# StripesCounter: A new image software for increment measurement in paleoclimate archives

Clara Boutreux, Patrick Brockmann, Mary Elliot, Matthieu Carré, Marc Gosselin

<figure><img src="image_1.png" /></figure>

## To cite this version:

Clara Boutreux, Patrick Brockmann, Mary Elliot, Matthieu Carré, Marc Gosselin. StripesCounter: A new image software for increment measurement in paleoclimate archives. Computers & Geosciences, 2026, pp.106104. 10.1016/j.cageo.2026.106104. hal-05453811

HAL Id: hal-05453811

https://hal.science/hal-05453811v1

Submitted on 12 Jan 2026

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
Journal Pre-proof

StripesCounter: A new image software for increment measurement in paleoclimate archives

Clara Boutreux, Patrick Brockmann, Mary Elliot, Matthieu Carré, Marc Gosselin

PII: S0098-3004(26)00001-4

DOI: https://doi.org/10.1016/j.cageo.2026.106104

Reference: CAGEO 106104

To appear in: *Computers and Geosciences*

Received Date: 12 September 2025

Revised Date: 9 December 2025

Accepted Date: 4 January 2026

<figure><img src="image_3.png" /></figure>

Please cite this article as: Boutreux, C., Brockmann, P., Elliot, M., Carré, M., Gosselin, M., StripesCounter: A new image software for increment measurement in paleoclimate archives, *Computers and Geosciences*, https://doi.org/10.1016/j.cageo.2026.106104.

This is a PDF of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability. This version will undergo additional copyediting, typesetting and review before it is published in its final form. As such, this version is no longer the Accepted Manuscript, but it is not yet the definitive Version of Record; we are providing this early version to give early visibility of the article. Please note that Elsevier's sharing policy for the Published Journal Article applies to this version, see: https://www.elsevier.com/about/policies-and-standards/sharing#4-published-journal-article. Please also note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

© 2026 Published by Elsevier Ltd.
StripesCounter: A new image software for increment measurement in paleoclimate archives

Clara Boutreux 1ª, Patrick Brockmann 2ᵇ, Mary Elliot 3ª, Matthieu Carré 4ᶜ and Marc Gosselin

5ᵈ

ᵃ Nantes Université, Univ Angers, Le Mans Université, CNRS, Laboratoire de Planétologie et Géosciences, LPG

UMR 6112, 44300 Nantes, France, ORCID(s): 0009-0001-5221-6829

ᵇ Laboratoire des Sciences du Climat et de l'Environnement-IPSL, Unité Mixte CEA-CNRS-UVSQ (UMR 8212),

Université Paris-Saclay, Orme des Merisiers, Gif-sur-Yvette, France

ᶜ Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques

ᵈ UMR 6566 Centre de Recherche en Archéologie, Archéosciences, Histoire, Campus de Beaulieu, Avenue du

Général Leclerc, 35000 Rennes, France

ARTICLE INFO

Keywords:

Sclerochronology

Increment

Climate

Age model

Bivalve

ENSO

Authorship contribution statement

Clara Boutreux (Author 1): developed the methodology, performed the analyses and wrote the manuscript. Patrick

Brockmann (Author 2): developed the software and programming code from start to finish. Mary Elliot (Author 3):

provided financial support and contributed to methodology, corrections, and manuscript revisions. Matthieu Carré
30 (Author 4): contributed to methodology and manuscript revisions. Marc Gosselin (Author 5): contributed to methodology and manuscript revisions.
31

32
33 ABSTRACT

34
35 Most natural paleoclimate archives are accretionary material presenting periodic structures that bear environmental 36 and/or chronological information. Here we present StripesCounter, an open access Python software designed for 37 automated banding detection and measurement. As a study case, 16-year long profiles of daily growth increment 38 measurements were conducted on a modern shell of the giant clam *Tridacna gigas*. High resolution images of shell 39 thin sections were obtained using a confocal laser scanning microscopy and processed using StripesCounter. We 40 demonstrate that StripesCounter provides highly reproducible and accurate results. The long time series of daily 41 increments indicate that *Tridacna gigas* growth is strongly modulated by seasonal oceanographic variations, reflecting 42 changes in sea surface temperature, precipitation, and salinity. Notably, growth profiles reveal semi-annual variations 43 related to semi-annual variations in environmental factors, potentially linked to ENSO events. This automated growth 44 increment analysis can be extended to other archives with cyclic structures, including tree rings, corals, and other 45 biogenic or abiotic laminated materials. StripesCounter offers a powerful and accessible tool for generating long high- 46 resolution, temporally explicit datasets, opening new perspectives for investigating rapid environmental changes 47 across diverse ecosystems and geological timescales.
48
49
50
## 1. Introduction

Incrementally growing biological archives, such as trees, corals, and bivalve shells, serve as valuable recorders of past environmental variability. These archives often display rhythmic growth patterns, such as annual rings in trees, seasonal density bands in corals, or daily to annual increments in bivalves, that result from biological responses to cyclic environmental forcings (e.g., temperature, tides, light availability, food supply, salinity) (Schöne et al., 2005; Carré et al., 2005; Butler et al., 2010; Van Wynsberge et al., 2017; Killam & Clapham, 2018). This information is not only valuable in its own right, but is also essential to construct robust and high-resolution age models. In geochemical approaches, an accurate age model is critical to assign environmental signals (e.g., δ¹⁸O, Mg/Ca, Sr/Ca) to the correct time point. Without precise age control, reconstructions of past seasonal or interannual climate signals become unreliable (Goodwin et al., 2003). Uncertainty in age models are a key challenge in this field, as errors in temporal alignment can obscure or distort the expression of rapid or cyclical climate events (Füllenbach et al., 2015; Wanamaker et al., 2012).

