Evaluating relationships among tree growth rate, shade tolerance, and browse tolerance following disturbance in an eastern deciduous forest
Bibliographic record
Abstract
Interspecific differences in shade tolerance among woody species are considered a primary driving force underlying forest succession. However, variation in shade tolerance may be only one of many interspecific differences that cause species turnover. For example, tree species may differ in their sensitivity to herbivory. Nonetheless, existing conceptual models of forest dynamics rarely explicitly consider the impact of herbivores. We examined whether browsing by white-tailed deer ( Odocoileus virginianus Zimmermann) alters the relationship between light availability and plant performance. We monitored growth and survival for seedlings of six woody species over 2 years within six windthrow gaps and the nearby intact forest in the presence and absence of deer. Browsing decreased seedling growth for all species except beech ( Fagus grandifolia Ehrh.). More importantly, browsing altered growth rankings among species. Increased light availability enhanced growth for three species when excluded from deer, but browsing obscured these relationships. Browsing also reduced survival for three species; however, survival rankings did not significantly differ between herbivory treatments. Our results indicated that browsing and light availability operated simultaneously to influence plant growth within these forests. Thus, existing models of forest dynamics may make inaccurate predictions of the timing and composition of species reaching the canopy, unless they can account for how plant performance varies as a result of a variety of environmental factors, including herbivory.
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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 | 0.002 |
| 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) | 0.000 | 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".