indicspecies (version 1.6.7)

indicators: Indicator analysis for species combinations

Description

Determines the indicator value of species combinations.

Usage

indicators(X, cluster, group, func="IndVal", max.order=5, At=0, Bt=0, sqrtIVt=0, nboot=0, alpha=0.05, XC=TRUE, enableFixed = FALSE, verbose = FALSE)
## S3 method for class 'indicators':
print(x, At=0, Bt=0, sqrtIVt=0, selection=NULL, confint=FALSE,...)
## S3 method for class 'indicators':
plot(x, type="sqrtIV", maxline=TRUE,...)

Value

An object of class indicators with:CA matrix describing all the combinations studied.XCA matrix containing the abundance/occurrence of each species combination.APositive predictive power of species combinations. If nboot>0 then this includes the lower and upper bounds of the confidence interval.BSensitivity of species combinations. If nboot>0 then this includes the lower and upper bounds of the confidence interval.sqrtIVSquare root of indicator value of species combinations. If nboot>0 then this includes the lower and upper bounds of the confidence interval.group.vecA logical vector indicating the membership to the target group.

encoding

UTF-8

Details

Function indicators creates explores the indicator value of the simultaneous occurrence of sets of species (i.e. species combinations). The method is described in De Cáceres{Caceres} et al. (2012) and is a generalization of the Indicator Value method of Dufrêne{Dufrene} & Legendre (1997). The maximum number of species conforming the species combination can be controlled using max.order. For each combination of species it determines its positive predictive value (A), sensitivity (B) and the square root of indicator value (sqrtIV). If nboot>0 then bootstrap confidence intervals are determined with the specified alpha level, as explained in De Cáceres{Caceres} & Legendre (2009). The combinations to be kept can be restricted to those whose positive predictive value, sensitivity and/or indicator value are equal or greater than input thresholds. Function print allows printing the results in a nice table, whereas plot draws the statistics against the order (i.e. the number of species) of the combination.

References

De Cáceres{Caceres}, M., Legendre, P., Wiser, S.K. and Brotons, L. 2012. Using species combinations in indicator analyses. Methods in Ecology and Evolution 3(6): 973-982.

De Cáceres{Caceres}, M. and Legendre, P. 2009. Associations between species and groups of sites: indices and statistical inference. Ecology 90(12): 3566-3574.

Dufrêne{Dufrene}, M. and P. Legendre. 1997. Species assemblages and indicator species: The need for a flexible asymetrical approach. Ecological Monographs 67:345-366.

See Also

pruneindicators, coverage, multipatt, strassoc, signassoc

Examples

library(stats)

data(wetland) ## Loads species data

## Creates three clusters using kmeans
wetkm = kmeans(wetland, centers=3) 

## Determine sensitivity of individual species
B=strassoc(wetland, cluster=wetkm$cluster,func="B") 

## Select species with more than 20% of sensitivity for the first group
sel=which(B[,1]>0.2) 

## Run indicator analysis with species combinations for the first group
sc= indicators(X=wetland[,sel], cluster=wetkm$cluster, group=1, verbose=TRUE, At=0.5, Bt=0.2)

#Prints the results
print(sc)

## Plots positive predictive power and sensitivity against the order of combinations
plot(sc, type="A")
plot(sc, type="B")

## Run indicator analysis with species combinations for the first group, 
## but forcing 'Orysp' to be in all combinations
sc2= indicators(X=wetland[,sel], cluster=wetkm$cluster, group=1, verbose=TRUE, At=0.5, Bt=0.2, enableFixed=TRUE)