Behavior Of Social Insects Research Articles

Robophysical study of excavation in confined environments

Soil-dwelling social insects build complex nests. Nest excavation is performed by multiple animals simultaneously and is governed by local interactions of the workers with other nest-mates and their surroundings. To investigate collective confined excavation challenges, we built groups of robotic excavators capable of performing hours of autonomous tunnel excavation in a model cohesive granular medium. Excavator behavior was governed by a simple set of rules triggered by interactions with the surrounding environment and other robots. The rate of tunnel growth and energetic costs of excavation were measured for groups of different numbers operating in wide and narrow tunnels. To extend the results to systems with large numbers of robots, we developed a cellular automata model. Experiments and simulations showed that in sufficiently wide tunnels an increase in the size of the excavating group increased the excavation rates without a significant increase in the energy consumption per robot. A decrease in the tunnel width resulted in a decrease in the excavation rates and increase in the energetic costs of excavation. We attribute this effect to the emergence of multiple time-consuming interactions (clogs) among excavating robots in the confined spaces. Although in all situations clogs were resolvable, clog resolution took longer in the systems with larger number of robots and narrower tunnels. We expect that our robotic system can be used to investigate the behavior of social insects in confined spaces as well as inspire more sophisticated search-and-rescue robotics.

PLASTICIDADE DO COMPORTAMENTO DE FORRAGEAMENTO EM FORMIGAS CORTADEIRAS

The foraging behavior in social insects is a complex activity that involves a self-organizing system and can be studied at the individual or at the colony level. In the ant society, not all individuals participate in the gathering of food. The workers who perform this task are often older and larger ants. In Attini ants, the foraging is a complex process that involves selection, cutting and transport of food resources to the nest. These ants use several sources of food to develop the symbiotic fungus, which provides food to the colony. The genera Atta and Acromyrmex , known as leaf-cutter ant, have a well-defined pattern of foraging determined by physical and chemical trails. In the basal genera the worker ants forage alone, hardly ever cut fresh vegetation, do not follow trails and there is no fidelity to the resource gathered. Besides the differences between groups, foraging behavior may be different in the same specie as consequence of environmental factors and the adaptability of workers. Other phenomena that make the Attines an interesting model to study foraging behavior are the polymorphism and polyethism. Several studies carried in Atta species demonstrated the existence behavioral plasticity of foraging behavior of ants. In this context, this review analyzes the adaptability of leaf-cutter ants to solve problems fitting their behavior, both individually and collectively to adopt the most convenient strategy to exploit as society several sources of food.

Symmetry breaking in mass-recruiting ants: extent of foraging biases depends on resource quality.

The communication involved in the foraging behaviour of social insects is integral to their success. Many ant species use trail pheromones to make decisions about where to forage. The strong positive feedback caused by the trail pheromone is thought to create a decision between two or more options. When the two options are of identical quality, this is known as symmetry breaking, and is important because it helps colonies to monopolise food sources in a competitive environment. Symmetry breaking is thought to increase with the quantity of pheromone deposited by ants, but empirical studies exploring the factors affecting symmetry breaking are limited. Here, we tested if (i) greater disparity between two food sources increased the degree to which a higher quality food source is favoured and (ii) if the quality of identical food sources would affect the degree of symmetry breaking that occurs. Using the mass-recruiting Pharaoh ant, Monomorium pharaonis, we carried out binary choice tests to investigate how food quality affects the choice and distribution of colony foraging decisions. We found that colonies could coordinate foraging to exploit food sources of greater quality, and a greater contrast in quality between the food sources created a stronger collective decision. Contrary to prediction, we found that symmetry breaking decreased as the quality of two identical food sources increased. We discuss how stochastic effects might lead to relatively strong differences in the amount of pheromone on alternative routes when food source quality is low.Significance statementPheromones used by social insects should guide a colony via positive feedback to distribute colony members at resources in the most adaptive way given the current environment. This study shows that when food resources are of equal quality, Pharaoh ant foragers distribute themselves more evenly if the two food sources are both of high quality compared to if both are of low quality. The results highlight the way in which individual ants can modulate their response to pheromone trails which may lead colonies to exploiting resources more evenly when in a resource rich environment.Electronic supplementary materialThe online version of this article (doi:10.1007/s00265-016-2187-y) contains supplementary material, which is available to authorized users.

Sickness Behavior in Honey Bees.

