% Carlo C. Maley
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% Give data to appear in the title section
% \fullpaper
 \title{Comparing causal factors in the\\diversification of species}
 \author{C. C. Maley\thanks{This work was supported in part by NDSEG
grant DAAH04-95-1-0557, the generosity of the MIT AI Lab and the
Harvard Herbarium.
I would like to thank Rod Brooks, Michael Donoghue, Hal Caswell, Dave
Cliff, Bob Berwick, and Miller Maley for their guidance
and support.}
                \affil{University of New Mexico, Department of Computer Science}
        }
 \date{18 November 1998}
 \abstract{

What have been the most important factors in the diversification of
life?  A configuration (individual-based) model ({MoD}) was constructed
to examine the origins and maintenance of species diversity through
time.  The model represents a species as a connected component in a
graph of the potential mating relationships between organisms.  This
allows us to detect speciation events and track species diversity over
time.  We may thereby test many ``untestable hypotheses'' for the causes of
diversification.  The results suggest that much of the emphasis placed
on the evolutionary innovations, in resource utilization and predation
interactions, in order to explain the diversification of a group, is
flawed at best.  Furthermore, habitat heterogeneity had little impact
on species diversity.  Instead, the model points to the importance of
geographical isolation as a primary causal factor for diversification,
along with the evolution of specialization and sexual selection, in
the form of positive assortative mating.  The influence
of assortative mating is particularly interesting because it is opaque
to the methods of paleobiology.  

}
% Here is the document

\begin{document}

\maketitle

\section{Introduction}\sectlabel{introduction}

Evolutionary biology is characterized by a relatively solid
understanding of microevolutionary phenomena, but little understanding
of macroevolutionary phenomena.  One of the most important open
questions of macroevolution is what have been the most important
causal factors that have driven the diversification of species. 

For clarity of discussion, we will consider a species to be a
reproductively isolated gene pool.  This is the dominant understanding 
of species in biology and is due in large part to the work of Ernst
Mayr\cite{mayr42,mayr63,mayr82a}.  The question then becomes what
factors tend to split gene pools apart and preserve them from
extinction?

Most of the hypotheses that attempt to answer this question are both
hard to test experimentally, and difficult to resolve in the fossil
record.  Thus, a model of diversification ({MoD}) has been developed
that instantiates enough of the
components of biological life to test and compare hypotheses for the
diversification of life within a common framework.

\section{Hypotheses}\sectlabel{hypotheses}

The hypotheses for the diversification of life fall into two broad
categories: abiotic and biotic explanations\cite{bent90}.  

\subsection{Abiotic causes of diversification}

Hypotheses for the diversification of species that depend on physical
characteristics of the environment inevitably make reference to
topology and/or climate.  The conventional wisdom in theoretical
biology holds that new species arise primarily from allopatric
speciation.  That is, populations of a species are separated
geographically (topologically in the broad sense) and slowly diverge.
This genetic divergence can emerge out of random genetic drift or
differential selection between the two populations, as long as there
are few enough migrants between the populations to avoid genetic
homogeneity.  Regardless of geographical separation,
 if two subpopulations experience different
climates, or more generally, different selective pressures, it is
reasonable to expect them to diverge and perhaps eventually become reproductively 
incompatible.  These are the two basic abiotic hypotheses.
Experiments on laboratory organisms show little effect of
geographical separation on hybrid fertility between members of the
subpopulations.  However, such experiments do demonstrate
that when allopatry (or any other manipulation that prevents viable
hybridization between subpopulations) is paired with divergent
selection, reproductive isolation does tend to evolve\cite{rice93}.

Cracraft\cite{crac85} has proposed that the fact of environmental complexity
may not be as important in the evolution of diversity as is change in
the environment.  He does not require the change to be in any one
particular direction, as long as the environment is dynamic.
Vermeij\cite{verm87} also cites changes in climate and topography as being
an important engine of speciation.  Perhaps these changes have
repeatedly isolated and then remixed populations, providing many
opportunities for the separation of gene pools.  A
simpler variation on this idea is Benton's suggestion\cite{bent90}
that perhaps the physical environment has become more complex and
fragmented over time.  This should tend to fragment the populations
and so boost speciation.  

