Climate Influence on Legacy Organochlorine Pollutants in Arctic

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Environmental Processes

Climate Influence on Legacy Organochlorine Pollutants in Arctic Seabirds Karen L Foster, Birgit M. Braune, Anthony J. Gaston, and Mark L. Mallory Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b07106 • Publication Date (Web): 28 Jan 2019 Downloaded from http://pubs.acs.org on January 29, 2019

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Environmental Science & Technology

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Partial Residuals

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Rain (mm)

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Climate Influence on Legacy Organochlorine

2

Pollutants in Arctic Seabirds

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Karen L. Fostera,b*, Birgit M. Braunec, Anthony J. Gastonc, Mark L. Malloryd

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aKaren

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Foster Environmental Research, Peterborough, ON, K9J 8L2, Canada of Modelling & Quantitative Methods (AMOD), Trent University, Peterborough, ON, K9L 0G2, Canada cEnvironment and Climate Change Canada, National Wildlife Research Centre, Carleton University, Ottawa, ON, K1A 0H3, Canada dBiology Department, Acadia University, Wolfville, NS, B4P 2R6, Canada bApplications

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Keywords: climate change, Arctic, seabirds, PCBs, organochlorine pesticides, DDT,

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chlorobenzenes, mirex, chlordanes

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ABSTRACT

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Changing climate can influence the transport of chemical pollutants into Arctic regions and their

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fate once there. However, the influence of weather or climate variables on organochlorine

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accumulation in Arctic wildlife, including seabirds, and associated timescale are poorly

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understood.

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variables for time lags of 0 to 10 yr and organochlorine pollutant concentrations spanning 1975-

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2014 in eggs of two seabird species (northern fulmar Fulmarus glacialis, thick-billed murre Uria

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lomvia) that breed in the Canadian High Arctic. The majority of variability in the data was

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associated with declining organochlorine emissions (up to 70.2 % for murres and 77.4 % for

We assessed the interannual relationships between a suite of weather/climate


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fulmars). Controlling for emissions using principal component ordination and general linear

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modelling, correlations with the North Atlantic Oscillation (NAO) were found for fulmars, and

24

with rainfall for murres, after a time lag of 4-9 yr between weather/climate conditions and egg

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collection. Our results suggest that with increasingly NAO+ conditions and increasing rainfall,

26

associated with climate change, concentrations of certain organochlorines such as

27

hexachlorobenzene and p,pʹ-DDE increased; dependent on seabird species and ecology, as well

28

as partitioning characteristics of the chemical. Analysis of a truncated version of the datasets

29

(2005-2014), consistent with typical time series lengths for environmental pollutants in Arctic

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wildlife, found correlations with precipitation for murres, but not with NAO for fulmars,

31

suggesting that longer time series better elucidate relationships with broad-scale climate indices.

32

Organochlorine pollutant datasets spanning 40 years, which is rare for Arctic wildlife, for two

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species of seabird were assessed and results highlight the association between weather/climate

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and pollutant accumulation in Arctic food webs and the critical role of ongoing monitoring to

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effectively elucidate these relationships.

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INTRODUCTION

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Climate change has been identified as a likely factor governing the fate of pollutants in

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the environment. This has implications for Arctic ecosystems, which serve as sinks for persistent

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organic pollutants (POPs), such as legacy organochlorines, predominantly produced and used at

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lower latitudes but restricted or banned in most circumpolar countries in the 1970s and 1980s (1-

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3). Widely agreed upon impacts of climate change on pollutants in Arctic ecosystems include

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the increased volatilization from primary sources (4), increased re-mobilization from

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environmental reservoirs or secondary sources such as soils, glaciers, sea ice and permafrost

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(5,6), and changes to air-water exchange owing to reductions in sea ice cover, increased

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precipitation and increased organic carbon inputs from terrestrial sources and altered carbon

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cycling in the Arctic Ocean (1,7-9).

48

Ecological implications of a warming climate for Arctic wildlife have been documented

49

(10). For Arctic seabirds, climate-related ecological shifts include effects on population size and

50

predation (11,12), prey availability (13-15), diet (16-18), as well as reproduction and foraging

51

behaviour (19-23). In some cases, ecological shifts have led to changes in accumulated POPs

52

(24,25). A study of organochlorines in thick-billed murre (Uria lomvia) eggs from two colonies

53

found that in the northern Hudson Bay colony, a dietary shift from cold water Arctic cod

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(Boreogadus saida) to sub-Arctic capelin (Mallotus villosus) resulted in a lower trophic position

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and a decreased rate of decline of accumulated organochlorine pollutants (26). In the High

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Arctic colony, an increase in stable nitrogen isotope (δ15N) levels, possibly owing to an increase

57

in fish consumption, led to more dramatic rates of decline of organochlorines but the magnitude

58

of declines were small compared to the effect of reduced emissions.


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Large-scale climate patterns such as the Arctic Oscillation (AO) and North Atlantic

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Oscillation (NAO) as well as local and regional weather, which are not always correlated (27),

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are expected to influence pollutant fate globally, including transfer into and within Arctic regions

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and accumulation in Arctic wildlife (1-3,7,28-33). AO and NAO are indices of atmospheric

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circulation with positive (+) cyclonic and negative (–) anticyclonic phases, and they have been

64

found to correlate with POPs concentrations in various Arctic wildlife (e.g., 24,34,35) and in a

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sub-Arctic terrestrial raptor (36). In seabirds, circulating blood levels of POPs in glaucous gulls

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(Larus hyperboreus) from the Norwegian Arctic were found to positively correlate with AO

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from the previous year and negatively correlate with AO from the current year, suggesting

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increased background levels of POPs under AO+ circulation patterns (37).

