Quantifying Sub‐Seasonal Growth Rate Changes in Fossil Giant Clams Using Wavelet Transformation of Daily Mg/Ca Cycles

Abstract

Plain Language Summary
Shells of giant clams exhibit growth bands, similar to tree rings, which form in both seasonal (visible by eye) and daily (resolvable by microscope) increments. However, the optical visibility of daily bands in fossil giant clam shells can be poor. Fortunately, growth bands are often accompanied by changes in the chemical composition of the shell. The incorporation of trace elements into the shell depends on environmental factors (like temperature and light) and biological controls, which are both characterized by cyclic daily variation. With our Python script Daydacna, we present a tool that enables daily resolution scale changes in growth rate to be evaluated using daily geochemical cycle lengths, that is, how much the shell has grown each day. Daydacna then creates an internal age model and converts the respective element compositions from being expressed over distance to being expressed over time. This information enables an unambiguous estimate of growth rate to be compared to elemental compositions, enabling (e.g.) potential (co)dependencies of these parameters to be identified. Time‐resolved data also allow to determine the timing of seasonal environmental changes, affecting the shell composition, with higher confidence and thus form an important basis for research on the seasonal aspects of the (paleo)climate.

Full text

1. Introduction
Tridacna (giant clams) provide decades-long, sub-daily resolvable archives of (sub)tropical reef environments.
Specifically, by analyzing the geochemical composition of their aragonitic shells, environmental parameters can
be reconstructed, such as temperature (e.g., Ayling etal.,2015; Batenburg etal.,2011; Pätzold etal.,1991), light
availability (e.g., Hori etal.,2015; Warter etal.,2018) and nutrient availability (Arias-Ruiz etal.,2017; Batenburg
etal.,2011; Elliot etal.,2009) as well as short-term disturbances of reef systems, such as storms and heavy precipita-
tion events (Komagoe etal.,2018; Yan etal.,2020). Tridacna emerged in the early Miocene (Harzhauser etal.,2008)
and are mostly mixotroph organisms (Jantzen etal.,2008), feeding on glucose produced by their photosymbionts
and filtering particulate nutrition from the environment (Kunzmann,2008). Their ability to record both seasonal
variability and short-term events as well as the excellent fossilization potential of their large dense aragonitic shells
make them well-suited paleoclimate archives even in “deep-time” (Batenburg etal.,2011; Harzhauser etal.,2008;
Abstract Shells of the giant clam Tridacna can provide decade-long records of past environmental
conditions via their geochemical composition and structurally through growth banding. Counting the daily
bands can give an accurate internal age model with high temporal resolution, but daily banding is not always
visually retrievable, especially in fossil specimens. We show that daily geochemical cycles (e.g., Mg/Ca)
are resolvable via highly spatially resolved laser-ablation inductively coupled plasma mass spectrometry
(LA-ICPMS; 3×33μm laser slit) in our Miocene (∼10Ma) specimen, even in areas where daily banding is not
visually discernible. By applying wavelet transformation on the measured daily geochemical cycles, we quantify
varying daily growth rates throughout the shell. These growth rates are thus used to build an internal age
model independent of optical daily band countability. Such an age model can be used to convert the measured
elemental ratios from a function of distance to a function of time, which helps evaluate paleoenvironmental
proxy data, for example, regarding the timing of sub-seasonal events. Furthermore, the quantification of
daily growth rates across the shell facilitates the evaluation of (co)dependencies between growth rates and
corresponding elemental compositions.
Plain Language Summary Shells of giant clams exhibit growth bands, similar to tree rings,
which form in both seasonal (visible by eye) and daily (resolvable by microscope) increments. However, the
optical visibility of daily bands in fossil giant clam shells can be poor. Fortunately, growth bands are often
accompanied by changes in the chemical composition of the shell. The incorporation of trace elements into
the shell depends on environmental factors (like temperature and light) and biological controls, which are both
characterized by cyclic daily variation. With our Python script Daydacna, we present a tool that enables daily
resolution scale changes in growth rate to be evaluated using daily geochemical cycle lengths, that is, how much
the shell has grown each day. Daydacna then creates an internal age model and converts the respective element
compositions from being expressed over distance to being expressed over time. This information enables an
unambiguous estimate of growth rate to be compared to elemental compositions, enabling (e.g.) potential (co)
dependencies of these parameters to be identified. Time-resolved data also allow to determine the timing of
seasonal environmental changes, affecting the shell composition, with higher confidence and thus form an
important basis for research on the seasonal aspects of the (paleo)climate.
ARNDT ETAL.
© 2023 The Authors. Geochemistry,
Geophysics, Geosystems published by
Wiley Periodicals LLC on behalf of
American Geophysical Union.
This is an open access article under
the terms of the Creative Commons
Attribution License, which permits use,
distribution and reproduction in any
medium, provided the original work is
properly cited.
Quantifying Sub-Seasonal Growth Rate Changes in Fossil
Giant Clams Using Wavelet Transformation of Daily Mg/Ca
Cycles
Iris Arndt1,2 , Douglas Coenen1,2 , David Evans1,2,3 , Willem Renema4,5, and Wolfgang Müller1,2
1Institute of Geosciences, Goethe University Frankfurt, Frankfurt am Main, Germany, 2Frankfurt Isotope and Element
Research Center (FIERCE), Goethe University Frankfurt, Frankfurt am Main, Germany, 3Now at School of Ocean and Earth
Science, University of Southampton, Southampton, UK, 4Marine Biodiversity Group, Naturalis Biodiversity Center, Leiden,
The Netherlands, 5Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, Amsterdam, The
Netherlands
Key Points:
• We present an approach to quantify
daily growth rates in mollusks with an
internal age model based on wavelet
transformation of Mg/Ca data
• The resulting highly resolved
elemental data versus time can be
used to evaluate the timing of (sub)
seasonal environmental changes
• The comparison of growth rate
and elemental composition in a
late-Miocene specimen indicates a (co)
dependence of Mg/Ca and growth rate
Supporting Information:
Supporting Information may be found in
the online version of this article.
Correspondence to:
I. Arndt,
arndt@em.uni-frankfurt.de
Citation:
Arndt, I., Coenen, D., Evans, D., Renema,
W., & Müller, W. (2023). Quantifying
sub-seasonal growth rate changes
in fossil giant clams using wavelet
transformation of daily Mg/Ca cycles.
Geochemistry, Geophysics, Geosystems,
24, e2023GC010992. https://doi.
org/10.1029/2023GC010992
Received 7 APR 2023
Accepted 5 SEP 2023
Author Contributions:
Conceptualization: Iris Arndt, Wolfgang
Müller
Data curation: Iris Arndt
Formal analysis: Iris Arndt
Funding acquisition: Iris Arndt,
Wolfgang Müller
Investigation: Iris Arndt, Wolfgang
Müller
Methodology: Iris Arndt, Douglas
Coenen, Wolfgang Müller
10.1029/2023GC010992
RESEARCH ARTICLE
1 of 20

Geochemistry, Geophysics, Geosystems
ARNDT ETAL.
10.1029/2023GC010992
2 of 20
Warter etal., 2015). As seasonality is an important component of the climate system, seasonally resolved data
greatly help in constraining palaeoclimate reconstructions and may be used to test the skill of climate models (Carré
& Cheddadi,2017; Cauquoin etal.,2019; Schmidt etal.,2014; Tierney etal.,2020).
Besides geochemical variability, macro and microstructures within the shell reflect changes in the environmental
setting of clams (Gannon etal.,2017). Tridacna shells feature seasonal (macroscopically visible at mm scales) to
daily (microscopically visible at μm scales) growth bands. Daily growth bands can be counted to determine time
intervals within recent giant clams (Duprey etal.,2015; Gannon etal.,2017; Sano etal.,2012; Zhao etal.,2023).
However, microscopic daily growth band visibility is not always sufficient for reliable counting, especially in
fossil shells (Figure1e, Figures S1 and S2 in Supporting InformationS1; Section3.2).
Alternatively, seasonal bands at the macroscale or annual cycles in the oxygen isotopic composition of the
shell (δ
18O) have been used to determine the ontogenetic age of fossil giant clams and to evaluate seasonal and
multiannual paleoenvironmental changes (Ayling etal.,2015; Batenburg etal.,2011; Driscoll etal.,2014; Warter
etal.,2015). However, in many tropical areas, seasonality is manifested not via large temperature variability but
rather via substantial changes in precipitation, which may complicate the interpretation of δ
18O records obtained
from Tridacna specimens and their usability for indicating yearly time intervals (Ma etal.,2020). This argues for
either an alternative macro-scale geochemical proxy or a reliable micro-scale analysis of the shell. Fortunately, it
is possible to measure daily increments within fossil Tridacna shells via spatially resolved geochemical analysis.
Analytical methods suitable for resolving daily changes in elemental composition are (Nano) Secondary Ion
Mass Spectrometry (e.g., Füllenbach etal.,2017; Hori etal.,2015; Sano etal.,2012; Yan etal.,2020), Electron
Probe Micro Analyzer measurements (Füllenbach etal.,2017; Hori etal.,2015), and Laser-Ablation Inductively
Coupled Plasma Mass Spectrometry (LA-ICPMS) (e.g., Warter & Müller,2017; Warter etal.,2018). LA-ICPMS
is a more rapid and inexpensive method (Warter & Müller,2017) and may therefore be advantageous in determin-
ing the ontogenetic ages of large (100–1,000mm) and long-lived Tridacna shells by daily band analysis.
Here we present a method applicable to the rapid generation of a reliable internal age model for fossil (Tridacna) shells.
Specifically, we show that daily resolved internal age models can be generated by assessing the changing wavelengths
of daily cycles of elemental ratios (e.g., Mg/Ca) measured via high resolution LA-ICPMS. We demonstrate the suita-
bility of this approach for assessing paleo-seasonality by enabling high-resolution analysis of geochemical parameters
over time rather than shell distance. Time resolved paleoenvironmental proxy data can be compared seasonally, which
improves the interpretation of reoccurring seasonal environmental changes. Additionally, we aim to further our under-
standing of the relationship between growth rate and geochemical proxies. Comparing the daily resolved growth rate
changes to geochemical data helps decipher growth rate dependent and independent proxy variability.
In general, the approach developed here is not only applicable to Tridacna but also to other organisms and
archives providing cyclic data sets (see Section5).
2. Materials and Methods
2.1. Sample Origin and Preparation
A large fossil Tridacna sample (TOBI; Figure1a) was sampled in East Borneo in early November 2013, near
Bontang (0°08′15″N, 117°26′21″E). The shell was recovered from a micritic bioclast packstone horizon within
silty shales and sandstones (Renema etal.,2015). Other bivalve shells and corals from the “Bontang Garden”
location have been dated via Strontium Isotope Stratigraphy, suggesting that fossils from this location can be
assigned to the Tortonian (late Miocene) (Renema etal.,2015).
The shell was externally cleaned with a brush, rinsed with tap water and dried before being cut along its maxi-
mum growth axis. The central slab was divided into smaller rectangular slabs (55×28×7mm) with slightly
overlapping areas (Figure1a). From the slabs, thin sections of 50μm thickness were cut using a G&N MP2 2R
300 surface grinder and polished with 3μm diamond suspension on a Logitech PM2A. These thin sections were
used for both imaging (e.g., Figures1c–1f) and LA-ICPMS analysis.
2.2. Microscope Imaging
In order to obtain information on the shell structure, microscope images of the polished thin sections were taken using
a Keyence VHX-6000 microscope with full ring incident light. For overview images we used 20× magnification,
Project Administration: Wolfgang
Müller
Resources: Willem Renema, Wolfgang
Müller
Software: Iris Arndt, Douglas Coenen
Supervision: David Evans, Wolfgang
Müller
Validation: David Evans, Wolfgang
Müller
Visualization: Iris Arndt
Writing – original draft: Iris Arndt
Writing – review & editing: Iris Arndt,
Douglas Coenen, David Evans, Wolfgang
Müller

