Linking Historic, Present and Future Spatial Variability of Soil Attributes in the
Greater Everglades Ecosystem
S. Grunwald, T.F.A. Bishop, and K. R. Reddy
Soil and Water Science Department, University of Florida, Gainesville, FL and
S. Newman
South Florida Water Management District
Introduction
Wetlands are known to accrete nutrients (N and P) and other contaminants. The
Greater Everglades ecosystem has been impacted by agricultural and urban
activities over several decades leading to phosphorus enrichment (DeBusk et al.,
1994; DeBusk et al., 2001; Newman et al., 1997). The degree of nutrient
enrichment depends both on nutrient loading and hydraulic retention time. This
effect is distinct in many wetlands, and most notably in several hydrologic units
of the Everglades, including water conservation areas, and the Everglades
National Park.
Some variables, such as vegetation have visible patterns and their spatial scales
are obvious. Remote sensing techniques can be employed to capture these
patterns. However, many other biogeochemical attributes in the soil and water
column are invisible, hence challenging to assess the spatial scales at which they
vary without first sampling exhaustively. Attributes can also vary at scales that
differ by several orders of magnitude simultaneously. Historic data are valuable to
assess the spatial variability of floc/detrital plant tissue, surface water and soils
attributes and guide future sampling designs and the assessment of environmental
quality. The identified spatial scale and autocorrelation from historic surveys are
beneficial to target future sampling locations reducing costs and labor. Our
objectives were to assess the spatial variability of selected soil quality indicators
in the Greater Everglades ecosystem using previous observations and to develop
optimized sampling designs for future studies.
Methodology
The variogram is the cornerstone of geostatistics, and it is therefore vital to
estimate it and model it correctly. Kriging requires the calculation of an
experimental semivariogram to which a theoretical model is fitted. This provides
a description of the spatial structure of the attribute (Fig. 1). The key features are
the nugget semivariance which is representative of the measurement error and
unmeasured variation at distances shorter than the smallest sampling interval. The
other important feature is the range at which the spatial autocorreclation becomes
0. The range marks the limit of spatial dependence, i.e., the distance at which
there is no spatial relationship between sampling points. Observations further
apart than the range are spatially independent. The sill is the semivariance at the
range and is the a priori variance of the process (Webster and Oliver, 2001). The
formula to calculate the semivariance is presented below (Eq. 1).
2
1 m(h)
Eq. 1: ˆ (h) 
 z ( xi )  z ( xi h )
2m(h) i 1
where
ˆ :
z(xi):
h:
m(h):
estimated semivariance
data values
lag vector (distance)
number of pairs of data points separated by the
particular lag vector
Fig. 1. An experimental semivariogram and model parameters.
In this study we used previous observations of chemical soil attributes shown in
Fig. 2 collected by the Wetland Biogeochemistry Laboratory (WBL), Soil and
Water Science Dept. University of Florida and U.S. Environmental Protection
Agency.
Survey
WCA-1 (WBL)
WCA-21 (WBL)
WCA-3 (WBL)
WCA-3 (WBL)
EPA
EPA
EPA
EPA
EPA
EPA
Date
9/1991
7/1990
2/1992
6/1992
4/1995
9/1995
5/1996
9/1996
5/1999
9/1999
# Sites
103
74
100
74
120
123
123
119
121
119
Soil Surveys
Florida EPA
WBL, UF
Hydrological Units
WCA-1
Big Cypress NP
Everglades NP
WCA-2a
WCA-2b
WCA-3a
WCA-3b
`
Fig. 2. Available soil attribute datasets used in this study.
Results
When designing a sampling scheme it is crucial to sample at distances smaller
than the range, and also to sample at very small distances to adequately
characterize the nugget semivariance. Therefore, the semivariogram parameters
for soil properties from previous soil surveys were used to provide a guide to the
required sampling density. A stratified random sampling design was chosen to
identify sample locations. Zones within each hydrological unit were identified
with k-means clustering of the kriged soil data (Hartigan & Wong, 1979). The
number of zones within each hydrological unit was chosen subjectively based on
prior expert knowledge of the study areas. Samples were randomly allocated
within each zone where the proportion of samples per zone is equal to the product
of the area and the within-zone standard deviation (SD) in total phosphorus (TP)
0-10 cm. This methodology was chosen to ensure that large zones with low
variability were not over-sampled and small zones with high variability were not
under-sampled. TP was chosen as it is the soil property of most interest in this
study. In each hydrological unit approximately 80% of the sampling stations were
identified using stratified random sampling. The remaining stations were allocated
to characterize the short range variability. Results for Water Conservation Area 1
(WCA-1) are given below (Table 1 and 2). The same methodology was applied to
the other hydrological units of Fig. 2.
Table 1. Semivariogram parameters for WCA-1 dataset (number of samples: 103; time of data
collection: September 1991) (Reddy et al., 1994a).
Attribute
Model
Nugget
Sill
Range (m)
Sample No.*
Total P 0-10cm
Spherical
92,662
71,892
21,675
19
Total N 0-10cm Spherical
14,164,940
21,982,981
21,298
20
Total C 0-10cm Spherical
8.94
12.91
20,591
21
*Sample number is based on a grid sampling scheme where the grid spacing is equal to one
quarter of the range parameter in the semivariogram. Therefore, a sample number between 19 and
21 was needed to characterize the spatial variability of Total N, P and C. This is for a grid
sampling scheme which have been found to be inefficient for characterizing spatial variability.
Instead a stratified random sampling scheme is suggested.
Table 2. Cluster statistics for WCA-1.
Cluster
Area (ha)
Mean TP 0-10cm
SD TP 0-10cm
Sample No.*
1
21,916
465.0
78.2
35/4
2
10,844
717.9
123.1
28/4
3
23,944
334.3
34.7
17/2
* Number to the left of the dash is number of samples randomly allocated within the cluster;
number to the right is the number of locations where short range variability will be sampled.
Discussion
Spatially explicit modeling of chemical, physical and biological attributes is
essential to understanding the structure and function of biodiversity at the
soil/water interface of wetlands. Characterization of these patterns is pivotal to
improve our understanding of factors that drive phosphorus retention and
mobilization across spatial and temporal scales. The suggested methodology
facilitates to improve the documentation of the ongoing restoration efforts in the
Everglades ecosystem.
Sabine Grunwald, Soil and Water Science Department, University of Florida,
Institute of Food and Agricultural Sciences, 2169 McCarty Hall, PO Box 110290,
Gainesville, FL 32611-0290, Phone: 352-392-4508, Fax: 352-392-3902, Email:
SGrunwald@mail.ifas.ufl.edu
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