Download Chapter 2: Evolution and Communication

Chapter 2: Evolution and Communication
The popular conception of evolution is “survival of the fittest”. The fittest individuals of a
generation survive, enabling themselves to reproduce and to pass their advantageous traits on to the
next generation. This is the simple principle Darwin put forward in The Origin of Species in 1859.
However, as it is far from trivial what makes an individual the fittest, what makes it survive and
reproduce, scientists have elaborated on the implications of evolution ever since.
To clarify the scope and the implications of evolutionary modelling, in this chapter I will discuss
some of the issues in evolution theory, using the notion of the replicator as a starting point (section
2.1). The survival of a replicator can be influenced by the environment (natural selection, section
2.2) or by other replicators (co-evolution, section 2.3). Replicators can be built out of smaller
replicators acting on a lower level of organisation or they can be part of a group of replicators on a
higher level. This so-called multi-level selection will be discussed in section 2.4. Finally I will
address cultural evolution in section 2.5, which is the application of evolutionary theory on the
emergence of culture, language and communication in general.
2.1
Replicators
The notion of a replicator was coined by Richard Dawkins in The Selfish Gene (1976) as the
generic unit of evolution, with the gene as a striking example. His theory states that evolution is a
generic phenomenon that the term replicators could also be applicable to other things than natural
organisms such as cultural units, the memes. According to Dawkins, replicators are the substrate for
evolution and the main prerequisite for evolutionary processes.
Replicators are self-replicating entities such as for example genes, computer viruses or chickens.
As each replication produces a new replicator, they will grow in number exponentially. Such
exponential growth can be observed in small microbe populations in a nutritious fluid, but also in
the growth of the global human population over the past thousands of years. As exponential growth
in infinite time will lead to infinite population sizes, limited resources such as space and food will
not allow it; populations eventually reach a more or less stable size.
Some replicators are more successful than others. The properties of the replicator that determine
their success we refer to as its phenotype, which is more or less determined by the genotype, the
12
replicated structure. The phenotype of a replicator can perform better or worse relative to the
environmental circumstances. In this respect the environment can be regarded as some kind of
function assigning a fitness value to every organism according to its properties, creating a fitness
landscape (Wright, 1932) with the fitness of the replicator on one axis and the dimensions of the
phenotype of the replicator on all the other axes. Replicators with high fitness will win the
competition from their competitors. Winning the competition enables them to produce more copies
of themselves. As this offspring will bare the same advantageous traits, the replicators with higher
fitness will increase in number. Eventually this selection process will most probably lead to the
take-over of the population by the replicator with the highest fitness.
However, there always is a chance on replication errors, mutations; the probability of a mutation
in an element of the replicator is small, but for a perfect replication, every element needs to be
copied perfectly. Therefore, the probability of an imperfect replication of the whole replicator is
high. A high mutation rate can cause replicators with high fitness to disappear from the population,
as they might only generate imperfect copies. If the replicators with higher fitness levels are not
able to pass their advantageous traits on to the next generation because the copies are mutated, they
will disappear from the population despite their high fitness. Then, the population is not able to
retain information and therefore is said to be below the information threshold. On the other hand,
mutations are also necessary to generate new, previously non-existent replicators. Mutations in
most cases will lower fitness, but in some cases the fitness for the new replicator will be higher.
These replicators will then out-compete the ones that were replicated perfectly and elevate the
population to higher fitness levels.
As long as the mutation rate is not too high, the match between the replicator population and the
environment it lives in will improve. Structures will evolve that can withstand the local conditions
better. For example, species of Anolis tree-lizards in the Caribbean, originating from a single
ancestral species, are known to have adapted their claws and bodyweights to the height they live on
in the trees (Losos, 2001). The match also improves by the evolution of structures for manipulation
of the environment, which is for example observable in nest building in birds. A third way the
match can improve is by changing location, as environmental circumstances do not have to be the
same everywhere.
Practically anything, which replicates good enough to stay above the information threshold and
is situated in an environment, can be a replicator, such as molecules, cells, individual organisms and
groups of organisms. A replicator does not even have to be part of the physical world as computer
programs (Koza, 1992) and even ‘cultural units’, memes, can function as replicators.
