optim.alpha {BAT}

R Documentation

Optimization of alpha diversity sampling protocols.

Description

Optimization of alpha diversity sampling protocols when different methods and multiple samples per method are available.

Usage

optim.alpha(comm, tree, methods, base, runs = 0, prog = TRUE)

Arguments

comm	A samples x species x sites array, with either abundance or incidence data.
tree	A phylo or hclust object (used only for PD or FD) or alternatively a species x traits matrix or data.frame to build a functional tree.
methods	A vector specifying the method of each sample (length must be equal to nrow(comm))
base	A vector defining a base protocol from which to build upon (complementarity analysis) (length must be equal to number of methods).
runs	Number of random permutations to be made to the sample order. Default is 1000.
prog	Present a text progress bar in the R console.

Details

Often a combination of methods allows sampling maximum plot diversity with minimum effort, as it allows sampling different sub-communities, contrary to using single methods. Cardoso (2009) proposed a way to optimize the number of samples per method when the target is to maximize sampled alpha diversity. It is applied here for TD, PD and FD, and for one or multiple sites simultaneously. PD and FD are calculated based on a tree (hclust or phylo object, no need to be ultrametric).

Value

A matrix of samples x methods (values being optimum number of samples per method). The last column is the average alpha diversity value, rescaled to 0-1 if made for several sites, where 1 is the true diversity of each site.

References

Cardoso, P. (2009) Standardization and optimization of arthropod inventories - the case of Iberian spiders. Biodiversity and Conservation, 18, 3949-3962.

Examples

comm1 <- matrix(c(1,1,0,2,4,0,0,1,2,0,0,3), nrow = 4, ncol = 3, byrow = TRUE)
comm2 <- matrix(c(2,2,0,3,1,0,0,0,5,0,0,2), nrow = 4, ncol = 3, byrow = TRUE)
comm <- array(c(comm1, comm2), c(4,3,2))
colnames(comm) <- c("Sp1","Sp2","Sp3")
methods <- c("Met1","Met2","Met2","Met3")
tree <- hclust(dist(c(1:3), method="euclidean"), method="average")
tree$labels <- colnames(comm)
optim.alpha(comm,,methods)
optim.alpha(comm, tree, methods)
optim.alpha(comm,, methods = methods, base = c(0,0,1), runs = 100)

---