To provide a “jump start” on setting up analyses for MacroecoDesktop, the sample parameter file below contains a variety of runs that perform different types of calculations on the demo dataset provided with Macroeco. This file, or individual runs from this file (consisting of a run title in square brackets and all subsequent lines until the next run title), can be copied and pasted into parameters files and modified as needed.
The lines beginning with the # symbol are comments. They are purely for information and are ignored by MacroecoDesktop. In some cases below, lines containing variables are prefaced by the # symbol, indicating that they are “commented out” and will not affect the analysis. Removing the # at the start of these lines will have the effect described in the associated comment for that line.
# The runs below provide examples of empirical data analysis, some with # model comparisons. # A simple species abundance distribution for the full plot [SAD] analysis = sad metadata = ANBO.txt models = logser_uptrunc; lognorm log_y = True # Log transform the y axis of output plots # Four separate SAD's for the four quadrants of the plot # cols is only required if it is not set in the metadata file [SAD4] analysis = sad metadata = ANBO.txt #cols = spp_col:spp; count_col:count; x_col:row; y_col:column splits = row:2; column:2 clean = True # Remove species with 0 individuals from SADs models = logser_uptrunc; lognorm log_y = True # Log transform the y axis of output plots # Empirical spatial abundance distribution for all 16 cells [SSAD] analysis = ssad metadata = ANBO.txt splits = row: 4; column: 4 # Species area relationship [SAR ANBO] analysis = sar metadata = ANBO.txt divs = 1,1;1,2;2,1;2,2;2,4;4,4 models = mete_sar_iterative #ear = True # Endemics area relationship instead of species area log_y = True log_x = True # Gridded commonality, calculating Sorensen index for each pair of cells [Commonality] analysis = comm_grid metadata = ANBO.txt #subset = row>=2;column>=2 # Use only cells in rows 2-3 and columns 2-3 cols = spp_col:spp; count_col:count; x_col:row; y_col:column #splits = row:2 # Perform analysis once for rows 0-1 and again for 2-3 divs = 2,2 #metric = Jaccard # Use Jaccard instead of Sorensen index models = power_law # O ring measure of distance decay # This measure is best suited to point count census data [Oring] analysis = o_ring metadata = ANBO.txt cols = spp_col:spp; count_col:count; x_col:row; y_col:column spp = 'crcr' bin_edges = 0, 1, 2, 3, 4 # The runs below provide examples of model exploration # pmf of geometric distribution [Geom-pmf] analysis = geom.pmf x = range(10) # x values from 0 to 9 p = 0.5 # Shape parameter of upper truncated geometric distribution [GeomUptrunc-p] analysis = geom_uptrunc.translate_args mu = 5 b = 20 # Fit parameters of lognormal to a small data set [Lognorm-fit] analysis = lognorm.fit_mle data = 2,2,5,8,4,3 # Draw random variates from a conditioned negative binomial distribution [Cnbinom-random] analysis = cnbinom.rvs mu = 10 k_agg = 2 b = 15 size = 10