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.
1 2 3 4 5 6 7 8  ## 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")

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 
metric 
Metric used for distances of environmental distances. See Details. 
parallel 
Number of parallel processes or a predefined socket
cluster. With 
x 

which 
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
environmental variables, scale
s 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^p1
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 BIOENV giving the name to the current function. Presumably BIOENV 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 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
. NB., 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
BrayCurtis index.
1 2 3 4 5 6 7 
Loading required package: permute
Loading required package: lattice
This is vegan 2.43
Call:
bioenv(formula = wisconsin(varespec) ~ log(N) + P + K + Ca + pH + Al, data = varechem)
Subset of environmental variables with best correlation to community data.
Correlations: spearman
Dissimilarities: bray
Metric: euclidean
Best model has 3 parameters (max. 6 allowed):
P Ca Al
with correlation 0.4004806
size correlation
P 1 0.2513
P Al 2 0.4004
P Ca Al 3 0.4005
P Ca pH Al 4 0.3619
log(N) P Ca pH Al 5 0.3216
log(N) P K Ca pH Al 6 0.2822
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