BIOGEOCHEMICAL INDICATORS OF
WATERSHED INTEGRITY
AND WETLAND EUTROPHICATION
Final Report
January 2004
Project Period
1999 -2003
U.S. Environmental Protection Agency
Grant Number: R-827641-01
Investigators:
K.R. Reddy, J.P. Prenger, M.M. Fisher, S. Grunwald, and A. Ogram
Soil and Water Science Department
and
W.D. Graham
Agricultural and Biological Engineering Department
University of Florida-IFAS, Gainesville, FL 32611
and
E.F. Lowe and L.W. Keenan
St. Johns River Water Management District
Palatka, FL
i
TABLE OF CONTENTS
Page
1
Executive Summary
1.0
Introduction
7
2.0
Technical Objectives
7
3.0
Description of Study Site
8
4.0
Project Milestones (FY2001)
8
4.1 Task 1: Determination Of Spatial Variability And Inter-Relationships
Of Biogeochemical Processes And Efficient Indicators.
8
4.1.1
Sample Collection
8
4.1.2
Soil Biogeochemical Analyses
8
4.1.3
Spatial Analysis of Physico-chemical Properties
10
4.1.4
Spatial Analysis of Microbial Biomass and
Labile Nutrient Pools
Spatial Analysis of Microbial Activities
12
4.1.5
4.2 Task 2: Temporal Variability Of Biogeochemical Processes
And Indicators.
16
23
4.2.1
Sample Collection
23
4.2.2
Soil Biogeochemical Analyses
23
4.2.3
Temporal Patterns of Physico-chemical Properties
23
4.2.4
Temporal Patterns of Microbial Biomass and
Labile Nutrient Pools
28
4.3 Task 3: Diversity And Composition Of Prokaryotic Groups Related
To C, N, And P Cycling In Wetlands.
34
4.4 Task 4: Spatial Distribution Of Biogeochemical Indicators In
Water, Litter And Soil.
36
4.4.1
Sample Collection
36
4.4.2
Soil Biogeochemical Analyses
36
4.4.3
Spatial Analysis of Physico-chemical Properties
36
4.4.4
Results
37
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4.5 Task 5: Validation Of Predictive Equations Using
Independent Measurements.
4.5.1
4.5.2
5.0
43
Evaluation of Empirical Relationships
Using Multivariate Statistics
43
Validation of Relationships
45
Conclusions
48
5.1 Task 1: Determination Of Spatial Variability And
Inter-Relationships Of Biogeochemical Processes And Efficient Indicators. 48
5.1.1
Spatial Analysis of Physico-chemical Properties
48
5.1.2
Spatial Analysis of Microbial Biomass and Labile
Nutrient Pools
48
5.1.3
Spatial Analysis Of Microbial Activities
50
5.2 Task 2: Temporal Variability Of Biogeochemical Processes
And Indicators.
51
5.2.1
Temporal Patterns Of Physico-Chemical Properties
51
5.2.2
Temporal Patterns Of Microbial Biomass And Labile
Nutrient Pools
51
5.3 Task 3: Diversity And Composition Of Prokaryotic Groups
Related To C, N, And P Cycling In Wetlands.
52
5.4 Task 4: Spatial Distribution Of Biogeochemical Indicators
In Water, Litter And Soil
52
5.5 Task 5: Validation Of Predictive Equations Using Independent
Measurements.
5.5.1
Evaluation Of Empirical Relationships Using
Multivariate Statistics
52
5.5.2
Evaluation Of Empirical Relationships With
An Independent Dataset
5.6 Overall Conclusions
52
52
53
6.0 Publications And Presentations
53
6.1 Publications (Submitted)
53
6.2 Publications (In Preparation)
53
6.3 Theses And Dissertations
53
6.4 Presentations
54
7.0 References
54
ii
EXECUTIVE SUMMARY
INTRODUCTION:
The purpose of this research project is to develop sensitive, reliable, rapid, and inexpensive indicators of
ecological integrity for use in large-scale ecosystem management and restoration. The premise is that wetlands will
more efficiently indicate the ecological integrity of the entire watershed for the following reasons: (1) Wetlands are
critical areas of the landscape. (2) Wetlands, as low lying areas in the landscape, receive inputs from all adjacent
uplands. (3) The response of a wetland to inputs from the uplands is patterned. Biogeochemical processes are good
candidates for efficient indicators of ecological integrity because they are potentially very sensitive. They are also
likely to be highly reliable in that ecological changes at this fundamental level will affect all species utilizing the
ecosystem. The central hypothesis of the proposed research is that rates of biogeochemical cycling of carbon,
nitrogen and phosphorus (C, N, and P) in wetlands can be used to indicate the ecological integrity of wetlands, and
that the concentrations of certain forms of these elements can accurately predict the rates of ecologically important
processes. In order to develop efficient indicators of eutrophication we addressed several objectives:
•
Identify the key biogeochemical processes impacted by nutrient loading and measure the rates of these
processes along the nutrient gradient.
