The Influence Of Fish Behaviour On Search Strategies Of Common Murres Uria Aalgein The Northwest Atlantic
Bibliographic record
Abstract
Although distribution patterns of seabirds at sea have been described for decades, it remains difficult to identify the mechanisms underlying these patterns.For instance, researchers focusing on prey dispersion as the primary determinant of seabird distribution have found high variability in the spatial overlap of bird and prey aggregations, partially due to the scale-dependent nature of such associations.We conducted a study to identify how the behaviour of capelin Mallotus villosus, the primary prey species of all vertebrate predators in the Northwest Atlantic, influences the search tactics of Common Murres Uria aalge while acting as central-place foragers during chick-rearing.The study was conducted from 1998-2002 on and around Funk Island, the largest colony of murres in eastern Canada (~ 400 000 breeding pairs), situated on the northeast coast of Newfoundland.We made direct measurements of (1) the distribution, abundance and spatial and temporal persistence of capelin aggregations within the foraging range from the colony (~ 100 km) in combination with (2) bio-physical habitat characteristics associated with capelin aggregations, and (3) individual-and population-level arrival and departure behaviour of murres from the colony.During July of 2000, capelin were found to be persistently abundant within specific 2.25 km blocks of transect ("hotspots").Further study revealed that capelin persisted in hotspots due to bio-physical characteristics suitable for demersal spawning and for staging areas and foraging areas prior to and after spawning.Directions of return and departure flights of murres measured from the colony did not match during the same observation period (~ 1h), indicating that murres departing the colony did not use information on prey distribution provided by the flight paths of flocks returning to the colony (Information Center Hypothesis).Specific commuting routes (regular flight paths) of murres toward and away from capelin hotspots, however, were obvious at sea, and feeding murres consistently marked the location of these hotspots.This provided excellent conditions for murres to locate capelin from memory and by cueing to activities of conspecifics (local enhancement).Hotspots were persistent across years in this region, presumably allowing marine predators to learn the locations of hotspots, resulting in the use of traditional feeding grounds through generations.Hotspots of predators and prey promote energy transfer among trophic levels, a key ecosystem process.Human predators also concentrate fishing activities within these areas and, thus, there is a need to identify hotspots for protection.Persistent hotspots would be particularly amenable to the design of marine protected areas defined by the habitats of marine predators and their prey.
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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) | 0.000 | 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 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".