Scale and strength of oak–mesophyte interactions in a transitional oak–hickory forest
Bibliographic record
Abstract
Forests in eastern North America are undergoing rapid compositional changes as they experience novel climate, disturbance, and pest conditions. One striking pattern is the replacement of canopy oaks (Quercus spp.) by mesic, fire-sensitive, shade-tolerant species like red maple (Acer rubrum L.). To gain insight into the successional patterns driving stand-level canopy oak replacement, we ask two questions: (i) What is the spatial association of oak and mesophyte recruitment compared with oak and mesophyte overstory individuals, and (ii) How do oaks and mesophytes differentially respond to canopy openings. We analyzed census data from a 23 ha forest plot surveyed in 2003, 2008, and 2014. We show that oak recruits are negatively associated with overstory red maples and black cherries (Prunus serotina Ehrh.), whereas mesophytic recruits were positively associated with overstory oaks. Second, we found that proximity to a dead overstory tree increased growth and survival for black cherries, increased growth for red maples, but had no effect on oaks. Black cherries and red maples are therefore better suited than oaks to take advantage of canopy openings and the moderate light available under adult oaks. These same fine scale competitive processes are contributing to canopy oak replacement across eastern North America.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".