About
Deborah M Gordon is a Professor in the Department of Biology at Stanford University. She studies how ant colonies work without central control using networks of simple interactions, and how these networks evolve in relation to changing environments. She received her BA in French from Oberlin College in 1976, her M.Sc. in Biology from Stanford University in 1977, and her PhD in Zoology from Duke University in 1984. Her academic appointments include being a member of the Bio-X program, the Stanford Woods Institute for the Environment, and the Wu Tsai Neurosciences Institute. Gordon's research projects include a long-term study of a population of harvester ant colonies in Arizona, studies of the invasive Argentine ant in northern California, arboreal ant trail networks, and ant-plant mutualisms in Central America. She joined the Stanford faculty in 1991 after a distinguished research career that included time at the Harvard Society of Fellows and postdoctoral research at Oxford and the University of London. Her contributions to the field have been recognized with numerous honors and awards, including the Quest Award from the Animal Behavior Society, fellowship in the Animal Behavior Society and the California Academy of Sciences, a Guggenheim Fellowship, and the Gores Award for excellence in teaching at Stanford.
Research topics
- Computer Science
- Biology
- Artificial Intelligence
- Sociology
- Ecology
- Evolutionary biology
- Geography
- Genetics
- Psychology
- Epistemology
- Mathematics
- Theoretical computer science
- Economics
- Data science
- Cognitive science
- Mathematical optimization
Selected publications
Distributed algorithms from arboreal ants for the shortest path problem
Proceedings of the National Academy of Sciences · 2023 · 11 citations
- Computer Science
- Computer Science
- Mathematics
Colonies of the arboreal turtle ant create networks of trails that link nests and food sources on the graph formed by branches and vines in the canopy of the tropical forest. Ants put down a volatile pheromone on the edges as they traverse them. At each vertex, the next edge to traverse is chosen using a decision rule based on the current pheromone level. There is a bidirectional flow of ants around the network. In a previous field study, it was observed that the trail networks approximately minimize the number of vertices, thus solving a variant of the popular shortest path problem without any central control and with minimal computational resources. We propose a biologically plausible model, based on a variant of the reinforced random walk on a graph, which explains this observation and suggests surprising algorithms for the shortest path problem and its variants. Through simulations and analysis, we show that when the rate of flow of ants does not change, the dynamics converges to the path with the minimum number of vertices, as observed in the field. The dynamics converges to the shortest path when the rate of flow increases with time, so the colony can solve the shortest path problem merely by increasing the flow rate. We also show that to guarantee convergence to the shortest path, bidirectional flow and a decision rule dividing the flow in proportion to the pheromone level are necessary, but convergence to approximately short paths is possible with other decision rules.
Goals and Limitations of Modeling Collective Behavior in Biological Systems
Frontiers in Physics · 2021 · 34 citations
Senior authorCorresponding- Computer Science
- Artificial Intelligence
- Computer Science
Local social interactions among individuals in animal groups generate collective behavior, allowing groups to adjust to changing conditions. Historically, scientists from different disciplines have taken different approaches to modeling collective behavior. We describe how each can contribute to the goal of understanding natural systems. Simple bottom-up models that describe individuals and their interactions directly have demonstrated that local interactions far from equilibrium can generate collective states. However, such simple models are not likely to describe accurately the actual mechanisms and interactions in play in any real biological system. Other classes of top-down models that describe group-level behavior directly have been proposed for groups where the function of the collective behavior is understood. Such models cannot necessarily explain why or how such functions emerge from first principles. Because modeling approaches have different strengths and weaknesses and no single approach will always be best, we argue that models of collective behavior that are aimed at understanding real biological systems should be formulated to address specific questions and to allow for validation. As examples, we discuss four forms of collective behavior that differ both in the interactions that produce the collective behavior and in ecological context, and thus require very different modeling frameworks. 1) Harvester ants use local interactions consisting of brief antennal contact, in which one ant assesses the cuticular hydrocarbon profile of another, to regulate foraging activity, which can be modeled as a closed-loop excitable system. 2) Arboreal turtle ants form trail networks in the canopy of the tropical forest, using trail pheromone; one ant detects the volatile chemical that another has recently deposited. The process that maintains and repairs the trail, which can be modeled as a distributed algorithm, is constrained by the physical configuration of the network of vegetation in which they travel. 3) Swarms of midges interact acoustically and non-locally, and can be well described as agents moving in an emergent potential well that is representative of the swarm as a whole rather than individuals. 4) Flocks of jackdaws change their effective interactions depending on ecological context, using topological distance when traveling but metric distance when mobbing. We discuss how different research questions about these systems have led to different modeling approaches.
