Literature DB >> 12202112

The dilemma of the selfish herd: the search for a realistic movement rule.

Steven V Viscido1, Matthew Miller, David S Wethey.   

Abstract

The selfish herd hypothesis predicts that aggregations form because individuals move toward one another to minimize their own predation risk. The "dilemma of the selfish herd" is that movement rules that are easy for individuals to follow, fail to produce true aggregations, while rules that produce aggregations require individual behavior so complex that one may doubt most animals can follow them. If natural selection at the individual level is responsible for herding behavior, a solution to the dilemma must exist. Using computer simulations, we examined four different movement rules. Relative predation risk was different for all four movement rules (p<0.05). We defined three criteria for measuring the quality of a movement rule. A good movement rule should (a) be statistically likely to benefit an individual that follows it, (b) be something we can imagine most animals are capable of following, and (c) result in a centrally compact flock. The local crowded horizon rule, which allowed individuals to take the positions of many flock-mates into account, but decreased the influence of flock-mates with distance, best satisfied these criteria. The local crowded horizon rule was very sensitive to the animal's perceptive ability. Therefore, the animal's ability to detect its neighbors is an important factor in the dynamics of group formation.

Mesh:

Year:  2002        PMID: 12202112     DOI: 10.1006/jtbi.2002.3025

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  12 in total

1.  The temporal selfish herd: predation risk while aggregations form.

Authors:  Lesley J Morrell; Graeme D Ruxton; Richard James
Journal:  Proc Biol Sci       Date:  2010-09-01       Impact factor: 5.349

2.  'Selfish herds' of guppies follow complex movement rules, but not when information is limited.

Authors:  Helen S Kimbell; Lesley J Morrell
Journal:  Proc Biol Sci       Date:  2015-10-07       Impact factor: 5.349

3.  State-dependent foraging rules for social animals in selfish herds.

Authors:  Sean A Rands; Richard A Pettifor; J Marcus Rowcliffe; Guy Cowlishaw
Journal:  Proc Biol Sci       Date:  2004-12-22       Impact factor: 5.349

Review 4.  From behavioural analyses to models of collective motion in fish schools.

Authors:  Ugo Lopez; Jacques Gautrais; Iain D Couzin; Guy Theraulaz
Journal:  Interface Focus       Date:  2012-10-03       Impact factor: 3.906

5.  Ontogeny of collective behavior reveals a simple attraction rule.

Authors:  Robert C Hinz; Gonzalo G de Polavieja
Journal:  Proc Natl Acad Sci U S A       Date:  2017-02-13       Impact factor: 11.205

6.  Motion-guided attention promotes adaptive communications during social navigation.

Authors:  B H Lemasson; J J Anderson; R A Goodwin
Journal:  Proc Biol Sci       Date:  2013-01-16       Impact factor: 5.349

Review 7.  The importance of individual variation in the dynamics of animal collective movements.

Authors:  Maria Del Mar Delgado; Maria Miranda; Silvia J Alvarez; Eliezer Gurarie; William F Fagan; Vincenzo Penteriani; Agustina di Virgilio; Juan Manuel Morales
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2018-05-19       Impact factor: 6.237

8.  The role of neighbours selection on cohesion and order of swarms.

Authors:  Angelo M Calvão; Edgardo Brigatti
Journal:  PLoS One       Date:  2014-05-08       Impact factor: 3.240

9.  Collective decision making and social interaction rules in mixed-species flocks of songbirds.

Authors:  Damien R Farine; Lucy M Aplin; Colin J Garroway; Richard P Mann; Ben C Sheldon
Journal:  Anim Behav       Date:  2014-09       Impact factor: 2.844

10.  Inferring the rules of social interaction in migrating caribou.

Authors:  Colin J Torney; Myles Lamont; Leon Debell; Ryan J Angohiatok; Lisa-Marie Leclerc; Andrew M Berdahl
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2018-05-19       Impact factor: 6.237

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