Effects of fire frequency and mowing on a temperate, derived grassland soil in south-eastern Australia
Bibliographic record
Abstract
Frequent disturbances such as fire are widely considered important drivers of plant composition and diversity in productive grassy ecosystems. Effects of fire frequency on grassland soils, however, are less well understood. We established replicated disturbance regimes in a high-quality, representative Themeda australis–Poa sieberiana-derived grassland in south-eastern Australia that had historically been burnt every 4–8 years. Effects on soil chemical, physical and biological properties were measured after 10 years of application of 2-, 4-, and 8-yearly burning, 2-yearly mowing and an undisturbed treatment. Contrary to other grassy ecosystems, there were no detectable effects of disturbance regime on total soil nitrogen and carbon, or a range of other soil chemical properties in the top 10 cm. However, a cumulative effect of burning on the grassland soil was evident from a suite of changes to soil surface properties, available nutrients and biological activity. In particular, on biennially burnt plots, reduced litter and plant protective cover were associated with increased soil surface compaction, decreased infiltration and decreased soil biological activity, which in turn were related to poor sward recovery after fire and drought. These relationships indicate potential for positive feedbacks whereby repeated removal of soil protective cover and changes to soil surface chemistry through very frequent burning ultimately lead to further reduction in soil protective cover through reduced productivity. However, this is only likely in extreme cases: data from unburnt plots indicated that soils that had historically been burnt every 4–8 years had not passed a threshold beyond which such soil changes were irreversible or damaging. Contrary to other predictions, cessation of burning for 13 years did not lead to detectable soil nutrient release through senescence of dominant grasses. Biennial mowing with slash retention was an effective alternative disturbance for maintaining sward vigour while avoiding soil surface damage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".