Herbivory eliminates fitness costs of mutualism exploiters
Bibliographic record
Abstract
A common empirical observation in mutualistic interactions is the persistence of variation in partner quality and, in particular, the persistence of exploitative phenotypes. For mutualisms between hosts and symbionts, most mutualism theory assumes that exploiters always impose fitness costs on their host. We exposed legume hosts to mutualistic (nitrogen-fixing) and exploitative (non-nitrogen-fixing) symbiotic rhizobia in field conditions, and manipulated the presence or absence of insect herbivory to determine if the costly fitness effects of exploitative rhizobia are context-dependent. Exploitative rhizobia predictably reduced host fitness when herbivores were excluded. However, insects caused greater damage on hosts associating with mutualistic rhizobia, as a consequence of feeding preferences related to leaf nitrogen content, resulting in the elimination of fitness costs imposed on hosts by exploitative rhizobia. Our experiment shows that herbivory is potentially an important factor in influencing the evolutionary dynamic between legumes and rhizobia. Partner choice and host sanctioning are theoretically predicted to stabilize mutualisms by reducing the frequency of exploitative symbionts. We argue that herbivore pressure may actually weaken selection on choice and sanction mechanisms, thus providing one explanation of why host-based discrimination mechanisms may not be completely effective in eliminating nonbeneficial partners.
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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) | 0.002 | 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".