Description Usage Arguments Details Value Note Author(s) References See Also Examples
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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comm |
Community data frame or a dissimilarity object or a square matrix that can be interpreted as dissimilarities. |
env |
Data frame of continuous environmental variables. |
method |
The correlation method used in |
index |
The dissimilarity index used for community data ( |
upto |
Maximum number of parameters in studied subsets. |
formula, data |
Model |
trace |
Trace the calculations |
partial |
Dissimilarities partialled out when inspecting
variables in |
... |
Other arguments passed to |
The function calculates a community dissimilarity matrix using
vegdist
. Then it selects all possible subsets of
environmental variables, scale
s the variables, and
calculates Euclidean distances for this subset using
dist
. Then it 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 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.
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
summary
method.
If you want to study the ‘significance’ of bioenv
results, you can use function mantel
or
mantel.partial
which use the same definition of
correlation.
However, bioenv
standardizes environmental variables to unit standard
deviation using function scale
and you must do the same
in mantel
for comparable results. Further, bioenv
selects variables to maximize the Mantel correlation, and significance
tests based on a priori selection of variables are biased.
Jari Oksanen
Clarke, K. R & Ainsworth, M. 1993. A method of linking multivariate community structure to environmental variables. Marine Ecology Progress Series, 92, 205–219.
vegdist
, dist
, cor
for underlying routines, monoMDS
and
metaMDS
for ordination, procrustes
for
Procrustes analysis, protest
for an alternative, and
rankindex
for studying alternatives to the default
Bray-Curtis index.
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