Resin ducts as resistance traits in conifers: linking dendrochronology and resin-based defences
Bibliographic record
Abstract
Conifers have evolved different chemical and anatomical defences against a wide range of antagonists. Resin ducts produce, store and translocate oleoresin, a complex terpenoid mixture that acts as both a physical and a chemical defence. Although resin duct characteristics (e.g., number, density, area) have been positively related to biotic resistance in several conifer species, the literature reporting this association remains inconclusive. Axial resin ducts recorded in annual growth rings are an archive of annual defensive investment in trees. This whole-life record of defence investment can be analysed using standard dendrochronological procedures, which allows us to assess interannual variability and the effect of understudied drivers of phenotypic variation on resin-based defences. Understanding the sources of phenotypic variation in defences, such as genetic differentiation and environmental plasticity, is essential for assessing the adaptive potential of forest tree populations to resist pests under climate change. Here, we reviewed the evidence supporting the importance of resin ducts in conifer resistance, and summarized current knowledge about the sources of variation in resin duct production. We propose a standardized methodology to measure resin duct production by means of dendrochronological procedures. This approach will illuminate the roles of resin ducts in tree defence across species, while helping to fill pivotal knowledge gaps in plant defence theory, and leading to a robust understanding of the patterns of variation in resin-based defences throughout the tree's lifespan.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".