•
Develop relationships between a process and its related easily measurable indicator.
•
Determine the spatial and temporal distribution of easily measurable indicators for a test wetland ecosystem.
•
Determine the spatial variations in biogeochemical processes, and develop spatial maps for various processes to
determine the extent of impact and risk assessment.
Validate the predictability of empirical relationships by making independent measurements of biogeochemical
processes in different wetland ecosystems.
•
The study area is Blue Cypress Marsh Conservation Area, an approximately 8,000 ha freshwater marsh in
the headwaters of the St Johns River, in east-central Florida. The project area is predominately a mosaic of marsh
communities. Approximately 75 % of the headwater floodplain has been drained for cattle, citrus, and row crop
usage. Virtually all nutrient runoff into the marsh has been eliminated; however, vegetation changes from native
sawgrass stands and maidencane flats to cattail and willow communities are still evident in areas of impact, and
sediment accretion rates have been altered. This area has three advantages for use as a study area: 1) it exhibits
gradients from severely impacted at some margins to apparently pristine in the interior; 2) it has multiple input
points for pollutants with information on the flow, water quality and upstream land uses for each source; 3) large
amounts of ancillary ecological data exists for the area.
RESEARCH SUMMARY:
TASK 1: DETERMINATION OF SPATIAL VARIABILITY AND INTER-RELATIONSHIPS OF
BIOGEOCHEMICAL PROCESSES AND EFFICIENT INDICATORS.
Three zones of the BCMCA were used as study sites: the Northeast (NE) and Southwest (SW) areas as
impacted sites (nutrient enriched) and the Northwest (NW) area as an unimpacted site. Two types of samples were
obtained at each station: soil and detritus. Vegetation types in the sampling areas were classified into following 6
groups: Typha, Typha/woody mix, Cladium, Cladium/woody mix, Panicum, and others. Woody mix includes the
following vegetation types: Salix sp., Myrica sp., Cephalanthus sp., and Royal fern. Biogeochemical processes
related to C, N, and P cycling were measured in plant litter and soil samples, and all samples were characterized for
basic physicochemical properties. Microbial indicators were analyzed on a subset of samples and correlated with the
basic physicochemical properties.
Physicochemical and microbial parameters varied significantly by region and by vegetation type. The
impacted areas (NE and SW) had higher total phosphorus (TP) than the unimpacted area (NW) for both detritus and
soil layers. TP levels also varied significantly among some vegetation types; however, TP was significantly higher
in impacted areas than unimpacted area regardless of vegetation type. The total carbon (TC) of detritus at SW was
significantly higher than NE although there was no significant difference between NW and SW and NE. For soil
TC, there was no significant difference among all 3 sites. Total nitrogen (TN) of detritus at NE was significantly
higher than NW and SW. The increase in TP concentration resulted in a lower C:P ratio in impacted areas. In
addition, the C:P ratio in Cladium, Cladium/woody mix, and Panicum areas were significantly higher than Typha
1
and Typha/woody mix for both detritus and soil layers. These results indicate higher phosphorus content in
impacted areas, especially in Typha sp. areas.
Microbial biomass nutrients varied significantly among impacted and non-impacted areas. Microbial
biomass carbon (MBC) in soil at NW was significantly higher than both NE and SW soil. In detritus layer,
however, there was no significant difference in the MBC content among the three areas. The MBC in soil in
Panicum area was significantly higher than most vegetation types: Cladium/woody mix, Typha, and Typha/woody
mix. For both the soil and detrital layers, MBN was not found to be significantly different between the three
regions, even when taking into account vegetative communities. Microbial biomass P in the NW soil was
significantly higher than the concentrations found in the NE and SW. There was no significant difference in the size
of the microbial biomass P associated with the detrital layer between the three regions. The labile organic pools also
differed significantly among sites. In the detrital layer, PMN and extractable NH4+ concentrations were found to be
significantly higher in the Northeast region than the NW and the Southwest regions. Within the soil, PMN rates
were significantly different between all three regions, with the NW region having the highest activity. However,
extractable NH4+ concentrations within the soil were significantly higher for the Northeast and Southwest regions
than the NW region. Higher PMN activity was found to be associated with lower C/N values occurring primarily in
the Northeast region.
