Data from: Predators, energetics and fitness drive neonatal reproductive failure in red squirrels
Bibliographic record
Abstract
Neonatal reproductive failure should occur when energetic costs of parental investment outweigh fitness benefits. However, little is known about the drivers of neonatal reproductive failure in free ranging species experiencing continuous natural variation in predator abundance and in the energetic and fitness costs and benefits associated with parental investment. Long-term comprehensive studies are required to better understand how biotic, abiotic, and life history conditions interact to drive occurrences of reproductive failure in the wild. Using 24 years (1987-2011) of reproductive data from a northern boreal population of North American red squirrels in southwestern Yukon, we examined the effects of predator abundance, energetics (resource availability, ambient temperature and litter size), and fitness benefits (probability of overwinter juvenile survival and maternal age) on occurrences of neonatal reproductive failure (494/2670 reproductive attempts; 18.5%). Neonatal reproductive failure was driven by a combination of predator abundance, and the energetic and fitness costs and benefits of parental investment. The abundance of mustelids and maternal age were positively related to the occurrence of neonatal reproductive failure. High energy costs associated with a combination of low resource availability and cold ambient temperatures or large litters, corresponded to increased occurrences of neonatal reproductive failure. However, the strength of these relationships was influenced by variation in juvenile overwinter survival (i.e. fitness benefits). We provide evidence that predation pressure is an important driver of neonatal reproductive failure. In addition, we found a trade-off occurs between resource-dependent energetic and fitness costs and benefits of raising the current litter to independence
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".