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common eiders (Somateria mollissima) from two colonies in the Norwegian Arctic (38),

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researchers found higher concentrations of circulating POPs (hexachlorobenzene, PCB-153, and

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p,pʹ-DDE) in blood when temperatures were low, suggesting a re-mobilization of hydrophobic

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chemicals stored in fat reserves metabolized to meet the increased energetic demand of the birds

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in colder conditions.

In a study of

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Modelling assessments suggest that the maximum response of POPs concentrations in an

75

ecosystem to changes in climate (39) or emissions (40-43) can lag the change itself, with the

76

precise lag time dependent on the physical-chemical properties of the chemical. PCB-153 in an

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Arctic marine ecosystem, for example, is predicted to have a response time of years between a

78

climate change-induced shift in exposure and the resultant change in a seabird food web (39).

79

Time lags of 4 to 7 yr between maximum PCB loadings to Lake Ontario and maximum PCB

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accumulation in sediments have been observed (40).

81

declines in PCBs in Lake Ontario food web components have also been observed, including

Similarly, time lagged responses to


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herring gull (Larus argentatus) eggs, with the estimated response time dependent on diet

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composition (40). Time lags of years to decades between changes in POPs emissions and

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resultant changes in accumulation in abiotic environmental compartments have also been

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predicted for some chemicals in remote Arctic receptor regions (41), oceans (42), and a generic

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evaluative environment (43). Yet, only zero or one yr time lags have been assessed for Arctic

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wildlife (34,35,37,38), which may be optimistic given the slow response of the marine ecosystem

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and food web to shifts in POPs emissions (39,41,42).

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Prince Leopold Island Migratory Bird Sanctuary (74°02′N, 90°05′W) supports one of the

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largest seabird colonies in the Canadian High Arctic, including approximately 100,000 breeding

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pairs of thick-billed murres and 16,000 breeding pairs of northern fulmars (Fulmarus glacialis;

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44). The ecology and POPs of murres and fulmars at Prince Leopold Island have been studied

93

since 1975. During this time, the diet of murres has remained consistent: 95 % fish (90 % of

94

which is Arctic cod) and 5 % marine invertebrates (16). An increase in the proportion of fish

95

was noted, but not significant. Similarly, the diet of fulmars has also remained fairly consistent:

96

fish (Arctic cod), cephalopods, polychaetes and crustaceans, though recent years suggest a higher

97

proportion of fish (45). Using previously reported POPs concentration data measured over 40

98

years in fulmar and murre eggs (46), and weather and climate data, we assessed potential

99

interannual climate effects on accumulated POPs and the timescale on which effects might occur

100

using principal component (PC) ordination and general linear modelling (GLM).


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MATERIALS AND METHODS

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Weather and Climate Data. We compiled an extensive suite of local and regional scale

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weather variables pertaining to atmospheric and oceanic environmental compartments, and air-

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to-ocean transfer (e.g., rain/snow), as well as large scale climate indices (e.g., AO, NAO), Arctic

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sea ice extent, and temperature anomalies (Table 1). Local and regional data were compiled for

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the Barrow Strait/Lancaster Sound region, or the nearest sampling station to Prince Leopold

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Island for 1965 through 2014, where available, to enable time lag analyses of zero to 10 yr.

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However, field- and satellite-based measurements were not always available for every year

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(Supporting information, Table S1). Depending on the data available, seasonal (winter and

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summer) and annual averages were compiled, or else data for ~June 20th; a date that reflects the

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start of egg laying for the majority of murres, and the end of egg laying for fulmars.

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POP

Concentrations

in

Seabird

Eggs.

Concentrations

of

organochlorine

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pesticides/metabolites and polychlorinated biphenyls (PCBs) in seabird eggs were measured

114

between 1975 and 2014 and methods of collection and analysis are provided elsewhere (26), as

115

are summarized mean annual concentrations (46). Data for 18 yr were available for murres and

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17 yr for fulmars. Two to five composite samples comprising equal aliquots of three eggs each

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were analyzed per species per year, resulting in sample sizes of n = 80 for fulmars and n = 81 for

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murres. However, all p-values were conservatively calculated for n = 18 for murres and n = 17

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for fulmars to reflect the number of sampling years (rather than the total number of samples).

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A subset of organochlorine chemicals was selected for each seabird species and consisted

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of PCB-31/28, -74, -99, -118, -153, -105, -138, -187, -183, -128, -156 (fulmar only), -180, -170,

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-199 (murre only), -194 (fulmar only), 1,2,3,4-tetrachlorobenzene (1,2,3,4-TCB; murre only),


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pentachlorobenzene (PnCB), hexachlorobenzene (HCB), octachlorostyrene (OCS; murre only),

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heptachlor epoxide (HE; murre only), oxychlordane, trans-nonachlor (fulmar only), cis-

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nonachlor, p,p’-DDE, dieldrin, p,p’-DDD (fulmar only), p,p’-DDT (fulmar only), photomirex

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(fulmar only), and mirex (fulmar only); a total of 22 chemicals for murres and 25 for fulmars.

