10.5061/DRYAD.Q573N5THJ
Clark, Casey
0000-0002-6311-9768
University of Washington
Horstmann, Lara
University of Alaska Fairbanks
Misarti, Nicole
University of Alaska Fairbanks
Data from: Walrus teeth as biomonitors of trace elements in Arctic marine
ecosystems
Dryad
dataset
2021
Trace elements
Contaminants
Heavy metals
Arctic marine mammals
Marine mammals
LA-ICP-MS
National Science Foundation
https://ror.org/021nxhr62
1263848
Bureau of Ocean Energy Management
https://ror.org/03tzscr25
Coastal Marine Institute*
North Pacific Research Board
https://ror.org/02nqgka20
Cooperative Institute for Alaska Research*
National Institute of General Medical Sciences
https://ror.org/04q48ey07
UL1GM118991, TL4GM118992, or RL5GM118990
National Oceanic and Atmospheric Administration
https://ror.org/02z5nhe81
Cooperative Agreements NA15OAR4320063 and NA20OAR4320271
Coastal Marine Institute
Cooperative Institute for Alaska Research
2021-02-11T00:00:00Z
2021-02-11T00:00:00Z
en
https://doi.org/10.1111/2041-210X.13482
https://doi.org/10.1093/conphys/coaa029
https://orcid.org/0000-0002-6311-9768
https://doi.org/10.1016/j.scitotenv.2021.145500
331689906 bytes
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CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Effective biomonitoring requires an understanding of the factors driving
concentrations of the substances or compounds of interest in the tissues
of studied organisms. Biomonitoring of trace elements, and heavy metals in
particular, has been the focus of much research; however, the complex
roles many trace elements play in animal and plant tissues can make it
difficult to disentangle environmental signals from physiology. This study
examined the concentrations of 15 trace elements in the teeth of 122
Pacific walruses (Odobenus rosmarus divergens) to investigate the
potential for walrus teeth as biomonitors of trace elements in Arctic
ecosystems. Elemental concentrations were measured across cementum growth
layer groups (GLGs), thereby reconstructing a lifetime history of element
concentrations for each walrus. The locations of GLGs were used to divide
trace element time series into individual years, allowing each GLG to be
associated with an animal age and a calendar year. The elements studied
exhibited a great deal of complexity, reflecting the numerous factors
responsible for generating tooth trace element concentrations. Generalized
linear mixed models were used to investigate the importance of age and sex
in explaining observed variation in trace element concentrations. Some
elements exhibited clear physiological signals (particularly zinc,
strontium, barium, and lead), and all elements except arsenic varied by
age and/or sex. Pearson correlations revealed that elements were more
strongly correlated among calendar years than among individual walruses,
and correlations of trace elements within individual walruses were
generally inconsistent or weak. Plots of average elemental concentrations
through time from 1945 – 2014 further supported the correlation analyses,
with many elements exhibiting similar patterns across the ~70 year period.
Together, these results indicate the importance of physiology in
modulating tooth trace element concentrations in walrus tooth cementum,
but suggest that many trace elements reflect a record of environmental
exposure and dietary intake/uptake.
