Prey assemblage isotopic variability as a tool for assessing diet and the spatial distribution of bowhead whale Balaena mysticetus foraging in the Canadian eastern Arctic
Bibliographic record
Abstract
The eastern Canada-West Greenland (EC-WG) bowhead whale Balaena mysticetus population is slowly recovering from the intensive commercial whaling of the 18th and 20th centuries.However, climate change, through effects on ice conditions and prey availability, is one of several threats that might affect bowhead whale recovery.In this study, we exploited the variability observed in isotopic signatures of prey assemblages across the eastern Arctic to examine variability in diet among bowhead whales (n = 202) and identify their potential foraging areas.We compared δ 13 C and δ 15 N isotope ratios of biopsied skin samples with those of potential zooplankton prey species collected across the Canadian eastern Arctic, and calculated the proportional contributions of various sources (zooplankton) to the diet of bowhead whales using a Bayesian stable isotope mixing model.A cluster analysis indicated some variability in isotopic composition among groups of individuals, but not between males and females or age classes.The isotopic model discounted Davis Strait and Disko Bay as potential foraging areas for bowhead whales, at least in spring and summer.Lancaster Sound, Baffin Bay and the Gulf of Boothia were the 3 main areas likely used for summer feeding, where bowhead whales fed primarily on large Arctic calanoid copepods (Calanus hyperboreus, C. glacialis, Metridia longa, and Paraeuchaeta spp.), mysids and euphausiids.While some inter-individual variability in diet was observed, the strong dependence of this endemic Arctic species on Arctic zooplankton may make them vulnerable to the predicted latitudinal shift in prey species composition caused by ongoing warming.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".