13
2.2
Natural Selection
Evolution of replicators causes the population to adapt to its surrounding environmental
conditions. However, in this statement environment refers to a general notion of ‘everything
around’. It is certainly not just restricted to stones and rain, but also includes other organisms that
are subject to evolution as well. Even members of the same species can be counted to the
environment. Natural selection is adaptation to a supposedly static environment, as opposed to the
dynamic environment of conspecifics and co-evolving species. The distinction between static and
dynamic depends on the focus of discussion. We will discuss some of the important issues on
natural selection to start with. Co-evolution will be discussed in section 2.3.
2.2.1
Adaptationism
The environment selects for better-adapted individuals, causing species to get more and more
adapted. According to the adaptationist paradigm, this is all the evolutionary change there is. Every
species only adapts to the environment and does so in an increasingly refined way, implying that the
traits of organisms are in form and function entirely determined by external factors. Thus, holds the
adaptationist, for every trait we find in a species, there has to be a cause in the environment.
Tinbergen (1963) in this context stated that all animal behaviour has proximate and ultimate
causes. Proximate explanations are pointing to the direct causes of behaviour coming from the
interaction between the environment and the animal’s genes and hormones. Ultimate causes are
evolutionarily grounded, explaining why the genes and hormones are the way they are, looking for
an adaptive explanation for every trait. For example, the blinking of an eye has a proximate cause in
the contraction of the ocular muscles and in the neural mechanism that makes these muscles
contract. The ultimate explanation would be that the eye needs to be kept wet, to regenerate etc.
Adaptationism is the common idea of how evolution works. Every day we see organisms around
us that are very well adapted to the environment they live in and everything seems to have its own
purpose. It made Daniel Dennett state in Darwin’s Dangerous Idea (1996): “Adaptationist
reasoning is not optional; it is the heart and soul of evolutionary biology”.
However, the idea that species are “sculptured” by the environmental conditions has faced severe
criticism. Gould & Lewontin (1979) argued elegantly that the building plans of organisms are so
much constrained by their evolutionary history, that these constraints actually are more important
than the selective forces in causing phenotypical change. Traits, genes or modules do not just adapt
separately to environmental conditions, but form a network of constraints on the possible ways in
which the organism can evolve. Not selection, but constraints and opportunities would determine
the largest part the direction of evolution. It contradicts the adaptationist paradigm of gradual
14
adaptation toward a global optimum. Rather, using the present opportunities, the population would
evolve towards a local optimum, determined by the constraints of the present form of the species.
2.2.2
Punctuated evolution
When looking at the fossil record, intermediate evolutionary steps are often absent. Species seem
to jump from phenotype to phenotype without the gradual change in between. A nice example
comes from the isolated island of Mauritius, home of the Bolyerine snakes. These snakes have a
property, which has not been found in any other terrestrial vertebrate: their upper jawbone has a
joint. Frazzetta (1970) argued that the transition from the fixed jaw to the flexible jaw could not
have been continuous as a bone simply cannot be “half broken”.
This has made Eldredge & Gould (1972; 1993) start questioning the scalability of the gradual
genetic and phenotypic change as seen on the micro-level. Somehow, gradual changes at the microlevel were causing jumps, punctuated equilibria, at the macro-level. If such a jump is advantageous,
it will cause rapid change in the phenotype of a species.
The notion of punctuated equilibria was in a way the reinstallation of an old theory on evolution
by Richard Goldschmidt, who argued that macro-mutations are creating hopeful monsters by
causing leaps in the phenotype space (1940). A small percentage of the hopeful monsters would be
viable, causing the population to make jumps in the phenotype. Gould inserted this theory into
Darwinian evolutionary theory and it is now widely accepted.
But how can evolution go about in almost discrete steps when we know that DNA changes
gradually, with a certain mutation chance? Every child that is born has a DNA at a more or less
fixed distance in genotype space from the DNA of the parents, thereby creating some kind of
molecular clock relating time to genotypic distance. Therefore one of the major questions about
punctuated evolution is how it takes place.
2.2.3
Neutrality
Kimura’s theory of neutral evolution (1983) explains the apparent contradiction between
phenotypic change at the micro and macro-level by looking at the structure of proteins. He argued
that a mutation in the DNA does not automatically implicate a change in the protein it encodes, as
the 3-dimensional structure and the binding site (active part) of the protein are the only defining
properties for its function. As long as the binding site and the spatial structure remain intact, a
mutation does not change anything to the functionality of the protein and is therefore referred to as
a neutral mutation. The set of structures linked together by neutral mutations is referred to as the
neutral network.
