Different vital rates of Engelmann spruce and subalpine fir explain discordance in understory and overstory dominance
Bibliographic record
Abstract
Studies of forest dynamics commonly assume that species composition of the seedling bank reflects the composition of the future forest canopy. However, many forest types exhibit persistent differences in relative dominance of species in the seedling bank versus the forest canopy. Species-specific differences in tree vital rates (e.g., in-growth, mortality, height growth, canopy residence time) across canopy positions may explain this discord in dominance between seedling banks and forest canopies. We tested for differences in tree vital rates for two widely distributed, coexisting species in subalpine forests of the Rocky Mountains, North America. We quantified seedling bank dynamics (>950 aged seedlings) and vital rates in permanent plots (>2500 trees) from 1982 to 2017 to determine if differences in vital rates explained the shift from seedling bank dominance by subalpine fir (Abies lasiocarpa (Hook.) Nutt.) to codominance of the main canopy by subalpine fir and Engelmann spruce (Picea engelmannii Parry ex Engelm.). Higher rates of fir recruitment into the main canopy were balanced by equally high rates of mortality, whereas spruce exhibited higher rates of net population increase and longer residence time in the main canopy. Projections of future forest trajectories from seedling bank composition can be improved by considering species-specific differences in vital rates.
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) | 0.001 | 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".