Plant Kin Recognition Enhances Abundance of Symbiotic Microbial Partner
Bibliographic record
Abstract
BACKGROUND: The stability of cooperative interactions among different species can be compromised by cheating. In the plant-mycorrhizal fungi symbiosis, a single mycorrhizal network may interact with many plants, providing the opportunity for individual plants to cheat by obtaining nutrients from the fungi without donating carbon. Here we determine whether kin selection may favour plant investment in the mycorrhizal network, reducing the incentive to cheat when relatives interact with a single network. METHODOLOGY/PRINCIPAL FINDINGS: We show that mycorrhizal network size and root colonization were greater when Ambrosia artemisiifolia L. was grown with siblings compared to strangers. Soil fungal abundance was positively correlated with group leaf nitrogen, and increased root colonization was associated with a reduced number of pathogen-induced root lesions, indicating greater benefit to plants grown with siblings. CONCLUSIONS/SIGNIFICANCE: Plants can benefit their relatives through investment in mycorrhizal fungi, and kin selection in plants could promote the persistence of the mycorrhizal symbiosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.003 | 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".