Extracting individual characteristics from population data reveals a negative social effect during honeybee defence

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F22%3A00126876" target="_blank" >RIV/00216224:14330/22:00126876 - isvavai.cz</a>

Result on the web

<a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010305" target="_blank" >https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010305</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1371/journal.pcbi.1010305" target="_blank" >10.1371/journal.pcbi.1010305</a>

Result language

angličtina

Original language name

Extracting individual characteristics from population data reveals a negative social effect during honeybee defence

Original language description

Honeybees protect their colony against vertebrates by mass stinging and they coordinate their actions during this crucial event thanks to an alarm pheromone carried directly on the stinger, which is therefore released upon stinging. The pheromone then recruits nearby bees so that more and more bees participate in the defence. However, a quantitative understanding of how an individual bee adapts its stinging response during the course of an attack is still a challenge: Typically, only the group behaviour is effectively measurable in experiment; Further, linking the observed group behaviour with individual responses requires a probabilistic model enumerating a combinatorial number of possible group contexts during the defence; Finally, extracting the individual characteristics from group observations requires novel methods for parameter inference. We first experimentally observed the behaviour of groups of bees confronted with a fake predator inside an arena and quantified their defensive reaction by counting the number of stingers embedded in the dummy at the end of a trial. We propose a biologically plausible model of this phenomenon, which transparently links the choice of each individual bee to sting or not, to its group context at the time of the decision. Then, we propose an efficient method for inferring the parameters of the model from the experimental data. Finally, we use this methodology to investigate the effect of group size on stinging initiation and alarm pheromone recruitment. Our findings shed light on how the social context influences stinging behaviour, by quantifying how the alarm pheromone concentration level affects the decision of each bee to sting or not in a given group size. We show that recruitment is curbed as group size grows, thus suggesting that the presence of nestmates is integrated as a negative cue by individual bees. Moreover, the unique integration of exact and statistical methods provides a quantitative characterisation of uncertainty associated to each of the inferred parameters.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Plos Computational Biology

ISSN

1553-734X

e-ISSN

1553-7358

Volume of the periodical

18

Issue of the periodical within the volume

9

Country of publishing house

US - UNITED STATES

Number of pages

20

Pages from-to

1-20

UT code for WoS article

000892078300002

EID of the result in the Scopus database

2-s2.0-85137869513