Nickel hyperaccumulation is independent of the leaf economics spectrum, although it may be linked to plant water balance in an ultramafic plant community from Sabah (Malaysia)
Bibliographic record
Abstract
Abstract Background and aims Nickel (Ni) hyperaccumulators are a group of plants able to store elevated amounts of this element in their leaves. Some studies indicate that hyperaccumulation may be associated with traits favouring fast resource capture or with traits favouring nutrient and water conservation, but there is no evidence for the role of nickel hyperaccumulation in the leaf economics spectrum. Our study aims to assess the differences in the leaf economics spectrum between Ni hyperaccumulators and non-hyperaccumulators. Methods We have conducted a field study involving five hyperaccumulators and ten non-hyperaccumulators growing on the same ultramafic community in Sabah (Malaysia). We measured two structural and seven chemical leaf traits and computed a Principal Component Analysis, which was complemented by a test of the phylogenetic signal of each trait and linear mixed models to assess the influence of each leaf trait on nickel accumulation. Results Our analyses inferred three principal components that reflected the main environmental constraints that shape the resource acquisition strategies of the studied ultramafic plant community: leaf economics spectrum, hyperaccumulation and water-use efficiency, and calcium to magnesium balance. Moreover, the linear mixed models indicated that carbon isotope discrimination and potassium concentrations had a significant effect on Ni accumulation, suggesting that nickel might replace partially potassium in its role in plant water balance. Conclusion Overall, the data suggest that in the community studied, Ni hyperaccumulation is independent of the leaf economics spectrum and related to plant water economy. More studies with other hyperaccumulator plants are needed to confirm these findings.
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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 | 0.001 | 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 | 0.000 |
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 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".