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Record W2970216449 · doi:10.1111/jeb.13531

Kin recognition and co‐operative foraging in<i>Drosophila melanogaster</i>larvae

2019· article· en· W2970216449 on OpenAlexafffund
Lucas Khodaei, Tristan A. F. Long

Bibliographic record

VenueJournal of Evolutionary Biology · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsWilfrid Laurier University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyDrosophila melanogasterForagingLarvaEvolutionary biologyKin recognitionDrosophila (subgenus)ZoologyEcologyGeneticsGene

Abstract

fetched live from OpenAlex

A long-standing goal for biologists and social scientists is to understand the factors that lead to the evolution and maintenance of co-operative behaviour between conspecifics. To that end, the fruit fly, Drosophila melanogaster, is becoming an increasingly popular model species to study sociality; however, most of the research to date has focused on adult behaviours. In this study, we set out to examine group-feeding behaviour by larvae and to determine whether the degree of relatedness between individuals mediates the expression co-operation. In a series of assays, we manipulated the average degree of relatedness in groups of third-instar larvae that were faced with resource scarcity, and measured the size, frequency and composition of feeding clusters, as well as the fitness benefits associated with co-operation. Our results suggest that larval D. melanogaster are capable of kin recognition (something that has not been previously described in this species), as clusters were more numerous, larger and involved more larvae, when more closely related kin were present in the social environment. These findings are discussed in the context of the correlated fitness-associated benefits of co-operation, the potential mechanisms by which individuals may recognize kin, and how that kinship may play an important role in facilitating the manifestation of this co-operative behaviour.

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 score0.456
Threshold uncertainty score0.133

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 head0.045
GPT teacher head0.243
Teacher spread0.198 · 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

Citations31
Published2019
Admission routes2
Has abstractyes

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