1 / 18

Using crowd-control simulations to predict behavior of motile organisms

Using crowd-control simulations to predict behavior of motile organisms . John E. Fauth Department of Biology Rex Oleson Institute for Simulation & Training (IST) D. J. Kaup Department of Mathematics & IST Linda Malone Department of Industrial Engineering & Management Systems Tom Clarke

erzsebet
Download Presentation

Using crowd-control simulations to predict behavior of motile organisms

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Using crowd-control simulations to predict behavior of motile organisms John E. Fauth Department of Biology Rex Oleson Institute for Simulation & Training (IST) D. J. Kaup Department of Mathematics & IST Linda Malone Department of Industrial Engineering & Management Systems Tom Clarke Institute for Simulation & Training (IST) University of Central Florida, Orlando, FL 32816

  2. Simulation Model • Goals of simulation: Understand and predict behaviors of organisms in complex environments • Social Potential Models 1. Flocking 2. Helbing-Molnar-Farkas-Vicsek 3. Lakoba-Kaup-Finkelstein Model

  3. Social Potential Models • Main use is path planning and obstacle avoidance • Used for robot motion • ci,jis a strength coefficient • ri,j is the radial distance • i,j is the inverse power

  4. 1. Flocking Model • Fluid-like motion built up from a series of independent entities • Each entity acts on its own local perception of the world • Based on 3 potentials • collision avoidance • velocity matching • flock centering

  5. Example: Bird flocking

  6. Social forces Physical forces (repulsion/attraction) (pushing, friction) + 2. Helbing-Molnar-Farkas-Vicsek (HMFV) Model • Hybrid model that combines social forces and physical forces: • Successfully modeled certain aspects of pedestrian motion • Faster is slower effect.

  7. 3. Lakoba-Kaup-Finkelstein (LKF) Model • Adjusted values of HMFV model to be physically correct. • Included additional social parameters required for realism. • Ability to learn and forget about location of an exit and walls. • Knowledge of locations is used to determine: • Direction in which a pedestrian is looking. • Attraction force to the exit (similarly, repulsion from walls). • Correctly reproduces realistic collective and individual behaviors.

  8. LKF Model • Equations are stiff Code has to resolve two disparate scales: • LARGE: distances about the size of the room ( ~ 10 m). • Small: distance between pedestrians when they come into contact ( ~ 1 cm). • New overlap algorithm developed to eliminate overlap among pedestrians. This allows stable solutions using the explicit 1st-order Euler method.

  9. Example: Crowd exiting room

  10. Generalizing the concept • Lakoba et al. developed crowd-control models to understand the collective behavior of pedestrians • Goal: understand and modify crowd behavior to reduce fatalities during emergencies: nightclub fires, stadium accidents, and subway or building bombings and other terrorist acts. • Method is generalizable to other motile, multicellular organisms.

  11. Niche partitioning in salamanders • Niche partitioning is a core concept in ecology. • Environmental gradients (ecotones) are common in nature. • Salamanders are an ideal model system. • Long history of research on plethodontid salamanders.

  12. Prior field experiments • Manipulated the presence and absence of several species of plethodontid salamanders: • Desmognathus, Plethodon, Eurycea, Gyrinophilus, Pseudotriton • Analyzed survival, growth, and microhabitat use of species along the aquatic-terrestrial ecotone.

  13. Model Environment cover objects Aquatic Terrestrial

  14. Social forces physical forces (repulsion/attraction) (pushing, friction) + Salamander Parameters • Regional Affinity (= microhabitat preference) • Cover Attraction • Cover Memory Attraction (~ site fidelity) • Food Attraction • Water Attraction • Salamander to Salamander Repulsion • No physical forces

  15. Simulation

  16. } enhances survival } shifts niches Predictions from Simulation • Parameters most important for producing niche partitioning (in order of importance): • Regional Affinity: yields microhabitat preference • Cover Attraction • Cover Memory Attraction • Food Attraction • Water Attraction • Salamander to Salamander Repulsion: • Yields one salamander per cover object • Move to cover when threatened.

  17. Conclusions • LKF Model yields specific predictions about forces driving niche partitioning, microhabitat shifts, and survival. • Input to simulations was only qualitative. • Output of simulations is quantitative. • Model is flexible and can be customized to diverse ecological scenarios.

  18. Future research • Interactions among different species of salamanders along differing environmental gradients. • Behavior of animals using ecopassages. • Dynamics of coral settlement on eutrophic and more pristine reefs.

More Related