Author: Vladimir F. Levchenko (Lew@ief.spb.su) Title: The Experience of Simulation of Evol

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====================================================================== Author: Vladimir F. Levchenko (Lew@ief.spb.su) Title: The Experience of Simulation of Evolutionary Process ====================================================================== THE EXPERIENCE OF SIMULATION OF EVOLUTIONARY PROCESS Vladimir F.Levchenko Institute of Evolutionary Physiology and Biochemistry of Russian Acad. Sci., St.Petersburg, 194223, Russia E-mail: Lew@ief.spb.su ABSTRACT 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. INTRODUCTION 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 model. 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. We have: 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 below, moreover). 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 one environment. 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: a) mutations, b) abolishing of populations which have non compatible features, c) calculation of survivals for all populations into each environment, 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), etc. The simulation, hence, models certain basic evolutionary phenomena: 1. Mutation. 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 other. 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 record. 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 species population. 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 species. REFERENCE LIST 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.

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