In dendrochronology, the identification and measurement of growth increments is standardized. Visual cross-dating, ring width measurement, and replication make it possible to construct robust composite chronologies (Speer, 2010). Sclerochronological methods, applied to marine or freshwater biogenic carbonates such as bivalve shells or corals are commonly derived by counting daily or yearly growth bands in shell cross-sections observed under reflected or transmitted light microscopy (Welsh et al., 2011; Arias-Ruiz et al., 2017; Mills et al., 2023). Some bivalve's families such as *Tridacna* spp. are made of successive daily growth increments and offer high-resolution archives of past environments (Yan et al., 2013; Liu et al., 2022; Wen et al., 2024). However, traditional techniques are time-consuming and often lack statistical rigor in uncertainty assessment. Common challenges include (i) manual annotation is labour-intensive and subject to observer bias, particularly when growth increments are faint or closely spaced. (ii) Resolution is constrained by imaging quality and the subjective interpretation of increment boundaries. (iii) Uncertainty is rarely quantified formally, making error propagation through age models difficult to assess or replicate. (iv) Reproducibility across users and specimens is limited when band detection lacks standardisation. These factors can lead to difficulties in differentiating closely spaced growth bands, increasing age model uncertainty (Surge & Schöne, 2013; Butler et al., 2013).

To meet these challenges, we developed a software tool tailored for the automated and semi-automated detection of growth bands in high-resolution images of biogenic carbonates (StripesCounter). Existing tools such as WinDendro
(Regent Instruments, 2009) and CooRecorder (Maxwell & Larsson, 2021), primarily designed for dendrochronology, can be adapted for sclerochronological use but often lack the spatial resolution or user-friendliness required for high-density datasets. StripesCounter was thus designed to offer an accessible, efficient, and resolution-appropriate solution. It comprises two main steps: (1) automatic detection of peaks from grayscale intensity profiles, and (2) manual correction to fine-tune peak positioning.

We tested the application on a modern specimen of *Tridacna gigas*, achieving high-resolution growth increment analysis at daily scale. Due to their specific habitat requirements, *Tridacna* clams are restricted to tropical shallow waters (usually ≤ 20 meters), predominantly associated with coral reefs (Hernawan, 2012; Griffiths & Klumpp, 1996). Their aragonitic (CaCO$_3$) shells display distinct daily growth increment (day-night coupling) and annual banding (Aharon & Chappell, 1986; Watanabe & Oba, 1999; Watanabe et al., 2004; de Winter et al., 2023), making them excellent targets for sclerochronological research as done for other bivalve species (Surge & Schöne, 2013; Butler et al., 2010; Trofimova et al., 2020; Reynolds et al., 2022; Uvanović et al., 2023). In this paper, we first present the workflow developed for StripesCounter, including sample preparation, high-resolution imaging, and band detection/editing. Secondly, we apply the method to a previously studied modern *Tridacna* shell (Welsh et al., 2011; Driscoll et al., 2014), and assess the reproducibility of the growth profiles. These tests enable detailed investigations of temporal variability in growth on daily to seasonal scales.

## 2. StripesCounter: an image software to measure stripes along grayscale profiles

### 2.1. Software description

We developed StripesCounter software to meet a specific need that existing market solutions either do not address or are not well-suited for. The software is optimized for high-resolution microscopy images (up to 25,000 by 25,000 pixels) and implements a sub-sampling based zooming algorithm to enable fast and seamless image exploration. The custom solution is designed to follow a simple workflow comprising three main phases. This workflow is illustrated from daily increments of black-and-white variation from a *Tridacna gigas* shell of Belitung Island, Indonesia. (Figure 1, Table 1).

In the first phase, an image is uploaded and displayed in grayscale. Basic adjustments such as contrast and brightness can be applied to clarify the visual data. Occasionally, inverting the grayscale enhances the visibility of key features, using the "inverse image" function (Table 1). Although the software provides basic tools for image manipulation, it
does not offer advanced processing algorithms. It was invented to meet the main task of peak detection and increment
measurement. For more complex image enhancements, it is recommended to use specialized image processing
software beforehand. Once adjusted, a scale is assigned to the image by creating a segment (by using the shift and
double click command) of a defined length using the tools "Define scale value" in pixel and "Define scale length" in
millimetre (Table 1).

In the second phase, peak detection is performed using a grayscale intensity profile extracted from the segment defined
by the user on the image. The grayscale intensity measured along the segments is averaged over the segment width,
which can be determined by the user in order to minimize the image noise effect (i.e. "Kernel size" referring to the
width of the local window applied to the profile and "Profile linewidth", which corresponds to the width of the segment
used for peak detection; Table 1). The peak detection relies on identifying local maxima that exceed a specified
threshold. It then applies distance and amplitude constraints to retain only the most relevant peaks in the signal. To
avoid bias in the increment measurements, the profiles should be taken perpendicular to the growth lines; however, in
cases where growth bands are not parallel, the segments defined in StripesCounter should remain as perpendicular as
possible to the growth bands (Figure 1). The visualization and adjustment of peak counts and spacing are facilitated
by the "Display peaks on current segment" toolbox (Table 1). Parameters such as segment thickness, profile
smoothing, and peak threshold can be fine-tuned to improve the accuracy of peak detection (respectively "Profile
linewidth", "Kernel size", "PeakUtils: maximum distance" and "PeakUtils: threshold" functions; Table 1).
Automatically detected peaks are then extracted using the "Extract peaks from profile" function. Additional segments
can be incorporated within a single scan (see the case study below with multiple segments from the 4 intensity
profiles). Each consecutive segments are then joined end to end, the first increment of a new segment (n+1) being
computed as the last increment of the precedented segment (n).

