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

PETROV, Tatjana, Matej HAJNAL, Julia KLEIN, David ŠAFRÁNEK and Morgane NOUVIAN. Extracting individual characteristics from population data reveals a negative social effect during honeybee defence. Plos Computational Biology. 2022, vol. 18, No 9, p. 1-20. ISSN 1553-734X. Available from: https://dx.doi.org/10.1371/journal.pcbi.1010305.

Basic information

Original name

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

Authors

PETROV, Tatjana (688 Serbia), Matej HAJNAL (703 Slovakia, belonging to the institution), Julia KLEIN (276 Germany), David ŠAFRÁNEK (203 Czech Republic, guarantor, belonging to the institution) and Morgane NOUVIAN (250 France)

Edition

Plos Computational Biology, 2022, 1553-734X

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Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 4.300

RIV identification code

RIV/00216224:14330/22:00126876

Organization unit

Faculty of Informatics

DOI

http://dx.doi.org/10.1371/journal.pcbi.1010305

UT WoS

000892078300002

Keywords in English

stochastic models; honeybee population; Markov population models; collective behaviour

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Abstract

V originále

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.