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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".