Before calculating diversity a metacommunity
object must be created. This object contains all the information needed to calculate diversity. In the following example, we generate a metacommunity (partition
) comprising two species ("cows" and "sheep"), and partitioned across three subcommunities (a, b, and c).
# Load the package into R library(rdiversity) # Initialise data partition <- data.frame(a=c(1,1),b=c(2,0),c=c(3,1)) row.names(partition) <- c("cows", "sheep")
The metacommunity()
function takes two arguments, partition
and similarity
. When species are considered completely distinct, an identity matrix is required, which is generated automatically if the similarity
argument is missing, as below:
# Generate metacommunity object meta <- metacommunity(partition = partition)
Note that a warning is displayed when abundances (rather than relative abundances) are entered into the partition
argument. Both are acceptable inputs.
When species share some similarity and a similarity matrix is available, then a similarity object (and the metacommunity object) is generated in the following way:
# Initialise similarity matrix s <- matrix(c(1, 0.5, 0.5, 1), nrow = 2) row.names(s) <- c("cows", "sheep") colnames(s) <- c("cows", "sheep") # Generate similarity object s <- similarity(similarity = s, dat_id = "my_taxonomic") # Generate metacommunity object meta <- metacommunity(partition = partition, similarity = s)
Alternatively, if a distance matrix is available, then a distance object is generated in the following way:
# Initialise distance matrix d <- matrix(c(0, 0.7, 0.7, 0), nrow = 2) row.names(d) <- c("cows", "sheep") colnames(d) <- c("cows", "sheep") # Generate distance object d <- distance(distance = d, dat_id = "my_taxonomic") # Convert the distance object to similarity object (by means of a linear or exponential transform) s <- dist2sim(dist = d, transform = "linear") # Generate metacommunity object meta <- metacommunity(partition = partition, similarity = s)
Each metacommunity
object contains the following slots:
@type_abundance
: the abundance of types within a metacommunity, @similarity
: the pair-wise similarity of types within a metacommunity, @ordinariness
: the ordinariness of types within a metacommunity, @subcommunity_weights
: the relative weights of subcommunities within a metacommunity, and@type_weights
: the relative weights of types within a metacommunity.This method uses a wrapper function to simplify the pipeline and is recommended if only a few measures are being calculated.
A complete list of these functions is shown below:
raw_sub_alpha()
: estimate of naive-community metacommunity diversity norm_sub_alpha()
: similarity-sensitive diversity of subcommunity j in isolation raw_sub_rho()
: redundancy of subcommunity j norm_sub_rho()
: representativeness of subcommunity j raw_sub_beta()
: distinctiveness of subcommunity j norm_sub_beta()
: estimate of effective number of distinct subcommunities sub_gamma()
: contribution per individual toward metacommunity diversity raw_meta_alpha()
: naive-community metacommunity diversity norm_meta_alpha()
: average similarity-sensitive diversity of subcommunities raw_meta_rho()
: average redundancy of subcommunities norm_meta_rho()
: average representativeness of subcommunities raw_meta_beta()
: average distinctiveness of subcommunities norm_meta_beta()
: effective number of distinct subcommunities meta_gamma()
: metacommunity similarity-sensitive diversity Each of these functions take two arguments, meta
(a metacommunity
object) and qs
(a vector of q values), and output results as a rdiv
object. For example, to calculate normalised subcommunity alpha diversity for q=0, q=1, and q=2:
# Initialise data partition <- data.frame(a=c(1,1),b=c(2,0),c=c(3,1)) row.names(partition) <- c("cows", "sheep") # Generate a metacommunity object meta <- metacommunity(partition) # Calculate diversity norm_sub_alpha(meta, 0:2)
However, if multiple measures are required and computational efficiency is an issue, then the following method is recommended (the same results are obtained).
This method requires that we first calculate the species-level components, by passing a metacommunity
object to the appropriate function; raw_alpha()
, norm_alpha()
, raw_beta()
, norm_beta()
, raw_rho()
, norm_rho()
, or raw_gamma()
. Subcommunity- and metacommunity-level diversities are calculated using the functions subdiv()
and metadiv()
. Since both subcommunity and metacommunity diversity measures are transformations of the same species-level component, this method is computationally more efficient.
# Initialise data partition <- data.frame(a=c(1,1),b=c(2,0),c=c(3,1)) row.names(partition) <- c("cows", "sheep") # Generate a metacommunity object meta <- metacommunity(partition) # Calculate the species-level component for normalised alpha component <- norm_alpha(meta) # Calculate normalised alpha at the subcommunity-level subdiv(component, 0:2) # Likewise, calculate normalised alpha at the metacommunity-level metadiv(component, 0:2)
In some instances, it may be useful to calculate all subcommunity (or metacommunity) measures. In which case, a metacommunity
object may be passed directly to subdiv()
or metadiv()
:
# Calculate all subcommunity diversity measures subdiv(meta, 0:2) # Calculate all metacommunity diversity measures metadiv(meta, 0:2)
# Taxonomic lookup table Species <- c("tenuifolium", "asterolepis", "simplex var.grandiflora", "simplex var.ochnacea") Genus <- c("Protium", "Quararibea", "Swartzia", "Swartzia") Family <- c("Burseraceae", "Bombacaceae", "Fabaceae", "Fabaceae") Subclass <- c("Sapindales", "Malvales", "Fabales", "Fabales") lookup <- cbind.data.frame(Species, Genus, Family, Subclass) # Partition matrix partition <- matrix(rep(1, 8), nrow = 4) colnames(partition) <- LETTERS[1:2] rownames(partition) <- lookup$Species
and assign values for each taxonomic level:
values <- c(Species = 0, Genus = 1, Family = 2, Subclass = 3, Other = 4)
tax2dist()
function:d <- tax2dist(lookup, values)
By default the tax2dist()
argument precompute_dist
is TRUE, such that a pairwise distance matrix is calculated automatically and is stored in d@distance
. If the taxonomy is too large, precompute_dist
can be set to FALSE, which enables pairwise taxonomic similarity to be calculated on the fly, in step 4.
