Plant Phenotypic Plasticity Belowground: A Phylogenetic Perspective on Root Foraging Trade‐Offs
Bibliographic record
Abstract
Many plants proliferate roots in nutrient patches, presumably increasing nutrient uptake and plant fitness. Nutrient heterogeneity has been hypothesized to maintain community diversity because of a trade-off between the spatial extent over which plants forage (foraging scale) and their ability to proliferate roots precisely in nutrient patches (foraging precision). Empirical support for this hypothesis has been mixed, and some authors have suggested that interspecific differences in relative growth rate may be confounded with measurements of foraging precision. We collected previously published data from numerous studies of root foraging ability (foraging precision, scale, response to heterogeneity, and relative growth rate) and phylogenetic relationships for >100 plant species to test these hypotheses using comparative methods. Root foraging precision was phylogenetically and taxonomically conserved. Using a historical and phylogenetically independent contrast correlations, we found no evidence of a root foraging scale-precision trade-off, mixed support for a relative growth rate-precision relationship, and no support for the widespread assumption that foraging precision increases the benefit gained from growth in heterogeneous soil. Our understanding of the impacts of plant foraging precision and soil heterogeneity on plants and communities is less advanced than commonly believed, and we suggest several areas in which further research is needed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".