The Red Queen and the seed bank: pathogen resistance of <i>ex situ</i> and <i>in situ</i> conserved barley
Bibliographic record
Abstract
Plant geneticists have proposed that the dynamic conservation of crop plants in farm environments (in situ conservation) is complementary to static conservation in seed banks (ex situ conservation) because it may help to ensure adaptation to changing conditions. Here, we test whether collections of a traditional variety of Moroccan barley (Hordeum vulgare ssp. vulgare) conserved ex situ showed differences in qualitative and quantitative resistance to the endemic fungal pathogen, Blumeria graminis f.sp. hordei, compared to collections that were continuously cultivated in situ. In detached-leaf assays for qualitative resistance, there were some significant differences between in situ and ex situ conserved collections from the same localities. Some ex situ conserved collections showed lower resistance levels, while others showed higher resistance levels than their in situ conserved counterparts. In field trials for quantitative resistance, similar results were observed, with the highest resistance observed in situ. Overall, this study identifies some cases where the Red Queen appears to drive the evolution of increased resistance in situ. However, in situ conservation does not always result in improved adaptation to pathogen virulence, suggesting a more complex evolutionary scenario, consistent with several published examples of plant-pathogen co-evolution in wild systems.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".