Changes in woody vegetation abundance and diversity after natural disturbances causing different levels of mortality
Bibliographic record
Abstract
Questions: How does woody vegetation abundance and diversity differ after natural disturbances causing different levels of mortality? Location: Abies balsamea–Betula papyrifera boreal mixed-wood stands of southeast Quebec, Canada. Methods: Woody vegetation abundance and diversity were quantified and compared among three disturbance-caused mortality classes, canopy gap, moderate-severity disturbances, and catastrophic fire, using redundancy analysis, a constrained linear ordination technique, and diversity indices. Results: Substantial changes in canopy tree species abundance and diversity only occurred after catastrophic fire. Shade-tolerant, late-successional conifer species remained dominant after canopy gap and moderate-severity disturbances, whereas shade-intolerant, early-successional colonizers dominated canopy tree regeneration after catastrophic fire. Density and diversity of mid-tolerant and shade-intolerant understory tree and shrub species increased as the impact of disturbance increased. Highest species richness estimates were observed after catastrophic fire, with several species establishing exclusively under these conditions. Relative abundance of canopy tree regeneration was most similar after canopy gap and moderate-severity disturbances. For the sub-canopy tree and shrub community, relative species abundances were most similar after moderate-severity disturbances and catastrophic fire. Vegetation responses to moderate-severity disturbances thus had commonalities with both extremes of the disturbance-caused mortality gradient, but for different regeneration layers. Conclusions: Current spatio-temporal parameters of natural disturbances causing varying degrees of mortality promote the development of a complex, multi-cohort forest condition throughout the landscape. The projected increase in time intervals between catastrophic fires may lead to reduced diversity within the system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".