Quercus ilex L. dieback is genetically determined: Evidence provided by dendrochronology, δ13C and SSR genotyping
Bibliographic record
Abstract
Quercus ilex L. dieback has been reported in several Mediterranean forests, revealing different degree of crown damages even in close sites, as observed in two Q. ilex forest stands in southern Tuscany (IT). In this work, we applied a novel approach combining dendrochronological, tree-ring δ13C and genetic analysis to test the hypothesis that different damage levels observed in a declining (D) and non-declining (ND) Q. ilex stands are connected to population features linked to distinct response to drought. Furthermore, we investigated the impact of two major drought events (2012 and 2017), that occurred in the last fifteen years in central Italy, on Q. ilex growth and intrinsic water use efficiency (WUEi). Overall, Q. ilex showed slightly different ring-width patterns between the two stands, suggesting a lower responsiveness to seasonal climatic variations for trees at D stand, while Q. ilex at ND stand showed changes in the relationship between climatic parameters and growth across time. The strong divergence in δ13C signals between the two stands suggested a more conservative use of water for Q. ilex at ND compared to D stand that may be genetically driven. Q. ilex at ND resulted more resilient to drought compared to trees at D, probably thanks to its safer water strategy. Genotyping analysis based on simple-sequence repeat (SSR) markers revealed the presence of different Q. ilex populations at D and ND stands. Our study shows intraspecific variations in drought response among trees grown in close. In addition, it highlights the potential of combining tree-ring δ13C data with SSR genotyping for the selection of seed-bearing genotypes aimed to preserve Mediterranean holm oak ecosystem and improve its forest management.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".