\subsection{Biotic causes of diversification}

Biotic causes for diversification have been more popular in the
literature as compared to abiotic causes.  Generally, biologists and
paleobiologists have engaged in a search for the evolution of some ``key innovation''
that catalyzed the diversification of their group of interest\cite{crac90,jabl90,schl96}.  Of
course, the set of characters that have been proposed as key innovations has 
been biased by the set of characters that can be observed in the
fossil record or inferred from phylogenies, as well as the measurement 
of diversity under consideration\cite{hunt98,jens90}.  

Raup\cite{raup82} observed a decline in the extinction rate of families
over time.  This should also lead to the observation of increasing
diversity over time.  Van Valen\cite{vanv84} argues that the observed
decreases in extinction rates may be due to the decrease in the overlap 
between species' niches and so a reduction of extinction through
competitive exclusion.  Niche overlap can be reduced in two ways.
Species may reduce the ``size'' of their niches through
specialization, or they may find and shift to the use of new,
unexploited resources. Benton\cite{bent90} emphasizes both of these
factors, specialization and innovation, as the most important causes
of diversification.  In fact, an innovation that helps to exploit a new set of 
under-utilized resources might lead to an increase in diversity
regardless of any change in extinction rates.

There is another realm of biotic causes of diversification that has
only recently received much attention.  Since species are determined
by reproduction, and reproduction involves a complex constellation of
both behaviors and physical traits, its stands to reason that
behavioral changes should have had an impact on diversification.
Unfortunately, it is all but impossible to recover behavioral
innovations from fossil data, except to the extent that the morphology 
of organisms evolved to support those behaviors.
Todd and Miller\cite{todd97,todd91} have shown in simulations that sexual
selection can quickly fracture populations into mutually exclusive
mating groups.  Other models\cite{liou94,fial92} have shown that positive assortative
mating can evolve under divergent selection on some other trait even
when the populations are mixed (sympatric).  All this suggests that
positive assortative mating may have played an important role in the
diversification of life. 

\section{Model}\sectlabel{model}


We approach model design for theoretical biology through a focus on
hypothesis testing.  All models are simplifications.  The difficulty
of modeling lies in the decisions of what to include and what to
exclude from the model.  We endeavor to include only those dynamics
which are necessary to test the hypothesis.  More cannot be easily
justified.  The hypotheses for the diversification of life require an
implementation of the following phenomena: organisms, reproduction,
heredity, and mutation.  This much is necessary to implement
microevolution.  Furthermore competition is required to model
extinction through competitive exclusion.  Hypotheses about niches, or at least
resource utilization, dictate the inclusion of discriminate predation and the
phenomenon of specialization.  In addition, geographical barriers,
migration, and habitats all feature prominently in hypotheses of
diversification. Finally, we are interested in species diversity.
Thus, we must have some representation of species as collections of
organisms.  As we have adopted the reproductive concept of
species\cite{mayr42,mayr63,mayr82a} we are obliged to implement sexual 
reproduction and reproductive barriers.  Furthermore, if one assumes
that offspring are generally not reproductively isolated from their
parents, then speciation, and indeed extinction, require the phenomena 
of death.  Some parents must die in order to fragment gene pools.

