Tree‐life history prior to death: two fungal root pathogens affect tree‐ring growth differently
Bibliographic record
Abstract
Summary This paper assesses whether tree‐ring patterns found in recently dead mountain pines (Pinus mugo Turra) infected by Armillaria spp. differ from those infected by Heterobasidion annosum, and determines whether and to what extent tree rings may be used as indicators of tree‐decline history (i.e. tree health conditions in relation to disease history) prior to death. Dendroecological and phytopathological analyses were undertaken in the Swiss National Park. The calendar year of death of the standing dead trees was determined by cross‐dating ring‐width patterns of dead trees to reference chronologies from living trees. This procedure is not, however, exact as there may be multiple intermittent missing rings. A remarkable discrepancy (up to 31 years) was found between the tree‐death year estimated through crown condition assessment (i.e. the presence or lack of green needles) and the date of the outermost tree ring (when tree‐ring production ceased). New needles may form and existing ones remain green for some years after the cambium at different heights along the stem has ceased activity and no new wood cells are being formed. Ring‐patterns in trees infected by Armillaria differ from those in trees infected by H. annosum. All dead trees infected by Armillaria had a slow growth decrease indicating suppression for several decades, and suggesting that Armillaria attacked trees that were already weakened by competition. In contrast, trees infected by H. annosum died over a very short period of time, although they may have been infected a long time previously. Nevertheless H. annosum seems to infect and kill trees directly , whereas Armillaria, at this site, is a secondary pathogen. This study demonstrates that tree rings may be used as indicators of the history of tree decline prior to tree death. However, the history of tree disease is difficult to reconstruct fully, e.g. tree rings do not enable the onset of infection to be dated.
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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) | 0.001 | 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".