Quantifying dietary pathways of proteins and lipids to tissues of a marine predator
Bibliographic record
Abstract
Abstract 1. Using measurements of naturally occurring stable isotopes in animal tissues is useful for monitoring diets of wide‐ranging species that would otherwise be logistically difficult to evaluate. However, differential metabolic routing of macromolecules within a consumer can be problematic when using stable isotope analysis of bulk tissues to trace dietary input. 2. We used stable isotope (δ 13 C, δ 15 N) analysis to examine polar bear Ursus maritimus diet, which includes both lipid‐rich blubber and the proteinaceous tissues of their marine mammal prey. Because the proportion of proteins and lipids consumed may depend on prey type and size, it was necessary to consider metabolic routing of these macromolecules separately in isotope mixing models. 3. Bayesian mixing models (MixSIR, version 1.04) were used to separately estimate protein (δ 13 C, δ 15 N) and lipid (δ 13 C) dietary inputs. We used existing knowledge of the relative lipid and protein intake for polar bears and isotopic information from both macromolecules to estimate overall diet composition. 4. The results for both male and female polar bears indicated that smaller prey (e.g. ringed seal Pusa hispida ) contributed the largest proportion to the protein‐metabolic pathway. In contrast, the largest proportion of the lipid‐metabolic pathway for both sexes tended to consist of larger prey (e.g. bearded seal Erignathus barbatus ). 5. The diet composition of male polar bears consisted of more large than small prey. Diet estimates for females overlapped to some degree with males but tended to consist of less large prey. 6. Synthesis and applications. Monitoring polar bear diet may help determine the effects of climate‐induced environmental changes in Arctic marine ecosystems including shifts in prey composition. Additionally, tracing origins of anthropogenic pollutants is currently a priority for wildlife managers concerned with the health of marine mammals. However, our results indicate using stable isotopes to infer dietary inputs when proportions of macromolecules fluctuate amongst food sources requires the sampling and analysis of multiple tissues representing distinct macromolecular metabolic pathways. In such cases, utilizing only proteinaceous tissues for analysis will result in erroneous dietary source estimates and inaccuracies when examining trophic‐level transfer of contaminants, especially those that are lipophylic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.002 | 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 teacher head, 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".