Factors influencing Brown Pelican (<i>Pelecanus occidentalis</i>) foraging movement patterns during the breeding season
Bibliographic record
Abstract
During the breeding season, seabird foraging behaviors are driven by a combination of individual- and external-based factors. This study evaluated how two individual-based factors (body condition and sex) and two external factors (nest stage and colony size), and their interactions, were related to movement. To do so, we used movement data obtained from 22 GPS-tag-equipped Brown Pelicans (Pelecanus occidentalis L., 1766) breeding in the northern Gulf of Mexico. In Brown Pelicans, the post-egg-hatching phase imposes increased foraging demands on breeding adults relative to the prehatching phase. This study demonstrates that the progression of the breeding period affects the nature and intensity of the relationship between individual-based factors and movement patterns. In particular, birds in relatively lower condition traveled greater distances during foraging trips during the energetically demanding posthatching phase, but not during the incubation stage. Contrary to many seabird species studied to date, neither colony size nor sex appeared to affect Brown Pelican movement patterns. Our results suggest that nest stage is the most important factor influencing foraging movements, and that it may modulate relationships between condition and movement. More refined measures of body condition and foraging behavior will allow further insights into the movement ecology of this seabird.
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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".