During an infection, animals suffer several changes in their normal physiology and behavior which may include lethargy, appetite loss, and reduction in grooming and general movements. This set of alterations is known as sickness behavior and although it has been extensively believed to be orchestrated primarily by the immune system, a relevant role for the central nervous system has also been established. The aim of the present work is to develop a simple animal model to allow studying how the immune and the nervous systems interact coordinately during an infection. We administered a bacterial lipopolysaccharide (LPS) into the thorax of honey bees to mimic a bacterial infection, and then we evaluated a set of stereotyped behaviors of the animals that might be indicative of sickness behavior. First, we show that this immune challenge reduces the locomotor activity of the animals in a narrow time window after LPS injection. Furthermore, bees exhibit a loss of appetite 60 and 90 min after injection, but not 15 h later. We also demonstrate that LPS injection reduces spontaneous antennal movements in harnessed animals, which suggests a reduction in the motivational state of the bees. Finally, we show that the LPS injection diminishes the interaction between animals, a crucial behavior in social insects. To our knowledge these results represent the first systematic description of sickness behavior in honey bees and provide important groundwork for the study of the interaction between the immune and the neural systems in an insect model.

Bioinspired algorithm for area surveillance using autonomous robots

Territorial surveillance plays a constantly increasing role in security. However, completely automatic surveillance using autonomous robots is hard to implement and maintain. Current methods described in literature propose systems that include direct communication of the robots or the use of a centralised system to coordinate the robots. These systems are prone to equipment failure and/or malicious attacks to the centralised system. In this paper, we propose a bioinspired algorithm that allows indirect communication between the robots that are considered minimally equipped. This is achieved by applying a parallel and distributed technique inspired by the emergent behaviour of social insects, namely ant colonies. In particular, the development of a collective memory for robots and areas covered is achieved subsequently through self-organisation of the autonomous robots to a continuous dynamic coverage of the test space. The algorithm is shown to have a robust behaviour and competitive performance. Several simulations run for various space sizes, different number of robots, different pheromone evaporation rates as well various percentages of space covered by obstacles. In all cases the efficacy of the proposed algorithm has been successfully proven when compared with other well known techniques.

Drosophila As a Genetically Tractable Model for Social Insect Behavior

The relatively simple communication, breeding and egg-making systems that govern reproduction in female Drosophila retain homology to eusocial species in which these same systems are modified to the social condition. Despite having no parental care, division of labour or subfertile caste, Drosophila may nonetheless offer a living test of certain sociobiological hypotheses framed around gene function. In this review, we make this case, and do so around the recent discovery that the non-social fly, Drosophila melanogaster, can respond to the ovary-suppressing queen pheromone of the honey bee Apis meliffera. Here, we first explain the sociobiological imperative to reconcile kin theory with molecular biology, and qualify a potential role for Drosophila. Then, we offer three applications for the fly-pheromone assay. First, the availability and accessibility of massive mutant libraries makes immediately feasible any number of open or targeted gene screens against the ovary-inhibiting response. The sheer tractability of Drosophila may therefore help to accelerate the search for genes in pheromone-responsive pathways that regulate female reproduction, including potentially any that are preserved with modification to regulate worker sterility in response to queen pheromones in eusocial taxa. Secondly, Drosophila’s powerful Gal4/UAS expression system can complement the pheromone assay by driving target gene expression into living tissue, which could be well applied to the functional testing of genes presumed to drive ovary activation or de-activation in the honey bee or other eusocial taxa. Finally, coupling Gal4 with UAS-RNAi lines can facilitate loss-of-function experiments against perception and response to the ovary inhibiting pheromone, and do so for large numbers of candidates in systematic fashion. Drosophila's utility as an adjunct to the field of insect sociobiology is not ideal, but retains surprising potential.

Trail-Using Ant Behavior Based Energy-Efficient Routing Protocol in Wireless Sensor Networks

Swarm Intelligence (SI) observes the collective behavior of social insects and other animal societies. Ant Colony Optimization (ACO) algorithm is one of the popular algorithms in SI. In the last decade, several routing protocols based on ACO algorithm have been developed for Wireless Sensor Networks (WSNs). Such routing protocols are very flexible in distributed system but generate a lot of additional traffic and thus increase communication overhead. This paper proposes a new routing protocol reducing the overhead to provide energy efficiency. The proposed protocol adopts not only the foraging behavior of ant colony but also the trail-using behavior which has never been adopted in routing. By employing the behaviors, the protocol establishes and manages the routing trails energy efficiently in the whole network. Simulation results show that the proposed protocol has low communication overhead and reduces up to 55% energy consumption compared to the existing ACO algorithm.