Geochemistry, Geophysics, Geosystems
ARNDT ETAL.
10.1029/2023GC010992
3 of 20
areas around the high-resolution laser-ablation (LA) tracks were documented at 400× magnification, and pictures up to
2000× magnification were taken of areas of specific interest, for example, within dark presumably semi-seasonal bands.
Where possible, daily bands were counted (Figures S3 and S4 in Supporting InformationS1) and a semiquantitative
analysis of daily band widths in different areas of the shell was conducted by counting daily increments over distance.
2.3. Phase Assessment via Attenuated Total Reflectance Fourier Transform InfraRed Spectrometry
(ATR-FTIR) and X-Ray Diffractometry (XRD)
Material from the rim of the shell was sampled using a handheld slow-rotating diamond-tipped drill. From the
inner shell, the material was cut out with a diamond blade saw and powdered by hand using an agate mortar. This
procedure was chosen to avoid any recrystallization of aragonite to calcite during the drilling process, as previously
observed (Aharon,1991; Foster etal.,2008). The mineralogical composition of the powder samples was measured
both by XRD, namely a PANalytical X’Pert PRO X-ray diffractometer, equipped with a copper X-ray tube and
a Ni-filter, and by ATR-FTIR, namely a Bruker Alpha II equipped with a diamond ATR attachment. ATR-FTIR
baseline subtraction was performed using a baseline measurement (12 scans) taken immediately prior to sample data
acquisition (24 scans), while data were collected between 350 and 4,000cm
−1 at a resolution of 4cm
−1.
2.4. LA-ICPMS
LA-ICPMS analysis was carried out using a RESOlution LR laser-ablation system equipped with a two-volume
S155 cell (Laurin Technic; Müller etal.,2009) coupled to a ThermoScientific Element XR sector field mass
spectrometer. Prior to analysis, thin sections were ultrasonically cleaned using ethanol.
Ablation took place in He atmosphere, with Ar added at the top of the funnel, and N2 admixed downstream of
the cell at ∼4ml/min. Tuning was performed using a 60μm spot, with 1μm/s scan speed and 5Hz repetition
rate. Following Warter etal.(2018), ablation was carried out using a rotatable rectangular laser slit. Here, we use
a 3×33μm slit, which has an ablation surface area similar to a 15μm circular spot, hence yielding equivalent
ICPMS signal intensities. All laser tracks were set parallel to the maximum growth axis, that is, orthogonal to
Figure 1. (a) Cross section of the late Miocene Tridacna shell (TOBI) with six slabs cut for analysis. The numbered points refer to the position of the three laser-ablation
tracks presented and discussed in this paper. (b) Ocean Data View map of the Indo-Pacific region with the sampling location marked by a star. (c) Thin section of the
ontogenetic oldest slab with presumably semi-seasonal dark banding. (d) Close-up of c with crossed lamellar crystal structure striking at an angle of ∼135° (dark gray
dotted line) and two dark bands striking at an angle of ∼45° (white dotted lines). (e) Close-up of d, showing the crossed lamellar crystal structure within part of a dark band.
These brownish and grayish structures likely reflect aragonite crystals of different orientations. The angle of the crystal structure changes around the dark banding, from
around 135° to around 110°. (f) Close-up of d, with crossed lamellar crystal structure and daily banding striking at an angle of ∼45° (red dotted line).

Geochemistry, Geophysics, Geosystems
ARNDT ETAL.
10.1029/2023GC010992
4 of 20
the daily bands. Precleaning took place at 7μm/s scan speed and 20Hz repetition rate, while measurements were
conducted at a scan speed of 1μm/s and 10Hz repetition rate. We monitored the isotopes
11B,
23Na,
24Mg,
27Al,
43Ca,
88Sr,
89Y, and
138Ba, with a total sweep time of 330ms, resulting in a sampling frequency of ∼0.34μm. We
do not further present or discuss
27Al data as they lie largely below the limit of detection.
89Y data were measured
to check any possible diagenetic alternation and are, like
27Al, only displayed with the full data set in Figure S5
of Supporting InformationS1. Instrumental tuning was performed on the standard reference material NIST SRM
612. The plasma was tuned to a ThO
+/Th
+ rate of 0.4%, a Th/U ratio of 0.97, and a normalized argon index
(Fietzke & Frische,2016) of 1.1, while double-charged production at 1.5% was monitored via the 22/44 ratio. At
10Hz, we did not use the signal smoothing squid in order to maximize the spatial resolution (Müller etal.,2009).
An overview of the measurement and tuning parameters can be found in Table S1 of Supporting InformationS1.
Element/Ca (El/Ca) ratios were calibrated using NIST SRM 612 as the bracketing external standard (Jochum
etal.,2011) with updated Mg values (Evans & Müller,2018) and
43Ca as the internal standard. Data quantifi-
cation follows Longerich etal.(1996) and was performed using the software Iolite 4 (Paton etal.,2011). Data
quality (accuracy and precision) was determined by measuring the MPI-DING glass KL2-G (Jochum etal.,2006)
and the nanopellet carbonate standard MACS-3NP (Garbe-Schönberg & Müller,2014) treated as unknowns and
analyzed in the same way as the samples; data are shown in Table1.
Further data evaluation and visualization was done using Python and facilitated through the use of the Python
libraries and packages NumPy (Harris et al., 2020), Pandas (Reback et al., 2022), and Matplotlib (Caswell
etal.,2022) as well as PyCWT based on Torrence and Compo(1998).
2.5. Wavelet-Analysis: New Python Script “Daydacna”
2.5.1. Data Pre-Treatment
Prior to further analysis, we removed outliers from the data set. Because the data are both cyclic and display long-
term trends, we detrended the data before determining outliers. Detrending was conducted by subtracting a 20-point
running mean from the original signal to remove most underlying variability. Subsequently, all data points outside
the two standard deviation range were selected as outliers. These data points were excluded from the original data
set and replaced with linearly interpolated values. Rejecting data points over two standard deviations resulted in a
clearer signal and less noisy wavelet scalogram compared with rejecting only points over three standard deviations.
2.5.2. Continuous Wavelet Transform (CWT) Basics
To evaluate the wavelength shifts within the daily El/Ca cycles, we applied CWT (Goupillaud etal.,1984) to the
data set. In CWT, an input function is multiplied by a wavelet. The wavelet used in this study is a real-value Morlet
wavelet (Morlet etal.,1982). This wavelet is continuously scaled and shifted in its position within a given range.
Measured mean (μg/g)±2 SD Reference value (μg/g)±2 SD Accuracy (%) Precision (% 2 RSD)
B MACS-3 10.13±1.84 8.9 13.82 18.13
KL2-G 2.88±0.40 2.73±0.28 5.49 13.73
Na MACS-3 7,711±1,053 5,900±800 30.70 13.66
KL2-G 17,051±544 17,434±593 −2.20 3.19
Mg MACS-3 1,948±108 1,756±272 10.91 5.55
KL2-G 43,519±1,296 44,263±543 −1.68 2.98
Sr MACS-3 7,205±238 6,760±700 6.58 3.31
KL2-G 355±8 356±8 −0.28 2.34
Ba MACS-3 77.3±7.2 58.7±4.0 31.70 9.31
KL2-G 127±2 123±5 3.25 1.50
Table 1
Results of Standard Reference Materials Measured Using the 3×33μm Slit Compared With Reference Values for MACS-3
(Na, Mg, Sr, Ba: Solution Based USGS-Data From Stephen Wilson (Pers. Comm, 2010); B: Laser-Ablation Inductively
Coupled Plasma Mass Spectrometry Data With Medium Mass Resolution and 193nm Wavelength; Jochum etal.,2012)
and KL2-G (GeoReM Preferred Values; Jochum etal.,2006); Accuracy and External Precision for Each Element

