The Consequences of Budding Speciation on Trees
Bibliographic record
Abstract
Paleobiologists have long sought to explain how alternative modes of speciation, including budding and bifurcating cladogenesis, shape patterns of evolution. Methods introduced over the past decade have paved the way for a renewed enthusiasm for exploring modes of speciation in the fossil record. However, the field does not yet have a strong intuition for how ancestor-descendant relationships, especially those that arise from budding speciation, might influence the shape of trees reconstructed for fossil or living clades. We developed a simulation approach based on classic paleobiological theory to ask what proportion of ancestral nodes in paleontological phylogenies are expected to correspond to sampled taxa under a range of preservational regimes. We compared our simulated results to empirical estimates of absolute fossil record completeness gathered from the literature and found that many fossilized clades of marine invertebrates are likely to display upwards of 80% sampled ancestors. Under a primarily budding model, phylogenies where 100% of the internal nodes correspond to named species are very possible for well-sampled clades at local and regional scales. We also leveraged our simulation approach to ask how budding might shape extant clades. We found that the ancestral signature of budding causes rampant hard polytomies (i.e., multifurcations), greatly impacting the shape of extant clades. Our results highlight how budding can yield dramatic and unrecognized effects on phylogenetic reconstruction of clades of both living and extinct organisms.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".