Predation risk and nest-site selection in the Inca tern
Bibliographic record
Abstract
Most tern species (Sternidae) are typically open-ground breeders; the Inca tern (Larosterna inca), however, breeds in crevices. This paper reports the first analysis of nest-site characteristics, predation rates, and breeding success in this species. We tested for evidence of natural selection on nest-site preferences in a colony subjected to high rates of predation by the peregrine falcon (Falco peregrinus). Characteristics of occupied sites differed from those of non-occupied sites. Terns selected sites with longer chambers, a greater number of cavities, and more overhead and lateral cover that were located close to the cliff edge. Predation was the main cause of breeding failure, and successful sites differed from unsuccessful sites, which is evidence for ongoing natural selection. Chicks at sites in larger crevices and more cavities remained at the site longer and were less likely to be depredated by peregrine falcons. Probably in response to the presence of predators, adults flew towards the colony in flocks, which "dissolved" at the cliff edge. Sites located far from the edge were more likely to be depredated and adults breeding there fed their chicks less frequently and, consequently, reared lighter chicks. The concordance between site preference and predation pressure on nest-site selection suggests that the use of non-preferred sites imposed a cost in the form of increased nest predation.
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 | 0.000 | 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) | 0.001 | 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".