An emerging view of coevolution in the legume–rhizobium mutualism
Bibliographic record
Abstract
Mutualisms are often framed as 'delicately balanced antagonisms' (Bronstein, 1994), with the net fitness benefits to both partners potentially masking underlying conflicts of interest. How commonly symbionts evolve to 'cheat' their hosts and hosts evolve to 'sanction' or 'control' uncooperative symbionts is the subject of debate, especially in legume-rhizobium interactions (Frederickson, 2013; Kiers et al., 2003). This kind of antagonistic coevolution should result in either arms-race dynamics characterized by repeated selective sweeps or fluctuating selection dynamics that leave signatures of balancing selection in host and symbiont genomes (Frederickson, 2013; Kortright et al., 2022; O'Brien et al., 2021). In a From the Cover article in this issue of Molecular Ecology, Epstein et al. (2022) combine GWAS and population genomic analyses to assess the evidence for positive or balancing selection consistent with ongoing, antagonistic coevolution between legumes and rhizobia. They found few genomic signatures of fitness conflicts between mutualistic partners, suggesting that legume and rhizobium fitness interests are largely aligned and symbiotic traits are mostly under stabilizing selection. In combination with other recent work (e.g. Batstone et al., 2020), the results of Epstein et al. (2022) indicate that there is little ongoing fitness conflict between legumes and rhizobia that shapes host and symbiont genomes in this system. It may be time to move beyond symbiont 'cheating' and host 'control' as the dominant paradigm for understanding how partners in mutualism coevolve.
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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 | 0.002 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".