kernel.arrangement | R Documentation |
Functional arrangement of a community, measuring the distribution of species within the total functional space at multiple spatial scales. This is a specific function to be used with an Hypervolume object, preferably, from kernel.build function.
kernel.arrangement(
comm,
stat = "PNCP",
distances = seq(0, 1, 0.01),
type = "SES",
alpha = 0.05,
runs = 99,
plot = TRUE
)
comm |
A 'Hypervolume' object, built using function kernel.build. |
stat |
statistic to be calculated. One of c("PNCP", "NNCP"), meaning "cumulative proportion of pairwise neighbors" and "cumulative proportion of nearest neighbors", respectively. |
distances |
vector of distances to be considered in calculations |
type |
Envelope type for testing significance. One of c("ecdf", "norm", "SES"), meaning "empirical cumulative distribution", "normalized envelope" (between 0-1, 0.5 indicate randomness, more than 0.5 - clustered; less than 0.5 - inhibition), and "standardized effect size" respectively. |
alpha |
alpha value to consider in significance testing (p-value). |
runs |
number of simulations for significance testing |
plot |
whether to plot the results |
This function measures the functional arrangement (Carvalho & Cardoso, 2025) of a n-dimensional hypervolume, namely the distribution of species within the total trait space at multiple spatial scales.
A list with observed PNCP or NNCP values, a matrix of simulated values at r distances (r x sim), and standard effect size.
Carvalho, J.C. & Cardoso, P. (2025) Quantifying species distribution within the functional space.
## Not run:
comm = c(100,3,0,5,3)
names(comm) = c("SpA", "SpB", "SpC", "SpD", "SpE")
trait = data.frame(body = c(1,2,3,4,2), beak = c(1,5,4,1,2))
rownames(trait) = names(comm)
hv = kernel.build(comm, trait, method.hv = "svm", svm.nu = 0.01, svm.gamma = 0.25)
kernel.arrangement(hv)
## End(Not run)
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