Oribates: Borcard's Obitatid Mite Data Set

In guenardg/constr.hclust: Space- And Time-Constrained Clustering Package

Borcard's Obitatid Mite Data Set

Description

Oribatid mite community data in a peat bog surrounding Lac Geai, QC, Canada

Usage

data(Oribates)

Format

A list with six elements:

fau

A data frame with 70 rows (sites) and 35 columns (species) whose contents are the abundances of the species in the sites.

env

A data frame with 70 rows (sites) and five columns (variables) whose contents are environmental variables taken on the sites.

xy

Cartesian coordinates of the sites in the study area.

link

A list of edges between neighboring locations (see details).

topo

A list of color values for representing the topography of the study area.

map

A raw color raster of the topography of the study area.

Details

Variables of oribatid$env are:

SubsDens

Substrate density (g/L).

WatrCont

Water content of the peat (g/L)

Substrate

A seven-level factor describing the substrate (more on that subject below.

Shrub

A three-level factor describing the presence and abundance of shrubs (mainly Ericaceae ) on the peat surface.

topo

A two-level factor describing the microtopography of the peat mat.

Levels of oribatid$env$Substrate are described as follows:

Sphagn1

Sphagnum magellanicum (with a majority of S. rubellum).

Sphagn2

Sphagnum rubellum.

Sphagn3

Sphagnum nemoreum (with a minority of S. angustifolium).

Sphagn4

Sphagnum rubellum and S. magellanicum in equal parts.

Litter

Ligneous litter.

Barepeat

Bare peat.

Interface

Interface between Sphagnum species.

Levels of oribatid$env$Shrub where: "none", "few", and "many" (the variable may also be considered semi-quantitative), whereas levels of oribatid$env$topo were "Blanket" (ie. flat) and "Hummock" (ie. raised).

Oribates$map is a color raster generated from Fig. 1 in Borcard et al. 1994. It has dimensions 244 (number of pixels along the Y axis) by 940 (number of pixels along the X axis) and describes an area of 2.6m (Y axis) by 10m (W axis) with a resolution of approximately 10.6mm per pixel. A higher resolution image from the same data can also be found as Fig. 1.1 in Borcard et al. 2018 (see references below). The X axis corresponds to locations going from the edge of the water to the edge of the forest. The Y axis correspond the distances along the lake's shore.

Author(s)

Daniel Borcard, <[e-mail]> and Pierre Legendre <[e-mail]>

References

Borcard, D. and Legendre, P. 1994. Environmental Control and Spatial Structure in Ecological Communities: An Example Using Oribatid Mites (Acari, Oribatei). Environ. Ecol. Stat. 1(1): 37-61 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/BF00714196")}

Borcard, D., Legendre, P., and Drapeau, P. 1992. Partialling out the spatial component of ecological variation. Ecology, 73, 1045-1055. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2307/1940179")}

Borcard, D.; Legendre, P.; and Gillet, F. 2018. Numerical Ecology with R (2nd Edition) Sprigner, Cham, Switzerland. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/978-3-319-71404-2")}

See Also

Data set oribatid from package ade4, which is another version of this data set.

Examples

data("Oribates",package="constr.hclust")

## A map of the study area with the links.
par(mar=rep(0,4L))
plot(NA,xlim=c(0,12),ylim=c(-0.1,2.5),yaxs="i",asp=1,axes=FALSE)
rasterImage(Oribates$map, 0, -0.1, 10, 2.5, interpolate=FALSE)
arrows(x0=0.15,x1=1.15,y0=0.1,y1=0.1,code=3,length=0.05,angle=90,lwd=2)
text(x=0.65,y=0.025,labels="1m")
invisible(
  apply(Oribates$link,1L,
        function(x,xy,labels) {
          segments(x0=xy[x[1L],1L],x1=xy[x[2L],1L],
                   y0=xy[x[1L],2L],y1=xy[x[2L],2L])
        },xy=Oribates$xy,labels=FALSE)
)
points(Oribates$xy,cex=1.25,pch=21,bg="black")
legend(10.1,2.5,legend=Oribates$topo[["Type"]],pt.bg=Oribates$topo[["RGB"]],
       pch=22L,pt.cex=2.5)

## Hellinger distance on the species composition matrix.
Oribates.hel <- dist(sqrt(Oribates$fau/rowSums(Oribates$fau)))

## Constrained clustering of the sites on the basis of their species
## composition.
Oribates.chclust <- constr.hclust(d=Oribates.hel, links=Oribates$link,
                                  coords=Oribates$xy)

## Plotting with different numbers of clusters.
par(mfrow=c(4,1),mar=c(0.5,0,0.5,0))
cols <- c("turquoise", "orange", "blue", "violet", "green", "red", "purple")
parts <- c(2,3,5,7)
for(i in 1L:length(parts)) {
  plot(NA, xlim=c(0,10), ylim=c(-0.1,2.5), xaxs="i", yaxs="i", asp=1,
       axes=FALSE)
  arrows(x0=0.15, x1=1.15, y0=0.1, y1=0.1, code=3, length=0.05, angle=90,
         lwd=2)
  text(x=0.65, y=0, labels="1m", cex=1.5)
  plot(Oribates.chclust, parts[i], links=TRUE, plot=FALSE,
       col=cols[round(seq(1,length(cols),length.out=parts[i]))], lwd=4,
       cex=2.5, pch=21, hybrids="single", lwd.hyb=0.25, lty.hyb=3)
  text(x=0.25, y=2.25, labels=LETTERS[i], cex=2.5)
}

guenardg/constr.hclust documentation built on July 13, 2024, 3:03 p.m.