Geochemistry, Geophysics, Geosystems
ARNDT ETAL.
10.1029/2023GC010992
5 of 20
The product of wavelet and input function is integrated, and the resulting coefficient can be used to determine the
correlation between wavelet and input function at a given frequency, including the position of the wavelet.
The resulting correlation between the wavelet and input function at a given interval and a given frequency of the
wavelet was used to determine the main frequency present in the input for said interval. The continuous shift in
location and frequency of the wavelet during its comparison to the input data allows for the analysis of shifts in
frequency over distance within the input data set itself. This is best visible in a scalogram in which the relative
correlation intensity is displayed in color with distance or time on the x-axis and wavelength on the y-axis (e.g.,
Figure2). In our new data evaluation script “Daydacna,” we use the Python package PyCWT (based on Torrence
and Compo(1998)) to calculate and display our CWTs.
2.5.3. Wavelet Correlation Maxima and Growth Rate Evaluation
Figures2a–2d illustrate the steps taken by the Daydacna script. We apply a combination of automated maxima
detection, manual correction, and smoothing to determine the wavelengths of daily cycles, hence directly trans-
lating to daily growth rates. These form the basis for the estimation of the number of days included in the data set.
We use a Monte Carlo approach to quantify the uncertainty of this estimation.
As a first step, the frequencies with the maximum correlation coefficient at a given point over distance are selected
to determine the main frequency within the input data (red lines, Figure2a). Here, we set the maximum limit for
detection to 40μm because daily band widths were optically determined to be 38μm or below throughout the shell
(see Section3.2). In step two, an initial interpolation and locally weighted scatterplot smoothing (LOWESS) statisti-
cal regression function based on Cleveland(1979) using the statsmodels Python module (Seabold & Perktold,2010)
is applied to smooth through the correlation maxima detected in step one (black line, Figure2b). Overlying lower
frequencies can at times show a clearer cyclicity in the data set and therefore have higher correlations than daily
cycles. Thus, the dominant wavelengths highlighted by Daydacna in the resulting graph may not always represent
daily cycles. To correct this issue, we implemented a mixture of automatic and manual adjustments. In areas where
overlying (greater than daily) frequencies dominate over longer distances, manual readjustment of the pathway
might be necessary. The user can choose a more likely pathway for such areas by selecting other points of high corre-
lation (blue dots, Figure2b). These manually set points within one pathway are in turn connected linearly (blue lines,
Figure2b), which overall replaces the automatically detected pathway in that data range. A Monte Carlo approach
was used to account for the uncertainty of manually selecting the approximate area of high correlation related to
the daily signal. Both x (distance) and y (wavelength) values of each manually selected point are randomly selected
(1,000 times) from a normal distribution using the point's coordinates as mean and 1% of the mean value as the
standard deviation. In a third step, maxima will be reselected automatically, but this time only close to the pathway
describing the approximate area in which the daily signal is expected based on Monte Carlo simulations. The upper
and lower boundaries of the range in which maxima are selected are also determined by a Monte Carlo simulation
with 1,000 iterations. The mean of the upper and lower boundaries is centered on the smoothed pathway plus or
minus 0.5 log2 units respectively. Based on these mean values, the boundaries are randomly sampled from a normal
distribution with a standard deviation of 0.05 (∼10% uncertainty). The smoothed pathway is processed as log2 of
the selected wavelengths; therefore, the range of interest is displayed uniformly across the scalogram (Figure2c).
Correlation maxima detected outside the area of interest, that is, far away from the smoothed detected pathway,
are automatically excluded in this second maxima detection step (Figure2c). Through microscope observations,
we found that growth rate changes are rather gradual (see Section3.2, Results). Therefore, we created smoothed
pathways through the detected maxima in the last step.Here we use a convolution algorithm for each of the 1,000
envelopes, rather than smoothing with LOWESS, because the computational time needed for the latter is more than
three orders of magnitude higher, making it unsuitable for Monte Carlo simulations on large data sets. However, the
convolution algorithm has boundary effects, impacting the rim domains from a distance equivalent to half of the
smoothing window. We cannot properly assess the uncertainty introduced by smoothing in these rim areas, but since
they make up only about 3% of the entire data set, we consider this contribution to be minor. The smoothing window
span is also simulated in 1,000 iterations using a Monte Carlo approach. Here, we chose a mean smoothing window
span of 400 data points with a standard deviation of 75. The resulting 50th percentile pathway describes the changes
in wavelengths over distance (dashed line, Figure2d) and the 2.5th and 97.5th percentiles are used to represent the
uncertainties (indicated in gray, Figure2d). Because of the aforementioned steps, different thresholds, and working
with linear and log2 scales, the resulting array of Monte Carlo simulated number of days in the sample is not always
normally distributed. As such, we use the 2.5th/97.5th percentiles in addition to the 2 SD values.

Geochemistry, Geophysics, Geosystems
ARNDT ETAL.
10.1029/2023GC010992
6 of 20
Our time series analysis approach provides a data set of wavelengths over distance, which traces the lengths of the
daily El/Ca cycles recorded within the shell. The wavelength of each daily cycle represents the shell growth per
day along the measured transect. With the smoothed growth rate pathway, we can quantify the growth rate in μm
per day for each given point, which allows us to quantify the time represented between two data points as a frac-
tion of a day. The cumulative sum of time between data points over distance along the shell results in an internal
age model based on daily growth rates. This allows us to obtain an overall estimate of the number of days included
Figure 2. Steps performed by the Daydacna script to assess the daily growth rate from the continuous wavelet transform (CWT) scalogram of the Mg/Ca data set
of LA track 2 (for details see Section3.3.2, Figure4). The y-scale is inverted and on a log2 scale. (a) Step 1: Correlation-maxima are selected within the range of
potentially occurring daily cycles (up to 40μm, based on observation). (b) Step 2: If these maxima are unlikely to represent daily cycles (criteria see Sections2.5.3
and4.2), an alternative area of interest can be selected manually, indicated in blue. (c) Step 3: An area of interest is defined around a smoothed curve of the selected
maxima and areas of manual correction excluding outliers and artificial jumps in growth rate. The black dashed lines indicate the 50th percentile of the Monte Carlo
simulated area of interest. Correlation maxima are detected for a second time but now only within the area of interest (red domains). In this way, low frequency
overlying signals with higher correlations than the daily signal are excluded. (d) Step 4: The maxima are interpolated and smoothed with different smoothing window
spans via Monte Carlo simulation. The 50th percentile curve, which is used to determine growth rates over distance, is indicated as the black dashed line. Confidence
intervals (2.5th and 97.5th percentiles; ∼2 SD) are displayed by the area shaded in gray.

Geochemistry, Geophysics, Geosystems
ARNDT ETAL.
10.1029/2023GC010992
7 of 20
in the measured LA track and quantify the distribution of time over distance. In order to assess and quantify the
uncertainties associated with the determination of days in a data set, we modify—via Monte Carlo simulations—
the parameters used to establish the daily growth rate, which have to be set but are difficult to determine. These
factors include the exact position of the high correlation area attributed to daily cyclicity by manual selection, the
range of area of interest around the approximate pathway of daily wavelengths and the smoothing window span.
Ultimately, we apply the internal age model derived as described above to display the measured data against time
rather than distance. When doing so, data points plotted versus time are no longer evenly spaced, in contrast to
plotting against distance. Nonetheless, this provides a test of the model as the daily cycles should all be displayed
as being equally long on the time scale (1day).
3. Results
3.1. Preservation of the Shell
While the outer rim (∼1cm) shows visible alteration, characterized by a change in color and at times visible
recrystallization, the inner part of the shell has an optically pristine appearance as it displays no signs of recrys-
tallization or dissolution (Figure1c). The original crystal structure is well preserved and visible in the microscope
images (Figure1d–1f). Furthermore, the El/Ca ratios (see below) are very similar to those of modern Tridacna
shells (Batenburg etal.,2011; Sano etal.,2012; Warter etal.,2018). The measured Y/Ca ratios do not indicate
diagenetic alteration (Figure S5 in Supporting InformationS1). XRD and ATR-FTIR measurements confirm that
the aragonite is preserved throughout the bulk of the shell analyzed here, whereas both calcite and aragonite are
detected in the powder samples from the outermost rim (Figures S6 and S7 in Supporting InformationS1).
3.2. Optical Analysis of Presumably (Sub)seasonal and Daily Bands
Along the cross-section of the shell, a conspicuously regularly occurring light-dark banding pattern is visible
(Figures1a and 1c). The banding pattern may be irregular, but especially in the ontogenetically older area of
the shell, a double-banding structure prevails (Figure1c). In areas where daily bands are visually resolvable, we
could count around 70–100days between the centers of the two bands close to each other, while the larger gaps
usually contain more than 200days. This indicates that the macroscopically visible dark bands in our sample are
likely to be produced seasonally.
The various microscopy techniques applied did not unequivocally resolve the daily bands throughout the shell. In
some areas of the shell daily bands are well resolvable with 400× magnification. In areas where daily bands are
not visible in 400× magnification, a five-fold increase in magnification generally did not result in better daily band
discernibility. We further unsuccessfully tried to enhance daily band visibility by treating slabs and thin sections
with Mutvei's solution, as described in Schöne etal.(2005), prior to microscopic assessment and imaging (Figure
S8 in Supporting InformationS1). In order to enhance the visibility of daily growth bands in recent specimens,
fluorescence microscopy has been utilized, which triggers the autofluorescence of the organic content within the
shell, which varies on a daily scale (Duprey etal.,2015; Ma etal.,2020; Yan etal.,2020). Our fossil sample did not,
however, display distinct continuous daily banding via fluorescence microscopy (Figure S9 in Supporting Informa-
tionS1). Furthermore, daily bands were not distinguishable via Scanning Electron Microscope imaging, nor did
their density difference allow them to be distinguished using μCT or via X-ray imaging (Figure S10 in Supporting
InformationS1). Daily geochemical El/Ca cycles, however, are preserved and resolvable in areas where the micro-
scopic bands were not well visible in our fossil specimen (Figure S1 in Supporting InformationS1).
The daily band visibility is slightly better in the ontogenetically youngest part of the shell, where the daily bands
are widest with a maximum of 38μm. However, daily band visibility and the lack thereof are not directly coupled
to ontogenetic age. Rather, there are bands and patches in which visibility is better or worse throughout the shell
section. In areas with good daily band visibility, we observed the following recurring patterns. We found that
daily band widths decrease towards the macroscopically visible dark (more translucent) presumably seasonal
bands. Daily band widths further decrease within the dark bands to well below 10μm and at times below 5μm
with a minimum of 2μm. A gradual increase in daily band width is seen at the end of the dark band until a rela-
tively constant daily band width is reached in the lighter (less translucent) parts of the shell (e.g., Figure S4 in
Supporting InformationS1). The observation of reduced widths of the microscopically visible daily growth bands
within the macroscopically visible darker presumably seasonal bands holds throughout the shell, linking the dark

Geochemistry, Geophysics, Geosystems
ARNDT ETAL.
10.1029/2023GC010992
8 of 20
bands to reduced growth of the organism. The crossed lamellar aragonite crystal structure (Figure1e–1g) shows
a steepened angle within the dark low growth bands (Figure1e).
Daily bands were counted along LA tracks in areas with good daily band visibility. A comparison of the counted
numbers of daily bands and Mg/Ca cycles confirmed their 1:1 relation. The counted sections also provide a basis
for checking the accuracy of the age estimates generated via our wavelet-analysis-based Python script Daydacna
(see Section3.3).
3.3. LA-ICPMS Data, Growth Rate, and Internal Age Model
In the following, we present the results for three LA tracks. We chose to run Daydacna on the Mg/Ca rather than
Sr/Ca data sets, because the daily cycles were more clearly expressed in Mg/Ca (compare Figure3 and Figure
S11 in Supporting InformationS1). The first LA track is from an area of relatively uniform growth within the
shell. The daily bands corresponding to the measurements were countable (Figure3). The second track includes
two dark presumably seasonal bands with reduced growth. Along this track, the daily bands were also countable
(Figure4). The third one includes four dark bands and is situated in an area where continuous counting of growth
bands was not possible (Figure5).
3.3.1. Track 1: Uniform Growth and Optically Countable Daily Bands
Along the first LA track, 145 daily bands were optically counted (Figure3a). The daily bands are relatively evenly
spaced in the microscope image. Mg/Ca values range from 0.09 to 0.44mmol/mol with a mean of 0.24mmol/mol.
Figure 3. (a) Microscope image along LA track 1. Daily bands are visible and counted to be 145 full bands (highlighted in black). (b) Measured Mg/Ca (light blue)
from the same track versus distance with resolvable daily cyclicity, with locally weighted scatterplot smoothing curve smoothed over 600 data points shown in gray.
(c) Continuous wavelet transform scalogram of the Mg/Ca signal with determined growth rates over distance (dashed line). (d) Internal age model indicating the range
of number of days across distance. The overall number of days±2 SD determined from daily Mg/Ca cycles by our Daydacna script is 148.2±0.08 with an offset of
3days (2%).