Communications Biology · 2020 · 29 citations
Senior authorCorresponding- Biology
- Evolutionary biology
- Genetics
Natural selection on collective behavior acts on variation among colonies in behavior that is associated with reproductive success. In the red harvester ant (Pogonomyrmex barbatus), variation among colonies in the collective regulation of foraging in response to humidity is associated with colony reproductive success. We used RNA-seq to examine gene expression in the brains of foragers in a natural setting. We find that colonies differ in the expression of neurophysiologically-relevant genes in forager brains, and a fraction of these gene expression differences are associated with two colony traits: sensitivity of foraging activity to humidity, and forager brain dopamine to serotonin ratio. Loci that were correlated with colony behavioral differences were enriched in neurotransmitter receptor signaling & metabolic functions, tended to be more central to coexpression networks, and are evolving under higher protein-coding sequence constraint. Natural selection may shape colony foraging behavior through variation in gene expression.
Movement, Encounter Rate, and Collective Behavior in Ant Colonies
Annals of the Entomological Society of America · 2020 · 26 citations
1st authorCorresponding- Computer Science
- Artificial Intelligence
- Biology
Spatial patterns of movement regulate many aspects of social insect behavior, because how workers move around, and how many are there, determines how often they meet and interact. Interactions are usually olfactory; for example, in ants, by means of antennal contact in which one worker assesses the cuticular hydrocarbons of another. Encounter rates may be a simple outcome of local density: a worker experiences more encounters, the more other workers there are around it. This means that encounter rate can be used as a cue for overall density even though no individual can assess global density. Encounter rate as a cue for local density regulates many aspects of social insect behavior, including collective search, task allocation, nest choice, and traffic flow. As colonies grow older and larger, encounter rates change, which leads to changes in task allocation. Nest size affects local density and movement patterns, which influences encounter rate, so that nest size and connectivity influence colony behavior. However, encounter rate is not a simple function of local density when individuals change their movement in response to encounters, thus influencing further encounter rates. Natural selection on the regulation of collective behavior can draw on variation within and among colonies in the relation of movement patterns, encounter rate, and response to encounters.
Recent grants
NIH · $607k · 2017
Collaborative Research: Search, Signals, and Information Exchange in Distributed Biological Systems
NSF · $95k · 2010–2014
The Relation of Foraging Activity and Reproductive Success in Red Harvester Ant Colonies
NSF · $270k · 2007–2012
Evolutionary ecology of a multi-species mutualism at a regional scale
NSF · $376k · 2009–2014
Frequent coauthors
- 18 shared
Nathan J. Sanders
University of Michigan–Ann Arbor
- 18 shared
Elizabeth G. Pringle
University of Nevada, Reno
- 17 shared
Nicole E. Heller
Carnegie Museum of Natural History
- 14 shared
Michael Greene
Novartis (United States)
- 12 shared
Randi Reppen
Northern Arizona University
- 11 shared
Megan E. Frederickson
University of Toronto
- 10 shared
Rodolfo Dirzo
Stanford University
- 10 shared
Daniel Friedman
University of California, Davis
Education
Ph.D.
Duke University
Other
Harvard Society of Fellows
Other
Oxford University
Other
University of London
Awards & honors
- Quest Award, Animal Behavior Society (2020)
- Fellow, Animal Behavior Society (2017)
- Fellow, Center for Advanced Study in the Behavioral Sciences…
- Fellow, California Academy of Sciences (2007-)
- Guggenheim Fellowship, Guggenheim Foundation (2001-02)
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