Nest-site habitat partitioning by Arctic, Common, and Roseate terns
Bibliographic record
Abstract
When closely related species that share aspects of their habitat niches nest in sympatry, selection is expected to favor species-specific differences in nest-site selection that allow birds to partition habitat. We tested habitat-partitioning theory in a minimally managed colony of closely related Arctic Terns (Sterna paradisaea), Common Terns (Sterna hirundo), and endangered Roseate Terns (Sterna dougallii) nesting on a rocky island in the Gulf of Maine, Nova Scotia, Canada. The site also consisted of a colony of Great Black-backed Gulls (Larus marinus), significant predators of tern eggs. Availability of nesting habitat is thought to limit growth of the Canadian Roseate Tern population, which preferentially nests in colonies with Common and Arctic terns. Accordingly, understanding how these three tern species partition nesting habitat will help guide efforts for identifying islands potentially suitable for Roseate Tern nesting, a priority strategy for the species’ recovery in Canada. We compared physical attributes of nest sites, i.e., rocks of different size classes and cover, and nearby densities of intra- and interspecific tern nests (i) between nest and random sites, i.e., nest-site selection; (ii), between successful and unsuccessful, i.e., predated, nests; and (iii) among the three species, i.e., habitat partitioning. All three tern species clustered their nests into sub-colonies, although Arctic Tern sub-colonies were positioned in open, more peripheral areas of the colony than those of the other two species. Roseate Terns established sub-colonies within Common Tern sub-colonies, but lateral cover at Roseate Tern nests, which was provided primarily by rocks, was six times greater than that at Common Tern nests. Greater levels of cover did not translate into improved nest success; however, the number of interspecific nests ≤ 2 m from a nest was linked to enhanced nest success for Common Terns, possibly reflecting efficient group defense against predation. We conclude that patterns of nest-site selection observed among Arctic, Common, and Roseate terns are consistent with habitat partitioning. Managers are encouraged to include the potential for rocky substrate to provide nest cover as a criterion for identifying tern islands that may be suitable for Roseate Tern nesting in Canada.
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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 | 0.000 | 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) | 0.000 | 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 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".