WATCHING termites construct a high-rise, air-conditioned apartment block out
of the red Kenyan soil, or a colony of ants in the rainforest sewing leaves
together to build a miniature metropolis, makes you wonder where the
intelligence for such intricate engineering comes from. Is the colony’s
behaviour just the sum of the activities of its individual members, or are we
dealing here with a property unique to the colony as a whole?
The first view is favoured by reductionists—those biologists who try to
reduce complex behaviour to the activities of simpler elements such as
individual organisms or, preferably, their genes. The second has given rise to
the concept of the colony as a superorganism, a coherent whole with properties
that are distinct from those of its constituents and which cannot be predicted
from them. This notion, though still not widely accepted, introduces a new level
of order to biology which in turn is helping to improve our understanding of
evolution.
The idea that there exists unpredictable “emergent” order in natural
processes did not originate in biology. It is, for example, well understood in
physics. Take the properties of the elements hydrogen and oxygen. These are
thoroughly understood in themselves. Yet nobody has ever succeeded in predicting
the properties of water from a knowledge of its constituent elements. Trying to
explain why water spirals down a plughole simply from a knowledge of atoms is an
impossible task. What’s missing is a knowledge of fluids—a whole level of
order above that of individual atoms.
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Physicists see water for what it is—a fluid. They develop theories
about its “high-level” properties that are consistent with the “low-level”
properties of atoms by working back and forth between the levels. There is
nothing unscientific about this approach. It simply acknowledges that certain
phenomena cannot be predicted. Turning back to biology and social insects, we
can ask what is the appropriate level to explain the cooperative behaviour of a
colony as a whole.
Probably the most popular approach to biological phenomena these days is to
try to explain them in terms of the properties of genes. According to
evolutionary thinking, if a genetic mutation changes an individual’s structure
or behaviour in a way that gives it a competitive edge over others, then it will
be favoured by natural selection and passed on to future generations.
Selfish bent
This is an explanation in terms of fitness—the capacity of an
individual to survive and reproduce. In recent years, biologists such as Bill
Hamilton and Richard Dawkins, both now at Oxford University, have extended this
argument to the genes themselves: it is genes that reproduce and are selected,
so they have become the ultimate units of natural selection. Evolution is now
widely understood in terms of selfish genes that are bent on leaving as many
copies of themselves behind as possible.
For ant colonies, this view has been further modified. If all the individuals
in a colony are closely related to one another—as they are in many species
of ant because they all have the same mother, the queen—then a genetic
change that gives an advantage to an individual will tend to be advantageous to
the whole colony. This is measured in terms of what is called “inclusive
fitness”. The cooperative activity seen in ant colonies is assumed to be just
such a characteristic that confers benefit on the group as a whole. And while we
might interpret cooperative behaviour as altruistic, Hamilton and Dawkins argue
that what we’re really seeing is the result of selfish genes looking out for
themselves via the colony.
Though fine in principle, this line of reasoning contains a logical gap.
Genes may affect the behaviour of individuals, but just how do they affect the
behaviour of the colony as a whole? After all, it is not genes that interact in
a colony but individual organisms. Just as hydrogen and oxygen atoms are
necessary but not sufficient to explain water spiralling down a plughole, so
genes—and even individual ants—alone cannot explain cooperative
behaviour. There is a level of order missing. We need to proceed more like
physicists and ask the question: what particular form of interaction between
ants can generate a particular type of collective behaviour? So let’s get
specific.
Order from chaos
At the University of Bath, Nigel Franks and his colleagues noticed rhythmic
activity patterns among workers tending the queen and the young in the brood
chambers of a species of Leptothorax, small ants that form colonies of
between 40 and 80 members. The team of workers is active for a time and then
becomes inactive, taking about half an hour to go through one cycle. How does
this collective rhythm arise? Does every ant behave in a cyclic fashion so that
the colony rhythm is simply an expression of synchrony among intrinsically
rhythmic individuals?
The person who answered this question is Blaine Cole, a researcher at the
University of Houston in Texas who works with another species within the same
genus of ant as that used at Bath. He filmed isolated ants and groups of various
sizes, and then analysed the activity patterns of individual ants. Cole
concluded that isolated individuals and individuals in sparsely populated groups
have a pattern of activity-inactivity that is described as deterministic chaos.
This is not a random pattern, but one which is so complex that it is impossible
for an observer to predict what a particular ant will do next.
Cole also found that when the density of ants reached a certain level, the
group displayed a collective activity-inactivity rhythm with about the same
cycle time as that found by Franks—around half an hour. How can collective
order emerge from chaotic individuals? Clearly, this must result from the way
they interact. But what kind of interaction?
Ants perceive their world primarily through their antennae. They have, on the
whole, very poor sight, whereas their antennae have exquisitely sensitive touch
and smell receptors. When ants meet, they communicate via their antennae. Active
ants may encounter either other active ants, or inactive ants. When the latter
happens, the inactive ant is stimulated into action.
Is this type of stimulation enough to produce a collective rhythm in a colony
of chaotic individuals? It is not obvious that this is the case, so I and my
colleagues Ricard Solé from the Polytechnic University of Catalonia,
Barcelona, and Octavio Miramontes, then at Britain’s Open University, designed a
computer model of an ant colony to see what we could learn. We described ants as
cellular automata, simple software agents that move about on a grid like a chess
board (see Diagram).
