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Investigating the dynamics of methylmercury
bioaccumulation in the Beaufort Sea shelf food
web: a modeling perspective†
Mi-Ling Li, *
ab
Emma J. Gillies,
b
Renea Briner,
a
Carie A. Hoover,
c
Kristen J. Sora,
d
Lisa L. Loseto,
ef
William J. Walters,
g
William W. L. Cheung
d
and Amanda Giang *
b
High levels of methylmercury (MeHg) have been reported in Arctic marine biota, posing health risks to
wildlife and human beings. Although MeHg concentrations of some Arctic species have been monitored
for decades, the key environmental and ecological factors driving temporal trends of MeHg are largely
unclear. We develop an ecosystem-based MeHg bioaccumulation model for the Beaufort Sea shelf (BSS)
using the Ecotracer module of Ecopath with Ecosim, and apply the model to explore how MeHg
toxicokinetics and food web trophodynamics affect bioaccumulation in the BSS food web. We show that
a food web model with complex trophodynamics and relatively simple MeHg model parametrization can
capture the observed biomagnification pattern of the BSS. While both benthic and pelagic production
are important for transferring MeHg to fish and marine mammals, simulations suggest that benthic
organisms are primarily responsible for driving the high trophic magnification factor in the BSS. We
illustrate ways of combining empirical observations and modelling experiments to generate hypotheses
about factors affecting food web bioaccumulation, including the MeHg elimination rate,
trophodynamics, and species migration behavior. The results indicate that population dynamics rather
than MeHg elimination may determine population-wide concentrations for fish and lower trophic level
organisms, and cause large differences in concentrations between species at similar trophic levels. This
research presents a new tool and lays the groundwork for future research to assess the pathways of
global environmental changes in MeHg bioaccumulation in Arctic ecosystems in the past and the future.
Environmental signicance
High levels of toxic methylmercury (MeHg) have been found in Arctic marine biota. Many environmental and ecological drivers can affect MeHg levels and
trends in biota, and their relative inuences are difficult to disentangle through monitoring data alone. We develop and evaluate an ecosystem-based bio-
accumulation model for the Beaufort Sea shelf, and apply it to explore how toxicokinetics and food web trophodynamics affect MeHg bioaccumulation. The
model is able to capture the observed biomagnication pattern of the BSS, and illustrates the key roles of population dynamics in determining concentrations in
biota and benthic organisms in elevating the biomagnication efficiency in the BSS. Future research can apply this model to assess the impact of different global
environmental changes on bioaccumulation in Arctic ecosystems.
1. Introduction
Human activities have greatly perturbed the natural biogeo-
chemical cycle of mercury (Hg).
1,2
Monomethylmercury (MeHg),
an organic form of Hg and a highly potent neurotoxicant, can
biomagnify in aquatic food webs, resulting in concentrations
that are at least a million times higher in predatory sh and
mammals than in seawater.
3
Mercury enters the Arctic Ocean
through a number of different pathways, including river
discharge, atmospheric deposition, snow and ice melt, and
coastal erosion (ranked from the most important to the least
important, though there remains substantial uncertainty).
4–6
Although remote and far away from major anthropogenic
sources, the Arctic has been impacted by global anthropogenic
a
School of Marine Science and Policy, University of Delaware, Newark, DE, USA.
E-mail: milingli@udel.edu
b
Institute for Resources, Environment & Sustainability, University of British Columbia,
Vancouver, BC, Canada. E-mail: amanda.giang@ubc.ca
c
Marine Affairs Program, Dalhousie University, Halifax, NS, Canada
d
Institute for the Oceans and Fisheries, University of British Columbia, Vancouver, BC,
Canada
e
Freshwater Institute, Fisheries and Oceans Canada, Winnipeg, MB, Canada
f
Centre for Earth Observation Science, Department Environment and Geography,
Clayton H. Riddell Faculty of Environment, Earth, and Resources, University of
Manitoba, Winnipeg, MB, Canada
g
Ken and Mary Alice Lindquist Department of Nuclear Engineering, Pennsylvania State
University, University Park, PA, USA
†Electronic supplementary information (ESI) available. See
https://doi.org/10.1039/d2em00108j
Cite this: DOI: 10.1039/d2em00108j
Received 13th March 2022
Accepted 9th May 2022
DOI: 10.1039/d2em00108j
rsc.li/espi
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emissions of Hg due to long-range transport and global distil-
lation.
7,8
Previous studies estimated that 70 to 95% of the
present-day Hg in Arctic marine mammals comes from
anthropogenic emissions.
9,10
Some Arctic apex predators have
the highest MeHg levels in the world, with measured concen-
trations exceeding the threshold for neurocognitive impairment
and liver diseases.
7
Many of these animals, such as beluga
whales and ringed seals, are also traditional foods that are
nutritionally, culturally, spiritually, and economically signi-
cant to Arctic Indigenous populations.
11
The seawater MeHg concentration is one of the key compo-
nents responsible for the spatial and temporal variability of
MeHg in marine biota. The vefold spatial difference observed
in the MeHg level of skipjack tuna across the Pacic Ocean can
be largely explained by the peak the MeHg concentration in the
water column.
12
Prior studies indicated that the decline of
seawater MeHg in the North Atlantic Ocean between 1990 and
2012, due to reduced anthropogenic Hg emissions and releases,
led to a decrease in MeHg levels of Atlantic bluen tuna from
the 1980s to the 2010s.
13,14
In the Arctic, in situ methylation of
inorganic Hg is considered to be the dominant source of
MeHg.
15
High MeHg concentrations are observed in Arctic
marine waters, driven by active methylation and reduced
demethylation due to lower solar radiation and colder temper-
atures.
16
Factors controlling the seawater MeHg concentration,
such as availability of inorganic Hg, microbial activity, solar
radiation, and temperature,
5,15,16
are all highly sensitive to
climate change and/or human activities. Hence, the rapid
warming in the Arctic and ongoing global environmental poli-
cies, including those that address climate change and anthro-
pogenic pollution, will likely have a large impact on MeHg
concentrations in seawater.
Recent work suggests that the most important pathways for
climate impacts may not be through contaminant loading or
biogeochemistry, but through food web dynamics. Schartup
et al. (2019) (ref. 13) suggested that ocean warming has altered
the bioenergetics and food web structure in the Gulf of Maine,
driving the trend of increasing MeHg concentrations in Atlantic
bluen tuna since the 2010s. This is likely also the case in the
Arctic, which is experiencing a rapid increase in sea surface
temperature and dramatic sea ice reduction.
17–19
Increasing
seawater temperatures could change a number of bioenergetic
parameters relevant to MeHg bioaccumulation in individual
organisms, including food ingestion, respiration, growth, and
elimination rates.
