Stefan M. Popp

office number

123 address street

Würzburg, BY 97237

I am interested in

What ants (and other critters) can learn, and how internal and external factors influence that. E.g.: Learning projects. I am mainly using Y-mazes in the lab for that.
How the specific animal and lab conditions and experimental setups influence the outcome of learning experiments. E.g.: Learning directional cues in the light vs dark or housing conditions
How do insects explore space to find resources? During my PhD I investigated the search strategies of ants and I plan on continuing that line of research.
Automated video tracking of animals. During my previous projects I gained experience with state of the art tracking methods with their potential and pitfalls. I am so interested in this that I also teach a BSc course on it.
The rules that make animals behave the way they do. E.g.: Modeling meandering movement or Collective movement
The biomechanics of behavior and neuroethology. I would love to collaborate with someone more knowledgeable in the ‘mechanics’ and ‘neuro’ aspects of that!
Isopods. I recently found a passion for them, including the amazing vivariums others build as a hobby. Some frequently traded and kept isopod species have not even been described yet.

I mostly work in the lab but do the occasional field work. In my current position, which will end in 2031, I will mostly be working on cognition, learning exploration, video tracking, and boredom in ants.

news

Aug 18, 2023	I officially got my PhD!
Jan 15, 2016	A simple inline announcement with Markdown emoji!
Nov 07, 2015	A long announcement with details

selected publications

Alignment with neighbours enables escape from dead ends in flocking models

Varun Joshi , Stefan Popp , Justin Werfel , and 1 more author

Journal of The Royal Society Interface, Aug 2022

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Coordinated movement in animal groups (flocks, schools, herds, etc.) is a classic and well-studied form of collective behaviour. Most theoretical studies consider agents in unobstructed spaces; however, many animals move in often complicated environments and must navigate around and through obstacles. Here we consider simulated agents behaving according to typical flocking rules, with the addition of repulsion from obstacles, and study their collective behaviour in environments with concave obstacles (dead ends). We find that groups of such agents heading for a goal can spontaneously escape dead ends without wall-following or other specialized behaviours, in what we term ‘flocking escapes’. The mechanism arises when agents align with one another while heading away from the goal, forming a self-stable cluster that persists long enough to exit the obstacle and avoids becoming trapped again when turning back towards the goal. Solitary agents under the same conditions are never observed to escape. We show that alignment with neighbours reduces the effective turning speed of the group while letting individuals maintain high manoeuvrability when needed. The relative robustness of flocking escapes in our studies suggests that this emergent behaviour may be relevant for a variety of animal species.
Ants combine systematic meandering and correlated random walks when searching for unknown resources

Stefan Popp , and Anna Dornhaus

iScience, Jan 2023

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Animal search movements are typically assumed to be mostly random walks, although non-random elements may be widespread. We tracked ants (Temnothorax rugatulus) in a large empty arena, resulting in almost 5 km of trajectories. We tested for meandering by comparing the turn autocorrelations for empirical ant tracks and simulated, realistic Correlated Random Walks. We found that 78% of ants show significant negative autocorrelation around 10 mm (3 body lengths). This means that turns in one direction are likely followed by turns in the opposite direction after this distance. This meandering likely makes the search more efficient, as it allows ants to avoid crossing their own paths while staying close to the nest, avoiding return-travel time. Combining systematic search with stochastic elements may make the strategy less vulnerable to directional inaccuracies. This study is the first to find evidence for efficient search by regular meandering in a freely searching animal.