Does salvage logging erase a key physical legacy of a tornado blowdown? A case study of tree tip-up mounds
Bibliographic record
Abstract
While large-scale wind disturbances are rare, they are nonetheless powerful drivers of plant community reassembly in temperate forests worldwide. These disturbances cause the formation of tree tip-up mounds that serve as regeneration niches, but the time scale at which novel plant communities develop on mounds is unknown. Moreover, salvage logging can cause mounds to “tip back down” and could therefore erase these microsites. Here, we test three hypotheses with a replicated field experiment: (1) novel plant communities rapidly form on tip-up mounds; (2) salvaging erases these microsites; and (3) “tipped-down” tip-up mounds are novel intermediate microsites. We salvaged a random half of four 3–6 ha blowdowns created by an F1 tornado, measured 249 mounds, and censused the vegetation on 48 mounds and 48 reference plots. Plant communities on mounds had two to three fewer species, 50% less cover, and lower diversity than reference communities. However, salvaging caused modest increases in species richness and diversity on mounds and caused 40% of mounds to tip back down. The physical characteristics and vegetation of these tipped-down “inclined mounds” were more similar to vertical mounds than to reference plots. Our results suggest that salvaging may increase microsite heterogeneity across the landscape by creating novel intermediate mounds.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".