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Record W4411852459 · doi:10.1098/rsbl.2025.0153

Foraging actively can be advantageous in heterogeneous environments

2025· article· en· W4411852459 on OpenAlexaff
Dylan J. Padilla Pérez, John M. VandenBrooks, Marla B. Sokolowski, Michael J. Angilletta

Bibliographic record

VenueBiology Letters · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsForagingBiologyForageTraitAdaptation (eye)EcologyOptimal foraging theoryFeeding behaviorExperimental evolutionLocal adaptationZoologyEvolutionary biologyPopulationGeneticsGene

Abstract

fetched live from OpenAlex

A wealth of evidence indicates that behavioural polymorphism is widespread in nature. While some organisms search for food by moving almost continuously throughout their environment, other organisms forage in one place for long periods of time. Although such a dichotomy has been previously documented in Drosophila melanogaster , the question remains which foraging strategy is better suited to maximize energy intake in a particular environment. We designed an experiment to evaluate whether the configuration of food in the environment alters the foraging behaviour of two larval strains. Assuming that one of the strains acquires more food than the other in a given environment, we examined whether variation in growth occurred between them. Our results indicate that foraging behaviour is a plastic trait, shaped by the configuration of food in the environment. Regardless of the foraging strategy, we found that larvae generally increased their locomotion when food was patchy rather than clumped. Even though we observed that some individuals actively sought food while others stayed foraging at nearby sites, we found no differences in growth rate between them. However, we suggest that foraging actively may be advantageous in polymorphic populations because such behaviour facilitates local adaptation via founder effect and gene flow.

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 score0.781
Threshold uncertainty score0.141

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 head0.015
GPT teacher head0.240
Teacher spread0.225 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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