MétaCan
Menu
Back to cohort
Record W4294609140 · doi:10.5194/bg-19-4107-2022

Carbon isotopic ratios of modern C <sub>3</sub> and C <sub>4</sub> vegetation on the Indian peninsula and changes along the plant–soil–river continuum – implications for vegetation reconstructions

2022· article· en· W4294609140 on OpenAlexaff
Frédérique Kirkels, Hugo J. de Boer, Paulina Concha Hernández, Chris Martes, Marcel T. J. van der Meer, Sayak Basu, Muhammed Usman, Francien Peterse

Bibliographic record

VenueBiogeosciences · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersAard- en Levenswetenschappen, Nederlandse Organisatie voor Wetenschappelijk OnderzoekNederlandse Organisatie voor Wetenschappelijk OnderzoekEidgenössische Technische Hochschule Zürich
KeywordsVegetation (pathology)Soil waterδ13CEnvironmental scienceIsotopes of carbonMonsoonGeologyDrainage basinHydrology (agriculture)Soil carbonTotal organic carbonStable isotope ratioSoil scienceEnvironmental chemistryClimatologyChemistryGeography

Abstract

fetched live from OpenAlex

Abstract. The large difference in the fractionation of stable carbon isotopes between C3 and C4 plants is widely used in vegetation reconstructions, where the predominance of C3 plants suggests wetter and that of C4 plants drier conditions. The stable carbon isotopic composition of organic carbon (OC) preserved in soils or sediments may be a valuable (paleo-)environmental indicator, based on the assumption that plant-derived material retains the stable carbon isotopic value of its photosynthetic pathway during transfer from plant to sediment. In this study, we investigated the bulk carbon isotopic values of C3 and C4 plants (δ13C) and of organic carbon (δ13Corg) in soils, river suspended particulate matter (SPM) and riverbed sediments to gain insight into the control of precipitation on C3 and C4 plant δ13C values and to assess changes in δ13Corg values along the plant–soil–river continuum. This information allows us to elucidate the implications of different δ13C end-members on C3 / C4 vegetation reconstructions. Our analysis was performed in the Godavari River basin, located in the core monsoon zone in peninsular India, a region that integrates the hydroclimatic and vegetation changes caused by variation in monsoonal strength. The basin has distinct wet and dry seasons and is characterised by natural gradients in soil type (from clay-rich to sandy), precipitation (∼ 500 to 1500 mm yr−1) and vegetation type (from mixed C3 / C4 to primarily C3) from the upper to the lower basin. The δ13C values of Godavari C3 plants were strongly controlled by mean annual precipitation (MAP), showing an isotopic enrichment of ∼ 2.2 ‰ from ∼ 1500 to 500 mm yr−1. Tracing δ13Corg values from plant to soils and rivers revealed that soils and riverbed sediments reflected the transition from mixed C3 and C4 vegetation in the dry upper basin to more C3 vegetation in the humid lower basin. Soil degradation and stabilisation processes and hydrodynamic sorting within the river altered the plant-derived δ13C signal. Phytoplankton dominated the δ13Corg signal carried by SPM in the dry season and year-round in the upper basin. Application of a linear mixing model showed that the %C4 plants in the different subbasins was ∼ 7 %–15 % higher using plant end-members based on measurement of the Godavari vegetation and tailored to local moisture availability than using those derived from data compilations of global vegetation. Including a correction for the 13C enrichment in Godavari C3 plants due to drought resulted in maximally 6 % lower estimated C4 plant cover. Our results from the Godavari basin underline the importance of making informed choices about the plant δ13C end-members for vegetation reconstructions, considering characteristics of the regional vegetation and environmental factors such as MAP in monsoonal regions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.225
Teacher spread0.200 · 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 teacher head, not a consensus.

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

Citations28
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueBiogeosciencesSame topicGeology and Paleoclimatology ResearchFrench-language works237,207