Microbial associates and social behavior in ants

Abstract

Current research in life sciences provides advances on how animal-associated microbes affect behavior and its underlying neurophysiology. However, studies in this field are often limited to individuals outside of their social context, and neglect social dynamics. Contrarily, animals and humans develop and live in complex societies where they constantly adjust physiology and behavior to social interactions. To improve our understanding of how microbes and hosts interact and produce phenotypes at social and group levels, we need to broaden our experimental approaches to a group-level dimension. Here we point out that eusocial insects, and ants in particular, are ideal models for this purpose. We first examine the most common types of microorganismal associations that ants engage in, and then briefly summarize what is known about the role of symbiotic microbes in ant social behavior. Finally, we propose future directions in the field in the light of recent technical advances in behavior measuring techniques.

Dual‐fluorescence imaging and automated trophallaxis detection for studying multi‐nutrient regulation in superorganisms

“…In a broader view, with empirical access to the flows of nutrients in a colony, one may study the properties and functions of ant food‐sharing networks while varying different ecologically relevant parameters, such as food source availability or quality, predation risk and competition. Furthermore, tracking different materials in the trophallactic fluid is expected to be useful in other contemporary research subjects, including: the trade‐offs between food and pathogen transmission through contact networks (Csata & Dussutour, 2019; Sendova‐Franks et al., 2010; Stroeymeyt et al., 2018), the different nutritional needs within colonies and their close symbionts (Crumière et al., 2020), the control of microbial communities ingested with food (Sclocco & Teseo, 2020) and regulatory effects of trophallactic fluid on differential larval development (LeBoeuf et al, 2016, 2018). Importantly, this method can potentially be applicable to other trophallaxis‐performing insects, including various species of ants (Czaczkes et al., 2019), bees (Fard et al., 2020), wasps (Suryanarayanan & Jeanne, 2008; Taylor & Jeanne, 2018) and termites (Poissonnier et al., 2018, 2020).…”

Socializing in an Infectious World: The Role of Parasites in Social Evolution of a Unique Rodent Family

“…The task-specific division of labor can also affect the microbiome of individuals, either because they differ in their exposure to microorganisms or because exchange of microorganisms occurs predominately between individuals carrying out the same task (Münger et al, 2018;Sinotte et al, 2020). In eusocial insects these differences are assumed to also increase the efficiency with which certain tasks are carried out and increase the extraction of nutrient that also fuel immune responses (Iorizzo et al, 2020;Sclocco and Teseo, 2020). Although the number of studies investigating the effects of the division of labor on the microbiome remains limited, the available information suggests distinct microbiomes for breeders and non-breeders as well as different worker castes in eusocial insects (Sclocco and Teseo, 2020;Sinotte et al, 2020).…”

Integrating real-time data analysis into automatic tracking of social insects

“…Although the technology underlying insect video tracking advances rapidly and significantly, many recently developed systems rely on offline data processing [11,[17][18][19][20][21]. This means that researchers often perform tracking on previously recorded videos and analyse data a posteriori.…”

“…This approach allows correcting errors and employing sophisticated and costly computational strategies, or even processing earlier datasets using future knowledge. On the other hand, offline data processing does not allow for simultaneous insights into already running experiments [ 22 ]. Graphics processing units (GPUs), which allow accelerating computation, begin to address this gap via integrating real-time data analysis [ 23 , 24 ] in the traditional way of conducting tracking-based studies on insect behaviour [ 12 , 25 ].…”