Author: Vladimir F. Levchenko (Lew@ief.spb.su)
Title: The Experience of Simulation of Evolutionary Process
THE EXPERIENCE OF SIMULATION OF EVOLUTIONARY PROCESS
Institute of Evolutionary Physiology and Biochemistry
of Russian Acad. Sci., St.Petersburg, 194223, Russia
The simulation model described below has been developed to
investigate the patterns and dynamics involved in the building of
evolutionary trees. The model is based upon darwinian theory.
The model can work with 12 types of environmental
conditions. Up to 9 populations can exist in any environment.
Every population is described by 25 features. Mutations are the
changes of features. The simulation program allows to draw
evolutionary trees on the screen of computer and to write the
results in a fail.
Fortran version of program has been created in 1987 for
PDP-11 and compatible computers and can be adapted for IBM PC.
The simulation model described below has been developed to
investigate the patterns and dynamics involved in the building of
evolutionary trees. The general model employed assumes only
certain specified ecological properties of populations and the
basic principle of interactions between them, and, hence, is
based upon darwinian theory.
While the ultimate objective of this simulation is greater
insight into the patterns produced by natural selection working
overlong periods of time, any simulation of evolution remains
constrained by inherent problems arising from the large number of
calculations involved and the resultant effects of small changes
in values at one time producing large changes in the system at a
later point in time.
It is important to note biological terminology used to
describe the work of model is a few metaphorical one. This is the
consequence of only some peculiarities of reality are enclosed in
The computer program was first developed in Fortran using a
RT11 system in 1987.
The program works by cycle. The operations on the each
cycles are related until 13 point of description of the model.
DESCRIPTION OF MODEL
We can not simulate all, it is necessary to predetermine the
properties and features of "model world" and the features of
object of the model.
1. 12 versions of "Environments".
2. Each environment is has up to 9 "Sub-environments"
(regions) each of which may be inhabited by only one population.
These 9 populations may be drawn from one or more species. Some
sub-environments can be free.
2.1. In the model we don't consider the quantity of
specimens of population and treat each population as having the
same number of individuals. More exactly: in environment can
exist doubles of some population. Each double we take into
account as independent population.
3. Any population is described 25 different "Features". In
the model all possible features are predetermined. Total number
of all possible features is 100 but description of any population
is some combination 25 features taken from this 100. For example:
dimension from 1M to 2M, 4 fins, predatory, roe...etc. (See
4. We call as "Property" the group of similar features. For
example: dimension (features here are: from 0.5 to 1, from 1 to
2, from 2 to 5,... for instance), extremities, type of
nourishment,.. etc. The total number of properties is 25 also.
Each population have one feature from each property.
5. "Mutations" is change of any feature(s) into
property(ies) in the model. Special tables exist in the model to
determine possible mutations into each property. For example: A
<-> B <-> C, where A,B,C are features into property. Note: to
transit from A to C for instance, it is necessary two step: A ->
B -> C. We can change the frequency of mutation on population
(i.e. mutation/population) by means special parameter of model.
6. The features, which can not exist in common we call "Non
compatible features". For example: skull does not exist, tooth
exist. Mutants which have as result of mutations such prohibited
combinations of features are abolished by using special table of
non compatible features.
7. Moreover. Each feature have value from 0 to 10. This is
adaptive value. For instance: the value of fins into water = 10,
but on the land = 0. The special matrix exist in the model and in
this matrix values of all features for all environment are set
using biological considerations. This are subjective values
certainly but it does not influence on results of simulation
which are interesting for us (for instance, the dynamic of grow
of evolutionary trees).
8. The "Survival" of population = summa of all (25) values
of features of population. Moreover, the survival = 0 if any one
(two...) value(s) = 0. (We suppose in this case that population
have feature(s) non compatible with environment).
We calculate in the cycle of work of program survivals of
each population for each environment, although any population
exist only into one environment in fact. This is necessary to
decide the question about a transition of population on the each
cycle. The survival of population may change after mutation.
9. The transition of population from initial (for it)
environment to other environment may take place if survival of
this population is more in other environment. But for realization
of transition it is necessary:
a) new environment has free sub-environments,
b) or the survival of population (which "wants" to transit)
is more then the survival some population in new environment.
9.1. We can simulate also additional difficulty for
transitions. When survival of population which "want" to transit
decreases on some quantity (special parameter).
10. Competition into environment: in each cycle of work of
program the population which have minimal survival is replaced by
population which have maximal survival (this is into each
environment). The replacement must occur if there are N>=2
populations in an environment, and if survivals of the
populations are not equal.
10.1. "Non hard competition". This is coexistence of
populations which have survival differed on the some quantity.
The simulation of competition is not produced if survivals differ
from each to other less then some quantity (special parameter).
11. If any region in any environment in empty, then the
population which has the largest "summa" of all populations in
all environments will colonise the empty region.
12. At the beginning we put usually one population into some
13. The program work by cycles. The description of job of
all acts in cycle is below.
The first cycle: we put one population in some environment.
