Evidence of population variation in drought tolerance during seed germination in four Banksia (Proteaceae) species from Western Australia
Bibliographic record
Abstract
Given the predicted changes in rainfall patterns for many Mediterranean climate regions, identifying seed tolerance to moisture stress in the earliest phase of plant development is an important consideration for species conservation, management and restoration. Here, we used polyethylene glycol (PEG 8000) to induce plant water deficit similar to drought stress in a field situation. Seeds of four Western Australia Banksia R.Br. (Proteaceae) species were incubated at seven levels of moisture potential (0 to -1.5 MPa) and three constant temperatures (10°C, 15°C and 20°C). In the absence of moisture stress, germination was uniformly high, but increasing drought stress led to reduced and delayed germination in all species. Overall, the threshold moisture potential value for a significant decline, and delay, in germination was –0.25 MPa. Results suggested that one species (B. coccinea) is likely to be most vulnerable to germination failure under predicted changes in rainfall patterns, whereas another (B. media) is likely to be less vulnerable. There was significant variation in population response to drought stress. However, this variation could not be explained by rainfall across species distributions. We discuss the PEG approach for assessing seed sensitivity to moisture stress, particularly in the context of shifting rainfall under 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 | 0.000 | 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".