kernel.evenness | R Documentation |
Functional evenness of a community, measuring the regularity of stochastic points distribution within the total functional space.
kernel.evenness(comm)
comm |
A 'Hypervolume' or 'HypervolumeList' object, preferably built using function kernel.build. |
This function measures the functional evenness (Mason et al., 2005) of a n-dimensional hypervolume, namely the regularity of stochastic points distribution within the total trait space (Mammola & Cardoso, 2020). Evenness is calculated as the overlap between the observed hypervolume and a theoretical hypervolume where traits and abundances are evenly distributed within the range of their values (Carmona et al., 2016, 2019).
A value or vector of evenness values for each site.
Carmona, C.P., de Bello, F., Mason, N.W.H. & Leps, J. (2016) Traits without borders: integrating functional diversity across scales. Trends in Ecology and Evolution, 31: 382-394.
Carmona, C.P., de Bello, F., Mason, N.W.H. & Leps, J. (2019) Trait probability density (TPD): measuring functional diversity across scales based on TPD with R. Ecology, 100: e02876.
Mason, N.W.H., Mouillot, D., Lee, W.G. & Wilson, J.B. (2005) Functional richness, functional evenness and functional divergence: the primary components of functional diversity. Oikos, 111: 112-118.
Mammola, S. & Cardoso, P. (2020) Functional diversity metrics using kernel density n-dimensional hypervolumes. Methods in Ecology and Evolution, 11: 986-995.
## Not run:
comm = rbind(c(100,3,0,5,3), c(3,2,5,1,0))
colnames(comm) = c("SpA", "SpB", "SpC", "SpD", "SpE")
rownames(comm) = c("Site 1", "Site 2")
trait = data.frame(body = c(1,2,3,4,4), beak = c(1,5,4,1,2))
rownames(trait) = colnames(comm)
hv = kernel.build(comm[1,], trait)
kernel.evenness(hv)
hv = kernel.build(comm[1,], trait, abund = FALSE)
kernel.evenness(hv)
hvlist = kernel.build(comm, trait, axes = 2)
kernel.evenness(hvlist)
## End(Not run)
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