Complex decision making in the slime mould Physarum polycephalum I
am interested in the decision making capabilities of slime
moulds. Slime moulds, which are unicellular, lack brains. Nevertheless,
we have recently shown that these simple organisms are capable of
flexible and complex behaviours.
For more information, please see: Reid, C. R., Latty, T.,
Dussutour, A. & Beekman, M. 2012. Slime mold uses an externalized
spatial “memory” to navigate in complex environments. Proceedings of the
National Academy of Sciences. Early Edition
Latty, T. & Beekman, M. 2010 Speed–accuracy trade-offs during foraging decisions in the acellular slime mould Physarum polycephalum. Proceedings of the Royal Society B: Biological Sciences.
Latty, T. & Beekman, M. 2010 Irrational decision-making in an amoeboid organism: transitivity and context-dependent preferences. Proceedings of the Royal Society B: Biological Sciences.
Beekman,M and Simpson SJ. Amoeboid organism solves complex nutritional
challenges. In press, Proceedings of the National Academy of Sciences.
Latty,TM and Beekman, M (2009). Food quality affects search strategy in the acellular slime mould, Physarum polycephalum. Behavioural Ecology 20: 1160 - 1167 :
Latty, TM and
Beekman, M (2009). Food quality, hunger and the risk of light exposure
effect patch choice decisions in the acellular slime mould Physarum polycephalum Ecology 91:22-27
Dynamic problem solving in self-organised biological systems
goal of this project is to understand how self-organized natural
systems are able to solve problems in dynamic environments. For example, bee colonies can adapt to changes in nectar concentration by re-allocating their workforce. Amazingly, they do this without designated
leaders. I am investigating the mechanisms that underlie dynamic decision making in three different self-organised systems:
ants, bees and slime moulds. This project is part of an international
collaboration between the labs of Dr. Madeleine Beekman, Dr. Martin
Middendorf, Dr. David Sumpter and Dr. Toshi Nakagaki.For more more information see:
and Beekman,M. Keeping track of changes: foraging performance of ant
colonies in dynamic environments. Animal Behaviour, in press.
C.R., Latty,T. & Beekman, M. Making a trail: Informed Argentine ants
lead colony to the best food by U-turning coupled with enhanced
pheromone-laying. Animal Behaviour , in press
Granovskiy,B, Latty,T; Duncan,M; Sumpter, D J.T, Beekman, M 2012. How dancing honey bees
keep track of changes: the role of inspector bees. Behavioural Ecology, 23, 588-596.
Self-organised transportation networks in ants
I am interested in the structure, function
and development of ant transportation networks. Human engineers and
urban planners face the task of designing efficient and cost effective
networks. Since building longer roads/tracks requires more
resources (and is therefore more costly), a challenge for engineers is to
design transportation networks that minimise resource use while still
maintaining connectivity between cities, stations etc. Similar problems are faced
by ant colonies which build trail networks to connect multiple nests to many
food sources. How do ants 'design' transportation networks in the absence of
centralised control? What, if anything, do ants optimise when building networks? This work has been done in close collaboration with computer scientist Kai Ramsch at the University of Leipzig.
For more information, please seeLatty, T.,
Ramsch, K., Ito, K., Nakagaki, T., Sumpter, D. J. T., Middendorf, M.
& Beekman, M. Structure and formation of ant transportation
networks. Journal of The Royal Society Interface.
Picture of ant network between three nests. The inset shows the shortest possible network (Steiner tree). Way to go ants!