Task-dependent influence of genetic architecture and mating frequency on division of labour in social insect societies

Abstract

Division of labour is one of the most prominent features of social insects. The efficient allocation of individuals to different tasks requires dynamic adjustment in response to environmental perturbations. Theoretical models suggest that the colony-level flexibility in responding to external changes and internal perturbation may depend on the within-colony genetic diversity, which is affected by the number of breeding individuals. However, these models have not considered the genetic architecture underlying the propensity of workers to perform the various tasks. Here, we investigated how both within-colony genetic variability (stemming from variation in the number of matings by queens) and the number of genes influencing the stimulus (threshold) for a given task at which workers begin to perform that task jointly influence task allocation efficiency. We used a numerical agent-based model to investigate the situation where workers had to perform either a regulatory task or a foraging task. One hundred generations of artificial selection in populations consisting of 500 colonies revealed that an increased number of matings always improved colony performance, whatever the number of loci encoding the thresholds of the regulatory and foraging tasks. However, the beneficial effect of additional matings was particularly important when the genetic architecture of queens comprised one or a few genes for the foraging task's threshold. By contrast, a higher number of genes encoding the foraging task reduced colony performance with the detrimental effect being stronger when queens had mated with several males. Finally, the number of genes encoding the threshold for the regulatory task only had a minor effect on colony performance. Overall, our numerical experiments support the importance of mating frequency on efficiency of division of labour and also reveal complex interactions between the number of matings and genetic architecture.

Neural Networks as Mechanisms to Regulate Division of Labor

“…To quantify the workers' efficiency in task allocation, we used a stochastic agent-based simulation to model a situation in which workers had to perform two distinct tasks (Tarapore et al 2010). Our aim was to mimic situations with two vital tasks, such as foraging and regulation of nest temperature.…”

“…In all simulations, the regulatory bounds were constantly fixed to the same values (140-160 items; Tarapore et al 2010). Changing the regulatory bounds would not qualitatively affect our results.…”

“…Consequently, at a given point in time, a worker could be engaged in the regulatory task, engaged in the foraging task (foraging worker), or inactive (idle worker). The fitness of colonies was a function of workers being able to perform both tasks efficiently (Tarapore et al 2010, see "Material and Methods").…”

“…To overcome these problems, we propose an extended responsethreshold model (ETM) that can result in an efficient task allocation for any stimulus conditions. We experimentally compare all models by means of directed evolution (Nolfi and Floreano 2000;Floreano and Keller 2010) in a foraging scenario that requires a dynamic reallocation of workers to different tasks according to colony needs (Tarapore et al 2010). Finally, we show that the response-threshold models can be formulated as artificial neural networks (McClelland et al 1986;Haykin 1998), which consequently constitute a comprehensive framework for modeling task allocation in social insects.…”

Selection methods regulate evolution of cooperation in digital evolution

“…While all five selection methods are frequently used to simulate differential selection (PSM in [19][20][21][22][23][24][25][26][27][28][29][30][31][32]; RSM in [33,34]; TPSM in [35][36][37]; TUSM in [38][39][40][41][42][43][44][45][46], TSM in [22,47,48]), the choice between them is rarely justified. Moreover, little attempt has been made to quantify the effects of selection methods on the dynamics of the digital evolution (but see [22,49]).…”