMFlim (by as much as a factor of 4) were significant for both Aurora and Nikita (ANOVA analysis, p < 0.05, Table S9b), even though the seasonal change in lipid assimilation efficiency for Aurora was small. Juno had statistically the same BMFlim value (around 60) throughout the year. The pattern of BMFF is more complex. Nikita and Juno had relatively low BMFF (1.5) during the hyperphagic period. Their BMFF continued increasing and reached a peak of 5 at the end of the hypophagic period, while Aurora’s BMFF peaked at 9 at the beginning of the hypophagic period. In short, BMFlim and BMFF of a bear can vary by as much as a factor of 4 during a year because of a period of hypophagia.

If the fugacity in the feces is reflective of the fugacity in the body of the polar bear, we might expect bears with higher biomagnification capability to also have higher fecal fugacities. This is observed since Aurora had the highest fugacity in its feces, followed by Nikita, and then Juno (Figure S5). We might also expect the fugacity in feces to decrease during a hyperphagic period due to a dilution effect and to increase during a hypophagic period due to bioamplification (i.e., contaminants become concentrated in a smaller lipid content in the body).43 This is observed for both Nikita (except for PCB-28) and Juno, but not Aurora (except for PCB-180) (Figure S5). While the fecal fugacity is likely to reflect the body burden of a contaminant, the contamination of the diet may also have some influence. While generally the dietary fugacity of PCBs decreased from hyperphagic to hypophagic periods (Table S7), we do not know the body burden of PCBs in each bear since blood samples were not available. Therefore, we cannot assess why the change in the fugacity in Aurora’s feces over time is not consistent with the concepts of biodilution and bioamplification. Aurora had a relatively high body weight and lipid content (e.g., 36% and ∼133 kg body fat in Feb 2021) compared to Nikita (32% and ∼105 kg body fat). A relatively small change in body fat may not result in a large enough change in body contamination to be observed as a change in fF. In fact, all three zoo-housed polar bears had a body mass (i.e., Aurora: ∼430 kg; Nikita: ∼360 kg; Juno: ∼330 kg at the end of hyperphagic period) at the extreme upper end of the range observed in female wild polar bears in North America (∼220 kg on average, with a maximum of 378 kg).44,45 It is also possible that fF cannot accurately reflect fB as Moser et al. have suggested (fB in humans can be 10-fold higher than fF).17

Changes in Fecal Microbial Composition and Potential Links with Biomagnification

Gut microbial composition can change rapidly with dietary shifts,24−27 while some gut microbes are capable of facilitating lipid absorption, which could directly promote biomagnification.24,28 Also, bacterial biomass is a major contributor (up to 54%) to organic matter in human feces.46 This may also be true for polar bears. Any change in the composition of the microbial community could influence ZF, and thus the fF, BMFlim, BMFF, and actual BMF. Here, we assessed fecal bacterial composition using a high throughput 16S rRNA gene sequencing approach. Although our sample size here is small, owing to the labor-intensive collection of biomagnification metrics, we aimed to generate hypotheses about potential relationships among BMFF, BMFlim, and gut bacterial composition for future investigation.

In comparing fecal bacterial composition from the three bears with three canines as an outgroup, we noted substantial differentiation (Figure 3A) and almost no overlap in the taxa present (Figure S6). This highlights a major challenge in trying to extrapolate from the few studies we have on gut bacterial relationships with lipid assimilation28−30 to species like polar bears, when they are conducted on lab-reared model organisms, like mice. Although bacteria could conceivably influence lipid assimilation in a wide range of organisms, the bacteria involved may necessarily differ widely, depending on factors like the diet and physiology of the host organisms.24−27 Interestingly, intraspecies differences (i.e., between different polar bears) and even differences in the same bear in a different feeding state can be nearly as large (Figures 3A,B and S6). This suggests that a hyperphagic/hypophagic cycle likely promotes massive gut bacterial turnover and that results here should be treated with caution, as different taxa could conceivably influence lipid assimilation at different points of the year. Future work could aim to scale these comparisons across more individual bears at different key physiological stages.

Figure 3.

Figure 3

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Results from a high-throughput 16S rRNA gene sequencing approach and a redundancy analysis. (A) Comparison of the gut microbiomes of different species; (B) relative abundance of gut microbes in the microbiome of the polar bears during hyperphagic (in March) and hypophagic (in July and November) periods; (C) redundancy analysis of different families and BMFlim (or BMFF).

