Title:
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Movement and information acquisition by super-organismic ants
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Some of the most successful organisms by total biomass are social
insects like ants: complex 'super-organisms' that work together so
closely that they can be considered as a single unit of natural selection.
A common problem in Bayesian statistics, and elsewhere, is to sample
from unknown probability distributions, with efficient sampling preferring
to locate regions of high probability first. I use Markov chain
Monte Carlo methods developed to solve such problems as models
of animal exploration, and use the idea of Kelly betting from information
theory to develop a probability matching model of collective
ant foraging. The technique of approximate Bayesian computation is
used to understand the ants' quorum sensing decision procedure as
approximating the probability that a location is the best option for a
new nest. These models allow the efficiency of such tasks to be measured
in informational terms. Studying the movement behaviour of
individual ants, I find evidence for motor planning, and relate this
to information processing effectiveness at the level of the individual
and colony. I identify a new method of collective exploration in T.
albipennis - avoiding the chemical markers left behind by previously
exploring nest mates - and show that this allows both real colonies
and a biomimetic MCMC method to sample from an unknown space
more efficiently. I report experimental work that finds colonies navigate more efficiently to a new nest site when a sloping landmark is present, compared
to a horizontal one. I identify lateralization in foraging Australian
meat ants, Iridomyrmex purpureus, and in T. albipennis, as the
ants prefer left turns when exploring unknown nest sites, and associate
this with slight asymmetries in their eyes. I also find that T.
albipennis males have much larger eyes than queens, consistent with a
'female-calling' mating system where the males fly to the queens for
matings.
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