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.
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## Default S3 method: 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 class 'formula' bioenv(formula, data, ...) bioenvdist(x, which = "best")
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
The dissimilarity index used for community data (
Maximum number of parameters in studied subsets.
Trace the calculations
Dissimilarities partialled out when inspecting
Metric used for distances of environmental distances. See Details.
Number of parallel processes or a predefined socket
The number of the model for which the environmental
distances are evaluated, or the
Other arguments passed to
The function calculates a community dissimilarity matrix using
vegdist. Then it selects all possible subsets of
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
metric defines distances in the given set of
environmental variables. With
metric = "euclidean", the
variables are scaled to unit variance and Euclidean distances are
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
the LHS is the data matrix and RHS lists the environmental variables.
The formula interface is practical in selecting or transforming
partial you can perform “partial”
analysis. The partializing item must be a dissimilarity object of
partial item can be used with any correlation
but it is strictly correct only for Pearson.
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.
The function returns an object of class
bioenv with a
If you want to study the ‘significance’ of
results, you can use function
mantel.partial which use the same definition of
bioenv standardizes environmental
variables depending on the used metric, and you must do the same in
mantel for comparable results (the standardized data are
returned as item
x in the result object). It is safest to use
bioenvdist to extract the environmental distances that really
were used within
bioenv selects variables
to maximize the Mantel correlation, and significance tests based on
a priori selection of variables are biased.
Clarke, K. R & Ainsworth, M. 1993. A method of linking multivariate community structure to environmental variables. Marine Ecology Progress Series, 92, 205–219.
for underlying routines,
metaMDS for ordination,
protest for an alternative, and
rankindex for studying alternatives to the default
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