20
At the population level, rising seawater
temperature has led to the northward expansion of subarctic
species into the Arctic and the recession of cold pools essential
for Arctic resident species, causing structural change in Arctic
marine food webs.
21
A number of studies have linked the
temporal trends of Hg in some Arctic species (e.g., polar bears,
ringed seals, beluga, and seabirds) to rapid sea-ice reduction,
which has altered the timing, intensity, and composition of
plankton production and induced ecosystem shifrom an ice-
associated food web to a pelagic one.
22–27
Measured concentrations of MeHg in Arctic species over time
represent the net effect of environmental factors governing Hg
loading, methylation and demethylation rates, and ecological
characteristics such as trophic interactions and bioener-
getics;
28–30
thus, it is oen challenging to use these empirical data
alone to identify the driving factors of changes in Hg levels in
biota. To elucidate the pathways of global environmental change
on MeHg bioaccumulation in Arctic ecosystems, an effective tool
that connects environmental factors (e.g., sea ice, temperature,
and contaminant loading) with food-web dynamics is urgently
needed. Here we develop an ecosystem-based bioaccumulation
model for the Beaufort Sea shelf ecosystem and provide a holistic
analysis of how the Hg burden of marine biota responds to
changes in environmental and ecological factors relevant to Hg
bioaccumulation. We aim to (1) test whether a food web model
with complex trophodynamics and relatively simple MeHg model
parametrization can capture observed patterns of MeHg bio-
accumulation at each trophic level; and (2) generate hypotheses
about the most inuential environmental and toxicokinetic
factors driving the variability of MeHg concentrations in sh and
marine mammals in the Beaufort Sea shelf ecosystem. This
research provides useful information for further assessing the
pathways of global environmental changes on MeHg bio-
accumulation in Arctic ecosystems in the past and the future.
2. Materials and methods
2.1. Study area
Our model area is the Canadian Beaufort Sea shelf (hereaer
referred to as the BSS, Fig. 1), the largest North American shelf
in the Arctic. The majority of the BSS is shallower than 200 m,
and north of the BSS is the Canada Basin, which extends
roughly 1130 km north and reaches a depth of 3600 m. This
region has experienced a range of climate change impacts,
including increased air and water temperature, decreased sea-
ice extent, a longer open water season, and more frequent and
extreme storms.
31,32
The BSS, which provides habitat for many
resident and migratory marine mammals and sh species, is
part of the homeland of the Inuvialuit people. Several studies
have reported Hg concentrations of environmental and bio-
logical samples collected in this region, including seawater,
15
plankton,
33–36
benthos,
33,37,38
and various sh species and ringed
seals.
27,33
In addition, as a sentinel species for ecosystem-based
monitoring of contaminant cycling and climate change, the
Eastern Beaufort Sea Beluga stock (“Beaufort beluga”hereaer)
and its Hg burden have been monitored for almost four decades
in Canada by a community-based biomonitoring program led
by the Fisheries Joint Management Committee (FJMC), a co-
management body with members appointed by both the Inu-
vialuit Game Council and the Government of Canada.
39
In
addition to Hg data, Inuvialuit harvesters have rich knowledge
of the ecosystem and the species inhabiting it (Fig. 1).
2.2. Food web
The trophodynamics of the BSS marine ecosystem across
different functional groups, ranging from primary producers to
top predators, for the period 1970–2012, have been previously
constructed using the Ecopath with Ecosim open-source
modeling soware suite (EwE) and trophic structured in
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agreement with stable isotope analysis.
40,41
For each functional
group, the EwE model setup simulates the whole population
that encompasses different sexes, ages, and body sizes. The
detailed characteristics of specic populations in the BSS food
web have been described in prior studies.
40,41
EwE is a widely
used trophodynamic ecosystem approach for simulating the
ow of mass and energy across the food web using information
including the biomass, feeding, production, and mortality rate
of components of a variety of functional groups.
42
In this study,
we use the BSS food web model built in Ecopath that reects the
average food web structure and dynamics between 2008 and
2012. It contains 31 functional groups, ranging from primary
producers and detritus to top predators (i.e., beluga whales)
(Table 1), and represents a mass-balanced ecosystem structure
in which biomass ows into a group via reproduction and
immigration, and equally, ows out of the group through
predation, harvest, and natural mortality.
2.3. Ecotracer simulation
We use Ecotracer, an EwE module, to simulate the bio-
accumulation of MeHg in the BSS ecosystem by tracking the
gains and losses of MeHg in all functional groups. The total
amount of MeHg in each functional group is calculated based
on ve processes: predator–prey interactions, direct uptake
from seawater, internal metabolism, internal decay, and
harvest. Walters and Christensen (2018)
45
provided a detailed
description of the Ecotracer module, and this tool has been
applied to simulate the dynamics of various contaminants in
marine ecosystems, including Hg,
44,46,47
persistent organic
pollutants,
46–48
microplastics,
49
and radioisotopes.
45,50,51
The intake amounts of MeHg for functional group icome
from the uptake of MeHg from either water (i.e.,m
i
B
i
C
o
in eqn
(1)) or food (i.e.,AE
iP
j¼prey
QjiCjin eqn (1)). The losses of MeHg for
group i are attributed to predation P
k¼predator
Qik!, harvest (H
i
),
natural mortality (MO
i
), and elimination of MeHg (E
i
),
including direct excretion and metabolic transformation
(namely demethylation) (eqn (2)). The sum of predation,
harvest, and natural mortality is the total mortality (i.e., the
ratio between production and biomass P/B), an indicator for the
population turnover rate. The P/B ratio for each group is
included in Table S1.†Our simulation generates the steady-
state MeHg concentration for each group (i.e., when intake ¼
loss):
Intake ½ton MeHg per year¼miBiCoþAEiX
j¼prey
QjiCj(1)
where C
o
represents the seawater MeHg concentration (ton per
km
2
); for group i, B
i
is the biomass (ton), m
i
is the direct
absorption rate of MeHg from water (km
2
per ton per year), and
AE
i
is the assimilation efficiency. Q
ji
is the consumption rate
(ton per year) of prey j by predator i, and C
j
is the MeHg
concentration in prey j (ton of MeHg per ton of biomass).
Loss ½ton MeHg per year
¼ X
k¼predator
Qik
Bi
þHiþMOiþEi!BiCi(2)
where Q
ik
is the rate of consumption (ton per year) of group i
due to predation by k and Qik
Bi
is the fraction of group i
Fig. 1 Map of the Canadian Beaufort Sea shelf and surrounding communities (the stars). The model area, Beaufort Sea shelf including Mackenzie
estuary, is defined by the 200 m contour (outlined in black) along the shelf-break in Canadian waters (map reproduced from Hoover et al. 2021
(ref. 40) with permission).
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consumed by predator k, H
i
is the mortality rate due to harvests
(per year), MO
i
(per year) is the natural or other mortality rate, E
i
(per year) is the elimination rate, and C
i
is the MeHg concen-
tration in predator i (ton of MeHg per ton of biomass).
