Seed-based approach for identifying flora at risk from climate warming
Bibliographic record
Abstract
In obligate seeding species, the germination niche is crucial for colonization and population survival. It is a high-risk phase in a plant's life cycle, and is directly regulated by temperature. Seeds germinate over a range of temperatures within which there is an optimum temperature, with thresholds above and below which no germination occurs. We suggest that abrupt changes in temperature associated with a warming climate may cause a disconnect between temperatures seeds experience and temperatures over which germination is able to occur, rendering obligate seeding species vulnerable to decline and extinction. Using a bidirectional temperature gradient system, we examined the thermal constraints in the germination niche of some geographically restricted species from the low altitude mountains of the Stirling Range, southern Western Australia, including seedlots from lowland populations of four of these species. We demonstrated that high temperatures are not a limiting factor for germination in some restricted species, signifying a lack of relationship between geographic range size and breadth of the germination niche. In contrast, we identified other restricted species, in particular Sphenotoma drummondii, as being at risk of recruitment failure as a consequence of warming: seeds of this species showed a strong negative relationship between percentage germination and increasing temperature above a relatively low optimum constant temperature (13°C). We found some ecotypic differences in the temperature profiles between seeds collected from montane or lowland populations of Andersonia echinocephala, and while specific populations may become more restricted, they are perhaps at less risk of extinction from climate warming. This seed-based approach for identifying extinction risk will contribute tangibly to efforts to predict plant responses to environmental change and will assist in prioritizing species for management actions, directing limited resources towards further investigations and can supplement bioclimatic modelling.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".