Stranger in a strange land: genetic variation of native insect resistance biomarkers in UK Sitka spruce (<i>Picea sitchensis</i> [Bong.] Carr.)
Bibliographic record
Abstract
Abstract Reforestation in the British Isles (UK and Ireland) has been dominated with the use of an exotic conifer tree species, Sitka spruce (Picea sitchensis [Bong.] Carr.). Sitka breeding in the UK was developed from a single provenance, the Haida Gwaii Islands (Canada), which is both well suited to the British climate and highly susceptible to the white pine weevil (Pissodes strobi L.) in its native range. We examined variation and heritability of insect resistance related traits and assessed potential trade-offs with tree growth in 50 full-sibling families and 13 clonally replicated genotypes growing in the UK. We measured bark levels of three terpenes (dehydroabietic acid, (+)-3-carene and terpinolene) shown to confer resistance to the white pine weevil in Sitka spruce’s native range, on the principle that these defence compounds may also contribute to pest resistance in the UK. We compared our results with published findings from the native range and also used individuals from a Haida Gwaii seed lot grown in the UK for comparison of terpene levels. Dehydroabietic acid content in the UK breeding population was similar to populations from resistant native populations, but (+)-3-carene and terpinolene levels were relatively low. Narrow sense heritability for dehydroabietic acid, (+)-3-carene and terpinolene was estimated as 0.20, 0.93 and 0.98, respectively from the full-sib data, and this evidence of genetic variance was supported by estimates of broad sense heritability from the smaller clonal study. Terpene content was found to be positively correlated to growth traits. The heritability estimates and genetic correlations indicate that selective breeding should be effective in raising levels in the UK breeding population of the three candidate terpenes implicated in weevil resistance. However, low levels observed indicate that other provenances from the native range may produce greater short-term improvements for two of the terpenes.
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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 | 0.001 |
| 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) | 0.001 | 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".