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 package 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! Here’s 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 x 6 ## expression min median `itr/sec` mem_alloc `gc/sec` ## <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl> ## 1 picante 11.9m 11.9m 0.00140 287.9GB 0.122 ## 2 caper 21.1s 21.1s 0.0474 21.6GB 0.284 ## 3 phyloregion 64.5ms 67.2ms 14.8 882.2KB 0
autoplot(res)
Comparing phyloregion against other packages in terms of speed and memory efficiency in computing evolutionary distinctiveness.
Here, phyloregion is several orders of magnitude faster and efficient in memory allocation than the other packages.
The function in phyloregion 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.
