CANAPE stands for “Categorical Analysis of Neo- And Paleo-Endemism,” and provides insight into the evolutionary processes underlying endemism (Mishler et al. 2014). The idea is basically that endemic regions may be so because either they contain range-restricted parts of a phylogeny that have unusually long branch lengths (paleoendemism), or unusually short branch lengths (neoendemism), or a mixture of both. Paleoendemism may reflect old lineages that have survived extinctions; neoendemism may reflect recently speciated lineages that have not yet dispersed.
Dataset
The canaper package comes with the dataset used in Mishler et al. (2014). Let’s load the data into memory:
The acacia dataset is a list including two items. The first, phy, is a phylogeny of Acacia species in Australia:
acacia$phy
#>
#> Phylogenetic tree with 510 tips and 509 internal nodes.
#>
#> Tip labels:
#> Pararchidendron_pruinosum, Paraserianthes_lophantha, adinophylla, semicircinalis, aphanoclada, inaequilatera, ...
#>
#> Rooted; includes branch lengths.
The second, comm, is a community dataframe with species as columns and rows as sites. The row names (sites) correspond to the centroids of 50 x 50 km grid cells covering Australia. The community matrix is too large to print out in its entirety, so we will just take a look at the first 8 rows and columns:
dim(acacia$comm)
#> [1] 3037 506
acacia$comm[1:8,1:8]
#> abbreviata acanthaster acanthoclada acinacea aciphylla
#> '-1025000:-1825000' 0 0 0 0 0
#> '-1025000:-1875000' 0 0 0 0 0
#> '-1025000:-1925000' 0 0 0 0 0
#> '-1025000:-1975000' 0 0 0 0 0
#> '-1025000:-2025000' 0 0 0 0 0
#> '-1025000:-2075000' 0 0 0 0 0
#> '-1025000:-2125000' 0 0 0 0 0
#> '-1025000:-2225000' 0 0 0 0 0
#> acoma acradenia acrionastes
#> '-1025000:-1825000' 0 0 0
#> '-1025000:-1875000' 0 0 0
#> '-1025000:-1925000' 0 0 0
#> '-1025000:-1975000' 0 0 0
#> '-1025000:-2025000' 0 0 0
#> '-1025000:-2075000' 0 0 0
#> '-1025000:-2125000' 0 0 0
#> '-1025000:-2225000' 0 0 0
Randomization test
There are many metrics that describe the phylogenetic diversity of ecological communities. But how do we know if a given metric is statistically significant? One way is with a randomization test. The general process is:
- Generate a set of random communities
- Calculate the metric of interest for each random community
- Compare the observed values to the random values
Observed values that are in the extremes (e.g, the top or lower 5% for a one-sided test, or either the top or bottom 2.5% for a two-sided test) would be considered significantly more or less diverse than random.
The main purpose of canaper is to perform these randomization tests.
canaper performs community matrix randomizations using the vegan package. There are a large number of pre-defined randomization algorithms available in vegan, as well as the option to provide a user-defined algorithm. Selecting the appropriate algorithm for analysis is not trivial, and can greatly influence results. For details about the pre-defined algorithms, see vegan::commsim().
This example also demonstrates one of the strengths of canaper: the ability to run randomizations in parallel. This is by far the most time-consuming part of CANAPE, since we have to repeat the calculations many (e.g., hundreds or more) times across the randomized communities to obtain reliable results. Here, we set the number of iterations (n_iterations; i.e., the number of swaps used to produce each randomized community) fairly high because this community matrix is large and includes many zeros; thorough mixing by swapping many times is required to completely randomize the matrix.
We will use a low number of random communities (n_reps) so things finish relatively quickly; you should consider increasing n_reps for a “real” analysis. We will use the swap randomization algorithm, which maintains species richness and abundance patterns while randomizing species identity (Gotelli2003?).
