Fire facilitates ground layer plant diversity in a Miombo ecosystem
Bibliographic record
Abstract
BACKGROUND AND AIMS: Little is known about the response of ground layer plant communities to fire in Miombo ecosystems, which is a global blind spot of ecological understanding. We aimed: (1) to assess the impact of three experimentally imposed fire treatments on ground layer species composition and compare it with patterns observed for trees; and (2) to analyse the effect of fire treatments on species richness to assess how responses differ among plant functional groups. METHODS: At a 60-year-long fire experiment in Zambia, we quantified the richness and diversity of ground layer plants in terms of taxa and functional groups across three experimental fire treatments of late dry-season fire, early dry-season fire and fire exclusion. Data were collected in five repeat surveys from the onset of the wet season to the early dry season. KEY RESULTS: Of the 140 ground layer species recorded across the three treatments, fire-maintained treatments contributed most of the richness and diversity, with the least number of unique species found in the no-fire treatment. The early-fire treatment was more similar in composition to the no-fire treatment than to the late-fire treatment. C4 grass and geoxyle richness were highest in the late-fire treatment, and there were no shared sedge species between the late-fire and other treatments. At a plot level, the average richness in the late-fire treatment was twice that of the fire exclusion treatment. CONCLUSIONS: Heterogeneity in fire seasonality and intensity supports diversity of a unique flora by providing a diversity of local environments. African ecosystems face rapid expansion of land- and fire-management schemes for carbon offsetting and sequestration. We demonstrate that analyses of the impacts of such schemes predicated on the tree flora alone are highly likely to underestimate impacts on biodiversity. A research priority must be a new understanding of the Miombo ground layer flora integrated into policy and land management.
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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 | 0.000 | 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) | 0.000 | 0.001 |
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".