Recent progress in the application of hydrogen isotopes from tree-ring lignin methoxy groups as a climate proxy
Bibliographic record
Abstract
Stable hydrogen isotope values of tree-lignin methoxy groups (δ2HLM) are increasingly applied to reconstruct the stable hydrogen isotope composition of precipitation (δ2Hprecip) and mean annual temperatures in mid-latitude regions. The climate reconstructions are typically derived considering an isotope fractionation from -200 mUr to -216 mUr between lignin methoxy groups and tree source water (Keppler et al. 2007, Anhäuser et al. 2017, Greule et al., 2021, Porter et al. 2022, Wieland et al. 2022). This empirical relationship was derived from different tree species collected along a European north-south transect at elevations below 1000 m above sea level. However, it is so far unknown how environmental and physiological factors such as tree age, tree species, salinity, elevation, or precipitation amount influence the biochemical hydrogen isotope fractionation between lignin methoxy groups and precipitation.We here present several recent investigations that show how environmental and tree physiological factors might influence δ2HLM values. For example, potential amount effects of precipitation are analysed using tree cores from the Carpathians, where the mountain barrier led to large precipitation events at the Luv site. In addition, potential age trends are studied using trees from Greece that are over 500 years old, and the phylogenetical range of δ2HLM values is assessed by comparing 70 different tree species grown under uniform climatic conditions. Finally, the influence of salinity is evaluated by analysing different mangrove tree species from Australia.The improvements and limitations of δ2HLM values as a climate proxy at different spatial and temporal scales will be discussed. In order to better reconstruct long-term climate variations, additional gain is expected from the cross-comparison of multiple isotope proxies, including stable carbon isotope values of cellulose and lignin methoxy groups, as well as stable oxygen isotopes of cellulose.References:Anhäuser, T., Greule, M., Polag, D., Bowen, G. J., and Keppler, F.: Mean annual temperatures of mid-latitude regions derived from δ2H values of wood lignin methoxyl groups and its implications for paleoclimate studies, Sci. Total Environ., 574, 1276–1282, https://doi.org/10.1016/j.scitotenv.2016.07.189, 2017.Greule, M., Wieland, A., and Keppler, F.: Measurements and applications of δ2H values of wood lignin methoxy groups for paleoclimatic studies, Quaternary Sci. Rev., 268, 107107, https://doi.org/10.1016/j.quascirev.2021.107107, 2021.Keppler, F., Harper, D. B., Kalin, R. M., Meier-Augenstein, W., Farmer, N., Davis, S., Schmidt, H. L., Brown, D. M., and Hamilton, J. T. G.: Stable hydrogen isotope ratios of lignin methoxyl groups as a paleoclimate proxy and constraint of the geographical origin of wood, New Phytol., 176, 600–609, https://doi.org/10.1111/j.1469-8137.2007.02213.x, 2007.Porter, T. J., Anhäuser, T., Halfar, J., Keppler, F., Csank, A. Z., and Williams, C. J.: Canadian Arctic Neogene temperatures reconstructed from hydrogen isotopes of lignin‐methoxy groups from sub‐fossil wood, Paleoceanogr. Paleoclimatology, 37, https://doi.org/10.1029/2021pa004345, 2022.Wieland, A., Greule, M., Roemer, P., Esper, J., and Keppler, F.: Climate signals in stable carbon and hydrogen isotopes of lignin methoxy groups from southern German beech trees, Clim. Past, 18, 1849–1866, https://doi.org/https://doi.org/10.5194/cp-18-1849-2022, 2022.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".