DOI: 10.5061/dryad.q573n5thj
title: Data from: Walrus teeth as biomonitors of trace elements in Arctic marine ecosystems

description: 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.
description: <i>Trace element analysis and data processing
 </i> 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 (<sup>51</sup>V), chromium
 (<sup>53</sup>Cr), manganese (<sup>55</sup>Mn),
 iron (<sup>57</sup>Fe), cobalt (<sup>59</sup>Co),
 nickel (<sup>60</sup>Ni), copper
 (<sup>63</sup>Cu), zinc (<sup>66</sup>Zn), arsenic
 (<sup>75</sup>As), strontium (<sup>88</sup>Sr),
 molybdenum (<sup>95</sup>Mo), silver
 (<sup>107</sup>Ag), cadmium (<sup>111</sup>Cd),
 barium (<sup>137</sup>Ba), and lead
 (<sup>208</sup>Pb) in walrus tooth cementum. Instrumental
 precision for the ICP-MS was ± 5 %. The internal standard for these
 analyses was <sup>43</sup>Ca, 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. <i>Growth layer group counts and
 designation of element concentrations to individual years
 </i> 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 (1<sup>st</sup> year of
 life), L2/D2 making up Age 1 (2<sup>nd</sup> 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. <i>Statistical methods</i>
 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 &lt; 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 <i>prospectr</i> (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
 <i>p</i> (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.