Simulated ecosystem threshold responses to co-varying temperature, precipitation and atmospheric CO2 within a region of Amazonia
Bibliographic record
Abstract
Aim Using a Dynamic Global Vegetation Model (DGVM), we assessed the independent and co-varying effects of temperature, precipitation and atmospheric CO2 on nonlinear (threshold) responses in carbon-based processes, and evaluated whether these underlying process thresholds translate to the ecosystem-scale. Location Amazon Basin, South America. Methods The Lund-Potsdam-Jena model (LPJ) was employed to determine responses in net primary production (NPP), heterotrophic respiration (RH), vegetation carbon (CV), soil carbon (CS), and plant functional type (PFT) composition to variations in temperature (± 9 °C relative to the control), precipitation (up to 80% reduction in rainfall relative to the control) and atmospheric CO2 (± 100 p.p.m.v. relative to the control). Results Our modelling experiments show that increases in temperature result in lower and steeper NPP and RH curves, indicating a thermal threshold at current temperature conditions. Under a combination of temperature and precipitation change, CV responds more to precipitation, while CS closely follows temperature gradients. Ecosystem thresholds, measured in terms of PFT composition stability, are surprisingly few. Simulations indicate an ecosystem threshold occurring at 80% reduction in rainfall; however, due to modelling limitations, this threshold is likely to occur at earlier drought stress conditions. Further empirical research on abiotic stress tolerance levels in tropical ecosystems must be performed in order to refine PFT descriptions used in DGVMs. Main conclusion In evaluating simulation scenarios that promote major changes in PFT assemblage, we conclude that the ‘natural’ Amazonian rain forest is resilient to environmental change, particularly to decreases in temperature and precipitation. Determining to what extent anthropogenic pressures have altered this resiliency is of utmost importance in predicting the future fate of the Amazon Basin.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".