Microbial activities varied by area. The activity of carbon acquisition enzyme β-glucosidase associated
with Typha detritus was elevated relative to that in detritus of other vegetation types, indicating more labile, easily
degraded material. The SOD of detritus at NW was significantly lower than that of NE and SW, and there was no
difference in SOD of detritus between NE and SW. Labile organic carbon (LOC) showed an identical pattern.
When the SOD values were compared by vegetation types, the SOD of detritus at Typha and Typha/woody mix
areas was significantly higher than Cladium and Cladium/woody mix areas. There was no significant difference in
SOD between Panicum and other vegetation types. Anaerobic respiration, as measured by CH4 production rate, was
significantly higher in detritus at SW site than NW site. The CH4 production rate from NE detritus was not
significantly different than either that of NW or SW. On the other hand, the NW soil produced significantly lower
amount of CH4 than NE and SW soil, while there was no significant difference between NE and SW soil CH4
production. When the CH4 production data was compared by vegetation types, the CH4 production rates in detritus
from Typha and Typha/woody mix areas were significantly higher than the detritus from Cladium and
Cladium/woody mix. An increase in anaerobic CO2 production rate was observed in both detritus and soil layers of
the SW impacted site as compared to the reference site (NW). When the anaerobic CO2 production rate was
compared by vegetation types, the detritus at Typha area had significantly higher CO2 production rate than Cladium
area. As a result, the Typha dominant area produced higher CO2 and CH4 than Cladium dominant area. Ratios such
as MBC/TC were compared by vegetation types, and was significantly higher for detritus in Typha, while in soil the
Panicum area had significantly higher MBC/TC ratio than Typha, Typha/woody mix, and Cladium/woody mix. The
MBC/Total LOC (TLOC) ratio for soil was significantly higher in Typha and Typha/woody mix areas than Cladium,
Cladium/woody mix, and Panicum areas. Results indicated that microbial parameters and their ratios were affected
by both nutrient level and vegetation types.
In general, acid phosphatase activity (APA) showed no definitive trends with regard to nutrient impact
when compared among the three areas. Comparison of MBP to TP suggested the microbial pool in the NW region
accounted for a larger portion of the total P at the 0-10 cm depth than in the NE and SW regions. The same regional
trends were seen in the detritus, with MBP accounting for a greater proportion of TP. These findings indicate that
the microbial community in the non-impacted region was more P efficient than the ones in the P impacted regions.
TASK 2: TEMPORAL VARIABILITY OF BIOGEOCHEMICAL PROCESSES AND INDICATORS.
Based on results from the spatial sampling, two sites were established in each of the reference and impacted
areas for bi-monthly sampling over 12 months in order to examine temporal variability. One site in the NE impacted
region had slightly elevated total phosphorus values (based on the spatial sampling), but retained the native Cladium
vegetation community. In January 2001, a large portion of the marsh burned, including two reference and the two
impacted sites in the southwest. Monitoring began in March 2001, after vegetation re-growth had begun. The
hydrologic condition drastically changed during the year of temporal study. Low rainfall caused a complete
drawdown of the marsh to occur from March to August 2001. Anaerobic conditions were not established until July
2001 in the NE and SW regions and September 2001 in the NW region.
Physio-chemical parameters including pH, bulk density, loss on ignition (LOI), TN, and TC in the 0-10 cm
soil layer across all three regions showed no site specific or regional differences. The temporal data showed the TP
content in the NE and SW regions was greater than the reference concentrations taken in the NW region, irrespective
2
of plant species. Distinct variation in the detrital TP concentrations between the six sites was observed, but did not
coincide with the designations of impacted and non-impacted regions, nor were they related to the dominant
vegetation species. Comparison of the C: N: P ratios indicates P-limitation in the system at three of the sites, the
two NW sites and the Cladium sp. NE site. There was significant difference in the N: P ratios within the NE region
between the two sites (Typha sp. and Cladium sp.). The total organic P (TPo) concentration accounted for
approximately 85 % of the TP at the 0-10 cm soil layer. Analysis of the TPo and TPi concentrations showed a
separation of the sites into two distinct groups with the NE Typha and SW sites having greater concentrations of
both TPo and TPi at both the detritus and soil levels when compared with NE Cladium sp and NW sites.
Microbial biomass nutrient pools varied among the three sites depending on sampling date. For soil, MBC
was higher in the early phase of temporal study (in March and June) for all three sites. For both detritus and soil,
LOC was higher in the early phase of the study for all three sites. Aerobic and anaerobic respiration for detritus
showed a general increase toward the end of temporal study. On the other hand, anaerobic CO2 production rate was
higher in the early phase of the study for soil in all three sites.
