Characterization of Fungal Pathogens Associated with White Pine Needle Damage (WPND) in Northeastern North America
Bibliographic record
Abstract
Eastern white pine is a crucial ecological and economic component of forests in the northern USA and eastern Canada, and is now facing an emerging problem in white pine needle damage (WPND). It is still unclear whether WPND results from one, or the combination of several fungal pathogens. Therefore, the first objective of this study was to characterize the fungi associated with WPND in the northeastern United States and document the damage being done to mature eastern white pine as a result of repeated defoliation. To date, 22 species of fungi, either cultured from diseased pine needles or formed fruiting bodies on pine needles were identified based on morphology and sequence data. Lecanosticta acicola and a putative new species of Septorioides were the species most frequently recovered from diseased needles, in addition to needle cast fungi Lophophacidium dooksii and Bifusella linearis, two obligate fungal pathogens that were frequently observed on pine needles in the northeast, but have not been known to cause excessive defoliation of eastern white pine. A second objective was to monitor yearly the health of 63 pairs of healthy and unhealthy trees in eight affected locations throughout New England. Since 2012, affected trees are increasingly and repeatedly chlorotic and defoliated every year. Trees that were initially healthy are now exhibiting symptoms. While L. acicola appears to be the primary pathogen causing WPND, several other common needle pathogens are being more frequently observed and the role of climate change may be important in the disease ecology of WPND. These defoliation events, while once a sporadic occurrence, have now become more frequent as observed in continued crown deterioration of eastern white pine in long-term monitoring plots followed during the course of this three-year study.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".