Ideality & Bio-Inspired Based Collaborative Bibliographic Search Method

Bibliographic search is a common entry point to any scientific work. It aims at gathering enough information to support the incoming thoughts. It is usually an unstructured process relying on both information retrieval and serendipity.Considering the search as an unstructured process, it could easily become a time consuming never ending process.In order to maximize the outcome of bibliographic search with limited time and resources, an approach is proposed. It focuses on how to structure such search without contradicting its need of serendipity thanks to different features. The first relies on the concept of Ideality. It aims at identifying and structuring the information to look for. The second feature focuses on identifying the scope and depth of the analysis related to any articles constituting the bibliography. These aspects are tackled thanks to an objective/subjective grading system with handling of the results inspired by the behavior of social insects. Another feature inspired by patent search (precision/recall indexes) focuses on identifying when to stop the article curation. Finally, parts of all these features can be coupled in order to generate a bibliographic search map. This map represents a means to share an overview of the work being done, allowing another person to take ownership of the bibliographic search. This feature provides the opportunity for structuring and strengthening collaborative bibliographic search approaches.

Wheeler and Whitehead: Process Biology and Process Philosophy in the Early Twentieth Century.

The case of William Morton Wheeler and Alfred North Whitehead represents a striking example of how biologists and philosophers engaged in a common enterprise in the early twentieth century. Both challenge the notion that the living world is composed of distinct organisms. Based on his studies of the behavior of social insects, Wheeler developed a concept of superorganisms that paved the way for a theory of emergent evolution. This paper argues that Whitehead, whose relation to academic biology has been largely ignored, drew on Wheeler's findings and integrated them into a universal philosophical cosmology.

A secured context-aware tourism recommender system using artificial bee colony and simulated annealing

Context-aware recommender systems have been developed to consider users' preferences in various contextual situations. While designing such systems, one immediate concern, is to preserve the integrity of the recommender and minimise the attack probability of biased users who may indirectly influence the outcome of the system. Several algorithms have been developed to identify malicious users in contextual environments. In this paper, we propose a reputation-controlled fish school (RCFS) algorithm to identify trustable users and utilise them in recommendations. In addition, we propose a recommendation algorithm that replicates the behaviour of social insects using a hybrid artificial bee colony (ABC) and simulated annealing (SA) technique. Finally, we demonstrate that the resulting feedback strategies can increase the effectiveness of the recommenders' decisions.

Track-a-Forager: a program for the automated analysis of RFID tracking data to reconstruct foraging behaviour

Behavioural studies make increasingly use of the passive radio-frequency identification (RFID) technology to monitor the foraging behaviour and activity patterns of individual animals over extended periods of time. Central place foragers, such as social insects, birds and many rodents have proved particularly well suited for this technology. As yet, however, there is no standardized methodology to filter and postprocess the data resulting from RFID scanners. Here we present a new user-friendly, publically available Java program named “Track-a-Forager” to analyse and rigorously filter RFID animal tracking data. The program is particularly suited and has special features to analyse social insect behaviour, but it is generic enough to analyse data obtained from any species. The implemented filtering algorithm consists of several well-defined steps to cluster multiple temporally clustered RFID scans of the same individual, determine events of leaving and entering the nest and/or feeder and reconstruct foraging trips for each individual. Track-a-Forager analyses RFID data independent of the used scanner system for eight different types of standard experimental setups that are common in foraging behaviour research. These setups differ with respect to whether or not foraging at an artificial feeder is monitored and the specific placement of the RFID scanners at the nest or feeder. As a real-life example, we show how Track-a-Forager enables one to reconstruct 75 % more foraging trips compared to if one were to use the raw data.

From division of labor to the collective behavior of social insects

‘Division of labor’ is a misleading way to describe the organization of tasks in social insect colonies, because there is little evidence for persistent individual specialization in task. Instead, task allocation in social insects occurs through distributed processes whose advantages, such as resilience, differ from those of division of labor, which are mostly based on learning. The use of the phrase ‘division of labor’ persists for historical reasons, and tends to focus attention on differences among individuals in internal attributes. This focus distracts from the main questions of interest in current research, which require an understanding of how individuals interact with each other and their environments. These questions include how colony behavior is regulated, how the regulation of colony behavior develops over the lifetime of a colony, what are the sources of variation among colonies in the regulation of behavior, and how the collective regulation of colony behavior evolves.

The Genome and Methylome of a Beetle with Complex Social Behavior, Nicrophorus vespilloides (Coleoptera: Silphidae).

