Author: Jonathan Chang

# Phylogenetic community assembly with the Fish Tree of Life

#### Jonathan Chang

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 5813 but only found 3961 species.
    #> * Ablabys binotatus
    #> * Ablabys macracanthus
    #> * Ablabys taenianotus
    #> * Abudefduf conformis
    #> * Abudefduf natalensis
    #> * ...and 1847 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
    #> Atlantic Ocean   623 238.1682      231.3588   2.5213665          100
    #> Indian Ocean    1213 233.1537      231.9486   1.3943958           76
    #> Pacific Ocean   1497 231.6829      231.7546   0.9395066           47
    #>                  mpd.obs.z mpd.obs.p runs
    #> Atlantic Ocean  2.70068771      1.00   99
    #> Indian Ocean    0.86421415      0.76   99
    #> Pacific Ocean  -0.07634417      0.47   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   623 41.86956       48.22864    1.1668943             1
    #> Indian Ocean    1213 34.99129       38.02963    0.7548866             1
    #> Pacific Ocean   1497 34.08644       34.99204    0.5509990             7
    #>                mntd.obs.z mntd.obs.p runs
    #> Atlantic Ocean  -5.449580       0.01   99
    #> Indian Ocean    -4.024901       0.01   99
    #> Pacific Ocean   -1.643559       0.07   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)
    }