Building resiliency in conifer forests: Interior spruce crosses among weevil resistant and susceptible parents produce hybrids appropriate for multi-trait selection
Bibliographic record
Abstract
Tree planting programs now need to consider climate change increasingly, therefore, the resistance to pests plays an essential role in enabling tree adaptation to new ranges through tree population movement. The weevil Pissodes strobi (Peck) is a major pest of spruces and substantially reduces lumber quality. We revisited a large Interior spruce provenance/progeny trial (2,964 genotypes, 42 families) of varying susceptibility, established in British Columbia. We employed multivariate mixed linear models to estimate covariances between, and genetic control of, juvenile height growth and resistance traits. We performed linear regressions and ordinal logistic regressions to test for impact of parental origin on growth and susceptibility to the pest, respectively. A significant environmental component affected the correlations between resistance and height, with outcomes dependent on families. Parents sourced from above 950 m a.s.l. elevation negatively influenced host resistance to attacks, probably due to higher P. engelmannii proportion. For the genetic contribution of parents sourced from above 1,200 m a.s.l., however, we found less attack severity, probably due to a marked mismatch in phenologies. This clearly highlights that interspecific hybrid status might be a good predictor for weevil attacks and delineates the boundaries of successful spruce population movement. Families resulting from crossing susceptible parents generally showed fast-growing trees were the most affected by weevil attacks. Such results indicate that interspecific 'hybrids' with a higher P. glauca ancestry might be genetically better equipped with an optimized resource allocation between defence and growth and might provide the solution for concurrent improvement in resistance against weevil attacks, whilst maintaining tree productivity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".