Response of the herbaceous layer to natural disturbance in North American forests
Bibliographic record
Abstract
Most work on the disturbance ecology of North American forests has focused on the tree canopy and woody understory, with little consideration of the herbaceous layer. Understanding how this species-rich and ecologically important layer responds to natural disturbances is needed as a guide for conservation. As a framework for this review, selected natural disturbances common to North American forests are evaluated in terms of a three-axis model of disturbance severity: percentage of tree canopy removed, percentage of understory vegetation removed, and percentage of forest floor and soil removed or disrupted. Eight factors, which control herbaceous-layer response, are linked to the three axes; these factors vary in dominance, depending on disturbance type and severity. The relative importances of different species' regeneration mechanisms also vary along severity gradients. The disturbance-severity model, along with the linked response factors and regeneration mechanisms, provide a framework for evaluating impacts of both natural and anthropogenic disturbances. Long-term studies of herbaceous-layer response to disturbance are needed, particularly mechanistic studies that focus on the relative importance of the eight factors controlling response. The conceptual model proposed herein needs to be tested across the full spectrum of severity levels. Finally, comparisons between natural and anthropogenic disturbances are needed.Key words: disturbance, forests, herbaceous layer, life-history characteristics, succession, understory.
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 | 0.000 | 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".