The Resistance of Swarm Ecosystem Under Environmental Variation

Evolving swarms can be used both to solve real-world problems and to study biological and ecological phenomena. We simulated an evolving swarm of birds under three different types of climate-change-related environmental variation a temperate environment becoming tropical, a temperate enviroment becoming a desert, and a tropical environment becoming a desert. We found that desertification increased expirations within the swarm and decreased population stability. The direction of the variation tropicalification or desertification had a greater impact on the dynamics of the swarm than the degree of variation when it came to these outcomes. The environmental variation also affected the genetics of the birds, with decreased food availability leading to collision avoidance genes being downplayed, and searching behavior for food being changed. High-intensity environmental variation led to less genetic stability post-change than lowerintensity environmental variation. Swarming and flocking behavior is ubiquitous throughout all scales of biological and physical systems. Swarming simulations were first developed by Reynolds (1987) using his boids, simple agents that moved according to a set of basic rules. Much later swarming behavior simulation work has focused on agent-based modeling, which is centered around the modeling of populations of individuals with rules governing their behavior (Mach and Schweitzer, 2003). Agent-based modeling has been used to study such subjects as the dynamics of mountian pine beetle infestations of forests (Perez and Dragicevic, 2010) and the dynamics of how bacteria aggregate to form microfilms (Lardon et al., 2011). Recent advances in robotics have made it possible to experiment with large-scale physical swarms of robots (Rubenstein et al., 2012). Swarm flying behavior is increasingly an area of interest, with a variety of algorithms and applications being developed. Karaboga (2005) used simulations of bee swarm flying to develop a numerical optimization method, and Su et al. (2009) modeled flocking behavior in the presence of a group leader. Optimization of heterogenous swarms of flying agents is challenging, but is potentially very useful in applications such as crop polination (Nagpal et al., 2011; Figure 1: Example of evolving swarm and environment. Berman et al., 2011). Flying swarms have also been applied in the lab to tasks such as chemical cloud detection (Kovacina et al., 2002) and dynamic communications relays (Hauert et al., 2008).