What makes nutrient-poor mediterranean heathlands so rich in plant diversity?
Bibliographic record
Abstract
Mediterranean heathlands with extremely low soil-nutrient concentrations (the fynbos of South Africa and the kwongan of Australia) have plant species diversities several times greater than one would expect from their areas. A combination of three factors provides a sufficient explanation for these diversities: First, poor soils favour shrubs that are killed by fire and reestablish from seed (‘seeders’). Otherwise, the frequent fires in most mediterranean heathlands favour shrubs that can re-sprout (‘sprouters’). Second, the numeric dominance of seeders on poor soil lowers their extinction rates. Third, seeders have relatively short generation times and thus increased speciation rates. Elevated speciation rates coupled with depressed rates of extinction lead to enhanced diversities. We elucidate this scenario and discuss evidence that favours the first factor. The evidence comes from 23 previously unanalysed sample plots surveyed by R.H. Whittaker and from two supplemental data sets. In mature fynbos and kwongan, 90 and 93% respectively of the shrub cover belongs to shrubs that re-seed after fire. In maquis (Israel), chaparral (California) and matorral (Chile), the proportion is considerably smaller. Mature strandveld, a South African shrubland superficially like fynbos but with richer soil, has only 29% seeders, although it is physically adjacent to fynbos. We suggest that nutrient-poor soil may favour seeders because the extra investment in underground organs is not worth the cost: pulses of nutrients released by fire lie mostly on top of the soil, inaccessible to new growth sprouting from subterranean lignotubers or epicormic buds.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".