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The influence of mating system on the intensity of parent–offspring conflict in primates

2005· article· en· W2116942267 on OpenAlexaff
Tristan A. F. Long

Bibliographic record

VenueJournal of Evolutionary Biology · 2005
Typearticle
Languageen
FieldPsychology
TopicPrimate Behavior and Ecology
Canadian institutionsQueen's University
Fundersnot available
KeywordsOffspringParent–offspring conflictBiologyInclusive fitnessKin selectionMatingParental investmentAltruism (biology)Sexual conflictSelfishnessSiblingPhylogenetic treeZoologyEvolutionary biologyDemographyEcologyGeneticsSocial psychologyDevelopmental psychologyPregnancyPsychologyGene

Abstract

fetched live from OpenAlex

An evolutionary conflict of interest exists between parents and their offspring over the partitioning of parental investment (PI) among siblings. When the direct fitness benefits to offspring of increased PI, outweigh the inclusive fitness costs from lost future sibling fitness, selection should favour the evolution of offspring selfishness over altruism. In theory, this conflict is heightened when females are not strictly monogamous, as current offspring should be less altruistic towards future half-siblings than they would be towards full-siblings. Using data collected on foetal growth rate (representing prenatal PI) in primates, I test the prediction from theory that the resolution of the parent-offspring conflict will be closer to the offspring's evolutionary optima in polyandrous species than in more monandrous species. Using phylogenetic comparative analysis, and controlling for allometry, I show that offspring are able to obtain more PI when the probability of future full-siblings decreases, and that this is most pronounced in taxa where there is the opportunity for direct foetal access to the maternal bloodstream. These results support the hypothesis that the resolution of prenatal PI conflict is influenced by both a species' mating system and by its placental structure.

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 score0.090
Threshold uncertainty score0.218

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 studies0.0000.000
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 head0.038
GPT teacher head0.323
Teacher spread0.285 · 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

Citations21
Published2005
Admission routes1
Has abstractyes

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