Function finds the best subset of environmental variables, so that the Euclidean distances of scaled environmental variables have the maximum (rank) correlation with community dissimilarities.
# S3 method for default
bioenv(comm, env, method = "spearman", index = "bray",
upto = ncol(env), trace = FALSE, partial = NULL,
metric = c("euclidean", "mahalanobis", "manhattan", "gower"),
parallel = getOption("mc.cores"), ...)
# S3 method for formula
bioenv(formula, data, ...)
bioenvdist(x, which = "best")The function returns an object of class bioenv with a
summary method.
Community data frame or a dissimilarity object or a square matrix that can be interpreted as dissimilarities.
Data frame of continuous environmental variables.
The correlation method used in cor.
The dissimilarity index used for community data (comm)
in vegdist. This is ignored if comm are dissimilarities.
Maximum number of parameters in studied subsets.
Model formula and data.
Trace the calculations
Dissimilarities partialled out when inspecting
variables in env.
Metric used for distances of environmental distances. See Details.
Number of parallel processes or a predefined socket
cluster. With parallel = 1 uses ordinary, non-parallel
processing. The parallel processing is done with parallel
package.
bioenv result object.
The number of the model for which the environmental
distances are evaluated, or the "best" model.
Other arguments passed to cor.
Jari Oksanen
The function calculates a community dissimilarity matrix using
vegdist. Then it selects all possible subsets of
environmental variables, scales the variables, and
calculates Euclidean distances for this subset using
dist. The function finds the correlation between
community dissimilarities and environmental distances, and for each
size of subsets, saves the best result. There are \(2^p-1\)
subsets of \(p\) variables, and an exhaustive search may take a
very, very, very long time (parameter upto offers a partial
relief).
The argument metric defines distances in the given set of
environmental variables. With metric = "euclidean", the
variables are scaled to unit variance and Euclidean distances are
calculated. With metric = "mahalanobis", the Mahalanobis
distances are calculated: in addition to scaling to unit variance,
the matrix of the current set of environmental variables is also
made orthogonal (uncorrelated). With metric = "manhanttan",
the variables are scaled to unit range and Manhattan distances are
calculated, so that the distances are sums of differences of
environmental variables. With metric = "gower", the Gower
distances are calculated using function
daisy. This allows also using factor
variables, but with continuous variables the results are equal to
metric = "manhattan".
The function can be called with a model formula where
the LHS is the data matrix and RHS lists the environmental variables.
The formula interface is practical in selecting or transforming
environmental variables.
With argument partial you can perform “partial”
analysis. The partializing item must be a dissimilarity object of
class dist. The
partial item can be used with any correlation method,
but it is strictly correct only for Pearson.
Function bioenvdist recalculates the environmental distances
used within the function. The default is to calculate distances for
the best model, but the number of any model can be given.
Clarke & Ainsworth (1993) suggested this method to be used for selecting the best subset of environmental variables in interpreting results of nonmetric multidimensional scaling (NMDS). They recommended a parallel display of NMDS of community dissimilarities and NMDS of Euclidean distances from the best subset of scaled environmental variables. They warned against the use of Procrustes analysis, but to me this looks like a good way of comparing these two ordinations.
Clarke & Ainsworth wrote a computer program BIO-ENV giving the name to the current function. Presumably BIO-ENV was later incorporated in Clarke's PRIMER software (available for Windows). In addition, Clarke & Ainsworth suggested a novel method of rank correlation which is not available in the current function.
Clarke, K. R & Ainsworth, M. 1993. A method of linking multivariate community structure to environmental variables. Marine Ecology Progress Series, 92, 205--219.
# The method is very slow for large number of possible subsets.
# Therefore only 6 variables in this example.
data(varespec)
data(varechem)
sol <- bioenv(wisconsin(varespec) ~ log(N) + P + K + Ca + pH + Al, varechem)
sol
summary(sol)
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