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Patterns of ectoparasitism in North American red squirrels (Tamiasciurus hudsonicus): Sex-biases, seasonality, age, and effects on male body condition

2015· article· en· W586313671 on OpenAlexaff
Jesse E. H. Patterson, Peter Neuhaus, Susan Kutz, Kathreen E. Ruckstuhl

Bibliographic record

VenueInternational Journal for Parasitology Parasites and Wildlife · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBiologySexual dimorphismImmunocompetenceBiological dispersalPopulationZoologyEcologyDemography

Abstract

fetched live from OpenAlex

Within many species, males are often more heavily parasitised than females. Several hypotheses have been proposed to explain this phenomenon, including immunocompetence handicaps, sexual size dimorphism and behavioural differences. Here we set out to test the latter two hypotheses and make inferences about the former by assessing patterns of ectoparasitism across various life-history stages in a population of North American red squirrels (Tamiasciurus hudsonicus). We also conducted an ectoparasite removal experiment to investigate the effects of ectoparasites on male body condition. We found that males were more intensely parasitized than females, but only during the mating period. There was no difference in ectoparasite intensity between male and female juveniles at birth or at emergence, suggesting that ectoparasites do not exploit male red squirrels for longer-range natal dispersal. Male red squirrels in our population were slightly heavier than females, however we did not find any evidence that this dimorphism drives male-biased ectoparasitism. Finally, we could not detect an effect of ectoparasite removal on male body mass. Our results lend support to the hypothesis that ectoparasites exploit their male hosts for transmission and that male red squirrels are important for the transmission dynamics of ectoparasites in this population; however, the mechanisms (i.e., immunocompetence, testosterone) are not known.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.715

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)0.0000.000

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 head0.024
GPT teacher head0.346
Teacher spread0.323 · 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

Citations15
Published2015
Admission routes1
Has abstractyes

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