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Chemical selection was based on detection in > 90 % of samples for the species and

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concentrations > PQL (practical quantification limit; five times the method detection limit,

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MDL). Selected chemicals also had declining concentration trends with time. For statistical

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analyses, non-detectable concentrations (i.e., no signal above baseline) and concentrations
0.8, |r| ≥ 0.6) and the time lags at which correlations were strongest.

147

Pearson correlations were also assessed for truncated versions of the fulmar and murre

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datasets. Data were truncated to 2005-2014, a period during which concentration variability was

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reduced, and correlations with weather/climate variables, time lagged as above, were assessed.

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We deduced a priori that maximum POPs accumulation in seabird eggs might lag climate

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by a minimum of three yr based on trophic exchange alone. The prey of fulmars and murres

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breeding on Prince Leopold Island range from zooplankton to Arctic cod (16,45,49), thus POPs

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likely pass through one to three trophic levels, including zooplankton food webs, which have

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also been shown to biomagnify POPs (50). Additionally, murres prey on Arctic cod that, based

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on cod size, are estimated to be more than two years old (49,51). Taken with predicted time lags

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of years to decades between climate/emission changes and maximum POPs concentration

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response reported in the literature as previously discussed (39-43), we conservatively opted to

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assess time lags of zero to 10 yr.

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Time lagged weather/climate variables identified in the preliminary PC correlation

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assessments were included in GLM analyses and the most parsimonious models selected using

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the difference in Akaike's Information Criteria (∆AICC) corrected for smaller sample size

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following a practical information-theoretic approach (52). Two models were considered for each

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chemical for each seabird species. The ‘base’ model consisted of Year + Constant. A second

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model was then assessed: base + NAO (for fulmars) or base + Rain (for murres). Substantial

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empirical support for both models was indicated when ∆AICC = 0-2, ∆AICC = 4-7 indicated

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considerably less support for the second best model, and >10 essentially no support for the


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second best model (52). A second set of exploratory GLMs were run on the most abundant

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PCBs (i.e., -118, -138, -153, and -180) for both fulmars and murres, substituting global annual

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emissions (“Emissions”, 53) for Year (i.e., GLMs were: Emissions + NAO + Constant for

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fulmars, and Emissions + Rain + Constant for murres). Concentrations from this model were

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compared to those from the Year-based models.

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To assist in the interpretation of results, three environmentally relevant partition

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coefficients: octanol-water (Log KOW), air-water (Log KAW), and octanol-air (Log KOA) were

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compiled using EPISuite™ v.4.11 (54; Supporting information, Table S2). All p-values were

175

two-tailed for α = 0.05. Some weather variables had insufficient non-zero values to include in

176

statistical analyses; these included winter rain accumulation and multiyear sea ice concentrations.

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Correlations between % lipid and stable nitrogen isotopes (δ15N) in eggs and weather/climate

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variables at time lags of zero to 10 yr were included in preliminary assessments, but aside from a

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moderate correlation between δ15N and summer NAO at a lag of 5 yr (r = -0.60) for fulmars no

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other correlations of acceptable power were found and % lipid and δ15N were not considered

181

further. All statistics were performed in SYSTAT Version 13.

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RESULTS

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A decline in concentrations of legacy POPs since 1975 in the eggs of murres and fulmars

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from Prince Leopold Island was evident (r = -0.2 to -0.91, also Figure 1), which corresponded

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with periods of reduced POPs production. Exploratory GLMs that substituted Emissions for

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Year yielded PCB-118, -138, -153 and -180 concentrations for both fulmars and murres that

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were highly correlated with those using Year (r = 0.94 – 0.97, all p 0.8). PC1 was highly correlated

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with sample collection year (r = -0.87, p < 0.001 for fulmars and r = -0.85, p < 0.001 for murres;

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Supporting information, Figure S1) and captured the confounding influence of declining

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emissions with time, thus PC1 was excluded from further analyses. The second extracted

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principal component (PC2) accounted for 10.8 % and 11.2 % of the variability in the fulmar and

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murre data, respectively. The third principal component (PC3) captured 5.7 % of the variability

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in the murre data. For fulmars, PC3 accounted for only 2.4 % of the data variability and, as

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might be expected, permitted little discrimination between chemical loadings. Thus, PC3 for

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fulmars was not considered further. Neither PC2 nor PC3 (murres only) were strongly correlated

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with Year for either species (r = -0.17 and -0.09 for murres, respectively, and r = -0.16 for

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fulmars), making them suitable variables for further analyses.


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PC2 loadings for fulmars grouped the chlorobenzenes (PnCB and HCB), chlordanes

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(oxychlordane, trans-nonachlor, cis-nonachlor), dieldrin, photomirex and mirex in the left

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quadrants, while PCBs and DDTs (DDE, DDD, DDT) grouped together in the right quadrants

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(Figure 2a). Preliminary correlation analyses between fulmar PC2 and weather/climate variables

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identified average annual NAO and local average annual atmospheric pressure as the only

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comparisons that yielded an acceptable power (Supporting information, Table S3). Correlation

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between fulmar PC2 and NAO was low (r < 0.06) at time lags of 0-3 yr (Supporting information,

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Figure S2a), but during lags 4-9 yr they were moderately negatively correlated; the lag of 7 yr

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provided the highest correlation (r = -0.61, p = 0.0093; Figure 2b). Similarly, correlations of

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PC2 with atmospheric pressure were low (r < 0.25) at time lags of 0-3 yr, but moderately

215

positively correlated during 4-9 yr lags, with the highest correlation also at a time lag of 7 yr (r =

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0.63, p = 0.0067; Supporting information, Table S3). Atmospheric pressure and NAO were

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highly correlated from 1965 to 2014 (r = -0.83, p < 0.001), thus NAO alone was selected for

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GLM analyses. Winter atmospheric pressure at a lag of 6 yr (r = 0.62, p = 0.0079; Supporting

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information, Table S3) was also correlated with PC2.

