Prey capture and selection throughout the breeding season in a deep‐diving generalist seabird, the thick‐billed murre
Bibliographic record
Abstract
Generalist seabirds forage on a variety of prey items providing the opportunity to monitor diverse aquatic fauna simultaneously. For example, the coupling of prey consumption rates and movement patterns of generalist seabirds might be used to create three‐dimensional prey distribution maps (‘preyscapes’) for multiple prey species in the same region. However, the complex interaction between generalist seabird foraging behaviour and the various prey types clouds the interpretation of such preyscapes, and the mechanisms underlying prey selection need to be understood before such an application can be realized. Central place foraging theory provides a theoretical model for understanding such selectivity by predicting that larger prey items should be 1) selected farther from the colony and 2) for chick‐feeding compared with self‐feeding, but these predictions remain untested on most seabird species. Furthermore, rarely do we know how foraging features such as handling time, capture methods or choice of foraging location varies among prey types. We used three types of animal‐borne biologgers (camera loggers, GPS and depth‐loggers) to examine how a generalist Arctic seabird, the thick‐billed murre Uria lomvia , selects and captures their prey throughout the breeding season. Murres captured small prey at all phases of a dive, including while descending and ascending, but captured large fish mostly while ascending, with considerably longer handling times. Birds captured larger prey and dove deeper during chick‐rearing. As central place foraging theory predicted, birds travelling further also brought bigger prey items for their chick. The location of a dive (distance from colony and distance to shore) best explained which prey type was the most likely to get caught in a dive, and we created a preyscape surrounding our study colony. We discuss how these findings might aid the use of generalist seabirds as bioindicators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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".