Measurement and modelling of primary sex ratios for species with temperature-dependent sex determination
Bibliographic record
Abstract
For many oviparous animals, incubation temperature influences sex through temperature-dependent sex determination (TSD). Although climate change may skew sex ratios in species with TSD, few available methods predict sex under natural conditions, fewer still are based on mechanistic hypotheses of development, and field tests of existing methods are rare. We propose a new approach that calculates the probability of masculinization (PM) in natural nests. This approach subsumes the mechanistic hypotheses describing the outcome of TSD, by integrating embryonic development with the temperature-sex reaction norm. Further, we modify a commonly used method of sex ratio estimation, the Constant Temperature Equivalent (CTE), to provide quantitative estimates of sex ratios. We test our new approaches using snapping turtles (Chelydra serpentina). We experimentally manipulate nests in the field, and find that the PM method is better supported than the modified CTE, explaining 69% of the variation in sex ratios across 27 semi-natural nests. Next, we use the PM method to predict variation in sex ratios across 14 natural nests over two years, explaining 67% of the variation. We suggest that the PM approach is effective and broadly-applicable to species with TSD, particularly for forecasting how sex ratios may respond to climate change. Interestingly, we also found that the modified CTE explained up to 64% of variation in sex ratios in a Type II TSD species, suggesting our modifications will be useful for future research. Finally, our data suggest that the Algonquin Park population of snapping turtles possesses resilience to biased sex ratios under climate change.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".