Persistence of rare species depends on rare events: demography, fire response and phenology of two plant species endemic to a semiarid Banded Iron Formation range
Bibliographic record
Abstract
The association of rare plant species and Banded Iron Formation (BIF) ranges in semiarid Western Australia is a noted phenomenon. These ranges are also a focus of iron ore exploration and mining. Decisions and planning required for development, conservation and management resulting from this interest, often consider translocation of these threatened species. Nonetheless, little is known about the ecology of BIF-endemic species to support any such decisions. We assessed population structure, patterns of growth, mortality, recruitment, reproduction and in situ seedbank persistence for two declared rare flora species. The shrub Darwinia masonii, and sedge Lepidosperma gibsonii are endemic to an area <40 km2 on the south-western boundary of the Australian arid zone. Both species were found to be long lived and slow growing, with evidence for reliance on rare events such as fire, and high rainfall years, including, for some processes, consecutive high rainfall years for growth, reproduction and recruitment. Retrieval and germination of seed batches shows that both species’ seedbanks are long-lived, with seasonal dormancy cycling. This, together with the ability of mature plants to survive through years not supporting growth, and, for L. gibsonii, to resprout after fire, are key mechanism for persistence in this unpredictable and low rainfall environment.
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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 | 0.001 | 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".