The effect of ontogeny, competition, site, and climate on background mortality for trees of nine species in Canadian boreal forest
Bibliographic record
Abstract
Background tree mortality can be defined as the death of trees that naturally occurs as stands develop, in the absence of major or sudden stand disturbances. The phenomenon is often linked to ontogeny and competition and generally affects individual trees, unlike catastrophic mortality, which affects most trees in the stand. To forecast stand characteristics and to estimate how stand development could change in response to changing climate, it is necessary to quantify background mortality and to identify the most important factors involved. Using data from 10 045 permanent sample plots, we modeled background tree mortality for the nine most abundant tree species of the eastern Canadian boreal forest. We used explanatory variables related to stand and tree ontogeny, competition, site characteristics, and climate to calibrate the models. We found that an increase in age, competition, and the presence of partial cut increased the mortality risk. However, the effect of DBH and site-related variables varied among species. We also found that higher temperatures, less precipitation, and higher aridity index values increased background tree mortality. According to mortality simulations under different future climate scenarios, background tree mortality could increase in the next decades for six of the nine tree species studied.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".