The Physiological Adjustments of Two Xerophytic Shrubs to Long-Term Summer Drought
Bibliographic record
Abstract
Adaptive characteristics of plants, such as those associated with photosynthesis and resource use efficiency, are usually affected by synthesis costs and resource availability. The impact of extreme climate events such as long-term drought on plant physiological functions needs to be examined, particularly as it concerns the internal management of water and nitrogen (N) resources. In this study, we evaluated the resource management strategies for water and N by xerophytic shrubs, Artemisia ordosica and Salix psammophila, under extreme summer drought. This was carried out by comparing the plants’ physiological status during periods of wet and dry summer conditions in 2019 and 2021. Compared with the wet period, A. ordosica and S. psammophila both decreased their light-saturated net carbon (C) assimilation rate (Asat), stomatal conductance (gs), transpiration rate (E), leaf N content per leaf area (Narea), and photosynthetic N use efficiency (PNUE) during the summer drought. Whether in wet or dry summers, the gas-exchange parameters and PNUE of A. ordosica were generally greater than those associated with S. psammophila. The instantaneous water use efficiency (IWUE) response to drought varied with species. As a drought-tolerant species, the A. ordosica shrubs increased their IWUE during drought, whereas the S. psammophila shrubs (less drought-tolerant) decreased theirs. The divergent responses to drought by the two species were largely related to differences in the sensitivity of gs, and as a result, E. Compared with A. ordosica, S. psammophila’s inferior plasticity regarding gs response affected its ability to conserve water during drought. Our research illustrates the need for assessing plasticity in gs when addressing plant adaptation to long-term drought. A high dry-season IWUE in xerophytic shrubs can benefit the plants by augmenting their C gain.
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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".