the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 19 May 2026
Short communication: A linear regression model for amino acid dating of Bithynia opercula from deep-core material
Abstract. Estimating numerical ages from the extent of amino acid racemisation observed in fossil biominerals has been the aim of many researchers in the field of amino acid geochronology. Here, the use of temperature profiles and independent age estimates to build a linear regression model for IcPD with uncertainty calculated using a Bayesian approach is explored. This work presents a pilot study to test the potential of this methodology and determine what next steps need to be taken to make this a viable approach to produce numerical ages from IcPD data. To progress this work, a comprehensive study of the sources of uncertainty in the model needs to be made.
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Status: open (until 29 Jul 2026)
- RC1: 'Comment on egusphere-2026-2261', Anonymous Referee #1, 28 May 2026 reply
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RC2: 'Comment on egusphere-2026-2261', Colin Murray-Wallace, 23 Jun 2026
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I enjoyed reading the manuscript because it attempts to address one of the long-standing difficulties in the application of amino acid racemization reactions to the dating of Quaternary successions. The challenge has been to try to reliably convert the extent of racemization to a numeric age. The vagaries of diagenetic temperature, as well as the preservation state of fossils has commonly rendered this a challenging task. This study is interesting because it incorporates samples that have experienced variable diagenetic temperatures due the effects of geothermal gradients, which appear not to be uniform within the Pannonia Basin, Hungary.
Some more specific comments appear below.
1. Line 65 it would be nice to see here, and explanation of why a linear model was selected.
2. I am not sure if this is possible, but can an outline of the margin of the Pannonian Basin be shown in Figure 1?
3. Line 103 Can the validity of this assumption be stated here?
4. Line 118 The Puspoki age model needs to be briefly described.
5. Line 127 Alanine is a Fast racemizing acid.
6. Line 137 What is the basis for the c. 49 ka age reported by Nelson (2024)?
7. Figure 2 - Is there any merit in producing three graphs - one for each amino acid? It might improve the clarity of the information and make it easier to see the structure of the data.
8. Line 152 '... time has passed since ...'
9. Line 159 Please keep spelling consistent within the document - racemization or racemisation.
10. Line 159 Please adopt in all instances 'compared with' throughout the text.
11. Line 186 D/L values
12. So, can you calculate some numeric ages and compare with the likely ages of the basin fill based on independent evidence?
Colin Murray-Wallace
Citation: https://doi.org/10.5194/egusphere-2026-2261-RC2
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This study presents a statistical model for estimating numerical ages from intra-crystalline protein decomposition (IcPD) data in fossil mollusc opercula. The authors use amino acid racemization data from seven boreholes in the Pannonian Basin, Hungary where opercula are buried at depths of 5-460 m combined with independently derived ages and measured geothermal gradients. The key innovation is incorporating both time and burial temperature into a single predictive framework using Bayesian inference. The model is tested on four samples, producing age estimates with broad uncertainty distributions (several hundred thousand years), with better performance on deeper, older material. The authors identify multiple sources of unaccounted uncertainty and propose this as a pilot study requiring substantial further development.
STRENGTHS
Strong Empirical Foundation. The study builds on previous analyses of material with known ages and measured geothermal gradients (Nelson et al., 2024). This is a solid foundation for developing a new methodology. The use of multiple boreholes (n=7) with variable geothermal gradients provides natural variation in the temperature-time space.
Sound Statistical Framework. The Bayesian approach to uncertainty estimation is appropriate, although I cannot speak to the accuracy of its execution. Using three amino acids (Ala, Glx, Val) with different racemization rates to calibrate age predictions is sensible.
Realistic of Burial Temperatures. Unlike earlier isothermal heating studies, this work attempts to model actual burial conditions, including varying temperatures with depth and time. The incorporation of variable geothermal gradients (40–57 °C/km) is a step toward more realistic modeling of racemization kinetics at environmental temperatures rather than laboratory conditions.
Transparent Acknowledgment of Limitations. The authors openly discuss sources of uncertainty (Püspöki age model uncertainties, geothermal gradient estimates, initial burial phase effects, climate influence near surface) and clearly identify this as preliminary work needing further refinement.
WEAKNESSES & CONCERNS
Unclear Explanation of Motivation and Significance. The paper needs additional context to better understand the motivation and significance of this study. For a non-specialist reader, the paper seems to imply that a numerical model is necessary to produce ages from amino acid data. The core contribution of developing a model that can estimate numerical ages from IcPD data without relying on direct calibration but instead using estimated temperature history is obscured. The paper would be improved by clearly explaining:
Without this context, the significance of the modeling effort is lost, and the results (broad age distributions) appear disappointing rather than a promising first step toward a new approach.
Unclear Applicability. The study focuses on material buried at depths >100 m, but the paper does not clearly articulate whether this model is specifically designed only for deep burial contexts or whether it is intended as a general approach applicable to any material where temperature history can be estimated. This distinction is crucial for understanding the significance and future potential of the work. The paper notes that shallow samples produce "uninformative" distributions, suggesting the approach breaks down near the surface where climate effects are strong. However, the paper does not explain whether this is a fundamental limitation of the model or a problem that future development might overcome. If the model applies only to deeply buried material, what is its practical scope for Quaternary dating, where most studies involve shallow deposits?
Limited Discussion of Kinetics. A major motivation for this work is to "provide a more accurate description of the kinetic behaviour of IcPD at lower temperatures." However, this stated goal is not explicitly addressed. There is minimal comparison with high-temperature data or discussion of whether the kinetic parameters differ meaningfully from what previous studies suggest.
Critical Missing Figure. A figure showing the actual IcPD vs. age data that underpins the model is missing. Such a visualization is essential context for readers to comprehend the study. I suggest using separate panels for each amino acid and color-coding the plot symbols by burial temperature.
OVERALL ASSESSMENT
This is a technically sound pilot study that tackles an understudied problem: how to extract numerical age information from amino acid data. The use of real field data, measured geothermal gradients, and Bayesian uncertainty quantification are commendable. However, the work is incomplete in its current form. The motivation and significance are unclear, the stated goal of characterizing low-temperature kinetics is underdeveloped, and the underlaying data are not clearly shown. It needs additional context and more attention to how a non-specialist might be able to comprehend and apply the approach. With these additions, this contribution would be suitable for publication.