Secondary disturbance following a deposit of volcanic tephra: a 30-year record from old-growth forest understory
Bibliographic record
Abstract
Forest disturbance is usually described by effects on trees, and small disturbances to forest understory are seldom studied. Nevertheless, effective analyses of succession need to consider both stand-replacing and subsequent “secondary” disturbances in both the canopy and understory. We estimated the characteristics of 13 types of secondary disturbances in old-growth forest understory, as well as change in the canopy cover, after the deposition of tephra (aerially transported volcanic ejecta) in 1980 from Mount St. Helens, Washington. We sampled 100 plots (1 m2) at each of four sites for vegetation change and types of disturbance at 10 different times from 1980 to 2010; we sampled the tree canopy above each plot in 1980 and 2016. The number of canopy gaps increased by 23% and mean gap dimension increased by 68% over 36 years, mostly from the loss of Abies amabilis (Douglas ex Loudon) Douglas ex Forbes. Secondary disturbance in understory affected 1.4% of stand area per year. The areas affected by soil disturbance and effects of woody litter were similar. Erosion, greater in deep than in shallow tephra, peaked in 1981, whereas most litter-caused disturbances increased after 2000. Less frequent litter-based disturbances covered greater area. Our results differ from conclusions about nonvolcanic understory disturbances. Secondary disturbances are variable, need more study, and are likely to affect many other systems.
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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 | 0.001 | 0.001 |
| 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".