{MoD} is a configuration\cite{casw92} model (a.k.a.,
individual-based or agent based model) at the level of the organism.
It bears a strong similarity to Echo\cite{holl92,holl93,hrab97,jone97} 
as well as a number of other models in Artificial
Life\cite{lind94,saru94,herr97}.  Each organism is described by 4 bit
pattern ``chromosomes'' where each 
bit represents a heritable character.  The first three ``ecological'' chromosomes
structure the predator-prey interactions between the organisms.  If the
prey template chromosome of one organism matches the bit pattern of
the predation resistance chromosome of another organism, the first
organism may prey upon the second.  This interaction is further
modified by the generalism chromosome in the predator.  The bits set
to 1 in
the generalism chromosome flag which loci in the prey template
chromosome are ignored when checking the match between predator and
prey.  The combination of the prey template and generalism chromosomes 
is called the search template of an organism. An organism with all 1's in its generalism chromosome
would be a complete generalist.  While an organism with all 0's would
be an extreme specialist, only able to digest organisms with one
particular predation resistance genotype.  The autotrophs (a
characteristic encoded by a non-mutating flag in each organism) match
their search template against a bit pattern
in the environment that represents the characteristics of the habitat
in their patch.  For the particular details
of the predation algorithm as well as the model in general, see
\cite{male98}. Competitive exclusion arises from the fact that the
autotrophs that better match their environment, and the heterotrophs
that better match the prey population of their patch will be more
likely to gather food and avoid starvation in any give time step than
their more poorly adapted competitors.

The fourth and last chromosome is the reproductive chromosome.  This
encodes all of the characters that influence reproductive isolation
between organisms, such as mating time, mating behaviors, reproductive 
organ morphology, gamete morphology, etc.  Reproductive barriers are
modeled by the rule that organisms can only mate with each other
if their reproductive chromosomes differ in at most 1 locus.  

Given
this implementation of reproductive barriers, we can unambiguously
identify the gene pools or species amongst the organisms.  if we
consider a graph where each extant reproductive genotype is a node and 
there is an edge between the genotypes that can mate (within
hamming distance 1 of each other), then a gene pool or species is just 
a connected component in this graph.  Over the generations, genes can
flow within these connected components but not between them.  A
speciation event occurs when all of the organisms bearing a particular 
reproductive genotype die, and thereby disconnect one of the
previously connected components\footnote{There are several strange
consequences of this implementation of reproductive barriers.  The
mating relationship is non-transitive.  Thus, while two organisms may
be members of the same species, they may not be able to produce
fertile offspring.  However, if two organisms can produce fertile
offspring, they must be members of the same species.  In addition, a
speciation event, the severing of a connected component of the
reproductive graph through the
removal of a node, may often lead to the creation of more than one
daughter species.  In other words, speciation events are not
necessarily just bifurcations.}.  While all of the ecological
chromosomes consist of 32 bits, the
reproductive chromosome is represented by 64 bits.  We doubled its size 
so as to reduce the chance that two species would coalesce into
one\footnote{The coalescence of species in the history of life is
thought to have been a rare event, but little data exists which bears
upon this issue.}.

The organisms are distributed on a 4 by 4 grid of patches, with each
patch able to hold 2048 organisms.  All ecological interactions occur
between organisms within the same patch.  Every organism is given a
chance to eat in each time step of the model.  If an organism fails to find 
a prey organism in its patch, it dies of starvation.  If an organism
survives for three consecutive turns, consuming three prey organisms,
it is given the chance to find a mate in its patch and reproduce.  The 
chromosomes of the offspring are constructed through two-point crossover
of the parental chromosomes in conjunction with a Poisson mutation rate
of on average 1 bit flip for every 10 new genomes.  The only migration 
between patches happens when a new organism was born.  New organisms take
a random walk of 0-2 steps to locate their new patch.  This random
walk is modified by a ``migratory barrier'' parameter associated with
each patch representing geographical obstacles to migration.  The
migratory barrier of a patch is a real number between 
0 and 1 which encodes the probability that an organism will be
prevented from entering the patch on a step of its random walk.  If
the new organism tries to settle in a patch that is already
full\footnote{Plants are not allowed to fill more than half of a patch 
(1024 organisms) so that they cannot compete with the animals for
space.} it has a 50\% chance of displacing (and killing) one of the
resident organisms of its type (autotroph or heterotroph).

