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Record W1968651839 · doi:10.1086/652470

Ontogenetic Change in Genetic Variance in Size Depends on Growth Environment

2010· article· en· W1968651839 on OpenAlexafffund
Caitlin Dmitriew, Mark W. Blows, Locke Rowe

Bibliographic record

VenueThe American Naturalist · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsOntogenyBiologyGenetic variationCompensatory growth (organ)Analysis of varianceVariance (accounting)Genetic variabilityEvolutionary biologyZoologyGeneticsStatisticsGenotypeGene

Abstract

fetched live from OpenAlex

Within populations, the amount of environmental and genetic variation present may differ greatly among traits measured at multiple times over ontogeny. Brief periods of food deprivation are often followed by a period of accelerated (compensatory) growth. Early laboratory studies likewise reported a contraction of genetic variance in size as maturation approached. However, studies of wild populations often contradict these laboratory results. One possibility is that environmentally imposed stress is exposing genetic variance not seen in the laboratory. We tested the effect of rearing environment (high or low food) on genetic variance in size traits measured at two ages in the ladybird beetle Harmonia axyridis. A substantial amount of genetic variance was present in all combinations of rearing environment by ontogenetic stage among males. The pattern of change in male variance in mass over ontogeny was of opposite sign in the two food treatments, which may reflect cryptic genetic variance that is apparent only under stress. The proportion of overall variance that was due to additive genetic effects was much lower in females than in males, which suggests that the underlying genetics of female growth trajectories differs from that males. Our experimental design afforded an initial exploration of the genetics of compensatory growth.

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 score0.501
Threshold uncertainty score0.997

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 studies0.0000.000
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 head0.023
GPT teacher head0.212
Teacher spread0.189 · 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

Citations29
Published2010
Admission routes2
Has abstractyes

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