Nitrogen allocation to offspring and milk production in a capital breeder
Bibliographic record
Abstract
Nitrogen (N) is a limiting nutrient for many herbivores, especially when plant availability and N content are low during the period of maternal investment, which is common for arctic ungulates. We used natural abundance of N isotopes to quantify allocation of maternal nitrogen to neonatal calves and milk in wild migratory caribou (Rangifer tarandus). We contrasted female-calf pairs from two herds in northern Quebec/Labrador, Canada: Rivière-George herd (RG; low population size with heavy calves) and the Rivière-aux-Feuilles herd (RAF; high population size and small calves). We assessed whether females of both herds relied on body protein or dietary N to produce the neonatal calf and milk at calving and weaning. Female caribou of both herds relied mostly on body N for fetal development. RAF females allocated less body N to calves than did RG females (92% vs. 95% of calf N), which was consistent with the production of calves that were 8% smaller in RAF than in RG. Allocation of body N to milk was also high for both herds, similar at calving for RAF and RG females (88% vs. 91% of milk N, respectively), but lower in RAF than RG females (95% vs. 99% of milk N) at weaning, which was consistent with a small but significantly greater reliance on dietary N supplies to support milk production at weaning. Female caribou used body protein stores to ensure a constant supply of N for fetal growth and milk production that minimized the effects of trophic mismatches on reproduction. The combination of migration and capital investment may therefore allow females to produce calves and attenuate the effects of both temporal and spatial mismatches between vegetation green-up and calf growth, which ultimately would reduce trophic feedbacks on population growth. Our data suggest that small changes in maternal allocation of proteins over the long period of gestation produce significant changes in calf mass as females respond to changes in resources that accompany changes in the size and distribution of the population.
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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 | 0.000 |
| 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) | 0.001 | 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".