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PC2 and PC3 loadings for murres distinguished between chlorobenzenes (1,2,3,4-TCB,

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PnCB, HCB) and OCS in the bottom left quadrant, PCBs and DDE in the right quadrants close to

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the origin, and chlordanes (HE, oxychlordane, cis-nonachlor) and dieldrin in the upper left

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quadrant (Figure 3a).

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variables found that PC3 versus annual average rainfall was the only correlation that yielded an

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acceptable power (Supporting information, Tables S4 and S5). Correlations between murre PC3

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and rainfall were poor at time lags of 0-4 yr (all |r| < 0.32; Supporting information, Figure S2b),

Correlation analyses between murre PC2/PC3 and weather/climate


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but moderately negative at lags of 5-8 yr; the strongest correlation being at a time lag of 6 yr (r =

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-0.60, p = 0.0085; Figure 3b).

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Groupings of organochlorine chemicals based on PC loadings for both fulmars and

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murres (Gaussian bivariate ellipses, p = 0.68; Figure 2a and 3a) reflected intrinsic environmental

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partitioning characteristics of the chemicals (Supporting information, Table S2). Fulmar PC2

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loadings were moderately but significantly correlated with Log KOW values (Figure 4a, r = 0.40,

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p = 0.049), whereas, murre PC3 loadings were highly negatively correlated with Log KAW,

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stretching from chlordanes and dieldrin (Log KAW = -3.07 to -5.45), to PCBs and DDE (Log KAW

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= -1.93 to -3.43), to chlorobenzenes and OCS (Log KAW = -1.16 to -2.03); representing a range

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of four orders of magnitude of KAW (Figure 4b, r = -0.77, p r < 0.26;

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Supporting information, Figure S3). Thus, average annual NAO at a time lag of 7 yr (“NAO”)

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and annual rain accumulation at a time lag of 6 yr (“Rain”) were assessed as predictors of

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individual chemical concentrations in fulmars and murres, respectively, using GLM.

243

General linear modelling (GLM). In fulmars, years of NAO+ conditions were followed

244

by increased concentrations of chlorobenzenes (PnCB, HCB), trans- and cis- nonachlor, dieldrin,

245

photomirex, and mirex in their eggs, as shown by the positive β-coefficients for NAO

246

(Supporting information, Table S6). For these chemicals, the NAO + Year + Constant model

247

was more parsimonious than the base model (∆AICC = 3.4 – 32.4). Partial residual plots

248

confirmed that when controlled for Year, concentrations of chlorobenzenes, trans-/cis-nonachlor,


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dieldrin, photomirex and mirex concentrations increased with NAO+ (Figure 5, Supporting

250

information, Figure S4 and S5). The GLM for oxychlordane yielded a negative β-coefficient for

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NAO, however, this was not a robust result – an outlier sample (studentized residuals = 3.33 to

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8.63) for most chemicals was removed for the purpose of plotting partial residuals and the slope

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for oxychlordane changed (from negative to positive). The base model was more parsimonious

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for DDE and most PCBs in fulmar eggs (∆AICC = 1.1 - 2.2), however, partial residual plots of

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DDTs (DDE, DDD, DDT) and PCBs exhibited a cyclical trend with NAO (Figure 5, Supporting

256

information, Figure S6 and S7).

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In murres, years of increased rainfall were followed by decreased concentrations of

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chlordanes (HE, oxychlordane, cis-nonachlor) as well as PCB-170 and -180 in eggs, as indicated

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by negative β-coefficients for Rain (Supporting information, Table S7). However, only the

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partial residual plot for oxychlordane yielded a convincingly negative trend (Figure 5).

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Chlorobenzene (1,2,3,4-TCB, PnCB, HCB), OCS, DDE, dieldrin and most PCB concentrations

262

increased with rainfall and had positive β-coefficients for Rain. Partial residual plots illustrate

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that when controlled for Year, concentrations of chlorobenzenes, OCS, DDE, dieldrin and most

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PCBs increased in murre eggs after increased rainfall, while concentrations of oxychlordane

265

decreased (Figure 5, Supporting information, Figure S8 to S10).

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Across the vast majority of organochlorine chemicals investigated, inclusion of NAO (for

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fulmars) and Rain (for murres) greatly improved model fit. For fulmars, model fit for 13

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chemicals indicated that the NAO model was most parsimonious (∆AICC = 0.66 – 32.44) and of

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these, considerably less support for the base model was found for eight chemicals with ∆AICC ≥

270

4. For the 12 chemicals for which the base model was the better fit, low ∆AICC suggested

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substantial empirical support for including NAO (∆AICC = 1.13 – 2.20). For murres, the model


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that included Rain was more parsimonious for 12 organochlorine pollutants (∆AICC = 0.12 –

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17.47) and of these, considerably less support was seen for the base model for eight chemicals

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with ∆AICC ≥ 4. For 10 chemicals the base model was the better fit, however, low ∆AICC still

275

suggested substantial empirical support for the Rain model (0.67 ≥ ∆AICC ≥ 2.20).