Trace element analysis and data processing Postcanine teeth from 122
Pacific walruses (Female: n = 93; Male: n = 29) were on loan from the
University of Alaska Museum in Fairbanks, Alaska, and the National Museum
of Natural History, in Washington DC. Specimens were collected between
1880 and 2016 (Table S1). The majority of these samples originated from
Alaska Native subsistence harvests in the communities of Gambell and
Savoonga on St. Lawrence Island, Alaska, though some of the earlier
specimens were collected during scientific expeditions. Because specimens
used in this study originated from museum collections and/or Alaska Native
subsistence harvests, this research was Institutional Animal Care and Use
Committee (IACUC) exempt. All specimens from contemporary subsistence
harvests were transferred to UAF for analysis under a Letter of
Authorization from the United States Fish and Wildlife Service (USFWS) to
Dr. L. Horstmann. A low speed, water-cooled saw equipped with a diamond
blade was used to create a 1.5 mm-thick longitudinal cross-section of the
center of the tooth. A 3000-grit diamond smoothing disc mounted on a
rotary polishing wheel was then used to polish this cross-section. Samples
were rinsed with ultra-pure water after polishing and allowed to air dry,
then rinsed and air dried again immediately prior to analysis. Trace
element analyses were conducted at the Advanced Instrumentation Lab,
University of Alaska Fairbanks (UAF), Fairbanks, Alaska. An Agilent 7500ce
Inductively Coupled Plasma Mass Spectrometer (ICP-MS; fitted with a cs
lens stack to improve sensitivity), coupled with a New Wave UP213 laser,
was used to measure concentrations of vanadium (51V), chromium (53Cr),
manganese (55Mn), iron (57Fe), cobalt (59Co), nickel (60Ni), copper
(63Cu), zinc (66Zn), arsenic (75As), strontium (88Sr), molybdenum (95Mo),
silver (107Ag), cadmium (111Cd), barium (137Ba), and lead (208Pb) in
walrus tooth cementum. Instrumental precision for the ICP-MS was ± 5 %.
The internal standard for these analyses was 43Ca, and the resulting
calcium-normalized element concentrations are reported in parts per
million (ppm). Measured elemental concentrations were compared with a
United States Geological Survey microanalytical phosphate standard
(MAPS-4), as well as a National Institute of Standards and Technology
Standard Reference Material (NIST SRM 610). All laser transects were
ablated using the following parameters: beam width = 25 μm; power = 55 %;
pulse frequency = 10 Hz; transect speed = 5 μm/s. Dwell times ranged from
0.002 – 0.15 seconds (Table S2). Locations of ablation transects were
selected to maximize distance from the root, where cementum growth layer
groups converge and become distorted, while also avoiding areas of tooth
wear near the crown, where not all cementum layers are present. Transects
were ablated starting at the interface between the dentin and the cementum
(first year of life), and ending at the outer edge of the tooth (final
year of life). Thus, elemental time series generated during these analyses
represented a lifetime record for each animal. Data extraction and
processing was conducted in Igor Pro version 6.37 using the Iolite
software package version 3.0. All statistical analyses were conducted
using R version 4.0.2 (R Core Team, 2020) with RStudio version 1.3.959
(RStudio Team, 2015). Limits of detection were calculated separately for
each analytical run using the standard method applied by Iolite (Longerich
et al., 1996). A value of one half the limit of detection was used to
replace data points that fell below the detection limits (U.S.
Environmental Protection Agency, 2000). Data points more than 4 standard
deviations from the mean were considered outliers and removed from
analysis (Tukey, 1977). These data points were typically single,
unrealistically high values, and were likely to represent instrumental
errors, rather than actual changes in tooth trace element concentrations.
Their removal is therefore unlikely to have impacted the results of this
study. Growth layer group counts and designation of element concentrations
to individual years After trace element analysis, photographs of walrus
teeth were taken using a Leica DFC295 camera coupled with a Leica M165 C
optical microscope using reflected light. All growth layer groups (GLGs)
in the tooth cementum were identified (Fay, 1982; Garlich-Miller et al.,
1993) and marked collaboratively by the authors (C.T.C., L.H., and N.M.),
and their positions were revisited on at least two additional days to
confirm their locations on the laser ablation transect (Fig. 1). Locations
of the growth layers were used to assign measured elemental concentrations
to individual years of life, with L1/D1 (the first light and dark layers)
representing Age 0 (1st year of life), L2/D2 making up Age 1 (2nd year of
life), and so on. All GLGs were counted to estimate the age of each animal
at death, and this information was used in tandem with the year of death
to associate GLGs with individual calendar years. Thus, an animal that was
Age 5 when it was harvested in 1995 would have GLGs grown in 1990 – 1995.