# Set a parallel back-end, with 3 CPUs running simultaneously
plan(multisession, workers = 3)
# Uncomment this to show a progress bar when running cpr_rand_test()
# progressr::handlers(global = TRUE)
# Set a random number generator seed so we get the same results if this is run again
set.seed(071421)
tic() # Set a timer
# Run randomization test
acacia_rand_res <- cpr_rand_test(
acacia$comm, acacia$phy,
null_model = "swap",
n_reps = 100, n_iterations = 100000)
#> [1] "Dropping tips from the tree because they are not present in the community data:"
#> [1] "Pararchidendron_pruinosum" "Paraserianthes_lophantha"
#> [3] "saligna" "clunies-rossiae"
toc() # See how long it took
#> 1336.808 sec elapsed
# Switch back to sequential (non-parallel) mode
plan(sequential)
If we hadn’t run it in parallel, it would have taken significantly longer!
Let’s take a peek at the output. cpr_rand_test() produces a lot of columns, so we’ll view this as a tibble, which prints out more nicely than a plain data.frame.
as_tibble(acacia_rand_res, rownames = "site")
#> # A tibble: 3,037 × 55
#> site pd_obs pd_rand_mean pd_rand_sd pd_obs_z pd_obs_c_upper pd_obs_c_lower
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 '-1025… 0.0145 0.0221 0.00408 -1.87 2 98
#> 2 '-1025… 0.0383 0.0503 0.00783 -1.53 8 92
#> 3 '-1025… 0.0379 0.0368 0.00640 0.171 67 33
#> 4 '-1025… 0.0571 0.0615 0.00865 -0.506 30 70
#> 5 '-1025… 0.0410 0.0420 0.00725 -0.149 49 51
#> 6 '-1025… 0.0100 0.00998 0.00153 0.0147 60 40
#> 7 '-1025… 0.0188 0.0224 0.00439 -0.836 24 76
#> 8 '-1025… 0.0435 0.0556 0.00799 -1.52 5 95
#> 9 '-1025… 0.0111 0.0103 0.00237 0.335 78 22
#> 10 '-1025… 0.0905 0.0875 0.00958 0.316 64 36
#> # … with 3,027 more rows, and 48 more variables: pd_obs_q <dbl>,
#> # pd_obs_p_upper <dbl>, pd_obs_p_lower <dbl>, pd_alt_obs <dbl>,
#> # pd_alt_rand_mean <dbl>, pd_alt_rand_sd <dbl>, pd_alt_obs_z <dbl>,
#> # pd_alt_obs_c_upper <dbl>, pd_alt_obs_c_lower <dbl>, pd_alt_obs_q <dbl>,
#> # pd_alt_obs_p_upper <dbl>, pd_alt_obs_p_lower <dbl>, rpd_obs <dbl>,
#> # rpd_rand_mean <dbl>, rpd_rand_sd <dbl>, rpd_obs_z <dbl>,
#> # rpd_obs_c_upper <dbl>, rpd_obs_c_lower <dbl>, rpd_obs_q <dbl>, …
For details about what each column means, see cpr_rand_test().
Classify endemism
The next step in CANAPE is to classify types of endemism. For a full description, see Mishler et al. (2014). In short, this defines endemic regions based on combinations of the p-values of phylogenetic endemism (PE; pe_obs) and PE measured on an alternative tree with all branch lengths equal (pe_alt). Here is a summary borrowed from the biodiverse blog, modified to use the variables as they are defined in canaper:
- If either
pe_obs or pe_alt_obs are significantly high then we look for paleo- or neo-endemism
- If
rpe_obs is significantly high then we have palaeo-endemism
- Else if
rpe_obs is significantly low then we have neo-endemism
- Else we have mixed age endemism, in which case
- If both
pe_obs and pe_alt_obs are highly significant (p < 0.01) then we have super endemism (high in both paleo and neo)
- Else we have mixed (some mixture of paleo, neo and nonendemic)
- Else if neither
pe_obs or pe_alt_obsare significantly high then we have a non-endemic cell
cpr_classify_endem() carries this out automatically on the results from cpr_rand_test(), adding a factor called endem_type:
acacia_canape <- cpr_classify_endem(acacia_rand_res)
table(acacia_canape$endem_type)
#>
#> mixed neo not significant paleo super
#> 191 9 2767 44 26
Classify signficance
A similar function to cpr_classify_endem() is available to classify significance of the randomization test, cpr_classify_signif(). Note that both of these take a data.frame as input and return a data.frame as output, so they are “pipe-friendly.” The second argument of cpr_classify_signif() is the name of the biodiversity metric that you want to classify. This will add a column *_signif with the significance relative to the random distribution for that metric. For example, cpr_classify_signif(df, "pd") will add the pd_signif column to df.
