Emergent weak home-range behaviour without spatial memory

Space-use problems have been well investigated. Spatial memory capacity is assumed in many home-range algorithms; however, actual living things do not always exploit spatial memory, and living entities can exhibit adaptive and flexible behaviour using simple cognitive capacity. We have developed an agent-based model wherein the agent uses only detected local regions and compares global efficiencies for a habitat search within its local conditions based on memorized information. Here, memorized information was acquired by scanning locally perceived environments rather than remembering resource locations. When memorized information matched to its current environments, the agent changed resource selection rules. As a result, the agent revisited previous resource sites while exploring new sites, which was demonstrating a weak home-range property.

[1]  Darcy R. Visscher,et al.  Memory keeps you at home: a mechanistic model for home range emergence , 2009 .

[2]  J. Deneubourg,et al.  Discrete dragline attachment induces aggregation in spiderlings of a solitary species , 2004, Animal Behaviour.

[3]  G. Viswanathan,et al.  Lévy flights and superdiffusion in the context of biological encounters and random searches , 2008 .

[4]  Paul R. Moorcroft,et al.  Home range analysis using a mechanistic home range model , 1999 .

[5]  Bryan F. J. Manly,et al.  Assessing habitat selection when availability changes , 1996 .

[6]  F. Ratnieks,et al.  Negative Feedback Enables Fast and Flexible Collective Decision-Making in Ants , 2012, PloS one.

[7]  Sheng-You Huang,et al.  Random walk with memory enhancement and decay. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Michael L. Morrison,et al.  Wildlife-habitat relationships , 1992 .

[9]  Robert A. Gitzen,et al.  Analysis of Animal Space Use and Movements , 2001 .

[10]  Wayne M. Getz,et al.  LoCoH: Nonparameteric Kernel Methods for Constructing Home Ranges and Utilization Distributions , 2007, PloS one.

[11]  Andrew Philippides,et al.  A Model of Ant Route Navigation Driven by Scene Familiarity , 2012, PLoS Comput. Biol..

[12]  Guillermo Abramson,et al.  Random-walk model to study cycles emerging from the exploration-exploitation trade-off. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Paul R Moorcroft,et al.  Mechanistic home range models capture spatial patterns and dynamics of coyote territories in Yellowstone , 2006, Proceedings of the Royal Society B: Biological Sciences.

[14]  Ken Cheng,et al.  How to navigate without maps: The power of taxon-like navigation in ants , 2012 .

[15]  Hugh P. Possingham,et al.  A SPATIALLY EXPLICIT HABITAT SELECTION MODEL INCORPORATING HOME RANGE BEHAVIOR , 2005 .

[16]  John Fieberg,et al.  Kernel density estimators of home range: smoothing and the autocorrelation red herring. , 2007, Ecology.

[17]  J. Fryxell,et al.  Are there general mechanisms of animal home range behaviour? A review and prospects for future research. , 2008, Ecology letters.

[18]  Simon Benhamou,et al.  How Memory-Based Movement Leads to Nonterritorial Spatial Segregation , 2015, The American Naturalist.

[19]  P. Moorcroft,et al.  Mechanistic home range analysis , 2006 .

[20]  J. Deneubourg,et al.  How do ants assess food volume? , 2000, Animal Behaviour.

[21]  J. Deneubourg,et al.  Self-organized structures in a superorganism: do ants "behave" like molecules? , 2006 .

[22]  A Bunde,et al.  Structural properties of self-attracting walks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  Ulrike E. Schlägel,et al.  Detecting effects of spatial memory and dynamic information on animal movement decisions , 2014 .

[24]  Wei Zhang,et al.  “True” self-attracting walk , 2001 .