Native forest pathogens facilitate persistence of Douglas-fir in old-growth forests of northwestern California
Bibliographic record
Abstract
Forest pathogens and insects can accelerate tree mortality, increase stand structural heterogeneity, and alter tree community composition. In northern California, the canopy trees Abies concolor var. lowiana (Gord. & Glend.) Lemmon (white fir) and Pseudotsuga menziesii var. menziesii (Mirbel) Franco (Douglas-fir) co-occur but vary in shade tolerance and regenerative abilities following disturbance. Field observations suggested that mortality and turnover of white fir exceeded that of Douglas-fir and that native pathogens may be important drivers in the absence of fire. Pathogens and bark beetles were sampled in old-growth white fir – Douglas-fir stands in northwestern California to assess their contribution to tree mortality, gap formation, and regeneration. We determined abundances and size class distributions of canopy trees, presence of pathogens and bark beetles, and causes of tree mortality. We sampled canopy gaps and closed-canopy forests for overstory species composition, cause of mortality of gap-maker trees, and regeneration of white fir and Douglas-fir. Root-rot fungi accounted for significantly higher mortality and gap formation in white fir than in Douglas-fir. Relative seedling–sapling density of Douglas-fir was higher in pathogen-induced canopy gaps than in closed-canopy forest. In the absence of fire, native forest pathogens enable regeneration and persistence of Douglas-fir by enhancing mortality of white fir, resulting in canopy gap formation.
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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 | 0.001 | 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".