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Record W2888910933 · doi:10.1111/ele.13144

Migratory monarchs that encounter resident monarchs show life‐history differences and higher rates of parasite infection

2018· article· en· W2888910933 on OpenAlexafffund
Dara A. Satterfield, John C. Maerz, Mark D. Hunter, D. T. Tyler Flockhart, Keith A. Hobson, D. Ryan Norris, Hillary Streit, Jacobus C. de Roode, Sonia Altizer

Bibliographic record

VenueEcology Letters · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsWestern UniversityUniversity of Guelph
FundersDivision of Environmental BiologyEnvironment CanadaUniversity of GuelphNational Science Foundation
KeywordsParasitismBiologyEcologyWildlifeBreedReproductive successDemographyPopulationHost (biology)

Abstract

fetched live from OpenAlex

Abstract Environmental change induces some wildlife populations to shift from migratory to resident behaviours. Newly formed resident populations could influence the health and behaviour of remaining migrants. We investigated migrant–resident interactions among monarch butterflies and consequences for life history and parasitism. Eastern North American monarchs migrate annually to Mexico, but some now breed year‐round on exotic milkweed in the southern US and experience high infection prevalence of protozoan parasites. Using stable isotopes (δ 2 H, δ 13 C) and cardenolide profiles to estimate natal origins, we show that migrant and resident monarchs overlap during fall and spring migration. Migrants at sites with residents were 13 times more likely to have infections and three times more likely to be reproductive (outside normal breeding season) compared to other migrants. Exotic milkweed might either attract migrants that are already infected or reproductive, or alternatively, induce these states. Increased migrant–resident interactions could affect monarch parasitism, migratory success and long‐term conservation.

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 score0.237
Threshold uncertainty score0.731

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 studies0.0000.001
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 head0.042
GPT teacher head0.239
Teacher spread0.197 · 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

Citations65
Published2018
Admission routes2
Has abstractyes

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