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Delayed timing as a strategy to avoid nest‐site competition: testing a model using data from starlings and flickers

2003· article· en· W2051336812 on OpenAlexfundaboutno aff
Karen L. Wiebe

Bibliographic record

VenueOikos · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaFrancis Crick Institute
KeywordsSturnusNest (protein structural motif)StarlingBiologySeasonal breederPopulationAvian clutch sizeEcologyWoodpeckerNest boxPredationCompetition (biology)ReproductionReproductive successHabitatDemography

Abstract

fetched live from OpenAlex

European starlings ( Sturnus vulgaris ) introduced to North America compete with native cavity‐nesting birds for nest sites. I examined whether natural selection could favour a strategy of delayed nesting in a population of northern flickers ( Colaptes auratus ), a native woodpecker, to reduce overlap in breeding phenology with starling competitors. I developed a mathematical model based on reproductive parameters for a population of flickers from central British Columbia, Canada. On average, 7% of flicker nests each year were usurped by starlings; daily probabilities of takeover declined through the season but were relatively low (1–2%). Flickers laid between 3 and 13 eggs and there was a negative correlation between clutch size and date. The probability of renesting after nest failure also declined during the season, but renesting intervals (2–19 days) were not associated with female age, or stage of nest loss. The model suggested that costs of delaying reproduction would outweigh benefits of an early start except when the probability of nest usurpation is very high (>75%) early in the season and declines rapidly through the summer. Thus, early laying dates should be favoured especially in northern latitudes where breeding seasons are short.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.690
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.092
GPT teacher head0.296
Teacher spread0.204 · 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 teacher head, not a consensus.

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

Citations80
Published2003
Admission routes2
Has abstractyes

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