A few studies have used high-throughput sequencing to assess the diversity and composition of polar bear gut microbiomes47,48 highlighting differences based on geography, which has been attributed to factors such as available diet. In general, the microbiomes in the wild polar bear’s gut are dominated by Firmicutes, Proteobacteria and Actinobacteria, and their diversity can increase significantly from spring to fall.47,48 Here, we see some overlap with respect to the major groups identified (e.g., high abundance of Bacillota and Pseudomonadota), but we uniquely show massive variability within individual bears, as well as fecal samples that are often dominated by a small number of classes (Enterobacterales, Clostridiales, and Peptostrepto-coccales). Across all bears, the relative abundance of Peptostreptococcales was highest in the hyperphagic period and that of Enterobacterales was highest at the start of the hypophagic period. Overall, a wider range of bacterial classes was abundant during the hypophagic period, especially in the feces of Nikita and Aurora (who are genetically related and shared the same diet), including the Lachnospirales, Bacteroidales, and Burkholderiales. Interestingly, the gut microbiome of Juno was much more consistent throughout the year (Figure 3A,B), perhaps reflecting the minor seasonal change in its diets. The broad temporal trends generally agree with studies in mice and humans that indicate a decreased relative abundance of Bacteroidota following consumption of a lipid-rich diet49 and increased abundance of Bacillota.50,51 The ratio between Bacillota and Bacteroidota has been proposed as an indicator of obesity,50 with a higher ratio indicative of higher weight gain. In all three bears, Bacteroidota were undetectable during hyperphagic sampling. However, the proportion of Pseudomonadota in the bears is much higher than would be typically seen in a human gut.24

The gut microbiome was also found to influence the health (e.g., promoting immune homeostasis and responses) and nutrient uptake in polar bears.52,53 Perhaps, it is a reason why Aurora has a higher body weight than her twin sister, Nikita. The gut microbiome can play a key role in lipid digestion and absorption,24 although this has not been widely shown across organisms. If this were broadly true, then it should influence the biomagnification capability of microbial hosts. This applies especially to microbes in the small intestine, where macronutrient digestion and absorption take place. Given the substantial differentiation in ASV composition across individuals and time points (see also Figure S7), we performed a redundancy analysis on bacterial composition at the Order level (Adj. R2 = 0.186), constrained by BMFF and BMFlim (Figure 3C). We observed a direct negative relationship between BMFlim and the relative abundance of Enterobacterales, while the Lactobacillales were also negatively associated with BMFlim and the Bacteroidales were positively associated. Interestingly, one of the main genera from the Enterobacterales in our study was Escherichia/Shigella, and E. coli has been shown to promote both lipid consumption and storage, depending on the composition of metabolites it produces.30 This further highlights the need to understand the unique relationships between gut microbes and different hosts, which may change with context. BMFF was positively associated with the relative abundance of Burkholderiales and negatively associated with that of the Peptostreptococcales. Romboutsia, one of the abundant genera of Peptostreptococcales in our data set, has been linked to higher lipid metabolism in rats,54 which could explain a negative association with BMFF. Again, this is very speculative from these data alone due to our small sample size and a lack of understanding for how gut microbes interact with this unique host.

Discussion

Comparison with Literature Data

Because studies of differences in biomagnification between individuals of the same species and the same individual at different feeding states are extremely rare, it is difficult to put the numerical results of our study into context. Moser and McLachlan reported feces–air equilibrium partitioning constants for humans,15 which are directly proportional to fecal fugacity capacities ZF. Differences in ZF for PCBs up to a factor of 2.8 among four human volunteers eating the same diet15 is very similar to the threefold difference in ZF we observed here for polar bears. Moser and McLachlan also investigated changes in ZF upon a change in diet.15 While eliminating meat, fish and other digestible products from the diet of two volunteers only led to a negligible change in ZF, an increase in the consumption of less readily digestible whole grains and vegetables almost doubled the ZF in two other volunteers.15 This compares with a decrease in Aurora’s and Nikita’s ZF by 24% and 74% on average, respectively, when removing most of the meat, fish, and other digestible products from their diet.

In this study, BMFlim and BMFF varied by as much as a factor of 3 between the three bears and by as much as a factor of 4 for the same bear during a year. While no similar data exist for any other species, we can compare these differences with those between species. Our previous studies showed a difference in BMFlim between Arctic wolves (∼40), adult dog (∼80) and polar bears (∼170) in the order of a factor of 2 to 4.12,13 Such interspecies difference can be explained by the difference in the dietary lipid content (influencing ZD), and the lipid assimilation efficiency of the animals (influencing both GF and ZF). A modeling study predicted maximum biomagnification factors (BMFmax) between ∼ 1 and 100 within the animal kingdom,2 with apex predators on the higher end (e.g., wolf adult: ∼100; polar bears: ∼70; bowhead whale: ∼20) and invertebrates, juvenile birds and cows on the lower end of that range (BMFmax < 10). For example, the BMFmax of adult wolf and dugong vary by a factor of 3, whereas a difference of a factor of 4 exists between the BMFmax values in polar bear and walrus. In other words, the differences in BMFlim we observe between individuals of the same species and for the same individual over time are comparable in magnitude to those between species.

