biomass {VisCollin}

Biomass Production in the Cape Fear Estuary

Description

Data collected by Rick Linthurst (1979) at North Carolina State University for the purpose of identifying the important soil characteristics influencing aerial biomass production of the marsh grass Spartina alterniflora in the Cape Fear Estuary of North Carolina. Three types of Spartina vegetation areas (devegetated “dead” areas, “short” Spartina areas, and “tall” Spartina areas) were sampled in each of three locations (Oak Island, Smith Island, and Snows Marsh)

Samples of the soil substrate from 5 random sites within each location–vegetation type (giving 45 total samples) were analyzed for 14 soil physico-chemical characteristics each month for several months.

Format

A data frame with 45 observations on the following 17 variables.

loc

location, a factor with levels OI SI SM

type

area type, a factor with levels DVEG SHRT TALL

biomass

aerial biomass in gm^{-2}, a numeric vector

H2S

hydrogen sulfide ppm, a numeric vector

sal

percent salinity, a numeric vector

Eh7

ester-hydrolase, a numeric vector

pH

acidity as measured in water, a numeric vector

buf

a numeric vector

P

phosphorus ppm, a numeric vector

K

potassium ppm, a numeric vector

Ca

calcium ppm, a numeric vector

Mg

magnesium ppm, a numeric vector

Na

sodium ppm, a numeric vector

Mn

manganese ppm, a numeric vector

Zn

zinc ppm, a numeric vector

Cu

copper ppm, a numeric vector

NH4

ammonium ion ppm, a numeric vector

Source

Rawlings, J. O., Pantula, S. G., & Dickey, D. A. (2001). Applied Regression Analysis: A Research Tool, 2nd Ed., Springer New York. Table 5.1.

References

R. A. Linthurst. Aeration, nitrogen, pH and salinity as factors affecting Spartina Alterniflora growth and dieback. PhD thesis, North Carolina State University, 1979.

Examples

data(biomass)
str(biomass)
biomass.mod <- lm (biomass ~ H2S + sal + Eh7 + pH + buf + P + K + Ca + Mg + Na +
                           Mn + Zn + Cu + NH4,
                 data=biomass)
car::vif(biomass.mod)

(cd <- colldiag(biomass.mod, add.intercept=FALSE, center=TRUE))
# simplified display
print(cd, fuzz=.3)