Either install from CRAN, or directly from GitHub using the code below. Note that GitHub is the development version of the package, which may or may not differ from what's on CRAN.
# From CRAN
install.packages("metacom")# From GitHub
# install.packages("devtools")
devtools::install_github("taddallas/metacom")
library("metacom")Description:
'metacom' provides functions for the analysis of the elements of
metacommunity structure (coherence, boundary clumping, &
turnover), following the pattern-based metacommunity framework of
Leibold & Mikkelson 2002 and Presley et al. 2010. This package is
designed to allow the user to distinguish between several
idealized patterns of metacommunity structure (Presley et al.
2010) utilizing any number of null model algorithms for the
randomization procedure. However, these metrics can also be used
in isolation, and without ordination via reciprocal averaging, and
instead, ordering along some biological gradient.
Definition of a metacommunity:
A metacommunity is a set of sites (e.g. plants in plant-pollinator
networks) associated through interactions (e.g. insect species
(columns) interact with plant species (rows) in plant-pollinator
networks). The pattern-based metacommunity concept, proposed by
Leibold & Mikkelson 2002 and expounded on by Presley et al. 2010,
allows for the evaluation of metacommunity structure by using
randomization techniques to discern between 10 patterns of
metacommunity structure. This is performed by ordinating
site-by-species interaction matrices and calculating three
metrics; coherence, boundary clumping & turnover.
The Elements of Metacommunity Structure Framework:
The metacom package calculates these three metrics; coherence is
calculated using the function Coherence(), boundary clumping with
BoundaryClump(), and turnover (from either species or range
perspective) using the Turnover() function. These functions are
consolidated in the metacommunity() function, which can be used to
calculate all three metrics. In order to interpret the output of
these functions, it will be helpful to read Leibold & Mikkelson
2002 and Presley et al. 2010, but to also read Ulrich and Gotelli
2013, as this paper outlines the difficulty seemingly inherent
with investigating community structure. Also, these functions do
not have to be used strictly in the Leibold and Mikkelson 2002
framework.
A cautionary note:
I caution the user to be aware that the creation of null matrices
can be performed to allow (or not allow) sites to be empty, or
species to not exist at any site (i.e. column sums and/or row sums
are allowed to be zero). This is controlled by the logical
argument 'allow.empty' in the Metacommunity(), NullMaker(),
Coherence(), and Turnover() functions. Restricting nulls to not
allow empty rows or columns may be biologically realistic, but it
also reduces the number of unique null matrices that can be built,
which will impact computation time, making it infeasible or
impossible in some situations. These situations occur when you
have a very sparse interaction matrix, and is also influenced by
null model algorithm ('method') that you choose.
The 'metacom' package is partially adapted from previous Matlab code written by Christopher Higgins (available here) and relies on many functions in the 'vegan' package (Oksanen et al. 2012).