Geochemistry, Geophysics, Geosystems
ARNDT ETAL.
10.1029/2023GC010992
9 of 20
The overall rather flat LOWESS curve, smoothed over 600 data points, and independent of daily cycles, is char-
acterized by little variability with values ranging between 0.20 and 0.26mmol/mol (Figure3b). Daily growth
rates determined by Daydacna show moderate variability and are in the range of 18–27μm/day (Figure3c). No
manual correction, to exclude maxima not representing daily cycles, was necessary in this region of the shell.
The Daydacna script determined 148.2±0.08days(2 SD) for this measurement (Figure3d). The precision is
relatively high in this example because the result is generated in a fully automated manner, and no additional vari-
ability is introduced by manual selection. The discrepancy between the 148days determined by the script and the
optically counted 145days is 2%. This offset between the number of days counted and determined by the script
appears to be caused by a slight underestimation of the growth rate in the rim areas of the scalogram (Figure3c).
3.3.2. Track 2: Variable Growth and Optically Countable Daily Bands
In the case of the second example, 271 daily bands were optically counted (Figure4a). The density of daily bands
seen in the microscope image is variable as the LA track crosses two presumably seasonal low growth areas.
Mg/Ca values range from 0.10 to 0.62mmol/mol with a mean of 0.29mmol/mol (Figure4b). The smoothed
Mg/Ca values (600 points, LOWESS) vary between 0.21 and 0.39mmol/mol. Two peaks in the Mg/Ca trend
around 1,400 and 3,150μm coincide with areas of denser daily banding (Figure4a). The detected wavelengths of
daily cycles in the scalogram (Figure4c) are variable and range between 9 and 27μm. In the regions of Mg/Ca
peaks, daily bands were overlain by longer term frequencies. Here, Daydacna detected and selected wavelength
Figure 4. (a) Microscope image along LA track 2. Daily bands are visible and indicate that this LA track includes 271days. (b) Measured Mg/Ca (light blue) from LA
track 2 versus distance, with daily cycles at times too narrow to be resolvable at the overview scale. The 600-point locally weighted scatterplot smoothing curve (gray)
shows a long-term trend with two maxima around 0.4mmol/mol, coinciding with areas with narrow daily banding in panel a. (c) The continuous wavelet transform
scalogram of the Mg/Ca signal shows variability in the determined growth rates with manual correction (dashed line). The 2.5th/97.5th percentile (∼2 SD) is displayed
by an area shaded in gray. The pathway without manual correction is displayed by a dotted line. (d) The internal age model (days vs. distance in blue) reflects the
non-linear growth rates determined. The thickness of the blue curve reflects the 2.5th to 97.5th percentile interval, also displayed by ret dotted lines in the inserted
distribution graph. The overall number of days±2 SD determined from the Mg/Ca signal for LA track 2 is 269.4±3.2. The offset of days counted versus determined
by Daydacna is 2days (0.7%). The blue dotted line represents the uncorrected age model.

Geochemistry, Geophysics, Geosystems
ARNDT ETAL.
10.1029/2023GC010992
10 of 20
maxima which are not related to daily bands. Hence, a manual correction was needed (Figure4c) in order to force
Daydacna to select the minor peaks reflecting the daily cyclicity. The manual correction is based on the optically
observed shortening of daily band intervals in the regions of the dark presumably seasonal bands. The variability
in growth rate can be seen in the age model displayed in time over distance (Figure4d). The time represented
in the measured shell section, as determined by Daydacna, is 269.4±3.2days(2 SD). The 271days counted
optically are within the 2 SD interval of prediction with an offset of 2days (0.7%).
3.3.3. Track 3: Variable Growth and Optically Not Countable Daily Bands
Daily bands were only partially optically visible along the third LA track, preventing continuous counting. The
LA track starts within a dark presumably seasonal band and passes through another three such bands (Figure5a).
Mg/Ca values vary from 0.15 to 1.10mmol/mol with a mean of 0.39 and the smoothed (600-point LOWESS) Mg/
Ca trend ranges between 0.27 and 0.72mmol/mol (Figure5b). Elevated Mg/Ca values are visible in and around
the dark bands (Figures5a and5b). The wavelengths determined by Daydacna range from 2 to 19μm (Figure5c).
Manual adjustments within the areas of the dark bands were needed for Daydacna to select the minor correlation
peaks at higher frequencies. While better correlated higher wavelengths are present at these points in the shell,
by comparison to other shell sections in which daily bands were clearly countable and based on the observation
that dark bands are connected to low growth areas, we judge that the shorter wavelengths are more likely to
represent daily cycles. In areas in which daily frequencies are not well represented (e.g., around 3,300μm, middle
Figure 5. (a) Microscope image along LA track 3. Daily bands are not optically resolvable. Four dark presumably seasonal bands are visible, one of which is located at
the left edge of the image. (b) Measured Mg/Ca (light blue) from LA track 3 versus distance, with daily cycles at times resolvable at the overview scale. The 600-point
smoothed locally weighted scatterplot smoothing curve (gray) shows a variable long-term Mg/Ca trend with maxima around the dark bands seen in Figure4a. (c)
The determined growth rates with manual correction (dashed line) and without manual correction (dotted line) on the continuous wavelet transform scalogram of the
detrended Mg/Ca are variable. The 2.5th/97.5th percentiles (∼2 SD) area around the corrected pathway is shaded in gray. (d) The changes in the slope of the internal
age model (solid blue curve) reflect the variability of growth over time, while the thickness of the blue curve reflects the 2.5th to 97.5th percentile interval, also
displayed by ret dotted lines in the inserted distribution graph. From the Mg/Ca signal, we determined that LA track 3 spans 519.6±11.0days(2 SD). The uncorrected
age model is shown in the blue dotted line.

Geochemistry, Geophysics, Geosystems
ARNDT ETAL.
10.1029/2023GC010992
11 of 20
of the third dark band), interpolation determines the most parsimonious pathway. The resulting growth rate is
variable and decreased in the dark band areas. The determined time contained in the measured shell section is
519.6±11.0days(2 SD).
3.4. Geochemical Composition Versus Time at Daily Resolution
The internal age models determined by Daydacna allow us to display the measured El/Ca data against time
(Figure6). The first LA track is ontogenetically the youngest, followed by the second and third tracks. Given that
these tracks have a very similar overall length (∼4,030μm), the decrease in observed duration of ∼150, ∼270,
and ∼520days indicates a trend towards reduced growth with increasing ontogenetic age as previously observed
in our microscope image analysis. Additionally, the presence and absence of dark bands characterized by reduced
growth affects the conversion of distance into time along the maximum growth axis of the shell, which Daydacna
can account for.
Displaying the data of track 1 against time (Figure6a; close-up in Figure6b), using the age model derived above
(Figure3), results in clearly visible daily cycles in the Mg/Ca and Sr/Ca data sets. Na/Ca and B/Ca co-vary and
display clear cycles with longer wavelengths of 4–10days that overlie the daily cycles. Mg/Ca and Ba/Ca are
characterized by low-amplitude long-term cyclic variability, while Sr/Ca shows no cyclicity other than on a daily
scale.
In track 2, daily cycles are difficult to resolve in the overview image (Figure6c). The close-up from 20 to 40days
(Figure6d) shows distinctive daily cycles in Mg/Ca and Sr/Ca. From the overview image, we can deduce that
phases of low growth, connected with high Mg/Ca values, last 1–2months. Mg/Ca cyclicity covaries positively
with Sr/Ca, whereas it displays a broadly negative relationship to Na/Ca. B/Ca behaves in an overall similar
manner to Na/Ca. Ba/Ca variability increases in low growth bands.
In track 3 (Figure6e), we observe that low-growth high Mg/Ca bands appear in the same half of the first and
second years. In the other half of the first year, low Mg/Ca values prevail. Mg/Ca and Na/Ca show opposing trends
in terms of their long-term variability, especially during low-growth, high Mg/Ca phases. B/Ca behaves similarly
to Na/Ca in regular growth areas, but unlike Na/Ca, B/Ca shows no strong shifts in low growth areas. The Sr/
Ca variability is in phase with Mg/Ca. Ba/Ca shows an increase again in variability in low-growth areas and is
lowest in the first low growth band of the second year. In the close-up view from 200 to 220days (Figure6f), all
elemental ratios display daily cyclicity, of which Mg/Ca and Sr/Ca are clearest.
4. Discussion
4.1. LA-ICPMS Data Evaluation With Daydacna
Daydacna is a new, purposely designed Python script which allows daily resolved internal age models for (fossil)
Tridacna shells to be generated from appropriately spatially resolved El/Ca data. Although it was only applied
here on one Tridacna specimen, it could also be used for similar data sets generated for different biogenic or
inorganic archives with incremental growth bands. Using the age model, the necessary high-resolution analysis
of the geochemical parameters against time rather than shell distance is possible. We envisage that this approach
could have multiple applications, such as quantified comparison between growth rate and elemental ratios, unam-
biguous interpretation of geochemical data in terms of seasonal paleoenvironmental change, and possible identi-
fication and quantification of extreme weather events (see Section4.4).
Despite the excellent preservation of the inner shell part of our sample “TOBI,” where neither microscope imag-
ing, phase (polymorph) analysis nor elemental ratios point towards recrystallization or alteration (see Section3.1),
daily increments could not be optically resolved in all parts of the inner shell. Instead, we observed that the daily
geochemical variability in fossil Tridacna can be retrieved even in areas where daily band visibility is very poor
(Figure5, Figure S1 in Supporting InformationS1). Optical evaluation of daily increments is practical within
certain smaller segments with good visibility, whereas using geochemical cycles to determine daily resolved
age models is advantageous for analyzing large shells with hundreds to thousands of days of growth, including
shell parts with poor optical visibility. Furthermore, creating daily resolved internal age models for long-lived
(several decades old) organisms using daily band counting would be very time consuming. Programs for auto-
mated image-based lamina detection have been available for over a decade (e.g., Meyer etal.,2006) and can

Geochemistry, Geophysics, Geosystems
ARNDT ETAL.
10.1029/2023GC010992
12 of 20
Figure 6. Elemental ratios for the three LA tracks (shown in Figures3–5), now displayed versus time. The ranges of the x and y axes are specific to each track in order
to best display the measured data. (a) Elemental ratios measured along LA track 1. Daily cyclicity is best visible in Mg/Ca and Sr/Ca. Overlying lower frequencies
are visible in B/Ca and Na/Ca, with corresponding close-up in (b). (c) Elemental ratios measured along LA track 2 over reduced growth areas (around 80–90, and
170–200days, respectively); (d) Close-up of c showing daily cycles for 20–40days. (e) Elemental ratios measured along LA track 3 with four reduced growth domains
(around 0–20, 70–90, 370–430, and 480–500days), with corresponding close-up (f) for 200–220days.

