Literature DB >> 24312727

A spatially explicit Bayesian framework for cognitive schooling behaviours.

Daniel Grünbaum1.   

Abstract

Social aggregations such as schools, swarms, flocks and herds occur across a broad diversity of animal species, strongly impacting ecological and evolutionary dynamics of these species and their predators, prey and competitors. The mechanisms through which individual-level responses to neighbours generate group-level characteristics have been extensively investigated both experimentally and using mathematical models. Models of social groups typically adopt a 'zone' approach, in which individuals' movement responses to neighbours are functions of instantaneous relative position. Empirical studies have demonstrated that most social animals such as fish exhibit well-developed spatial memory and other advanced cognitive capabilities. However, most models of social grouping do not explicitly include spatial memory, largely because a tractable framework for modelling acquisition of and response to historical spatial information has been lacking. Using fish schooling as a focal example, this study presents a framework for including cognitive responses to spatial memory in models of social aggregation. The framework utilizes Bayesian estimation parameters that are continuously distributed in time and space as proxies for animals' spatial memory. The result is a hybrid Lagrangian-Eulerian model in which the effects of cognitive state and behavioural responses to historical spatial data on individual-, group- and population-level distributions of social animals can be explicitly investigated.

Entities:  

Keywords:  behavioural algorithm; individual-based model; partial differential equation model; spatial statistics

Year:  2012        PMID: 24312727      PMCID: PMC3499124          DOI: 10.1098/rsfs.2012.0027

Source DB:  PubMed          Journal:  Interface Focus        ISSN: 2042-8898            Impact factor:   3.906


  8 in total

1.  From individuals to aggregations: the interplay between behavior and physics.

Authors:  G Flierl; D Grünbaum; S Levins; D Olson
Journal:  J Theor Biol       Date:  1999-02-21       Impact factor: 2.691

2.  Animal movement, search strategies and behavioural ecology: a cross-disciplinary way forward.

Authors:  Luca Giuggioli; Frederic Bartumeus
Journal:  J Anim Ecol       Date:  2010-03-22       Impact factor: 5.091

3.  Coarse-grained analysis of stochasticity-induced switching between collective motion states.

Authors:  Allison Kolpas; Jeff Moehlis; Ioannis G Kevrekidis
Journal:  Proc Natl Acad Sci U S A       Date:  2007-03-27       Impact factor: 11.205

4.  Collective memory and spatial sorting in animal groups.

Authors:  Iain D Couzin; Jens Krause; Richard James; Graeme D Ruxton; Nigel R Franks
Journal:  J Theor Biol       Date:  2002-09-07       Impact factor: 2.691

5.  Non-linear advection-diffusion equations approximate swarming but not schooling populations.

Authors:  Daniel Grünbaum; Karen Chan; Elizabeth Tobin; Michael T Nishizaki
Journal:  Math Biosci       Date:  2008-06-12       Impact factor: 2.144

6.  Biased random walk models for chemotaxis and related diffusion approximations.

Authors:  W Alt
Journal:  J Math Biol       Date:  1980-04       Impact factor: 2.259

7.  Collective animal behavior from Bayesian estimation and probability matching.

Authors:  Alfonso Pérez-Escudero; Gonzalo G de Polavieja
Journal:  PLoS Comput Biol       Date:  2011-11-17       Impact factor: 4.475

8.  Multi-scale inference of interaction rules in animal groups using Bayesian model selection.

Authors:  Richard P Mann; Andrea Perna; Daniel Strömbom; Roman Garnett; James E Herbert-Read; David J T Sumpter; Ashley J W Ward
Journal:  PLoS Comput Biol       Date:  2012-01-05       Impact factor: 4.475

  8 in total
  1 in total

1.  Spatial memory-based behaviors for locating sources of odor plumes.

Authors:  Daniel Grünbaum; Mark A Willis
Journal:  Mov Ecol       Date:  2015-05-04       Impact factor: 3.600

  1 in total

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