Long-term influence of disturbance-generated microsites on forest structural and compositional development
Bibliographic record
Abstract
Wind disturbance generates heterogeneous microsite structures, including downed logs, windthrow mounds, and pits. While these structures can provide opportunities for regeneration of certain tree species, the long-term influence of microsites and microsite heterogeneity on forest development has not been quantified. We used long-term measurements of a formerly old-growth Tsuga canadensis – Pinus strobus forest severely damaged by a category 3 hurricane in 1938 to quantify the impact of microsite conditions on overstory composition and structure. We asked (i) “What are the patterns in live-tree size, growth, and mortality five and seven decades after disturbance?” and (ii) “What roles do microsite heterogeneity and the presence of disturbance-generated microsites play in long-term forest development following disturbance?” We compared live-tree (>2 cm DBH) development and survival to microsite heterogeneity at the 100 m2 scale. Microsite diversity was positively related to overstory species diversity and stem density and negatively related to average tree size. We propose that plots with higher microsite diversity may have experienced more severe local disturbance, which allowed more species and individuals to establish and created varied niches that allowed these individuals to coexist and generate greater stand-level diversity. These persistent relationships highlight how microsite conditions affect forest development after severe disturbances.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".