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Of fleas and geese: the impact of an increasing nest ectoparasite on reproductive success

2010· article· en· W1979539999 on OpenAlexaboutno aff
Vanessa B. Harriman, Ray T. Alisauskas

Bibliographic record

VenueJournal of Avian Biology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
Fundersnot available
KeywordsAltricialPrecocialBiologyNest (protein structural motif)Reproductive successGooseEcologyZoologyBrood parasiteAnatidaeFleaBird nestPopulationParasitismHost (biology)PredationDemography

Abstract

fetched live from OpenAlex

Past studies on the relationship between nest ectoparasites and avian fitness have been primarily limited to altricial hosts. Life history strategies of precocial and altricial birds vary considerably, limiting our ability to infer the effect of nest parasites on fitness of precocial species. Ross's Chen rossii and lesser snow goose Chen caerulescens caerulescens populations have been growing at unprecedented high rates. New limiting factors on vital rates of these precocial birds may arise after populations have been released from previously regulating factors. The flea Ceratophyllus vagabundus vagabundus is an apparently newly emerging nest parasite in the arctic goose colony at Karrak Lake, Nunavut, Canada. We examined the relationship between flea abundance (measured by the proportion of goose eggs covered by blood in each nest) and goose reproductive success from 2001–2004. In three of four years of study, nest success was inversely related to flea abundance in nests. Despite the potential for high costs to individuals, the overall effects of fleas on goose nesting success have thus far been small. We demonstrated that nest parasites negatively influence reproductive success of precocial bird hosts despite host life history strategy of leaving the nest quickly after hatch, which results in minimal exposure to nest parasites compared to altricial birds that raise their young in the nest.

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 score0.203
Threshold uncertainty score0.399

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.309
Teacher spread0.298 · 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

Citations23
Published2010
Admission routes1
Has abstractyes

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