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Truncation (2005-2014). Truncated concentrations were, for the most part, not well

277

correlated with Year (-0.57 < r < 0.36), though PCB-170 in murres was moderately correlated (r

278

= -0.67) and not considered further. In murres, PnCB concentrations were positively correlated

279

with annual precipitation (i.e., the sum of rain and snow accumulation) after a 6 yr time lag (r =

280

0.76, p = 0.011), chlorobenzenes (1,2,3,4-TCB, PnCB, HCB) were moderately correlated (r =

281

0.67-0.76) with annual rain, while chlordanes (HE, oxychlordane, cis-nonachlor) and dieldrin

282

were less well correlated (r = 0.12-0.32) with annual rain, and DDE and PCBs were intermediate

283

(r = 0.20-0.68). The power of these correlations was low, nonetheless, these trends generally

284

followed those identified for murres using the full dataset. In fulmars, however, correlations

285

were not found with NAO, nor with other climate indices, with one exception. PCB-156 was

286

both negatively and positively correlated with a number of climate indices at various time lags,

287

but these correlations likely reflect the unusually high percent standard deviation (70 %) of PCB-

288

156.


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DISCUSSION

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We have found patterns of correlation between weather and climate and the

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concentrations of some organochlorine legacy POPs in Arctic seabird eggs using unique, 40 yr

292

datasets for two species. Preliminary correlations between extracted PCs and all weather/climate

293

variables identified NAO (and sea level atmospheric pressure) for fulmars at time lags of 4-9 yr

294

and rainfall for murres at time lags of 5-8 yr as the variables of most relevance to the interannual

295

variability of organochlorine concentrations in eggs. Time lags of 7 and 6 yr, respectively, for

296

fulmars and murres yielded correlations of sufficiently high statistical power.

297

relationships were further examined for each individual chemical using GLMs. Since locally

298

measured sea level atmospheric pressure was well correlated with the broader scale regional

299

NAO for the time period studied (1965-2014), GLM investigations for fulmars focused on only

300

NAO as a main effect.

These

301

Across all organochlorine chemicals investigated, inclusion of NAO (for fulmars) and

302

rainfall (for murres) substantially improved GLM model fit and suggested a strong relationship

303

with organochlorine concentrations in seabird eggs, consistent with the PC correlation analyses.

304

Years of NAO+ conditions were followed by years of higher concentrations of chlorobenzenes,

305

trans-/cis-nonachlor, dieldrin, photomirex and mirex in fulmar eggs, and NAO- was associated

306

with the opposite trends. Concentrations of PCBs and DDTs were found to cycle with maxima

307

at approximately NAO = 0 and minima at NAO = -1 and +1; which is discussed below. Murre

308

GLM results suggested that years of higher rainfall were followed by years when concentrations

309

of chlorobenzenes, OCS, DDE, dieldrin and most PCBs were higher, while concentrations of

310

HE, oxychlordane, cis-nonachlor, PCBs-170 and -180 were lower.


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Declining concentrations of organochlorine POPs in murre and fulmar eggs have been

312

shown previously, and are attributed to declines in historic production of these chemicals (e.g.,

313

26,46). We included Year in GLM models to control for this effect, in the absence of emissions

314

data for the majority of studied chemicals. However, using emissions data for PCBs that were

315

available (53), our exploratory emissions-based GLM for four PCBs yielded modelled

316

concentrations highly correlated with those from year-based GLMs for both fulmars and murres.

317

We conclude that historic emissions data that become available for other legacy organochlorine

318

chemicals should be included in predictive models of accumulation in Arctic wildlife, with

319

consideration for the time lag necessary for long-range transport to Arctic regions (41), but if

320

such data are not yet available year is a suitable proxy.

321

Time lags of zero up to 10 yr between weather/climate occurrences and seabird egg

322

collections were investigated to assess possible staggered effects of weather and climate on POPs

323

in seabird eggs. Lags of up to 10 yr we viewed as conservative, given that measurement- and

324

modelling-based assessments have suggested that time lags of years to decades may be relevant

325

for some chemicals, particularly with respect to marine ecosystems (39,41,42). Our results are in

326

agreement with these studies and support the consideration of a broader timeframe than currently

327

considered in assessments of climate change effects on POPs in Arctic wildlife (34,35,37,38),

328

particularly given the unknowns associated with climate driven shifts in background

329

concentrations of POPs via primary or secondary sources, and the complex processes that relay

330

these shifts into environmental compartments, into the base of the food web and up to top

331

predators.

332

Clues as to the mechanism for the associations between accumulated organochlorine

333

concentrations in fulmar and murre eggs and NAO and rainfall, respectively, may be gleaned


16


Page 18 of 34

334

from partitioning properties and inter-chemical comparisons.