The neutrality of a mutation however does not imply that it has no consequences. A protein has
15
the spatial structure with the lowest energy value, but one mutation could make another folding
more efficient. The protein would fold differently and get another function. Mutations can be
neutral for one folding, but still change the properties of others, thereby enabling the genome to
improve its search through the genotype space by ‘walking’ the neutral path. In this way, neutral
evolution would influence the adaptation abilities of a species, its evolvability.
Neutral evolution can provide an explanation for the existence of the punctuated equilibria. In
the stable periods the population walks along the neutral path to create opportunities for adaptation
and higher fitness. Moreover, supporting evidence for the importance of neutral evolution comes
from the theory of RNA-evolution. RNA molecules are hoped to offer the building block of prebiotic evolution as these strings of base pairs can replicate themselves and act as some kind of
enzyme at the same time. The theory of neutral evolution and neutral networks could provide the
necessary stability of pre-biotic RNA-molecules, while retaining evolvability of the structure
(Huynen et al., 1996).
Kimura claimed that the amount of neutrality in the genome is more important for the direction
of evolution than (natural) selection. He proved mathematically that in many situations most of the
mutations that spread through a population are neutral and will not have any selective advantage.
Evolutionary change due to selection pressure would be a relatively rare occasion, almost only
occurring in reaction to the opportunities created by neutral evolution. Kimura’s claim is
controversial as it drastically reduces the influence of natural selection on the course of evolution.
I have discussed three issues concerning natural selection, i.e. adaptationism, punctuated
equilibria and neutrality, roughly defining the relation between the static environment and the
replicator. In the following section, the focus will be on the interaction between a replicator and the
living, dynamic, co-evolving world.
2.3
Selection by other organisms
Co-evolution can be described as the process of reciprocal evolutionary change (Janzen, 1980;
Thompson, 1994), which is a very general definition, applying to any case of mutually influencing
evolutionary processes. In this sense co-evolution includes reciprocal evolutionary change between
different replicator populations, but also between sexes or different subpopulations. As the
performance of the replicators depends on other evolving replicators, the environment that the
replicator has to match is continuously changing. Such a fitness landscape is often called a fitness
seascape.
Co-evolution only occurs when the involved replicator populations maintain long-term contact.
16
Under the continuous mutual selection pressure, associations between different populations can
evolve, referred to as symbiosis.
The relationship between the populations can be cooperative when there is a mutual benefit as in
mutualism. In a mutualistic relationship both species often evolve to facilitate the cooperation. Bees
for example want to get nectar from the flowers while flowers need the bees to fertilise their seeds.
Therefore both the bee and the flower have evolved all kinds of mechanisms to guide the bee
toward the flower.
However, most of the times co-evolution will be a consequence of a conflict of interests as in a
predator-prey relationship, in parasitism, in competition or in mate selection. In parasitism the
parasite profits from its host, just as the predator profits from its prey. In this conflict of interests the
parasite will try to take as much advantage of the other as possible, which the host will try to
prevent. The result is continuous mutual adaptation, resulting in an arms race of adaptive traits. In
competition both groups use the same resource, such as light, water, food or space. A special kind
of conflict exists between males and females in sexually reproducing populations, generally referred
to as sexual selection, which I will discuss in the next paragraph.
2.3.1
Sexual Selection
Evolution in diploid (sexual) species is sometimes called an exam with two papers: to be
successful, an individual does not only have to survive, it also has to gain mating opportunities with
the opposite sex (Noble, 1998). Males will try advertising themselves by emitting signals and
females will try evaluating these signals in order to choose the best mate.
The males and the females seem to have the same interest, i.e. to get as much offspring as
possible, but they do not on all accounts. As the amount of offspring for the female is usually
limited, her interests are in the genetic quality of her partner. The male however, does not have any
physical limitations on the amount of offspring he can have. His only objective is to be chosen by as
many females as possible, no matter their quality. He will try to signal to all females that he is the
best male she can get, while the female is trying to know from his signal whether he is right or not.
