compana: Compositional Analysis of Habitat Use

Description

compana performs a classical compositional analysis of habitat use (Aebischer et al., 1993).

Usage

compana(used, avail, test = c("randomisation", "parametric"),
        rnv = 0.01, nrep = 500, alpha = 0.1)

Value

Returns a list of the class compana:

used: the matrix of used habitats
avail: the matrix of available habitats
type.test: a character string. Either "randomisation" or "parametric"
test: the results of the test of habitat selection
rm: the ranking matrix: a square matrix with nh rows and nh columns, where nh is the number of habitat types under study. At the intersection of the row i and of the column j, there is a "+" when the habitat i is more used than the habitat in column, and "-" otherwise. When the difference is significant, the sign is tripled.
rmnb: the matrix containing the number of animals used to perform the tests in rm.
rank: the rank of the habitat types. It is equal to the number of "+" for each habitat type in row of rm.
rmv: the matrix of statistics used to build rm. If (test = "parametric"), the matrix contains the values of t, in the t-test comparing the row and the column habitat. If (test = "randomisation"), the matrix contains the mean difference between the used and available log-ratios (see Aebischer et al., 1993).
profile: the profile of preferences: resource types are sorted so that the left type is the most preferred and the right type is the most avoided. Habitats for which the intensity of habitat selection is similar (no significant difference) are connected by a line.

Arguments

used: a matrix or a data frame describing the percentage of use of habitats (in columns) by animals (in rows).
avail: a matrix or a data frame describing the percentage of availability of habitats (in columns) by animals (in rows).
test: a character string. If "randomisation", randomisation tests are performed for both the habitat ranking and the test of habitat selection. If "parametric", usual parametric tests are performed (chi-square for the test of habitat selection and t-tests for habitat ranking).
rnv: the number replacing the 0 values occurring in the matrix used.
nrep: the number of repetitions in the randomisation tests.
alpha: the alpha level for the tests.

Author

Clement Calenge clement.calenge@ofb.gouv.fr

Details

The compositional analysis of habitat use has been recommended by Aebischer et al. (1993) for the analysis of habitat selection by several animals, when the resources are defined by several categories (e.g. vegetation types).

This analysis is carried out in two steps: first the significance of habitat selection is tested (using a Wilks lambda). Then, a ranking matrix is built, indicating whether the habitat type in row is significantly used more or less than the habitat type in column. When this analysis is performed on radio-tracking data, Aebischer et al. recommend to study habitat selection at two levels: (i) selection of the home range within the study area, and (ii) selection of the relocations within the home range. The first level is termed second-order habitat selection on Johnson's scale (1980), and the second one, third-order habitat selection.

When zero values are found in the matrix of used habitats, they are replaced by a small value (by default, 0.01), according to the recommendations of Aebischer et al. (1993).

When zero values are found in the matrix of available habitats, the function compana uses the procedure termed "weighted mean lambda" described in Aebischer et al. (1993: Appendix 2), instead of the usual lambda (see examples). Zero values can be found in the matrix of available habitats when the third-order habitat selection is under focus. In this case, it may occur that some habitat types are available to some animals and not to the others.

Note that this method rely on the following hypotheses: (i) independence between animals, and (ii) all animals are selecting habitat in the same way (in addition to "traditional" hypotheses in these kinds of studies: no territoriality, all animals having equal access to all available resource units, etc.). The function eisera can be used as a preliminary to identify whether this is indeed the case (see examples).

References

Aebischer, N. J. and Robertson, P. A. (1992) Practical aspects of compositional analysis as applied to pheasant habitat utilisation. pp. 285--293 In: Priede, G. and Swift, S. M. Wildlife telemetry, remote monitoring and tracking of animals.

Aebischer, N. J., Robertson, P. A. and Kenward, R. E. (1993) Compositional analysis of habitat use from animal radiotracking data. Ecology, 74, 1313--1325.

Johnson, D. H. (1980) The comparison of usage and availability measurements for evaluating resource preference. Ecology, 61, 65--71.

Examples

Run this code


## The examples presented here
## are the same as those presented in
## the paper of Aebischer et al. (1993)


#############################
## Pheasant dataset: first
## example in Aebischer et al.

data(pheasant)

## Second order habitat selection
## Selection of home range within the
## study area (example of parametric test)
pheana2 <- compana(pheasant$mcp, pheasant$studyarea,
                   test = "parametric")
pheana2

## The ranking matrix:
print(pheana2$rm, quote = FALSE)

## Third order habitat selection
## (relocation within home range)
## We remove the first pheasant of the analysis
## (as in the paper of Aebischer et al.)
## before the analysis
pheana3 <- compana(pheasant$locs[-1,], pheasant$mcp[-1,c(1,2,4)])
pheana3

## The ranking matrix:
print(pheana3$rm, quote = FALSE)



#############################
## Squirrel data set: second
## example in Aebischer et al.

data(squirrel)

## Second order habitat selection
## Selection of home range within the
## study area
squiana2 <- compana(squirrel$mcp, squirrel$studyarea)
squiana2

## The ranking matrix:
print(squiana2$rm, quote = FALSE)


## However, note that here, the hypothesis of identical use 
## on which this analysis relies is likely to be false.
## Indeed, an eisera indicates:

us <- round(30 * squirrel$locs / 100)
av <- squirrel$studyarea
ii <- eisera(us, av, scannf = FALSE)
scatter(ii, grid = FALSE, clab = 0.7)

## There are clearly two groups of animals.  In such cases,
## compositional analysis is to be avoided in this case.




## Third order habitat selection
## (relocation within home range)
## We remove the second column
## (as in the paper of Aebischer et al.)
squiana3 <- compana(squirrel$locs[,-2], squirrel$mcp[,-2])
squiana3

## The ranking matrix:
print(squiana3$rm, quote = FALSE)

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