Does more maternal investment mean a larger brain? Evolutionary relationships between reproductive mode and brain size in chondrichthyans
Why this work is in the frame
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Bibliographic record
Abstract
Chondrichthyans have the most diverse array of reproductive strategies of any vertebrate group, ranging from egg-laying to live-bearing with placental matrotrophy. Matrotrophy is defined as additional maternal provisioning beyond the yolk to the developing neonate; in chondrichthyans, this occurs through a range of mechanisms including uterine milk, oophagy, uterine cannibalism and placentotrophy. Chondrichthyans also exhibit a wide range of relative brain sizes and highly diverse patterns of brain organisation. Brains are energetically expensive to produce and maintain, and represent a major energetic constraint during early life in vertebrates. In mammals, more direct maternal–fetal placental connections have been associated with larger brains (steeper brain–body allometric scaling relationships). We test for a relationship between reproductive mode and relative brain size across 85 species from six major orders of chondrichthyans by using several phylogenetic comparative analyses. Ordinary least-squares (OLS) and reduced major axis (RMA) regression of body mass versus brain mass suggest that increased maternal investment results in a larger relative brain size. Our findings were supported by phylogenetic generalised least-squares models (pGLS), which also highlighted that these results vary with evolutionary tempo, as described by different branch-length assumptions. Across all analyses, maximum body size had a significant influence on the relative brain size, with large-bodied species (body mass >100 kg) having relatively smaller brains. The present study suggests that there may be a link between reproductive investment and relative brain size in chondrichthyans; however, a more definitive test requires a better-resolved phylogeny and a more nuanced categorisation of the level of maternal investment in chondrichthyans.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 it