Testing prescribed burning to shift an agronomic grass community to a diverse native plant community
Bibliographic record
Abstract
Prescribed burning can be an effective land management tool. Here, we study changes in plant diversity and composition following experimental fire disturbance in microcosm units extracted from a twenty-five-year-old historically reclaimed grassland located at Highland Valley Copper mine in British Columbia (B.C.), Canada. Experimental microcosm units were dominated by agronomic grass species Elymus lanceolatus, Thinopyrum intermedium and Bromus inermis. The disturbance treatment was fire intensity, represented by three levels (light, moderate, and heavy), replicated six times per treatment. Fire intensity was controlled by modifying the weight of dried litter applied to each microcosm unit (50 g,150 g, 200g), along with the time each grass turf was burned (10 s, 15 s, 20 s). One day after the fire treatment was applied, microcosm units were seeded with a native species mix consisting of six grassland species common to southern B.C. to examine effectiveness of plant establishment postburn. Disturbance treatments resulted in higher overall alpha diversity, richness, evenness, and beta diversity. Plant community changes included colonization of seeded native forbs, grasses, and legumes in response to disturbance. Aboveground net primary productivity (ANPP) was net neutral within the light and moderate burning disturbance treatments but resulted in increased ANPP with heavy disturbance. Litter mass reduced plant diversity and ANPP, indicating that litter was a major factor in plant community dynamics. These results suggest disturbance by burning leads to short term positive plant community response towards increasing diversity of semi-arid grasslands, and aids in shifting plant communities to higher diversity composed of an increase in native plant species. Our results also suggest that without active management the gains observed in native species establishment might quickly be out shadowed and restricted by the previously dominant agronomic plant community.
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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 | 0.000 | 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".