Impact of Nesting Mode, Diet, and Taxonomy in Structuring the Associated Microbial Communities of Amazonian Ants

Author:

Chanson Anaïs12ORCID,Moreau Corrie S.3ORCID,Duplais Christophe4ORCID

Affiliation:

1. Université de Guyane, UMR8172 Ecofog, Campus AgroParisTech, CNRS, Cirad, INRAE, Université des Antilles, 97304 Kourou, France

2. Department of Life Sciences, College of Science, University of Lincoln, Lincoln LN6 7TS, UK

3. Departments of Entomology and Ecology & Evolutionary Biology, Cornell University, Ithaca, NY 14850, USA

4. Department of Entomology, Cornell AgriTech, Cornell University, Geneva, NY 14456, USA

Abstract

Studies of ant biodiversity are important to understand their group better, as well as to extend our knowledge on the evolution of their associated organisms. Host-associated microbial communities, and particularly bacterial communities, are shaped by different host factors such as habitat, diet, and phylogeny. Here, we studied the structures of bacterial and microbial eukaryote communities associated with Amazonian ants collected from two habitats: the rainforest and the city. We collected 38 ant species covering a large taxonomic range, and we used 16S rRNA and 18S rRNA amplicon sequencing to study the impact of the host’s ecological and phylogenetic factors on their microbial communities. Our results show that (1) habitat does not structure ant microbial communities, (2) ant diet and nesting mode impact bacterial communities, while only nesting mode structures microbial eukaryote communities, and (3) microbial diversity is not correlated with host phylogeny, although several ant genera have conserved bacterial communities. As we continue to uncover the diversity and function of insect-associated microbes, this work explores how host ecology and evolutionary history shape ant microbial communities.

Funder

Investissement d’Avenir

National Science Foundation

Publisher

MDPI AG

Subject

Nature and Landscape Conservation,Agricultural and Biological Sciences (miscellaneous),Ecological Modeling,Ecology

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