Task partitioning in insect societies. I. Effect of colony size on queueing delay and colony ergonomic efficiency

The collection and handling of colony resources such as food, water, and nest construction material is often divided into subtasks in which the material is passed from one worker to another. This is known as task partitioning. When material is transferred directly from one individual to another, queueing delays frequently occur because individuals must sometimes wait for a transfer partner. A stochastic simulation model was written to study the effect of colony size on these delays. Queueing delay decreases roughly exponentially with colony size because stochastic fluctuations in the arrival of individuals are lower in larger colonies. These results support empirical studies of Polybia occidentalis and other theoretical studies of honeybees. The effect of the relative number of individuals in the two subtask groups was also studied. There is a unique optimal ratio of the number of workers associated with each of the subtasks that simultaneously minimizes mean queueing delay and maximizes colony nectar-processing rate. Deviations from this optimal ratio, for example, as a result of forager mortality or changes in nectar productivity that affect foraging trip duration, increase mean queueing delays greatly, especially in smaller colonies

BEEHAVE: A systems model of honeybee colony dynamics and foraging to explore multifactorial causes of colony failure

Journal Article© 2014 The Authors. Journal of Applied Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly citedSummary: A notable increase in failure of managed European honeybee Apis mellifera L. colonies has been reported in various regions in recent years. Although the underlying causes remain unclear, it is likely that a combination of stressors act together, particularly varroa mites and other pathogens, forage availability and potentially pesticides. It is experimentally challenging to address causality at the colony scale when multiple factors interact. In silico experiments offer a fast and cost-effective way to begin to address these challenges and inform experiments. However, none of the published bee models combine colony dynamics with foraging patterns and varroa dynamics. We have developed a honeybee model, BEEHAVE, which integrates colony dynamics, population dynamics of the varroa mite, epidemiology of varroa-transmitted viruses and allows foragers in an agent-based foraging model to collect food from a representation of a spatially explicit landscape. We describe the model, which is freely available online (www.beehave-model.net). Extensive sensitivity analyses and tests illustrate the model's robustness and realism. Simulation experiments with various combinations of stressors demonstrate, in simplified landscape settings, the model's potential: predicting colony dynamics and potential losses with and without varroa mites under different foraging conditions and under pesticide application. We also show how mitigation measures can be tested. Synthesis and applications. BEEHAVE offers a valuable tool for researchers to design and focus field experiments, for regulators to explore the relative importance of stressors to devise management and policy advice and for beekeepers to understand and predict varroa dynamics and effects of management interventions. We expect that scientists and stakeholders will find a variety of applications for BEEHAVE, stimulating further model development and the possible inclusion of other stressors of potential importance to honeybee colony dynamics. © 2014 The Authors. Journal of Applied Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society.Biotechnology and Biological Sciences Research Council (BBSRC

Organisation of foraging in ants

In social insects, foraging is often cooperative, and so requires considerable organisation. In most ants, organisation is a bottom-up process where decisions taken by individuals result in emergent colony level patterns. Individuals base their decisions on their internal state, their past experience, and their environment. By depositing trail pheromones, for example, ants can alter the environment, and thus affect the behaviour of their nestmates. The development of emergent patterns depends on both how individuals affect the environment, and how they react to changes in the environment. Chapters 4 – 9 investigate the role of trail pheromones and route memory in the ant Lasius niger. Route memories can form rapidly and be followed accurately, and when route memories and trail pheromones contradict each other, ants overwhelmingly follow route memories (chapter 4). Route memories and trail pheromones can also interact synergistically, allowing ants to forage faster without sacrificing accuracy (chapter 5). Home range markings also interact with other information sources to affect ant behaviour (chapter 6). Trail pheromones assist experienced ants when facing complex, difficult-to-learn routes (chapter 7). When facing complicated routes, ants deposit more pheromone to assist in navigation and learning (chapter 7). Deposition of trail pheromones is suppressed by ants leaving a marked path (chapter 5), strong pheromone trails (chapter 7) and trail crowding (chapter 8). Colony level ‘decisions’ can be driven by factors other than trail pheromones, such as overcrowding at a food source (chapter 9). Chapter 10 reviews the many roles of trail pheromones in ants. Chapters 11 – 14 focus on the organisation of cooperative food retrieval. Pheidole oxyops workers arrange themselves non-randomly around items to increase transport speeds (chapter 11). Groups of ants will rotate food items to reduce drag (chapter 12). Chapters 13 and 14 encompass the ecology of cooperative transport, and how it has shaped trail pheromone recruitment in P. oxyops and Paratrechina longicornis. Lastly, chapter 15 provide a comprehensive review of cooperative transport in ants and elsewhere

Social Integrating Robots Suggest Mitigation Strategies for Ecosystem Decay

We develop here a novel hypothesis that may generate a general research framework of how autonomous robots may act as a future contingency to counteract the ongoing ecological mass extinction process. We showcase several research projects that have undertaken first steps to generate the required prerequisites for such a technology-based conservation biology approach. Our main idea is to stabilise and support broken ecosystems by introducing artificial members, robots, that are able to blend into the ecosystem's regulatory feedback loops and can modulate natural organisms' local densities through participation in those feedback loops. These robots are able to inject information that can be gathered using technology and to help the system in processing available information with technology. In order to understand the key principles of how these robots are capable of modulating the behaviour of large populations of living organisms based on interacting with just a few individuals, we develop novel mathematical models that focus on important behavioural feedback loops. These loops produce relevant group-level effects, allowing for robotic modulation of collective decision making in social organisms. A general understanding of such systems through mathematical models is necessary for designing future organism-interacting robots in an informed and structured way, which maximises the desired output from a minimum of intervention. Such models also help to unveil the commonalities and specificities of the individual implementations and allow predicting the outcomes of microscopic behavioural mechanisms on the ultimate macroscopic-level effects. We found that very similar models of interaction can be successfully used in multiple very different organism groups and behaviour types (honeybee aggregation, fish shoaling, and plant growth). Here we also report experimental data from biohybrid systems of robots and living organisms. Our mathematical models serve as building blocks for a deep understanding of these biohybrid systems. Only if the effects of autonomous robots onto the environment can be sufficiently well predicted can such robotic systems leave the safe space of the lab and can be applied in the wild to be able to unfold their ecosystem-stabilising potential

Honey Bee Health

Over the past decade, the worldwide decline in honey bee populations has been an important issue due to its implications for beekeeping and honey production. Honey bee pathologies are continuously studied by researchers, in order to investigate the host–parasite relationship and its effect on honey bee colonies. For these reasons, the interest of the veterinary community towards this issue has increased recently, and honey bee health has also become a subject of public interest. Bacteria, such as Melissococcus plutonius and Paenibacillus larvae, microsporidia, such as Nosema apis and Nosema ceranae, fungi, such as Ascosphaera apis, mites, such as Varroa destructor, predatory wasps, including Vespa velutina, and invasive beetles, such as Aethina tumida, are “old” and “new” subjects of important veterinary interest. Recently, the role of host–pathogen interactions in bee health has been included in a multifactorial approach to the study of these insects’ health, which involves a dynamic balance among a range of threats and resources interacting at multiple levels. The aim of this Special Issue is to explore honey bee health through a series of research articles that are focused on different aspects of honey bee health at different levels, including molecular health, microbial health, population genetic health, and the interaction between invasive species that live in strict contact with honey bee populations