Testing for conserved and novel mechanisms underlying phenotypic evolution requires a diversity of genomes available for comparison spanning multiple independent lineages. For example, complex social behavior in insects has been investigated primarily with eusocial lineages, nearly all of which are Hymenoptera. If conserved genomic influences on sociality do exist, we need data from a wider range of taxa that also vary in their levels of sociality. Here, we present the assembled and annotated genome of the subsocial beetle Nicrophorus vespilloides, a species long used to investigate evolutionary questions of complex social behavior. We used this genome to address two questions. First, do aspects of life history, such as using a carcass to breed, predict overlap in gene models more strongly than phylogeny? We found that the overlap in gene models was similar between N. vespilloides and all other insect groups regardless of life history. Second, like other insects with highly developed social behavior but unlike other beetles, does N. vespilloides have DNA methylation? We found strong evidence for an active DNA methylation system. The distribution of methylation was similar to other insects with exons having the most methylated CpGs. Methylation status appears highly conserved; 85% of the methylated genes in N. vespilloides are also methylated in the hymentopteran Nasonia vitripennis. The addition of this genome adds a coleopteran resource to answer questions about the evolution and mechanistic basis of sociality and to address questions about the potential role of methylation in social behavior.

Differential necrophoric behaviour of the ant Solenopsis invicta towards fungal-infected corpses of workers and pupae.

Necrophoric behaviour is critical sanitation behaviour in social insects. However, little is known about the necrophoric responses of workers towards different developmental stages in a colony as well as its underlying mechanism. Here, we show that Solenopsis invicta workers display distinct necrophoric responses to corpses of workers and pupae. Corpses of workers killed by freezing (dead for <1 h) were carried to a refuse pile, but pupal corpses would take at least 1 day to elicit workers' necrophoric response. Metarhizium anisopliae-infected pupal corpses accelerated the necrophoric behaviour of resident workers, with 47.5% of unaffected corpses and 73.8% infected corpses discarded by 1 day post-treatment). We found that fungus-infected pupal corpses had a higher concentration of fatty acids (palmitic acid, oleic acid and linoleic acid) on their surface. We experimentally confirmed that linoleic and oleic acids would elicit a necrophoric response in workers. The appearance of linoleic and oleic acids appeared to be chemical signals involved in recognition of pupal corpses, and M. anisopliae infection could promote the accumulation of fatty acids on surface of pupal corpses resulting in accelerated necrophoric responses of workers.

Beyond pheromones: evolving error-tolerant, flexible, and scalable ant-inspired robot swarms

For robot swarms to operate outside of the laboratory in complex real-world environments, they require the kind of error tolerance, flexibility, and scalability seen in living systems. While robot swarms are often designed to mimic some aspect of the behavior of social insects or other organisms, no systems have yet addressed all of these capabilities in a single framework. We describe a swarm robotics system that emulates ant behaviors, which govern memory, communication, and movement, as well as an evolutionary process that tailors those behaviors into foraging strategies that maximize performance under varied and complex conditions. The system evolves appropriate solutions to different environmental challenges. Solutions include the following: (1) increased communication when sensed information is reliable and resources to be collected are highly clustered, (2) less communication and more individual memory when cluster sizes are variable, and (3) greater dispersal with increasing swarm size. Analysis of the evolved behaviors reveals the importance of interactions among behaviors, and of the interdependencies between behaviors and environments. The effectiveness of interacting behaviors depends on the uncertainty of sensed information, the resource distribution, and the swarm size. Such interactions could not be manually specified, but are effectively evolved in simulation and transferred to physical robots. This work is the first to demonstrate high-level robot swarm behaviors that can be automatically tuned to produce efficient collective foraging strategies in varied and complex environments.

Towards Collective Knowledge Systems: The Intersection of the Social Web, the Semantic Web, and Machine Learning