The final phase involves manual editing, allowing for the displacement, addition or removal of peaks if accuracy needs
to be improved, for instance in the case of noisy images (i.e. right click on segment to add a peak, left click on a peak
to delete it). In the case of growth increment measurements, it is essential that the peaks are as close to the middle of
the growth line as possible to obtain the most representative distance of growth. After validation of the profile, the
data can be exported as a CSV file for further analysis (i.e. "Save segments and peaks" function; Table 1). The CSV
file contains eight variables: 'n' indicates the peak number; 'xPixel' and 'yPixel' represent the coordinates in the image;
'segment' denotes the segment number; 'peakNumbInSegment', 'distanceInSegment', and 'distanceCumulated'
correspond to distance metrics within and across segments; and 'stripeLength' represents the distance between two consecutive peaks. The software also supports saving images with the segmented data or loading previously created segments onto new images allowing several measurement sessions to be conducted over long period of time (respectively "Save image with segments and peak" and "Load segments and peaks"). The CSV file can be imported into other software, such as Geographic Information Systems (GIS), to integrate and process additional spatial or attribute data.

141

## 2.2. Installation procedure

The application is available from a GitHub repository and consists of a python Matplotlib-based application with a PyQt interface. It is designed to run in a Conda environment, with the installation process facilitated by a requirements.txt file to ease the creation of a dedicated environment. The necessary packages to be installed include PyQt5, scikit-image, opencv-python, opencv-contrib-python, opencv-python-headless, shapely, pycairo, matplotlib, peakutils, and pandas. The current version of the software described in this paper is StripesCounter V.12.30. The application can be accessed via this link: [StripesCounter GitHub Repository](https://github.com/yourusername/stripescounter). DOI reference is: [DOI: 10.5281/zenodo.15228837](https://doi.org/10.5281/zenodo.15228837)

150

## 3. Using StripesCounter for paleoclimate and climate analyses: a study case on *Tridacna* spp.

### 3.1. Sample description

The present study investigates a modern specimen of *Tridacna gigas* (MT03-7), collected alive by local inhabitants from the Huon Peninsula in northern Papua New Guinea near the Vitiaz strait in 2002 and previously studied (Welsh et al., 2011). Oxygen isotope profiles derived from this 16 years old specimen illustrate that *T. gigas* was a faithful recorder of changes in water temperature and salinity associated with El Niño and La Niña events occurring between 1986 and 2002 (Welsh et al., 2011). An initial age model was established by traditional manual counting under a microscope; the mean width of daily growth bands was estimated with a regular step, every millimetre and adjustments were done using geochemical profiles when the growth bands were not visible (Welsh et al., 2011). We have used this published age model as a reference for comparison with the new automated method developed using the StripesCounter software (Figure 2).

154
155
156
157
158
159
160
161
162
## 3.2. Sample preparation and data input

A slice was cut using a rock saw from the centre to the ventral edge of the giant clam shell along the growth axis (Welsh et al., 2011). The thickness was reduced until the growth lines were visible in transmitted light. Thin sections are polished to a uniform surface. In general, the thickness of thin sections, depending on the sample, is around 30µm.

In the case of MT03-7, the thickness of the thin sections is 40 µm, which is a good compromise between visualisation of the growth bands and a fracture-free sample. The growth analysis was performed on the inner layer, which is better preserved than the other layers (Pätzold et al., 1991) and offers a higher resolution of growth bands, as previously done by Elliot et al. (2009) or Welsh et al. (2011) for giant clam shell. Afterwards, the sections were scanned using a laser scanning confocal microscope (LSCM) at the Centre Hospitalier Universitaire de Nantes, providing high-resolution images that reveal daily growth increments (supplementary information). The scan acquisition was performed using a Nikon A1R confocal microscope equipped with a 10x/0.45 objective. The images were captured with a resolution of 1.2 µm per pixel under transmitted light. The dataset contains 3 thin sections/scans (and is available on the supplementary information 1): MT03-7-3 represents the juvenile phase, MT03-7-2 and MT03-7-1 represent the adult phase.

## 3.3. Data processing and results

The microscope scan image is imported into StripesCounter_v12.30, following a standardised process described in the II.1 section. Uncertainties in the increment width record are assessed by conducting several parallel profiles. In this study we compare four parallel profiles from the inner layer of the whole shell. Each profile comprises three main parts corresponding to the three scans of the shell (multiple segment function). The maximum distance between the extremal profiles (red D & green C) is 1.2 cm. The mean distance between each profile is 3.2 mm (Figure 2, Supplementary information 1 & 2). Profiles A, B and C were made from the juvenile phase to the adult phase, while profile D was made in the opposite direction, from the adult phase to the juvenile phase, in order to have a dataset highlighting different counting conditions. The profiles were also compared to what was done in the initial study of Welsh et al., (2011) (Figure 2a & b).