Only complete GLGs with a fully formed light and dark layer were used for
analysis of trace element concentrations by animal age or calendar year.
Statistical methods Trace element data were natural log-transformed prior
to statistical analysis to ensure their distributions approximated
normality. Generalized linear mixed models (GLMMs) were run using the R
package ‘lme4’ (Bates et al., 2015) to test for relationships between
concentrations of each trace element and individual walrus age, as well as
test for differences between males and females. These analyses were
restricted to ages 0 – 15, to ensure that ≥15 male and female walruses
were represented at each age. Model selection was conducted using Akaike’s
Information Criterion corrected for small sample sizes (AICc), where
models with the lowest AICc score were considered to best explain the
variability in the data (Burnham and Anderson, 2002). In instances where
more than one model had a DAICc < 2, the model with the least
parameters was selected. Prior to running the GLMMs, random effects were
selected using restricted maximum likelihood (“REML = ‘TRUE’” in the
‘lmer()’ function) and using AICc selection on the fully-parameterized
models with varying random effects. Random effects tested included random
intercepts for individual ID (“(1|id)”) and calendar year (“(1|year)”),
and a combination of both of these intercepts, as well as correlated
(“(age|id)”) and uncorrelated (“(age||id)”) random intercepts and slopes
for individual ID by animal age, with and without a random intercept for
calendar year. After choosing the random effects, model selection was
conducted on five models with varying combinations of fixed effects for
individual age and sex (Table S3). Both Sr and Ba exhibit large,
non-linear changes in early life associated with nursing and weaning
(Clark et al., 2020b), thus GLMMs were only conducted for ages ≥ 5, where
the weaning signal is no longer present in the data. Individuals with five
or more elemental concentrations classified as outliers (i.e., falling
more than 4 standard deviations from the mean concentration of all
individuals; Tukey, 1977) were excluded from the GLMM for that element.
This resulted in the omission of one individual from the GLMMs for Cu and
Pb. Model predictions and 95% confidence intervals were calculated using
the ‘bootpredictlme4’ R package, which uses a bootstrapping approach (1000
iterations, in this case) to generate confidence intervals (Duursma,
2017). Pearson’s correlations were used to investigate relationships among
trace elements within the lives of individual walruses, among walruses,
and among calendar years. Correlation coefficients were calculated for the
time series of 15 trace elements for each individual walrus, and the
resulting correlation matrices were averaged for males and females to
calculate mean within-individual correlations for each sex. There was high
variability within the high-resolution elemental time series, possibly
resulting from microscale variations in tooth structure or instrumental
noise, which led to almost universally low correlations among elements
within individual walruses. To better compare correlations among
underlying trends in the data, the elemental time series were smoothed
using a Savitzky-Golay filter from the R package prospectr (Stevens and
Ramirez-Lopez, 2014) with a window of 15 data points. The Savitzky-Golay
algorithm smooths the data by fitting a local polynomial regression of
order p (3, in this case) to the data points in the window.
Within-individual correlations were calculated using these smoothed time
series. To examine correlations among walruses, mean (natural
log-transformed) elemental concentrations were calculated for each
individual, and correlations among these mean values were computed
separately for males and females. Finally, to examine correlations among
elemental concentrations by calendar year, mean (natural log-transformed)
trace element concentrations were calculated for male and female walruses
for each year. Correlations among calendar years were restricted to years
in which elemental concentrations from at least three (and usually ≥ 5)
individuals were available, which resulted in a time series from 1945 –
2014. Correlation coefficients were then calculated for each element
across these years. Changes in mean (untransformed) elemental
concentrations from 1945 – 2014 were examined visually. As for the GLMMs,
changes in Sr and Ba through time were calculated using only data from
ages ≥ 5. This resulted in slightly smaller sample sizes, but allowed for
the inclusion of these two elements in this analysis. Pearson’s
correlations between male and female trace element concentrations were
calculated for the period from 1945 – 2014 and interpreted alongside the
visual examinations.