About

Anna Dornhaus is a professor leading the Social Insect Lab at the University of Arizona, where she and her team study social insect colonies, including ants and bees, to understand complex social systems. Their research focuses on how insects work together, investigating collective behaviors such as group organization and individual actions that enable effective problem solving, task allocation, search, and decision-making. By understanding how collective patterns emerge from individual behaviors, their work aims to inform the design of better computing systems, improve human society, and provide insights into broad principles of biology. In addition to her research, Professor Dornhaus is deeply committed to science education and outreach. She supports K-12 teachers and students through initiatives like the Cricket Project, which allows young learners to engage directly with scientific inquiry by studying insect behavior. This project integrates multiple disciplines including reading, writing, math, and science standards, fostering active problem-solving and hands-on learning. Professor Dornhaus advocates for science as a powerful approach to discovery and problem-solving, emphasizing the scientific method's role in achieving objective insights and addressing complex challenges beyond intuitive solutions.

Research topics

• Computer Science
• Biology
• Ecology
• Communication
• Psychology
• Mathematics
• Algorithm
• Statistical physics
• Genetics
• Statistics
• Physics

Selected publications

• Bumble bees that follow a stricter routine innovate less: Foraging behaviors, environmental complexity, and how they relate to novel problem solving

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-03-09

  articleOpen accessSenior author

  Abstract The ability of animals to innovate - solve novel problems - can shape their ecology and evolution. Here we investigate how individual traits and environmental complexity relate to successful solving of a novel problem. We presented foraging bumble bees ( Bombus impatiens ) with artificial flowers of not-previously-encountered shapes and recorded the bees’ latency to access nectar. We measured individual foraging traits across multiple trips with simple flowers that did not require innovation, and bees were foraging either in a simple or complex environment (cluttered flight arena). Bees in complex environments took longer to find and were less likely to land on novel flowers, indicating that environmental complexity may take up cognitive resources and make search more difficult. However, we did not find an effect of environmental treatment on the ability or time to access reward in novel flowers once bees had landed on them. In contrast, behavioral traits significantly predicted how quickly bees ‘solved’ novel flowers. In particular, overall foraging tempo as well as routine formation, i.e. how much bees followed a fixed route on known flowers, predicted innovation - faster bees innovated faster, and bees with more repetitive foraging sequences were slower to solve the novel tasks. Overall, while the degree of evolutionary ‘novelty’ in tasks or solutions is always hard to evaluate, our findings demonstrate that environment and individual traits may affect innovation in different ways. Individuals in simple environments may be more likely to detect , and individuals that are generally faster and have a lower tendency to develop fixed routines may be more likely to solve , novel tasks.

  PublisherOA PDFDOI

• What good is modeling? Introducing biology students to theory

  arXiv (Cornell University) · 2026-04-14

  preprintOpen accessSenior author

  Theory and empirical science should be in constant dialogue, but often find it hard to understand one another. Here we describe a graduate-level university course we developed to improve matters. The course was designed to help empirically-focused biology graduate students read and understand theory papers, despite little prior mathematical training. It uses several evidence-based principles of modern teaching: backwards design, active learning, and just-in-time teaching. We believe that this or similar curricular content, emphasizing the nature of evidence and the role of theory in science, will improve critical thinking and scientific progress.

  PublisherDOI

• Aliens Are Likely to Be Smart But Not “Intelligent”: What Evolution of Cognition on Earth Tells Us about Extraterrestrial Intelligence

  2026-04-17

  articleOpen access1st authorCorresponding

  How likely is it that we will find aliens like the ones in so many science fiction stories–people who possess self-awareness and cognitive ability comparable to ours, but who arose from an independent evolutionary origin? Here I make the argument that if life has evolved on other planets, it may well eventually acquire complexity equivalent to that found on Earth. The resulting lifeforms may be good problem-solvers, including predicting their environment and the behavior of social partners, using tools, learning, and otherwise flexibly and adaptively responding to information: these are all traits common among organisms on Earth. However, on Earth, humanlike intelligence is unique. No other animal appears to have the same level of cognitive complexity, ability to use abstract and endlessly flexible communication, and ability to capitalize on social division of labor as humans do. Surprisingly, we do not know why this is the case: why are we the only ones with this level of intelligence on our own planet? This is not an unsolvable question in principle: we know the answer to many evolutionary “why” questions when it comes to animal intelligence. In the case of humans, however, natural selection to increase individual reproduction seems insufficient as explanation. Perhaps it is: sexual selection, the evolution of an exaggerated trait unnecessary for survival but impressive to potential mates, much like a peacock’s tail or a nightingale’s song, may be the most plausible explanation for the evolution of the human brain. If this is true, then we should expect cognitive ability, i.e. learning, memory, abstraction, and many other elements of intelligence to be commonplace in the galaxy as they are among organisms on Earth; but exaggerated intelligence as in humans may be a rare accident of chance, as rare as a peacock’s tail.

  PublisherOA PDFDOI

• What good is modeling? Introducing biology students to theory

  ArXiv.org · 2026-04-14

  articleOpen accessSenior author

  Theory and empirical science should be in constant dialogue, but often find it hard to understand one another. Here we describe a graduate-level university course we developed to improve matters. The course was designed to help empirically-focused biology graduate students read and understand theory papers, despite little prior mathematical training. It uses several evidence-based principles of modern teaching: backwards design, active learning, and just-in-time teaching. We believe that this or similar curricular content, emphasizing the nature of evidence and the role of theory in science, will improve critical thinking and scientific progress.

