A dendrochronological analysis of a disturbance–succession model for oak–pine forests of the Appalachian Mountains, USA
Bibliographic record
Abstract
Disturbance–succession models describe the relationship between the disturbance regime and the dominant tree species of a forest type. Such models are useful tools in ecosystem management and restoration, provided they are accurate. We tested a disturbance–succession model for the oak–pine ( Quercus spp. – Pinus spp.) forests of the Appalachian Mountains region using dendrochronological techniques. In this model, fire promotes pines, while fire suppression, bark beetle outbreaks, and ice storms encourage oaks. We analyzed nine Appalachian oak–pine stands for species establishment dates and the occurrence of fires and canopy disturbances. We found no evidence that fire preferentially promoted the establishment of pine more than oak, nor did we find any evidence that canopy disturbances or periods of no disturbance facilitated the establishment of oak more than pine. Rather, we found that both species groups originated primarily after combined canopy and fire disturbances, and reduction of fire frequency and scope coincided with the cessation of successful oak and pine regeneration. Currently, heath shrubs are slowly dominating these stands, so we present a revised disturbance–succession model for land managers struggling to manage or restore oak–pine forests containing a dense ericaceous understory.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".