Geochemistry, Geophysics, Geosystems
ARNDT ETAL.
10.1029/2023GC010992
13 of 20
facilitate daily band counting on the scale of years if the band visibility is sufficient. However, even in areas with
good banding visibility, the success of computer-based image assessment of daily bands is lower than that of
geochemical assessment (Warter & Müller,2017).
Daily geochemical cycles have previously been used in rudist shells to obtain a reliable daily resolved age model
due to their better resolvability than daily banding (de Winter etal.,2020). We have extended this approach and
provide, to our knowledge, the first internal age model in clams based on CWT of daily geochemical cycles.
We provide a semi-automated script (Daydacna) which quantifies the wavelengths of the correspondent daily
geochemical cycles. As the wavelength of each daily cycle indicates how much the shell grew during that day,
changes in the daily wavelength over time directly translate to daily growth rates. This approach tackles both
the issue of low visibility of daily banding and the time effort of counting daily signals and measuring daily
signal distances over timespans of months to decades. Furthermore, one set of elemental ratio measurements can
provide an age model that is directly applicable to related geochemical proxies, allowing paleoenvironmental
interpretations.
4.2. Before Applying Daydacna
To derive reasonable daily growth rates, some knowledge of the analyzed sample is needed. We interpret and
restrict the selection of wavelengths, which represent daily cycles, based on the following observations, which
need to be reconsidered for each sample before Daydacna is applied. All adaptations can be modified in the
code if the underlying observations are not applicable to the specific sample under consideration; however, if the
Daydacna code is run without adaptation, the following assumptions have to be met to obtain reliable results.
1. One band is one daily cycle:
It has previously been shown that Tridacna shells contain daily banding and daily cycles in Sr/Ca and Mg/Ca,
likely linked to the light-related dependency of the incorporation of these elements in the shell, which could
be connected to photosymbiotic activity (Sano etal.,2012; Warter & Müller,2017; Warter etal.,2018) and/or
light enhanced calcification (Rossbach etal.,2019; Sano etal.,2012). In previous Tridacna culturing studies,
Warter etal.(2018) demonstrated that the culturing period in days was equivalent to the number of bands and
cycles in several El/Ca ratios. The sample presented here (“TOBI”) is a fossil specimen, therefore we cannot
independently verify how much time is contained in the shell. We observed that the Mg/Ca cycles, measured
via LA-ICPMS, coincide with a microscopically detected banding pattern, similar to that observed daily in
scale by Warter etal.(2018). Overall, we are confident that the El/Ca cycles in the range of a few to tens of
μm observed in our fossil Tridacna are daily. The evaluation of the temporal interval of the analyzed cycles is
crucial for successfully applying Daydacna. Other bivalves, for example, mussels, have shown semi-diurnal
elemental cycles caused by semi-diurnal tidal influence (e.g., Sano etal.,2021). To analyze such samples
using Daydacna, the expected time interval per cycle would need to be adapted.
2. Choice of appropriate widths of daily growth:
The range of daily band width observed optically within the analyzed clam fell between 2 and 38μm. Bands
over 30μm wide are mostly apparent in the ontogenetic youngest part of the shell. After the first decade of
life, daily growth bands in TOBI are mostly in the range of 5–25μm. We decided to limit the range in which
correlation maxima are selected to wavelengths below 40μm to include all daily signals but reject long-term
trends, such as seasonal signals. Shell growth rates are variable even within Tridacnids (from a few μm/day
(this study) to 140μm/day (Killam etal.,2021)) and can range higher for other bivalves like Pecten maximus
(e.g., Gillikin etal.,2008) or remain in the range of few μm/day for other calcifiers with daily banding like
foraminifera (Spero etal.,2015). Therefore, it is crucial to study the sample and choose an appropriate range
for daily band selection.
3. Areas displaying macroscopically dark bands coincide with reduced daily band width:
We observed a linkage between the macroscopically dark (translucent) bands and low growth rate (see
Section3.2). The signal of daily cycles in the low growth rate range, close to or below the spot size (i.e., slit
width; 3μm), can be reduced in amplitude due to smoothing effects. Sinusoidal signals with wavelengths of
the spot size/n, with n∈ℕ, cannot be detected. In these areas of reduced daily cycle amplitudes, the daily
signal is often overlain by cyclicities with larger wavelengths. The overlying signal is not reduced in amplitude
and may dominate during the evaluation of correlation maxima in the scalogram, making it more likely to
be automatically selected by the script than the daily signal. Therefore, manual corrections are often needed

Geochemistry, Geophysics, Geosystems
ARNDT ETAL.
10.1029/2023GC010992
14 of 20
in these areas of low growth rate, implying that in such cases the shorter wavelengths should preferably be
selected manually.
4. Shifts in growth rate are gradual:
Based on microscope images, shifts in daily band widths are gradual rather than abrupt such that slight differ-
ences in width between individual daily bands can be averaged out without biasing the age model overall.
As such, smoothed correlation curves of the scalogram are used to select the correlation maxima most likely
related to daily cycles and the growth rate pathways are smoothed again to exclude unrealistically abrupt
short-term variability in the suggested growth rate.
4.3. Daydacna Limitations
A prerequisite for the effective use of our Daydacna script for internal age assessment is the availability of appropriate
data sets on shell structure (imaging) and geochemical composition (Section4.2). The major limitation of the script is
that not all auto-selected correlation maxima are necessarily related to daily cycles, which necessitates some manual
adjustment. In the case of the specimen studied here, we observed that the daily cycle signal is usually well visible in
the scalogram. The lower limit of cyclic signal detection is twice the sampling frequency (e.g., Åström,1969), allow-
ing the detection of cycles with wavelengths down to 0.7μm in our data. But due to smoothing effects caused by the
fact that the spot size (3μm) is larger than the sampling steps (∼0.35μm), we cannot detect sinusoidal signals with
wavelengths of the spot size/n with n∈ℕ (i.e., 3, 1.5, 1, and 0.75μm). Cyclic signal detection below the spot size is
both severely reduced in amplitude and discontinuous and thus less reliable. We recommend using a spot size below
the smallest expected wavelengths to analyze the cyclic signal. For our specimen, optically determined daily banding
as well as detected daily cycle wavelengths are rarely below the spot size. However, cyclic signals with wavelengths
above the spot size also have a smoothing-induced reduction in amplitude, which is more pronounced the closer the
wavelength is to the spot size. The resulting biases in the automatic selection of correlation maxima toward longer
wavelengths can affect the resulting internal age model. This underlines the importance of the manual adjustment
option, which allows manually selected points to replace those automatically selected in the first step.A range of inter-
est is set via Monte Carlo simulation in the case of both manual and automatic selection, in which Daydacna auto-
matically searches for maxima a second time. An exact selection of the minor correlation peaks associated with daily
cycle wavelengths is not needed as the position of the point is subsequently varied via Monte Carlo simulation, such
that similar pathway corrections lead to equivalent results with deviations of no more than a few percentage. However,
it is possible to manually select areas which do not represent daily cycles, which can lead to erroneous growth rates.
The resulting growth model should therefore be checked in a few test cases before applying it to larger data sets.
To assess whether the resultant model is realistic, two approaches can be taken. In areas with countable daily
growth bands, a comparison between the number of daily growth bands and the number of days suggested by the
age model can be conducted to check the accuracy of the model (see results; Figures3 and4). This is especially
suitable for testing the model on shorter intervals within the shell before selecting larger shell segments. In areas
without countable daily bands and across longer areas of the shell, the evenness of daily cycles over time can be
checked via wavelet transformation of the geochemical data onto their timescale. However, note that the wavelet
transformation can only be applied to evenly spaced data. As the measured data are redistributed from distance
to time by Daydacna, the resulting data points will be unevenly distributed. Therefore, resampling of the data
along the interpolated pathways between the measured data points must be conducted beforehand. After rescaling
the geochemical signal over time, daily cycles should be uniform and, by definition, 1day long. In the resulting
wavelet scalogram of the detrended data set, we expect the highest correlation around the one-day wavelength if
the manual selection of the daily wavelength area is accurate (Figure S12 in Supporting InformationS1). If there
are areas where the correlation maxima strongly deviate from the 1-day line, the time contained in this interval
may have been over- or underestimated. In this case, the section of data should be reconsidered by adapting the
manual selection of the pathway such that the resulting scalogram of the daily cycles shows frequencies in the
1-day range.

Geochemistry, Geophysics, Geosystems
ARNDT ETAL.
10.1029/2023GC010992
15 of 20
4.4. Daydacna Applications
4.4.1. El/Ca Variability With Growth Rate
Macroscopically visible darker seasonal bands and thus areas with low growth rates coincide with higher Mg/Ca,
as has previously been shown in other mollusks (e.g., Lorens & Bender,1977; Schleinkofer etal.,2021). Here,
we demonstrate one advantage of deriving a continuous growth rate model by testing whether Mg/Ca and other
elemental ratios correlate with the growth rates determined (Figure7). Mg/Ca values and growth rate correlate
nonlinearly and negatively with an R
2 value of 0.71, increasing to 0.84 in the case of the corresponding smoothed
data set that excludes daily variability. Similarly, Sr/Ca shows a nonlinear and negative trend with an R
2 value of
0.31 for the complete data set and 0.58 for the smoothed data set. Na/Ca shows a nonlinear but positive trend with
a growth rate mostly in the low growth areas, with overall poor correlation coefficients. Both B/Ca and Ba/Ca
show an essentially linear increase with lower growth rates albeit with a shallow slope and lower coefficient of
correlation (R
2 value of 0.07 for both ratios for the untreated data and 0.1 for the smoothed data). By correlating
Ba/Ca to growth rate, we could, however, confirm our previous observation (Figures6c and6e) that Ba/Ca vari-
ability increases in low growth areas. Similarly, the Mg/Ca and Sr/Ca variability is higher for low growth rates.
4.4.2. Seasonal Environmental Impacts
Evaluating seasonal paleoenvironmental data on daily resolved time scales rather than over distance improves
data comparability over several years and between different organisms and time periods. In the following section,
we briefly discuss the sub-seasonally-resolved paleoenvironmental information that can be gleaned from different
Figure 7. Correlation curves of growth rate versus El/Ca ratios of all three LA tracks. All data (n=35,061) are displayed on the left in semi-transparent blue dots
to better show the data density, while the data smoothed over 120 points (n=294) are displayed on the right in semi-transparent circles. Dashed lines represent the
upper and lower 95% prediction band (95% chance that the next data point is within that area). The 95% confidence interval lies almost indistinguishably close to the
regression line (orange) due to the large data set and is therefore not displayed.

