Combining Stable-Isotope and Body-Composition Analyses to Assess Nutrient-Allocation Strategies in Breeding White-Winged Scoters (<i>Melanitta fusca</i>)
Bibliographic record
Abstract
Birds meet the energy and nutrient demands of egg formation by using dietary (exogenous) sources, somatic (endogenous) nutrients, or combinations of both. Therefore, understanding plasticity in resource acquisition and the allocation strategies that are used is important for predicting how ecosystem changes across a species' range could affect vital rates. Sources of egg nutrients have traditionally been assessed through analyses of body composition, but stable-isotope analysis has provided a new tool in cases where animal tissues differ isotopically from the local food webs where they breed. We provide the first simultaneous comparison of these two techniques and test the “seasonally variable nutrient threshold hypothesis.” Using body-composition analysis of White-winged Scoters (Melanitta fusca) collected at the northern extent of their range, we inferred that protein in rapidly developing ovarian follicles was derived entirely from dietary sources but that follicle lipids were most likely derived largely from somatic reserves. Stable-isotope analysis confirmed that most protein was derived from dietary sources during early (70.7–86.6%) and late (83.4–94.4%) spring collection periods, but dietary lipids were not distinguishable isotopically from stored somatic lipids to estimate lipid contributions to eggs. This pattern differed from that at the southern limit of the species' breeding range, where both lipid and protein for egg formation were derived from exogenous sources. Although our results were consistent with plasticity in resource-allocation strategies among sites (latitudinal variation), they did not support the seasonally variable nutrient threshold hypothesis within a site. We discuss the benefits of using both techniques simultaneously to assess reproductive strategies of birds that migrate between isotopically distinct areas.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".