predict.enfa {adehabitat} | R Documentation

## Habitat Suitability Maps Built from the ENFA

### Description

`predict.enfa` computes habitat suitability maps using the Ecological-Niche Factor Analysis and the Mahalanobis distances method.

### Usage

    predict.enfa(object, index, attr, nf, ...)

### Arguments

`object` | an object of class `enfa`
`index` | an integer vector giving the position of the rows of `tab` in the initial object of class `kasc`.
`attr` | an object of class `kasc` or `mapattr`.
`nf` | the number of axes of specialization kept for the predictions. By default, all axes kept in `object` are used
`...` | further arguments passed to or from other methods

### Details

The predictions are based on the position of the niche defined by the ENFA within the multidimensional space of environmental variables. The ENFA produces row coordinates for each pixel, which are used with the function `mahalanobis`. For each pixel, this function computes the Mahalanobis distances from the barycentre of the niche.

Actually, the function `predict.enfa` is identical to the function `mahasuhab`, except that the habitat suitability map is computed using the axes of the ENFA, instead of the raw data.

### Value

Returns a raster map of class `kasc`.

### Author(s)

Mathieu Basille basille@biomserv.univ-lyon1.fr

### References

Clark, J.D., Dunn, J.E. and Smith, K.G. (1993) A multivariate model of female black bear habitat use for a geographic information system. Journal of Wildlife Management, 57, 519–526.

Hirzel, A.H., Hausser, J., Chessel, D. & Perrin, N. (2002) Ecological-niche factor analysis: How to compute habitat-suitability maps without absence data? Ecology, 83, 2027–2036.

### See Also

`mahalanobis` for information on the computation of Mahalanobis distances. `mahasuhab` for more details on the computation of habitat suitability maps using the Mahalanobis distances.

### Examples

    ## Not run:
    data(lynxjura)

    map <- lynxjura$map

    ## We keep only "wild" indices.
    tmp <- lynxjura$loc[,4] != "D"
    locs <- lynxjura$locs[tmp, c("X","Y")]
    dataenfa1 <- data2enfa(map, locs[tmp, c("X","Y")])

    (enfa1 <- enfa(dataenfa1$tab, dataenfa1$pr,
                   scannf = FALSE))

    ## Compute the prediction
    pred <- predict(enfa1, dataenfa1$index, dataenfa1$attr)
    image(pred)
    contour(pred, col="green", add=T)
    points(locs, col = "red", pch = 16)
    ## Lighter areas are the most preferred areas
    ## End(Not run)

[Package adehabitat version 1.4 Index]