MacroecoDesktop RecipesΒΆ

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
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
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
analysis = ssad

metadata = ANBO.txt
splits = row: 4; column: 4

# Species area relationship
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
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
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
analysis = geom.pmf

x = range(10)  # x values from 0 to 9
p = 0.5

# Shape parameter of upper truncated geometric distribution
analysis = geom_uptrunc.translate_args

mu = 5
b = 20

# Fit parameters of lognormal to a small data set
analysis = lognorm.fit_mle

data = 2,2,5,8,4,3

# Draw random variates from a conditioned negative binomial distribution
analysis = cnbinom.rvs

mu = 10
k_agg = 2
b = 15
size = 10