How do plant communities differ between fire refugia and fire‐generated early‐seral vegetation?
Bibliographic record
Abstract
Abstract Aims Wildfires in dry forest ecosystems in western North America are producing fire effects that are more severe than historical estimates, raising concerns about the resilience of these landscapes to contemporary disturbances. Despite increasing fire activity, relatively little is known about the structure and composition of fire refugia — unburned or low‐severity burned patches where trees survived fire — or the degree to which their understory composition differs from fire‐generated early‐seral forests. Locations Four recent large fires in dry mixed‐conifer forest in eastern Oregon, USA. Methods We sampled vegetation and environmental factors in 187 plots (100‐m 2 ) in fire refugia ( n = 52) and stand‐replacement patches (SRPs) ( n = 135). We used non‐metric multidimensional scaling, indicator species analysis, and randomization tests to compare understory plant communities and diversity in fire refugia and SRPs 12–17 years post‐fire. Results Understory plant communities in fire refugia and SRPs showed strong compositional affinities, but exhibited shifts in reproductive trait prevalence and differences in landscape‐scale species richness. There were no differences in plot‐scale species richness or the occurrence of two prominent invasive annual grasses ( Bromus tectorum, Ventenata dubia ). The abundance of common obligate seeding species was similar between plot types, but resprouting and seed‐banking species became substantially more abundant in SRPs. Significantly more plant species occurred only in fire refugia, and significantly fewer in SRPs, than expected by chance. Conclusions Similarities in understory plant communities between fire refugia and early‐seral forest in SRPs provide evidence for the resilience of historically frequent‐fire forest understory communities to contemporary fire effects. However, fire refugia may serve an ecologically important role as patches with relatively intact forest structure, seed sources that contribute to post‐fire forest recovery, and as reservoirs of species and age structure that may be absent from the higher‐severity burned matrix.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".