\section{Testing the model}

\Figwide[htb]{barriers}
{\includegraphics[width=6in]{barrier-climate-biodiv-bw.eps}}
{The effect of migratory barriers and habitat heterogeneity on
biodiversity.  The means of 50 runs in each condition are shown along
with standard error bars.  Migratory barriers have a strong impact on diversity only
when those barriers are high.  Habitat heterogeneity appears to slightly
increase diversity but the effect is not statistically significant.}


The model was subjected to four tests in order to determine if its
ecological and evolutionary dynamics were notably unrealistic as
compared with accepted theory.  We tested to see if the model
demonstrated predator-prey population oscillations, trophic cascades,
competitive exclusion, and adaptation.

Stable predator-prey oscillations do not appear at all three trophic
levels unless the implementation of predation is stochastic and there
is some representation of competitive interference between predators.
This may be modeled as a spatial distribution within a patch such
that not all predators may pursue a particular prey organism.  We
chose to implement this restriction by the following equation.
 \begin{equation}
 Pr[\text{predation}] = ce^{-dn - m}
 \end{equation}
where $c$ is the {\em prey-location\/} parameter, $d$ is the {\em
predation-distribution\/} parameter, $n$ is the number of times that
the prey organism has escaped predation this time step (simulating
competitive interference of the predators), and $m$ is the number of bits that
mismatch between the predator's search template and the prey's
predation resistance characters.  When the {\em predation-distribution\/} is 0, there is
effectively no spatial structure.  With this elaboration,
predator-prey oscillations are stable at all trophic levels over a
wide range of parameter settings.

The model also exhibits a trophic cascade\cite{boot97}.
Trophic cascades are observed in the population levels of a species when its
predator and then its predator's predator are introduced to the
ecosystem.  In the absence of predation, a species should expand until 
its population reaches the carrying capacity of its environment.  When 
a predator is introduced to the ecosystem, the prey species population 
generally declines to a level below the carrying capacity of the
environment.  Finally, when the predator's predator is added to the
ecosystem, the predator's population is reduced, thus allowing some
recovery in population levels of the initial prey species.  This
cascade effect is readily observable in {MoD}.

Competitive exclusion also occurs in the model.  The model was seeded
with two plant species, one slightly better adapted to the habitat (at
1 locus).  The model was tested with the inferior competitor's
search template matching the habitat bit pattern at 1, 8, 16, 24, 28
and 31 loci.  In each case, the superior competitor's search template matched the 
habitat in one additional locus.  The model was seeded with equal
population sizes of each species.  A competitive exclusion event was
recorded if one species expanded to 90\% of the 
total population in less than 2000 time steps.  The model was run 100
times for each of the 6 competitions.  In all cases, the superior
competitor excluded the inferior one.

The previous experiments were carried out in the absence of
evolution.  The mutation rate was set to 0.  In order to test that
simple adaptation occurs in {Mod}, the model was initialized with an
autotroph that matched its habitat in 24 out of 32 loci.  This is the
expected proportion of matches if both the prey template and the
generalism loci were evolving randomly.  Adaptation should appear as a 
increase in the degree of match between the plant and its environment
above the level for random mutation.  The model was run 32 times for
mutation rates of $0.02, 0.11, 0.155, 0.2, 1.1$ and 2 bits flipped on
average per genome.  In all cases the plants evolved adaptations to their
environment as determined by a one-tailed t-test relative to neutral
evolution ($p < 0.001$).  



\section{Experiments and results}\sectlabel{results}

\Fig[htb]{summary-hom}
{\includegraphics[height=3in]{div-sum-hom.eps}} %[width=248bp]
{A summary of manipulations to species diversity.  The
chart shows the manipulations in the context of a homogeneous environment
with no migratory barriers. Each
effect has been normalized by its relevant baseline. The bars
indicate the magnitude of an effect relative to the baseline.  The
light grey
bars indicate that the manipulation had no statistically significant
effect.  Black bars indicate a significant effect, with $p < 0.05$.}

