habits on temporal trends of per- and polyfluoroalkyl substances in two Arctic top predators

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1 Supporting information for Emission changes dwarf the influence of feeding habits on temporal trends of per- and polyfluoroalkyl substances in two Arctic top predators Heli Routti, * Jon Aars, Eva Fuglei, Linda Hanssen, Karen Lone, Anuschka Polder, Åshild Ø. Pedersen, Sabrina Tartu, Jeffrey M. Welker, and Nigel G. Yoccoz, Norwegian Polar Institute, Fram Centre, Tromsø, Norway Norwegian Institute for Air Research, Fram Centre, Tromsø, Norway Norwegian University of Life Sciences, Campus Adamstua, Oslo, Norway University of Alaska Anchorage, Department of Biological Sciences, Anchorage, Alaska, United States UiT-The Arctic University of Norway, Department of Arctic and Marine Biology, Tromsø, Norway Number of pages: 39 Number of tables: 9 Number of figures: 5 S1

2 Field sampling Adult female polar bears were immobilized from a helicopter by remote injection of tiletamine hydrochloride and zolazepam hydrochloride (Zoletil Forte Vet ; Virbac, France). A vestigial premolar tooth was used for age estimation, 1 unless age was already known based on earlier capture. Each polar bear was classified according to their breeding status. Solitary bears were alone or together with a male, AF-COYs were together with 1 or 2 cubs of the year, and, AF-YRLs were with 1 or 2 yearlings (cubs aged between 1 and 2 years). Blood samples were collected in heparinized tubes, which were kept in cold and in the dark until they were centrifuged within 10 h (3500 rpm, 10 minutes). Red blood cells and plasma were frozen and stored at -20 o C until analysis. The National Animal Research Authority (NARA), Norway, approved all procedures. We calculated body condition index (BCI) as previously described for polar bears based on body mass and body length. 2 For the bears that were not weighted in the field (n=62), we estimated body mass with 8% accuracy. 3 Breeding status, age and body condition index for each sampling year are given in Table S1. Proxies for feeding habits Stable isotope values of carbon and nitrogen (δ 15 N and δ 13 C, respectively) were used as proxies for feeding habits of polar bears and arctic foxes. δ 15 N and δ 13 C are expressed as stable isotopic ratios ( 15 N/ 14 N and 13 C/ 12 C) relative to isotopic ratios in international standards, i.e. atmospheric air for nitrogen and Pee Dee Belemnite carbonate for carbon. Analyses of stable nitrogen and carbon isotopes in polar bear red blood cell samples has been described previously. 4 Arctic fox muscle samples were analysed for δ 15 N and δ 13 C according to methods by Ehrich et al. 5 Briefly, the polar bear and arctic fox samples were dried and grounded to a fine powder in a bead-mill homogenizer. S2

3 The combustion analyses for polar bear samples were conducted at the Environment and Natural Resources Institute Stable Isotope Laboratory at the University of Alaska, Anchorage. Long-term records of internal standards (BWBII keratin, freeze dried moose [Alces alces] blood, peach leaves and purified methionine) yield an analytical precision of 0.10 for δ 15 N and 0.11 for δ 13 C.Measured values in reference materials are given in Table S3. Combustion analyses for artic fox samples were performed at the Stable Isotopes in Nature Laboratory (SINLAB), New Brunswick, Canada. Quality assurance for the same sample series, including measured values for commercially available and in-house secondary isotopic reference materials, have been reported previously. 6 We corrected the muscle δ 13 C values for lipid content using model-based normalisation for muscular tissue if the C/N ratio was between 3.5 and 7 (97% of the data) as recommended. 5 Proxies for food availability We used sea ice habitat quality available for individual polar bears as a proxy for availability of seals to polar bears. Polar bear habitat preference likely reflects the occurrence and availability of seals to polar bears. 7 First, we categorized surrounding areas of Svalbard according to the abundance of preferred sea ice habitat. The yearly ( ) distribution and duration of preferred sea ice habitat was based on resource selection function (RSF) models. The RSF models, based on polar bear tracking data from 224 females between 1991 and 2015, predict the probability of use of a habitat as a function of sea ice concentration, and distance to various thresholds of sea ice concentrations and bathymetry. 7 The predictions from the model of habitat selection were used to categorize optimal polar bear habitat, the seasonal cut-off values for this was set so the optimal habitat included 70% of all polar bear locations in each season. Daily predictions of distribution of optimal habitat were summed to give number of days of optimal habitat in each year. According to the maps produced by RSF S3