We can chain them together as follows:
acacia_canape <-
cpr_classify_endem(acacia_rand_res) |>
cpr_classify_signif("pd") |>
cpr_classify_signif("rpd") |>
cpr_classify_signif("pe") |>
cpr_classify_signif("rpe")
# Take a look at one of the significance classifications:
table(acacia_canape$pd_signif)
#>
#> < 0.01 < 0.025 > 0.975 > 0.99 not significant
#> 296 219 25 22 2475
Visualize results
With the randomizations and classification steps taken care of, we can now visualize the results to see how they match up with those of Mishler et al. (2014).
Note that the results will not be identical because we have used a different randomization algorithm from the paper and because of stochasticity in the random values.
Here is Figure 2, showing the results of the randomization test for PE, RPE, PE, and RPE:
# Fist do some data wrangling to make the results easier to plot (add lat/long columns)
acacia_canape <- as_tibble(acacia_canape, rownames = "site") |>
separate(site, c("long", "lat"), sep = ":") |>
mutate(across(c(long, lat), parse_number))
a <- ggplot(acacia_canape, aes(x = long, y = lat, fill = pd_signif)) +
geom_tile() +
# cpr_signif_cols is a color palette in canaper for significance colors
scale_fill_manual(values = cpr_signif_cols, name = "Phylogenetic diversity") +
guides(fill = guide_legend(title.position = "top", label.position = "bottom"))
b <- ggplot(acacia_canape, aes(x = long, y = lat, fill = rpd_signif)) +
geom_tile() +
scale_fill_manual(values = cpr_signif_cols, name = "Relative phylogenetic diversity") +
guides(fill = guide_legend(title.position = "top", label.position = "bottom"))
c <- ggplot(acacia_canape, aes(x = long, y = lat, fill = pe_signif)) +
geom_tile() +
scale_fill_manual(values = cpr_signif_cols, name = "Phylogenetic endemism") +
guides(fill = guide_legend(title.position = "top", label.position = "bottom"))
d <- ggplot(acacia_canape, aes(x = long, y = lat, fill = rpe_signif)) +
geom_tile() +
scale_fill_manual(values = cpr_signif_cols, name = "Relative phylogenetic endemism") +
guides(fill = guide_legend(title.position = "top", label.position = "bottom"))
a + b + c + d + plot_annotation(tag_levels = "a") & theme(legend.position = "top")
And here is Figure 3, showing the results of CANAPE:
a <- ggplot(acacia_canape, aes(x = long, y = lat, fill = endem_type)) + geom_tile() +
# cpr_endem_cols is a color palette in canaper for endemism colors
scale_fill_manual(values = cpr_endem_cols) +
guides(fill = guide_legend(title.position = "top", label.position = "bottom")) +
theme(legend.position = "bottom", legend.title = element_blank())
b <- ggplot(acacia_canape, aes(x = pe_alt_obs, y = pe_obs, color = endem_type)) +
geom_abline(slope = 1, color = "darkgrey") +
geom_point() +
scale_color_manual(values = cpr_endem_cols) +
labs(
x = "Phylogenetic endemism on comparison tree",
y = "Phylogenetic endemism on actual tree"
) +
theme_bw() +
theme(legend.position = "none")
a + b + plot_layout(ncol = 1) + plot_annotation(tag_levels = "a")
