Skull variation in Afro-Eurasian monkeys results from both adaptive and non-adaptive evolutionary processes
Bibliographic record
Abstract
Afro-Eurasian monkeys originated in the Miocene and are the most species-rich modern primate family. Molecular and fossil data have provided considerable insight into their evolutionary divergence, but we know considerably less about the evolutionary processes that underlie these differences. Here, we apply tests developed from quantitative genetics theory to a large (n > 3000) cranio-mandibular morphometric dataset, investigating the relative importance of adaptation (natural selection) and neutral processes (genetic drift) in shaping diversity at different taxonomic levels, an approach applied previously to monkeys of the Americas, apes, hominins, and other vertebrate taxa. Results indicate that natural selection, particularly for differences in size, plays a significant role in diversifying Afro-Eurasian monkeys as a whole. However, drift appears to better explain skull divergence within the subfamily Colobinae, and in particular the African colobine clade, likely due to habitat fragmentation. Small and declining population sizes make it likely that drift will continue in this taxon, with potentially dire implications for genetic diversity and future resilience in the face of environmental change. For the other taxa, many of whom also have decreasing populations and are threatened, understanding adaptive pressures similarly helps identify relative vulnerability and may assist with prioritising scarce conservation resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".