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Record W1964467538 · doi:10.1086/605962

Maternal Effects Increase Within‐Family Variation in Offspring Survival

2009· article· en· W1964467538 on OpenAlexfundno aff
Wendy L. Reed, Mark E. Clark, Carol M. Vleck

Bibliographic record

VenueThe American Naturalist · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
FundersDelta WaterfowlNational Science Foundation
KeywordsBiologyOffspringHatchingMaternal effectPopulationJuvenileZoologyNatural selectionAvian clutch sizeSelection (genetic algorithm)Population sizeEcologyReproductionDemographyPregnancyGenetics

Abstract

fetched live from OpenAlex

Maternal effects are environmental components of phenotypes that complicate relationships between natural selection and evolution because they often affect phenotypes and fitness simultaneously. We studied the effects of egg size variation on juvenile survival in a population of American coots (Fulica americana). We experimentally evaluated egg size variation at three levels: across the population, within natal nests, and within foster nests. Natal nests accounted for the most variation in population egg size. Within clutches, early-laid eggs were larger than later-laid eggs, with the exception of first-laid eggs, which were small. In the fostering experiment, posthatching survival was most strongly related to egg size relative to natal siblings and natal hatching order and less so to egg size within foster nests. These effects on survival were found even though young from natal nests were neither raised together nor raised by genetic parents. These results indicate that females allocate resources unequally among offspring such that offspring from larger, early-laid eggs have higher survival than offspring from smaller, later-laid eggs, regardless of their size relative to foster siblings or to mean population egg size. These results suggest that egg size variation can be maintained through selection on maternal investment strategies and not on egg size per se.

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 score0.046
Threshold uncertainty score0.602

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 head0.006
GPT teacher head0.233
Teacher spread0.227 · 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

Citations32
Published2009
Admission routes1
Has abstractyes

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