Data Simulation 
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Data sets can be simulated that represent samples taken from geometric, log series, truncated log normal or broken stick models of species abundance. The simulated data sets produced become the active data set. Therefore, if you have made changes to an existing data set open in SDR, it should be saved before starting a simulation.
To create a simulated data set simply choose the type of distribution you want from the Simulation dropdown menu on the top bar of the program, fill in the required parameters with reasonable values, and click OK.
When the parameter input window is first opened, Species Diversity and Richness will have automatically entered some parameters that will produce sensible numbers. It is possible to choose parameters that will produce nonsense data. A summary of the parameters required, and their meaning, is given below. No. of samples: The number of independent data sets created – the columns in the data set. No. of species: This is the number of species in the community. No. of individuals: For a geometric distribution you need to state the number of individuals in each sample. K: The parameter for the geometric distribution, it must lie between 0 and 1. Alpha: The log series diversity parameter, typical values lie between 4 and 6. No. of Species in sample: The number of species captured in each sample. This parameter is required to simulate the log series. Mean: The mean species abundance in the population or hypothetical sampling area. SD: The standard deviation of population abundance for the hypothetical population. proportion – this is the proportion of the individuals in each hypothetical population that is captured, it must lie between 0 and 1. Maximum Sp Abundance: The abundance of the commonest species in the hypothetical population.
Once the parameters have been entered, the simulations are produced by clicking OK. The resulting data set will be displayed in a standard data grid. Click OK, and the simulated data will be available for use by all the Species Diversity and Richness methods. The user can save the data as a .csv file by choosing File: Save As.
