ENSO effects on the transpiration of eastern Amazon trees
Bibliographic record
Abstract
Tree transpiration is important in the recycling of precipitation in the Amazon and might be negatively affected by El Niño–Southern Oscillation (ENSO)–induced droughts. To investigate the relative importance of soil moisture deficits versus increasing atmospheric demand (VPD) and determine if these drivers exert different controls over tree transpiration during the wet season versus the dry season (DS), we conducted sap flow measurements in a primary lowland tropical forest in eastern Amazon during the most extreme ENSO-induced drought (2015/2016) recorded in the Amazon. We also assessed whether trees occupying different canopy strata contribute equally to the overall stand transpiration ( T stand ). Canopy trees were the primary source of T stand . However, subcanopy trees are still important as they transpired an amount similar to other biomes around the globe. Tree water use was higher during the DS, indicating that during extreme drought trees did not reduce transpiration in response to low soil moisture. Photosynthetically active radiation and VPD exerted an overriding effect on water use patterns relative to soil moisture during extreme drought, indicating that light and atmospheric constraints play a critical role in controlling ecosystem fluxes of water. Our study highlights the importance of canopy and subcanopy trees to the regional water balance and highlights the resilience to droughts that these trees show during an extreme ENSO event. This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications’.
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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 | 0.000 |
| 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) | 0.001 | 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".