'Metacommunity' is a wrapper for the analysis of the three elements of metacommunity structure (coherence, boundary clumping, & turnover) following the framework of Leibold & Mikkelson 2002. It is important to note here that the results of boundary clumping and turnover are only relevant if the matrix is significantly positively coherent (i.e. empirical matrix has fewer embedded absences than null matrices).
Metacommunity(comm, scores = 1, method = "r1",
turnoverMethod = "EMS", sims = 1000, order = TRUE,
orderNulls = FALSE, allowEmpty = FALSE, binary = TRUE,
verbose = FALSE, seed = 1)community data in the form of a presence absence matrix
Axis scores to ordinate matrix. 1: primary axis scores (default) 2: secondary axis scores. See Details.
null model randomization method used by 'nullmaker'. See details.
(default='EMS') 'EMS' option generates null matrices as in Leibold and Mikkelson's original framework. However, null models that randomize occurrences -- introducing embedded absences (bad), but in a far less constrained null space (good) -- can also be used
number of simulated null matrices to use in analysis
logical argument indicating whether to ordinate the interaction matrix or not. Default is TRUE. See details.
logical argument indicating whether to ordinate the null matrices. Default is FALSE.
logical argument indicating whether to allow null matrices to have empty rows or columns
logical argument indicating whether to ordinate the community matrix based on abundance or binary (default) data.
Logical. Prints a graphical progress bar that tracks the creation of null matrices. Useful for conservative null models on large and/or sparse data.
seed for simulating the null model. Null matrices should be repeatable.
A list of length 4, containing; Comm -- ordinated matrix used to calculate coherence, boundary clumping & turnover
Coherence --output of the Coherence() function, giving information on the number of embedded absences and the significance relative to simulated null matrices
Turnover -- output of the Turnover() function, testing the number of species replacements relative to simulated null matrices
Boundary -- output of the BoundaryClump() function, which calculates the Morisita's index, assessing significance using a chi-squared test
'method' is an argument handed to functions in the 'vegan' package. Leibold & Mikkelson advocated the use of equiprobable rows and columns (provided that rows and columns had at least one entry). This method is called 'r00'. Methods maintaining row (site) frequencies include 'r0','r1' & 'r2'. The default method argument is 'r1', which maintains the species richness of a site (row totals) and fills species ranges (columns) based on their marginal probabilities. Arguably the most conservative null algorithm is the fixed row - fixed column total null, which can be attained using many of swap algorithms described in the vegan package (sequential methods like 'tswap', 'swap', and non-sequential 'quasiswap' and 'backtracking'). See the help file for 'commsim' or Wright et al. 1998 for more information.
If 'order' is FALSE, the interaction matrix is not ordinated, allowing the user to order the matrix based on site characteristics or other biologically relevant characteristics. The 'orderNulls' argument allows the user to ordinate the null matrices. While creating a more conservative test, this may negate the null model implemented (fixed row and column sums will not be maintained)
Dallas,T. 2014. metacom: an R package for the analysis of metacommunity structure. Ecography. DOI:10.1111/j.1600-0587.2013.00695.x
Leibold, M.A. and G.M. Mikkelson. 2002. Coherence, species turnover, and boundary clumping: elements of meta-community structure. Oikos 97: 237 - 250.
Presley, S. J., C. L. Higgins, and M. R. Willig. 2010. A comprehensive framework for the evaluation of metacommunity structure. Oikos 119:908-917
Wright, D.H., Patterson, B.D., Mikkelson, G.M., Cutler, A. & Atmar, W. (1998). A comparative analysis of nested subset patterns of species composition. Oecologia 113, 1-20.
# NOT RUN {
#define an interaction matrix
data(TestMatrices)
intmat <- TestMatrices[[7]]
#determines the elements of metacommunity structure
ems.test <- Metacommunity(intmat, method='r1', sims=100, scores=1)
# }
Run the code above in your browser using DataLab