All initial concentrations of MeHg are set to zero for all
functional groups and seawater. MeHg is released into the
modeled environment through a base inow rate and is lost
through biological uptake and base volume exchange. No
information is available for the net input of MeHg into the BSS
system, so we set the simulated BSS seawater MeHg concen-
tration to match the observed peak concentration (0.224 pM) of
a shelf-break station in the Beaufort Sea (71N 04.9330, 133W
39.0720),
15
through a constant abiotic ow rate through a system
of 1.05 10
5
ton MeHg per km
2
per year. Limited seawater
MeHg data are available for the BSS. Because coastal shelves
generally have higher MeHg concentrations than the adjacent
continental slope and open ocean, as a result of direct MeHg
inputs from rivers and wetlands and elevated MeHg production
in benthic sediment and water,
8,52–54
we use the observed peak
MeHg concentration between the shelf and slope to represent
the average seawater MeHg level on the BSS. We run a 100 year
simulation and the MeHg concentrations of all functional
groups reach a steady state between 40 and 50 years.
The trophic interactions in the BSS food web have been
characterized in Ecopath through the literature and stable
isotope data in previous studies, which account for temperature
and the individual body size (e.g.,sh length and weight) when
calculating the mortality and consumption rate for each func-
tional group.
40,41
Other parameters required for characterizing
MeHg dynamics in Ecotracer include the direct absorption rate,
assimilation efficiency of MeHg from food, and elimination rate.
We estimated these parameters using values or equations
generated in prior studies (Table 1, see the detailed methodology
for parameterization in the ESI†). We applied direct absorption of
MeHg from water to low-trophic level organisms (i.e.,benthic
plants, phytoplankton, zooplankton and benthos), as this is one
Table 1 Model input in Ecotracer for simulating MeHg dynamics in the Beaufort Sea food web (see detailed description of each group in ESI
Table S1)
Group name
a
Initial conc. (t t
1
)
Direct absorption rate
b
(km
2
per ton per year)
Proportion of
contaminant assimilated
c
Elimination rate
e
(per year)
Beluga 0 0 0.85 0.100
Bowhead 0 0 0.85 0.200
Ringed Seal 0 0 0.85 0.250
Bearded Seal 0 0 0.85 0.250
Char & Dolly Varden 0 0 0.85 0.328
d
Ciscos & Whitesh 0 0 0.85 0.367
d
Salmonids 0 0 0.85 0.363
d
Small Nearshore Forage Fish 0 0 0.85 0.415
d
Arctic & Polar Cods 0 0 0.85 0.450
d
Capelin 0 0 0.85 0.636
d
Flounder & Benthic Cods 0 0 0.85 0.231
d
Small Benthic Marine Fish 0 0 0.85 0.388
d
Other Fish 0 0 0.85 0.388
d
Arthropods 0 0.001 0.85 1.825
d
Bivalves 0 0.00027 0.65 1.825
d
Echinoderms 0 0.00027 0.65 1.825
d
Mollusks 0 0.00027 0.65 1.825
d
Worms 0 0.00027 0.65 1.825
d
Other Benthos 0 0.00027 0.85 1.825
d
Jellyshes 0 0.0000158 0.85 1.825
d
Macro-Zooplankton 0 0.0000158 0.85 1.241
d
Medium Copepods 0 0.000378 0.6 3.468
d
Large Copepods 0 0.00011 0.85 2.190
d
Other Meso-Zooplankton 0 0.0002 0.6 7.410
d
Micro-Zooplankton 0 0.00134 0.6 11.607
d
Large Pelagic Producers 0 0.00024 0 0
Small Pelagic Producers 0 0.0024 0 0
Ice Algae 0 0.0006 0 0
Benthic Plants 0 0.0001182 0 0
Pelagic Detritus 0 0 0 0
Benthic Detritus 0 0 0 0
a
Detailed description of the species composition in each group can be found in Table S1.
b
Only applies to benthos, zooplankton, and primary
producers. See ESI 1.1 for calculations of each group.
c
Adopted the average assimilation efficiencies in the literature for zooplankton,
43
bivalves
and mollusks,
13
worms,
13
and sh.
13
Arthropods, echinoderms, other benthos, and jellies were assumed to have the same assimilation efficiency
as macro-zooplankton. Marine mammals were assumed to have the same assimilation efficiency as sh.
d
Set as zero for the low elimination
rate scenario.
e
The elimination rates of pilot whale, baleen whale, and seal from Booth and Zeller 2005 (ref. 44) are used for beluga, bowhead,
and ringed and bearded seals here. The average elimination rate of bivalves from Pan and Wang 2011 are used for all benthos. The rate for
other lower trophic level organisms is calculated and details can be found in ESI 1.2.
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of the dominant pathways for their MeHg accumulation.
55–57
Since MeHg accumulated in higher trophic level groups
predominantly comes from dietary intake,
58–60
we assume no
direct uptake of MeHg from water in these groups. The direct
absorption rate in phytoplankton is calculated based on the
phytoplankton size classes and the DOC concentrations in the
seawater collected from the Beaufort Sea shelf (120 mMC
61
).
43
The direct absorption rate in zooplankton is a function of its
mass and temperature, as described by Schartup et al. (2018).
43
To estimate the direct absorption rate of Arctic benthos, we rst
derive a linear relationship between the MeHg absorption rate
and ltration rate based on the experimental results of various
bivalve species at room temperature.
57
We then calculate the
MeHg absorption rate of Arctic bivalves based on the ltration
rate of Arctic clams
62
and account for temperature effects on the
MeHg uptake rate in cold waters.
63
We describe the calculations
of the direct absorption rate for each group in detail in ESI 1.1.†
We assume that elimination of MeHg occurs in all
consumers and not in producers. The elimination rates of
MeHg in zooplankton and sh are calculated based on the body
mass and temperature using previously published equations
(Schartup, 2018 (ref. 43) and Trudel, 1997 (ref. 91)) (see ESI
1.2†). Prior studies that used the Ecotracer module to simulate
the Hg dynamics in marine or freshwater ecosystems have all
set elimination rates of MeHg in sh and lower trophic level
organisms as zero due to slow excretion and inefficient internal
demethylation.
44,46,47
We therefore run and compare the simu-
lations with and without empirical elimination rates of MeHg
for sh and lower trophic levels. In vivo demethylation of MeHg
occurs widely in marine mammals, which transforms MeHg
into labile inorganic Hg and HgSe nanoparticles.
64–69
No eld or
lab data are available for estimating the elimination or deme-
thylation rate of MeHg for marine mammals (e.g., seals and
whales). Here we use the literature values for similar marine
mammals previously estimated by ecosystem modeling
approaches to represent the elimination rate (i.e., excretion and
demethylation) of MeHg in these organisms
44
(Table 1).
