Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous AbstractInfluence of hydraulic retention time on the psychrophilic hydrolysis/acidogenesis of proteins    Next AbstractAnts combine object affordance with latent learning to make efficient foraging decisions »

Elife


Title:Experimental investigation of ant traffic under crowded conditions
Author(s):Poissonnier LA; Motsch S; Gautrais J; Buhl J; Dussutour A;
Address:"Research Center on Animal Cognition (CRCA), Center for Integrative Biology (CBI), Toulouse University, CNRS, UPS, 31062 Toulouse, France. Arizona State University, Tempe, United States. School of Agriculture, Food and Wine, The University of Adelaide, Adelaide, Australia"
Journal Title:Elife
Year:2019
Volume:20191022
Issue:
Page Number: -
DOI: 10.7554/eLife.48945
ISSN/ISBN:2050-084X (Electronic) 2050-084X (Linking)
Abstract:"Efficient transportation is crucial for urban mobility, cell function and the survival of animal groups. From humans driving on the highway, to ants running on a trail, the main challenge faced by all collective systems is how to prevent traffic jams in crowded environments. Here, we show that ants, despite their behavioral simplicity, have managed the tour de force of avoiding the formation of traffic jams at high density. At the macroscopic level, we demonstrated that ant traffic is best described by a two-phase flow function. At low densities there is a clear linear relationship between ant density and the flow, while at large density, the flow remains constant and no congestion occurs. From a microscopic perspective, the individual tracking of ants under varying densities revealed that ants adjust their speed and avoid time consuming interactions at large densities. Our results point to strategies by which ant colonies solve the main challenge of transportation by self-regulating their behavior"
Keywords:"Animals Ants/*physiology Behavior, Animal/*physiology Feeding Behavior Food Models, Biological Movement/*physiology Pheromones Population Density Running Time Factors ants collective behavior ecology foraging linepithema humile self-organization traffic;"
Notes:"MedlinePoissonnier, Laure-Anne Motsch, Sebastien Gautrais, Jacques Buhl, Jerome Dussutour, Audrey eng DMS-1515592/National Science Foundation/International Research Support, U.S. Gov't, Non-P.H.S. England 2019/10/23 Elife. 2019 Oct 22; 8:e48945. doi: 10.7554/eLife.48945"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 06-11-2024