abstract: In social insect colonies, as with individual animals, the rates of biological processes scale with body size. The remarkable explanatory power of metabolic allometry in ecology and evolutionary biology derives from the great diversity of life exhibiting a nonlinear scaling pattern in which metabolic rates are not proportional to mass, but rather exhibit a hypometric relationship with body size. While one theory suggests that the supply of energy is a major physiological constraint, an alternative theory is that the demand for energy is regulated by behavior. The central hypothesis of this dissertation research is that increases in colony size reduce the proportion of individuals actively engaged in colony labor with consequences for energetic scaling at the whole-colony level of biological organization. A combination of methods from comparative physiology and animal behavior were developed to investigate scaling relationships in laboratory-reared colonies of the seed-harvester ant, Pogonomyrmex californicus. To determine metabolic rates, flow-through respirometry made it possible to directly measure the carbon dioxide production and oxygen consumption of whole colonies. By recording video of colony behavior, for which ants were individually paint-marked for identification, it was possible to reconstruct the communication networks through which information is transmitted throughout the colony. Whole colonies of P. californicus were found to exhibit a robust hypometric allometry in which mass-specific metabolic rates decrease with increasing colony size. The distribution of walking speeds also scaled with colony size so that larger colonies were composed of relatively more inactive ants than smaller colonies. If colonies were broken into random collections of workers, metabolic rates scaled isometrically, but when entire colonies were reduced in size while retaining functionality (queens, juveniles, workers), they continued to exhibit a metabolic hypometry. The communication networks in P. californicus colonies contain a high frequency of feed-forward interaction patterns consistent with those of complex regulatory systems. Furthermore, the scaling of these communication pathways with size is a plausible mechanism for the regulation of whole-colony metabolic scaling. The continued development of a network theory approach to integrating behavior and metabolism will reveal insights into the evolution of collective animal behavior, ecological dynamics, and social cohesion. / Dissertation/Thesis / Ph.D. Biology 2012

Many animal groups benefit from making decisions collectively. For example, colonies of many ant species are able to select the best possible nest to move into without every ant needing to visit each available nest site. Similarly, honey bee colonies can focus their foraging resources on the best possible food sources in their environment by sharing information with each other. In the same way, groups of human individuals are often able to make better decisions together than each individual group member can on his or her own. This phenomenon is known as "collective intelligence", or "wisdom of crowds." What unites all these examples is the fact that there is no centralized organization dictating how animal groups make their decisions. Instead, these successful decisions emerge from interactions and information transfer between individual members of the group and between individuals and their environment. In this thesis, I apply mathematical modeling techniques in order to better understand how groups of social animals make important decisions in situations where no single individual has complete information. This thesis consists of five papers, in which I collaborate with biologists and sociologists to simulate the results of their experiments on group decision-making in animals. The goal of the modeling process is to better understand the underlying mechanisms of interaction that allow animal groups to make accurate decisions that are vital to their survival. Mathematical models also allow us to make predictions about collective decisions made by animal groups that have not yet been studied experimentally or that cannot be easily studied. The combination of mathematical modeling and experimentation gives us a better insight into the benefits and drawbacks of collective decision making, and into the variety of mechanisms that are responsible for collective intelligence in animals. The models that I use in the thesis include differential equation models, agent-based models, stochastic models, and spatially explicit models. The biological systems studied included foraging honey bee colonies, house-hunting ants, and humans answering trivia questions.