A comparison of all growth profiles reveals growth rates ranging from 16 mm to 2 mm per year (or 0.05 mm to > 0.01mm per day), consistent with published growth patterns for bivalves and specifically for *Tridacna* species (Aharon, 1991; Elliot et al., 2009; Welsh et al., 2011, Liu et al., 2022). Manual increment counting (Figure 2a)
typically provides an estimation of the daily growth in millimetre, whereas StripesCounter produces daily-resolution estimates, offering a substantial gain in accuracy and temporal precision. In terms of processing time, a single profile of comparable length requires at least one week of continuous work using the manual methods, while StripesCounter can extract and process four complete profiles within a single day. The results indicate a decrease in mean growth rates from the juvenile to the adult phase, superimposed by significant higher frequency-daily-monthly variations. Comparison of the 4 profiles illustrates that the reproducibility is very good. For example, the profiles exhibit strong quantitative consistency, with only a 0.07-year difference (less than a month) across the four profiles as an estimate of age (in years), considering that each peak corresponds to a single day with a continuous growth hypothesis (no growth stops). Profile A (yellow) estimates an age of 15.50 years with a total of n= 5647 daily growth increments, Profile B (blue) 15.48 years n= 5641 increments, and Profile C (green) 15.43 years n= 5619 increments, based on the transformation of the cumulative growth distance into age (Figure 2b). Similarly, Profile D (red) estimates an age of 15.48 years n= 5636 increments, further demonstrating the consistency and reliability of the results (Figure 2b). To evaluate the similarity between the four growth profiles, spectral and statistical analyses, as well as correlation matrix, were performed using R (R Core Team, 2023) with the Pearson product-moment correlation. A vertical boxplot provides a clear visualization of the overall distribution of increment widths (see Supplementary Information 3). The distributions are similar across all profiles, with a mean growth rate of 0.022-0.023 mm.day⁻¹, values ranging from 0.001 mm (minimum) to 0.08 mm (maximum), and interquartile values of 0.013 mm and 0.028-0.030 mm. To further assess the correlation between the profiles, a different approach was applied to the raw time-domain data (Figure 2b). The growth series were then interpolated to match a common maximum time (i.e. the same number of data: 5647 increments or 5647 days), a necessary step for comparative analysis. This interpolation was performed using the "approx()" function in R software, homogenising the number of data across all series for a fair comparison to obtain a robust mean. For the shortest time profile (C), 28 days were added randomly to enable statistical analysis, which corresponds to 0.4% of the total time data set. Then, a correlation matrix based on Pearson correlation coefficient was calculated to quantify the relationships between the growth series, revealing significant positive correlations, with a mean value of R =0.7 (Supplementary Information 4). The correlation may be slightly reduced due to micro-differences related to the curves of the lines, which are sometimes thicker in certain areas of the shell. Therefore, it is preferable to create several profiles in order to gain an all-round view of the shell. Overall, the
218 StripesCounter software provides comparable growth profiles. The sample's growth record can be used to establish
219 an accurate age model while also enabling the analysis of growth variations.

220

221 3.4. Detrending process

222 Although there are detail differences, profiles A, B, C, and D exhibit similar trends. Analysis of seasonal cycles reveals
223 repetitive patterns in growth rates that correspond to annual growth cycles, with phases of increase and decrease
224 suggesting influences of both ontogenetic and environmental factors like sea surface temperature variations or the
225 food availability. Despite the potential for small local differences in some growth increments of the inner layer of the
226 shell, the overall environmental factors consistently influence organism growth. From the set of interpolated profiles,
227 a master chronology was established (Figure 2c). Utilizing all profiles helps to smooth out any variations or outliers
228 present in individual profiles. By applying the chron() function (Cook & Kairiukstits, 1990) from The
229 Dendrochronology Program Library package in R ("DplR," Bunn, 2008; Bunn, 2010), a standard chronology was
230 created (Figure 2C) based on the profiles mean at the same daily growth points with the longest profile A as the profile
231 reference. This technique, common in dendrochronology, has also been applied to sclerochronology studies, such as
232 Reynolds et al. (2022). From the standardized chronology, it is necessary to correct for the ontogenetic trend to extract
233 the environmental signal. Different detrending methods have been applied to allow comparison between individuals
234 (Figure 2d). The "ModNegExp()" which fits a negative exponential model to capture the age-related trend and subtract
235 it; the "Spline()" function, applied to a smoothed curve uses a default frequency response in which 50% of the variance
236 is preserved at a wavelength equal to 67% of the series length to extract long-term trends; and the "Friedman()" based
237 on the Friedman's smoothing algorithm with the default setting from the DplR package. Also, the standard growth
238 index (SGI) described by Schöne, 2003 was used. This index is commonly used in sclerochronology. It provides a
239 measure of the deviation in growth relative to the mean trend. This procedure removes the autocorrelation between
240 the mean and the variance of the data, yielding a stationary series of relative growth anomalies. The strong correlation
241 between growth variance and mean disappears with the standard growth index. All these methods show the same
242 growth variations, but with different amplitudes (Figure 2d). In order to confirm the close similarity between the
243 different detrending methods, spectral analyses were performed to identify the dominant frequencies present in the
244 detrended growth series (Supplementary information 5). We estimated the power spectral density (PSD) using the
245 spec.pgram() routine in R, which employs a tapered Fourier periodogram. The PSD was then computed using a fast
Fourier transform (FFT) with a 10% cosine taper (taper = 0.1) to minimise spectral leakage at the boundaries. No additional smoothing or detrending was applied within the function. The resulting power spectra were normalised by their maximum power to enable direct comparison between the four detrending methods (SGI, Spline, Modified Negative Exponential and Friedman). The main frequencies are consistently preserved across methods, with dominant period peaks appearing around ~297 days and ~376 days (i.e., annual periodicities), indicating a stable and method-independent frequency structure. Additionally, the SGI, Friedman, and Spline methods also exhibit a peak at ~176 days (semi-annual), confirming that this periodicity is genuinely present in the data rather than an artefact of the detrending procedure. For the Spline and ModNegExp functions, frequencies between 0 and 0.001 are due to edge effects.

