smoky: Whittaker's Great Smoky Mountains Vegetation Data (USA)
In phytomosaic/fitNMDS: Fitting Resampled and Reciprocal NMDS Models

Description Usage Format Details Source References Examples

A list of two data.frames ‘spe’ and ‘env’ derived from Whittaker's (1956) Table 3, showing vegetation changes along a moisture gradient in Great Smoky Mountains National Park, USA.

smoky

A list of 2 data.frames:

spe 12 observations of 41 woody plant species
env 12 observations of 7 nvironmental variables

Species matrix values are abundance of tree species in 12 stations between 3500 and 4500 ft. Abundances are percentages of total woody plant stems per station > or = 1-inch diameter). Each station is an aggregate of 1-7 plots of variable size and sampling intensity. Presences < 0.5 matrix.

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

where 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. ”

Location: Great Smoky Mountains of Tennessee and North Carolina, USA.

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

# visualize the species matrix
data(smoky)
z <- smoky$spe
ecole::plot_heatmap(z, xord=FALSE, yord=FALSE, yexp=1.7,
     logbase=10, asp=1)

# following Whittaker's Fig. 4 (top):
e <- sapply(env[,1:4], FUN=function(x)(x-min(x))/(max(x)-min(x)))
e <- cbind(e, 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)
}

# following Whittaker's Fig. 4 (middle):
nm <- c('tilia_heterophylla','halesia_monticola',
         'tsuga_canadensis','quercus_alba','pinus_pungens')
e <- sapply(spe[,names(spe)%in%nm],
           FUN=function(x)(x-min(x))/(max(x)-min(x)))
e <- cbind(e, moisture=env$moisture)
plot(1:12, ylim=c(0,1), type='n', las=1, bty='l', ylab='')
for(i in 1:5){
     points(e[,i], pch=16, col=i)
     lines(loess(e[,i] ~ e[,'moisture']), col=i)
}