Adaptations to winter-wet ironstone soils: a comparison between rare ironstone Hakea (Proteaceae) species and their common congeners
Bibliographic record
Abstract
In south-western Australia, a rare plant community is found on shallow, winter-wet ironstone soils, which occur on coastal plains as isolated islands in a matrix of surrounding deeper sandy soils. To test for local adaptation of species endemic to these communities and potential inhibitory effects of ironstone soils on other species, we compared two rare ironstone Hakea species with four of their common congeners. The common congeners were chosen from nearby winter-wet habitats on deeper sandy soils and from non-wetland woodland habitats (i.e. two species in each habitat group). Seedlings of all species were grown on ironstone soil and subjected to waterlogging in a glasshouse experiment. Significant habitat-related differences emerged only when seedlings were waterlogged. When compared with their controls, shoot and root growth rates of ironstone endemics were less affected by waterlogging than those of their common congeners. This was partly associated with their large accumulation of leaf starch, and their substantial adventitious-root formation. Leaves of ironstone endemics also exhibited consistently higher concentrations of Cu and Zn. In contrast to the effect of waterlogging in the glasshouse experiment, natural waterlogging of seedlings transplanted into ironstone communities led to high mortality, but only in the non-wetland Hakea species. Mortality was strongly associated with the intensity of flooding events, with very small differences in inundation level (10–15 mm) strongly influencing seedling survival. Our results suggest that the chemistry of the waterlogged ironstone soil, and species adaptations to it, are important for understanding distribution patterns of these Hakea species.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".