### 3.5. Results: Growth patterns of sample MT03-7

The SGI of specimen MT03-7 displays clear variability across multiple timescales between 1986 and 2002. A dominant annual pattern is evident, characterised by regular oscillations that reflect a seasonal growth cycle. Superimposed on this, higher-frequency fluctuations point to the presence of a recurrent semi-annual signal. Wavelet analysis of the SGI (Figure 3) supports this interpretation, revealing two distinct bands of periodicity, centred around 6 and 12 months, corresponding to semi-annual and annual growth rhythms, respectively.

Notably, both the SGI and wavelet power spectra reveal growth disturbances at specific points during the organism's lifespan. These perturbations are particularly pronounced at the onset of growth, between the 6th and 7th years, and again between the 11th and 12th years—periods during which the semi-annual signal appears weakened or disrupted (respectively 1922-1993 and 1997-1998. Comparison with the Multivariate ENSO Index (MEI) (Wolter & Timlin, 2011; https://psl.noaa.gov/enso/mei/) suggests that these anomalies coincide with El Niño events, indicating a potential link between ENSO-related environmental stress and the suppression or alteration of high-frequency growth patterns in *Tridacna* (Figure 3).

### 4. Conclusions

In this study, we demonstrate a rapid and automated approach for analysing and measuring long series of daily growth bands in *Tridacna* spp. By leveraging advanced imaging techniques and the newly developed StripesCounter software, we efficiently generate detailed growth records with high temporal resolution. Unlike traditional manual counting methods, StripesCounter provides a fast, standardized, and objective way to detect growth increments, enabling
quantitative analyses and statistical assessments of growth dynamics. This automated approach significantly enhances the accuracy and reproducibility of growth records, while facilitating robust age modelling and improving the integration of sclerochronological data with geochemical proxies, such as trace element and stable isotope records. Beyond paleoclimate reconstructions, the robustness and versatility of StripesCounter make it applicable to a wide range of environmental research fields, from pollution bioindication to the study of biological responses to climate variability.

The high-resolution growth profile records enable the reconstruction of short-lived climatic events, such as tropical storms or rapid climatic oscillations like ENSO (Liu et al., 2022; Ma et al., 2020; Yan et al., 2020). The potential of this method extends beyond bivalves, offering valuable applications for diverse archives, such as corals, sclerosponges, tree rings, laminated sediments. By studying these archives, it is possible to capture climate variability at sub-seasonal timescales, improving our understanding of rapid environmental changes and their impacts on ocean-atmosphere dynamics.

## 5. Acknowledgments

The authors would like to thank Laurent Lenta for his work in preparing the observation slides and ensuring the high quality of the results. We acknowledge the IBISA MicroPICell facility (Biogenouest), member of the national infrastructure France-Bioimaging supported by the French national research agency (ANR-10-INBS-04) for providing access to the laser scanning confocal microscope (LSCM). Also, to thank INSU MOSCA for the financial support. M.G. postdoctorate was supported by the ANR project NéoArabia (ANR-16-CE03-0007).

## Code availability section

StripesCounter

Contact: patrick.brockmann@lsce.ipsl.fr (01.60.08.32.24)

Hardware requirements: None

Program language: Python 3

Software required: See https://github.com/PaleoIPSL/StripesCounter/blob/main/requirements.txt and https://github.com/PaleoIPSL/StripesCounter/tree/main

Program size: 22MB for the zip project
302 The source codes are available for downloading at the link: https://github.com/PaleoIPSL/StripesCounter

303

304

305
306 References

307 Aharon, P., 1991. Recorders of reef environment histories: stable isotopes in corals, giant clams, and calcareous algae.

308 Coral Reefs 10, 71–90. https://doi.org/10.1007/BF00571826

309 Aharon, P., Chappell, J., 1986. Oxygen isotopes, sea level changes and the temperature history of a coral reef

310 environment in New Guinea over the last 105 years. Palaeogeography, Palaeoclimatology, Palaeoecology 56,

311 337–379. https://doi.org/10.1016/0031-0182(86)90101-X

312 Arias-Ruiz, C., Elliot, M., Bézos, A., Pedoja, K., Husson, L., Cahyarini, S.Y., Cariou, E., Michel, E., La, C.,

313 Manssouri, F., 2017. Geochemical fingerprints of climate variation and the extreme La Niña 2010–11 as

314 recorded in a Tridacna squamosa shell from Sulawesi, Indonesia. Palaeogeography, Palaeoclimatology,

315 Palaeoecology 487, 216–228. https://doi.org/10.1016/j.palaeo.2017.08.037

316 Bunn, A.G., 2010. Statistical and visual crossdating in R using the dpIR library. Dendrochronologia 28, 251–258.

317 https://doi.org/10.1016/j.dendro.2009.12.001

318 Bunn, A.G., 2008. A dendrochronology program library in R (dpIR). Dendrochronologia 26, 115–124.

319 https://doi.org/10.1016/j.dendro.2008.01.002

320 Butler, P.G., Richardson, C.A., Scourse, J.D., Wanamaker, A.D., Shammon, T.M., Bennell, J.D., 2010. Marine climate

321 in the Irish Sea: analysis of a 489-year marine master chronology derived from growth increments in the shell

322 of the clam Arctica islandica. Quaternary Science Reviews 29, 1614–1632.