Geochemistry, Geophysics, Geosystems
ARNDT ETAL.
10.1029/2023GC010992
16 of 20
elemental ratios. Overall, an investigation across greater time spans would be needed for an in depth paleoenviron-
mental interpretation, as reproducible seasonal patterns would become more evident. However, the observations,
especially from track 3, already point towards a long-term trend in shell geochemistry with seasonal variability.
The low growth bands, visible as dark bands in the shell, occur typically twice a year and are associated with
long-term changes in Mg/Ca, Sr/Ca, Ba/Ca, and Na/Ca. Mg/Ca and to a lesser extent Sr/Ca values are increased
in low growth areas (Figure7), whereas Na/Ca decreases in these bands. The measured Ba/Ca is characterized by
a higher degree of variability in seasonal intervals of low growth.
Mg/Ca displays a strong relationship with growth rate (Section4.4.1, Figure7). Previous studies link Mg/Ca
ratios in Tridacna to temperature (Arias-Ruiz etal.,2017; Ayling etal.,2015; Warter etal., 2015) as well as
light availability and physiological stress, especially on short (daily) time scales (Warter etal.,2018). In Arctica
islandica shells, a correlation between increased Mg and increased organics (in the form of the organic matrix)
has been observed (Schöne etal.,2010) as well as a dependency on the crystal structure (Schöne etal.,2013). In
Tridacna, Mg/Ca has also been suggested to be growth-dependent given the increasing Mg/Ca with ontogenetic
age (e.g., Elliot etal.,2009; Warter etal.,2015). We propose that the growth rate also impacts Mg/Ca at much
shorter time scales, from daily to seasonal. On the daily level, the Mg/Ca ratios provide the clearest of the El/
Ca cycles. This might be due to the impact of reduced growth during the night, characterized by elevated Mg/Ca
values, and vice versa (cf., Warter etal.,2018). In bivalves, reduced growth and elevated Mg/Ca values can also
be coupled to a relative increase in organic content (Schleinkofer etal.,2021).
More broadly, these findings suggest that it is unlikely that a single environmental parameter governs shell Mg/
Ca. Instead, both temperature and light are impacting the growth rate, crystal structure, and possibly incorpo-
ration of organics into the organism shell and thus affect Mg/Ca values. Decoupling the effect of temperature,
light, and growth rate in experimental setups would greatly aid the development of the proxy. However, given that
Mg/Ca is dominantly driven by growth rate (or some factor that correlates with growth rate), irrespective of the
primary cause of the change in growth rate, the Mg/Ca “proxy” may find utility as a tracer of the combined set
of processes that can impact the calcification rate. We expect that during the rainy season, irradiance is reduced
because of reduced direct sunlight and higher turbidity. This causes reduced growth and high Mg/Ca values. Mg/
Ca may thus be used to track growth rate changes, possibly linked to seasonal rainfall. Hence, the recording of the
rainy season, coupled to the passing of the Intertropical Convergence Zone (ITCZ) twice a year, may make Mg/
Ca in Tridacna a potential tracer for past ITCZ positioning in tropical regions.
Sr/Ca values show a slight increase in low growth areas, but no strong correlation between growth rate and Sr/
Ca could be established. Contradicting observations have been documented regarding the interpretation of Sr/Ca
as a paleoenvironmental proxy in Tridacna. While some studies show a partly species-dependent, inverse corre-
lation between Sr/Ca and SST (Mei etal.,2018; Yan etal.,2013), other studies found no significant correlation
(Arias-Ruiz etal.,2017; Batenburg etal.,2011; Elliot etal.,2009; Warter etal.,2015). On a smaller scale, the
diurnal Sr/Ca cycles have been linked to diurnal changes in irradiance (Hori etal.,2015; Sano etal.,2012). Sr/Ca
variability has also been linked to physiological performance and growth (Carré etal.,2006; Gillikin etal.,2005).
In our data, long-term Sr/Ca trends are in phase with Mg/Ca but the correlation to growth rate is weaker in the
former (Figure7). A dependency of Sr/Ca on solar irradiance could explain the high Sr/Ca values associated with
the dark seasonal banding, which we interpret as growth during the rainy season in which the reef experiences
reduced direct sunlight and increased water turbidity.
Ba/Ca in Tridacna has been proposed as an indicator of primary productivity (Arias-Ruiz etal.,2017; Elliot
etal.,2009; Hori etal.,2015) and other mollusks (e.g., Fröhlich etal.,2022; Gillikin etal.,2008; Marali etal.,2017).
Short and intense Ba/Ca peaks have also been connected to storm events in Tridacna (Komagoe etal.,2018) and
Tsunamis in mussels (Sano etal.,2021). A higher variability during low growth (Figure7) might indicate an
increased storm activity during the rainy season, additionally impacting turbidity and nutrient availability through
influx and resuspension and thus introducing higher variability in primary productivity.
Na/Ca is yet to be established as a paleoenvironmental proxy in Tridacna. Na/Ca has been positively corre-
lated with salinity (negatively to freshening) in marine mollusks (Findlater etal.,2014) and estuarine barnacles
(Gordon etal.,1970). Similarly, Na/Ca was suggested to be a proxy for salinity in foraminifera (Wit etal.,2013).
However, the very weak relationship of Na/Ca to salinity in core-top samples and similarly weak relationships to
other environmental factors, such as temperature, seawater [CO3
2−], and bottom water Ωcalcite, make its successful
application as a salinity proxy unlikely in most settings (Gray etal.,2023). If the Na/Ca-salinity relationship in

Geochemistry, Geophysics, Geosystems
ARNDT ETAL.
10.1029/2023GC010992
17 of 20
mollusks is indeed expressed more strongly than in foraminifera, the Na/Ca decrease observed in periods with
low growth rates could be related to a freshening happening in the rainy season. However, more experimental data
are needed to robustly interpret this elemental ratio.
5. Conclusions and Outlook
We present our Python script Daydacna, which enables an age model to be created, using CWT analysis of
elemental data of samples with daily resolution chemical banding, applied here to a fossil Tridacna shell. A key
advantage of this approach is that the spatially resolved measured data are assigned to regular time intervals using
an age model based on the data itself. This approach avoids potential errors introduced by matching the geochem-
ical data set to the optical data set and reduces the dependence on the optical resolvability of daily banding,
making it both more time efficient and applicable to more samples compared to optical approaches. By applying
the age, seasonal changes in environmental proxy data over time can be assessed at the daily temporal resolution.
Furthermore, the daily growth rates are quantified across the shell and can be compared to elemental ratios. We
show that Mg/Ca is growth rate dependent and suggest considering the influence of metabolism and growth rate
driven incorporation of organic matter and trace elements in addition to the influence of external environmental
factors (e.g., temperature or light) impacting the element incorporation into the shell. Further in-depth evaluation
of growth rate changes, how they are impacted by environmental changes and how they affect elemental incor-
poration is needed, based on culturing studies with controlled environmental settings (cf., Warter etal.,2018).
The Daydacna script with its CWT approach to reconstruct daily cycles provides a basis for further paleoseason-
ality and growth rate studies in Tridacna (and potentially other long-lived mollusks with daily chemical growth
bands). Ultimately, we envisage that this approach of combining daily resolved geochemical data with a daily
growth model will facilitate the interpretation of geochemical data sets from clams in terms of paleo(extreme)
weather evaluation, given that several El/Ca systems have been shown to relate closely to light intensity and/
or chemical and physical properties of the seawater (e.g., Arias-Ruiz et al., 2017; Ayling etal., 2015; Hori
etal.,2015; Sano etal.,2012; Warter etal.,2015,2018). Tridacna shells can provide decade-long records of the
tropics and subtropics from the early Miocene onwards (Harzhauser etal.,2008). They are therefore especially
well-suited for the generation of decade-long paleo(extreme)weather and seasonality records in the context of
Neogene (sub)tropical climate reconstructions.
Although Daydacna was designed to evaluate daily cycles in Tridacna samples, it can be applied to other
archives, types of data sets, or cyclicities. Potential high-resolution archives suitable for Daydacna include other
bivalves, corals, coralline algae, otoliths, other marine calcifiers, speleothems and teeth. If geochemical data are
not retrievable in sub-daily resolution, other types of cyclic data sets can be used to evaluate daily cycles with
Daydacna, such as a lightness or color curve across a sample which shows distinct daily banding throughout the
shell. If the lowest measurable cyclicity in the data set is tidal, yearly, or even in the scale of Milankovitch cycles,
the script could still be applied by adjusting the defined length for one cycle, as long as the time represented by
one cycle is well known. Lastly, if low frequency cycles of known time spans are available within the sample
(e.g., yearly cycles), Daydacna can also be used to assess which time interval fits best to the high frequency
cycles of the same data set (e.g., diurnal or semi-diurnal).
Data Availability Statement
The laser-ablation data fed into Daydacna to create the internal age models in this study are available at Zenodo
with licence CC BY 4.0 (Arndt etal.,2023). Version 1 of Daydacna used to create an internal age model and
redistribute data from distance to time is available at Zenodo with licence CC BY 4.0 (Arndt & Coenen,2023).
References
Aharon, P. (1991). Recorders of reef environment histories: Stable isotopes in corals, giant clams, and calcareous algae. Coral Reefs, 10(2), 71–90.
https://doi.org/10.1007/BF00571826
Arias-Ruiz, C., Elliot, M., Bézos, A., Pedoja, K., Husson, L., Cahyarini, S. Y., etal. (2017). Geochemical fingerprints of climate variation and
the extreme La Niña 2010–11 as recorded in a Tridacna squamosa shell from Sulawesi, Indonesia. Palaeogeography, Palaeoclimatology,
Palaeoecology, 487, 216–228. https://doi.org/10.1016/j.palaeo.2017.08.037
Arndt, I., & Coenen, D. (2023). Daydacna [Code]. Zenodo. https://doi.org/10.5281/zenodo.8334594
Acknowledgments
We are grateful for a scholarship of the
Stiftung Polytechnische Gesellschaft that
provided financial support for the corre-
sponding author as well as funding via the
Deutsche Forschungsgemeinschaft grant
(DFG MU 3739/6-1). We thank Linda
Marko, Alexander Schmidt, and Richard
Albert for technical support during
LA-ICPMS measurements, data reduction
and microscope imaging. Maria Bladt,
Sören Tholen, and Niels Parawitz are
thanked for the preparation of the sample
slabs and thin sections. Our gratitude also
goes to Renate Rabenstein for X-ray and
μCT imaging and Wolfgang Schiller for
fluorescence microscope imaging. We
thank Rainer Petschick and Ruben Ritt-
berger for assisting with the visualization
of the XRD and ATR-FTIR data, respec-
tively. We gratefully acknowledge discus-
sions with Viola Warter and Max Furs-
man. Aspects of this research (DC, DE,
WM) were co-funded through the VeWA
consortium (Past Warm Periods as Natu-
ral Analogues of our high-CO2 Climate
Future) by the LOEWE programme of
the Hessen Ministry of Higher Education,
Research and the Arts, Germany. FIERCE
is financially supported by the Wilhelm
and Else Heraeus Foundation and by the
Deutsche Forschungsgemeinschaft (DFG:
INST 161/921-1 FUGG, INST 161/923-1
FUGG and INST 161/1073-1 FUGG),
which is gratefully acknowledged. This
is FIERCE contribution No. 140. Open
Access funding enabled and organized by
Projekt DEAL.

