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Record W2992263131 · doi:10.5194/bg-16-4613-2019

Effects of leaf length and development stage on the triple oxygen isotope signature of grass leaf water and phytoliths: insights for a proxy of continental atmospheric humidity

2019· article· en· W2992263131 on OpenAlexaff
Anne Alexandre, Elizabeth A. Webb, Amaëlle Landais, Clément Piel, Sébastien Devidal, Corinne Sonzogni, Martine Couapel, Jean-Charles Mazur, Monique Pierre, Frédéric Prié, Christine Vallet‐Coulomb, Clément Outrequin, Jacques Roy

Bibliographic record

VenueBiogeosciences · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsWestern University
FundersCentre National de la Recherche ScientifiqueAgence Nationale de la Recherche
KeywordsPhytolithIsotopes of oxygenδ18OTransectFractionationRelative humidityIsotope analysisStable isotope ratioEnvironmental chemistryAtmospheric sciencesEnvironmental scienceChemistryBotanyGeologyBiologyEcologyGeochemistryGeographyPhysics

Abstract

fetched live from OpenAlex

Continental relative humidity (RH) is a key climate parameter, but there is a lack of quantitative RH proxies suitable for climate model–data comparisons. Recently, a combination of climate chamber and natural transect calibrations have laid the groundwork for examining the robustness of the triple oxygen isotope composition ( δ ′18 O and 17 O-excess) of phytoliths, that can preserve in sediments, as a new proxy for past changes in RH. However, it was recommended that besides RH, additional factors that may impact δ ′18 O and 17 O-excess of plant water and phytoliths be examined. Here, the effects of grass leaf length, leaf development stage and day–night alternations are addressed from growth chamber experiments. The triple oxygen isotope compositions of leaf water and phytoliths of the grass species F. arundinacea are analysed. Evolution of the leaf water δ ′18 O and 17 O-excess along the leaf length can be modelled using a string-of-lakes approach to which an unevaporated–evaporated mixing equation must be added. We show that for phytoliths to record this evolution, a kinetic fractionation between leaf water and silica, increasing from the base to the apex, must be assumed. Despite the isotope heterogeneity of leaf water along the leaf length, the bulk leaf phytolith δ ′18 O and 17 O-excess values can be estimated from the Craig and Gordon model and a mean leaf water–phytolith fractionation exponent ( λ Phyto-LW ) of 0.521. In addition to not being leaf length dependent, δ ′18 O and 17 O-excess of grass phytoliths are expected to be impacted only very slightly by the stem vs. leaf biomass ratio. Our experiment additionally shows that because a lot of silica polymerises in grasses when the leaf reaches senescence (58 % of leaf phytoliths in mass), RH prevailing during the start of senescence should be considered in addition to RH prevailing during leaf growth when interpreting the 17 O-excess of grass bulk phytoliths. Although under the study conditions 17 O-excess Phyto do not vary significantly from constant day to day–night conditions, additional monitoring at low RH conditions should be done before drawing any generalisable conclusions. Overall, this study strengthens the reliability of the 17 O-excess of phytoliths to be used as a proxy of RH. If future studies show that the mean value of 0.521 used for the grass leaf water–phytolith fractionation exponent λ Phyto-LW is not climate dependent, then grassland leaf water 17 O-excess obtained from grassland phytolith 17 O-excess would inform on isotope signals of several soil–plant-atmosphere processes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.013
GPT teacher head0.219
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2019
Admission routes1
Has abstractyes

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