Photosynthetic sensitivity to drought varies among populations of Quercus ilex along a rainfall gradient
Bibliographic record
Abstract
Drought frequency and intensity are expected to increase in the Mediterranean as a consequence of global climate change. To understand how photosynthetic capacity responds to long-term water stress, we measured seasonal patterns of stomatal (SL), mesophyll (MCL) and biochemical limitations (BL) to net photosynthesis (Amax) in three Quercus ilex (L.) populations from sites differing in annual rainfall. In the absence of water stress, stomatal conductance (gs), maximum carboxylation capacity (Vcmax), photosynthetic electron transport rate (Jmax) and Amax were similar among populations. However, as leaf predawn water potential (Ψl,pd) declined, the population from the wettest site showed steeper declines in gs, Vcmax, Jmax and Amax than those from the drier sites. Consequently, SL, MCL and BL increased most steeply in response to decreasing Ψl,pd in the population from the wettest site. The higher sensitivity of Amax to drought was primarily the result of stronger stomatal regulation of water loss. Among-population differences were not observed when gs was used instead of Ψl,pd as a drought stress indicator. Given that higher growth rates, stature and leaf area index were observed at the wettest site, we speculate that hydraulic architecture may explain the greater drought sensitivity of this population. Collectively, these results highlight the importance of considering among-population differences in photosynthetic responses to seasonal drought in large scale process-based models of forest ecosystem function.
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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) | 0.000 | 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 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".