4 models, preferred habitat was consistently available for more days of the year on the east side of Svalbard than in the west side of Svalbard over the whole study period (Figure S1). As tracks were not available for all individual polar bears, habitat quality could not be assigned based on the areas each individual traversed prior to sampling. Instead, we divided Svalbard into two with the eastern side considered as high quality habitat and western side of Svalbard as low quality habitat. Among the 137 individual polar bears studied, 83 were equipped with satellite telemetry collars when sampled or in previous years. For these bears, we used telemetry data to categorize whether they had mainly used the better habitat on the east side of Svalbard or the poorer habitat on the west side of Svalbard. For the bears that were not equipped with collars, we used the capture position to assign them to the better habitat on the eastern side or the poorer habitat on the western side. Although polar bears may move over large areas, they have a circannual movement patterns with season-specific fidelity to areas. 8,9 To determine availability of sea ice cover for individual foxes, we first calculated the average sea ice cover in Isfjorden for each month (November-February) between 1997 and The calculations based on daily sea ice maps of Isfjorden ( and only sea ice cover >80% (close drift ice and fast ice) was included in the calculations. To get a sea ice cover index for individual foxes with known trapping date (n=99) we used the sum of the monthly average sea ice cover between 1 st of November (beginning of the trapping season) and the month of when the fox was trapped. For foxes without known trapping date (n=14), we summed the monthly sea ice cover of Isfjorden between November and February and divided it by two. Analyses of PFAS Due to the availability of different body compartments for the two species, we monitored PFASs in plasma of polar bears and liver of arctic foxes. According to studies on beef cattle S4

5 (Bos Taurus), the half-life of PFOS is similar for plasma and liver. 10 However, PFAS halflifes are highly variable among species and different compounds. 11,12 Polar bear samples were analysed at the Laboratory of Environmental Toxicology, Norwegian University of Life Sciences (NMBU). The arctic fox samples were analyzed at the Norwegian Institute for Air Research. The 17 PFASs monitored in polar bear plasma (Table 1) included perfluorobutane sulfonate (PFBS), perfluorohexane sulfonate (PFHxS) and perfluorooctane sulfonate (PFOS), and monitored C 6-14 PFCAs included perfluorohexanoate (PFHxA), perfluoroheptanoate (PFHpA), perfluorooctanoate (PFOA), perfluorononanoate (PFNA), perfluorodecanoate (PFDA) and perfluoroundecanoate (PFUnDA), perfluorododecanoate (PFDoDA), perfluorotridecanoate (PFTrDA), perfluorotetradecanoate (PFTeDA). In addition, we analyzed the following PFOS precursors: perfluorooctane sulfonamide (FOSA) and its derivates N-methyl and N-ethyl FOSA (N-MeFOSA, N-EtFOSA), N-methyl and N-ethyl perfluorooctane sulfonamidoethanol (N-MeFOSE, N-EtFOSE). C 6-7 and C PFCAs, FOSA and its derivates, and PFBS were analyzed in a reduced amount of samples (n=70-131; Table S3). The samples were analyzed in three batches in (Table S3). The method for extraction and clean-up of the samples was described previously Briefly, plasma samples (1 ml) were weighed and internal mass-labelled (M) standards (20 ng/ml) and methanol were added to the samples. The samples were cleaned-up by adding approximately g active coal to each sample and the extracts were evaporated to a final volume of 0.5 ml. Separation and quantification of PFAS in the extracts from 2012 and 2014 were conducted as previously described 14,15. Briefly, the final extracts were analysed by separation on a highperformance liquid chromatographer (HPLC) with a Discovery C18 column (15 cm x 2.1 mm x, 5 µm, Supelco, Sigma-Aldrich, Oslo, Norway), connected to a pre-column; Supelguard Discovery C 18 column (2 cm x 2.1 mm x, 5 µm, Supelco, Sigma-Aldrich, Oslo, Norway). S5