This competitive co-evolution sometimes causes an evolutionary arms race leading to outrageous
traits as for example the feathers of the peacock and the face of the Mandrill.
The apparent arbitrariness of sexual signals observed in nature made Darwin state in “The
descent of Man” (1871) that any trait could become subject of sexual selection provided a female
preference for it. If females would rather choose males with a certain trait than males without it, this
trait will become more frequent and eventually will take over the population. A process referred to
as runaway or Fisherian selection.
17
However, Zahavi (1975) showed among others that a trait needs have some reference to the
quality of a male to become an honest sexual signal. That is the only way in which it is possible to
avoid low quality males from ‘cheating’ and signalling a higher quality. Only then the trait can
become evolutionary stable.
2.4
Multi-level Selection
Richard Dawkins argued that it is wrong to see individuals as the replicators. Although he posed
the replicator theory as a generic principal, he identified the genes as the only replicators and that
the genes use organisms as a means to replicate. He stated that organisms are just the product of
cooperation of an enormous number of genes. They form a vehicle that has to win the war for the
replicators that built it, allowing them to replicate.
The theory of selfish genes is a very appealing and straightforward view on evolutionary
dynamics, but it is also a rather simplified one. The gene is positioned in the centre of the universe
and everything else is explained from its perspective. It ignores the fact that genes are not the only
replicators present.
At many different levels of organisation replicators are operational, from genes to cells to
organisms to groups of organisms. All these replicator levels bring along their own evolutionary
dynamics, and will all be influencing the evolutionary process. A computational model of Savill &
Hogeweg (1997) shows how selection between large patterns in a population can influence the
survival and reproduction of individuals. The extra level of selection even provoked selection
pressure for a higher death rate of the individuals. Beneath I will discuss two relevant issues that are
strongly related with multi-level selection.
2.4.1
Self-organisation in space
The spiral wave patterns in the Savill & Hogeweg model from the last section are a good
example of a process called self-organisation, which is the emergence of complex patterns from
simple principles or rules. Typically the emerging structure cannot be deduced from the principles
without going through the whole process. A famous theoretical example of this idea is the Game-ofLife by Conway, in which simple transition rules determine the state of a square on a grid in the
next time step, taking the ‘neighbourhood’ as input. This simple scheme produces stable and
moving patterns that cannot be predicted from the transition rules, even providing a platform for
universal computation.
It is crucial to self-organisation that each replicator is only able to access the information that is
18
present in its neighbourhood. As long as this ‘localness’ is accounted for, space can be anything,
from real space to conceptual space. Natural processes are always occurring in real space, which
therefore should be interesting to investigate for self-organising processes. Marée (1999)
constructed an extensive spatial model of a self-organising process to explain the behaviour of the
slime mould Dyctyostelium Discoideum. This is a species, which in its lifecycle takes the form of a
single cellular amoeba as well as a multi-cellular slug. The model showed that a population of
unicellular organisms following to simple local behaviour rules could organise to behave as a multicellular organism (slug/fruiting body).
Boerlijst & Hogeweg (1991) proposed self-organisation as a substrate for evolution. They stated
that the higher-level structures emerged through self-organisation can act as replicators themselves,
making the parameters determining the specific structure subject to evolution.
2.4.2
Time
Different timescales are involved in the production of animal behaviour. It is a common practice
to see processes taking place on an ecological and on an evolutionary timescale as separate. This
implies that in evolutionary processes the (ecological) population dynamics do not have any
influence on the process, while population dynamics infer that the species involved are strictly
monomorphic.
However, quite contrary to the common conviction, the dynamics of these two timescales do not
always add up. Savill & Hogeweg (1997) showed in their model that a very instable host-parasitoïd
system could be stabilised by including evolutionary change of the organisms. Although the time
scale of the evolutionary change is large, it can nevertheless influence the population dynamics. On
the other hand, the evolutionary change in the model could only be explained from the population
dynamics. Through self-organisation structures emerge with a much larger lifespan than an
individual that influence the evolutionary change of the replicators.
2.5
From signalling to communication
2.5.1
Communication
Communication, as defined by Jason Noble (1998), is proper signalling. This means that a signal
becomes communication as soon as both the sender and the receiver have acquired the
communicating behaviour or pattern through evolutionary adaptation. This is the case in most
human utterances, but also in numerous animal signals. Many examples are known of animals using
alarm calls to warn their conspecifics for predators or threats to chase them away. Traits that did not
evolve as a signal do not count as proper signals, excluding the spotting of a deer by a lion as
19
communication.