2.4. Data analysis
2.4.1. Comparing the model output with observations. We
calculate the average of observed mean concentrations across
each empirical study as the overall observed mean MeHg
concentration. To assess the model bias for each functional
group, we calculate the ratio between modeled (M) and observed
MeHg concentrations (O) (eqn (3)) and normalized mean bias
(eqn (4)). The results are shown in Table S1.†
M=O ratio ¼M
O(3)
Normalized mean bias ¼P
n
1
ðMOÞ
P
n
1
O
(4)
where nis the number of empirical studies that generate
observed mean MeHg values.
2.4.2. Estimating trophic magnication of MeHg. The
trophic magnication factor (TMF) has been widely used as a tool
for assessing chemical bioaccumulation in different ecosys-
tems.
70
For a given chemical, manyfactors, such as ecological and
ecosystem characteristics, data treatment, and study design can
lead to variability and uncertainty in estimated TMFs.
70
Here we
derive the model predicted TMF for the BSS food web and assess
its conformity to empirical studies as a tool to evaluate our model
performance and gain mechanistic understanding of BSS food
web biomagnication, rather than accurately quantifying the
degree of MeHg bioaccumulation in this food web.
We run a simple linear regression of modeled MeHg
concentrations across the entire BSS food web against their
respective trophic levels previously determined by Hoover et al.
(2021)
40
(eqn (5)). We then calculate the TMF as the antilog of
the regression slope (eqn (6)). As beluga and bowhead whales
migrate between the Beaufort, Chukchi, and Bering Seas
annually, they are transient species that may not exclusively t
in the BSS food web. To reect the extent of biomagnication in
the BSS ecosystem, we produced the TMF of resident organisms
in the BSS by excluding beluga and bowhead whales. Prior
studies reveal substantial differences between the TMFs of
organic pollutants in the piscivorous and marine mammalian
food webs from the same Arctic ecosystem.
71
We also calculated
the TMF of the piscivorous food web (i.e., predatory sh as apex
predators) for comparing TMFs between piscivorous and
marine mammalian food webs in the BSS:
log
10
[MeHg] ¼a+bTL (5)
TMF ¼10
b
(6)
where [MeHg] and TL are the MeHg concentration and trophic
level of organisms in the BSS food web, respectively.
2.4.3. Sensitivity analysis. To investigate the most inuen-
tial parameters responsible for MeHg bioaccumulation in the
BSS food web (as represented in our model), we conduct sensi-
tivity analyses of seawater and benthic detritus MeHg concen-
trations and the toxicokinetic parameters regarding the uptake,
biotransformation, and elimination of MeHg in each broad biota
category (i.e., marine mammals, sh, benthos, zooplankton,
producers). We perturb each parameter by 10% of the original
amount, and the sensitivity is calculated using eqn (7):
Sensitivity ¼Dy=y
Dx=x(7)
where xis a specic parameter and yis the simulated MeHg
concentration of each organism. Dyis the change in the
MeHg concentration (y) because of the change (Dx)ofthe
input parameter. Dx/xis xed as 10% in our sensitivity
analysis.
3. Results and discussion
3.1. Model performance and evaluation
Given the EwE model setup, our simulation produces average
MeHg concentrations for each functional group population. We
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found that the simulated population-wide MeHg concentra-
tions of most BSS functional groups are comparable to their
respective published values (i.e., within SE) without MeHg
elimination in sh and lower trophic level organisms, as shown
in Fig. 2a. In contrast, the application of empirical elimination
rates of MeHg in sh and lower trophic levels leads to apparent
underestimation of MeHg concentrations in most groups and
we discuss the underlying reasons in Section 3.4. Herein, we
only evaluate the model output from the runs with no elimi-
nation of MeHg applied in sh and lower trophic level
organisms.
The challenges of using Ecotracer to simulate MeHg bio-
accumulation in a food web have been noted in prior studies,
indicated by large model bias (M/O ratio ¼0.016 to 0.056)
46
or
lack of overlap between predicted and observed ranges.
47
Compared to these previous studies, our model simulation
shows signicant improvement (M/O ratio ¼0.088 to 0.86;
normalized mean bias ¼0.91 to 0.14, across functional
groups) and we attribute the better performance to the well-
characterized nature of the BSS food web and detailed param-
eterization of the direct absorption rate of lower trophic level
organisms based on the body size and temperature. Compared
with available MeHg observations of BSS biota, our model tends
to underestimate MeHg concentrations except for two highly
migratory marine mammals –Beaufort beluga (M/O ratio ¼
1.74; normalized mean bias ¼0.74) and bowhead whales (M/O
ratio ¼1.17; normalized mean bias ¼0.17) (Fig. 2a and Table
S1†). This may result from underestimating the seawater MeHg
concentration and/or the uptake of MeHg at the base of the BSS
food web given the great inuence of these factors on the MeHg
concentration in all BSS biota (see Section 3.2).
The estimated MeHg concentrations in the pelagic food web
(e.g., phytoplankton, zooplankton, and bowhead) match the
observed values better than organisms from the benthic food
web (e.g., worms, mollusks, arthropods, and benthic sh)
(Fig. 2a). Marine sediment is the major MeHg production
source in contaminated coastal regions,
72–77
while active meth-
ylation in marine waters is considered the dominant source of
MeHg in the (sub)Arctic.
15,54,78
Our results suggest that the BSS
sediment could still play an appreciable role in contributing to
MeHg accumulated in benthic organisms and, consequently,
their predators (e.g.,sh and marine mammals). The inability
to capture legacy Hg contributions and in situ methylation in
sediment in the current Ecotracer module setup may drive the
overall underestimation performance. To better represent food-
web MeHg bioaccumulation in many shallow coastal ecosys-
tems, including the BSS, we suggest adding functions that take
into account the direct loading of MeHg to the benthic
ecosystem by the deposition of river-born particles and the
methylation of legacy and present inorganic Hg in the benthic
environment in Ecotracer.
74,79
In addition, MeHg concentra-
tions in the water column vary by depth and organisms foraging
at different depths could receive different Hg burdens,
80–82
which is currently not accounted for in this model due to lack of
information on the vertical MeHg prole in the BSS. Future
work generating data of MeHg variability by depth and incor-
porating this information into another EwE module, Ecospace,
could provide more accurate simulation of MeHg bio-
accumulation in each functional group.
Fig. 2 (a) Comparison between log transformed modeled and empirical MeHg concentrations (mgg
1
wet weight) of Beaufort Sea shelf food
web. The blue line signifies the 1 : 1 ratio. The compiled dataset of empirical concentrations and standard errors (SE) indicated as error bars can be
found in the ESI.†(b) Methylmercury biomagnification in the Beaufort Sea shelf food web. The dotted lines represent regression of MeHg
concentrations of all or subsets of organisms in the food web. The regression slopes for black, yellow, and red lines are 1.05 0.10 (SE), 0.99
0.10, and 0.89 0.11, corresponding to TMFs of 11.1, 9.8, and 7.8.
