'BoundaryClump' calculates the Morisita's Index (Morisita 1962) for
presence-absence interaction matrices, using a chi-squared test to assess
significance.
Usage
BoundaryClump(comm, order = TRUE, scores = 1, binary = TRUE)
Arguments
comm
community data in the form of a presence absence matrix
order
logical argument indicating whether to ordinate the interaction
matrix or not. See details.
logical argument indicating whether to ordinate the community
matrix based on abundance or binary (default) data.
Details
This statistic is not based on randomization methods, so the function only
requires a presence-absence interaction matrix and two arguments regarding
the ordination of the empirical matrix.
The default is the range perspective, meaning that the analyses of boundary
clumping and species turnover compare the distribution of species among
sites. If the 'community' perspective is desired, transpose the matrix
before analysis using the transpose function ('t()'). However, the author
cautions against misinterpretation of the community perspective, as the
biological meaning of turnover and boundary clumping differ between
perspectives.
Boundary clumping, quantified by the Morisita's index, is a measure of the
degree to which species range boundaries overlap. This measure, and species
turnover, cannot be interpreted unless the network is significantly coherent
(see 'Coherence()').
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.
Value
'BoundaryClump' returns a data frame containing the calculated
Morisita's index ('index'), the corresponding p-value ('P'), and the degrees
of freedom ('df').
The p-value is based on a chi-squared test comparing the Morisita's index to
a value of 1. If the Morisita's index is less than 1, a left-tailed test is
performed (less clumping than expected by chance).
If the Morisita's index is greater than 1, a right-tailed test is performed
(more clumping han expected by chance)
Author(s)
Tad Dallas
References
Morisita, M. 1962. Id-index, a measure of dispersion of
individuals. Res. Popul. Ecol. 4, 1-7.
Examples
## define an interaction matrix
data(TestMatrices)
intmat=TestMatrices[[1]]
## analysis of boundary clumping
bound.test <- BoundaryClump(intmat, order=TRUE, scores=1, binary=TRUE)
## prints a single row data.frame containing the Morisita's index, p-value, and degrees of freedom
bound.test