The tree mortality regime in temperate old-growth coniferous forests: the role of physical damage
Bibliographic record
Abstract
Aspects of the tree mortality regime were characterized for old-growth conifer forests in Mount Rainier National Park, Washington, USA, using individual tree (stems ≥5 cm diameter at breast height (dbh)) records from a network of permanent forest research plots. Average annual forest-wide mortality rates of trees ≥15 cm dbh never exceeded 1% on a stem-density or basal-area basis; mortality was slightly higher for stems <15 cm dbh. Physical agents of mortality (uprooting, stem breakage, and crushing by falling debris) accounted for approximately 40% and 45% of mortality events in trees <15 and ≥15 cm dbh, respectively. These physical processes were chronic sources of mortality: they were not associated with a single or few disturbance events. Preexisting decay fungi were associated with trees that died proximately due to stem breakage (41%) and uprooting (22%), consistent with a predisposing role of decay fungi in trees that die due to mechanical damage. Given the importance of physical processes in the tree mortality regime, we suggest that a richer mechanistic understanding of the causes and consequences of tree mortality in natural forests will be achieved with models that consider the physical, as well as the physiological, attributes of trees and forests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".