Lipid Assimilation Efficiency Predicts BMFlim, but Not the BMFF

Including the data reported in Chen et al.,12,13 our group has now determined 13 BMFlim and 10 BMFF values for PCBs using the method described herein. While these numbers are small, they constitute a significant amount of work (e.g., a total of 348 individual silicone-coated vials or jars were used in equilibration experiments). In addition to polar bears, these values represent a domestic dog (as an adult and as a juvenile) and zoo-housed Arctic wolves. We find a reasonably strong relationship between lipid assimilation efficiency of an organism’s digestive system and the measured BMFlim (R2 = 0.76) (Figure 4). This suggests that it might be possible to estimate the BMFlim simply from the lipid content in food and feces as well as the dietary ingestion and fecal egestion rates. This is because, on average, lipids can account for three-quarters of the fecal fugacity capacity (Table S10). The contribution is even higher for the highly chlorinated PCB congeners; e.g., on average lipids contribute 90% of the bulk ZF for PCB-153.

Figure 4.

Figure 4

Open in a new tab

Relationship between the average BMFlim (left) or BMFF (right) in zoo-housed polar bears and their lipid assimilation efficiency. Both the BMFlim and lipid assimilation efficiency were calculated based on the adjusted fecal egestion rate.

The method described here is complex and labor-intensive, requiring expensive trace analytical instrumentation and difficult and time-consuming experiments. It is also dependent on the availability of large amounts of fecal matter, limiting the method to reasonably large organisms. In contrast, the determination of dietary and fecal lipid contents is much easier and requires much smaller sample sizes. We therefore suggest that investigations on differences in the biomagnification capabilities of POPs between species, between individuals of the same species, and of the same individual at different times could rely on simple determinations of the dietary and fecal lipid content. One caveat is that the regression in Figure 4 relies exclusively on carnivores and more studies may be required to establish its validity for organisms with divergent gut morphology.

The relationship between the lipid assimilation efficiency and BMFF is less strong (R2 = 0.50) (Figure 4). A weaker relationship may occur because fF is influenced by the history of an organism’s exposure to a contaminant. In other words, the contamination of an organism’s tissues, and therefore also the fugacity in its feces fF, is at least as much dependent on past dietary intake as it is due to uptake from the most recent meal. An organism with a history of exposure to a contaminant should have a high fB, which is reflected in a high fF. When the digesting foods in the gut lumen equilibrate with the gut tissues, the fF/fD ratio will be smaller in an organism with a high fB than in an organism with a low fB.

Implications

Biomonitoring studies often reveal large differences in the concentrations of biomagnifying contaminants between individuals. This is also true for studies on polar bears.20,55−62 Differences are typically attributed to differences in the contamination of the environment,60,63,64 in feeding strategies and therefore dietary composition,65−67 in the rates of elimination between females and males (e.g., as a result of elimination processes unique to females, such as parturition and lactation),68,69 in activity levels and therefore energy expenditures,70 or in age (with older individuals possibly retaining memory of higher exposures in the past).71 Here we show that interindividual differences in biomagnification capabilities can be a major contributor to such differences in body concentrations. Two female polar bears, who are twins and throughout their entire lives had identical diets and living environment, differ by as much as a factor of 4 in how efficiently they absorb dietary lipids and therefore absorb contaminants into their tissues. It is conceivable that bears that are not as genetically similar, that feed differently, and/or have different hunting strategies or habitat could deviate much more strongly in their biomagnification capabilities. The current study also suggests that the gut microbiome could help explain why such large differences in the dietary lipid assimilation can occur, but this would require a much more extensive study.

Limitations of the Study and a Path Forward

In this study, we determined BMFF instead of the actual BMF because the assumption that fF is equal to fB is likely not appropriate for polar bears.17 It may still be possible to estimate the actual BMF, since for humans the contamination of feces (CF) with selected POPs (e.g., polychlorinated dibenzo-p-dioxins and dibenzofurans, PCBs, chlorinated pesticides, and polybrominated diphenyl ethers) was found to be strongly related to contaminant levels in blood, CBlood.72−74 The key is to determine the correlation between CF and CBlood. With a sufficiently large sample number, it may be possible to obtain a correlation that is suitable for reliably estimating CBlood from measured CF. If ZBlood is estimated from the lipid content in blood and Zlipids, then fBlood can be calculated using an equation of type eq 5. Finally, the actual BMF is obtained as the ratio of fBlood/fD. We were not able to do this in this study, because animal welfare considerations limit the drawing of blood from polar bears housed in the Toronto Zoo.

Acknowledgments

We acknowledge funding from a Discovery Grant of the Natural Sciences and Engineering Research Council of Canada (grant number: RGPIN-2020-04142; F.W.). We thank Michael McLachlan for his insights.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.4c03302.

  • Sample and silicone-coated vial preparation; equilibrium passive sampling; analytical methods; quality assurance; list of analytes and samples; chemical properties of selected compounds; chemical concentration in samples and polymer, recovery rate, Z-values, fugacities and chemical activity in diets and feces, relative contribution of lipids to the bulk Z-values; BMFlim and feces-based BMFF; performance of equilibrium sampling, fugacity capacities of selected PCB congeners in different biological samples in different time periods, changes in fugacity of selected PCBs in polar feces at different feeding states, adjustment to fecal egestion rate, and gut microbial community composition in three zoo-housed polar bears (PDF)

The authors declare no competing financial interest.

Supplementary Material

es4c03302_si_001.pdf (2.2MB, pdf)

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