Distributional patterns of a marine bird and its prey: habitat selection based on prey and conspecific behaviour
Bibliographic record
Abstract
We examined distributional patterns of a pursuit-diving seabird, the common murre Uria aalge, and its fish prey, capelin Mallotus villosus, within the avian foraging range of the largest murre colony in eastern North America: Funk Island, Newfoundland.During chick-rearing, the foraging habitat was previously partitioned into: (1) a high-quality area, 45 km from the colony where energyrich capelin schools were found, which were spatially and temporally persistent and (2) a low-quality area, 60 km from the colony where schools were composed of lower-energy capelin that were ephemeral.At the scale of the foraging range (meso-scale: 1 to 100 km), murres were highly clustered into 25% of the surveyed area, with fewer murres in the low-quality relative to the high-quality area.There were tighter associations among murre and capelin aggregations in the low-quality (1.2 ± 0.2 km) relative to the high-quality area (2.6 ± 0.4 km).This likely resulted from the divergent capelin behaviour and, thus, different foraging strategies used by murres to search for (e.g.memory vs local enhancement) and capture prey.At fine spatial scales (250 m 2 ) within foraging areas, murres were found at lower densities (mode: 2 murres), revealing that interference competition among individuals may be important during prey capture.Modeling revealed that at > 50 murres per 250 m 2 in the highquality area, a murre would have a > 90% chance of increasing its foraging efficiency by switching to forage in the low-quality area.Overall, this scale-dependent aggregative behaviour of murres suggests that cooperative foraging among conspecifics may be important in locating prey at the scale of a foraging range, or murres may simply aggregate in areas of high prey abundance, but competitive interactions among conspecifics become important at the scale of prey capture.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".