Zombie ants: Precise manipulation of social insect behavior by a microbe

2016 ◽  
Author(s):  
David Hughes
Keyword(s):  
Social Insect ◽  
Insect Behavior

scholarly journals Embedding Swarm Intelligence in Internet of Things Networks: A Social Insect Approach

2020 ◽  
pp. 80-87
Author(s):  
Sergio V Davalos, Ph.D.
Keyword(s):  
Internet Of Things ◽  
Swarm Intelligence ◽  
Social Insect ◽  
Insect Behavior ◽  
Agent Based Modeling ◽  
The Internet ◽  
Evolutionary Adaptation ◽  
Agent Based ◽  
The Internet Of Things

This paper introduces a framework for embedding intelligence in the Internet of Things (IoT) networks. The framework draws upon agent-based modeling, swarm intelligence, social insect behavior, and evolutionary adaptation. The key principles for each of these areas are first discussed. These concepts are then discussed from an IoTs perspective. The resulting capabilities and potential of embedding this type of intelligence are outlined.

scholarly journals Reappraising Social Insect Behavior through Aversive Responsiveness and Learning

PLoS ONE ◽  
2009 ◽  
Vol 4 (1) ◽  
pp. e4197 ◽  
Author(s):  
Edith Roussel ◽  
Julie Carcaud ◽  
Jean-Christophe Sandoz ◽  
Martin Giurfa
Keyword(s):  
Social Insect ◽  
Insect Behavior

scholarly journals Movement, Encounter Rate, and Collective Behavior in Ant Colonies

2020 ◽  
Author(s):  
Deborah M Gordon
Keyword(s):  
Collective Behavior ◽  
Task Allocation ◽  
Social Insect ◽  
Local Density ◽  
Movement Patterns ◽  
Insect Behavior ◽  
Nest Size ◽  
Encounter Rate ◽  
Encounter Rates ◽  
Nest Choice

Abstract Spatial patterns of movement regulate many aspects of social insect behavior, because how workers move around, and how many are there, determines how often they meet and interact. Interactions are usually olfactory; for example, in ants, by means of antennal contact in which one worker assesses the cuticular hydrocarbons of another. Encounter rates may be a simple outcome of local density: a worker experiences more encounters, the more other workers there are around it. This means that encounter rate can be used as a cue for overall density even though no individual can assess global density. Encounter rate as a cue for local density regulates many aspects of social insect behavior, including collective search, task allocation, nest choice, and traffic flow. As colonies grow older and larger, encounter rates change, which leads to changes in task allocation. Nest size affects local density and movement patterns, which influences encounter rate, so that nest size and connectivity influence colony behavior. However, encounter rate is not a simple function of local density when individuals change their movement in response to encounters, thus influencing further encounter rates. Natural selection on the regulation of collective behavior can draw on variation within and among colonies in the relation of movement patterns, encounter rate, and response to encounters.

Swarm Intelligence

1999 ◽  
Author(s):  
Eric Bonabeau ◽  
Marco Dorigo ◽  
Guy Theraulaz
Keyword(s):  
Artificial Intelligence ◽  
Swarm Intelligence ◽  
Social Insects ◽  
Operations Research ◽  
Social Insect ◽  
Multiagent System ◽  
Collective Intelligence ◽  
Insect Behavior ◽  
Changing Environments ◽  
Interacting Agents

Social insects--ants, bees, termites, and wasps--can be viewed as powerful problem-solving systems with sophisticated collective intelligence. Composed of simple interacting agents, this intelligence lies in the networks of interactions among individuals and between individuals and the environment. A fascinating subject, social insects are also a powerful metaphor for artificial intelligence, and the problems they solve--finding food, dividing labor among nestmates, building nests, responding to external challenges--have important counterparts in engineering and computer science. This book provides a detailed look at models of social insect behavior and how to apply these models in the design of complex systems. The book shows how these models replace an emphasis on control, preprogramming, and centralization with designs featuring autonomy, emergence, and distributed functioning. These designs are proving immensely flexible and robust, able to adapt quickly to changing environments and to continue functioning even when individual elements fail. In particular, these designs are an exciting approach to the tremendous growth of complexity in software and information. Swarm Intelligence draws on up-to-date research from biology, neuroscience, artificial intelligence, robotics, operations research, and computer graphics, and each chapter is organized around a particular biological example, which is then used to develop an algorithm, a multiagent system, or a group of robots. The book will be an invaluable resource for a broad range of disciplines.

Effects of rhizobacteria on the biology and behavior of Plutella xylostella (Lepidoptera: Plutellidae)

2017 ◽  
Vol 43 (2) ◽  
pp. 195
Author(s):  
Robson Thomaz Thuler ◽  
Fernando Henrique Iost Filho ◽  
Hamilton César De Oliveira Charlo ◽  
Sergio Antônio De Bortoli
Keyword(s):  
Plutella Xylostella ◽  
Diamondback Moth ◽  
Insect Behavior ◽  
Control Treatment ◽  
Pupal Weight ◽  
Larval Feeding ◽  
Direct Effects ◽  
Plant Feeding ◽  
Kluyvera Ascorbata ◽  
And Behavior

Plant induced resistance is a tool for integrated pest management, aimed at increasing plant defense against stress, which is compatible with other techniques. Rhizobacteria act in the plant through metabolic changes and may have direct effects on plant-feeding insects. The objective of this study was to determine the effects of cabbage plants inoculated with rhizobacteria on the biology and behavior of diamondback moth, Plutella xylostella (Lepidoptera: Plutellidae). Cabbage seeds inoculated with 12 rhizobacteria strains were sowed in polystyrene trays and later transplanted into the greenhouse. The cabbage plants with sufficient size to support stress were then infested with diamondback moth caterpillars. Later, healthy leaves suffering injuries were collected and taken to the laboratory to feed P. xylostella second instar caterpillars that were evaluated for larval and pupal viability and duration, pupal weight, and sex ratio. The reduction of leaf area was then calculated as a measure of the amount of larval feeding. Non-preference for feeding and oviposition assays were also performed, by comparing the control treatment and plants inoculated with different rhizobacterial strains. Plants inoculated with the strains EN4 of Kluyvera ascorbata and HPF14 of Bacillus thuringiensis negatively affected the biological characteristics of P. xylostella when such traits were evaluated together, without directly affecting the insect behavior.

Evaluasi Awal Ketahanan Beberapa Kultivar Talas Terhadap Serangan Kumbang Talas, Papuana spp. (Coleoptera : Scarabaeidae)

Agrotek ◽  
2018 ◽  
Vol 2 (3) ◽  
Author(s):  
Alexander Yaku
Keyword(s):  
South Pacific ◽  
Insect Behavior ◽  
Natural Resistance ◽  
The South ◽  
White Colour ◽  
Selection Of

Taro beetles, Papuana spp., (Coleoptera: Scarabaeidae) are pests of taro (Colozasia esculenta) and a number of other plants in the South Pacific regions including Papua. The pest have become serious threat for taro farmers because it damage corm resulting in the loss of production. To control the taro beetle, 104 taro cultivars were evaluated for their natural resistance. The experiment was conducted in the Unipa Farm located in East Amban. Results of research shows that there are three cultivars being highly resistance and 36 cultivars being only resistance. The taro with white colour of corm flesh seems to be a non-preference factor that alter insect behavior, resulting in the selection of alternate taro cultivars.

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