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Avian cholera, post‐hatching survival and selection on hatch characteristics in a long‐lived bird, the common eider <i>Somateria mollisima</i>

2010· article· en· W2094178938 on OpenAlexfundno aff
Sébastien Descamps, Mark R. Forbes, H. Grant Gilchrist, Oliver P. Love, Joël Bêty

Bibliographic record

VenueJournal of Avian Biology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaArcticNet
KeywordsBiologyEiderHatchingCholeraOutbreakPopulationZoologyEcologyDemographyVirology

Abstract

fetched live from OpenAlex

Infectious diseases can have dramatic impacts on animal population dynamics, but how they influence vital rates remains understudied. We took advantage of the appearance of an avian cholera epizootic in an arctic colony of common eiders Somateria mollissima to study variation in juvenile survival and selection on hatch characteristics in relation to this highly infectious disease. Avian cholera is one of the most important infectious diseases affecting wild birds and is thought to primarily affect adult survival. Here, we show that avian cholera was associated with a 90% decline in duckling survival, leading to almost zero recruitment. Before the cholera outbreak, there was significant stabilizing selection on hatching date and significant positive directional selection on hatching mass. During cholera outbreaks, selection on hatch characteristics was no longer significant. These results were based on a low sample of surviving ducklings in cholera years, but suggested that date and mass at hatching did no longer affect duckling survival in the presence of cholera. These effects of avian cholera on post‐hatching survival were likely not only the consequence of the disease per se , but also a consequence of an increase in predation rates that followed the emergence of avian cholera. Our results emphasize the dramatic direct and indirect impacts that infectious disease can have on vital rates, and thus population dynamics.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.128
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.252
Teacher spread0.243 · 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.

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

Citations34
Published2010
Admission routes1
Has abstractyes

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