6 Detection and quantification was accomplished with a tandem mass spectrometry (MS-MS) system (API 3000, LC/MS/MS System). For the batch run in 2015, the extractions were analysed by Agilent 1200 series SL coupled to Agilent 6460 Triple Quadrupole LC/MS. Separation was performed by Supelco Discovery C 18 column. All compounds were quantified against mass-labelled internal standards with a few exceptions: PFTrDA and PFTeDA were quantified against MPFDoDA, and PFBS against MPFHxS. The limit of detection (LOD) was three times signal to noise ratio found in the samples (Table S4). The quality assurance procedures included three procedural blank, one blind and two recovery samples for each series. PFASs were detected in blanks at low concentrations (<0.25 ng/ml; Table S3). Because recovery of PFASs in spiked reference samples varied between batches due to instrumental variation (73-120%; Table S3), all the samples were corrected for the average recovery of PFASs in the spiked reference samples of the batch. The laboratory participates to the inter-laboratory comparison entitled the Arctic Monitoring and Assessment Programme (AMAP) Ring Test for Persistent Organic Pollutants in Human Serum. The results for the AMAP human serum samples (average per batch) were within +/ 11% of the reference values, except for PFOA in one batch and PFOS in two batches for which the mean quantified concentrations were +24%, 22% and 29% of the assigned values, respectively We monitored 16 PFASs in artic fox liver samples. Analyzed PFSAs were PFHxS, perfluoroheptane sulfonate (PFHpS), PFHxS, linear and branched isomers of PFOS, and perfluorodecane sulfonate (PFDS), whereas the monitored C 7-14 PFCAs were PFHpA, PFOA, PFNA, PFDA, PFUnDA, PFDoDA, PFTrDA and PFTeDA. As potential PFOS and PFCA precursors we analyzed FOSA and 6:2 and 8:2 fluorotelomer sulfonates (FTSA), the latter two ones only in 99 samples (Table S5). The arctic fox samples were analyzed in two batches at the Norwegian Institute for Air Research. First, we used data from 14 samples analyzed in 2013 for another study, 16 then the remaining analyses run in 2015 were conducted using S6

7 similar methods by the same laboratory. One gram of liver was transferred to a 15 ml polypropylene centrifugation tube. Twenty μl of a 100 ng/ml mass-labelled internal standard mixture was added before adding of 8 ml acetonitrile (Lichrosolv, Merck, Germany). After extraction, 3 x 10 minutes on an ultrasonic bath, the supernatant was transferred to a new tube and the volume reduced to approximately 1 ml using a RapidVap (Rapid Vap; Labconco Corp., Kansas City, MO, USA). The sample was vortexed with acidified ENVI-Carb 120/400 (Supelco, PN, USA). 17 Finally, 5 ng of bpfda was added as the recovery standard. Prior to analysis, an aliquot of 100 μl was transferred to a vial and mixed with an equal amount of 2 mm aqueous ammonium acetate (NH 4 OAc, 99%, Sigma-Aldrich, St. Louis, MO, USA). More details about the analysis are found in Hanssen et al. 18 A blank and a control (AM-SY 1306 for the Arctic Monitoring and Assessment Programme Ring Test for Persistent Organic Pollutants in Human Serum) was processed with each batch of 20. No PFASs were detected in the blanks. The results for the control samples were within +/ 10% of reference values, except for PFNA and PFUnDA for which the mean quantified concentrations were 30 and 20% of reference values, respectively. Recovery for the 13 C labelled internal standards varied between %. S7

