Microsite and climatic controls of tree population dynamics: an 18‐year study on cliffs
Bibliographic record
Abstract
1 We studied a cliff-face forest ecosystem dominated by a single long-lived tree species that has been previously shown to have slow, pulsed recruitment. We assessed the degree to which microsite and climatic variability over a long period of time control recruitment, morbidity and mortality of trees at a previously disturbed cliff site. 2 We sampled more than 2000 Thuja occidentalis (eastern white cedar) over an 18-year period using a series of dynamic cohorts. We also examined a smaller area more intensively for 9 years. 3 Microsite and climate both played a role in controlling emergence and survival. Seedlings emerged preferentially in horizontal microsites such as large ledges and shelves but survival there was poor, whereas crevices and smaller ledges had lower emergence but the best survival. Decaying logs, cliff edges, vertical cliff faces and the smallest ledges proved unsuitable for seedling recruitment. Very few seedlings survived for more than 5 years. 4 While spring and summer climate influenced emergence and early survival, climate effects decreased with increasing plant size and mortality at the later stages of recruitment was independent of climate. 5 Drought and pathogens were the most common causes of mortality in horizontal habitats, while drought and rockfall were important in vertical habitats. 6 There appear to be a finite number of safe sites on cliff faces, and recruitment to those sites limits the demographic changes in tree populations over time. 7 Long-term studies on long-lived species have the value of sorting real, but unimportant, short-term variation in plant response to climate and site conditions from the long-term trends that are principally responsible for moulding the structure of the ecosystem.
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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 | 0.001 | 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 | 0.001 | 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".