Synthesizing the phylogenetic evidence for mutualism-associated diversification
Bibliographic record
Abstract
Mutualisms are associations in which interacting species provide services or resources to each other. It has been suggested that being party to a mutualism can spur the diversification of the interacting species due to several potential hypothesized mechanisms. There is empirical evidence to both support and refute this prediction. However, this evidence comes from a variety of different methodologies, some of which have been found to be unreliable when the phylogenetic model is misspecified, and different data types and it is therefore difficult to weigh together. Here, we synthesize phylogenetic comparative datasets and analyze the data in a consistent manner using both sister-clade comparisons and hidden-trait state-dependent speciation and extinction models. The results are mixed-for the majority of the datasets we find no evidence for an effect on diversification rates in either direction, with several showing significant positive associations and a few showing significant negative associations. In contrast to the generally mixed findings between datasets, we find that qualitative results to be consistent when analyzing taxonomically overlapping datasets using different methods, suggesting that the detected variation in diversification is due to the nature of the mutualism and not due to differences in methodology.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".