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Large differences between simulated and observed MeHg
concentrations (M/O ratio ¼0.088 to 0.22) appear in groups that
encompass a wide range of species or genera, such as macro-
zooplankton, arthropods, and small benthic sh (Fig. 2a). The
highly heterogeneous species composition of these functional
groups leads to the large variability in MeHg concentrations
observed in eld measurements. For example, the macro-
zooplankton group of the BSS food web includes krill, shrimp,
mysids, and amphipods
40
and the simulated MeHg concentra-
tion for this group is 1.8 ng g
1
wet weight. The MeHg
concentrations of eld collected macro-zooplankton vary from
3.2 1.8 (mean SD) ng g
1
wet weight in omnivorous krill
(Thysanoessa spp.) to 65 10 ng g
1
wet weight in carnivorous
circumpolar shrimp (Eualus gaimardii),
36,38
with amphipods and
mysids falling in that range (Table S1†). The simulated MeHg
concentration of each functional group represents its average
MeHg level, thus it may not match the empirical data of any
single species, particularly non-dominant species of that group.
The Hg burden of Beaufort beluga whales has been moni-
tored for almost four decades and a previous study showed that
the average MeHg concentrations in muscle tissues of this
population varied within a relatively small range (0.97–1.4 mg
g
1
wet weight) between 2005 and 2012.
39
Our simulated beluga
MeHg concentration (2.1 mgg
1
wet weight) is higher than the
empirical range, and we attribute the difference to their
migratory foraging behavior, which is currently not accounted
for in the EwE representation of the BSS, as elaborated in
Section 3.5.
3.2. Sensitivity analyses of environmental and toxicokinetic
factors
3.2.1. Seawater and benthic detritus MeHg. The modeled
MeHg concentration in all functional groups is highly sensitive
to the seawater MeHg levels (sensitivity z1), indicating that
simulated changes in seawater MeHg will result in proportional
changes in the biota MeHg burden. The MeHg concentrations
of all groups respond little to the change in the initial MeHg
concentration of benthic detritus (sensitivity close to zero). This
is because in our Ecotracer representation of the system, the
benthic detritus in the BSS is largely driven by sinking particles
containing mostly ice algae and to a lesser extent phyto-
plankton, which ultimately obtain MeHg from seawater. Varia-
tion in the initial MeHg level of benthic detritus alone only
leads to a transient change of MeHg in benthic organisms, and
the seawater MeHg concentration is the factor that dictates the
steady-state MeHg concentration of these organisms in the
model.
Climate change likely inuences MeHg concentrations in
BSS seawater in multiple ways. Leitch et al. (2007)
53
showed that
the Mackenzie River input is the dominant Hg source in the
Beaufort Sea. The warming of the Mackenzie Basin is likely to
increase both inorganic Hg and MeHg riverine uxes to the
Beaufort Sea due to permafrost melt, increases in freshwater
discharge, and more frequent extreme events (ooding, storms,
and forest res).
52,53
Riverine MeHg bound to the terrestrial
dissolved organic matter (DOM) can be resistant to degradation
and is biologically available.
83
With the rise in temperature,
inorganic Hg inputs, terrestrial DOM discharge, and the
microbial methylation of inorganic Hg in the BSS water column
would likely be enhanced.
52,53,84
In the meantime, the dramatic
reduction in sea ice and longer open-water periods lead to
increased light penetration in ice-free seawater thereby
fostering photodegradation of MeHg. To date, little quantitative
data exist to infer the net effect of these environmental vari-
abilities on MeHg production and degradation in the Arctic.
Given the high sensitivity of the MeHg burden in biota to
seawater MeHg levels, an accurate assessment of climatic
impacts on the net MeHg production (or loss) in the Arctic
marine ecosystems should be among the top research priorities
for addressing the impacts of global environmental changes on
MeHg bioaccumulation in Arctic food webs.
3.2.2. Low trophic level organisms. All producers (benthic
plants, phytoplankton, and ice algae) obtain MeHg from the
water via passive absorption,
55
hence their MeHg concentra-
tions are only controlled by their direct absorption rate (Fig. 3;
Table S2†). Zooplankton, which obtain MeHg from both water
and food uptake, are sensitive to direct absorption rates of
phytoplankton and zooplankton, and the assimilation effi-
ciency of MeHg in zooplankton. Benthic organisms, such as
bivalves and arthropods, are pivotal for transferring MeHg to
sh and marine mammals in the BSS marine ecosystem. Pan
and Wang (2011) (ref. 57) illustrated the strong ability of
bivalves to obtain MeHg from both dissolved and dietary pha-
ses. Detrital materials, which are ultimately derived from ice
algae and phytoplankton, are the major dietary sources of
benthic organisms in the BSS. Our sensitivity analysis suggests
that the MeHg burden in benthic organisms is controlled by
three factors: direct absorption rate and assimilation efficiency
of MeHg in benthos, and the direct absorption rate in
producers, with the dissolved MeHg direct uptake rate playing
a dominant role (Fig. 3; Table S2†). We hypothesize that this is
partially due to the underestimation of MeHg intake from
benthic detritus for the reasons elaborated in Section 3.1. In
addition, the direct absorption rate of Beaufort Sea benthic
organisms was estimated based on the relationship between
MeHg uptake and ltration rates derived in laboratory experi-
ments on subtropical bivalves, as this information is unavail-
able for other types of benthos. The high uncertainty of this
parameter, combined with its large inuence in upper trophic
level MeHg concentrations, underscores the need to charac-
terize the biodynamics of the MeHg accumulation in non-
bivalve benthos and in benthic organisms living in cold
marine environments.
Our model assumes that all MeHg uptake by sh species
comes from ingested food. Fish species in the BSS consume
a variety of lower-trophic level organisms, which explains that
factors related to the MeHg concentrations in producers,
zooplankton and benthos all have an impact on sh MeHg
concentrations. Although dietary preferences differ across these
sh groups, a general pattern of sensitivity coefficients is found:
direct absorption rate of MeHg in benthos (sensitivity: 0.70 to
0.87) and assimilation efficiency in sh (sensitivity: 0.18 to 0.43)
are the most inuential factors associated with sh MeHg
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concentrations (Fig. 3). This highlights the importance of the
benthic pathway for transferring MeHg to sh, as discussed in
detail earlier.
