Passerines Diversification

This article aims to replicate the case study developed by Araujo et al. (in review). We will also present the workflow executed within the study for both a single passerine phylogeny and multiple passerine phylogenies, in order to control the results for phylogenetic uncertainty Rangel et al. (2015).

Before starting our analysis, we’ll need to load the ape, ggplot2, and ggpubr packages. Additionally, if required, install the devtools to invoke the treesliceR package:

# Loading the packages that we'll use
library(ape)
library(ggplot2)
library(ggpubr)

Now, we’ll need to load (and install if necessary) the treesliceR package:

# Loading it
library(treesliceR)

1. Passerines study for a single phylogeny

1.1. First framework example

Herein, we’ll compare the passerines tip diversification rates (DR) before and after the Australian aridification (~33 Mya). Our used passerines phylogenies are subsets of the global birds phylogenies made available by Jetz et al. (2012).

Thus, primarily, we can load the passerines phylogeny available within the package by calling the pass_trees object:

tree <- pass_trees[[1]]

Now, we need to split the phylogeny into two distinct portions, one representing the last 33 millions of years ago (Mya) and another representing the cladogenesis events before this period. To capture the root slice before 33 Mya, we can slice our phylogeny tipwardly (i.e., from tips to root) using the squeeze_tips() function. Alternatively, the phylogeny slice of the last 33 Mya can be obtained through the squeeze_root() function, which slices a phylogeny rootwardly (i.e, from root to tips).

Since DR calculations are dependent on the node’s path to a given tip, we set the argument dropNodes = TRUE in our functions to remove those “void nodes” (nodes with zero branch length) to avoid biases in our DR estimates:

recent <- squeeze_root(tree = tree, time = 33, dropNodes = T)
old <- squeeze_tips(tree = tree, time = 33, dropNodes = T)

Let’s compare our original phylogeny with those cut ones:

oldpar <- par(mfrow = c(1, 3)) # Setting an 1x3 graphical display
plot(tree, main = "Complete tree", show.tip.label = F); axisPhylo()
plot(old, main = "Old tree", show.tip.label = F); axisPhylo()
plot(recent, main = "Recent tree", show.tip.label = F); axisPhylo()
par(oldpar) # Returning to the original display

Then, we need to calculate the DR separately for each phylogeny slice while finding the difference between them. To calculate the DR, we can use the DR() function available within the treesliceR package:

DR_diff <- DR(tree = recent)[,2] - DR(tree = old)[,2]

Now we’ll assign these DR differences to a data frame inside our tree object containing passerines information:

tree$Species_info$DR_diff <- DR_diff

To calculate the mean and standard deviation differences per family, we use the tapply() function to aggregate the calculated DRs by passerine families:

# tapply() for means
fam_DR <- tapply(tree$Species_info$DR_diff, tree$Species_info$Family, mean)
# tapply() for standard deviations
fam_DR_sd <- tapply(tree$Species_info$DR_diff, tree$Species_info$Family, sd)

To visualize DR differences among families, we’ll create a new data frame with our outputs:

# Creating the families DR data frame
fam_df <- data.frame(Family = names(fam_DR), DR_diff = fam_DR, DR_sd = fam_DR_sd)
# Sorting them based on DR's value
fam_df <- fam_df[order(fam_df$DR_diff),]
# Turning this order into a factor to plot it
fam_df$Family <- factor(fam_df$Family, levels = fam_df$Family)

Now, we can create a graph similar to the one displayed in Araujo et al. (in review) using the ggplot2 package:

ggplot(fam_df, aes(x = Family, y = DR_diff,
                    ymin = DR_diff - DR_sd,
                    ymax = DR_diff + DR_sd)) +
  geom_pointrange(color = "#d90429") +
  geom_hline(yintercept = 0, linetype="dashed", color = "black") +
  coord_flip() + theme_minimal() + 
  theme(axis.title = element_text(size = 13),
        axis.text = element_text(size = 10),
        axis.line = element_line(colour = "black"),
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank()) +
  ylab(expression(paste(DR["recent"]-DR["past"]))) + xlab(NULL)

Notice that the final output is slightly different from what was observed in Araujo et al. (in review). However, remember that here we executed the analysis only for a single passerine phylogeny. A more comprehensive assessment using all phylogenies could be carried out in the sections below (for example in the section “2. Passerines study for multiple phylogenies”).