8 Table S1. Biological information for female polar bears sampled around Svalbard, , by number of samples for area (habitat quality high vs. low), breeding status, and time-point for PFAS-analyses. Age, body condition index (BCI; arbitrary units), δ 13 C and δ 15 N values in red blood cells are given as mean ± standard deviation. Solitary female bears were alone or together with a male, AF-COYs were together with 1 or 2 cubs of the year, and AF-YRLs were with 1 or 2 yearlings (cubs aged between 1 and 2 years). The 192 samples collected represented 137 individual females. Twenty-eight females were captured 2-8 times. Year Habitat quality Breeding status Age BCI δ 13 C δ 15 N PFAS-analyses High Low Solitary AF-YRL AF-COY Years ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± S8

9 Table S2. Number of individual arctic foxes by sex, body condition and age is given for each trapping season during the period The body condition index refers to fat deposits and ranges from 1 (low; barely measurable fat) to 4 (extensive fat). δ 13 C and δ 15 N values measured in muscle tissue are given as mean ± standard deviation. Trap Sex Body condition Age δ 13 C δ 15 N Season F M y 2y ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 0.55 Total S9

10 Table S3. Measured δ 15 N and δ 13 C values for commercially available and in-house (University of Alaska Anchorage, UAA) secondary isotopic reference materials analysed with polar bear samples. Difference from expected values SD for difference Mean SD δ15n BWBII keratin (UAA) Purified methionine (Alfa Aesar) Freeze dried moose (Alces alces; UAA) Peach Leaves (UAA) δ13c BWBII keratin(uaa) Purified methionine (Alfa Aesar) Freeze dried moose (Alces alces; UAA) Peach Leaves (UAA) S10

11 Table S3. Limit of detection (LOD; ng/ml) and recovery (%) of PFASs in spiked reference material, and, concentrations (ng/ml) of PFASs in blank samples for polar bear batches analyzed in 2012, 2014 (with API 3000 instrument) and in 2015 (Agilent 6560). SD: standard deviation; n.d.: not detected; n.a.: not analysed 2012 (n=62) 2014 (n=60) 2015 (n=70) LOD Recovery Blank LOD Recovery Blank LOD Recovery Blank Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD PFBS a PFHxS n.d a PFOS n.d a FOSA n.a. n.a a N-MeFOSA n.a. n.a n.d N-MeFOSE n.a. n.a n.d N-EtFOSA n.a. n.a n.d N-EtFOSE n.a. n.a n.d PFHxA n.a. n.a a PFHpA n.a. n.a a PFOA a n.d a PFNA n.d a PFDA n.d a PFUnDA n.d a PFDoDA n.a a PFTrDA n.a a PFTeDA n.a. n.a a a Concentrations in blanks were subtracted from PFAS concentrations in samples. S11

12 Table S4. Limit of detection (LOD; ng/g ww), number of samples (n) analyzed and % of plasma samples above LOD for PFASs analyzed in polar bears during n.a. = not analyzed. N- MeFOSA N- MeFOSE N- N- EtFOSA EtFOSE FOSA PFBS PFHxS PFOS PFHxA PFHpA PFOA PFNA PFDA PFUnDA PFDoDA PFTrDA PFTeDA LOD n n.a n.a n.a n.a n.a n.a n.a n.a n.a n.a n.a n.a n.a n.a n.a n.a n.a n.a 2012 n.a n.a n.a n.a n.a n.a n.a n.a n.a 2013 n.a n.a n.a n.a n.a n.a n.a n.a n.a S12

13 Table S5. Limit of detection (LOD; ng/g ww), number of samples (n) analyzed and % of liver samples above LOD for PFASs analyzed in arctic foxes during 11 trapping seasons. Trap season FOSA PFHxS PFHpS PFOSlin PFOSbr PFDS 6:2 FTSA 8:2 FTSA PFHpA PFOA PFNA PFDA PFUnDA PFDoDA PFTrDA PFTeDA LOD n S13

14 Table S6. Model selection table for models explaining concentrations of PFASs in adult female polar bears according to ΔAIC and AIC weights. Degrees of freedom (df) and adjusted R 2 for fixed effects only are given. Continuous predictor variables were standardized prior analyses. Predictors include δ 15 N and δ 13 C values in red blood cells, habitat quality (good vs. poor), breeding status (solitary, with cubs of the year or yearlings), body condition (arbitrary units) and age (years). Intercept s(year) δ 13 C δ 15 N ln(pfhxs) Habitat quality Breeding status Body condition Age df ΔAIC AIC weight R 2 S14