323 https://doi.org/10.1016/j.quascirev.2009.07.010

324 Carré, M., Bentaleb, I., Blamart, D., Ogle, N., Cardenas, F., Zevallos, S., Kalin, R.M., Ortlieb, L., Fontugne, M., 2005.

325 Stable isotopes and sclerochronology of the bivalve Mesodesma donacium: Potential application to Peruvian

326 paleoceanographic reconstructions. Palaeogeography, Palaeoclimatology, Palaeoecology 228, 4–25.

327 https://doi.org/10.1016/j.palaeo.2005.03.045

328 Cook, E.R., Kairiukstis, L.A. (Eds.), 1990. Methods of Dendrochronology. Springer Netherlands, Dordrecht.

329 https://doi.org/10.1007/978-94-015-7879-0

330 de Winter, N.J., Killam, D., Fröhlich, L., de Nooijer, L., Boer, W., Schöne, B.R., Thébault, J., Reichart, G.-J., 2023.

331 Ultradian rhythms in shell composition of photosymbiotic and non-photosymbiotic mollusks. Biogeosciences

332 20, 3027–3052. https://doi.org/10.5194/bg-20-3027-2023

333 Driscoll, R., Elliot, M., Russon, T., Welsh, K., Yokoyama, Y., Tudhope, A., 2014. ENSO reconstructions over the

334 past 60 ka using giant clams (Tridacna sp.) from Papua New Guinea. Geophysical Research Letters 41, 6819–

335 6825. https://doi.org/10.1002/2014GL061446

336 Elliot, M., Welsh, K., Chilcott, C., McCulloch, M., Chappell, J., Ayling, B., 2009. Profiles of trace elements and stable

337 isotopes derived from giant long-lived Tridacna gigas bivalves: Potential applications in paleoclimate studies.

338 Palaeogeography, Palaeoclimatology, Palaeoecology 280, 132–142.

339 https://doi.org/10.1016/j.palaeo.2009.06.007

340 Füllenbach, C.S., Schöne, B.R., Mertz-Kraus, R., 2015. Strontium/lithium ratio in aragonitic shells of *Cerastoderma*

341 *edule* (Bivalvia) — A new potential temperature proxy for brackish environments. Chemical Geology 417, 341–

342 355. https://doi.org/10.1016/j.chemgeo.2015.10.030
343 Goodwin, D.H., Flessa, K.W., Schöne, B.R., Dettman, D.L., 2001. Cross-Calibration of Daily Growth Increments,
344 Stable Isotope Variation, and Temperature in the Gulf of California Bivalve Mollusk Chione cortezi:
345 Implications for Paleoenvironmental Analysis. PALAIOS 16, 387–398. https://doi.org/10.2307/3515578

346 Griffiths, C., Klumpp, D., 1996. Relationships between size, mantle area and zooxanthellae numbers in five species
347 of giant clam (Tridacnidae). Mar. Ecol. Prog. Ser. 137, 139–147. https://doi.org/10.3354/meps137139

348 Hernawan, U.E., 2012. Taxonomy of Indonesian giant clams (Cardiidae, Tridacninae). Biodiversitas Journal of
349 Biological Diversity 13. https://doi.org/10.13057/biodiv/d130303

350 KILLAM, D.E., CLAPHAM, M.E., 2018. IDENTIFYING THE TICKS OF BIVALVE SHELL CLOCKS:
351 SEASONAL GROWTH IN RELATION TO TEMPERATURE AND FOOD SUPPLY. PALAIOS 33, 228–
352 236. https://doi.org/10.2110/palo.2017.072

353 Liu, C., Zhao, L., Zhao, N., Yang, W., Hao, J., Qu, X., Liu, S., Dodson, J., Yan, H., 2022. Novel methods of resolving
354 daily growth patterns in giant clam (Tridacna spp.) shells. Ecological Indicators 134, 108480.
355 https://doi.org/10.1016/j.ecolind.2021.108480

356 Ma, X., Yan, H., Fei, H., Liu, C., Shi, G., Huang, E., Wang, Y., Qu, X., Lian, E., Dang, H., 2020. A high-resolution
357 δ18O record of modern *Tridacna gigas* bivalve and its paleoenvironmental implications. Palaeogeography,
358 Palaeoclimatology, Palaeoecology 554, 109800. https://doi.org/10.1016/j.palaeo.2020.109800

359 Maxwell, R.S., Larsson, L.-A., 2021. Measuring tree-ring widths using the CooRecorder software application.
360 Dendrochronologia 67, 125841. https://doi.org/10.1016/j.dendro.2021.125841

361 Mills, K., John, E.H., Muir, D.D., Santodomingo, N., Johnson, K.G., Hussein, M.A.S., Sosdian, S., 2023. Growth
362 responses of mixotrophic giant clams on nearshore turbid coral reefs. Coral Reefs 42, 593–608.
363 https://doi.org/10.1007/s00338-023-02366-8

364 Patzold, J., Heinrichs, J.P., Wolschendorf, K., Wefer, G., 1991. Correlation of stable oxygen isotope temperature
365 record with light attenuation profiles in reef-dwelling Tridacna shells. Coral Reefs 10, 65–69.
366 https://doi.org/10.1007/BF00571825

367 Reynolds, D.J., Von Biela, V.R., Dunton, K.H., Douglas, D.C., Black, B.A., 2022. Sclerochronological records of
368 environmental variability and bivalve growth in the Pacific Arctic. Progress in Oceanography 206, 102864.
369 https://doi.org/10.1016/j.pocean.2022.102864

