Phylogenetic beta diversity standardized for species beta diversity

Source: R/phylobeta_ses.R

This function computes the standard effect size of phylogenetic beta diversity by correcting for changes in species beta diversity. The novelty of this function is its ability to utilize sparse community matrix making it possible to efficiently randomize very large community matrices spanning thousands of taxa and sites.

phylobeta_ses(
  x,
  phy,
  index.family = "simpson",
  model = c("tipshuffle", "rowwise", "colwise"),
  reps = 1000,
  ...
)

Arguments

x

a (sparse) community matrix, i.e., an object of class matrix or Matrix.

phy

a phylogenetic tree (object of class phylo).

index.family

the family of dissimilarity indices including “simpson”, “sorensen” and “jaccard”.

model

The null model for separating patterns from processes and for contrasting against alternative hypotheses. Available null models include:

  • “tipshuffle”: shuffles phylogenetic tip labels multiple times.

  • “rowwise”: shuffles sites (i.e., varying richness) and keeping species occurrence frequency constant.

  • “colwise”: shuffles species occurrence frequency and keeping site richness constant.

reps

Number of replications.

...

Further arguments passed to or from other methods.

Value

A data frame of results for each community or grid cell

  • phylobeta_obs: Observed phylobeta in community

  • phylobeta_rand_mean: Mean phylobeta in null communities

  • phylobeta_rand_sd: Standard deviation of phylobeta in null communities

  • phylobeta_obs_z: Standardized effect size of phylobeta vs. null communities \(= (phylobeta_obs - phylobeta_rand_mean) / phylobeta_rand_sd\)

  • reps: Number of replicates

References

Proches, S., Wilson, J.R.U. & Cowling, R.M. (2006) How much evolutionary history in a 10 x 10m plot? Proceedings of Royal Society B 273: 1143-1148.

Examples

library(ape)
library(Matrix)
tree <- read.tree(text ="((t1:1,t2:1)N2:1,(t3:1,t4:1)N3:1)N1;")
com <- sparseMatrix(c(1,3,4,1,4,5,1,2,3,4,5,6,3,4,6),
  c(1,1,1,2,2,2,3,3,3,3,3,3,4,4,4),x=1,
  dimnames = list(paste0("g", 1:6), tree$tip.label))

phylobeta_ses(com, tree, model="rowwise")
#> $phylobeta_obs
#>           g1        g2        g3        g4        g5
#> g2 0.0000000                                        
#> g3 0.2000000 0.0000000                              
#> g4 0.0000000 0.0000000 0.0000000                    
#> g5 0.0000000 0.0000000 0.2500000 0.0000000          
#> g6 0.3333333 0.0000000 0.0000000 0.0000000 0.3333333
#> 
#> $phylobeta_rand_mean
#>           g1        g2        g3        g4        g5
#> g2 0.0000000                                        
#> g3 0.2000000 0.0000000                              
#> g4 0.0000000 0.0000000 0.0000000                    
#> g5 0.0000000 0.0000000 0.2500000 0.0000000          
#> g6 0.3333333 0.0000000 0.0000000 0.0000000 0.3333333
#> 
#> $phylobeta_rand_sd
#>    g1 g2 g3 g4 g5
#> g2  1            
#> g3  1  1         
#> g4  1  1  1      
#> g5  1  1  1  1   
#> g6  1  1  1  1  1
#> 
#> $phylobeta_obs_z
#>    g1 g2 g3 g4 g5
#> g2  0            
#> g3  0  0         
#> g4  0  0  0      
#> g5  0  0  0  0   
#> g6  0  0  0  0  0
#> 
#> $reps
#> [1] 1000
#>