Phosphorus nutrition of phosphorus-sensitive Australian native plants: threats to plant communities in a global biodiversity hotspot
Bibliographic record
Abstract
South-western Australia harbours a global biodiversity hotspot on the world's most phosphorus (P)-impoverished soils. The greatest biodiversity occurs on the most severely nutrient-impoverished soils, where non-mycorrhizal species are a prominent component of the flora. Mycorrhizal species dominate where soils contain slightly more phosphorus. In addition to habitat loss and dryland salinity, a major threat to plant biodiversity in this region is eutrophication due to enrichment with P. Many plant species in the south-western Australian biodiversity hotspot are extremely sensitive to P, due to a low capability to down-regulate their phosphate-uptake capacity. Species from the most P-impoverished soils are also very poor competitors at higher P availability, giving way to more competitive species when soil P concentrations are increased. Sources of increased soil P concentrations include increased fire frequency, run-off from agricultural land, and urban activities. Another P source is the P-fertilizing effect of spraying natural environments on a landscape scale with phosphite to reduce the impacts of the introduced plant pathogen Phytophthora cinnamomi, which itself is a serious threat to biodiversity. We argue that alternatives to phosphite for P. cinnamomi management are needed urgently, and propose a strategy to work towards such alternatives, based on a sound understanding of the physiological and molecular mechanisms of the action of phosphite in plants that are susceptible to P. cinnamomi. The threats we describe for the south-western Australian biodiversity hotspot are likely to be very similar for other P-impoverished environments, including the fynbos in South Africa and the cerrado in Brazil.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".