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

Deterministic Response Threshold Models of Reproductive Division of Labor Are More Robust Than Probabilistic Models in Artificial Ants

We implement an agent-based simulation of the response threshold model of reproductive division of labor. Ants in our simulation must perform two tasks in their environment: forage and reproduce. The colony is capable of allocating ant resources to these roles using different division of labor strategies via genetic architectures and plasticity mechanisms. We find that the deterministic allocation strategy of the response threshold model is more robust than the probabilistic allocation strategy. The deterministic allocation strategy is also capable of evolving complex solutions to colony problems like niche construction and recovery from the loss of the breeding caste. In addition, plasticity mechanisms had both positive and negative influence on the emergence of reproductive division of labor. The combination of plasticity mechanisms has an additive and sometimes emergent impact.

Role transformation of fecundity and viability: The leading cause of fitness costs associated with beta-cypermethrin resistance in Musca domestica

Fitness is closely associated with the development of pesticide resistance in insects, which determines the control strategies employed to target species and the risks of toxicity faced by non-target species. After years of selections with beta-cypermethrin in laboratory, a strain of housefly was developed that was 684,521.62-fold resistant (CRR) compared with the susceptible strain (CSS). By constructing ≤ 21 d and ≤ 30 d life tables, the differences in life history parameters between CSS and CRR were analyzed. The total production numbers of all the detected development stages in CRR were lower than in CSS. Except for the lower mortality of larvae, all the other detected mortalities in CRR were higher than in CSS. ♀:♂ and normal females of CRR were also lower than those of CSS. For CRR, the relative fitness was 0.25 in the ≤ 21 d life table and 0.24 in the ≤ 30 d life table, and a lower intrinsic rate of increase (rm) and net reproductive rate (Ro) were detected. Based on phenotype correlation and structural equation model (SEM) analyses, fecundity and viability were the only directly positive fitness components affecting fitness in CRR and CSS, and the other components played indirect roles in fitness. The variations of the relationships among fitness, fecundity and viability seemed to be the core issue resulting in fitness differences between CRR and CSS. The interactions among all the detected fitness components and the mating frequency-time curves appeared to be distinctly different between CRR and CSS. In summary, fecundity and its related factors separately played direct and indirect roles in the fitness costs of a highly beta-cypermethrin-resistant housefly strain.

Regulatory mechanism predates the evolution of self-organizing capacity in simulated ant-like robots

The evolution of complexity is one of the prime features of life on Earth. Although well accepted as the product of adaptation, the dynamics underlying the evolutionary build-up of complex adaptive systems remains poorly resolved. Using simulated robot swarms that exhibit ant-like group foraging with trail pheromones, we show that their self-organizing capacity paradoxically involves regulatory behavior that arises in advance. We focus on a traffic rule on their foraging trail as a regulatory trait. We allow the simulated robot swarms to evolve pheromone responsiveness and traffic rules simultaneously. In most cases, the traffic rule, initially arising as selectively neutral component behaviors, assists the group foraging system to bypass a fitness valley caused by overcrowding on the trail. Our study reveals a hitherto underappreciated role of regulatory mechanisms in the origin of complex adaptive systems, as well as highlights the importance of embodiment in the study of their evolution.

To Cooperate or Not to Cooperate: Why Behavioural Mechanisms Matter

Mutualistic cooperation often requires multiple individuals to behave in a coordinated fashion. Hence, while the evolutionary stability of mutualistic cooperation poses no particular theoretical difficulty, its evolutionary emergence faces a chicken and egg problem: an individual cannot benefit from cooperating unless other individuals already do so. Here, we use evolutionary robotic simulations to study the consequences of this problem for the evolution of cooperation. In contrast with standard game-theoretic results, we find that the transition from solitary to cooperative strategies is very unlikely, whether interacting individuals are genetically related (cooperation evolves in 20% of all simulations) or unrelated (only 3% of all simulations). We also observe that successful cooperation between individuals requires the evolution of a specific and rather complex behaviour. This behavioural complexity creates a large fitness valley between solitary and cooperative strategies, making the evolutionary transition difficult. These results reveal the need for research on biological mechanisms which may facilitate this transition.

