Phenotypic plasticity accounts for changes in plant phosphorus‐acquisition strategies from mining to scavenging along a gradient of soil phosphorus availability in South American <i>Campos</i> grasslands
Bibliographic record
Abstract
Abstract Plants have evolved numerous traits to acquire phosphorus (P). Correspondingly, soil P availability modulates the functional composition of many plant communities. However, it is unclear to what extent plant species modulate the expression of different P‐acquisition strategies (phenotypic plasticity). Moreover, how variation in soil‐P availability interacts with plant phenotypic plasticity and species turnover to determine what P‐acquisition strategies are present in highly diverse communities? To address these questions, we assessed associations between plant‐available soil P and the magnitude of several P‐acquisition traits in both individual species and plant communities in Campos grasslands. Root phosphatase activity (phosphomonoesterase and phosphodiesterase), leaf manganese (Mn) concentration (a proxy for carboxylate concentration in the rhizosphere) and arbuscular mycorrhizal (AM) colonization were assessed in 105, 52 and 54 native plant species, respectively, sampled across three to seven plant communities with contrasting concentrations of plant‐available soil P. Furthermore, root diameter and plant cover of those species were also quantified. Variation in P‐acquisition strategies among species was large: 157‐ and 118‐fold for phosphatases, 96‐fold for leaf [Mn] and 39‐fold for AM colonization. Between half and two‐thirds of the variation in community‐weighted mean P‐acquisition traits was accounted for by the interactive factors plant‐available soil P, soil pH and root diameter. At low‐P availability, phosphatases and carboxylate exudation (P‐mining traits) and thin roots predominated, particularly at low soil pH. At higher P availability, AM associations (P‐scavenging trait) and thicker roots were more common. Synthesis . Phenotypic plasticity was a major source of variation in the response of P‐acquisition traits to soil properties, particularly for P‐mining traits. Our results reveal that the plasticity of the expression of plant P‐acquisition strategies in individual species was more important than changes in species presence or cover as a mechanism underlying shifts between P‐mining and P‐scavenging strategies as plant P availability varied across communities.
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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 | 0.000 |
| 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".