The pattern of activity-inactivity of our “ants” was driven
by a neural network that produced a chaotic output, so that in isolation they
behaved like the real ants observed by Cole. But the ants could also interact
with one another: whenever an active ant reached a position on the grid next to
an inactive ant, the lazy ant was stimulated out of its lethargy.
Now, as Cole had done with real ants, we began to increase the number of
virtual ants on the grid. As the density increased, the amount of activity per
individual increased, simply because of the higher frequency of stimulation
between individuals. Then, at a particular density, a distinct rhythm began to
emerge over the “colony” as a whole. With further increases in density, the
rhythm became distinct and well-defined
(see Graphs).
Unpredictable rhythms
So model ants, behaving chaotically and interacting “socially” by
stimulation, can generate a collective rhythm throughout a colony. This is a
clear case of emergent behaviour: it was impossible to predict the collective,
rhythmic pattern just from a knowledge of the chaotic behaviour of the
individuals. Just as in physics, we worked backwards from an unexplained
behaviour of a system to its possible origins within the pattern of interactions
between the system’s components.
Using our model, we could explore the impact on the colony of different
patterns of interaction. What would happen if active ants could stimulate other
active ants, for example? We found that the most important type of interaction
for generating the rhythmic patterns was stimulation of inactive ants by active
ants. Cole found this to be true for real ants too. We could now begin to be
more precise about the way in which genes may be influencing observed behaviour
through the properties of individual ants.
In the model there is a parameter that defines how sensitive ants are to
being stimulated by their companions. We found that if this sensitivity is too
small, activation never spreads across the colony to produce collective rhythms,
no matter how high the density of ants. On the other hand, if the sensitivity is
too high, then above a certain density the colony becomes continuously active
and again no collective rhythm emerges.
This suggests that genes affecting an individual’s response to stimulation
need to be regulated. Is this range large or small? If small, then there may be
a significant price to be paid for accurately regulating the relevant genes to
keep the sensitivity within the required bounds.
However, the model showed that there is in fact a wide range over which the
sensitivity can vary and still produce collective rhythms. This suggests that
rhythmic activity patterns are robust consequences of colonial living. They may,
in fact, be hard to avoid. Collective rhythm is an emergent property that has
been described as “order for free”.
Unexpected altruism
This result may also help to explain another aspect of colonial life. For
many species, including bees, wasps and termites, the members of a colony may
not be closely related but still cooperate. This seems to run counter to the
spirit of the selfish gene. The theory’s proponents solve this puzzle by arguing
that the cost of recognising alien genes in other colony members is too high and
would lead to losses in efficiency. Order for free offers a more direct and
simple solution.
There is one other important property of colonial rhythms that the model
revealed. Rhythmic activity emerges suddenly above a critical density. But what
type of discontinuity are we dealing with? The model showed that it has the
characteristics of what physicists call a phase transition: a sudden change of
state from one type of order to another. One example is the sudden appearance of
magnetic properties in iron as it is cools below a critical temperature. Above
this temperature the kinetic energy of the atoms, each of which behaves as a
magnetic dipole, is too high for them to become aligned, so they move
independently of one another. Below the critical temperature, the kinetic energy
falls low enough to let the dipoles line up, north pole to south pole along the
metal, so that a collective magnetic field emerges.
The ants are doing something similar, but now it is density, and hence the
amount of stimulation between them, that is the critical parameter. We see from
the model that at the critical density, what was a collection of individuals
doing their own local thing begins to change as global order emerges via
activation waves that propagate through the entire colony.
It’s time to go back to real colonies and ask if they actively regulate their
densities. And if so, where on the density spectrum do they lie: close to the
lower transition to disorder—at the edge of chaos—or well into the
ordered regime? Again, Franks and his colleagues have made important
observations here. They have shown that ants do regulate the mean density of
workers in the brood chamber, though it is not clear how they do it. The
preferred density of the colony appears to be near the edge of chaos.
They have also suggested what the adaptive value of rhythmic activity may be
to the colony. Within the brood chamber, the workers look after the queen and
the young, feeding and cleaning them and organising the brood so that the
youngest are closest to the queen. Franks contends that if workers had chaotic
patterns of activity-inactivity in the brood chamber, there would be a haphazard
distribution of care over the brood as a whole. But with a rhythmic pattern, the
workers are all active within the brood chamber at the same time. They then
distribute care more uniformly because workers tend to seek untended brood to
look after.
So, the suggestion goes that the rhythmic behaviour improves a colony’s
method of nurturing its young and increases its fitness, giving it a competitive
edge. This is a plausible suggestion that fits well with mainstream evolutionary
thinking. Of course, we could have reached the same conclusion just through
discussion of genes and fitness, with no discussion of collective rhythm. But
fitness explanations explain only why a particular pattern of behaviour
persists, not how it is generated and how difficult or easy it is to produce.
This is where the science of complexity and the study of emergent phenomena come
into play. They bridge a serious gap in evolutionary thinking. In the process,
they are shifting the emphasis from genes and fitness to emergent order as a
primary source of evolutionary novelty.
Every “ant” on the computerised grid (see Diagram)
has an activity-inactivity cycle that is chaotic. And when an active ant (orange) occupies
a position next to an inactive ant (grey), the lethargic insect becomes mobile too.
With a sparse population of ants, activity across the grid as a whole tends to be chaotic
(see Graphs). But as the density increases, a rhythmic
pattern emerges. The colony begins to behave like a single, pulsating superorganism.FIG-mg21385301.JPG