3.2.3. Marine mammals. The sensitivity of MeHg concen-
trations in mammals to different toxicokinetic parameters of
lower trophic level organisms reects their feeding strategies
(Fig. 4). The most inuential factor for varying MeHg burden in
piscivorous marine mammals, including beluga, ringed seals,
and beard seals, is the direct absorption rate of MeHg in
benthos, the same as the most sensitive parameter for marine
mammals' diet –sh. In contrast, bowhead whales, a lter-
feeding marine mammal species that feed primarily on
zooplankton, are highly sensitive to the direct absorption rate of
phytoplankton and zooplankton and the assimilation efficiency
of MeHg in zooplankton, the factors controlling the MeHg
content of zooplankton (Fig. 4).
In vivo demethylation of MeHg is widely observed across
marine mammals and is considered the main mechanism for
MeHg elimination; therefore, the MeHg concentrations of
beluga, bowhead whales, and bearded seals are highly sensitive
to their demethylation rates (sensitivity: 0.52 to –0.74) (Fig. 4).
Compared with other marine mammals, ringed seals are less
sensitive to the demethylation rate (sensitivity: 0.31) (Fig. 4).
This results from their rapid population turnover (i.e., high
mortality rate P/B) in the BSS ecosystem due to hunting activi-
ties by polar bears and humans. As mortality and MeHg elimi-
nation are the two main mechanisms for MeHg loss in each
functional group (see eqn (2)), the higher mortality rate means
greater importance of population turnover relative to MeHg
elimination in determining MeHg concentrations in any given
group. Ringed seals are the major diet of polar bears in the
ecosystem and are harvested by Inuvialuit communities.
85
The
predation and hunting mortality rates of ringed seals are 10 and
6 times higher than those of bearded seals, which largely
explains the simulated difference in MeHg concentrations and
sensitivity to the MeHg elimination rate between these two
species. Prior studies found that age classes, feeding strategies,
and trophic level can play roles in determining the MeHg
concentrations of phocid seals (i.e., ringed, bearded, spotted,
and harbor seals) in the Alaskan and Canadian Arctic.
86,87
Our
model simulation demonstrates that food web trophodynamics,
particularly top-down interactions including predation and
shing activities, could also have a large impact on population
average MeHg concentrations of these species, thus stressing
the importance of the food web context when interpreting the
Hg burden.
3.3. MeHg biomagnication in the BSS food web
Polar systems are known to biomagnify MeHg more efficiently
than ecosystems at lower latitudes due to longer food chains,
limited biomass dilution, and slower excretion of MeHg at
colder temperatures.
88
We estimate that the TMF of the entire
BSS food web is 11.1, similar to the empirical TMF value (10.1)
of the Beaufort Sea estuarine and shelf food web with beluga
whales as an apex predator.
33
The simulated and observed TMF
values of the BSS food web are among the highest TMFs of
Fig. 3 Sensitivity of simulated MeHg concentrations in primary producers, zooplankton, benthos, and fish to each input parameter, as measured
through the sensitivity coefficient. The further the sensitivity coefficient is from 0, the more sensitive the simulated MeHg concentration is to
changes in the input parameter. Sensitivity coefficients are generated by decreasing or increasing each parameter by 10% of the baseline.
Coefficients were largely symmetrical between increases and decreases, so the plot only shows the data generated by increasing the input
parameter by 10%. Full details of these sensitivity coefficients for each parameter can be found in Table S2.†
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MeHg reported for other polar marine ecosystems (range: 3.0 to
11.3), including Baffin Bay, Svalbard, West Greenland, Melville
Sound, and Gulf of Amundsen.
88
Although the TMF is a rela-
tively simple metric given complex real-world food webs, it has
utility as a screening metric to highlight important areas for
further comparative research between ecosystems.
88
Our results
suggest that there may be highly efficient biomagnication of
MeHg throughout the BSS food web relative to other polar
systems, and highlight the potential vulnerability of elevated
MeHg exposure in the BSS sh and marine mammals, and the
Inuvialuit communities who rely on these wildlife as nutri-
tionally and culturally important subsistence food.
The estimated TMFs of the BSS food web without migratory
species (i.e., beluga and bowhead whales) and without marine
mammals are 9.8 and 7.8, respectively, slightly lower but
statistically indifferent from the TMF of the entire BSS food web
at the a¼0.05 level. This is consistent with the nding that
there is no signicant effect of the food web composition on the
TMF in other marine ecosystems as summarized by Lavoie et al.
(2013),
88
but contrary to the recent observation in Antarctic
marine ecosystems where the TMF is higher when endotherms
are included.
89
As many endotherms like marine mammals and
birds are migratory, we suggest that the inuence of the food
web composition on the TMF observed in some studies may
originate from their MeHg uptake via foraging outside of the
studied food web. We illustrate how Beaufort beluga MeHg
concentrations vary depending on the extent of foraging in
different feeding grounds in Section 3.5.
Similar to many other coastal shelf ecosystems, the BSS has
a highly coupled benthic and pelagic food web.
40
Fig. 2b illus-
trates that both benthic and pelagic biomass production are
important contributors for transferring MeHg to higher trophic
level organisms like sh. The heavy reliance on benthic organ-
isms that contain higher levels of MeHg than pelagic ones leads
to the high TMF in the BSS food web (Fig. 2b). Although at similar
trophic levels, both simulated and literature MeHg concentra-
tions in benthos are almost one order of magnitude higher than
those in zooplankton. The major dietary component of benthic
organisms is benthic detritus, largely composed of sinking
particles on the sea oor, while phytoplankton are the dominant
food source for zooplankton. Benthic detritus and phytoplankton
have comparable simulated MeHg concentrations (benthic
detritus: 0.26 ng g
1
wet weight; phytoplankton: 0.06 to 0.30 ng
g
1
wet weight) (Table S1†). Thus, the higher MeHg concentra-
tions in benthos than zooplankton result from the greater bio-
magnication step between benthic detritus and benthos (BMF:
15–75) than the one between phytoplankton and herbivorous/
omnivorous zooplankton (BMF: 2–20) (Fig. 2b). We attribute
the elevated biomagnication at the base of the BSS benthic food
web to population dynamics because turnover in benthos is on
an average 15 times slower than that of zooplankton communi-
ties. In other words, benthos generally have a much longer life-
time to accumulate MeHg than zooplankton. These ndings
emphasize the necessity of incorporating population turnover or
biomass dilution in addition into trophic levels for predicting
MeHg concentrations of marine organisms.
