Stand- and plot-level changes in a boreal forest understory community following wildfire
Bibliographic record
Abstract
Background: Boreal forest understory plant communities are known to be resilient to fire – the species composition of stands after a fire is quite similar to the pre-fire composition. However, we know little about recovery of individual plants within particular locations in forest stands (i.e. plot-level changes) since we usually do not have pre-fire data for plots.Aims: We wanted to determine whether species recruited into the same or different locations in a Pinus banksiana stand that experienced a severe wildfire.Methods: We used pre-existing permanent plots to evaluate the cover of understory after an unplanned wildfire.Results: Across the entire stand nine of 47 species showed a significant change in cover. The largest change in a plant functional group was in the mosses, with all species present before fire being eliminated. There was no change in species diversity or total cover. At the plot level, species composition showed a much greater change. An average of 47% of the species present in a plot before the fire were absent in the same plot after the fire, and the total species turnover in plots was 88% of the species present before the fire. The plots showed a similar shift in species composition.Conclusions: These results confirm that boreal forest communities show a high degree of resilience to fire, but within a forest stand species will be found in different locations following fire, potentially exposing them to a different set of biotic and abiotic conditions in these new locations.
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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 | 0.001 | 0.000 |
| 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) | 0.001 | 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".