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Record W2994944218 · doi:10.1111/ele.13436

The new kid on the block: immigrant males win big whereas females pay fitness cost after dispersal

2019· letter· en· W2994944218 on OpenAlexaffabout
April Robin Martinig, Andrew G. McAdam, Ben Dantzer, Jeffrey E. Lane, David W. Coltman, Stan Boutin

Bibliographic record

VenueEcology Letters · 2019
Typeletter
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsUniversity of SaskatchewanUniversity of GuelphUniversity of Alberta
Fundersnot available
KeywordsBiological dispersalImmigrationOffspringDemographyCompetition (biology)EcologyPopulationBiologyGeographySociologyPregnancy

Abstract

fetched live from OpenAlex

Dispersal is nearly universal; yet, which sex tends to disperse more and their success thereafter depends on the fitness consequences of dispersal. We asked if lifetime fitness differed between residents and immigrants (successful between-population dispersers) and their offspring using 29 years of monitoring from North American red squirrels (Tamiasciurus hudsonicus) in Canada. Compared to residents, immigrant females had 23% lower lifetime breeding success (LBS), while immigrant males had 29% higher LBS. Male immigration and female residency were favoured. Offspring born to immigrants had 15-43% lower LBS than offspring born to residents. We conclude that immigration benefitted males, but not females, which appeared to be making the best of a bad lot. Our results are in line with male-biased dispersal being driven by local mate competition and local resource enhancement, while the intergenerational cost to immigration is a new complication in explaining the drivers of sex-biased dispersal.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.222
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.007

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 head0.016
GPT teacher head0.226
Teacher spread0.210 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations40
Published2019
Admission routes2
Has abstractyes

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