However, it is difficult to see why signalling would evolve through evolutionary adaptation, as it
may lower the survival chances of the signaller. The receiver gets crucial information for its
survival, as in the case of the alarm calls, whereas the sender gets nothing and only attracts attention
of predators. Why would an individual make a sacrifice for the sake of the group?
Such altruism is controversial as it shows the evolution of non-selfish genes coding for
signalling, which seems in direct contradiction with the selfish genes of Richard Dawkins.
Therefore it has been tried to explain such behaviours as being directed to relatives, so the sacrifice
would still be advantageous for the genes. Such kin-selection however can only account for a part of
the reported observations as the groups of individuals that profit from the signaller is often much
larger than just the relatives
To explain altruism other than by kinship, the theory of group selection was the prevailing
evolutionary paradigm until the 1960’s. The theory of natural selection was applied to groups and
individuals were argued to have evolved altruistic traits for the better of the group. The discovery of
the DNA in 1953 made many biologists realise that selection indeed only could take place on an
individual level and group selection was dismissed (Williams, 1966) as doing something for the
best of the group would not help your own genes proliferate any better. However, Száthmary &
Demeter (1987) showed that compartmentalisation could re-install group selection as a force in
evolution. Their stochastic corrector model showed that as long as the population is divided in
isolated subpopulations, the groups with more altruists perform better, causing the entire share of
altruistic individuals in the population to increase.
Self-organisation in space can provide an emerging subdivision in the population, making group
selection a feasible option. It is indeed well known for showing altruistic effects, e.g. in modelling
experiments on autocatalytic nets (Boerlijst & Hogeweg, 1991). On higher organisation levels,
selection can act inversely to the selection on the individual level (see section 2.4). In this way
organisms can be selected for to act altruistic as long as it is advantageous to the replicators on the
higher selection level.
Signalling could also very well be selected for through sexual selection. As described in section
2.3.1, emitting certain signals can be revealing about the quality of the signaller and therefore be
preferred by the other sex. In that case, the sender would not be altruistic, but would only bring
himself in danger in order to emit the right signal for the other sex.
2.5.2
Social learning
Social learning refers to the collection of all possible learning mechanisms to learn from
20
conspecifics, most importantly imitation learning (Galef, 1988). It is a mechanism that helps
organisms to cope with their environment without having to find out everything by themselves. The
system of common knowledge that arises as a consequence is what we will call culture.
Social learning is common in nature and can be observed in nature in numerous species. A
striking example is the blue titmouse (Parus caeruleus), which learns from conspecifics how to
open milk bottles in order to get the cream out, as observed by amateur birdwatchers in the 1920’s.
A comparable observation is done with a Japanese macaque (Macaca fuscata) that found out that
the washing of sweet potatoes makes them tastier. In the following years this knowledge spread
through the population until eventually the whole troop showed the washing behaviour (Kawai,
1965).
Social learning is susceptible to error, as the road from the signal of one organism to its
understanding and reproduction of a second organism is one of many dangers. Noise may
compromise the signal in every step: the signal may be badly emitted due to an incapable signaller,
noise from the environment may interfere, the perception can be biased by the physical properties of
the perception system of the receiver and the reproduction of the signal depends on the signalling
capacities of the receiver. All these factors might possibly obstruct clear transmission.
2.5.3
Cultural Evolution
As soon as signals are socially learned, they start leading a life of their own as a meme, a cultural
unit. They enter their own evolutionary process supervening on the communicating population
(Durham, 1990). Evolution of memes is often referred to as memetic or cultural evolution. Some
species come to accumulating multiple memes in a cultural system. However, such cumulative
cultural evolution, is almost only reported in songbirds, chimpanzees and humans (Boyd &
Richerson, 1996).
Cultural evolution is similar to Darwinian evolution (Dawkins, 1976). The memes adapt to their
host population (natural selection) or to other memes (cf. co-evolution). Higher levels of
organisation are present in the form of meme sets, languages and cultures. Mutation of memes is
caused by imperfect transmission, like mutations in genes. Their persistence is determined by the
willingness to use them of the individuals in the population.
