Linking the Investigations of Character Evolution and Species Diversification
Bibliographic record
Abstract
Variation in diversification rates is often studied by investigating traits related to species' ecology and life history. Often, however, it is unknown whether these traits evolve gradually or in punctuated bursts during speciation. Using phylogenetic data and species' present-day trait information, we present a novel approach to assessing the mode of character change while accounting for trait-dependent speciation and extinction. Our model, "Binary-State Speciation and Extinction-node enhanced state shift" (BiSSE-ness), estimates both the rate of change occurring along lineages and the probability of change occurring during speciation, as well as independent speciation and extinction rates for each character state. Using simulations, we found that BiSSE-ness is able to distinguish along-lineage and speciational change and accurately estimate the parameters associated with character change and diversification rates. We applied BiSSE-ness to an empirical primate data set and found evidence for along-lineage changes in primate mating systems and social behaviors, whereas shifts in habitat were associated with speciation. In cases where trait changes may be linked to the speciation process itself (e.g., niche-related traits), BiSSE-ness provides a suitable framework with which to simultaneously address questions regarding species diversification and character change.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".