Foraging space as a limited resource: inter- and intra-specific competition among sympatric pursuit-diving seabirds
Bibliographic record
Abstract
Competition is thought to play a fundamental role in structuring avian communities, yet this has been difficult to quantify and demonstrate in marine ecosystems. We tested for fine-scale competition over foraging space between sympatric pursuit-diving seabirds, Marbled Murrelet ( Brachyramphus marmoratus (J.F. Gmelin, 1789)) and Common Murre ( Uria aalge (Pontoppidan, 1763)). We simultaneous assessed the effects of inter- and intra-specific competition among these predators, predicting that the larger Common Murres would out-compete the smaller Marbled Murrelets for foraging space. A theodolite was used to map the fine-scale (±2 m) distributions of birds on the water; distance from shore measurements and nearest-neighbour spatial statistics quantified the spatial overlap and segregation between species. Species distributions differed with respect to distance from shore, but overlapped extensively within 1200 m of the shoreline. Nearest-neighbour statistics, assessed with randomization tests, showed Marbled Murrelets foraging farther from Common Murres (mean distances 294 m) than from other Marbled Murrelets (95 m), but groups of Common Murres foraged with similar spacing among conspecifics (266 m) and competitors (186 m). These results suggest avoidance of Common Murres by Marbled Murrelets (interspecific competition) but intraspecifc competition among Common Murres. Avoidance behaviour may minimize the impacts of aggression or competition, but by avoiding Common Murres, the Marbled Murrelets may also be reducing their foraging opportunities.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".