370 Schöne, B.R., 2003. A ‘clam-ring’ master-chronology constructed from a short-lived bivalve mollusc from
371 the northern Gulf of California, USA. The Holocene 13, 39–49. https://doi.org/10.1191/0959683603hl593rp

372 Schone, B.R., Houk, S.D., Freyre Castro, A.D., Fiebig, J., Oschmann, W., Kroncke, I., Dreyer, W., Gosselck, F., 2005.
373 Daily Growth Rates in Shells of Arctica islandica: Assessing Sub-seasonal Environmental Controls on a Long-
374 lived Bivalve Mollusk. PALAIOS 20, 78–92. https://doi.org/10.2110/palo.2003.p03-101

375 Speer, J.H., 2010. Fundamentals of Tree-ring Research. University of Arizona Press.

376 Surge, D.M., Schöne, B.R., 2013. Bivalve Sclerochronology, in: Rink, W.J., Thompson, J. (Eds.), Encyclopedia of
377 Scientific Dating Methods. Springer Netherlands, Dordrecht, pp. 1–14. https://doi.org/10.1007/978-94-007-
378 6326-5_165-1
379 Trofimova, T., Alexandroff, S.J., Mette, M.J., Tray, E., Butler, P.G., Campana, S.E., Harper, E.M., Johnson, A.L.A., 380 Morrongiello, J.R., Peharda, M., Schöne, B.R., Andersson, C., Andrus, C.F.T., Black, B.A., Burchell, M., 381 Carroll, M.L., DeLong, K.L., Gillanders, B.M., Grønkjær, P., Killam, D., Prendergast, A.L., Reynolds, D.J., 382 Scourse, J.D., Shirai, K., Thébault, J., Trueman, C., de Winter, N., 2020. Fundamental questions and applications 383 of sclerochronology: Community-defined research priorities. Estuarine, Coastal and Shelf Science 245, 106977. 384 https://doi.org/10.1016/j.ecss.2020.106977

385 Uvanović, H., Peharda, M., Pavin, N., Thébault, J., Mazzoldi, C., Mihanović, H., Župan, I., 2023. Developing a 386 sclerochronology network in the Adriatic Sea: Growth synchrony among populations of *Callista chione*. 387 Regional Studies in Marine Science 64, 103009. https://doi.org/10.1016/j.rsma.2023.103009

388 Van Wynsberge, S., Andréfouët, S., Gaertner-Mazouni, N., Wabnitz, C.C.C., Menoud, M., Le Moullac, G., Levy, P., 389 Gilbert, A., Remoissenet, G., 2017. Growth, Survival and Reproduction of the Giant Clam *Tridacna maxima* 390 (Röding 1798, Bivalvia) in Two Contrasting Lagoons in French Polynesia. PLoS ONE 12, e0170565. 391 https://doi.org/10.1371/journal.pone.0170565

392 Wanamaker, A.D., Butler, P.G., Scourse, J.D., Heinemeier, J., Eiríksson, J., Knudsen, K.L., Richardson, C.A., 2012. 393 Surface changes in the North Atlantic meridional overturning circulation during the last millennium. Nat 394 Commun 3, 899. https://doi.org/10.1038/ncomms1901

395 Watanabe, T., Oba, T., 1999. Daily reconstruction of water temperature from oxygen isotopic ratios of a modern 396 *Tridacna* shell using a freezing microtome sampling technique. J. Geophys. Res. 104, 20667-20674. 397 https://doi.org/10.1029/1999JC900097

398 Watanabe, T., Suzuki, A., Kawahata, H., Kan, H., Ogawa, S., 2004. A 60-year isotopic record from a mid-Holocene 399 fossil giant clam (*Tridacna gigas*) in the Ryukyu Islands: physiological and paleoclimatic implications. 400 Palaeogeography, Palaeoclimatology, Palaeoecology 212, 343-354. https://doi.org/10.1016/S0031- 401 0182(04)00358-X

402 Welsh, K., Elliot, M., Tudhope, A., Ayling, B., Chappell, J., 2011. Giant bivalves (*Tridacna gigas*) as recorders of 403 ENSO variability. Earth and Planetary Science Letters 307, 266-270. https://doi.org/10.1016/j.epsl.2011.05.032

404 Wen, H., Yan, H., Liu, C., Zhao, N., Liu, S., Dodson, J., Han, T., Geng, J., Zhou, P., 2024. Seasonal to interannual 405 variations of daily growth rate of a *Tridacna* shell from Palau Island, western Pacific, and paleoclimatic 406 implications. Palaeogeography, Palaeoclimatology, Palaeoecology 647, 112258. 407 https://doi.org/10.1016/j.palaeo.2024.112258

408 Wolter, K., Timlin, M.S., 2011. El Niño/Southern Oscillation behaviour since 1871 as diagnosed in an extended 409 multivariate ENSO index (MEI.ext). Intl Journal of Climatology 31, 1074-1087. 410 https://doi.org/10.1002/joc.2336

411 Yan, H., Shao, D., Wang, Y., Sun, L., 2013. Sr/Ca profile of long-lived *Tridacna gigas* bivalves from South China 412 Sea: A new high-resolution SST proxy. Geochimica et Cosmochimica Acta 112, 52-65. 413 https://doi.org/10.1016/j.gca.2013.03.007