For murres, the majority of

335

organochlorine chemicals studied increased in concentration in eggs following years of heavier

336

rainfall, which supports findings that POPs increase in Arctic sea ice melt-ponds following

337

precipitation events (56). PC3 distinguished between chlorobenzenes and OCS, PCBs and DDE,

338

and chlordanes and dieldrin; chemical groupings that correspond with declining Log KAW values,

339

which was shown by the significant decline in PC3 loadings with Log KAW. Since PC3 was

340

found to decline with rainfall, our results suggest that years of heavier rainfall were followed by

341

higher concentrations of higher Log KAW chemicals, such as HCB and PnCB, which have an

342

increased likelihood of being found in air (55) and therefore available for atmospheric

343

scavenging. Oxychlordane, with the lowest Log KAW of the organochlorines investigated here,

344

supported this trend with a concentration decline in years following heavier rainfall, however, it

345

must be noted that oxychlordane is also a major metabolite of technical chlordane in murres (57)

346

and a source apportionment assessment of metabolism, background, and climate associated

347

sources is merited.

348

For fulmars, the concentrations of most organochlorines studied increased in eggs under

349

NAO+ conditions. NAO+ circulation is associated with the transport of POPs originating in

350

industrialized source regions in Eurasia and eastern North America into the Arctic, and is

351

particularly effective in winter (7,30), although, the colder temperatures of winter may hinder the

352

release of POPs from secondary sources (2). Associations between NAO+ and magnified POPs

353

accumulation in Arctic wildlife have been found (34,35,37). For fulmars, the yearly averaged

354

NAO explained more variability in the dataset than winter NAO, possibly owing to the increased

355

contribution of secondary sources during the warmer seasons - higher annual temperatures in


17

Page 19 of 34


356

Arctic regions have been associated with increased re-mobilization of POPs from secondary

357

sources (5).

358

In our analyses, PC2 loadings for fulmar egg organochlorines distinguished between

359

chlorobenzenes, chlordanes, dieldrin, photomirex, mirex and PCB-187, and the DDTs and

360

remaining PCBs; a grouping that corresponds with Log KOW, and was confirmed by a moderate,

361

but significant, positive correlation between fulmar PC2 loadings and Log KOW. The negative

362

correlation between PC2 and NAO, therefore, indicated that NAO+ conditions favored chemicals

363

such as chlorobenzenes with lower Log KOW. Chlorobenzenes, based on Log KOW, Log KAW and

364

Log KOA, are considered “multiple hoppers;” or chemicals with increased long-range

365

atmospheric transport mobility to Arctic regions (41), and it follows that these would be the

366

chemicals most impacted by increased atmospheric advection into Arctic regions, which occurs

367

under NAO+ conditions (7,30). The negative correlation between PC2 and NAO also indicated

368

that NAO- conditions favored increased concentrations of higher Log KOW chemicals such as the

369

majority of PCBs in fulmar eggs. These higher Log KOW chemicals are mostly “single hoppers”

370

(41) and less readily mobile than the multiple hoppers, which supports the muted influence of

371

NAO+ as shown by GLM partial residual plots.

372

concentrations of higher Log KOW chemicals follow years of neutral NAO. It is probable that

373

given the multiplicity of factors associated with NAO phases, that competing factors are at play.

374

For example, depending on the source region of emissions, NAO+ conditions may also decrease

375

the transport of POPs into the Arctic (2). Future studies utilizing both POPs concentration time

376

series for Arctic wildlife and models capturing competing chemical fate processes under various

377

climate change scenarios are recommended to further elucidate the underlying mechanisms.

However, it is unclear why maximum


18


Page 20 of 34

378

The association between organochlorine concentrations in the eggs of fulmars and murres

379

and weather/climate variables was particularly interesting considering individual seabird life

380

histories. Murres feed closer to the colony (generally 500 km) (58,59). The

382

importance of the more locally relevant variable of rainfall for murres and of the broader North

383

Atlantic Ocean conditions for fulmars aligns with distinctions in feeding range.

384

recorded locally may be a better indicator of conditions where the murres forage than broad-scale

385

climate indices (e.g., NAO, AO). Also, we recognize that fulmars overwinter in the North

386

Atlantic from the Labrador Sea to the northeast Atlantic (60), and murres in Davis Strait –

387

northern Labrador Sea and south to waters off Newfoundland (61) and there is some evidence

388

that the body burden of POPs in female migrating Arctic seabirds can be influenced by

389

overwintering location, and that influence can be passed on to chicks (62,63). However, we

390

contend that contributions of contaminants accrued overwinter to eggs are probably small

391

compared to contaminants in local food chains for these income breeders (e.g., 64).

Rainfall

392

Interannual assessment of weather/climate variables and pollutants in Arctic regions, for

393

example, contrasting chemical partitioning and fate under + as opposed to – modes of NAO or

394

AO in the Arctic, is thought to provide insight into likely climate-driven shifts over a longer

395

temporal scale (e.g., 7). The results of this study would, therefore, suggest that under a climate

396

change scenario where NAO is increasingly positive, concentrations of chlorobenzenes, trans-

397

/cis-nonachlor, dieldrin, photomirex and mirex are likely to increase in fulmar eggs, at least

398

initially, whereas concentrations of PCBs and DDTs cycle with NAO.

399

associated with climate change (65) is likely to lead to increased concentrations of

Increased rainfall


19

Page 21 of 34


400

chlorobenzenes, OCS, DDE, dieldrin and PCBs in the eggs of murres, at least initially, and

401

decreased concentrations of oxychlordane.

