Multifaceted evolution of dental morphology during the diversification of the bat superfamily Noctilionoidea
Bibliographic record
Abstract
Abstract Noctilionoid bats went through one of the most extensive ecomorphological diversifications among mammals. Dietary ecology has been identified as a driver of noctilionoid morphological diversification. However, the macroevolutionary trajectories of changes dental morphology remain understudied. Studies indicate that variation in dental traits correlate with specialisation to different diets, implying differing patterns in phenotypic variability. We compared macroevolutionary trajectories across dental features quantifying five different traits using metrics of dental topography and size. Studying a sample of 110 species, we reconstructed the mode and tempo of dental evolution. We found multiple bursts of dental diversification through time, each involving different dental traits. Trait diversification was associated with different dietary radiations and could be traced to different nodes. Shifts in adaptive regimes were found in four traits, all of them concentrated within family Phyllostomidae. Evolutionary rate covariation differed across traits. We found low evolutionary covariation between measures of dental size and topography. Evolutionary modelling indicated dental traits evolved under different modes, signalling independent evolutionary trajectories. Support for diet-based models of stabilising and stochastic evolution across traits highlights the overarching effect of diet during dental evolution in Noctilionoidea. Our results support a complex and multifaceted model of evolution during noctilionoid dental morphological diversification.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".