Geochemistry, Geophysics, Geosystems
ARNDT ETAL.
10.1029/2023GC010992
18 of 20
Arndt, I., Coenen, D., Evans, D., Renema, W., & Müller, W. (2023). Supplementary Data to ‘Quantifying sub-seasonal growth rate changes in
fossil giant clams using wavelet transformation of daily Mg/Ca cycles’ in Geochemistry, Geophysics, Geosystems [Dataset]. Zenodo. https://
doi.org/10.5281/zenodo.7755711
Åström, K. J. (1969). On the choice of sampling rates in parametric identification of time series. Information Sciences, 1(3), 273–278. https://
doi.org/10.1016/S0020-0255(69)80013-7
Ayling, B. F., Chappell, J., Gagan, M. K., & McCulloch, M. T. (2015). ENSO variability during MIS 11 (424–374 ka) from Tridacna gigas at
Huon Peninsula, Papua New Guinea. Earth and Planetary Science Letters, 431, 236–246. https://doi.org/10.1016/j.epsl.2015.09.037
Batenburg, S. J., Reichart, G.-J., Jilbert, T., Janse, M., Wesselingh, F. P., & Renema, W. (2011). Interannual climate variability in the Miocene:
High resolution trace element and stable isotope ratios in giant clams. Palaeogeography, Palaeoclimatology, Palaeoecology, 306(1–2), 75–81.
https://doi.org/10.1016/j.palaeo.2011.03.031
Carré, M., Bentaleb, I., Bruguier, O., Ordinola, E., Barrett, N. T., & Fontugne, M. (2006). Calcification rate influence on trace element
concentrations in aragonitic bivalve shells: Evidences and mechanisms. Geochimica et Cosmochimica Acta, 70(19), 4906–4920. https://doi.
org/10.1016/j.gca.2006.07.019
Carré, M., & Cheddadi, R. (2017). Seasonality in long-term climate change. Quaternaire. Revue de l’Association Française Pour l’étude Du
Quaternaire, 28(2), 173–177. https://doi.org/10.4000/quaternaire.8018
Caswell, T. A., Droettboom, M., Lee, A., de Andrade, E. S., Hoffmann, T., Klymak, J., etal. (2022). matplotlib/matplotlib: REL: V3.5.2 [Soft-
ware]. Zenodo. https://doi.org/10.5281/zenodo.6513224
Cauquoin, A., Werner, M., & Lohmann, G. (2019). Water isotopes – Climate relationships for the mid-Holocene and preindustrial period simu-
lated with an isotope-enabled version of MPI-ESM. Climate of the Past, 15(6), 1913–1937. https://doi.org/10.5194/cp-15-1913-2019
Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368),
829–836. https://doi.org/10.2307/2286407
de Winter, N. J., Goderis, S., Malderen, S. J. M. V., Sinnesael, M., Vansteenberge, S., Snoeck, C., etal. (2020). Subdaily-scale chemical variability
in a Torreites Sanchezi rudist shell: Implications for rudist paleobiology and the Cretaceous day-night cycle. Paleoceanography and Paleocli-
matology, 35(2), e2019PA003723. https://doi.org/10.1029/2019PA003723
Driscoll, R., Elliot, M., Russon, T., Welsh, K., Yokoyama, Y., & Tudhope, A. (2014). ENSO reconstructions over the past 60 ka using giant clams
(Tridacna sp.) from Papua New Guinea. Geophysical Research Letters, 41(19), 6819–6825. https://doi.org/10.1002/2014GL061446
Duprey, N., Lazareth, C. E., Dupouy, C., Butscher, J., Farman, R., Maes, C., & Cabioch, G. (2015). Calibration of seawater temperature
and δ
18Oseawater signals in Tridacna maxima’s δ
18Oshell record based on in situ data. Coral Reefs, 34(2), 437–450. https://doi.org/10.1007/
s00338-014-1245-z
Elliot, M., Welsh, K., Chilcott, C., McCulloch, M., Chappell, J., & Ayling, B. (2009). Profiles of trace elements and stable isotopes derived from
giant long-lived Tridacna gigas bivalves: Potential applications in paleoclimate studies. Palaeogeography, Palaeoclimatology, Palaeoecology,
280(1–2), 132–142. https://doi.org/10.1016/j.palaeo.2009.06.007
Evans, D., & Müller, W. (2018). Automated extraction of a five-year LA-ICP-MS trace element data set of ten common glass and carbonate refer-
ence materials: Long-term data quality, optimisation and laser cell homogeneity. Geostandards and Geoanalytical Research, 42(2), 159–188.
https://doi.org/10.1111/ggr.12204
Fietzke, J., & Frische, M. (2016). Experimental evaluation of elemental behavior during LA-ICP-MS: Influences of plasma conditions and limits
of plasma robustness. Journal of Analytical Atomic Spectrometry, 31(1), 234–244. https://doi.org/10.1039/C5JA00253B
Findlater, G., Shelton, A., Rolin, T., & Andrews, J. (2014). Sodium and strontium in mollusc shells: Preservation, palaeosalinity and palaeotem-
perature of the Middle Pleistocene of eastern England. Proceedings of the Geologists’ Association, 125(1), 14–19. https://doi.org/10.1016/j.
pgeola.2013.10.005
Foster, L. C., Andersson, C., Høie, H., Allison, N., Finch, A. A., & Johansen, T. (2008). Effects of micromilling on δ
18O in biogenic aragonite.
Geochemistry, Geophysics, Geosystems, 9(4), Q04013. https://doi.org/10.1029/2007GC001911
Fröhlich, L., Siebert, V., Walliser, E. O., Thébault, J., Jochum, K. P., Chauvaud, L., & Schöne, B. R. (2022). Ba/Ca profiles in shells of Pecten
maximus – A proxy for specific primary producers rather than bulk phytoplankton. Chemical Geology, 593, 120743. https://doi.org/10.1016/j.
chemgeo.2022.120743
Füllenbach, C. S., Schöne, B. R., Shirai, K., Takahata, N., Ishida, A., & Sano, Y. (2017). Minute co-variations of Sr/Ca ratios and microstructures
in the aragonitic shell of Cerastoderma edule (Bivalvia) – Are geochemical variations at the ultra-scale masking potential environmental
signals? Geochimica et Cosmochimica Acta, 205, 256–271. https://doi.org/10.1016/j.gca.2017.02.019
Gannon, M. E., Pérez-Huerta, A., Aharon, P., & Street, S. C. (2017). A biomineralization study of the Indo-Pacific giant clam Tridacna gigas.
Coral Reefs, 36(2), 503–517. https://doi.org/10.1007/s00338-016-1538-5
Garbe-Schönberg, D., & Müller, S. (2014). Nano-particulate pressed powder tablets for LA-ICP-MS. Journal of Analytical Atomic Spectrometry,
29(6), 990–1000. https://doi.org/10.1039/C4JA00007B
Gillikin, D. P., Lorrain, A., Navez, J., Taylor, J. W., André, L., Keppens, E., etal. (2005). Strong biological controls on Sr/Ca ratios in aragonitic
marine bivalve shells. Geochemistry, Geophysics, Geosystems, 6(5), Q05009. https://doi.org/10.1029/2004GC000874
Gillikin, D. P., Lorrain, A., Paulet, Y.-M., André, L., & Dehairs, F. (2008). Synchronous barium peaks in high-resolution profiles of calcite and
aragonite marine bivalve shells. Geo-Marine Letters, 28(5), 351–358. https://doi.org/10.1007/s00367-008-0111-9
Gordon, C. M., Carr, R. A., & Larson, R. E. (1970). The influence of environmental factors on the sodium and manganese content of Barnacle
shells. Limnology & Oceanography, 15(3), 461–466. https://doi.org/10.4319/lo.1970.15.3.0461
Goupillaud, P., Grossmann, A., & Morlet, J. (1984). Cycle-octave and related transforms in seismic signal analysis. Geoexploration, 23(1),
85–102. https://doi.org/10.1016/0016-7142(84)90025-5
Gray, W. R., Evans, D., Henehan, M., Weldeab, S., Lea, D. W., Müller, W., & Rosenthal, Y. (2023). Sodium incorporation in foraminiferal calcite:
An evaluation of the Na/Ca salinity proxy and evidence for multiple Na-bearing phases. Geochimica et Cosmochimica Acta, 348, 152–164.
https://doi.org/10.1016/j.gca.2023.03.011
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., etal. (2020). Array programming with NumPy.
Nature, 585(7825), 357–362. https://doi.org/10.1038/s41586-020-2649-2
Harzhauser, M., Mandic, O., Piller, W. E., Reuter, M., & Kroh, A. (2008). Tracing back the origin of the Indo-Pacific Mollusc Fauna: Basal Tridacninae
from the Oligocene and Miocene of the Sultanate of Oman. Palaeontology, 51(1), 199–213. https://doi.org/10.1111/j.1475-4983.2007.00742.x
Hori, M., Sano, Y., Ishida, A., Takahata, N., Shirai, K., & Watanabe, T. (2015). Middle Holocene daily light cycle reconstructed from the stron-
tium/calcium ratios of a fossil giant clam shell. Scientific Reports, 5(1), 8734. https://doi.org/10.1038/srep08734
Jantzen, C., Wild, C., El-Zibdah, M., Roa-Quiaoit, H. A., Haacke, C., & Richter, C. (2008). Photosynthetic performance of giant clams, Tridacna
maxima and T. squamosa, Red Sea. Marine Biology, 155(2), 211–221. https://doi.org/10.1007/s00227-008-1019-7

