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Record W4229803414 · doi:10.1093/condor/103.4.793

Relationships Between Black-Legged Kittiwake Nest-Site Characteristics and Susceptibility to Predation by Large Gulls

2001· article· en· W4229803414 on OpenAlexaffabout
Melanie Massaro, John W. Chardine, Ian L. Jones

Bibliographic record

VenueOrnithological Applications · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsNest (protein structural motif)PredationLarusFledgeHerringEcologyBiologyHerring gullGeographyZoologyFishery

Abstract

fetched live from OpenAlex

Abstract We quantified the relationship between Black-legged Kittiwake (Rissa tridactyla) nest-site characteristics and risk of predation by Great Black-backed (Larus marinus) and Herring (L. argentatus) Gulls at Gull Island, Newfoundland, Canada, during 1998 and 1999. We monitored kittiwake nesting cliffs to identify nest sites attacked by large gulls and compared characteristics of attacked and successful nests among four study plots. We also examined which nest sites were attacked by Herring or Great Black-backed Gulls during calm (≤10 km hr−1) or windy conditions (>10 km hr−1). We found that kittiwake nests on plots with fewer nests were more likely to be attacked by gulls and less likely to fledge young. Nest density and nest location relative to the cliffs' upper edges significantly affected the risk of gull predation. Breeding success was correlated with nest density and ledge width and differed significantly among plots. Regardless of wind conditions both gull species were more likely to attack nests located on upper sections of cliffs than nests on lower sections. However, during calm conditions, nest sites located on narrow ledges were less likely to be attacked by Great Black-backed Gulls. Our results demonstrate that for kittiwake colonies where predation is an important source of breeding failure, the size of subcolonies and nest density affect the survival of kittiwake offspring. Relación entre las Características de los Sitios de Nidificación de Rissa tridactyla y la Susceptibilidad a la Depredación por parte de Gaviotas Resumen. Cuantificamos la relación entre las características de los sitios de nidificación de Rissa tridactyla y el riesgo de depredación por parte de Larus marinus y L. argentatus en la Isla Gull, Newfoundland, Canadá, durante 1998 y 1999. Con el objetivo de identificar los nidos atacados por gaviotas de gran tamaño, monitoreamos acantilados de nidificación de R. tridactyla en cuatro localidades de estudio y comparamos las características de los nidos atacados y exitosos. También examinamos qué nidos fueron atacados por L. marinus o por L. argentatus durante condiciones de viento calmo (≤10 km hr−1) o ventosas (>10 km hr−1). Encontramos que los nidos de R. tridactyla ubicados en localidades con menor número de nidos tuvieron mayor probabilidad de ser atacados y menor probabilidad de criar volantones. Tanto la densidad como la ubicación de los nidos en relación al vértice superior del acantilado afectaron significativamente el riesgo de depredación por gaviotas. El éxito de cría se diferenció significativamente entre localidades y se correlacionó con la densidad de nidos y con el ancho de la plataforma. Independientemente de la velocidad del viento, las dos especies de gaviotas atacaron con mayor probabilidad a los nidos ubicados en las secciones superiores del acantilado que en las secciones inferiores. Sin embargo, durante condiciones de viento calmo, los nidos localizados en plataformas angostas presentaron una menor probabilidad de ser atacados por L. marinus. Nuestros resultados demuestran que para colonias de R. tridactyla en las cuales la depredación de nidos es un factor importante en el fracaso reproductivo, el tamaño de las subcolonias y la densidad de nidos afecta la supervivencia de la progenie de R. tridactyla.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.999

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)0.0020.003

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 head0.025
GPT teacher head0.265
Teacher spread0.240 · 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; both teacher heads agree on what is shown here.

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

Citations12
Published2001
Admission routes2
Has abstractyes

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