\Fig[htb]{summary-het}
{\includegraphics[height=3in]{div-sum-het.eps}}
{A summary of
manipulations to species diversity in the context of a heterogeneous environment and $0.95$
migratory barriers.  Here the baseline is a heterogeneous environment with $0.95$
barriers.  In the case of the
manipulations to the barriers, the relevant baseline is a
heterogeneous habitat with no migratory barriers.  Again black
indicates a statistically significant effect with $p < 0.05$.}
 

{MoD} was used to test ten different hypotheses for the
diversification of life.  In all cases, the model was initialized with 
three genetically homogeneous species (plant, herbivore, and
carnivore) and then run for 5000
time steps.  The number of surviving species was recorded at the end
of that time.  The model was run 50 times under each parameter
setting.  In most cases, the baseline for comparison was the result of 
running the model with no migratory barriers and the same habitat
pattern in every patch (a homogeneous environment).  Averaged over 50
runs, the baseline condition results in only 2.5 surviving species.
Furthermore, most experiments were also run under ``heterogeneous
environmental conditions.''  This meant $0.95$ migratory barriers and
the random flipping of 8 bits in each habitat pattern (resulting in an 
expected average of 10.3 bits different between any two patches).
Under the heterogeneous environmental conditions, an average of $9.32$
species are observed after 5000 time steps. 

The first two experiments examined the effect of geographical
isolation on diversity and the interaction of geographical
isolation with habitat heterogeneity.  The model was run under $0,
0.5, 0.9, 0.95$ and $0.99$
migratory barriers.  The third experiment examined the effects of
habitat heterogeneity by flipping 8 bits in every patch before running 
the model.  The results of these experiements can be seen in
\fig{barriers}.  The following experiments on abiotic factors tested
Cracraft's\cite{crac85} and Vermeij's\cite{verm87} theories that
change in the abiotic environment has been an important cause of
diversification.  In the fourth experiment, the habitat bit patterns
of the patches were independently modified by flipping a bit, selected randomly, once 
very 156 times steps in each patch.  This meant that by the end of
5000 time steps, the habitat in each patch would have changed 32
times.  The fifth experiment varied the migratory barriers of all the
patches over time so that they followed a sine wave with a period of
500 time steps, a maximum value of $1.0$ and a minimum value of 0.  In this
way the subpopulations were repeatedly isolated and mixed.  The sixth
experiment involved allowing the migratory barriers of the patches to
individually and independently follow a random walk between 0 and 1
with increasing or decreasing steps of $0.05$ every time step.

The seventh experiment examined the effect of allowing evolutionary
innovations with the potential for species to capitalize on
unexploited resources in their environment.  A restricted innovation
condition was modeled by
 preventing mutations in 10 loci of the generalism chromosome
(set to 0), 10 loci of the prey template chromosome (set to 0), and 10
loci of the predation resistance chromosome (set to 0).
This was then compared to evolution when mutations, and thus
adaptations, were allowed in all 32 loci of those chromosomes.
Similarly, the eighth experiment examined the effects of the evolution
of specialism by fixing 10 bits of the generalism chromosome (set to
1).  In this restricted specialism condition, all organisms were
forced to be generalists in those 10 loci.  This was compared to the
case where species could evolve specialism in all 32 loci of their
generalism chromosome. Finally, the ninth and tenth experiments
examined the effects of assortative mating on diversification.  Both
positive and negative assortative mating were examined.  Assortative
mating was modeled by allowing an organism to consider up to 8
potential mates in its patch.  Each of these potential mates were
still required to differ from the organism in no more than one locus
of their reproductive chromosome. In addition to this, the
organism compared its predation resistance chromosome characters---a
proxy for observable phenotypic characters---to those of its mates and 
either selected the most similar (positive assortment) or most
dissimilar (negative assortment) of the candidates for mating.  The results off all these
experiments are summarized in \fig{summary-hom} and \fig{summary-het}.


