To what extent do new fossil discoveries change our understanding of clade evolution? A cautionary tale from burying beetles (Coleoptera:<i>Nicrophorus</i>)
Bibliographic record
Abstract
Divergence time estimates derived from phylogenies are crucial to infer historical biogeography and diversification dynamics. Yet, the impact of fossil record incompleteness on macroevolutionary reconstructions remains equivocal. Here, we investigate to what extent gaps in the fossil record can impinge downstream evolutionary inferences in the beetle family Silphidae. Recent discoveries have pushed back the fossil record of this group from the Eocene into the Jurassic. We estimated the divergence times of the family using both its currently understood fossil record and the fossil record known prior to these recent discoveries. All fossil calibrations were informed with different parametric distributions to investigate the weight of priors on posterior age estimates. Based on time-calibrated trees, we assessed the impact of fossil calibrations on the inference of ancestral ranges and diversification rate dynamics in the genus Nicrophorus. Depending upon the selected sets of fossil constraints, the age discrepancies had a major impact on the macroevolutionary inferences: the biogeographic extrapolations relative to paleogeography are markedly contrasting, and the calculated rates at which species form or go extinct (and when they varied) are strikingly different. We show that soft prior distributions do not necessarily alleviate such shortcomings therefore preventing the inference of reliable macroevolutionary patterns in groups presenting a taphonomic bias in their 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 | 0.038 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".