Using Multivariate Statistics to Identify Sensitive Biogeochemical Indicators in
the Northern Everglades
Corstanje, R., and Reddy, K. R.
Wetland Biogeochemistry Laboratory,
Soil and Water Science Department, University of Florida-IFAS, FL
Portier, K.M.
Statistics Department, University of Florida, FL
The extent of vegetation displacement in the Northern Everglades and resulting
changes in environmental conditions (loss of slough habitat, substrate quality for
e.g.) has resulted in a need for a sensitive set of indicator(s) that prelude changes
in the vegetative communities in response to nutrient enrichment. Microbial
communities play a critical role in nutrient cycling, mediating and responding to
nutrient levels. Consequently, multiple microbial parameters have been shown to
individually respond to nutrient enrichment. However, in this complex array of
indicator forms, which is the most sensitive response? Certain data analysis
techniques are capable of screening multivariate data arrays in order to select the
best “explanatory” variables, i.e. those variables that best distinguish grades of
ecological disturbance.
In this study, we used the extensive data base collected by researchers at the
Wetland Biogeochemistry Laboratory for the past 12 years (Table 1). These
biogeochemical data were collected along the eutrophic gradient in the Water
Conservation Area 2A (WCA 2A) of the Everglades.
We found that when complete set of variables describing chemical composition of
soils were used, observations clustered naturally in multivariate groups that, with
some misclassification, fell in line with the sites. No misclassification for the
samples obtained from the impacted area; slight misclassification for the
intermediate and unimpacted site. This precluded the necessity of an a priori
classification of the data.
Table 1 Description of the soil chemical and biological parameters used in the
data analysis
Chemical Parameters
Total Phosphorus (mg/kg)
TP
Total Inorganic Phosphorus (mg/kg)
TPi
Labile Inorganic Phosphorus (mg/kg)
Lab. Pi
Inorganic Phosphorus (mg/kg)
In Pi
Fulvic Associated P (mg/kg)
FAP
Humic Associated P (mg/kg)
HAP
Residue Organic Phosphorus (mg/kg)
ResidueP
Labile Organic Phosphorus (mg/kg)
Lab. Po
Total Nitrogen (g/kg)
TN
Total Organic Nitrogen (mg/kg)
TON
Total Kjedhal Nitrogen (mg/kg)
TKN
Ammonia Nitrogen (mg/kg)
NH4-N
Total Carbon (g/kg)
TC
Total organic carbon (mg/kg)
TOC
Loss on Ignition (%)
LOI
Calcium (mg/kg)
Ca
Magnesium (mg/kg)
Mg
Iron (mg/kg)
Fe
Aluminum (mg/kg)
Al
Biological Parameters
Microbial Biomass Phosphorus (mg/kg)
MBP
Potential Mineralizable Phosphorus (mg/kg/d)
PMP
Potential Mineralizable Nitrogen (mg/kg/d)
PMN
Microbial Biomass Nitrogen (mg/kg)
MBN
Microbial Biomass Carbon (mg/kg)
MBC
Alkaline Phosphatase Activity (ug/g/h)
APA
B-glucosidase activity (ug /g /h)
Beta
Dehydrogenases activity (ug /g /h)
Dehyd
Peptidase Peptidase activity (ug /g /h)
Anaer. C02 Anaerobic Microbial Respiration (ug/g/d)
Stepwise discrimination of the
chemical soil characteristics resulted
in two Discriminant functions that
best described group membership. A
plot of the first two functions (1
and 2 resulting in Can1 and Can2
respectively) is presented in Fig. 2,
which illustrates the potential of the
canonical Discriminant functions.
The set of parameters that
compromise Can1 are powerful at
Figure 2 Stepwise Canonical Discriminant
distinguishing
between the impacted
Analysis of chemical data set on clusters
areas
and
the
derived from the chemical characterization
data. Biplot of the first two canonical variables
transitional/unimpacted
areas.
(Can1 and Can2 or 1 and 2).
Likewise the set of parameters that
compromise Can2 are capable at
separating the intermediates site from the unimpacted site. 
The first two raw canonical variables employed in the discrimination are;

1 = 0.0001*Ca + 0.26*TN – 0.026*TPi – 0.03*Al – 0.106*Lab. Po – 0.025* HAP – 0.001*Mg
2= 0.00004*Ca – 0.47*TN – 0.011*TPi + 0.01*Al + 0.015* HAP + 0.001*TOC – 0.01*FAP
These functions are a subset of chemical variables that are the best predictors of
group membership, i.e. site. In comparing the standardized coefficients, Can1
consists of a contrast of extractable Ca and total N content to the total inorganic P,
extractable Al, labile organic P, humic acid associated P, and extractable Mg
(Canr2=.833) and Can2 consists of a contrast of mainly Ca, Al and fulvic
associated P with total N and total inorganic P (Canr2=.167).
We
then
projected
the
microbiological soil characteristics
of the observations on the groups
established by the chemical soil
characteristics.
The
biological
response
variables
were
extraordinarily
successful
(no
misclassification) at predicting
group
membership
of
the
observations. Discriminatory data
Figure 3 Stepwise Canonical Discriminant
analysis indicated that mainly the
Analysis of biological data set on clusters
microbiological
measures
derived from the chemical characterization
associated
with
the
phosphorus
data. Biplot of the first two canonical variables
(Can1 and Can2 or 1 and 2).
cycle
were
determinant
in
predicting group (site) membership.
The first two raw canonical variables employed in the discrimination are;
1 = 0.0089*MBP – 0.14*PMP + 0.020*PMN + 0.00023*APA
2 = 0.013*MBP + 0.026*PMP – 0.023*PMN – 0.00041*APA
In comparing the standardized coefficients, Can 1 consists of a contrast microbial
biomass P and alkaline phosphatase with potential mineralizable P (Canr2=.694),
Can 2 contrasts microbial biomass P with potential mineralizable N (Canr2=.306).
Of the entourage of biogeochemical indicators of ecological disturbance,
microbiological measures were more adept at distinguishing a nutrient enriched
site, a reference site and an intermediate site. We therefore show that biological
responses variables are valid tripwire indicators in case of nutrient impacts,
resulting in a selection of Everglades specific sensitive biogeochemical indicators.
Ron Corstanje
Wetland Biogeochemistry Laboratory
Soil and Water Science
University of Florida
106 Newel Hall
Gainesville
Florida 32611-0510
phone 352 3921804 321
fax 352 392 3399
corstanje@mail.ifas.ufl.edu