\section{Discussion}

The first surprise was that the increased capacity for innovations in
resource utilization had no significant effect on diversification.
This was the dynamic that Benton\cite{bent90} thought best explained
the increasing diversification of life.  The same experiment was run
with as many as 28 of the 32 loci fixed, restricting adaptive space
dramatically.  Yet, even this extreme restriction does not
significantly lower diversity levels.  Perhaps, in the context of no
migratory barriers and environmental heterogeneity, diversity is so
low in any case that evolving with just 4 bits in the ecological
chromosomes is still not particularly restrictive.  However, even in
the context of $0.95$ migratory barriers and a heterogeneous
environment, which results in an average of $9.32$ species when there
are no restrictions on innovation, these extreme restrictions have no
statistically significant effect at the $p = 0.05$ level (diversity =
$8.08$).  This results suggests that we should question one of the
basic assumptions of the key innovation perspective.  Why should
expansion in adaptive space stimulate diversification?  There is no
direct relationship between adaptive space (a theoretical construct in 
any case) and speciation.  Speciation depends on reproductive
barriers.  We should certainly expect that key innovations that modify 
characters involved in reproduction, and perhaps even geographical
isolation, should have a strong impact on diversification.  But it is
much more of a stretch to think that the availability of new resources in and
of themselves should stimulate diversification.  

This is probably a good time to visit some of the caveats of the
model. First of all, the model is extremely simple and abstract.  At the base of
the food web there is only one resource, energy.  The physical
environment is not rich by any stretch of the imagination.  The biotic 
environment is only rich to the extent that the organisms diversify.
Second, the spatial, population, and temporal scales are far below the
scales at which most Paleobiologists work.  With only 16 patches, a 
maximum of 32K organisms, and 5000 time steps (roughly 2000
generations) the model represents evolution and ecosystems on a
regional and millenial scale at best.  Perhaps at geological scales,
there are key innovations that spark adaptive radiations in clades due 
to some novel way of utilizing resources.  Third, it should be noted
that we have said nothing about morphological diversity.  We have
strictly considered only the reproductive concept of species.  In
the absence of a better understanding of the genetic basis of
speciation, all of the diversity dynamics of the model have taken
place on a binary hypercube (the space of possible reproductive
genotypes in the model).  

These caveats may also help to explain the difference between
Rice and Hostert's\cite{rice93} findings that habitat heterogeneity had a strong
effect on reproductive isolation and the model's result that habitat
heterogeneity had little effect on diversification.  {MoD's}
representation of a habitat is, again, extremely simplified.  It only
directly affects the autotrophs.  Yet, if one looks closely at Rice
and Hostert's review, we see that divergent selection was only
effective at producing reproductive isolation when the subpopulations
were physically isolated, the hybrids were removed, or the divergent
selection was particularly intense (e.g., 95\% of the organisms were
killed each generation).  In the later case, reproductive isolation
arises because the reproductive characters ``hitchhike'' along with
the characters that are being intensely selected.  Selection is too
intense and quick for recombination to allow the reproductive traits
to evolve independently of the selected traits.  
Simple disruptive selection on an arbitrary character does not tend to 
produce reproductive isolation\cite{rice93}.  Most of the experimental cases where
divergent selection leads to reproductive isolation are not
represented in the experiments on habitat heterogeneity in {MoD}. The
one exception is the case of divergent selection in allopatry
(physical isolation).  In {MoD} there was one case, when the migratory 
barriers where $0.9$, that habitat heterogeneity had a significant
effect on diversity levels ($p < 0.05$).

Let it not be said that niche space has no influence over diversity.
Under conditions of habitat heterogeneity and $0.95$ migratory
barriers, specialization does have a significant impact on diversity.
When species are allowed to evolve specialism at all loci of their
generalism chromosome, the diversity jumped from $6.7$ to $9.32$.
However, specialization only became important in the presence of
migratory barriers and divergent selection. The introduction of heterogeneous habitats
with migratory barriers effectively creates more (geographically
protected) niches upon which species might specialize.

