aravo: Distribution of Alpine plants in Aravo (Valloire, France)

In ade4: Analysis of Ecological Data: Exploratory and Euclidean Methods in Environmental Sciences

Distribution of Alpine plants in Aravo (Valloire, France)

Description

This dataset describe the distribution of 82 species of Alpine plants in 75 sites. Species traits and environmental variables are also measured.

Usage

data(aravo)

Format

aravo is a list containing the following objects :

spe

is a data.frame with the abundance values of 82 species (columns) in 75 sites (rows).

env

is a data.frame with the measurements of 6 environmental variables for the sites.

traits

is data.frame with the measurements of 8 traits for the species.

spe.names

is a vector with full species names.

Details

The environmental variables are:

Aspect	Relative south aspect (opposite of the sine of aspect with flat coded 0)
Slope	Slope inclination (degrees)
Form	Microtopographic landform index: 1 (convexity); 2 (convex slope); 3 (right slope); 4 (concave slope); 5 (concavity)
Snow	Mean snowmelt date (Julian day) averaged over 1997-1999
PhysD	Physical disturbance, i.e., percentage of unvegetated soil due to physical processes
ZoogD	Zoogenic disturbance, i.e., quantity of unvegetated soil due to marmot activity: no; some; high

The species traits for the plants are:

Height	Vegetative height (cm)
Spread	Maximum lateral spread of clonal plants (cm)
Angle	Leaf elevation angle estimated at the middle of the lamina
Area	Area of a single leaf
Thick	Maximum thickness of a leaf cross section (avoiding the midrib)
SLA	Specific leaf area
Nmass	Mass-based leaf nitrogen content
Seed	Seed mass

Source

Choler, P. (2005) Consistent shifts in Alpine plant traits along a mesotopographical gradient. Arctic, Antarctic, and Alpine Research, 37,444–453.

Examples

data(aravo)
coa1 <- dudi.coa(aravo$spe, scannf = FALSE, nf = 2)
dudienv <- dudi.hillsmith(aravo$env, scannf = FALSE, nf = 2, row.w = coa1$lw)
duditrait <- dudi.pca(aravo$traits, scannf = FALSE, nf = 2, row.w = coa1$cw)
rlq1 <- rlq(dudienv, coa1, duditrait, scannf = FALSE, nf = 2)
plot(rlq1)

ade4 documentation built on Feb. 16, 2023, 7:58 p.m.