Strengthened mutualistic adaptation between teosinte and its rhizosphere biota in cold climates
Bibliographic record
Abstract
Summary While abiotic environments consistently shape local adaptation, the strength of local adaptation to biotic interactions may vary more. One theory, COCO (CO-evolutionary Outcomes across Conditionality), predicts it may be strongest where species experience greater stress, because stress increases fitness impacts of species interactions. For example, in plant interactions with rhizosphere biota, positive outcomes increase with stress from low soil fertility, drought and cold. To investigate the influence of abiotic stress gradients on adaptation between plants and rhizosphere biota, we used a greenhouse common garden experiment recombining teosinte, Zea mays ssp. mexicana (wild relative of maize), and rhizosphere biota, collected across a stress gradient (elevational variation in temperature, precipitation, and nutrients). We found stronger local adaptation between teosinte and rhizosphere biota from colder, more stressful sites, as expected by COCO. However, biota from less stressful, warmer sites provided greater average benefits across teosinte populations. Links between plant traits and 20-element profiles of plant leaves explained fitness variation, persisted in the field, were influenced by both plants and biota, and largely reflected patterns of local adaptation. In sum, we uncovered greater local adaptation to biotic interactions in colder sites, and that both plants and rhizosphere biota affect the expression of plant phenotypes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".