{MoD} exhibits a striking influence of geographical
barriers on diversification.  Simply increasing the migratory barriers 
to high levels led to a dramatic increase in the number of species.
This matches the dominant theory of speciation.  Most speciation
is allopatric, where populations are physically separated and slowly
diverge to the point of reproductive isolation.  Note that
\fig{barriers} shows that the relationship between migratory barriers
and diversity is non-linear.  This is why the experiment with randomly 
fluctuating barriers produces significantly more species than its
baseline where the all the migratory barriers were held constant at
$0.5$.  

Of particular theoretical interest is the effects of positive
assortment.  Such behavioral characteristics are difficult to recover
from fossil data and so have gone largely untested as factors in
speciation. Positive assortative mating significantly increases
diversity in {MoD} under both homogeneous environmental conditions
with no migratory barriers, and under heterogeneous environments with
$0.95$ barriers.  Positive assortment acts as a proxy for geographical 
separation.  The populations tend to fracture into the clusters of
similar phenotypes (whatever characteristics organisms are using to
select their mates) with little mating between these subpopulations.
The subpopulations may then diverge to the point of reproductive isolation.
  
\section{Conclusions}\sectlabel{conclusions}

The results of the model must be predicated on acceptance of the
simplifications in the abstractions and their implementations in the
simulations.  The results only speak to diversity as identified by
isolated gene pools.  But as such, {MoD} suggests that geographical
isolation has been of central importance to the diversification of
life.  Secondarily, positive assortment and specialization may have
played important parts in the drama.  In addition, the results help to
focus our thinking about key innovations.  There is no particular
reason to think that an innovation related to resource utilization
should spark diversification.  Rather, innovations that affect
reproduction or migration should take primacy in our theories for the
causes of an adaptive radiation.  These predictions should be tested
with laboratory experiments\cite{rice85,ehrm65,deol80}, perhaps with
{\em Drosophila\/} or {\em Saccharomyces},
as well as comparative phylogenetics\cite{mooe97,sand96,sand94,slow90,slow91}.

The study of complex systems thrives on the application of knowledge
in one field to provide insights and techniques for the study of
another.  The processes of evolution have been imported into computer
science in the form of evolutionary algorithms (EAs).  But before we try to
extend these results to evolutionary algorithms, we should pause and
consider the basis of the analogy and the disanalogy between
biological and artificial evolution.  Most of the work in EAs has been 
focused on finding a good or even optimal ``solution'' to a particular 
problem.  This contrasts with biology and models of biology like {MoD} 
in which the only ``problem'' is to survive and reproduce.  In other
words, the fitness function in {MoD} and in biology is endogenous.
Secondly, most EAs, with a few exceptions\cite{hill92,sumi94}, lack coevolutionary dynamics and so have a static
fitness function.  Finally, to the extent that artificial evolutionary
systems have been used to examine
diversity, they have tended to focus on genotype
diversity\cite{deb89,beda92,saru94,hrab97,jone97} rather than species
diversity.

In most formulations of EAs, diversity is of interest as a mechanism
for searching solution space to find many good solutions, not just the 
best.  This is essentially a genotype diversity issue.  However, one
of the main lessons of genetic algorithms (GAs) for machine learning is that 
populations can often search solution spaces more efficiently than
individuals.  Species diversity may thus form the basis for efficient
search of solution space.  Deb and Goldberg have already applied
positive assortative mating to GAs in order to locate all the local
maxima in the fitness landscape.  They had particular success when
they combined positive assortative mating with ``fitness sharing''
which negatively related a solution's fitness to the population
density of its local in solution space. The addition of fitness
sharing effectively imposes a sort of niche space on the solution
space by locally limiting the reproductive resources in solution
space. Our specialization results would suggest that reducing the
distance across which fitness must be shared should boost the absolute 
number of ``species'' in the GA.  However, it is unclear whether
packing more ``species'' next to each other would improve the GA's
ability to locate the fitness optima.  Adding geographical separations 
between subpopulations in GA's is a well known technique for helping
to prevent premature convergence.  {MoD} was primarily developed as a
tool for theoretical biology, to investigate macroevolutionary
dynamics.  Much work remains for the future to translate those insights
about biology to other fields.  

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