Weak local adaptation to drought in seedlings of a widespread conifer
Bibliographic record
Abstract
Abstract There is an urgent need for better understanding how populations of trees will respond to predictable changes in climate and the intensification of extreme weather events such as droughts. The distribution of adaptive traits in seedlings is a crucial component of population adaptive potential and its characterization is important for development of management approaches mitigating the effects of climate change on forests. In this study, we used a large-scale common garden drought experiment to characterize the variation in drought tolerance, growth, and plastic responses to extreme summer drought in seedlings of 73 natural provenances of the two main varieties of Douglas-fir ( Pseudotsuga menziesii var. menziesii and var. glauca ), sampled across most of their extensive natural ranges. We detected large differences between the two Douglas-fir varieties for all traits assessed, with var. glauca showing higher tolerance to drought but slower height growth and less plasticity than var. menziesii . Surprisingly, signals of local adaptation to drought within varieties were weak within var. glauca and nearly absent within var. menziesii . Temperature-related variables were identified as the main climatic drivers of clinal variation in drought tolerance and height growth species-wide, and in height growth within var. menziesii . Furthermore, our data indicate that higher plasticity under extreme droughts could be maladaptive in var. menziesii . Overall, our study suggests that genetic variation for drought tolerance in seedlings is maintained primarily within rather than among provenances within varieties and does not compromise growth at early stages of plant development. Given these results, assisted gene flow is unlikely to help facilitate adaptation to drought within Douglas-fir varieties, but selective breeding within provenances could accelerate adaptation.
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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".