Canopy tree growth responses following selection harvest in seven species varying in shade tolerance
Bibliographic record
Abstract
We used tree ring measurements to investigate the temporal response of basal area increment (BAI) of canopy trees following selection harvests by sampling across a chronosequence of stands with known harvest dates in tolerant hardwood (Great Lakes – St. Lawrence) stands in central Ontario. Seven tree species of various shade tolerances ranged widely in their responses to reduced competition. The more shade-tolerant species responded more positively: shade-tolerant species showed an average increase in BAI of 35% 4–15 years postharvest compared with 16% for mid-tolerant species and –7.5% for intolerant species. All species showed a time-lag in postharvest growth responses, with maximum growth responses occurring between 3 and 15 years postharvest. Tree size was the most important factor determining the magnitude of BAI response, with smaller trees consistently responding more than larger trees. We suggest that higher growth responses to selection harvests among shade-tolerant species may contribute to declines in mid-tolerant species abundance in selection-managed stands. More broadly, interspecific variability in canopy tree responses to forest disturbance appears to follow patterns distinct from seedling and sapling responses, with important implications to forest community dynamics in both managed and unmanaged forests.
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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 | 0.000 |
| 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 | 0.000 | 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".