Repeated disturbance through chaining and burning differentially affects recruitment among plant functional types in fire-prone heathlands
Bibliographic record
Abstract
Managing fire regimes is increasingly recognised as important for biodiversity conservation in fragmented agricultural landscapes in fire-prone regions. In the global biodiversity hotspot of south-west Western Australia, chaining and burning is a novel technique for facilitating fire management. Vegetation is first dislodged using a chain, then after a period of curing, burnt. The effects on plant communities are largely unstudied, despite the potential consequences of combining two disturbance events. We hypothesised that outcomes would vary depending on plant functional types defined by disturbance response. We compared plant community composition and recruitment and resprouting of plant functional types in mallee-heath subject to chaining and burning, burning only and neither of these. The effects of chaining and burning did not differ from only burning at the community level. Importantly, however, we recorded 90% fewer recruits of serotinous, obligate seeders in chained and burnt compared with only burnt plots, and a 44% decrease in their species richness. By contrast, recruits of obligate seeding shrubs and fire-ephemeral herbs with persistent soil-stored seed banks increased by 166% in chained and burnt plots. Sprouters showed little difference. We conclude that chaining and burning is likely to significantly alter vegetation composition, and potentially poses a significant threat to serotinous, obligate seeders. These impacts require consideration in fire management planning.
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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 | 0.000 |
| 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".