More partners, more ranges: generalist legumes spread more easily around the globe
Bibliographic record
Abstract
How does mutualism affect range expansion? On the one hand, mutualists might thrive in new habitats thanks to the resources, stress tolerance or defence provided by their partners. On the other, specialized mutualists might fail to find compatible partners beyond their range margins, limiting further spread. A recent global analysis of legume ranges found that non-symbiotic legumes have been successfully introduced to more ranges than legumes that form symbioses with rhizobia, but there is still abundant unexplained variation in introduction success within symbiotic legumes. We test the hypothesis that generalist legumes have spread to more ranges than specialist legumes. We used published data and rhizobial 16S rRNA sequences from GenBank to quantify the number of rhizobia partners that associate with 159 legume species, spanning the legume phylogeny and the globe. We found that generalist legumes occur in more introduced ranges than specialist legumes, suggesting that among mutualists, specialization hinders range expansions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".