402

To identify greater than decadal variability in climate cycles, time series data should be

403

longer than 30 yr (66). Climate cycling in the Arctic is thought to occur on a timeline of six

404

years to decades (67). Yet, the typical length of environmental pollutant monitoring time series

405

in Arctic biota is around a decade (1-3), and very few monitoring datasets for POPs in Arctic

406

wildlife exceed the span of 40 yr covered by the datasets used here. Assessments of a truncated

407

version of the murre dataset (2005-2014) found positive correlations between annual

408

precipitation and chlorobenzenes (most notably PnCB) at a time lag of 6 yr, as well as low power

409

correlations with the other organochlorine chemicals, which, in part, reflects the trends identified

410

with the full dataset. For fulmars, however, no correlations with NAO, or indeed with any

411

climate index, were found. Thus, we conclude that a time series of shorter, more typical length

412

for POPs would be too short to effectively detect the trends with weather and climate identified

413

using the full dataset.

414

The single largest barrier to studies of weather and climate influence on environmental

415

pollutants in the Arctic is limited availability of consistent, long-term monitoring data; an issue

416

that has been noted previously (7). Long-term pollutant monitoring datasets for Arctic wildlife

417

are valuable and comparatively rare because of the greater logistical challenges and operational

418

costs for field work than at more southerly latitudes. However, the importance of these data is

419

clear and the optimization of methods to use them effectively to assess the impact of changing

420

climate on pollutants within Arctic ecosystems can only increase their value in the future.


20


Page 22 of 34

murres fulmars 0.8 0.20 0.16 0.12 0.08 0.04 0.00

PCB

0.6 0.4 0.2 0.0 0.15

Oxychlordane

700 600 500 400 300 200 100 0

Global Emissions (Tonnes)

421

0.12 0.09

Concentration (mg kg-1 ww)

0.06 0.03 0.00 0.15

HCB

0.12 0.09 0.06 0.03 0.00 Dieldrin

0.03 0.02 0.01 0.00

DDE

0.4 0.3 0.2 0.1 0.0 1970

422 423 424 425

1980

1990

2000

2010

Year Figure 1 Concentrations of selected contaminants in fulmar (○) and murre (●) eggs from Prince Leopold Island (mean ± SE; fulmar n = 2-5/yr, murre n = 3-5/yr), with predominant periods of POPs production shaded grey (68-71). Red line shows global Σ4PCB emissions (53). Σ4PCBs is the sum of PCBs-118, -138, -153 and -180.

426


21

Page 23 of 34


1.0

a)

0.5

0.0

Oxychlordane

b)

r = -0.61, p = 0.0093

r = 0.63, p = 0.0067

4 2

DDD nonachlors (cis/trans) 194 DDE 156 138 105 118 99 180 170 183 31/28 74 128 DDT PMirex Mirex 187 153 Dieldrin HCB

PC2

PC3 Loadings

6

0 -2

-0.5

PnCB

-4 -1.0 -1.0

427 428 429 430

-6 -0.5

0.0

0.5

PC2 Loadings

1.0

-2

-1

0

NAO

1

1012

1014

Pressure (

Figure 2 Northern fulmar PCA results: a) Principal component (PC2 and PC3) loadings for organochlorine chemicals with Gaussian bivariate ellipses for chemical groups shown (p = 0.68), b) PC2 scores with average annual NAO lagged to 7 yr. Chemical groups identified by loadings correspond with Log KOW (Figure 4a).

431


22


0.5

433 434 435

4

-0.5

b)

2

Dieldrin

0.0

r = -0.60, p = 0.0085

3

cis-nonOxychlordane HE

-1.0 -1.0

432

a)

180 170 138183 153 199 187 105 118 74DDE 99 31/28 OCS 128 HCB 1,2,3,4-TCB PnCB

PC3

PC3 Loadings

1.0

Page 24 of 34

1 0 -1 -2

-0.5

0.0

0.5

PC2 Loadings

1.0

40

60

80

100

Rain (mm)

120

Figure 3 Thick-billed murre PCA results: a) Principal component (PC2 and PC3) loadings for organochlorine chemicals with Gaussian bivariate ellipses for chemical groups shown (p = 0.68), b) PC3 scores with annual rain accumulation lagged to 6 yr. Chemical groups identified by loadings correspond with Log KAW (Figure 4b).


23

Page 25 of 34


0.4

0.6

Loadings PC3

0.2

Loadings PC2

0.8

a)

r = 0.40, p = 0.049

0.0 -0.2 -0.4 -0.6

0.4 0.2 0.0 -0.2

-0.8

-0.4

-1.0

-0.6 4

5

6

7

Log KOW

8

9

b)

r = -0.77, p < 0.001

-6

-5

-4

-3

-2

-1

0

Log KAW

437 438 439

Figure 4 Principal component loadings versus relevant equilibrium partition coefficients for a) fulmar PC2 loadings versus Log KOW, and b) murre PC3 loadings versus Log KAW.