After this in each cycle we have:
b) abolishing of populations which have non compatible
c) calculation of survivals for all populations into each
d) transitions (if it is possible),
e) competition in each environment,
f) filling of all free positions into partial filled
environment (note: only into environments into which some
quantity of already populations exist),
g) return to a),
The simulation, hence, models certain basic evolutionary
2. Negative selection of non-viable individuals.
3. Migration and colonization processes.
4. Competitive exclusion of a species by another species.
Computer experiments show that some peculiarities
of competitive interrelation are very important. In particularly,
the capacity of coexistence of populations having difference
survival in one environment is essential. The special parameters
have included in the model to investigate similar effects.
THE RESULTS AND DISCUSSION
Since we were looking at large-scale patterns in
phylogenetic evolution, we needed to chose an appropriately
high-level of taxonomic grouping for our study. We selected the
phylum Chordata to provide data and to give us a comparative
phylogeny with which we might evaluate our results. Of course,
this programing complex suits completely for many other (and non
biological even) objects.
The "Mutations," hence, in our modeling correspond to major
changes inadaptive traits. They represent adaptive breakthroughs
such as major changes in size, or in the nature of respiration.
In nature, of course, the actual time taken for such events is on
the order of .5-1 million years.
It should be noted that these are "Macromutational" changes
and the model therefore deals with the consequences of such major
adaptive shifts, rather than their causes.
Our modeling of the environment was handled in a similarly
broad manner: for example we posited the existence of 4 aquatic,
3 fresh water, and 3 land environments. To this, we added 2
environments in which an amphibial adaptation would be optimal.
We began the simulation of evolutionary change in our model
with a population having characteristics similar to Lanceolatus.
At each iteration of the program, processes of macromutation and
migration were simulated. Hence, given the rate for the evolution
of these kinds of adaptive change, each iteration was the
equivalent of between .5 and 1 million years. Generally, the
simulation was carried out through 40 - 60 cycles, thereby
modeling evolutionary events occurring over periods of some
200-300 million years.
The simulation of macro-evolutionary patterns studied here
demonstrated that certain unexpected effects, such as
"Reimigration of near descendants" and some others (see below)
may be observed in the patterns produced by evolutionary
processes. This result demonstrates that the "Reimigration of
near descendants" is the significant factor of evolution. If to
say by another words, the results of the simulation demonstrated
that patterns of allopatric speciation followed by competitive
replacement in the original region is a common pattern over
evolutionary time and has major effects upon the nature of the
evolutionary trees which the program generates.
Other interesting results have been obtained in experiments
concerning to the change of parameters of competition. We uncover
hard competition provokes such processes when the ones of
specialization predominate processes of filling out of free
environment. This process appears to be a function of the
mutation rate. More exactly: the increase of frequency of
mutations promotes in the intensification of this tendency.
In result of this experiments we obtain that in case of hard
competition the lateral branches of evolutionary trees don't grow
almost and are the trunk composed from biological forms the
survival of them increases gradually. Many environments remained
unfilled. In biological terminology this is means that under
these conditions, the amount of diversification is minimized as
is the rater at which new habitats are colonized. Decreased
competition into environments also tends to increase the
diversification and the rate of migration and colonization!
The simulation also allows us the opportunity to investigate
how evolutionary trees constructed from a sub-set of the total
number of traits differ from those in which all trait states are
know. Construction of evolutionary trees from partial data, of
course, is the rule in biology, with areas of study such as
paleontology, genetics and evolutionary physiology having to work
with less than complete knowledge of the total trait assemblage
of the organisms studied.
Since the simulation contains a "total" data set, we may
then easily compare the "true" phylogenies with others
constructed from incomplete data. Here, we followed the usual
convention of establishing ancestor - descendant and "sister
species" relationships by minimizing the number of modified trait
states. We found that trees reconstructed from incomplete
information differed significantly from the "true" trees.
Moreover, this is means that trees are built for various
biological sciences need not consider as ones differed
paradoxically but can be described as trees completing each
All in all, however, the model of macro-evolutionary
processes produced by our program, tends to confirm certain basic
assumptions generally used in reconstructing the paleontological
1. The probability of return to ancestral forms is very low;
evolution is non reversible.
2. The overall pattern of branching in evolution is a result
of random changes, which have not significance for survival of
3. Some peculiarities of ecological evolution is
predetermined. This is the consequence of the same results in
particularity concerning to filling of environments may be
obtained in case of various parameters of model (frequency of
mutations for instance).
4. Highly specialized species seldom produce descendant
Levchenko V.F. "A physic-ecological conception of biosphere
evolution." [in Russian] In Proceeding of the 1990 7th
International Symposium "Simulation of system in biology and
medicine" (Praha), 59-63.
Levchenko V.F. 1992."Directedness of biological evolution as a
consequence of the biosphere development [in Russian] Zhurnal
obshchei biologii Vol. 53, no 1(Jan-Feb): 58-70.
Levchenko V.F. and Menshutkin V.V. 1988. "Simulation of
macroevolutionary process" Zhurnal evolucionnoi biokhimii i
fiziologii. Vol. 23, no. 5(Sep-Oct): 668-673.
Levchenko V.F. and Starobogatov Ya.I. 1990 "Succession changes
and evolution of ecosystems." [in Russian] Zhurnal obshchei
biologii Vol. 51, no 5(May): 619-631.