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Record W2379643049 · doi:10.1111/evo.12951

Antagonistic natural and sexual selection on wing shape in a scrambling damselfly

2016· article· en· W2379643049 on OpenAlexaff
David Outomuro, Linus Söderquist, Viktor Nilsson‐Örtman, Maria Cortázar‐Chinarro, Cecilia Lundgren, Frank Johansson

Bibliographic record

VenueEvolution · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of Toronto
FundersVetenskapsrådet
KeywordsBiologyDamselflyNatural selectionEvolutionary biologySelection (genetic algorithm)Sexual selectionWingScramblingNatural (archaeology)ZoologyOdonataPaleontologyArtificial intelligence

Abstract

fetched live from OpenAlex

Wings are a key trait underlying the evolutionary success of birds, bats, and insects. For over a century, researchers have studied the form and function of wings to understand the determinants of flight performance. However, to understand the evolution of flight, we must comprehend not only how morphology affects performance, but also how morphology and performance affect fitness. Natural and sexual selection can either reinforce or oppose each other, but their role in flight evolution remains poorly understood. Here, we show that wing shape is under antagonistic selection with regard to sexual and natural selection in a scrambling damselfly. In a field setting, natural selection (survival) favored individuals with long and slender forewings and short and broad hindwings. In contrast, sexual selection (mating success) favored individuals with short and broad forewings and narrow-based hindwings. Both types of selection favored individuals of intermediate size. These results suggest that individuals face a trade-off between flight energetics and maneuverability and demonstrate how natural and sexual selection can operate in similar directions for some wing traits, that is, wing size, but antagonistically for others, that is, wing shape. Furthermore, they highlight the need to study flight evolution within the context of species' mating systems and mating behaviors.

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 score0.918
Threshold uncertainty score0.108

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 head0.020
GPT teacher head0.237
Teacher spread0.217 · 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

Citations38
Published2016
Admission routes1
Has abstractyes

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