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Record W3002589385 · doi:10.1086/708271

Parental Age Effects and the Evolution of Senescence

2020· article· en· W3002589385 on OpenAlexaff
Patrick Barks, Robert A. Laird

Bibliographic record

VenueThe American Naturalist · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsSenescenceBiologyOffspringReproductive valueFecundityDemographyPopulationAgeingLongevitySelection (genetic algorithm)ReproductionPaternal careEcologyEvolutionary biologyGenetics

Abstract

fetched live from OpenAlex

Most theory on the evolution of senescence implicitly assumes that all offspring are of equal quality. However, in addition to age-related declines in survival and fecundity (classically defined senescence), many organisms exhibit age-related declines in offspring quality, a phenomenon known as a parental age effect. Theoretical work suggests that parental age effects may alter age trajectories of selection and therefore shape the evolution of senescence; however, to date, these analyses have been limited to idealized life cycles and models of maternal care in human populations. To gain a broader understanding of how parental age effects may shape age trajectories of selection, we extend the classic age-structured population projection model to also account for parental age structure and apply this model to empirical data from an aquatic plant known to exhibit parental age effects (the duckweed Lemna minor), as well as a diverse set of simulated life cycles. Our results suggest that parental age effects alter predictions from classic theory on the evolution of senescence. Age-related declines in offspring quality reduce the relative value of late-life reproduction, leading to steeper age-related declines in the force of natural selection than would otherwise be expected and potentially favoring the evolution of more rapid rates of senescence.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.974
Threshold uncertainty score0.396

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)0.0000.000

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 head0.010
GPT teacher head0.217
Teacher spread0.207 · 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

Citations24
Published2020
Admission routes1
Has abstractyes

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