Collective Knowledge Systems are information systems that utilize the knowledge made available through Social Media by using Semantic Web technology. The current state of the Social Web, comprising all the content made available through regular user participation, can best be described as “collected intelligence,” i.e., value mainly ensues from this knowledge being available rather than directly usable in a computational context.The potential of collective intelligence is powerfully illustrated in nature by, for example, the behaviour of social insects. Some of these evolutionary adaptations have been successfully abstracted and captured in computational systems, such as Ant Colony Optimization, amongst many others. Referring to collective intelligence in the context of the human cyberspace, Pierre Levy synthesised the latter as “enabling members of delocalized communities to interact within a mobile landscape of signification,” stressing that “the greater the number of collective intellects with which an individual is involved, the more opportunities he has to diversify his knowledge and desire.” His vision on collective intelligence of the sort emerging in cyberspace expands to dramatically alter the course of development of (knowledge) economies.What remains after we have mechanized agriculture, industry and messaging technologies? The economy will centre, as it does already, on that which can never be fully automated, on that which is irreducible: the production of the social bond, the relational. Those who manufacture things will become scarcer and scarcer, and their labour will become mechanized, augmented, automated to a greater and greater extent. The final frontier will be the human itself, that which can’t be automated: the creation of sensible worlds, invention, relation, the continuous recreation of the community. What kind of engineering will best meet the needs of a growing economy of human qualities?While it may be argued that systems such as Wikipedia (and its more machine-friendly version -- DBpedia), Twitter, and Google are systems that exhibit characteristics of Collective Knowledge Systems, we argue that none of these systems fully adheres to the functionality originally specified in: next to the collective knowledge stored by the systems, i.e., collected knowledge, the system should be able to “create new value from the collected data.” It is here that the vast amounts of content on the Social Web can be morphed from a universe of unstructured data into knowledge that is available in a computational context. And it is here that Semantic Web technologies, through their ability to infer new knowledge from known facts, can crucially contribute.

ESA Founders Pictured on the Cover

ESA Founders Pictured on the Cover

Nature-Inspired Swarm Intelligence and Its Applications

In 1989 Gerardo Beni and Jing Wang first proposed the name ―Swarm Intelligence‖ in their paper ―Swarm Intelligence in Cellular Robotic Systems‖. Some remarkable observations of different researchers are studied in this paper, like the proximity principle, the quality principle, the principle of diverse response, the principle of stability, the principle of adaptability. To enhance the capabilities of robot and different systems, researchers started to exploit the behavior of natural systems. Swarm groups are governed by three rules, move in the same direction as your neighbor, remain close to your neighbor, and avoid collision with your neighbor .Characteristics of swarm groups are emergence and stigmergy. Different insects like ants, wasps, termites carry out a work locally for global goal with sufficient flexibility as they are not controlled centrally. In this paper the existing research works are analysed to show the behavior in social insects by using self-organization, positive feedback, negative feedback, amplification of fluctuation, multiple interactions. It has also been observed that these insects are almost blind and memoryless, still they communicate indirectly among themselves for stigmergic effect by using pheromone. Implementation of swarm intelligence in robotics i.e., swarm robots are narrated. The limitations of swarm robots as well as factors behind the success of swarm robotics have also been encompassed. Finally authors focus on swarm robots applications in telecommunication fields, civil engineering and digital image processing.

Variable threshold algorithm for division of labor analyzed as a dynamical system.

Division of labor is a widely studied aspect of colony behavior of social insects. Division of labor models indicate how individuals distribute themselves in order to perform different tasks simultaneously. However, models that study division of labor from a dynamical system point of view cannot be found in the literature. In this paper, we define a division of labor model as a discrete-time dynamical system, in order to study the equilibrium points and their properties related to convergence and stability. By making use of this analytical model, an adaptive algorithm based on division of labor can be designed to satisfy dynamic criteria. In this way, we have designed and tested an algorithm that varies the response thresholds in order to modify the dynamic behavior of the system. This behavior modification allows the system to adapt to specific environmental and collective situations, making the algorithm a good candidate for distributed control applications. The variable threshold algorithm is based on specialization mechanisms. It is able to achieve an asymptotically stable behavior of the system in different environments and independently of the number of individuals. The algorithm has been successfully tested under several initial conditions and number of individuals.

Lévy flights and self-similar exploratory behaviour of termite workers: beyond model fitting.

Animal movements have been related to optimal foraging strategies where self-similar trajectories are central. Most of the experimental studies done so far have focused mainly on fitting statistical models to data in order to test for movement patterns described by power-laws. Here we show by analyzing over half a million movement displacements that isolated termite workers actually exhibit a range of very interesting dynamical properties –including Lévy flights– in their exploratory behaviour. Going beyond the current trend of statistical model fitting alone, our study analyses anomalous diffusion and structure functions to estimate values of the scaling exponents describing displacement statistics. We evince the fractal nature of the movement patterns and show how the scaling exponents describing termite space exploration intriguingly comply with mathematical relations found in the physics of transport phenomena. By doing this, we rescue a rich variety of physical and biological phenomenology that can be potentially important and meaningful for the study of complex animal behavior and, in particular, for the study of how patterns of exploratory behaviour of individual social insects may impact not only their feeding demands but also nestmate encounter patterns and, hence, their dynamics at the social scale.