A predator's perspective of nest predation: predation by red squirrels is learned, not incidental
Bibliographic record
Abstract
Nest predation has been used to explain aspects of avian ecology ranging from nest site selection to population declines. Many arguments rely on specific assumptions regarding how predators find nests, yet these predatory mechanisms remain largely untested. Here we combine artificial nest experiments with behavioural observations of individual red squirrels Tamiasciurus hudsonicus to differentiate between two common hypotheses: predation is incidental versus learned. Specifically, we tested: 1) whether nest survival could be explained solely by a squirrel's activity patterns or habitat use, as predicted if predation was incidental; or 2) if predation increased as a squirrel gained experience preying on a nest, as predicted if predation was learned. We also monitored squirrel activity after predation to test for evidence of two search mechanisms: area-restricted searching and use of microhabitat search images. Contrary to incidental predation and in support of learning, squirrels did not find nests faster in areas with high use (e.g. forest edges). Instead, survival of artificial nests was strongly related to a squirrel's prior experience preying on artificial nests. Experience reduced nest survival times by over half and increased predation rates by 150–200%. Squirrels returned to and doubled their activity at the site of a previously preyed on nest. However, neither area-restricted searching nor microhabitat search images can explain how squirrels located artificial nests more readily with experience. Instead, squirrels likely used cues associated with the nests or eggs themselves. Learning implies that squirrels could be increasingly effective predators as the density or profitability of nests increases. Our results add support to the view that nest predation is complex and broadly influenced (e.g. by predator experience, motivation), and is unlikely to be predicted consistently by simple relationships with predator activity, abundance or habitat.
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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 | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".