Exploring the Relationships Between Environmentally Influenced Foraging Behaviour, Energetics, and Breeding Decisions in Arctic Breeding Common Eiders
Bibliographic record
Abstract
Migratory species breeding in polar environments face highly seasonal conditions and significant temporal and spatial constraints which can limit their ability to reproduce successfully. Effective energetic management is especially important at high latitudes, where a shortened breeding season and variable spring climatic conditions can restrict seasonal food availability, impact ability to initiate reproduction and successfully raise offspring. We explored this paradigm in marine benthic foraging common eiders (Somateria mollissima), at a long-term studied colonial nesting site at East Bay (Mitivik) Island, Nunavut, Canada. Eiders rely on marine prey to gain the energy necessary to invest in reproduction when arriving at spring breeding grounds. To successfully breed, females must gain energetic stores quickly to invest in egg production and to fuel a fasted incubation period prior to duckling hatching. As such, variation in spatiotemporal movement and foraging behaviour which provides access to required resources while minimizing energetic costs should be key predictors of successful breeding. To examine how and what it takes for a female eider to successfully breed in the Arctic, we integrated GPS biologging technology to explore variability in behaviours and energetic time budgets across the breeding season. We also explored foraging distance metrics, and how breeding stage and body condition might impact spatial and temporal use of the landscape. We then examined whether spatial use and foraging hot spots varied inter-annually and under changing temporal constraints, and whether spatial use may impact energetic management for breeding and non-breeding individuals differently. Understanding and quantifying intra- and inter-individual variation in foraging decisions during this life history stage provides critical missing information on the proximate mechanisms driving breeding investment decisions within the context of a stochastic environment. By extension, this thesis will evaluate how pre-laying foraging decisions ultimately impact fitness and survival and support critical habitat selection to inform on the development of a Marine Protected Area (MPA) around Southampton Island, Nunavut.
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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 | 0.001 | 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".