Factors controlling the triple oxygen isotope composition of grass leaf water and phytoliths: insights for paleo-environmental reconstructions
Bibliographic record
Abstract
The oxygen isotope signature of leaf water is used to trace several processes at the soil-plant-atmosphere interface. During photosynthesis, it is transferred to the oxygen isotope signature of atmospheric CO2 and O2, which can be used for reconstructing past changes in gross primary production. The oxygen isotope signature of leaf water additionally imprints leaf organic and mineral compounds, such as phytoliths, used as paleoclimate and paleovegetation proxies when extracted from sedimentary materials. Numerous experimental and modelling studies were dedicated to constrain the main parameters responsible for changes in the δ18O of leaf water. Although these models usually correctly depict the main trends of 18O-enrichment of the leaf water when relative humidity decreases, the calculated absolute values often depart from the observed ones by several ‰. Moreover, the δ18O of leaf water absorbed by plants is dependent on the δ18O value of meteoric and soil waters that can vary by several ‰ at different space and time scales. These added uncertainties make our knowledge of the parameters responsible for changes in the δ18O of leaf water and phytoliths flawed. Changes in the triple oxygen isotope composition of leaf water, expressed by the 17O-excess, are controlled by fewer variables than changes in δ18O. In meteoric water the 17O-excess varies slightly as it is weakly affected by temperature or phase changes during air mass transport. This makes the soil water fed by meteoric water and the atmospheric vapour in equilibrium with meteoric water changing little from a place to another. Hence the 17O-excess of leaf water is essentially controlled by the evaporative fractionation. The latest depends on the ratio of vapor pressure in the air to vapor pressure in the stomata intercellular space, close to relative humidity. Leaf water evaporative fractionation can lead to 17O-excess negative values that can exceed most of surficial water ones. Here we present the outcomes of several recent growth chamber and field studies, for the purpose of i) refining the grass leaf water and phytoliths δ18O and 17O-excess modelling, ii) assessing whether the δ18O and 17O-excess of grass leaf water can be reconstructed from phytoliths, and iii) examining the precision of the 17O-excess of phytoliths as a new proxy for past changes in continental atmospheric relative humidity. Atmospheric continental relative humidity is an important climate parameter poorly constrained in global climate models. A model-data comparison approach, applicable beyond the instrumental period, is essential to progress on this issue. However, there is currently a lack of proxies allowing quantitative reconstruction of past continental relative humidity. The 17O-excess signature of phytoliths could fill this gap.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".