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Record W4405920301 · doi:10.1111/ele.14505

Inheritance of Material Wealth in a Natural Population

2024· article· en· W4405920301 on OpenAlexaff
Murielle Ålund, S. Eryn McFarlane, Arild Husby, Jonas Knape, Tomas Pärt, Päivi M. Sirkiä, Franz J. Weissing, David Wheatcroft, Yishu Zhu, Anna Qvarnström

Bibliographic record

VenueEcology Letters · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsYork University
FundersVetenskapsrådetSvenska Forskningsrådet FormasRoyal Swedish Academy of Sciences
KeywordsBiologyInheritance (genetic algorithm)HeritabilityPopulationEvolutionary biologyAdaptation (eye)Genetic FitnessNatural selectionLocal adaptationEcologyGeneticsDemographyBiological evolutionGene

Abstract

fetched live from OpenAlex

ABSTRACT Evolutionary adaptation occurs when individuals vary in access to fitness‐relevant resources and these differences in ‘material wealth’ are heritable. It is typically assumed that the inheritance of material wealth reflects heritable variation in the phenotypic abilities needed to acquire material wealth. We scrutinise this assumption by investigating additional mechanisms underlying the inheritance of material wealth in collared flycatchers. A genome‐wide association analysis reveals a high genomic heritability ( h 2 = 0.405 ± 0.08) of access to caterpillar larvae, a fitness‐relevant resource, in the birds' breeding territories. However, we find little evidence for heritable variation in phenotypic abilities needed to acquire this material wealth. Instead, combined evidence from simulations, experimental and long‐term monitoring data indicate that inheritance of material wealth is largely explained by philopatry causing a within‐population genetic structure across a heterogeneous landscape. Therefore, allelic variants associated with high material wealth may spread in the population without having causal connections to traits promoting local adaptation.

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 score0.830
Threshold uncertainty score0.152

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 head0.010
GPT teacher head0.230
Teacher spread0.220 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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