414
415 List of Figures

416 **Figure 1: Workflow of StripesCounter software and interface description.**

417 The procedure begins with the data input (top left) displaying a natural sample image with daily increments of black-and-white variation from a *Tridacna gigas* shell of Belitung Island, Indonesia. 1) StripesCounter process (1): the image is uploaded and a scale is created by defining a calibration segment (red line). In this example, the image's scale is 3.2 mm for 2,500 pixels. 2) StripesCounter process (2): a new profile is drawn corresponding to the segment position across the increment. The intensity profile is extracted. 3) StripesCounter process (3): peaks position corresponding to the daily increments are extracted automatically detected and can be manually editable. At the end, the data output (bottom right) provides a time series of increment width (growth rate) in function of cumulative distance. StripesCounter Toolbox (right) showing various adjustable parameters for image processing, including contrast, brightness, kernel size, and profile line width. Options for peak detection, scale definition, and image inversion are also available, along with buttons to detect scales, extract peaks, and save the analysed image with segments and peaks. See software description paragraph above
<figure><img src="image_4.png" /></figure>
<caption>Figure 2: Shell MT03-7 daily growth variations in mm.day⁻¹ reported as a function of year of growth.</caption>

The growth axis extends from left (most juvenile phase) to right (most adult phase). a) Age model according to Welsh et al. (2010) using the low resolution traditional counting method. b) Profile comparison using age for smoothing at 20 points in mm.day⁻¹ (yellow: profile A, blue: profile B, green: profile C, red: profile D). c) Standardized chronology from all profiles of MT03-7, the 30 points moving average derive from the standardized chronology. The growth axis extends from right (most juvenile phase) to left (most adult phase). d) Detrending methods comparison from the standardized chronology.

<figure><img src="image_5.png" /><figcaption>Figure 3: Standard Growth Index of MT03-7 variability and the ENSO impact.</figcaption></figure>

(top) Growth and environmental data from 1986 to 2002. The pink curve is the daily standard growth index (SGI) of MT03-7, the black curve is the 30 points moving average derive from the SGI chronology and the brown curve is the

430

431

432

433

434

435

436

437

438

439
440 monthly Multivariate ENSO Index. (bottom) Wavelet analysis of the SGI, the colour scale corresponds to the power
441 of the signal, with the period in function of time. The dashed lines are the 6 and 12 months periods. The black outline
442 of the wavelet corresponds to the 95% confidence interval. The vertical black lines highlight the main ENSO events
443 (1986 - 1988, 1992 - 1994 and 1997 - 1998).

444
445

<figure><img src="image_6.png" /></figure>

446

447 List of Tables

448 Table 1:

449
<table><thead><tr><td>Parameters</td><td>Purpose</td><td>Description</td></tr></thead><tbody><tr><td>Inverse image</td><td>Image visualisation</td><td>Inverts grayscale values.</td></tr><tr><td>Contrast level</td><td>Image enhancement</td><td>Adjusts image contrast to emphasize lighthigh-dark variations.</td></tr><tr><td>Brighness level</td><td>Image enhancement</td><td>Modifies overall image brightness.</td></tr><tr><td>Kernel size</td><td>Signal smoothing</td><td>Sets the width of the local averaging window applied to the grayscale profile to reduce noise.</td></tr><tr><td>Profile line width</td><td>Signal analysis</td><td>Sets the width of the segment used for peak detection.</td></tr><tr><td>PeakUtils - Minimum distance</td><td>Peak detection</td><td>Specifies the minimum separation between two adjacent peaks to avoid duplicate detection.</td></tr><tr><td>PeakUtils - Threshold</td><td>Peak detection</td><td>Defines the normalised intensity threshold above which a local maximum is identified as a peak.</td></tr><tr><td>Display peaks on current segment</td><td>Visualisation</td><td>Toggles display of detected peaks on the active segment.</td></tr><tr><td>Reverse profile</td><td>Signal orientation</td><td>Reverses the direction of the intensity profile.</td></tr><tr><td>Detect scale</td><td>Image calibration</td><td>Extracts the pixel scale from a user-drawn segment.</td></tr><tr><td>Define scale value</td><td>Image calibration</td><td>Sets the conversion factor from millimeters to pixels.</td></tr><tr><td>Define scale length</td><td>Image calibration</td><td>Sets the conversion factor from pixels to millimeters.</td></tr><tr><td>Define scale</td><td>Image calibration</td><td>Combines pixel length and real-world length to apply a calibration factor across all measurements.</td></tr><tr><td>Extract peaks from profile</td><td>Data extraction</td><td>Extracts detected peaks with their positions, spacing, and amplitudes for CSV export.</td></tr><tr><td>Load segments and peaks</td><td>Data import</td><td>Loads previously saved measurement sessions.</td></tr><tr><td>Save segments and peaks</td><td>Data export</td><td>Saves the current set of detected peaks and segments as a CSV file.</td></tr><tr><td>Capture displayed image</td><td>Image export</td><td>Captures the current image display.</td></tr><tr><td>Delete last segment</td><td>Editing</td><td>Removes the last user-defined segment.</td></tr><tr><td>Save image with segments and peaks</td><td>Image export</td><td>Saves the full image with segment and peak overlays.</td></tr></tbody></table>
Highlights

StripesCounter: A new image software for increment measurement applied to periodicity analysis of paleoclimate archives

Clara Boutreux, Patrick Brockmann, Mary Elliot, Matthieu Carré, Marc Gosselin

* StripesCounter provides fast increment detection in giant clam shell.

* StripesCounter ensures highly reproducible results across replicate analyses.

* StripesCounter quantifies seasonal and semi-annual growth in giant clam shells.
**Declaration of interests**

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

☐ The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

<figure><img src="image_7.png" /></figure>