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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".