BAT (version 2.11.0)

kernel.beta: Beta diversity partitioning using kernel density hypervolumes.

Description

Pairwise beta diversity partitioning into replacement and net difference in amplitude components of n-dimensional hypervolumes.

Usage

kernel.beta(comm, func = "jaccard", comp = FALSE)

Value

Five pairwise distance matrices, one per each of the five beta diversity components. If comp = TRUE also three distance matrices with beta diversity components.

Arguments

comm

A 'HypervolumeList' object, preferably built using function kernel.build.

func

Partial match indicating whether the Jaccard or Soerensen family of beta diversity measures should be used. If not specified, default is Jaccard.

comp

Boolean indicating whether beta diversity components (shared and unique fractions) should be returned

Details

Computes a pairwise decomposition of the overall differentiation among kernel density hypervolumes. The beta diversity measures used here follow the partitioning frameworks developed by Podani & Schmera (2011), Carvalho et al. (2012) and Legendre (2019) and later expanded to PD and FD by Cardoso et al. (2014), where Btotal = Brepl + Brich or Btotal = Bgain + Bloss. Btotal = total beta diversity, reflecting both volume replacement and loss/gain; Brepl = beta diversity explained by replacement of volume alone; Brich = beta diversity explained by volume loss/gain (richness differences) alone; Bgain = beta diversity explained by volume gain from T1 to T2; Bloss = beta diversity explained by volume lost from T1 to T2. See Carvalho & Cardoso (2020) and Mammola & Cardoso (2020) for the full formulas of beta diversity used here.

References

Carvalho, J.C. & Cardoso, P. (2020) Decomposing the causes for niche differentiation between species using hypervolumes. Frontiers in Ecology and Evolution, 8: 243.

Carvalho, J.C., Cardoso, P. & Gomes, P. (2012) Determining the relative roles of species replacement and species richness differences in generating beta-diversity patterns. Global Ecology and Biogeography, 21, 760-771.

Cardoso, P., Rigal, F., Carvalho, J.C., Fortelius, M., Borges, P.A.V., Podani, J. & Schmera, D. (2014) Partitioning taxon, phylogenetic and functional beta diversity into replacement and richness difference components. Journal of Biogeography, 41, 749-761.

Legendre, P. (2019) A temporal beta-diversity index to identify sites that have changed in exceptional ways in space–time surveys. Ecology and Evolution, 9: 3500-3514.

Mammola, S. & Cardoso, P. (2020) Functional diversity metrics using kernel density n-dimensional hypervolumes. Methods in Ecology and Evolution, 11: 986-995.

Podani, J. & Schmera, D. (2011) A new conceptual and methodological framework for exploring and explaining pattern in presence-absence data. Oikos, 120, 1625-1638.

Examples

if (FALSE) {
comm <- rbind(c(1,1,1,1,1), c(1,1,1,1,1), c(0,0,1,1,1),c(0,0,1,1,1))
colnames(comm) = c("SpA","SpB","SpC","SpD", "SpE")
rownames(comm) = c("Site 1","Site 2","Site 3","Site 4")

trait <- cbind(c(2.2,4.4,6.1,8.3,3),c(0.5,1,0.5,0.4,4),c(0.7,1.2,0.5,0.4,5),c(0.7,2.2,0.5,0.3,6))
colnames(trait) = c("Trait 1","Trait 2","Trait 3","Trait 4")
rownames(trait) = colnames(comm)

hvlist = kernel.build(comm, trait)
kernel.beta(hvlist)
hvlist = kernel.build(comm, trait, axes = 0.9)
kernel.beta(hvlist, comp = TRUE)
}