Here’s a quick example to show how we could use the fishtree package to conduct some phylogenetic community analyses. First, we load fishtree and ensure that the other packages that we need are installed.

library(ape)
library(fishtree)
loadNamespace("rfishbase")
#> <environment: namespace:rfishbase>
loadNamespace("picante")
#> <environment: namespace:picante>
loadNamespace("geiger")
#> <environment: namespace:geiger>

Next we’ll start downloading some data from rfishbase. We’ll be seeing if reef-associated ray-finned fish species are clustered or overdispersed in the Atlantic, Pacific, and Indian Oceans.

# Get reef-associated species from the `species` table
reef_species <- rfishbase::species(fields = c("Species", "DemersPelag"))
reef_species <- reef_species[reef_species$DemersPelag == "reef-associated", ]

# Get native and endemic species from the Atlantic, Pacific, and Indian Oceans
eco <- rfishbase::ecosystem(species_list = reef_species$Species)
valid_idx <- eco$Status %in% c("native", "endemic") & eco$EcosystemName %in% c("Atlantic Ocean", "Pacific Ocean", "Indian Ocean") 
eco <- eco[valid_idx, c("Species", "EcosystemName")]

# Retrieve the phylogeny of only native reef species across all three oceans.
phy <- fishtree_phylogeny(species = eco$Species)
#> Warning: Requested 5898 but only found 3975 species.
#> * Abudefduf conformis
#> * Abudefduf natalensis
#> * Abudefduf nigrimargo
#> * Acanthaluteres brownii
#> * Acanthanectes rufus
#> * ...and 1918 others

We’ll have to clean up the data in a few ways before sending it to picante for analysis. First, we’ll need to convert our species-by-site data frame into a presence-absence matrix. We’ll use base::table for this, and use unclass to convert the table into a standard matrix object.

sample_matrix <- unclass(table(eco))
dimnames(sample_matrix)$Species <- gsub(" ", "_", dimnames(sample_matrix)$Species, fixed = TRUE)

Next, we’ll use geiger::name.check to ensure the tip labels of the phylogeny and the rows of the data matrix match each other.

nc <- geiger::name.check(phy, sample_matrix)
sample_matrix <- sample_matrix[!rownames(sample_matrix) %in% nc$data_not_tree, ]

Finally, we’ll generate the cophenetic matrix based on the phylogeny, and transpose the presence-absence matrix since picante likes its columns to be species and its rows to be sites.

cophen <- cophenetic(phy)
sample_matrix <- t(sample_matrix)

We’ll run picante::ses.mpd and picante::ses.mntd with only 100 iterations, to speed up the analysis. For a real analysis you would likely increase this to 1000, and possibly test other null models if your datasets have e.g., abundance information.

picante::ses.mpd(sample_matrix, cophen, null.model = "taxa.labels", runs = 99)
#>                ntaxa  mpd.obs mpd.rand.mean mpd.rand.sd mpd.obs.rank mpd.obs.z
#> Atlantic Ocean   614 237.4919      231.3450   2.5127364          100 2.4463188
#> Indian Ocean    1206 233.2451      231.6289   1.1537890           93 1.4007702
#> Pacific Ocean   1487 231.7967      231.6479   0.9044225           56 0.1644762
#>                mpd.obs.p runs
#> Atlantic Ocean      1.00   99
#> Indian Ocean        0.93   99
#> Pacific Ocean       0.56   99
picante::ses.mntd(sample_matrix, cophen, null.model = "taxa.labels", runs = 99)
#>                ntaxa mntd.obs mntd.rand.mean mntd.rand.sd mntd.obs.rank
#> Atlantic Ocean   614 42.05075       48.36370    1.4103035             1
#> Indian Ocean    1206 34.96860       38.18936    0.6738824             1
#> Pacific Ocean   1487 34.20019       35.15455    0.5341215             3
#>                mntd.obs.z mntd.obs.p runs
#> Atlantic Ocean  -4.476309       0.01   99
#> Indian Ocean    -4.779410       0.01   99
#> Pacific Ocean   -1.786771       0.03   99

The Atlantic and Indian Oceans are overdispersed using the MPD metric, and all three oceans are clustered under the MNTD metric. MPD is thought to be more sensitive to patterns closer to the root of the tree, while MNTD is thought to more closely reflect patterns towards the tips of the phylogeny.

We can confirm these patterns visually by running the following code, which will plot the phylogeny and add colored dots (red, green, and blue) to indicate whether a tip is associated with a specific ocean basin.

plot(phy, show.tip.label = FALSE, no.margin = TRUE)
obj <- get("last_plot.phylo", .PlotPhyloEnv)

matr <- t(sample_matrix)[phy$tip.label, ]
xx <- obj$xx[1:obj$Ntip]
yy <- obj$yy[1:obj$Ntip]
cols <- c("#1b9e77", "#d95f02", "#7570b3")
for (ii in 1:ncol(matr)) {
  present_idx <- matr[, ii] == 1
  points(xx[present_idx] + ii, yy[present_idx], col = cols[ii], cex = 0.1)
}