15 8 Table S6 continued Intercept s(year) δ 13 C δ 15 N Habitat quality Breeding status Body condition Age df ΔAIC AIC weight ln(pfos) R 2 S15

16 9 Table S6 continued Intercept s(year) δ 13 C δ 15 N Habitat quality Breeding status Body condition Age df ΔAIC AIC weight ln(pfoa) R 2 S16

17 Table S6 continued Intercept s(year) δ 13 C δ 15 N Habitat quality Breeding status Body condition Age df ΔAIC AIC weight R 2 10 ln(pfna) S17

18 11 Table S6 continued Intercept s(year) δ 13 C δ 15 N Habitat quality Breeding status Body condition Age df ΔAIC AIC weight ln(pfda) R 2 S18

19 Table S6 continued Intercept s(year) δ 13 C δ 15 N Habitat quality Breeding status Body condition Age df ΔAIC AIC weight R 2 12 ln(pfunda) S19

20 Table S6 continued Intercept s(year) δ 13 C δ 15 N Habitat quality Breeding status Body condition Age df ΔAIC AIC weight R 2 13 ln(pfdoda) S20

21 Table S6 continued Intercept s(year) δ 13 C δ 15 N Habitat quality Breeding status Body condition Age df ΔAIC AIC weight R 2 14 ln(pftrda) S21

22 15 Table S6. Continued Intercept s(year) δ 15 N δ 13 C Habitat quality df AIC ΔAIC AIC weight ln(pfbs) ln(pfhpa) R 2 16 ln(pfteda) S22

23 Table S7. Model selection table for models explaining concentrations of PFASs in arctic foxes according to ΔAIC and AIC weights. Degrees of freedom (df) and adjusted R 2 are given. We standardized all the predictor variables prior analyses (δ 15 N and δ 13 C in muscle, index for sea ice cover, number of reindeer carcasses and body condition ranging from 1-barely measurable fat to 4-extensive fat). Intercept s(year) δ 15 N δ 13 C Sea ice cover Reindeer mortality Body condition df ΔAIC AIC weight ln(pfhxs) R 2 ln(pfhps) S23

24 Table S7 continued Intercept s(year) δ 15 N δ 13 C Sea ice cover Reindeer mortality Body condition df ΔAIC AIC weight ln(pfos) R 2 22 ln(pfoa) S24

25 Table S7 continued Intercept s(year) δ 15 N δ 13 C Sea ice cover Reindeer mortality Body condition df ΔAIC AIC weight ln(pfna) R 2 23 ln(pfda) S25

26 Table S7 continued Intercept s(year) δ 15 N δ 13 C Sea ice cover Reindeer mortality Body condition df ΔAIC AIC weight ln(pfunda) R 2 24 ln(pfdoda) S26

27 25 26 Table S7 continued Intercept s(year) δ 15 N δ 13 C Sea ice cover Reindeer mortality Body condition df ΔAIC AIC weight ln(pftrda) R 2 S27

28 Table S8. Concentrations (ng/g wet weight) of perfluoroalkyl sulfonates (PFSA) and carboxylates (PFCA) in a) plasma samples from adult polar bear females and b) liver samples from young arctic foxes collected from Svalbard during the period and , respectively. PFSA a PFCA b Year Median Min Max Median Min Max a) polar bear plasma b) arctic fox liver , , , a polar bears: PFHxS+PFOS; arctic foxes: PFHxS+PFHpS+PFOS b polar bears: C 8-11 PFCAs; arctic foxes C 8-14 PFCAs 33 S28

