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A predator's perspective of nest predation: predation by red squirrels is learned, not incidental

2009· article· en· W2144107421 on OpenAlexaff
Shawna A. Pelech, James N. Smith, Stan Boutin

Bibliographic record

VenueOikos · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPredationNest (protein structural motif)PredatorEcologyHabitatBiologyPopulationNest boxDemography

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.278
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations32
Published2009
Admission routes1
Has abstractyes

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