Assessing population‐level response to interacting temperature and moisture stress
Bibliographic record
Abstract
Abstract Greenhouse experiments have been pivotal to predicting the likely response of tree species to future climate. However, there are some common inadequacies in the inferences derived from many of the studies. Moisture and temperature effects are tightly coupled but in controlled experiments, only a few studies acknowledged the interacting nature of these factors. Furthermore, there is evidence that population‐level plasticity is relevant to plant survival in novel environments. We posit that an inference derived from response to a single climatic factor is likely incomplete and hypothesised that adaptive properties inherent in population‐level plasticity mediate plant growth in novel environments. We tested this hypothesis using a greenhouse experiment involving four populations of white birch ( Betula papyrifera Marsh) grown under two temperatures and two moisture regimes. We examined variations in their photosynthetic rates ( A ), water‐use efficiency (WUE), water potential ( ψ pd ) and stomatal conductance ( g s ). We also investigated variations in their height growths, height relative growth rates ( RGR ht ), and biomass accumulations. Interaction of temperature and moisture was consistently significant for most of the traits. Contrary to expectation, population from cold climate had the highest growth in the high temperature treatments while a coastal population had the highest WUE in low water treatments and also showed greatest decline in growth responses. Some of the results also suggest that there is an overriding effect of phenotypic plasticity over local adaption in white birch. Collectively, the results underscore the growing awareness that populations would likely respond differently in the event of climate change.
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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 | 0.001 | 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 | 0.000 | 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".