Evidence map›Paper›PMID 39541398›Full record

ArticlePLoS biology2024

Immigrant birds learn from socially observed differences in payoffs when their environment changes.

Michael Chimento, Gustavo Alarcón-Nieto, Lucy M Aplin

Abstract read
In one paragraph

Article in PLoS biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Michael ChimentoDepartment of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland.ORCID 0000-0001-5697-1701
Gustavo Alarcón-NietoCognitive and Cultural Ecology Research Group, Max Planck Institute of Animal Behavior, Radolfzell, Germany.
Lucy M AplinDepartment of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland.

Funding

Deutsche Forschungsgemeinschaft (DFG) EXC 2117-422037984
6 · The paper itself

Abstract

Longstanding theory predicts that strategic flexibility in when and how to use social information can help individuals make adaptive decisions, especially when environments are temporally or spatially variable. A short-term increase in reliance on social information under these conditions has been experimentally shown in primates, including humans, but whether this occurs in other taxa is unknown. We asked whether migration between spatially variable environments affected social information use with a large-scale cultural diffusion experiment with wild great tits (Parus major) in captivity, a small passerine bird that can socially learn novel behaviors. We simulated an immigration event where knowledgeable birds were exchanged between groups with opposing preferences for a socially learned foraging puzzle, living in similar or different environments. We found evidence that both immigrants and residents were influenced by social information and attended to the rewards that others received. Our analysis supported the use of a payoff-biased social learning by immigrants when both resources and habitat features were spatially variable. In contrast, immigrants relied more-so on individual learning when payoffs or the environment were unchanged. In summary, our results suggest that great tits assess the payoffs others receive and are more influenced by socially observed differences in payoffs when environmental cues differ in their new environment. Our results provide experimental support for the hypothesis that spatial variability is a strong driver for the evolution of social learning strategies.

Indexed as

Social BehaviorAnimal MigrationAnimalsBehavior, AnimalEcosystemEnvironmentFemaleLearningMalePasseriformesSocial Learning

Identifiers

PMID39541398
PMCPMC11563421

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.