Fig. 4 Sensitivity of simulated MeHg concentrations in four types of marine mammals to each input parameter, as measured through the
sensitivity coefficient. The further the sensitivity coefficient is from 0, the more sensitive the simulated MeHg concentration is to changes in the
input parameter. Negative coefficients indicate opposing directions of change (i.e., an increase in MeHg elimination results in a decrease in the
concentration). Sensitivity coefficients are generated by decreasing or increasing each parameter by 10% of the baseline. Coefficients were
largely symmetrical between increases and decreases, so the plot only shows the data generated by increasing the input parameter by 10%. Full
details of these sensitivity coefficients for each parameter can be found in Table S2.†
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In addition, Pomerleau et al. (2016)
36
reported higher total
Hg and MeHg concentrations in marine zooplankton collected
in the BSS than those from ve other Arctic regions, including
the Laptev Sea, Chukchi Sea, Canadian Arctic Archipelago,
Hudson Bay, and northern Baffin Bay. Hence, it is possible that
the BSS pelagic food web also has elevated bioconcentration
between water and phytoplankton and/or greater bio-
magnication between phytoplankton and zooplankton relative
to other Arctic ecosystems. Further research effort on MeHg
bioaccumulation at the base of Arctic marine food webs is
required to fully elucidate the reasons for elevated MeHg
burden in the BSS marine biota.
3.4. Population-wide MeHg elimination rate
If empirical elimination rates of MeHg in sh and lower trophic
levels are applied, the simulated MeHg concentrations in
benthos, shes, and piscivorous marine mammals are on an
average 9 times lower than the observed values. The discrepancy
between simulated and observed values indicates that the
empirical MeHg elimination rates, oen derived from
controlled laboratory experiments on a small number of indi-
viduals, do not accurately reect the MeHg toxicokinetics in sh
and lower trophic levels in the natural environment. Following
other studies,
13,90
we used the chronic MeHg exposure equation
based on the sh body size and water temperature to estimate
the MeHg elimination rates in free-ranging sh in the eld.
91
The data used for deriving this equation are largely from studies
in which sh were exposed to MeHg in articial ways (e.g.,
concentrated MeHg solution and food spiked with MeHg).
Recent evidence from both eld and laboratory studies showed
that sh fed with naturally contaminated prey have 2.4 to 5.5
times lower MeHg elimination rates than those estimated by
using the chronic exposure equation.
92
Future work incorpo-
rating factors that can cause differences in MeHg elimination
rates in sh and macroinvertebrates, such as age, sex, and
species,
20,57,92,93
may improve the applicability of the laboratory
derived MeHg elimination rates in eld studies.
In addition to MeHg elimination from sh bodies,
91,92
mortalityistheotherrouteofMeHglossinanygivenfunctional
group (eqn (2)). In EwE, when the biomass is at a steady state, the
mortality rate is calculated as the ratio between production and
biomass and reects how fast the population turns over. In the
eld, relatively fast reproduction, growth, predation, and
mortality oen occurs in low-trophic level organisms like
plankton, benthos, and sh. Population dynamics are therefore
directly related to the total MeHg burden in a functional group,
and thereby the average MeHg concentration of the population.
For example, the recruitment of new sh adds new MeHg into the
sh population and mortality due to predation, shing, and other
reasons removes the MeHg in deceased shoutofthegroup.The
simulations with no MeHg elimination in sh and lower trophic
level organisms yield the MeHg concentrations comparable to
observed levels, indicating that population turnover may be the
dominant factor for explaining the MeHg loss in each group.
A recent study based on a 15 year whole-ecosystem experi-
ment showed little to no loss of Hg in northern pike in 6 to 8
years aer the cessation of Hg spike addition to a boreal lake.
94
The authors hypothesized that population turnover, rather than
the slow MeHg elimination, drives the change in the MeHg
concentration of the sh population in the eld.
94
Our study
further corroborates this proposition by using a modeling
approach to illustrate that a food web model with well-dened
population dynamics is adequate to predict population-wide
MeHg concentrations in lower trophic level organisms; there-
fore, it is appropriate to assume a negligible amount of MeHg
eliminated by these functional groups throughout their life-
time. Nevertheless, it is worth mentioning that MeHg elimina-
tion is an important route of Hg loss in species with slow
population turnover in the eld and/or with enhanced deme-
thylation capacity, such as marine mammals.
3.5. Bering Sea vs. Beaufort Sea foraging
We nd that the simulated MeHg concentration of Beaufort
beluga whales (2.1 mgg
1
wet weight) is 1.8 times higher than
the observed mean Hg concentrations (1.2 mgg
1
wet weight),
which implies that we may have overestimated the MeHg intake
from food. The model simulates the beluga MeHg concentra-
tion assuming that they exclusively forage in the BSS year-
round. However, Beaufort beluga whales only spend summer
in the eastern Beaufort Sea and Mackenzie Delta region, and
they migrate through the Chukchi Sea to the Bering Sea where
they spend winter,
39,95,96
with the feeding contribution of the
Beaufort Sea unknown. The consistency between simulated and
observed MeHg concentrations in major dietary items of beluga
(e.g., Arctic cod, cisco & whitesh) suggests that belugas' dietary
exposure of MeHg in the Beaufort Sea is not overestimated.
Therefore, we postulate that the lower-than-simulated MeHg
concentrations for this beluga stock mainly results from
consuming shes with lower MeHg content in the Bering and
Chukchi Seas compared to those in the BSS.
The Beaufort Sea shelf is known to have elevated Hg
concentration in the water column and marine food chain
compared to other Arctic regions. Seawater in the BSS was found
to have much higher MeHg concentrations (0.134 pM at Chla
max, and 0.227 pM at oxycline),
8,15
in comparison to less than
the detection limit (<0.020 pM) throughout the water columns
across six stations in the Bering Sea.
97
The marine zooplankton
collected in the BSS exhibited higher MeHg concentrations
relative to other Arctic regions such as the Laptev Sea, Chukchi
Sea, Canadian Arctic Archipelago, Hudson Bay and northern
Baffin Bay.
36
We compared the published Hg values of common
prey items of Beaufort beluga whales, such as Arctic cod, Pacic
herring, and other species. The MeHg concentrations of the
same prey species are 2.0 to 2.7 times lower in the Bering Sea
than the ones collected in the Beaufort Sea (Table S3†), sup-
porting our hypothesis. Given the relatively high reporting limit
of some sh Hg data collected in the Bering Sea
98
and that this
beluga stock frequently consumes lower-trophic level organ-
isms in the Bering Sea (e.g., shrimp and octopus),
99
we antici-
pate that the ratio (R) of average MeHg concentrations in the
belugas' prey intake from the Beaufort vs. Bering Sea may be
greater than that observed.
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Here we explore how Beaufort beluga MeHg concentrations
vary depending on the extent of feeding in the Bering Sea vs.
Beaufort Sea, under two scenarios of the concentration ratio (R
¼2 or 4) between the two Arctic regions (Fig. 5). The observed
MeHg concentration of beluga came from subsistence harvests
in the Mackenzie Delta,
39
where about 90% of the landed catch
of Beaufort beluga in the past occurred in July, shortly aer their
spring migration from Bering Sea.