Evolution of Self-Organized Task Specialization in Robot Swarms

Division of labor is ubiquitous in biological systems, as evidenced by various forms of complex task specialization observed in both animal societies and multicellular organisms. Although clearly adaptive, the way in which division of labor first evolved remains enigmatic, as it requires the simultaneous co-occurrence of several complex traits to achieve the required degree of coordination. Recently, evolutionary swarm robotics has emerged as an excellent test bed to study the evolution of coordinated group-level behavior. Here we use this framework for the first time to study the evolutionary origin of behavioral task specialization among groups of identical robots. The scenario we study involves an advanced form of division of labor, common in insect societies and known as "task partitioning", whereby two sets of tasks have to be carried out in sequence by different individuals. Our results show that task partitioning is favored whenever the environment has features that, when exploited, reduce switching costs and increase the net efficiency of the group, and that an optimal mix of task specialists is achieved most readily when the behavioral repertoires aimed at carrying out the different subtasks are available as pre-adapted building blocks. Nevertheless, we also show for the first time that self-organized task specialization could be evolved entirely from scratch, starting only from basic, low-level behavioral primitives, using a nature-inspired evolutionary method known as Grammatical Evolution. Remarkably, division of labor was achieved merely by selecting on overall group performance, and without providing any prior information on how the global object retrieval task was best divided into smaller subtasks. We discuss the potential of our method for engineering adaptively behaving robot swarms and interpret our results in relation to the likely path that nature took to evolve complex sociality and task specialization.

Emergence of polymorphic mating strategies in robot colonies

Polymorphism has fascinated evolutionary biologists since the time of Darwin. Biologists have observed discrete alternative mating strategies in many different species. In this study, we demonstrate that polymorphic mating strategies can emerge in a colony of hermaphrodite robots. We used a survival and reproduction task where the robots maintained their energy levels by capturing energy sources and physically exchanged genotypes for the reproduction of offspring. The reproductive success was dependent on the individuals' energy levels, which created a natural trade-off between the time invested in maintaining a high energy level and the time invested in attracting mating partners. We performed experiments in environments with different density of energy sources and observed a variety in the mating behavior when a robot could see both an energy source and a potential mating partner. The individuals could be classified into two phenotypes: 1) forager, who always chooses to capture energy sources, and 2) tracker, who keeps track of potential mating partners if its energy level is above a threshold. In four out of the seven highest fitness populations in different environments, we found subpopulations with distinct differences in genotype and in behavioral phenotype. We analyzed the fitnesses of the foragers and the trackers by sampling them from each subpopulation and mixing with different ratios in a population. The fitness curves for the two subpopulations crossed at about 25% of foragers in the population, showing the evolutionary stability of the polymorphism. In one of those polymorphic populations, the trackers were further split into two subpopulations: (strong trackers) and (weak trackers). Our analyses show that the population consisting of three phenotypes also constituted several stable polymorphic evolutionarily stable states. To our knowledge, our study is the first to demonstrate the emergence of polymorphic evolutionarily stable strategies within a robot evolution framework.

Selection methods regulate evolution of cooperation in digital evolution

A key, yet often neglected, component of digital evolution and evolutionary models is the 'selection method' which assigns fitness (number of offspring) to individuals based on their performance scores (efficiency in performing tasks). Here, we study with formal analysis and numerical experiments the evolution of cooperation under the five most common selection methods (proportionate, rank, truncation-proportionate, truncation-uniform and tournament). We consider related individuals engaging in a Prisoner's Dilemma game where individuals can either cooperate or defect. A cooperator pays a cost, whereas its partner receives a benefit, which affect their performance scores. These performance scores are translated into fitness by one of the five selection methods. We show that cooperation is positively associated with the relatedness between individuals under all selection methods. By contrast, the change in the performance benefit of cooperation affects the populations' average level of cooperation only under the proportionate methods. We also demonstrate that the truncation and tournament methods may introduce negative frequency-dependence and lead to the evolution of polymorphic populations. Using the example of the evolution of cooperation, we show that the choice of selection method, though it is often marginalized, can considerably affect the evolutionary dynamics.

Interindividual variability in social insects - proximate causes and ultimate consequences

