enfa {adehabitat} | R Documentation |
enfa
performs an Ecological-Niche Factor Analysis.
hist.enfa
draws histograms of the row scores or of the initial
variables of the ENFA.
data2enfa
prepares data (kasc and localizations) to be analyzed
by the ENFA.
enfa(tab, pr, scannf = TRUE, nf = 1) hist.enfa(x, scores = TRUE, type = c("h", "l"), adjust = 1, Acol, Ucol, Aborder, Uborder, Alwd = 1, Ulwd = 1, ...) data2enfa(kasc, pts)
tab |
a data frame describing the available units |
pr |
a vector giving the utilization weights associated to each unit |
scannf |
logical. Whether the eigenvalues barplot should be displayed |
nf |
an integer indicating the number of kept specialization axes |
x |
an object of class enfa |
scores |
logical. If TRUE , the histograms display
the row scores of the ENFA. If FALSE , they display the
niche on the environmental variables (in this case, this is equivalent to
histniche ) |
type |
what type of plot should be drawn. Possible types are: * "h" for histograms,* "l" for kernel density estimates (see ?density ).By default, type = "h" is used. If type = "l" is used,
the position of the mean of each distribution is indicated by dotted
lines |
adjust |
if type = "l" , a parameter used to control the
bandwidth of the density estimates (see ?density ) |
Acol |
if type = "h" , a color to be used to fill the histogram
of the available pixels. if type = "l" , a color to be used for the
kernel density estimates of the available pixels |
Ucol |
if type = "h" , a color to be used to fill the histogram
of the used pixels. if type = "l" , a color to be used for the
kernel density estimates of the used pixels |
Aborder |
color for the border of the histograms of the available pixels |
Uborder |
color for the border of the histograms of the used pixels |
Alwd |
if type = "l" , the line width of the kernel density
estimates of the available pixels |
Ulwd |
if type = "l" , the line width of the kernel density
estimates of the used pixels |
kasc |
a raster map of class kasc |
pts |
a data frame with two columns, giving the coordinates of the species locations |
... |
further arguments passed to or from other methods |
The niche concept, as defined by Hutchinson (1957), considers the
ecological niche of a species as an hypervolume in the multidimensional
space defined by environmental variables, within
which the populations of a species can persist. The Ecological-Niche
Factor Analysis (ENFA) has been developped by Hirzel et al. (2002) to
analyse the position of the niche in the ecological space. Nicolas
Perrin (1984) described the position of the niche in the n-dimensional
space using two measures: the M-specialization (hereafter termed marginality)
and the S-specialization (hereafter termed specialization). The
marginality represents the squared distance of the niche barycentre from
the mean available habitat. A large specialization corresponds to a
narrow niche relative to the habitat conditions available to the species.
The ENFA first extracts an axis of marginality (vector from the average of available habitat conditions to the average used habitat conditions). Then the analysis extracts successives orthogonal axes (i.e. uncorrelated), which maximises the specialization of the species. The calculations used in the function are described in Hirzel et al. (2002).
enfa
returns a list of class enfa
containing the
following components:
call |
original call. |
tab |
a data frame with n rows and p columns. |
pr |
a vector of length n containing the number of points in each pixel of the map. |
nf |
the number of kept specialization axes. |
m |
the marginality (squared length of the marginality vector). |
s |
a vector with all the eigenvalues of the analysis. |
lw |
row weights, a vector with n components. |
li |
row coordinates, data frame with n rows and nf columns. |
co |
column coordinates, data frame with p rows and nf columns. |
mar |
coordinates of the marginality vector. |
tab |
a data frame with n rows and p columns. |
pr |
a vector of length n containing the number of points in each pixel of the map. |
index |
an integer vector giving the position of the rows of
tab in the initial object of class kasc . |
attr |
an object of class mapattr with the attributes of the
initial kasc . |
Mathieu Basille basille@biomserv.univ-lyon1.fr
Hutchinson, G.E. (1957) Concluding Remarks. Cold Spring Harbor Symposium on Quantitative Biology, 22: 415–427.
Perrin, N. (1984) Contribution à l'écologie du genre Cepaea (Gastropoda) : Approche descriptive et expérimentale de l'habitat et de la niche écologique. Thèse de Doctorat. Université de Lausanne, Lausanne.
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.
niche
, kselect
for other types of
analysis of the niche, when several species are under studies,
niche.test
to perform a test of the marginality and the
tolerance of the niche, and scatter.enfa
to have a
graphical display of objects of class enfa
.
## Not run: data(lynxjura) map <- lynxjura$map ## We keep only "wild" indices. tmp <- lynxjura$locs[,4]!="D" locs <- lynxjura$locs[tmp, c("X","Y")] hist(map, type = "l") ## The variable artif is far from symetric ## We perform a square root transformation ## of this variable ## We therefore normalize the variable 'artif' map[,4] <- sqrt(map[,4]) hist(map, type = "l") ## We prepare the data for the ENFA (dataenfa1 <- data2enfa(map, locs)) ## We then perform the ENFA (enfa1 <- enfa(dataenfa1$tab, dataenfa1$pr, scannf = FALSE)) hist(enfa1) hist(enfa1, scores = FALSE, type = "l") ## randomization test and scatterplot (renfa <- randtest(enfa1)) plot(renfa) scatter(enfa1) ## End(Not run)