Individual and population dietary specialization decline in fin whales during a period of ecosystem shift
Bibliographic record
Abstract
This study sought to estimate the effect of an anthropogenic and climate-driven change in prey availability on the degree of individual and population specialization of a large marine predator, the fin whale (Balaenoptera physalus). We examined skin biopsies from 99 fin whales sampled in the St. Lawrence Estuary (Canada) over a nine year period (1998-2006) during which environmental change was documented. We analyzed stable isotope ratios in skin and fatty acid signatures in blubber samples of whales, as well as in seven potential prey species, and diet was quantitatively assessed using Bayesian isotopic models. An abrupt change in fin whale dietary niche coincided with a decrease in biomass of their predominant prey, Arctic krill (Thysanoessa spp.). This dietary niche widening toward generalist diets occurred in nearly 60% of sampled individuals. The fin whale population, typically composed of specialists of either krill or lipid-rich pelagic fishes, shifted toward one composed either of krill specialists or true generalists feeding on various zooplankton and fish prey. This change likely reduced intraspecific competition. In the context of the current "Atlantification" of northern water masses, our findings emphasize the importance of considering individual-specific foraging tactics and not only population or group average responses when assessing population resilience or when implementing conservation measures.
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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 | 0.001 |
| 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) | 0.001 | 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".