From populations to individuals: understanding foraging niche dynamics, individual specialization, and behavioral plasticity in the common murre (Uria aalge) and razorbill (Alca torda) in northeastern Newfoundland
Bibliographic record
Abstract
Understanding variation among individuals, populations, and species provides insight into sensitivity to change. The goal of this thesis was to examine individual specialization, niche partitioning, and phenotypic plasticity in two seabirds, the common murre (Uria aalge) and razorbill (Alca torda) breeding in northeastern Newfoundland. Using GPS tracking and stable isotopes, we examined individual- and population-level foraging ecology in relation to changing prey availability. For common murres, we found high within-individual variation in foraging trips and low spatial overlap, indicative of flexible behavior, contrasting a degree of dietary consistency. At the population level, murres and razorbills exhibited spatial segregation and divergent dive characteristics, contrasting high dietary overlap. As prey availability shifted, individuals exhibited reduced energy costs with dietary and behavioral plasticity to exploit highly available prey. Together, these findings support flexible foraging strategies for both species and provide insight into how individuals and populations interact and respond to environmental variation.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".