Add clumped (aggregated) positions to a species abundance distribution. Clumping is simulated using a Thomas cluster process, also known as Poisson cluster process (Morlon et al. 2008, Wiegand & Moloney 2014)
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Species abundance vector (integer)
Mean displacement (along each coordinate axes) of a point from
its mother point (= cluster centre).
Number of mother points (= cluster centres).
If this is a single value, all species have the same number of clusters.
Mean number of points per cluster. If this is
a single value, species have the same average number of points per cluster.
If this is a vector of the same length as
Extent of the community in x-direction (numeric vector of length 2)
Extent of the community in y-direction (numeric vector of length 2)
To generate a Thomas cluster process of a single species this
function uses a C++ re-implementation of the function
rThomas in the package
There is an inherent link between the parameters
cluster_points. For every species the
abundance has to be equal to the number of clusters
mother_points) times the number of points per cluster
abundance = mother_points * cluster_points
Accordingly, if one of the parameters is provided, the other one is directly
calculated from the abundance. Values for
mother_points override values
cluster_points. If none of the parameters is specified, it is assumed
that for every species there is a similar number of clusters and of points
mother_points = cluster_points = √(abundance),
In this case rare species have few clusters with few points per cluster, while abundant species have many clusters with many points per cluster.
A community object as defined by
Morlon et al. 2008. A general framework for the distance-decay of similarity in ecological communities. Ecology Letters 11, 904-917.
Wiegand and Moloney 2014. Handbook of Spatial Point-Pattern Analysis in Ecology. CRC Press
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abund <- c(10,20,50,100) sim1 <- sim_thomas_coords(abund, sigma = 0.02) plot(sim1) # Simulate species "ranges" sim2 <- sim_thomas_coords(abund, sigma = 0.02, mother_points = 1) plot(sim2) # Equal numbers of points per cluster sim3 <- sim_thomas_coords(abund, sigma = 0.02, cluster_points = 5) plot(sim3) # With large sigma the distribution will be essentially random (see Details) sim4 <- sim_thomas_coords(abund, sigma = 10) plot(sim4)
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