Trajectories of forest composition, structure, and productivity following an experimental ice storm disturbance
Bibliographic record
Abstract
Disturbance creates structural legacies that are important drivers of functional and compositional stabilities in forested ecosystems. We used an experimental ice storm disturbance to evaluate effects of disturbance severity on structural legacies and their functional consequences. We evaluated canopy structural characteristics (height, density, openness, and complexity) before and after disturbance using data from terrestrial LiDAR. We compared trajectories of structural characteristics and functional outcomes (composition, mortality, and productivity) among treatments and relative to controls. We found significant post-disturbance change for all canopy structural characteristics especially at higher severity levels, with persistent legacy effects on mean canopy height and canopy complexity. There were limited changes in biomass, productivity, and composition, and mortality did not vary significantly among treatments. There was limited evidence for linkages between structural and functional responses, but plots that retained greater complexity had higher stability of net primary productivity. Our findings indicate persistent structural legacies associated with ice storm disturbance, but declining structural legacies over time may affect interactions with subsequent disturbance or stressors. Improved understanding of these trajectories could help with predicting outcomes of changing disturbance regimes associated with global change.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".