A review of niche segregation across sympatric breeding seabird assemblages
Bibliographic record
Abstract
Breeding seabirds challenge the concept of niche segregation among competing species because similar competitors with comparable life histories can coexist in large multi‐species colonies. This makes them an ideal model organism for studying the Hutchisonian niche model, which proposes interspecific niche segregation, across n ‐dimensions. Recent advances in assessment of ecological niches have improved our understanding of the mechanisms leading to at‐sea segregation. We examine 152 published studies investigating one or more of three niche spaces in breeding seabirds: dietary items, stable isotopes (isotopic niche) and spatial distribution (horizontal and vertical). Most studies focused on one rather then multiple niche spaces in combination. When multiple niche spaces were combined, higher segregation than overlap was reported, compared with when niche spaces were assessed individually, as is predicted by the n ‐dimensional hypervolume concept. Studies investigating vertical (diving) foraging dimensions in addition to the more traditional horizontal (spatial) assessment reported more spatial segregation than overlap, compared with studies focusing only on horizontal or vertical dimensions. Segregation increased with colony size, suggesting an effect of competition. Segregation also increased during chick‐rearing, when taxa were more phylogenetically distant, and when foraging ecology was more similar. To gain a comprehensive understanding of sympatric competitors' interactions and their ecological niche space, it is important to combine approaches and standardize methodologies. Embracing multidimensional approaches to assess niche segregation in seabird species can inform effective conservation and management practices in marine ecosystems.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".