Sorting of persistent morphological polymorphisms links paleobiological pattern to population process
Bibliographic record
Abstract
Abstract Biological variation fuels evolutionary change. Across longer timescales, however, polymorphisms at both the genomic and phenotypic levels often persist longer than would be expected under standard population genetic models such as positive selection or genetic drift. Explaining the maintenance of this variation within populations across long time spans via balancing selection has been a major triumph of theoretical population genetics and ecology. Although persistent polymorphisms can often be traced in fossil lineages over long periods through the rock record, paleobiology has had little to say about either the long-term maintenance of phenotypic variation or its macroevolutionary consequences. I explore the dynamics that occur when persistent polymorphisms maintained over long lineage durations are filtered into descendant lineages during periods of demographic upheaval that occur at speciation. I evaluate these patterns in two lineages: Ectocion , a genus of Eocene mammals, and botryocrinids, a Mississippian cladid crinoid family. Following origination, descendants are less variable than their ancestors. The patterns by which ancestral variation is sorted cannot be distinguished from drift. Maintained and accumulated polymorphisms in highly variable ancestral lineages such as Barycrinus rhombiferus Owen and Shumard, 1852 may fuel radiations as character states are sorted into multiple descendant lineages. Interrogating the conditions under which trans-specific polymorphism is either maintained or lost during periods of demographic and ecological upheaval can explain how population-level processes contribute to the emergent macroevolutionary dynamics that shape the history of life as preserved in the fossil record.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".