Articles | Volume 22, issue 23
https://doi.org/10.5194/bg-22-7687-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-22-7687-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Dec 2025
Research article |
| 08 Dec 2025
| 08 Dec 2025
Utilizing probability estimates from machine learning and pollen to understand the depositional influences on branched GDGT in wetlands, peatlands, and lakes
Amy Cromartie
CORRESPONDING AUTHOR
Université Côte d'Azur, CNRS, CEPAM, UMR 7264, 06300 Nice, France
Cindy De Jonge
Geological Institute, ETH Zürich, 8092 Zurich, Switzerland
Guillemette Ménot
ENS de Lyon, Université Lyon 1, CNRS, UMR 5276 LGL-TPE, 69364 Lyon, France
Mary Robles
Aix-Marseille Univ., CNRS, IRD, INRAE, Coll France, UMR 34 CEREGE, 13545 Aix-en-Provence, France
ISEM, Univ. Montpellier, CNRS, IRD, 34090 Montpellier, France
Lucas Dugerdil
ENS de Lyon, Université Lyon 1, CNRS, UMR 5276 LGL-TPE, 69364 Lyon, France
ISEM, Univ. Montpellier, CNRS, IRD, 34090 Montpellier, France
Odile Peyron
ISEM, Univ. Montpellier, CNRS, IRD, 34090 Montpellier, France
Marta Rodrigo-Gámiz
Department of Stratigraphy and Paleontology, University of Granada, 18071 Granada, Spain
Jon Camuera
Unit of Botany, Faculty of Pharmacy, Complutense University of Madrid, 28040 Madrid, Spain
Maria Jose Ramos-Roman
Instituto de Investigación en Cambio Global Universidad Rey Juan Carlos 28933, Madrid, Spain
Gonzalo Jiménez-Moreno
Department of Stratigraphy and Paleontology, University of Granada, 18071 Granada, Spain
Claude Colombié
Univ Lyon, UCBL, ENSL, UJM, CNRS, LGL-TPE, Villeurbanne, F-69622 France
Lilit Sahakyan
Institute of Geological Sciences, National Academy of Sciences of Republic of Armenia, Yerevan 0019, Armenia
Sébastien Joannin
ISEM, Univ. Montpellier, CNRS, IRD, 34090 Montpellier, France
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Lucas Dugerdil, Sébastien Joannin, Odile Peyron, Isabelle Jouffroy-Bapicot, Boris Vannière, Bazartseren Boldgiv, Julia Unkelbach, Hermann Behling, and Guillemette Ménot
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Since the understanding of Holocene climate change appears to be a relevant issue for future climate change, the paleoclimate calibrations have to be improved. Here, surface samples from Mongolia and Siberia were analyzed to provide new calibrations for pollen and biomarker climate models. These calibrations appear to be more powerful than global calibrations, especially in an arid central Asian context. These calibrations will improve the understanding of monsoon Holocene oscillations.
Cody C. Routson, Darrell S. Kaufman, Nicholas P. McKay, Michael P. Erb, Stéphanie H. Arcusa, Kendrick J. Brown, Matthew E. Kirby, Jeremiah P. Marsicek, R. Scott Anderson, Gonzalo Jiménez-Moreno, Jessica R. Rodysill, Matthew S. Lachniet, Sherilyn C. Fritz, Joseph R. Bennett, Michelle F. Goman, Sarah E. Metcalfe, Jennifer M. Galloway, Gerrit Schoups, David B. Wahl, Jesse L. Morris, Francisca Staines-Urías, Andria Dawson, Bryan N. Shuman, Daniel G. Gavin, Jeffrey S. Munroe, and Brian F. Cumming
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We present a curated database of western North American Holocene paleoclimate records, which have been screened on length, resolution, and geochronology. The database gathers paleoclimate time series that reflect temperature, hydroclimate, or circulation features from terrestrial and marine sites, spanning a region from Mexico to Alaska. This publicly accessible collection will facilitate a broad range of paleoclimate inquiry.
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Short summary
BrGDGT (branched glycerol dialkyl glycerol tetraethers) are a molecular biomarker utilized for paleotemperature reconstructions. One issue, however, with utilizing brGDGTs is that the distribution differs in relation to sediment environments (i.e., peat, lake, soil). We utilize the probability estimate outputs from five machine learning algorithms and a new modern brGDGT database to track provenance change and apply these models to two downcore records utilizing pollen, non-pollen polymorphs, and XRF (X-ray fluorescence) to confirm the models’ accuracy.
BrGDGT (branched glycerol dialkyl glycerol tetraethers) are a molecular biomarker utilized for...
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