dist2sim()
function:s <- dist2sim(d, "linear")
metacommunity()
function:meta <- metacommunity(partition, s)
meta_gamma(meta, 0:2)
Phylogenetic diversity measures can be broadly split into two categories – those that look at the phylogeny as a whole, such as Faith’s (1992) phylogenetic diversity (Faith’s PD), and those that look at pairwise tip distances, such as mean pairwise distance (MPD; Webb, 2000). The framework of measures presented in this package is able to quantify phylogenetic diversity using both of these methods.
# Example data tree <- ape::rtree(4) partition <- matrix(1:12, ncol=3) partition <- partition/sum(partition)
phy2dist()
function: d <- phy2dist(tree)
By default the phy2dist()
argument precompute_dist
is TRUE, such that a pairwise distance matrix is calculated automatically and is stored in d@distance
. If the taxonomy is too large, precompute_dist
can be set to FALSE, which enables pairwise taxonomic similarity to be calculated on the fly, in step 4.
dist2sim()
function:s <- dist2sim(d, "linear")
metacommunity()
functionmeta <- metacommunity(partition, s)
meta_gamma(meta, 0:2)
tree <- ape::rtree(4) partition <- matrix(1:12, ncol=3) partition <- partition/sum(partition) colnames(partition) <- letters[1:3] row.names(partition) <- paste0("sp",1:4) tree$tip.label <- row.names(partition)
phy2branch()
functions <- phy2branch(tree, partition)
metacommunity()
functionmeta <- metacommunity(partition, s)
meta_gamma(meta, 0:2)
Note that: a metacommunity that was generated using this approach will contain three additional slots:
@raw_abundance
: the relative abundance of terminal species (where types are then considered to be historical species),@raw_structure
: the length of evolutionary history of each historical species@parameters
: parameters associated with historical speciespinfsc50
must be installed for this example to work.library(rdiversity) vcf_file <- system.file("extdata", "pinf_sc50.vcf.gz", package = "pinfsc50") #read in twice: first for the column names then for the data tmp_vcf <- readLines(vcf_file) vcf_data <- read.table(vcf_file, stringsAsFactors = FALSE) # filter for the columns names vcf_names <- unlist(strsplit(tmp_vcf[grep("#CHROM",tmp_vcf)],"\t")) names(vcf_data) <- vcf_names partition <- cbind.data.frame(A = c(rep(1, 9), rep(0, 9)), B = c(rep(0, 9), rep(1, 9))) partition <- partition/sum(partition)
gen2dist()
function:d <- gen2dist(vcf)
dist2sim()
function:s <- dist2sim(d, transform = 'l')
Note: the dist2sim()
function contains an optional argument, max_d
, which defines the distance at which pairs of individuals have similarity zero. If not supplied this is set to the maximum distance observed in the distance matrix. If comparing different windows on a genome, for example, it is necessary to ensure max_d
is the same for each analysis.
metacommunity()
function:rownames(partition) <- rownames(s@similarity) meta <- metacommunity(partition, s)
norm_meta_beta(meta, 0:2)
partition <- matrix(sample(6), nrow = 3) rownames(partition) <- paste0("sp", 1:3) partition <- partition / sum(partition) d <- matrix(c(0,.75,1,.75,0,.3,1,.3,0), nrow = 3) rownames(d) <- paste0("sp", 1:3) colnames(d) <- paste0("sp", 1:3) d <- distance(d, "my_taxonomy") s <- dist2sim(d, "linear") meta <- metacommunity(partition, s)
partition <- matrix(sample(6), nrow = 3) rownames(partition) <- paste0("sp", 1:3) partition <- partition / sum(partition) s <- matrix(c(1,.8,0,.8,1,.1,0,.1,1), nrow = 3) rownames(s) <- paste0("sp", 1:3) colnames(s) <- paste0("sp", 1:3) s <- similarity(s, "my_functional") meta <- metacommunity(partition, s)
repartition()
tree <- ape::rtree(5) tree$tip.label <- paste0("sp", 1:5) partition <- matrix(rep(1,10), nrow = 5) row.names(partition) <- paste0("sp", 1:5) partition <- partition / sum(partition) s <- phy2branch(tree, partition) meta <- metacommunity(partition, s) new_partition <- matrix(sample(10), nrow = 5) row.names(new_partition) <- paste0("sp", 1:5) new_partition <- new_partition / sum(new_partition) new_meta <- repartition(meta, new_partition)
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