Relationships between juvenile tree survival and tree density, shrub cover and temperature vary by size class based on ratios of abundance
Bibliographic record
Abstract
Global change drivers are altering forest dynamics, yet how these factors influence tree survival across early developmental stages (i.e., seedling to recruited sapling) over large geographies is not well understood. We developed a novel approach to evaluate controls on seedling and sapling survival. This approach was demonstrated on a set of systematic forest inventory plots across the northeastern USA in which seedlings were tallied within six height classes, allowing for a detailed assessment of the stages at which demographic bottlenecks in juvenile tree development are often observed. Forest inventory subplots containing a study species were divided into overlapping bins along an environmental or ecological gradient, and ratios of abundance between successive size classes were used to infer relative survival rates. Relationships between 10 common tree species and tree density, shrub cover, and mean annual temperature were assessed. As seedling height class increased, we observed shifts from positive to negative associations with shrub cover and large tree density. Our results suggest that observed patterns of sapling and tree abundance may belie complex and sometimes opposing influences on seedling survival that are important to quantify when predicting and managing for successful tree recruitment and future canopy tree composition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".