Predator‐mediated interactions between preferred, alternative and incidental prey in the arctic tundra
Bibliographic record
Abstract
Apparent competition between prey is hypothesized to occur more frequently in environments with low densities of preferred prey, where predators are forced to forage for multiple prey items. In the arctic tundra, numerical and functional responses of predators to preferred prey (lemmings) affect the predation pressure on alternative prey (goose eggs) and predators aggregate in areas of high alternative prey density. Therefore, we hypothesized that predation risk on incidental prey (shorebird eggs) would increase in patches of high goose nest density when lemmings were scarce. To test this hypothesis, we measured predation risk on artificial shorebird nests in quadrats varying in goose nest density on Bylot Island (Nunavut, Canada) across three summers with variable lemming abundance. Predation risk on artificial shorebird nests was positively related to goose nest density, and this relationship was strongest at low lemming abundance when predation risk increased by 600% as goose nest density increased from 0 to 12 nests ha −1 . Camera monitoring showed that activity of arctic foxes, the most important predator, increased with goose nest density. Our data support our incidental prey hypothesis; when preferred prey decrease in abundance, predator mediated apparent competition via aggregative response occurs between the alternative and incidental prey items.
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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 | 0.000 |
| 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".