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)
requireNamespace("rfishbase")
#> Loading required namespace: rfishbase
requireNamespace("picante")
#> Loading required namespace: picante
requireNamespace("geiger")
#> Loading required namespace: geiger
#> Registered S3 method overwritten by 'geiger':
#> method from
#> unique.multiPhylo ape
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$Species)
#> Warning: Requested 5788 but only found 3997 species. Missing names:
#> * Conger esculentus
#> * Lutjanus guttatus
#> * Lutjanus inermis
#> * Lutjanus jordani
#> * Lutjanus lemniscatus
#> * ...and 1786 others
We’ll have to clean up the data in a few ways before sending it to picante
for analysis. First, we’ll need to convert our species-by-site data frame into a presence-absence matrix. We’ll use base::table
for this, and use unclass
to convert the table
into a standard matrix
object.
sample_matrix <- unclass(table(eco))
dimnames(sample_matrix)$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.
We’ll run ses.mpd
and 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
#> Atlantic Ocean 637 238.3167 232.2084 1.8066282 100
#> Indian Ocean 1222 233.5305 231.8798 1.2929589 86
#> Pacific Ocean 1511 232.1380 231.8682 0.7284014 65
#> mpd.obs.z mpd.obs.p runs
#> Atlantic Ocean 3.3810650 1.00 99
#> Indian Ocean 1.2766983 0.86 99
#> Pacific Ocean 0.3702948 0.65 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 637 41.62559 47.83711 1.4396703 1
#> Indian Ocean 1222 34.98367 37.89510 0.6658603 1
#> Pacific Ocean 1511 34.00154 34.90489 0.4914791 4
#> mntd.obs.z mntd.obs.p runs
#> Atlantic Ocean -4.314548 0.01 99
#> Indian Ocean -4.372430 0.01 99
#> Pacific Ocean -1.838023 0.04 99
The Atlantic and Indian Oceans are overdispersed using the MPD metric, and all three oceans are clustered under the MNTD metric. MNTD is thought to be more sensitive to patterns closer to the root of the tree. We can confirm these patterns visually:
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)
}