Implications of plant metabolic source-sink feedbacks for modelling the terrestrial carbon balance
Bibliographic record
Abstract
Dynamic global vegetation models (DGVMs) are used to attribute historical and forecast future atmosphere-land carbon (C) exchange. While mean long-term behaviour across DGVMs is compatible with observational constraints on the global C cycle over recent decades, differences between models are high both in annual fluxes and attribution of the long-term net carbon uptake to drivers such as atmospheric CO2, indicating significant uncertainty regarding process understanding. These models are largely C source-driven, with behaviour primarily determined by the environmental responses of photosynthesis. However, real plants are integrated wholes, with feedbacks between sources (e.g. photosynthesis) and sinks (e.g. growth) resulting in homeostatic concentrations of metabolites such as sugars. An approach to implementing such behaviour in a plant growth model is presented and its implications for responses to environmental factors assessed. The approach uses Hill functions to represent inhibition of C sources (net photosynthesis) and activation of sinks (structural growth) based on sugar concentrations. The model is parameterised for a mature tropical rainforest site and its qualitative behaviour is found to be consistent with experimental observations. Key findings are that sinks and sources strongly regulate each other. For example, doubling potential net photosynthesis (i.e. the rate that would occur without feedback) results in growth increasing by only 1/3 at equilibrium, with increased sugar concentration causing feedback-inhibition of photosynthesis. A C source-only driven response, as in current DGVMs, would result in close to a doubling of growth. Hence, in this approach, environmental factors that affect potential net photosynthesis, such as atmospheric CO2, have greatly reduced effects on growth and net C uptake when homeostatic behaviour of sugars is considered. Implications for understanding and modelling the global carbon cycle are discussed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".