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Passerines Distribution

Source: vignettes/Passeriformes-distribution.Rmd
Passeriformes-distribution.Rmd

This article aims to map the species richness distributions of older and young lineages of Australian passerines (obtained from Jetz et al. (2012)). Using the treesliceR tools, we’ll compare the spatial distribution between these lineages and explore emergent biodiversity patterns in space.

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

# 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. Pruning the passerines phylogeny

Before conducting our analysis, it is necessary to load the passerines’ phylogeny. We will call the pass_trees object, which is accessible from the treesliceR package. This object comprises 100 passerine phylogenies, but for the sake of simplicity we will use only the first one:

tree <- pass_trees[[1]]

To evaluate the species richness distribution of both older and younger lineages, it is essential to split the phylogenetic tree of these lineages based on a specific criterion. Therefore, we will categorize lineages as older species if their last branching events are higher than the 75th quantile of the overall passerines age distribution. This can be done by using the prune_tips() function:

older <- prune_tips(tree, 0.75, qtl = T)

Now, we will classify lineages as younger if their ages are smaller than the 25th quantile of the overall passerine ages. However, because we aim to keep in the phylogeny those species originating after our quantile threshold, we will set the argument method = 2 (for more details, see ?prune_tips()):

younger <- prune_tips(tree, 0.25, qtl = T, method = 2)

Let’s visualize both the original and pruned phylogenies:

oldpar <- par(mfrow = c(1, 3)) # Setting an 1x3 graphical display
plot(tree, main = "All species", show.tip.label = F); axisPhylo()
plot(older, main = "Older species", show.tip.label = F); axisPhylo()
plot(younger, main = "Younger species", show.tip.label = F); axisPhylo()
par(oldpar) # Returning to the original display

2. Distribution of species richness (older vs younger)

Before calculating the species richness in each one of our categories (young and old lineages), we need to identify the older and younger species present in the pruned phylogenies:

older <- older$tip.label
younger <- younger$tip.label

Now, we need to evaluate the distribution of these species. To do this, we’ll load the passerines species occurrence matrix in the pass_mat object. Let’s take a look at the first tree species in the matrix:

head(pass_mat[, 1:3])
#>   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

Then, we can create subsets of this matrix for both older and younger species. Let’s begin with the older ones:

positions <- which(colnames(pass_mat) %in% older)
old_mat <- pass_mat[, positions]

Now, let’s proceed by subsetting assemblages that contain the younger species:

positions <- which(colnames(pass_mat) %in% younger)
yng_mat <- pass_mat[, positions]

Now, we can load a grid map of Australia and merge species richness information from young and old species onto it:

grid <- AU_grid
grid$SR_old <- rowSums(old_mat)
grid$SR_yng <- rowSums(yng_mat)

Then, visualize the species richness patterns in both categories:

# Younger
AU_yng <- ggplot() +
          geom_sf(data = grid, aes(fill = SR_yng)) +
          scale_fill_gradient(low = "white", high = "red") +          
          labs(fill = "SR (Young)") +
          theme_void()
# Older
AU_old <- ggplot() +
          geom_sf(data = grid, aes(fill = SR_old)) +
          scale_fill_gradient(low = "white", high = "red") +
          labs(fill = "SR (Old)") +
          theme_void()
# Plotting
ggarrange(AU_yng, AU_old, labels = c("a)", "b)"), ncol = 2, nrow = 1)

3. Mismatches in species richness distribution

As observed above, there is a spatial correspondence between the species richness of younger and older lineages. However, the extent of is this relationship is uncertain. Let’s fit a simple linear regression to quantify this relationship:

model <- lm(SR_yng ~ SR_old, data = grid)

Now, let’s examine the summarized results:

summary(model)
#> 
#> Call:
#> lm(formula = SR_yng ~ SR_old, data = grid)
#> 
#> Residuals:
#>      Min       1Q   Median       3Q      Max 
#> -10.5402  -2.8631  -0.4006   2.6029  12.1718 
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)    
#> (Intercept) -1.16669    0.56283  -2.073   0.0385 *  
#> SR_old       0.85690    0.02403  35.658   <2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 3.999 on 718 degrees of freedom
#> Multiple R-squared:  0.6391, Adjusted R-squared:  0.6386 
#> F-statistic:  1271 on 1 and 718 DF,  p-value: < 2.2e-16

Note that its coefficient of determination is high and significant (R² = 63%!!!!). Thus, areas with high richness of younger lineages will generally also exhibit high richness of older lineages. These areas could indicate different dynamics of diversification rates over time, concurrently influencing the patterns of species richness of lineages of different depths.

Let’s dive a bit more into this example and examine a scatterplot depicting the species richness of both categories:

plot(SR_yng ~ SR_old, data = grid,
     ylab = "Species richness (younger)",
     xlab = "Species richness (older)")
# Regression line
abline(model, col = "red")

Despite the stronger relationship between the species richness of both young and old lineages, the deviations in these patterns could indicate important and unidentified biogeographical processes influencing the currently observed species richness of these lineages.

Therefore, let’s quantify the magnitude of these deviations from our model using the residuals function and spatialize them:

grid$res <- residuals(model)
# Mapping it
ggplot() + 
  geom_sf(data = grid, aes(fill = res)) +
  scale_fill_gradient2(low = "red", mid = "grey80", high = "blue") +
  labs(fill = "Residuals") +
  theme_void()

Areas in blue indicate a higher predominance of younger lineages than expected, while in red indicates that older lineages are predominant. Areas colored in grey indicate that the observed species richness of younger and older species are close to the richness expected by our model.

Using these residuals, we can possibly infer the processes leading to the differences between the species richness of older and younger lineages. Interestingly, these differences observed in the residuals are highly structured in space. There is a higher predominance of older lineages in deserts, which could be attributed to a processes of lower diversification rates within these locations over time. On the other hand, younger lineages predominate in humid areas and islands, indicating locations where diversification rates are higher and continually generate recent lineages. It is worth to mention that both mechanisms mentioned here are only hypotheses that deserves further empirical testing.

That’s all folks!

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Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. and Mooers, A. O. 2012. The global diversity of birds in space and time. - Nature 491(7424): 444–448. https://doi.org/10.1038/nature11631

Sonne, J., Dalsgaard, B., Borregaard, M. K., Kennedy, J., Fjeldså, J. and Rahbek, C. 2022. Biodiversity cradles and museums segregating within hotspots of endemism. - Proceedings of Royal Society: B. 289: 20221102. https://doi.org/10.1098/rspb.2022.1102