96,100
Given belugas' foraging
behavior in the Chukchi and Bering Seas prior to summering in
the BSS and the long half-life time of MeHg in other mammals
(51 days in ringed seals,
68
50–80 days in human pop-
ulations
101,102
), the MeHg uptake in the Chukchi and Bering seas
should account for a signicant fraction of the MeHg burden in
these beluga whales, even aer they forage in Beaufort for 1–2
months prior to the harvest. We estimate that only 16 to 44% of
MeHg in Beaufort beluga whales comes from their food uptake
in the Beaufort Sea (Fig. 5), revealing the necessity of moni-
toring Hg contamination in other feeding grounds (i.e., Chuk-
chi and Bering Seas) for further understanding the levels and
trends of the Hg burden in Beaufort belugas.
A range of tools can be applied to provide insight into the
dietary composition of top predators, such as fatty acid signa-
tures and stable isotope ratios.
33,103,104
However, it is oen
difficult to assess the apportionment of the food or contami-
nant uptake across regions for highly migratory species
foraging a wide range of prey at various geographic locations.
Here we illustrate a method of estimating the MeHg contribu-
tion between two foraging grounds for Beaufort beluga using
a combination of empirical data from eld studies and
ecosystem modeling that simulates bounding case MeHg
concentrations (i.e. assuming full feeding in each location: one
for the Beaufort Sea and one for the Bering Sea). This method is
applicable to other migratory species for assessing their expo-
sure sources of various bioaccumulative pollutants.
Like Beaufort beluga whales, Bering–Chukchi–Beaufort
bowhead whales summering in the BSS region spend fall in the
Chukchi Sea and winter in the Bering Sea.
105
In contrast to
beluga whales, the model-simulated concentration of bowhead
whales (0.023 mgg
1
) based on the assumption of feeding solely
in the Beaufort Sea is very close to the literature value (0.020 mg
g
1
). The observed bowhead Hg data come from the subsistence
hunt in Barrow,
106
which occurs during both spring and fall as
whales migrate between the Bering and Beaufort Seas.
105,107
We
postulate that the similarity between the literature value and
model simulation is due to (1) the large fraction of samples
from the fall hunt which captures the bowhead whales aer
feeding in the Beaufort Sea for the whole summer,
107
and/or (2)
a similar MeHg concentration in their diet (i.e., small to
moderate sized crustaceans, such as euphausiids and cope-
pods) between the Bering, Chukchi, and Beaufort Seas. We
cannot exclude the possibility that the relatively small sample
size of the observations (N¼33) may not reect the average
MeHg concentrations of this population. Additional informa-
tion on MeHg concentrations of this bowhead stock, and crus-
taceans across Bering–Chukchi–Beaufort feeding grounds, will
enable a better estimate of the MeHg contribution for bowhead
whales across different Arctic regions.
4. Conclusions
Here we developed an ecosystem-based MeHg bioaccumulation
model that has a detailed representation of the BSS trophody-
namics, and relatively simple representation of MeHg tox-
icokinetics. The model is able to capture the highly efficient
biomagnication in the BSS food web and simulated MeHg
concentrations of most BSS functional groups are comparable
to their respective published values. The results suggest that the
heavy reliance on benthic organisms with higher levels of
MeHg, compared to pelagic ones, leads to the high bio-
magnication efficiency in the BSS food web. Future develop-
ment of the Ecotracer module to account for the methylation of
inorganic Hg reservoirs in the benthic environment can
improve the representation of food-web MeHg bioaccumulation
in many shallow coastal ecosystems. Prior studies have not been
able to attribute the observed temporal trend of beluga Hg
burdens to individual anthropogenic, environmental, or
ecological factors.
22,39
Our model integrates environmental
factors and food-web dynamics, thus providing a tool that can
be further applied to holistically examine how ecological and
environmental change drivers interact and which contributes
the most to the observed temporal evolution of Hg concentra-
tions in this beluga stock.
While incorporating the MeHg elimination rate in marine
mammals is essential for simulating the MeHg burden in these
animals, we nd that the application of the experimentally
derived MeHg elimination rate in sh, invertebrates, and
plankton largely underestimates the MeHg concentrations in
these groups. The results indicate that population turnover,
rather than MeHg elimination, plays a dominant role in
Fig. 5 The fraction of MeHg in Beaufort beluga whales from feeding in
the Beaufort Sea calculated based on different ratios (R) between the
MeHg concentration of beluga diet in the Beaufort Sea and in the
Bering Sea. The horizontal dashed line and the yellow shade indicate
the observed beluga MeHg concentrations (mean standard error;
wet weight based) across 2005–2012 from subsistence harvests in the
BSS. The arrows point to the calculated fractions of MeHg in Beaufort
beluga whales from foraging in the Beaufort Sea.
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removing MeHg from populations and determines the
population-wide MeHg concentration in each functional group.
Our results suggest that the direct elimination of MeHg from
sh and lower trophic level organisms is likely negligible at
a population-level due to fast population turnover in the eld
and inefficient demethylation. This nding is consistent with
the recent results from a whole-ecosystem experiment, and to
the best of our knowledge, this study is the rst explicit illus-
tration of this effect using an ecosystem modeling approach.
At present, the interactions between climate change and Hg
cycling in the Arctic is poorly understood, which limits our
ability to assess climate change impacts on MeHg in the Arctic
marine food web and any implications for human exposure.
108
The results of the sensitivity analyses highlight that the
seawater MeHg concentration and direct absorption rate of
dissolved MeHg in benthos and plankton are among the most
inuential environmental and toxicokinetic factors driving the
variability of MeHg concentrations in sh and marine
mammals in the Beaufort Sea shelf ecosystem. However, these
variables are also amongst the most poorly constrained. Further
research to better quantify these important parameters, and
their likely evolution with global environmental change (such as
increased human activity, and global warming) will be critical
for estimating future MeHg impacts in this sensitive ecosystem.
Conflicts of interest
All authors declared no conicts of interest.
Acknowledgements
We gratefully acknowledge input and feedback from the Inu-
vialuit Game Council, and the contribution of beluga whale
harvesters in the Inuvialuit Settlement Region to the long-
standing biomonitoring project and published observational
data from which was used in this study. We also thank all other
investigators that contributed to the rich environmental and
biotic monitoring data sets of the Beaufort Sea shelf. We thank
Dr Juan Jose Alava for his helpful advice on Ecotracer parame-
terization. This project was funded by the Northern Contami-
nants Program of Canada (M-45; AG, ML, CH, LL), a Natural
Sciences and Engineering Research Council of Canada
Discovery Grant (RGPIN-2018-04893; AG, ML, EG), and a Natural
Sciences and Engineering Research Council Canada Graduate
Scholarship Master's level (to EG). C. Hoover and L. Loseto
would like to acknowledge the Fisheries Joint Management
Committee, Fisheries and Oceans Canada, Manitoba Centres of
Excellence Fund, and ArcticNet for funding contributions to the
Ecopath with Ecosim model.
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