Species properties and recovery from disturbance: Forest herbs buried by volcanic tephra
Bibliographic record
Abstract
Abstract Question:How do forest herb species differ from each other in their spatial and temporal dynamics during recovery from volcanic disturbance, and how are dynamics related to species traits? Location:Northeast of Mount St. Helens, Washington, USA. Methods:Following deposition of volcanic tephra in 1980, we measured herb density and cover in permanent 1‐m2plots during 1980‐2005 in three old‐growth forests with differing tephra depths. For 26 species, we calculated the frequency of plots with residuals (individuals that survived the eruption) versus re‐establishment on the tephra, timing of establishment, turnover, influence of nearby conspecific plants, importance of three components of cover increase, and relationship of flowering frequency to succession. Results:Tephra depth affected species behavior. Deep tephra produced fewer residuals, a greater increase in shoot size, and more shoot turnover; favored species that established late; and allowed establishment of early seral herbs. Nearby presence of conspecifics increased permanence of first establishment and rate of plot occupancy. Most species spread significantly in deep tephra but not in shallow tephra. Among species, frequency of flowering increased with fewer residuals, later establishment, and higher turnover. Species behavior seldom differed among growth forms based on leaf longevity and vegetative spread. Conclusion:Population dynamics at the small‐plot scale differed from those of the entire population. The timing and permanency of establishment and mechanism of expansion differed among species and with tephra depth. There was some consistency among species with similar habitat breadth and degree of flowering, but little consistency associated with the usual growth form classification.
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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 | 0.000 | 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) | 0.001 | 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".