Sorting of ancestral polymorphism and its impact on morphological phylogenetics and macroevolution
Bibliographic record
Abstract
Intraspecific phenotypic variation provides the basic substrate upon which the evolutionary processes that give rise to morphological innovation, such as adaptation, operate. Work in living clades has shown standing population-level variation fuels ecological speciation and gives rise to adaptive radiations. Despite its importance in evolutionary biology, the role of intraspecific variation in shaping phylogenetic and macroevolutionary patterns and processes has remained underexplored. I introduce a model of morphological evolution that accommodates polymorphism. The model describes the stochastic gain and loss of phenotypic character states within taxa, i.e., anagenesis, and the sorting of ancestral polymorphic variation during speciation, i.e., cladogenesis. I explore the behavior of the model using simulations, then deploy it to reconstruct evolutionary relationships between the highly variable species belonging to the Cretaceous echinoid genus Micraster. The analysis revealed strong statistical support for several contentious relationships. The clade depicts a pattern where morphological variation accumulates within a small number of ancestral lineages and then is sorted into descendants without being fully replenished by anagenetic gains. This disproportionate maintenance of variation within early taxa and loss among later taxa could provide a link between the population processes that maintain intraspecific variation and the radiation and decline of clades.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 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".