Using niche breadth theory to explain generalization in mutualisms
Bibliographic record
Abstract
For a mutualism to remain evolutionarily stable, theory predicts that mutualists should limit their associations to high-quality partners. However, most mutualists either simultaneously or sequentially associate with multiple partners that confer the same type of reward. By viewing mutualisms through the lens of niche breadth evolution, we outline how the environment shapes partner availability and relative quality, and ultimately a focal mutualist's partner breadth. We argue that mutualists that associate with multiple partners may have a selective advantage compared to specialists for many reasons, including sampling, complementarity, and portfolio effects, as well as the possibility that broad partner breadth increases breadth along other niche axes. Furthermore, selection for narrow partner breadth is unlikely to be strong when the environment erodes variation in partner quality, reduces the costs of interacting with low-quality partners, spatially structures partner communities, or decreases the strength of mutualism. Thus, we should not be surprised that most mutualists have broad partner breadth, even if it allows for ineffective partners to persist.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".