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Record W2117354036 · doi:10.1098/rspb.2013.1116

Beyond lifetime reproductive success: the posthumous reproductive dynamics of male Trinidadian guppies

2013· article· en· W2117354036 on OpenAlexafffund
Andrés López‐Sepulcre, Swanne P. Gordon, Ian G. Paterson, Paul Bentzen, David N. Reznick

Bibliographic record

VenueProceedings of the Royal Society B Biological Sciences · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaAgence Nationale de la RechercheNational Science Foundation
KeywordsSemelparity and iteroparityBiologyFecundityReproductive successPopulationReproductionSpermZoologyReproductive biologyEcologyDemographyGenetics

Abstract

fetched live from OpenAlex

In semelparous populations, dormant germ banks (e.g. seeds) have been proposed as important in maintaining genotypes that are adaptive at different times in fluctuating environments. Such hidden storage of genetic diversity need not be exclusive to dormant banks. Genotype diversity may be preserved in many iteroparous animals through sperm-storage mechanisms in females. This allows males to reproduce posthumously and increase the effective sizes of seemingly female-biased populations. Although long-term sperm storage has been demonstrated in many organisms, the understanding of its importance in the wild is very poor. We here show the prevalence of male posthumous reproduction in wild Trinidadian guppies, through the combination of mark-recapture and pedigree analyses of a multigenerational individual-based dataset. A significant proportion of the reproductive population consisted of dead males, who could conceive up to 10 months after death (the maximum allowed by the length of the dataset), which is more than twice the estimated generation time. Demographic analysis shows that the fecundity of dead males can play an important role in population growth and selection.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.262
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.221
Teacher spread0.205 · 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.

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

Citations79
Published2013
Admission routes2
Has abstractyes

Explore more

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