Low frequency scattering from dynamic fish schools based on collective animal behavior modeling

2015 ◽  
Vol 137 (4) ◽  
pp. 2361-2361
Author(s):  
Simón E. Alfaro ◽  
Jorge Cellio ◽  
Maria P. Raveau ◽  
Christopher Feuillade
Keyword(s):  
Animal Behavior ◽  
Low Frequency ◽  
Behavior Modeling ◽  
Fish Schools ◽  
Collective Animal Behavior

scholarly journals Low frequency scattering from dynamic fish schools based on collective animal behavior modeling

2015 ◽  
Author(s):  
Simon E. Alfaro ◽  
Jorge Cellio ◽  
Maria P. Raveau ◽  
Christopher Feuillade
Keyword(s):  
Animal Behavior ◽  
Low Frequency ◽  
Behavior Modeling ◽  
Fish Schools ◽  
Collective Animal Behavior

scholarly journals Ontogeny of collective behavior reveals a simple attraction rule

2017 ◽  
Vol 114 (9) ◽  
pp. 2295-2300 ◽  
Author(s):  
Robert C. Hinz ◽  
Gonzalo G. de Polavieja
Keyword(s):  
Animal Behavior ◽  
Danio Rerio ◽  
Collective Behavior ◽  
Centralized Control ◽  
Potential Significance ◽  
Fish Schools ◽  
Animal Development ◽  
Collective Animal Behavior ◽  
Moving Groups ◽  
Attraction Behavior

The striking patterns of collective animal behavior, including ant trails, bird flocks, and fish schools, can result from local interactions among animals without centralized control. Several of these rules of interaction have been proposed, but it has proven difficult to discriminate which ones are implemented in nature. As a method to better discriminate among interaction rules, we propose to follow the slow birth of a rule of interaction during animal development. Specifically, we followed the development of zebrafish, Danio rerio, and found that larvae turn toward each other from 7 days postfertilization and increase the intensity of interactions until 3 weeks. This developmental dataset allows testing the parameter-free predictions of a simple rule in which animals attract each other part of the time, with attraction defined as turning toward another animal chosen at random. This rule makes each individual likely move to a high density of conspecifics, and moving groups naturally emerge. Development of attraction strength corresponds to an increase in the time spent in attraction behavior. Adults were found to follow the same attraction rule, suggesting a potential significance for adults of other species.

scholarly journals An Algorithm for Global Optimization Inspired by Collective Animal Behavior

2012 ◽  
Vol 2012 ◽  
pp. 1-24 ◽  
Author(s):  
Erik Cuevas ◽  
Mauricio González ◽  
Daniel Zaldivar ◽  
Marco Pérez-Cisneros ◽  
Guillermo García
Keyword(s):  
Global Optimization ◽  
Animal Behavior ◽  
Food Source ◽  
Collective Motion ◽  
Global Optimum ◽  
Migration Routes ◽  
Collective Behaviors ◽  
Biological Laws ◽  
Harvesting Efficiency ◽  
Collective Animal Behavior

A metaheuristic algorithm for global optimization called the collective animal behavior (CAB) is introduced. Animal groups, such as schools of fish, flocks of birds, swarms of locusts, and herds of wildebeest, exhibit a variety of behaviors including swarming about a food source, milling around a central locations, or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency, to follow better migration routes, to improve their aerodynamic, and to avoid predation. In the proposed algorithm, the searcher agents emulate a group of animals which interact with each other based on the biological laws of collective motion. The proposed method has been compared to other well-known optimization algorithms. The results show good performance of the proposed method when searching for a global optimum of several benchmark functions.

scholarly journals Probabilistic models of individual and collective animal behavior

PLoS ONE ◽  
2018 ◽  
Vol 13 (3) ◽  
pp. e0193049 ◽  
Author(s):  
Katarína Bod’ová ◽  
Gabriel J. Mitchell ◽  
Roy Harpaz ◽  
Elad Schneidman ◽  
Gašper Tkačik
Keyword(s):  
Animal Behavior ◽  
Probabilistic Models ◽  
Collective Animal Behavior

scholarly journals Inference of Causal Information Flow in Collective Animal Behavior

2016 ◽  
Vol 2 (1) ◽  
pp. 107-116 ◽  
Author(s):  
Warren M. Lord ◽  
Jie Sun ◽  
Nicholas T. Ouellette ◽  
Erik M. Bollt
Keyword(s):  
Information Flow ◽  
Animal Behavior ◽  
Causal Information ◽  
Collective Animal Behavior

The low‐frequency scattering action of fish schools

1994 ◽  
Vol 95 (5) ◽  
pp. 3020-3020
Author(s):  
Joel Garrelick ◽  
Miguel C. Junger ◽  
Mary L. Lamberton
Keyword(s):  
Low Frequency ◽  
Fish Schools

scholarly journals Low frequency scattering from fish schools: A comparison of two models

2014 ◽  
Author(s):  
Maria P. Raveau ◽  
Christopher Feuillade
Keyword(s):  
Low Frequency ◽  
Fish Schools
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