Gene-Net  v 1.0     Introduction Model principles References Installation and Downloads Input files User manual Contacts

where d is the Kronecker index :

Figure 1 : Stralher Classification.

Strahler's classification can organize the different segments of a stream network into a hierarchy. Consequently, the stream outlet will have the highest index value, corresponding to the river network order. This classification puts forward general geometric laws. Among them, Horton's laws (1945) describe the way stream networks are organized (Figure 1). These laws express the so-called bifurcation ratio R_B and length ratio R_L, also known as Horton's ratios. A great number of experimental studies on stream networks (e.g.  La Barbera, Rosso, 1990; Tarboton et al., 1990; Rosso et al., 1991) revealed that these ratios are rather stable and fluctuate between 3 and 5 for R_B and between 1.5 and 3.5 for R_L. The calculation of the fractal dimension of a stream network varies according to the observation scale; the stream network is therefore a multifractal object (Rodriguez-Iturbe, Rinaldo, 1997). Horton's laws also make it possible to work out the R_B and R_L (equation 2).
      (2)
Where  is the average value of the morphometric lengths of w order and   is the number of morphometric lengths of w order.
Then the fractal dimension D can be computed by (equation 3).
        (3)

Population module:
Populations located on the river network are characterized by the life cycle of the study species. Life cycles are designed for freshwater organisms restricted to watercourses, with or without larval stages. Equation (4) was used for a damselfly species, Calopteryx splendens (Harris, 1782). Life cycle of odonates is composed by egg, larval, immature adult (young) and breeder stages. Each stage is characterized by a survival rate which allows calculation of the population growth rate l.
        (4)
Where S_egg, S_larvae, S_young and S_breeder are respectively the survival rates of eggs, larvae, young adults and breeders. f, the fertility rate represents the number of eggs laid by a female in average.
A population consists in individuals whose sex and alleles are randomly drawn. Allele frequencies follow the Dirichlet model. Furthermore, Hardy Weinberg equilibrium is assumed in each population. The implementation of the life cycle is applied sequentially by the reproduction of individuals within a population and survival rates for different stages. When reproduction occurs, females are fertilized by at least one male. An egg results in the random sampling of one allele from each parent. One or more populations are located on selected places on the network. For a given population, the displacement of individuals takes place according to a migration rate parameter. Displacement from a node to another is governed by a uniform probability density function which allows upstream and downstream migrations. This probability density function gives the individual ability to move along some distance. This travel distance is assigned to an individual at birth and remains constant throughout the life cycle. The spatial migration process leads to an increase in the size of populations that were originally under the carrying capacity threshold. Boundary nodes (boundaries of the river catchment) are linked to upper nil nodes. For these nodes special conditions are applied. These are Dirichlet’s boundary conditions where the number of individuals remains constant when the density threshold is reached.

Simulation module:
The simulated demographic and genetic information are stored into output text files for later use with any visualization data software. Information available are ,  and  values and the regression slope between geographic and genetic distances, isolation by distance (Rousset, 1997) of any population, at any time and at any location. The figure 2 shows an isolation by distance pattern obtained from the species Calopteryx splendens in a river network whose parameters are R_B = 3 and R_L = 2 and n = 3 after 100 generations. Gene-Net outputs files containing sampled genetic diversity in the ARLEQUIN format (Excoffier et al., 2005).

Installation in GUI version:

    1. To run Gene-Net in GUI version you need a Java Runtime Machine (JRM) installed on your PC. Most of the time JRM is installed by default. If you do not have a JRM, download the JRM on the site http://www.java.com/fr/download/.

    2. Then download the archive genenet.zip which contains two applications: the graphical user interface (genenet.jar) and the computing program (genenet.exe). Unzip the archive into a directory (D:/ is for instance).

    3. Double click genenet.jar to launch the GUI (the program genenet.exe is called by the GUI).

    4. It is necessary to configure the GUI. Go to "Settings". In "Gene-Net directory" choose the directory where genenet.jar and genenet.exe are located (if you have unziped the archive in D:/, then genenet.jar and genenet.exe are in D:/genenet/. Then in "Gene-Net binary" selecte the program genenet.exe (if you have unziped the archive in D:/, then genenet.exe is in D:/genenet/

    5. Click "Start". The Settings window opens. With the two top buttons "Clear all parameters" and "Default Parameters" you can respectively delete all parameters value and give default values. Click "Start" again to run the simulation.
 

Installation in Consol version:

    1. Download the archive genenetconsol.zipwhich contains two files: the computing program (genenet.exe) and the inputfile of the parameters (parameters.input). Unzip the archive into a directory (D:/ for instance).

    2. In a script consol (e.g. DOS) type the command : genenet.exe parameters.input