1.2. Second framework example

Herein, we’ll calculate the CpB-rate of Australian passerines for both turnover and nestedness components. Firstly, we’ll need to load the assemblages containing the species matrix stored within the package by calling the internal object pass_mat. Additionally, to calculate the beta-diversity metrics, it is necessary to provide the adjacency matrix containing the focal cells and their respective neighborhoods, which can be accessed through the AU_adj object. Let’s examine the header of the assemblage matrix containing the species, focusing on the first four columns (or species):

head(pass_mat[, 1:4])
#>   Heteromyias_albispecularis Myzomela_obscura Taeniopygia_guttata
#> 1                          0                1                   0
#> 2                          0                1                   0
#> 3                          0                1                   0
#> 4                          0                1                   0
#> 5                          0                1                   0
#> 6                          0                1                   0
#>   Dicaeum_hirundinaceum
#> 1                     1
#> 2                     1
#> 3                     1
#> 4                     1
#> 5                     1
#> 6                     1

We can run some sensitivity analysis to find the most parsimonious number of slices to assess the CpB-rate patterns. But first, we need to create a vector containing our desired number of slices for assessment:

vec <- c(250, 500, 750, 1000, 1250, 1500, 1750, 2000)

Let’s run the sensitivity analysis for both turnover and nestedness components:

sens_turn <- CpR_sensitivity(tree = tree, vec = vec, samp = 100,
                             mat = pass_mat, adj = AU_adj, rate = "CpB", comp = "turnover")
sens_nest <- CpR_sensitivity(tree = tree, vec = vec, samp = 100,
                             mat = pass_mat, adj = AU_adj, rate = "CpB", comp = "nestedness")

So, we can visualize our sensitivity analysis using the CpR_sensitivity_plot function. We use the ggplot2 syntax to add a vertical line showing our selected number of slices to run our subsequent analysis:

# Store each graph within a respective object
turn_sens_plot <- CpR_sensitivity_plot(sens_turn, rate = "CpB", stc = "mean") + 
  geom_vline(xintercept = 1000, linetype="dashed", color = "black")
nest_sens_plot <- CpR_sensitivity_plot(sens_nest, rate = "CpB", stc = "mean") + 
  geom_vline(xintercept = 1000, linetype="dashed", color = "black")
# To plot them together
ggarrange(turn_sens_plot, nest_sens_plot,
                    labels = c("a)", "b)"), ncol = 2, nrow = 1)

Now, we can finally calculate the CpB-rates for turnover and nestedness components, in this case, under a multisite approach (PS: this may take a few minutes):

# For turnover component
turn <- CpB(tree = tree, n = 1000, mat = pass_mat, adj = AU_adj, comp = "turnover")
# For nestedness component
nest <- CpB(tree = tree, n = 1000, mat = pass_mat, adj = AU_adj, comp = "nestedness")

Finally, we can plot these CpB over time and map them. To map them, we’ll use an Australian grid map stored within our package in the object AU_grid. Let’s plot these patterns for the turnover component:

turn_1 <- CpR_graph(data = turn, rate = "CpB", qtl = TRUE)
turn_2 <- CpR_graph(data = turn, rate = "CpB", qtl = TRUE, map = AU_grid)
# To plot them together
ggarrange(turn_1, turn_2,
                    labels = c("a)", "b)"), ncol = 2, nrow = 1)

And we can do the same for the nestedness component:

nest1 <- CpR_graph(data = nest, rate = "CpB", qtl = TRUE)
nest2 <- CpR_graph(data = nest, rate = "CpB", qtl = TRUE, map = AU_grid)
# To plot them together
ggarrange(nest1, nest2,
                    labels = c("a)", "b)"), ncol = 2, nrow = 1)