What drives biological diversification? detecting traits under species selection
Bibliographic record
Abstract
Species selection - heritable trait-dependent differences in rates of speciation or extinction - may be responsible for variation in both taxonomic and trait diversity among clades. While initially controversial, interest in species selection has been revived by the accumulation of evidence of widespread trait-dependent diversification. In my thesis, I developed and applied a number of new likelihood-based methods for investigating species selection by detecting the association between species traits and speciation or extinction rates. These methods are explicitly phylogenetic and incorporate simple, but commonly used, models of speciation, extinction, and trait evolution; I assume throughout that speciation and extinction can be modelled as a birth-death process where rates depend in some way on one or more traits, and that these traits evolve under a Markov process. In particular, I extended the BiSSE (Binary State Speciation and Extinction) method to allow use with incompletely resolved phylogenies, and developed analogous methods for multi-state discrete traits or combinations of binary traits (MuSSE; Multi-State Speciation and Extinction) and quantitative traits (QuaSSE; Quantitative State Speciation and Extinction). I tested the statistical performance of the methods using simulations, investigating their performance with variation in tree size, degree of resolution, number of traits, and departure from the true model. I used each method to consider a different biological question; I found that sexual dimorphism was shortlived but associated with elevated rates of speciation in shorebirds; that solitariness and monogamy are associated with decreased speciation rates in primates (showing that a previous analysis was robust to treating both traits simultaneously); and that body size was a poor predictor of speciation rates in primates. In chapter 5, I extended this analysis of body size to all mammals, and investigated if within-lineage increases in body size (Cope's rule) were balanced by species selection against large bodied species. I found little support for this hypothesis, with clade-specific differences in the direction of species selection and idiosyncratic variation in speciation rates. Together, the methods I have developed allow testing of long-standing hypotheses about causes of variation in biological diversity.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".