Foraging behaviour of Thick-billed Murres (Uria lomvia) in northern Hudson Bay
Bibliographic record
Abstract
The foraging behaviour of seabirds has been well-studied, but the role of energy costs and prey type in determining foraging behaviour is still poorly known. To investigate how energy costs influence the foraging behaviour of Thick-billed Murres, a generalist seabird, at Coats Island, Nunavut, I attached positively (n=9), negatively (n=10) and neutrally (n=9) buoyant handicaps and drag handicaps of cross-sectional area equivalent to three (2.8 cm2;n=8) and six (5.6 cm2;n=6) percent of murre body cross-sectional area. To investigate how murres modify their foraging behaviour for different prey types, I attached time-depth-temperature recorders to chick-rearing murres (n=23 in 2004;n=33 in 2005;n= 60 in 2006) and monitored dive behaviour on the dive bout preceding the delivery of prey items observed at the colony. When buoyancy was altered, or drag increased; murres reduced dive depth, dive duration, ascent rates, descent rates and time spent diving. Handicapped murres did not alter surface pause duration, but surface pause duration increased for a given dive duration, agreeing with predictions from foraging theory. Thus, murres altered dive behaviour in response to increasing energy costs. Dive behaviour for the following prey: fish doctor, squid, amphipods, daubed shanny, sand lance and Arctic shanny was discriminated from each other at the 80% or 95% confidence level by minimum convex polygons on a discriminant analysis of dive variables and, therefore, were considered "specialist" prey items. Specifically, amphipods were captured after V-shaped dives near the colony with a slow descent rate, squid were captured after deep V-shaped dives and fish doctor were captured after a long series of U-shaped dives in warm water far from the colony. Dive behaviour for Arctic cod, capelin and sculpin, overlapped both with each other and with the behaviour associated with other prey items and, therefore, were classified as "generalist" prey items. In general, V-shaped dives preceded deliveries of pelagic prey items and U-shaped dives preceded deliveries of benthic prey items. The relationship between surface pause, dive depth and dive duration also varied with prey type. For example, surface pause duration decreased weakly (but significantly) with prey mass (R2=0.01-0.04) and was unrelated to prey type (schooling vs. benthic); dive diration for a given depth increased with prey mass (R2=0.17) and was longer for benthic items, presumably because benthic dives involved less energy expenditure. Thus, dive behaviour clearly reflected prey type and, therefore, perceived energy gain. Distance flown for a given prey item and average mass of prey items declined over the season, suggesting that murres depleted prey from waters near the colony. This conclusion was also supported by a tradeoff between depth and distance and a trend towards increasing prey mass with flight distance. Consequently, I concluded that seabird foraging behaviour is influenced by energy costs, prey type and degree of prey depletion. A thorough understanding of these issues is necessary to use seabird foraging behaviour as an indicator for prey abundance or distribution.
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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".