Genes and memes co-evolve. If a meme would cause higher rates of reproduction, than genes
promoting the usage of this meme will be selected for. Consequently, the frequencies of both the
gene and the meme will go up. However a conflict of interests between genes and memes is
possible as well, because of the wider transmission possibilities of memes; memes can be
transmitted to any individual that is close by, whereas genes can only be transmitted vertically. A
21
process being very harmful to a gene, such as committing a suicide bombing might cause the idea
behind it to spread (horizontally) in a more successful way.
Gene-culture interaction might also take place at higher selection levels of cultural evolution as
socially learning populations tend to show spatial variation in the signal. The signals will almost
always be learned from neighbours and relatives and due to the decreasing ability for social learning
with increasing age learning for the larger part take place during childhood. As only a small and
unrepresentative fraction of the entire population will be close enough to act as a teacher, spatial
patterns might emerge, as has been shown to happen under some circumstances in a computational
model by Goodfellow & Slater (1986). Such self-organisation in space is known for its ability to
create extra levels of selection (see section 2.4.1), which raises the question, what could be the
impact of dialects on evolution.
The emergence of dialects through self-organisation in a socially learning population might
enable selection on dialects themselves, as soon as migration between them would be impaired.
However, at least within the songbird literature, there is an ongoing debate about whether such
impairment is present or not (see Slabbekoorn & Smith, 2002 for an overview). Craig and Jenkins
(1982) argued in a theoretical article, that would the existence of dialects cause reduced migration
across the border, the result would then be a cultural arms race towards higher complexity of the
used memes. I will discuss their line of thought in more detail in the next chapter.
2.6
Modeling evolution with individual-based simulations
By now it has become clear that the implication of the evolutionary principles reaches farther
than just optimisation. Here we will discuss the use of individual-based evolutionary simulation
models in general, and our model in particular.
What do we refer to when speaking of
individual-based
simulation
models?
Seth
Bullock (1997) defined simulations as “models
(...) which character of its dynamics is
determined experimentally, through allowing
the simulation to unfold over time”. Individualbased simulation models explore a minimal set
of low-level assumption that can lead to certain
high-level emergent phenomena. Drawing an
analogy between an emergent feature from a
Figure 2.1:copied from Noble (1998)
22
model and a natural phenomenon might provide us with ideas about the underlying causes of this
phenomenon (see picture). Evolutionary simulation models will show the evolutionary
consequences for a species, as a consequence of low-level assumptions about the interaction
between the individuals.
When modelling natural or cultural evolution with individual-based simulations, assumptions are
made about the individuals and their interaction with the environment (including other individuals),
to see what the consequences are for the gene or meme pool of the population. In this way it is
possible to make certain inferences on the evolutionary causes for certain traits that we observe in
natural species today. However, making the right assumptions is a hard job. Many assumptions that
have a large impact on the dynamics of the simulation are made implicit, often without a conscious
choice of the modeller. Moreover, it is inherent to evolutionary simulations, that the consequences
of many assumptions can only be known by running the simulation. Simulation models sometimes
are so much constrained, that they are pushing deterministically toward the evolution of the
expected behaviour (W.Y.W.I.W.Y.G., What You Want Is What You Get: Wheeler et al., 2002).
This trap can be avoided if the model is constrained by the properties of natural organisms. It is
informative to investigate what are the minimal requirements to make the outcome of the model
match the observations from nature. Of course, such a model still only shows a possible course of
evolution.
A powerful way to use evolutionary simulation models is the search for generic patterns in
evolutionary simulations. If simple local interactions can produce emergent patterns, than these
patterns can be expected to show up regularly in various processes and scales. An example of such a
paradigm system is the spiral wave (Savill & Hogeweg, 1997) that can be observed in many
different surroundings. Such patterns might form substrates for evolution (see self-organisation)
changing the selection pressure for the individuals in the population. Identifying more of such
paradigm systems might increase out understanding of the observable result of evolution.
In our simulation model, the individuals will learn from each other, introducing an extra
dimension to the dynamics of the population. As we want to use our model for the exploration of
bird song dynamics, we will have to get the constraints from the theory on songbirds. In the next
chapter, the literature on this subject will be discussed.
23