Natural root grafting in <i>Picea mariana</i> to cope with spruce budworm outbreaks
Bibliographic record
Abstract
Spruce budworm (Choristoneura fumiferana Clem.) outbreaks cause extensive mortality and growth reductions throughout boreal forests in eastern North America. As tree vulnerability to defoliation remains partially unexplained by tree and stand attributes, we hypothesized that root grafting might attenuate the negative impact of severe defoliation in tree growth. Two experimental sites in the Abitibi-Témiscamingue region dominated by black spruce (Picea mariana Mill.) were harvested and hydraulically excavated to study tree growth in 36 trees in relation to root grafting and the last spruce budworm outbreak using dendroecological methods. Root grafts reduced the negative effects of defoliation by maintaining stable growth in connected trees during epidemic periods. Among dominant trees, growth releases immediately after the outbreak were uniquely observed in grafted trees. Among suppressed trees, grafted trees tended to grow more than non-grafted trees when defoliation severity was the highest. Carbohydrate transfers through root grafts and enhanced efficiency to acquire resources may explain the better performance of grafted trees under scenarios of limited carbon supply. This study reinforces the growing body of literature that suggests root grafting as a cooperative strategy to withstand severe disturbances and highlights the key role of root grafting in stand dynamics to cope with periodic outbreaks.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".