Phylogenetic community assembly with the Fish Tree of Life

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
species <- rfishbase::fb_tbl("species")
species <- species[species$DemersPelag == "reef-associated", ]
reef_species <- paste(species$Genus, species$Species)

# Get native and endemic species from the Atlantic, Pacific, and Indian Oceans
eco <- rfishbase::ecosystem(species_list = reef_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 5899 but only found 3877 species.
#> • Abudefduf conformis
#> • Abudefduf natalensis
#> • Abudefduf nigrimargo
#> • Abantennarius analis
#> • Abantennarius bermudensis
#> • ...and 2017 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   598 237.7817      232.3279    2.276850          100 2.3953203
#> Indian Ocean    1175 233.6108      231.7154    1.164264           96 1.6280237
#> Pacific Ocean   1449 232.1826      231.7453    0.938854           69 0.4657706
#>                mpd.obs.p runs
#> Atlantic Ocean      1.00   99
#> Indian Ocean        0.96   99
#> Pacific Ocean       0.69   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   598 41.73364       48.70549    1.2035540             1
#> Indian Ocean    1175 35.12921       37.96675    0.7147039             1
#> Pacific Ocean   1449 34.27236       34.97047    0.5277975            10
#>                mntd.obs.z mntd.obs.p runs
#> Atlantic Ocean  -5.792716       0.01   99
#> Indian Ocean    -3.970235       0.01   99
#> Pacific Ocean   -1.322672       0.10   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)
}

Jonathan Chang