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labdsv (version 1.3-1)

duleg: Dufrene-Legendre Indicator Species Analysis

Description

Calculates the indicator value (fidelity and relative abundance) of species in clusters or types.

Usage

duleg(taxa,clustering,numitr=1000)
## S3 method for class 'duleg':
summary(object, p=0.05, \dots)

Arguments

taxa
a matrix or data.frame of samples with species as columns and samples as rows
clustering
a vector of numeric cluster memberships for samples, or a classification object returned from pam, or optpart, <
numitr
the number of randomizations to iterate to calculate probabilities
object
an object of class duleg
p
the maximum probability for a species to be listed in the summary
...
additional arguments to the summary function

Value

  • a list with components:
  • relfrqrelative frequency of species in classes
  • relaburelative abundance of species in classes
  • indvalthe indicator value for each species
  • maxclsthe class each species has maximum indicator value for
  • indclsthe indicator value for each species to its maximum class
  • pvalthe probability of obtaining as high an indicator values as observed over the specified iterations
  • The summary function simply returns the sum of probabilities for the species.

Details

Calculates the indicator value d of species as the product of the relative frequency and relative average abundance in clusters. Specifically,

where: $p_{i,j} =$ presence/absence (1/0) of species $i$ in sample $j$; $x_{i,j}$ = abundance of species $i$ in sample $j$; $n_c =$ number of samples in cluster $c$; for cluster $c$ in set $K$; $$f_{i,c} = {\sum_{j \in c} p_{i,j} \over n_c}$$ $$a_{i,c} = {(\sum_{j \in c} x_{i,j}) / n_c \over \sum_{k=1}^K ((\sum_{j \in k} x_{i,j}) / n_k)}$$ $$d_{i,c} = f_{i,c} \times a_{i,c}$$

References

Dufrene, M. and Legendre, P. 1997. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol. Monogr. 67(3):345-366.

See Also

isamic

Examples

Run this code
data(bryceveg) # returns a vegetation data.frame
    dis.bc <- dsvdis(bryceveg,'bray/curtis') # returns a dissimilarity matrix
    clust <- sample(1:5,nrow(bryceveg),replace=TRUE)
    duleg(bryceveg,clust)

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