29 Table S9. Annual change (%) with 95% confidence intervals of PFAS concentrations in polar bears and arctic foxes from Svalbard, Norway. The adjusted changes are corrected for annual variation in feeding habits and food availability (δ 13 C, δ 15 N in both species, and sea ice cover and reindeer carcasses in arctic foxes) in addition to other variables (body condition in both species, and, habitat quality, age and breeding status for polar bears) in case these variables were included in top ranked additive mixed models (GAMM, Table S6-S7). The non-adjusted trends were derived from top ranked GAMMs excluding the continuous predictors reflecting feeding habits and food availability. Linearity of the trends was investigated using GAMM plots (Figure 1, S2-S3). Polar bear plasma Arctic fox liver time adjusted not adjusted time adjusted not adjusted PFBS (-12.4, 12.4) PFHxS (-12.5, -4.8) -9.0 (-12.7, -5.1) (-16, -3.8) (-16.9, -5.2) PFHxS (-1.1, 11.2) 5.1 (-1.0, 11.5) PFHpS (-15.9, -4.2) -8.7 (-12.5, -4.8) a PFOS (-17.8, -10.4) (-17.7, -10.6) (-18.1, -4.8) -7.9 (-13.2, -2.2) a PFOS (-3.4, 6.8) 1.6 (-3.3, 6.7) (-13.7, 63.7) PFHpA (-3.2, 4.9) -0.6 (-23.4, 28.9) PFOA (-2.0, 1.1) -1.5 (-3.0, 0.1) (-3, 0.4) -1.8 (-3.9, 0.3) PFNA (2, 4.2) 2.5 (1.5, 3.5) (-0.3, 3.8) 0.4 (-2.0, 2.9) PFDA (2, 4.2) 2.3 (1.2, 3.4) (1.6, 6.0) 2.1 (-0.4, 4.7) PFUnDA (1.8, 4.3) 2.0 (0.7, 3.2) (1.6, 7.0) 6.3 (-1.3, 14.5) b PFDoDA (1.5, 4.6) 1.7 (0.2, 3.2) (-0.3, 5.4) 8.6 (0.5, 17.4) b PFTrDA (1.5, 5.2) 1.6 (-0.1, 3.5) (0.9, 7.0) 11.1 (2.1, 20.9) b PFTeDA (-0.3, 4.7) 1.1 (-13.3, 17.9) a ; b since S29

30 Figure S1. Distribution and duration of optimal polar bear habitat around Svalbard, modeled by a resource selection function using sea ice concentration, bathymetry and distance to 15% and 75% sea ice concentration. Maps show number of days in each year the pixels were characterized as optimal habitat. S30

31 PFHxS PFOS PFOA PFNA PFDA PFUnDA Figure S2. Diagnostic residual plots for the highest ranked additive mixed models that explain ln-transformed concentrations of PFASs in adult female polar bears. S31

32 PFDoDA PFTrDA PFBS PFHxA PFTeDA Figure S2 continued S32

33 PFHxS PFHpS PFOS PFOA PFNA PFDA Figure S3. Diagnostic residual plots for the highest ranked additive mixed models that explain ln-transformed concentrations of PFASs in arctic foxes. S33

34 PFUnDA PFDoDA PFTrDA Figure S3 continued S34

35 Figure S4. Measured vs. adjusted (adj) temporal trends of PFAS concentrations in plasma of female polar bears from Svalbard (n=70-192), Norway. The trends are derived from additive mixed models and given with 95% confidence intervals. The adjusted changes are corrected for annual variation in feeding habits (δ 13 C, δ 15 N) in addition to other variables (body condition, habitat quality, age and breeding status) in case these variables were included in top ranked additive mixed models (GAMM; Table S6). The non-adjusted trends were derived from the top ranked GAMMs excluding the continuous predictors reflecting feeding habits. The y-axes show partial residuals of the models. S35

36 Figure S5. Measured vs. adjusted (adj) temporal trends of PFAS concentrations in liver of 113 arctic foxes from Svalbard. The trends are derived from additive mixed models and given with 95% confidence intervals. Year is the only predictor for the non-adjusted measured trends, whereas the adjusted trends are derived of the highest ranked models (Table S7) i.e., the effects of year have been controlled for other variables selected in the models. The y-axes show partial residuals of the models. S36

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