Experimental and field evidence indicate that islet-nesting tundra birds experience reduced nest predation and benefit indirectly from high snow goose densities
Bibliographic record
Abstract
Abstract Landscape features can shape the occurrence and strength of predator-prey interactions by influencing predation risk and prey distribution. In the High Arctic, some bird species select nesting sites with physical features that limit the access by their main terrestrial predator, the arctic fox, though these features do not always provide full protection. We investigated how nest microhabitat characteristics and prey availability modulate nest survival in tundra birds that select pond and lake islets as breeding sites. Over four summers, we analyzed the survival of 132 cackling goose and 55 glaucous gull nests located on islets or on pond and lake shores within a 150 km 2 area occupied by a snow goose colony on Bylot Island, Nunavut, Canada. We also analyzed survival of 537 artificial nests deployed over three summers. We found that islets act as partial prey refuges, with higher nest survival rates on islets than on pond and lake shores. Nest survival generally increased with islet distance from shore, but we found little evidence of this effect for cackling geese and glaucous gulls, which avoided nesting on islets near shore. Moreover, water depth surrounding islets had little to no influence for any nest type. Nest mortality was much higher in a year with relatively low snow goose nest density, suggesting a short-term positive indirect effect of this colonial nesting bird on species nesting on islets. Since the arctic fox was virtually the sole predator of artificial nests, our findings indicate that annual variation in nest survival on islets were driven by a shift in fox foraging behavior in response to changes in prey availability across the landscape. Our study, which integrates multi-year monitoring and field experiments, highlights the interplay between microhabitat selection and predator-multi-prey dynamics in the arctic tundra.
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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) | 0.003 | 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".