Resource allocation of a deep-diving Arctic seabird, the thick-billed murre (Uria lomvia), in response to sea-ice variation during the chick-rearing period
Bibliographic record
Abstract
Abstract: Presently, inter-annual variability in sea ice dynamics is increasing in Arctic regions. Sea ice dynamics, such as the timing of ice-melt and sea ice extent, highly influence trophic dynamics in marine Arctic systems, leading to changes in the abundance and distribution of fish and invertebrates. Thick-billed murres (Uria lomvia) - a pursuit diving seabird that preys upon pelagic fish, benthic fish, and invertebrates - are thus expected to be negatively impacted by shifts in marine Arctic community structure. As such foraging behaviour, including the decision to balance self-feeding and chick-provisioning, will be influenced by environmental conditions, and mediated by either adult or chick body condition. In a low ice year, associated with reduced food availability, adults may choose to maximize self-feeding to maintain adult condition, while reducing chick provisioning at the cost of chick condition. Alternatively, adults may maintain or increase chick-provisioning to maximize chick condition, while reducing self-feeding at the cost of adult condition. To investigate the impact of environmental conditions on resource allocation decisions in this system we fitted adult murres with GPS accelerometers during the incubation and chick-rearing periods at Coats Island, Nunavut, Canada in 2018 and 2019. To assess the success of foraging trips for adult energetic condition we blood sampled murres before/after GPS deployments to measure energetic hormones (corticosterone), metabolites (non-esterified fatty acids, beta-hydroxybutyrate, and triglycerides), and stable isotopes (δ13C and δ15N). To assess the success of foraging trips for chick energetic condition, we conducted feeding watches to identify prey types and chick-provisioning rates. By determining how variation in environmental conditions impacts resource allocation decisions, we can predict how breeding success and ultimately fitness will be impacted in rapidly changing Arctic ecosystems. Authors: Alyssa Eby¹, Allison Patterson², Kyle Elliott², H. Grant Gilchrist³, Oliver Love¹ ¹University of Windsor, ²McGill University, ³Environment and Climate Change Canada
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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 | 0.000 | 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".