Introduction
Evolutionary distinctiveness is a metric that quantifies how isolated a species is on a phylogenetic tree – some species have few or no close living relatives.
The standard method for calculating evolutionary distinctiveness is
either by using the R packages picante or
caper. For very large trees, such calculation is a
memory-intensive operation and a bottle-neck for these algorithms.
Because of these challenges, we developed a new method in our
phyloregion package that speeds up the process
significantly to produce results in seconds! See how:
Let’s try computing evolutionary distinctiveness for a tree with 5,000 species:
library(ape)
library(ggplot2)
# packages we benchmark
library(phyloregion)
library(picante)
library(caper)tree <- ape::rcoal(5000)
ed_picante <- function(x) picante::evol.distinct(x, type="fair.proportion")
ed_caper <- function(x) caper::ed.calc(x)
ed_phyloregion <- function(x) phyloregion::evol_distinct(x, type="fair.proportion")
res <- bench::mark(picante=ed_picante(tree),
caper=ed_caper(tree),
phyloregion=ed_phyloregion(tree), check=FALSE)
summary(res)## # A tibble: 3 × 6
## expression min median `itr/sec` mem_alloc `gc/sec`
## <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
## 1 picante 4.02m 4.02m 0.00415 NA 18.9
## 2 caper 9.17s 9.17s 0.109 NA 5.45
## 3 phyloregion 65.3ms 70.56ms 13.9 NA 1.98autoplot(res)
Here, phyloregion is several orders of magnitude faster and
efficient in memory allocation than the other packages.
The function in phyloreegion is called
evol_distinct and it is used as follows:
evol_distinct(tree, type = c("equal.splits", "fair.proportion"), ...)If you find this vignette tutorial useful, please cite in publications as:
Daru, B.H., Karunarathne, P. & Schliep, K. (2020) phyloregion: R package for biogeographic regionalization and macroecology. Methods in Ecology and Evolution 11: 1483-1491. doi: 10.1111/2041-210X.13478.