440


24

Oxychlordane

Partial Residuals (Concentration, mg kg-1 ww) PCB 153 HCB

DDE


0.2

0.15

0.1

0.10

0.0

0.05

-0.1

0.00

-0.2

-0.05

-0.3 0.03

-0.10 0.08

0.02 0.04

0.01 0.00

0.00

-0.01 -0.02

-0.04

-0.03 0.12

0.03

0.08

0.02

0.04

0.01

0.00 0.00

-0.04 -0.08

-0.01

-0.12 0.09

-0.02 0.015

0.06

0.010

0.03

0.005

0.00

0.000

-0.03

-0.005

-0.06 -2.0 -1.5 -1.0 -0.5

441 442 443 444 445

Page 26 of 34

-0.010 0.0

0.5

1.0

1.5

20

40

60

NAO

80

100

120

140

Rain (mm)

Figure 5 Partial residual plots of the concentrations (mg kg-1 ww) of four chemicals in the eggs of fulmars (○) and murres (●) when year is controlled for versus average annual NAO (lagged 7 yr) and annual accumulation of rain (lagged 6 yr) for fulmars and murres, respectively. One outlier was removed for fulmars for all chemicals (studentized residuals = 3.33 to 8.63).


25

Page 27 of 34


Table 1 Climate and weather time series data compiled for this study include atmospheric, oceanic, and sea ice variables, as well as air-ocean transfer (i.e., atmospheric scavenging), and teleconnection indices. Data compiled for this study were for 1965-2014 as available. Annual, summer (June, July, August), and winter (December, January, February, March) averages compiled except where noted. Category Air

Variable Surface Air Temperature Wind Speed Temperature Anomalies

Air-Ocean Transfer

Sea Level Pressure Precipitation

Unit °C km h-1 °C

hPa mm

Ocean

Sea Surface Temperature (SST)

°C

Sea Ice

Sea Ice Conc.

%

Sea Ice Conc.

%

Teleconnections

Arctic Sea Ice Extent North Atlantic Oscillation (NAO) Arctic Oscillation (AO)

Pacific North America index (PNA) Multivariate El Niño-Southern Oscillation (MEI)

106 km2

Data Type and Treatment Seasonal means computed from monthly means Seasonal means computed from monthly means Temperature departures from long-term average of reference period (1901-2000) for June Seasonal means computed from monthly means Total precipitation, snow and rain accumulation computed as the sum of monthly totals

Region Resolute

Source Environment and Climate Change Canada, http://ec.gc.ca/dccha-ahccd/

Resolute

Environment and Climate Change Canada, http://ec.gc.ca/dccha-ahccd/

Northern Hemisphere

https://www.ncdc.noaa.gov/, (72)

Resolute


Resolute


Computed from satellite imagery; yearly 15 day composite July 20th-Aug 3rd (ice cover in some years prevented SST measurements June 20th) Total, multiyear, and first year ice concentrations computed from ice charts; used data for the week of June 18th Satellite brightness measurements; average June values for six regions

Lancaster Sound

St. Lawrence Global Observatory, http://slgo.ca/en/remotesensing/data.html

Eastern Parry: Lancaster to Eastern Barrow region Six marine regions: all data within 25 km of colonies Northern Hemisphere North Atlantic

Canadian Ice Service Digital http://iceweb1.cis.ec.gc.ca/IceGraph/

Summer sea ice extent in June Principal component based index Wintertime sea level pressure (SLP) based northern annular mode (NAM) index; seasonal means computed Historical index standardized to 1981 to 2010 climatology; means computed Bimonthly values; seasonal and yearly means computed

Arctic

Western Canada associated with Asian jet stream Tropical Pacific

Archive,

IceGraph

2.5,

National Snow & Ice Data Center (NSIDC), Polaris, http://nsidc.org/data/polaris/, (73)

http://nsidc.org/data/seaice_index/archives.html; (74) Climate Analysis Section, National Center for Atmospheric Research (NCAR), https://climatedataguide.ucar.edu/climate-data, (75) Climate Prediction Center, National Oceanic and Atmospheric Administration (NOAA), http://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/ao.shtml, (76) Climate Prediction Center, National Oceanic and Atmospheric Administration (NOAA), http://www.cpc.ncep.noaa.gov/data/teledoc/pna.shtml https://www.esrl.noaa.gov/psd/enso/mei/, (77)


26


Supporting Information.

Page 28 of 34

POPs concentration data are entered into the Environment and

Climate Change Canada Data Catalogue and will be available through the Government of Canada’s Open Government site (open.canada.ca/en) in the near future. Additionally, data are available from the authors upon request. Tables: data availability for POPs and weather/climate variables, POPs partition coefficients, preliminary PC correlations, GLM results. Figures: PC1 versus sample collection year, r values at time lags 0 to 10 yr, climate/weather variable values (1965-2014), GLM partial residual plots, comparison of Year- and Emission-based GLM models. This material is available free of charge via the Internet at http://pubs.acs.org. Corresponding Author *[email protected]; (705) 768-2081 ACKNOWLEDGMENT The authors thank D. Mackay (Trent University), S. Findlay (University of Ottawa) and R. Macdonald (Department of Fisheries and Oceans, DFO, Emeritus) for insightful suggestions early in the project and anonymous reviewers for constructive comments and suggestions. We also thank those individuals maintaining the climate variable databases and public access websites, and the field assistants over the years who endured often challenging conditions to acquire eggs for analyses. Financial support provided by the Northern Contaminants Program (NCP) of Indigenous and Northern Affairs Canada, Environment and Climate Change Canada, and Acadia University.


27

Page 29 of 34


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Climate Influence on Legacy Organochlorine Pollutants in Arctic

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