Geochemistry, Geophysics, Geosystems
ARNDT ETAL.
10.1029/2023GC010992
19 of 20
Jochum, K. P., Scholz, D., Stoll, B., Weis, U., Wilson, S. A., Yang, Q., etal. (2012). Accurate trace element analysis of speleothems and biogenic
calcium carbonates by LA-ICP-MS. Chemical Geology, 318–319, 31–44. https://doi.org/10.1016/j.chemgeo.2012.05.009
Jochum, K. P., Stoll, B., Herwig, K., Willbold, M., Hofmann, A. W., Amini, M., etal. (2006). MPI-DING reference glasses for in situ microanal-
ysis: New reference values for element concentrations and isotope ratios. Geochemistry, Geophysics, Geosystems, 7(2), Q02008. https://doi.
org/10.1029/2005GC001060
Jochum, K. P., Weis, U., Stoll, B., Kuzmin, D., Yang, Q., Raczek, I., etal. (2011). Determination of reference values for NIST SRM 610–617 glasses
following ISO guidelines. Geostandards and Geoanalytical Research, 35(4), 397–429. https://doi.org/10.1111/j.1751-908X.2011.00120.x
Killam, D., Al-Najjar, T., & Clapham, M. (2021). Giant clam growth in the Gulf of Aqaba is accelerated compared to fossil populations. Proceed-
ings of the Royal Society B: Biological Sciences, 288(1957), 20210991. https://doi.org/10.1098/rspb.2021.0991
Komagoe, T., Watanabe, T., Shirai, K., Yamazaki, A., & Uematu, M. (2018). Geochemical and microstructural signals in giant clam Tridacna
maxima recorded typhoon events at Okinotori Island, Japan. Journal of Geophysical Research: Biogeosciences, 123(5), 1460–1474. https://
doi.org/10.1029/2017JG004082
Kunzmann, A. (2008). Physiological performance of giant clams (Tridacna spec.) in a recirculation system. In ICRS Conference Proceedings
(pp.316–320). Retrieved from https://nsuworks.nova.edu/occ_icrs/1
Longerich, H. P., Jackson, S. E., & Günther, D. (1996). Inter-laboratory note. Laser ablation inductively coupled plasma mass spectrometric
transient signal data acquisition and analyte concentration calculation. Journal of Analytical Atomic Spectrometry, 11(9), 899–904. https://doi.
org/10.1039/JA9961100899
Lorens, R. B., & Bender, M. L. (1977). Physiological exclusion of magnesium from Mytilus edulis calcite. Nature, 269, 5631–5794. https://doi.
org/10.1038/269793a0
Ma, X., Yan, H., Fei, H., Liu, C., Shi, G., Huang, E., etal. (2020). A high-resolution δ
18O record of modern Tridacna gigas bivalve and its paleoen-
vironmental implications. Palaeogeography, Palaeoclimatology, Palaeoecology, 554, 109800. https://doi.org/10.1016/j.palaeo.2020.109800
Marali, S., Schöne, B. R., Mertz-Kraus, R., Griffin, S. M., Wanamaker, A. D., Matras, U., & Butler, P.G. (2017). Ba/Ca ratios in shells of Arctica
islandica – Potential environmental proxy and crossdating tool. Palaeogeography, Palaeoclimatology, Palaeoecology, 465, 347–361. https://
doi.org/10.1016/j.palaeo.2015.12.018
Mei, Y., Shao, D., Wang, Y., Yang, Z., Yang, W. Q., Gao, Y., etal. (2018). Measurement of Sr/Ca ratio in Tridacna spp. Shells from South
China Sea: A comparison of SR-XRF and ICP-OES analysis methods. Spectroscopy and Spectral Analysis, 38(5), 1640–1647. https://doi.
org/10.3964/j.issn.1000-0593(2018)05-1640-08
Meyer, M. C., Faber, R., & Spötl, C. (2006). The WinGeol Lamination Tool: New software for rapid, semi-automated analysis of laminated
climate archives. The Holocene, 16(5), 753–761. https://doi.org/10.1191/0959683606hl969rr
Morlet, J., Arens, G., Fourgeau, E., & Glard, D. (1982). Wave propagation and sampling theory; Part I: Complex signal and scattering in multi-
layered media. Geophysics, 47(2), 203–221. https://doi.org/10.1190/1.1441328
Müller, W., Shelley, M., Miller, P., & Broude, S. (2009). Initial performance metrics of a new custom-designed ArF excimer LA-ICPMS system
coupled to a two-volume laser-ablation cell. Journal of Analytical Atomic Spectrometry, 24(2), 209–214. https://doi.org/10.1039/B805995K
Paton, C., Hellstrom, J., Paul, B., Woodhead, J., & Hergt, J. (2011). Iolite: Freeware for the visualisation and processing of mass spectrometric
data. Journal of Analytical Atomic Spectrometry, 26(12), 2508–2518. https://doi.org/10.1039/C1JA10172B
Pätzold, J., Heinrichs, J. P., Wolschendorf, K., & Wefer, G. (1991). Correlation of stable oxygen isotope temperature record with light attenuation
profiles in reef-dwelling Tridacna shells. Coral Reefs, 10(2), 65–69. https://doi.org/10.1007/BF00571825
Reback, J., jbrockmendel, McKinney, W., Bossche, J. V. D., Roeschke, M., Augspurger, T., etal. (2022). pandas-dev/pandas: Pandas 1.4.3 [Soft-
ware]. Zenodo. https://doi.org/10.5281/zenodo.6702671
Renema, W., Warter, V., Novak, V., Young, J. R., Marshall, N., & Hasibuan, F. (2015). Ages of Miocene fossil localities in the northern Kutai
Basin (East Kalimantan, Indonesia). PALAIOS, 30(1), 26–39. https://doi.org/10.2110/palo.2013.127
Rossbach, S., Saderne, V., Anton, A., & Duarte, C. M. (2019). Light-dependent calcification in Red Sea giant clam Tridacna maxima. Biogeo-
sciences, 16(13), 2635–2650. https://doi.org/10.5194/bg-16-2635-2019
Sano, Y., Kobayashi, S., Shirai, K., Takahata, N., Matsumoto, K., Watanabe, T., etal. (2012). Past daily light cycle recorded in the strontium/
calcium ratios of giant clam shells. Nature Communications, 3(1), 761. https://doi.org/10.1038/ncomms1763
Sano, Y., Okumura, T., Murakami-Sugihara, N., Tanaka, K., Kagoshima, T., Ishida, A., etal. (2021). Inf luence of normal tide and the Great
Tsunami as recorded through hourly-resolution micro-analysis of a mussel shell. Scientific Reports, 11(1), 19874. https://doi.org/10.1038/
s41598-021-99361-2
Schleinkofer, N., Evans, D., Wisshak, M., Büscher, J. V., Fiebig, J., Freiwald, A., etal. (2021). Host-influenced geochemical signature in the
parasitic foraminifera Hyrrokkin sarcophaga. Biogeosciences, 18(16), 4733–4753. https://doi.org/10.5194/bg-18-4733-2021
Schmidt, G. A., Annan, J. D., Bartlein, P.J., Cook, B. I., Guilyardi, E., Hargreaves, J. C., etal. (2014). Using palaeo-climate comparisons to
constrain future projections in CMIP5. Climate of the Past, 10(1), 221–250. https://doi.org/10.5194/cp-10-221-2014
Schöne, B. R., Dunca, E., Fiebig, J., & Pfeiffer, M. (2005). Mutvei’s solution: An ideal agent for resolving microgrowth structures of biogenic
carbonates. Palaeogeography, Palaeoclimatology, Palaeoecology, 228(1–2), 149–166. https://doi.org/10.1016/j.palaeo.2005.03.054
Schöne, B. R., Radermacher, P., Zhang, Z., & Jacob, D. E. (2013). Crystal fabrics and element impurities (Sr/Ca, Mg/Ca, and Ba/Ca) in shells of
Arctica islandica – Implications for paleoclimate reconstructions. Palaeogeography, Palaeoclimatology, Palaeoecology, 373, 50–59. https://
doi.org/10.1016/j.palaeo.2011.05.013
Schöne, B. R., Zhang, Z., Jacob, D., Gillikin, D. P., Tütken, T., Garbe-Schönberg, D., etal. (2010). Effect of organic matrices on the determi-
nation of the trace element chemistry (Mg, Sr, Mg/Ca, Sr/Ca) of aragonitic bivalve shells (Arctica islandica) – Comparison of ICP-OES and
LA-ICP-MS data. Geochemical Journal, 44(1), 23–37. https://doi.org/10.2343/geochemj.1.0045
Seabold, S., & Perktold, J. (2010). Statsmodels: Econometric and statistical modeling with Python. In Proceedings of the Python in Science
Conferences (pp.92–96). https://doi.org/10.25080/Majora-92bf1922-011
Spero, H. J., Eggins, S. M., Russell, A. D., Vetter, L., Kilburn, M. R., & Hönisch, B. (2015). Timing and mechanism for intratest Mg/Ca variability
in a living planktic foraminifer. Earth and Planetary Science Letters, 409, 32–42. https://doi.org/10.1016/j.epsl.2014.10.030
Tierney, J. E., Poulsen, C. J., Montañez, I. P., Bhattacharya, T., Feng, R., Ford, H. L., etal. (2020). Past climates inform our future. Science,
370(6517), eaay3701. https://doi.org/10.1126/science.aay3701
Torrence, C., & Compo, G. P. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79(1), 61–78. https://
doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2
Warter, V., Erez, J., & Müller, W. (2018). Environmental and physiological controls on daily trace element incorporation in Tridacna crocea from
combined laboratory culturing and ultra-high resolution LA-ICP-MS analysis. Palaeogeography, Palaeoclimatology, Palaeoecology, 496,
32–47. https://doi.org/10.1016/j.palaeo.2017.12.038

Geochemistry, Geophysics, Geosystems
ARNDT ETAL.
10.1029/2023GC010992
20 of 20
Warter, V., & Müller, W. (2017). Daily growth and tidal rhythms in Miocene and modern giant clams revealed via ultra-high resolution LA-ICPMS
analysis—A novel methodological approach towards improved sclerochemistry. Palaeogeography, Palaeoclimatology, Palaeoecology, 465,
362–375. https://doi.org/10.1016/j.palaeo.2016.03.019
Warter, V., Müller, W., Wesselingh, F. P., Todd, J. A., & Renema, W. (2015). Late Miocene seasonal to subdecadal climate variability in the Indo-
West Pacific (East Kalimantan, Indonesia) preserved in giant clams. Palaios, 30(1), 66–82. https://doi.org/10.2110/palo.2013.061
Wit, J. C., de Nooijer, L. J., Wolthers, M., & Reichart, G. J. (2013). A novel salinity proxy based on Na incorporation into foraminiferal calcite.
Biogeosciences, 10(10), 6375–6387. https://doi.org/10.5194/bg-10-6375-2013
Yan, H., Liu, C., An, Z., Yang, W., Yang, Y., Huang, P., etal. (2020). Extreme weather events recorded by daily to hourly resolution biogeo-
chemical proxies of marine giant clam shells. Proceedings of the National Academy of Sciences, 117(13), 7038–7043. https://doi.org/10.1073/
pnas.1916784117
Yan, H., Shao, D., Wang, Y., & Sun, L. (2013). Sr/Ca profile of long-lived Tridacna gigas bivalves from South China Sea: A new high-resolution
SST proxy. Geochimica et Cosmochimica Acta, 112, 52–65. https://doi.org/10.1016/j.gca.2013.03.007
Zhao, N., Yan, H., Luo, F., Yang, Y., Liu, S., Zhou, P., et al. (2023). Daily growth rate variation in Tridacna shells as a record of tropical
cyclones in the South China Sea: Palaeoecological implications. Palaeogeography, Palaeoclimatology, Palaeoecology, 615, 111444. https://
doi.org/10.1016/j.palaeo.2023.111444