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Opposing effects of allogrooming on disease transmission in ant societies
Authors:Fabian J Theis  Line V Ugelvig  Carsten Marr  Sylvia Cremer
Institution:1.Helmholtz Zentrum München–German Research Center for Environmental Health, Institute of Computational Biology, Ingolstädter Landstrasse 1, Neuherberg 85764, Germany;2.Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Boltzmannstraße 3, Garching 85748, Germany;3.IST Austria (Institute of Science and Technology Austria), Am Campus 1, Klosterneuburg 3400, Austria
Abstract:To prevent epidemics, insect societies have evolved collective disease defences that are highly effective at curing exposed individuals and limiting disease transmission to healthy group members. Grooming is an important sanitary behaviour—either performed towards oneself (self-grooming) or towards others (allogrooming)—to remove infectious agents from the body surface of exposed individuals, but at the risk of disease contraction by the groomer. We use garden ants (Lasius neglectus) and the fungal pathogen Metarhizium as a model system to study how pathogen presence affects self-grooming and allogrooming between exposed and healthy individuals. We develop an epidemiological SIS model to explore how experimentally observed grooming patterns affect disease spread within the colony, thereby providing a direct link between the expression and direction of sanitary behaviours, and their effects on colony-level epidemiology. We find that fungus-exposed ants increase self-grooming, while simultaneously decreasing allogrooming. This behavioural modulation seems universally adaptive and is predicted to contain disease spread in a great variety of host–pathogen systems. In contrast, allogrooming directed towards pathogen-exposed individuals might both increase and decrease disease risk. Our model reveals that the effect of allogrooming depends on the balance between pathogen infectiousness and efficiency of social host defences, which are likely to vary across host–pathogen systems.
Keywords:host–  pathogen interactions  epidemiology  SIS model  social interaction patterns  social immunity  sanitary behaviours
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