Fluctuating reproductive output and environmental stochasticity: do years with more reproducing females result in more offspring?
Bibliographic record
Abstract
Reproduction is a key life-history process often constrained by abiotic conditions, which affect resource availability and influence reproductive output, including the number of females in a population that reproduce within a given year. We investigated whether population-level fluctuations in reproductive effort (i.e., the number of nesting females) result in fluctuations in the number of offspring produced under environmentally stochastic conditions. Here we show that timing and frequency of tropical storms constrain reproductive success in green sea turtles ( Chelonia mydas (L., 1758)); in years when storms arrive early or when multiple storms occur most green sea turtle nests are inundated by seawater and fail to hatch. Although equal proportions of the nests were destroyed by tropical storms in peak and non-peak nesting years, significantly more hatchlings emerged from nests during peak nesting years. Thus, the cyclic patterns of green sea turtle reproduction result in cyclic patterns of hatchling emergence under high levels of nest failure owing to seawater inundation. Ultimately, green sea turtle reproductive success is constrained by the timing of tropical storms in relation to the nesting season. Continuing increases in the severity of tropical storms from changing global climates could contribute to a higher proportion of nesting seasons with low reproductive success, such that population growth rates are slowed, which may have long-term negative effects on the ability of this species to recover to historical levels.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".