Response of ecosystem intrinsic water use efficiency and gross primary productivity to rising vapor pressure deficit
Bibliographic record
Abstract
Abstract Elevated vapor pressure deficit (VPD) due to drought and warming is well-known to limit canopy stomatal and surface conductance, but the impacts of elevated VPD on ecosystem gross primary productivity (GPP) are less clear. The intrinsic water use efficiency (iWUE), defined as the ratio of carbon (C) assimilation to stomatal conductance, links vegetation C gain and water loss and is a key determinant of how GPP will respond to climate change. While it is well-established that rising atmospheric CO 2 increases ecosystem iWUE, historic and future increases in VPD caused by climate change and drought are often neglected when considering trends in ecosystem iWUE. Here, we synthesize long-term observations of C and water fluxes from 28 North American FLUXNET sites, spanning eight vegetation types, to demonstrate that ecosystem iWUE increases consistently with rising VPD regardless of changes in soil moisture. Another way to interpret this result is that GPP decreases less than surface conductance with increasing VPD. We also project how rising VPD will impact iWUE into the future. Results vary substantially from one site to the next; in a majority of sites, future increases in VPD (RCP 8.5, highest emission scenario) are projected to increase iWUE by 5%–15% by 2050, and by 10%–35% by the end of the century. The increases in VPD owing to elevated global temperatures could be responsible for a 0.13% year −1 increase in ecosystem iWUE in the future. Our results highlight the importance of considering VPD impacts on iWUE independently of CO 2 impacts.
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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".