  PublisherOA PDF

• Optimal competitors: the balance of attraction and choices of mutualists, like pollinators, drives facilitation and may promote crop pollination

  Proceedings of the Royal Society B Biological Sciences · 2025-11-01

  articleOpen access1st authorCorresponding

  When two species use the same resource, this typically leads to competition, such as when different plants aim to attract the same mutualist pollinators. However, more flowers may also attract more pollinators to an area, such that one or both 'competitors' actually benefit from the other's presence. For example, it has been argued that strips of wildflowers planted next to crops may attract pollinators who 'spill over' into the crop. Here, we mathematically examine facilitation and competition in consumer attraction. Contrary to previous claims, no accelerating benefits of density on attraction per se are necessary for facilitation. Instead, under very general assumptions, facilitation can be generated by an imbalance between local competition and joint long-distance attraction of consumers; for example, a low presence of highly attractive 'wildflowers' should lead to benefits to a crop. In this mechanism, how pollinator attraction to a patch increases with density of plants is a key factor. Our results generalize to many contexts where local competition may trade off with joint long-distance attraction of consumers, and we show that the exact relationship between competitor density and attraction of consumers can qualitatively shape outcomes, including facilitation or competition.

  PublisherDOI

• Author response for "Optimal competitors: the balance of attraction and choices of mutualists, like pollinators, drives facilitation and may promote crop pollination"

  2025-05-28

  peer-review1st authorCorresponding
  PublisherDOI

• Persist or Give up? Fire ants motivated to search for a high-quality food source even if they don’t know how to find it

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-12-25

  articleOpen accessSenior author

  Abstract Finding resources for the colony is one of the most difficult and risky tasks for a social insect worker. A worker on a foraging trip can face a number of challenges, including interference from other individuals, her own errors, and environmental disturbances. Collectively, colonies may use a variety of strategies to minimize the impact of such perturbations on the foraging process. Here, we investigated how individual Solenopsis xyloni ant workers react to perturbation of an established pheromone trail. We trained foragers from colonies in the field to either a low or high concentration sucrose solution in a feeder on a T-maze setup, then replaced a section of floor covering, removing a section of the pheromone trail previously laid. We found that while ants made correct choices on the T-maze when the trail was intact, their choices did not differ from chance when the trail was absent, indicating strong reliance on a pheromone trail (and not, for example, memory) to return to the resource. Moreover, when the trail was absent, we found that a majority of ants abandoned the resource, and that even the ants that were able to reach the resource did not repair the perturbed trail. However, with a high-quality resource, more ants persisted in attempting to reach it (instead of abandoning). We interpret these responses in the framework of robustness mechanisms discussed in systems biology. Our study thus links individual and collective responses to perturbations, and provides an empirical example of how information use interacts with system robustness. Statements and declarations The authors have no competing interests to declare that are relevant to the content of this article.

  PublisherDOI

• Author response for "Optimal competitors: the balance of attraction and choices of mutualists, like pollinators, drives facilitation and may promote crop pollination"

  2025-09-18

  peer-review1st authorCorresponding
  PublisherDOI

• Author response for "Optimal competitors: the balance of attraction and choices of mutualists, like pollinators, drives facilitation and may promote crop pollination"

  2025-08-20

  peer-review1st authorCorresponding
  PublisherDOI

• A comparison of turn identification methods on high-frequency movement trajectories reveals potential comparability issues between studies of movement ecology

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-04-19

  preprintOpen accessSenior author

  Abstract High-frequency animal tracks must often be subsampled to allow a simple analysis of the movement on the most meaningful scale for the respective study. One way of achieving this is to identify ‘biologically significant turns’, compared to heading changings caused by ‘noise’. Many ‘turn identification’ methods have been developed, but the accuracy and consistency of such methods have rarely been validated against ground truth trajectories with known ’true’ turns and noise. We analyze simulated tracks with known parameters as well as two empirical tracks and identify turns with 10 different frequently used resampling methods. We assess the specificity and sensitivity of identifying the location of turns and compare the known mean step length and turn angle of the paths with the resampled trajectories. We found great accuracy differences between, and sometimes within, methods, even on simulated tracks of the same characteristics. Results of some methods were also highly sensitive to the user-set threshold the method requires (e.g. max angle). Overall, the best-performing methods in this study were DP and MRPA, methods used in human mobility research, and TPA, which is mostly used in primate research. We thus advise caution when comparing results of studies using different resampling methods and recommend justifying the use of the resampling method in addition to quantifying the sensitivity of results to the threshold value. This study is also an appeal to authors of novel turn identification methods to consider thorough comparisons in different scenarios with a wide range of previous methods, including those developed outside the movement ecology discipline.

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: ABI Development: A User-friendly Tool for Highly Accurate Video Tracking

  NSF · $207k · 2016–2020

• Collaborative Proposal: ABI Innovation: Rapid, Interactive, Visual Mining of Biological Motion

  NSF · $279k · 2013–2016

• Adaptive distribution of morphological specialists in social insects: New insights into the evolution of division of labor

  NSF · $450k · 2009–2013

• BCSP: The Emergence of Inactivity: Adaptive Task Allocation in Complex Distributed Systems, or Why Are There so Many Lazy Ants?

  NSF · $695k · 2015–2020

Frequent coauthors

• Nigel R. Franks

  University of Bristol

  26 shared

• Benjamin Blonder

  17 shared

• Joshua S. Hoskinson

  15 shared

• J. A. Banks

  University of California, Berkeley

  15 shared

• Rebecca Lipson

  15 shared

• Alan Strauss

  15 shared

• Austin Cruz

  Central University of the Caribbean

  15 shared

• Daniel Charbonneau

  University of Arizona

  15 shared

Labs

• Dornhaus LabPI

  The Dornhaus Lab conducts research on social insects, focusing on the behavior and social structure of ants.