Offspring size resolves a population growth paradox in rays and skates
Bibliographic record
Abstract
Abstract The maximum intrinsic population growth rate, r max , is a key determinant of the limits for sustainable fishing and is increasingly used in risk assessments. Metabolic theory suggests that r max scales with adult body size, temperature (and hence depth) such that smaller-bodied species and those in warmer, shallower waters have greater r max and, therefore, will be less sensitive to overexploitation. However, warm shallow-water tropical rays have lower r max than cold deep-water temperate skates contra to the metabolic expectation. To resolve this paradox, we build from recent advances that suggest that offspring size may be key to understanding r max . Specifically, we examine how r max is related to adult size, offspring size, temperature, and depth across 85 ray and skate species. Our results show that offspring size mediates relationships between r max , adult body size, temperature, and depth. Indeed, tropical rays had, on average, larger offspring and lower r max compared to the temperate skates, despite living in warmer, shallower waters. Thus, despite the expectation from theory that tropical species should have faster life histories compared to temperate species, our result explains why tropical rays are actually less resilient. It remains unclear as to why tropical rays have such large offspring but we speculate that this is due to greater predation risk in shallow tropical waters driving the additional maternal investment in offspring size via the evolution of viviparity and matrotrophy. Our work highlights the complex relationships among life histories and the environment and may help explain global biogeographic patterns of intrinsic sensitivity to overexploitation.
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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 | 0.002 |
| 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 | 0.000 |
| 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".