Presentation of the phenomenon
 
 

Origin of the study
 

As the name of the model explains it, it has been created by the American biologist Alfred Lotka and  the Italian mathematician Vito Volterra. Proposed in 1925, its objective is to simulate interactions between predators and prey in an ecosystem.
It take account by the use of 4 parameters of :


 

The equations


 
 

The model is discribed by two equations.
In order to be more concretened, we will use rabbits as prey and foxs as predators.
R represents the population of rabbits and F for foxs.
 
 

Studies of the critical point
It is easy to find two different points whose coordinates are :
        and 
 
 
 

In order to study the stablity of these points, we represents R and F by Re+R* and Fe+F* :


 
 
 

 For the point (0,0),
the system is equivalent to :

It shows that this point is unstable due to the positivity of a.
 
 
 

For the point (a/b,c/(d*b)),
The problem is less easy to solve:

By using the value of Re and Fe,


 

and by taking into account that can be neglected in comparison with and , this system is equivalent to :


 
 
 


 

The eigen values are . It is impossible to conclude for the stability of the system
 
 
 
 
 
 
 
 



Method of programming :
 

Discretisation

The method of discretisation is a uncentred scheme :


 

The system becomes equivalent to :


 
 

Program

Matlab is used to make the simulation of the phenomemon; the program is in the file named volterra.m :

It allows to choose
 
 
 
 
 
 
 
 
 
 
 
 
 




 
 
 
 

Results obtained and interpretations
 

Realistic case :

The paramaters which permit a realistic simulation of the model are :

a=0.04
b=0.0005
c=0.2
d=0.1
Graph of the simulation :
 


 

The phenomenon has a periodical oscillation of its population.
 
 
 
 
 
 

Influence of the parameters and interpretation in biology:
 

For this study, I use a too important time step, which is responsible for th growth of the population. Neverthless, I decided to let these results because they show that an error in numerical simulation is easy to make. (this error is presented at the end of the report)
In spite of this, interpretations on parameters are good.
 
 

Parameters a :
The augmentation of this parameters makes increase the population.
From the point of view of biology,  the rise of a represent an increase of the population of rabbits and as a consequence the development of foxs.
 
 
a=0.004 a=0.4

 
Paramaters b :
The parameters b has the opposite role of the parameters a.
The result obtains is here logical because when the death rate of rabbits rises, the consequence for the rabbits population and then as a consequence for foxs are negative .
 
 
b=0.005 b=0.05

 
 
Paramaters c :
As c increases, the population is more important.
When c is less important, the population of foxs decreases less ( in the left part of the graphe, when number of rabbits is very small ) than when c is more important. Consequently, the population of rabbits grows slower and so the development of population is too very slowest.
 
 
c=0.02 c=0.2

 
 
 
Parameters d :
The decrease of d makes the population rises more before the stabilisation.
 
 
d=0.01 d=0.1

 
 
 
 

An interesting simulation mistake:

This evolution is ploted for  :

a=0.04
b=0.05
c=0.2
d=0.1
Initially, there are 4 rabbits and 1 fox.
 
 

100 iterations

1000 iterations 
\
1500 iterations

5000 iterations

5000 iterations

40000 iterations 

 

With a time step too important, the population.of rabbits and fox continue to grow. Why ? Because of the unprecision of the calculation. During the simulation,the calculation near the singular points  aren't accurate, which provock a small error and, consequently,the graph doesn't close itself as it done it fot the perfect case.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 




 
 
 
 
 
 

Conclusion
 

This study was interesting. It gives me the possibility to discover a simple application of hydrodynamics instability and problem of time step that can appears in a simulation. Neverthless, I didn't success in finding the caracter stable or unstable of the second critical point.
From the point of view of Biology, the model is quite interested too and it is an original application of a mathematical model.