smoky: Whittaker's Great Smoky Mountains Vegetation Data (USA)
In phytomosaic/ecole: ecole: School of Ecology Package

Description Usage Format Details Source References Examples

Trees and environment, showing vegetation changes along a moisture gradient in Great Smoky Mountains National Park, USA. This is Whittaker's (1956) Table 3.

smoky

A list of 2 data.frames:
- spe 12 observations of 41 woody plant species,
- env 12 observations of 7 environmental variables.

Species matrix values are abundance of tree species in 12 stations, (percentages of total woody plant stems per station > or = 1-inch diameter). Each station is an aggregate of 1 to 7 plots of variable size and sampling intensity (!). Presences < 0.5 percent are coded as 0.1 here.

Environmental matrix values are:
- mesic mesic indicator value,
- submesic submesic indicator value,
- subxeric subxeric indicator value,
- xeric xeric indicator value,
- treect count of trees per station,
- nsites count of sites per station,
- moisture position on a putative moisture gradient, ranging from 1 = mesic to 12 = xeric.
The first four variables are species weighted averages as moisture indicator values.

Whittaker's caption verbatim:
“Table 3. Composite transect of moisture gradient between 3500 and 4500 ft, distribution of trees along gradient. Transect along the moisture gradient from mesic valley sites (Sta. 1) to xeric southwest slope sites (Sta. 12), based on 46 site counts including 4906 stems from elevations between 3500 ft and 4500 ft. All figures are percentages of total stems in station from 1-in. diameter class up. ”

Table 3 in Whittaker (1956).

Whittaker, R. H. 1956. Vegetation of the Great Smoky Mountains. Ecological Monographs 26:2–80.

# split into two data.frames
data(smoky)
spe <- smoky$spe
env <- smoky$env

# describe the species abundance matrix
mx_diversity(spe)
mx_valid(spe)

# visualize the species abundance matrix
plot_heatmap(spe, xord='wa', yord='wa', logbase=10, yexp=1.7,
    asp=1)

# roughly following Whittaker's Fig. 4, top:
e <- cbind(sapply(env[,1:4], standardize), moisture=env$moisture)
plot(1:12, ylim=c(0,1), type='n', las=1, bty='l', ylab='')
for(i in 1:4){
     points(e[,i], pch=16, col=i)
     lines(loess(e[,i] ~ e[,'moisture']), col=i)
}