New plant hydraulic architecture reproduces impacts of droughts in the Amazon rainforest
Bibliographic record
Abstract
The Amazon rainforest has been hit by extreme drought events in recent decades. Thereby, plant hydraulics are essential to better understand the impacts of droughts on single plants and whole forest ecosystems. Plant hydraulic mechanisms such as stomatal closure and leaf water potential are very complex, still posing challenges for current vegetation model development and parameterization. Here, we present the new hydraulic architecture of the Dynamic Global Vegetation Model LPJ-GUESS, accounting for leaf stomatal responses to plant water status and subsequent drought-induced mortality. We show that when applying the model to the Amazon rainforest we can reproduce the observed increasing trend in carbon losses and the decreasing trend in net carbon sink from plot observations over the past two decades. Our model simulations suggest that the increasing historical trend in carbon losses from mortality can be explained by hydraulic failure and associated mortality. The high biodiversity of the Amazon tropical rainforest poses further challenges for process-based models. Here we present an approach to include the diversity of plant responses to drought by simulating 37 individual Plant Functional Types (PFTs) differing in their leaf water potential regulation- and resistance to soil water stress, and provide a simple solution how to cover a wide range of species and species-specific parameters. Future modelling studies should also take species interaction and competition of different hydraulic strategies into account.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".