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Record W4389951088 · doi:10.1038/s41598-023-48571-x

Rapid seasonal changes in phenotypes in a wild Drosophila population

2023· article· en· W4389951088 on OpenAlexfundno aff
Takahisa Ueno, Akiko Takenoshita, Kaiya Hamamichi, Mitsuhiko Sato, Yuma Takahashi

Bibliographic record

VenueScientific Reports · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsnot available
FundersInstitute of GeneticsJapan Society for the Promotion of ScienceToyota FoundationSumitomo Foundation
KeywordsBiologyPopulationPhenotypeEcologyDrosophila (subgenus)Adaptation (eye)WingConfoundingPhenotypic traitPhenotypic plasticityZoologyGeneticsDemographyGene

Abstract

fetched live from OpenAlex

Seasonal environmental change is one of the most rapid and striking environmental variables. Although relatively rapid adaptation to environmental changes over several years or several decades has been described in many taxa, rapid responses to seasonal environments are delicate, and therefore, the detection of the evolutionary responses requires sensitive methods. In this study, we examined seasonal changes in phenotypes related to thermal tolerance and morphological traits of Drosophila lutescens collected at the spring and autumn periods from a single location. We first demonstrated that flies in the two seasonal periods were almost genetically identical using double-digest restriction site-associated DNA sequencing and analysis. Using an experimental design to eliminate the effect of possible confounding factors that influence phenotypes (i.e., maternal effects and the environmental conditions in which each phenotype was analyzed), we showed that the heat tolerance of D. lutescens was significantly higher in the autumn population than in the spring population. Furthermore, cold tolerance was slightly higher in the spring population than in the autumn one. Although wing length and thorax length did not change significantly between seasons, the ratio of wing length to thorax length changed significantly between them. These results suggest that seasonal environmental heterogeneity induces rapid phenotypic changes within a year. Finally, we discuss the possibility of rapid evolutionary responses to seasonal changes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.238
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2023
Admission routes1
Has abstractyes

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