Predictable ecological dynamics over incredibly small spatial scales influence early-life phenotypes in a species with temperature-dependent sex determination
Bibliographic record
Abstract
Phenotype-environment associations in neonatal animals may arise in wild environments by virtue of ecological dynamics within the nest. Such dynamics may be of special importance to the evolution of temperature-dependent sex determination (TSD), an enigmatic trait that can be adaptive when the incubation temperatures that affect sexual differentiation also have differential effects on the fitness of the sexes. To infer the causal effects of the nest environment on fitness-relevant phenotypes, we apply structural equation modelling (SEM) to a 14-year dataset of 3085 individual embryos whose position in 179 wild snapping turtle nests could be estimated. We find that temperature has a positive effect on hatchling size, and that the same temperatures that predict hatchling size also predict the sex of hatchlings. Further, the probability that embryos develop as males is correlated with hatchling size in the wild, where across all environments, males are slightly and significantly larger than females at hatching. Our SEM reveals that the covariance between size and sex arises because of temperature effects on size, and because of a predictable covariance between egg placement within the nest coupled with maternal effects on egg size. Finally, embryos deep in the nest have a high probability of becoming male even in the hottest years. Our study suggests ecological dynamics occurring within the nest are an interesting and underappreciated source of phenotypic variation. Our study also supports the view that TSD is an adaptive trait, rather than a neutral trait, by showing consistent associations between phenotype and temperature in wild nests of a TSD reptile.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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 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".