Individuals within social groups often show consistent differences in behaviour across time and context. Such interindividual differences and the evolutionary challenge they present have recently generated considerable interest. Social insects provide some of the most familiar and spectacular examples of social groups with large interindividual differences. Investigating these within-group differences has a long research tradition, and behavioural variability among the workers of a colony is increasingly regarded as fundamental for a key feature of social insects: division of labour. The goal of this review is to illustrate what we know about both the proximate mechanisms underlying behavioural variability among the workers of a colony and its ultimate consequences; and to highlight the many open questions in this research field. We begin by reviewing the literature on mechanisms that potentially introduce, maintain, and adjust the behavioural differentiation among workers. We highlight the fact that so far, most studies have focused on behavioural variability based on genetic variability, provided by e.g. multiple mating of the queen, while other mechanisms that may be responsible for the behavioural differentiation among workers have been largely neglected. These include maturational, nutritional and environmental influences. We further discuss how feedback provided by the social environment and learning and experience of adult workers provides potent and little-explored sources of differentiation. In a second part, we address what is known about the potential benefits and costs of increased behavioural variability within the workers of a colony. We argue that all studies documenting a benefit of variability so far have done so by manipulating genetic variability, and that a direct test of the effect of behavioural variability on colony productivity has yet to be provided. We emphasize that the costs associated with interindividual variability have been largely overlooked, and that a better knowledge of the cost/benefit balance of behavioural variability is crucial for our understanding of the evolution of the mechanisms underlying the social organization of insect societies. We conclude by highlighting what we believe to be promising but little-explored avenues for future research on how within-colony variability has evolved and is maintained. We emphasize the need for comparative studies and point out that, so far, most studies on interindividual variability have focused on variability in individual response thresholds, while the significance of variability in other parameters of individual response, such as probability and intensity of the response, has been largely overlooked. We propose that these parameters have important consequences for the colony response. Much more research is needed to understand if and how interindividual variability is modulated in order to benefit division of labour, homeostasis and ultimately colony fitness in social insects.

Implications of behavioral architecture for the evolution of self-organized division of labor

Division of labor has been studied separately from a proximate self-organization and an ultimate evolutionary perspective. We aim to bring together these two perspectives. So far this has been done by choosing a behavioral mechanism a priori and considering the evolution of the properties of this mechanism. Here we use artificial neural networks to allow for a more open architecture. We study whether emergent division of labor can evolve in two different network architectures; a simple feedforward network, and a more complex network that includes the possibility of self-feedback from previous experiences. We focus on two aspects of division of labor; worker specialization and the ratio of work performed for each task. Colony fitness is maximized by both reducing idleness and achieving a predefined optimal work ratio. Our results indicate that architectural constraints play an important role for the outcome of evolution. With the simplest network, only genetically determined specialization is possible. This imposes several limitations on worker specialization. Moreover, in order to minimize idleness, networks evolve a biased work ratio, even when an unbiased work ratio would be optimal. By adding self-feedback to the network we increase the network's flexibility and worker specialization evolves under a wider parameter range. Optimal work ratios are more easily achieved with the self-feedback network, but still provide a challenge when combined with worker specialization.

Evolution of self-organized division of labor in a response threshold model

Division of labor in social insects is determinant to their ecological success. Recent models emphasize that division of labor is an emergent property of the interactions among nestmates obeying to simple behavioral rules. However, the role of evolution in shaping these rules has been largely neglected. Here, we investigate a model that integrates the perspectives of self-organization and evolution. Our point of departure is the response threshold model, where we allow thresholds to evolve. We ask whether the thresholds will evolve to a state where division of labor emerges in a form that fits the needs of the colony. We find that division of labor can indeed evolve through the evolutionary branching of thresholds, leading to workers that differ in their tendency to take on a given task. However, the conditions under which division of labor evolves depend on the strength of selection on the two fitness components considered: amount of work performed and on worker distribution over tasks. When selection is strongest on the amount of work performed, division of labor evolves if switching tasks is costly. When selection is strongest on worker distribution, division of labor is less likely to evolve. Furthermore, we show that a biased distribution (like 3:1) of workers over tasks is not easily achievable by a threshold mechanism, even under strong selection. Contrary to expectation, multiple matings of colony foundresses impede the evolution of specialization. Overall, our model sheds light on the importance of considering the interaction between specific mechanisms and ecological requirements to better understand the evolutionary scenarios that lead to division of labor in complex systems.

Neural networks as mechanisms to regulate division of labor

In social insects, workers perform a multitude of tasks, such as foraging, nest construction, and brood rearing, without central control of how work is allocated among individuals. It has been suggested that workers choose a task by responding to stimuli gathered from the environment. Response-threshold models assume that individuals in a colony vary in the stimulus intensity (response threshold) at which they begin to perform the corresponding task. Here we highlight the limitations of these models with respect to colony performance in task allocation. First, we show with analysis and quantitative simulations that the deterministic response-threshold model constrains the workers' behavioral flexibility under some stimulus conditions. Next, we show that the probabilistic response-threshold model fails to explain precise colony responses to varying stimuli. Both of these limitations would be detrimental to colony performance when dynamic and precise task allocation is needed. To address these problems, we propose extensions of the response-threshold model by adding variables that weigh stimuli. We test the extended response-threshold model in a foraging scenario and show in simulations that it results in an efficient task allocation. Finally, we show that response-threshold models can be formulated as artificial neural networks, which consequently provide a comprehensive framework for modeling task allocation in social insects.