Microbial biomass N was significantly higher in the Southwest and NW regions than the Northeast region
for the soil and detritus layers. Significantly lower MBN was present for detritus associated with Cladium
communities from both the NW and Northeast regions. For extractable NH4+, a seasonal effect was observed as well
as a significant difference in seasonal patterns between the three regions within both the soil and detrital layers. For
the detritus layer, a significant difference in PMN rates was found over time with the Northeast and Southwest
regions having higher mineralization rates than the NW region. Extractable NH4+ concentrations were found to be
significantly higher in the Northeast and Southwest regions within the detritus layer, corresponding to PMN rates.
PMN rates and extractable NH4+ concentrations were not significantly different within the soil layer between the
impacted and unimpacted regions and therefore, apparently not influenced by total P concentrations. PMN rates
within the detritus layer were significantly lower for NW (Cladium) vegetation than other vegetative communities.
This corresponds to the low PMN rates in the NW region reflecting a higher C/N ratio or lower decomposition rate
of the Cladium substrate. No significant difference between vegetative communities was found for PMN within the
soil. For the detritus layer, total P was found to influence PMN rates and extractable NH4+concentrations.
The relative proportion of MBP in the soil was significantly greater in the NW region when compared to
the NE and SW regions. The elevated concentrations of MBP in the non-impacted area could be related to the
dominant vegetation, Panicum sp., which has thick, fine root masses. The distribution of detrital MBP demonstrated
no significant differences between any of the sites but the MBP proportions do suggest a trend among similar
vegetation communities. The distribution of the forms of P followed regional TP trends at the 0-10 cm soil layer;
however, the detrital TP distribution was more clearly linked to vegetative communities than to the soil TP
concentrations. The organic P fraction of the soil showed fluctuations over time in the MBP, LOP, FAP, HAP and
residual Po. Microbial biomass changed by seasonality with maxima occurring in the summer (June and July) and
winter (December and January) months.
Results indicate that most of the microbial parameters were significantly affected by seasonality. Although
many of the microbial parameters were significantly affected by time and station individually, many of them did not
show significant difference when time and station effects were combined. The one-year temporal study showed that
differences in vegetation type, soil conditions and hydrology had an impact on the TP of the soil. The majority (>
85 %) of the soil and detritus TP was in the organic form. Total inorganic P, which is more readily available for
microbial uptake and utilization by vegetation, did not show significant seasonal or site variations. The fraction of
organic P most significantly affected by seasonal and site effects was LOP with reduced concentrations during the
summer months of June and July correlating with the drought period. The detrital layer was influenced by the
dominant type of vegetation present more than differences in soil TP. Overall the TP concentrations appear to be
diminishing overtime, indicating the system is redistributing P and recovering. Variations in soil characteristics as a
result of fluctuations in water levels appeared to have a greater effect on the P pools than the two surface fires that
occurred in BCM prior to sampling. Changes in hydrology appear to have a sustained impact over several sampling
periods, dependent on duration of unsaturated conditions. Fire can be a sudden and intense impact to the system;
however, it appeared to have a minimum impact on the soil P over the year focused on by the first sampling period
after the event.
TASK 3: DIVERSITY AND COMPOSITION OF PROKARYOTIC GROUPS RELATED TO C, N, AND P
CYCLING IN WETLANDS.
The objectives of this task were to: 1) investigate the diversity and composition of prokaryotic groups
related to carbon cycling in Blue Cypress Marsh; 2) to understand the diversity of indicator groups with respect to
3
nutrient loading in the BCM impacted versus non-impacted sites; and 3) to examine the structure-function
relationships of syntrophic-methanogenic groups with respect to the nutrient loading in impacted versus nonimpacted sites. Using Polymerase Chain Reaction (PCR) based cloning and sequencing or analytical techniques such
as Terminal Restriction Fragment Length Polymorphisms (T-RFLP), shifts in composition of assemblages within
the methanogens were investigated. In addition, methane concentrations were monitored weekly in microcosms
using soil from impacted and non-impacted areas of BCMCA. Addition of sulfate inhibited methanogenesis in the
NE Typha soils when butyrate was the substrate whereas for propionate, methanogenesis increased two fold. This
reflects the need of syntrophic bacteria for sulfate to oxidize propionate. These results indicate a competetion
between the sulfate reducing bacteria (SRBs) and the syntrophs for carbon donors (propionate and or butyrate).
However, in the non-impacted NW Cladium site, exogenous addition of sulfate completely inhibited
methanogenesis, indicating the greater involvement of SRBs there.
Ribosomal DNA clones were grouped into operational taxonomic units based on their RFLP patterns.
Representative clones having different and similar RFLP patterns were then sequenced and their sequences
compared and aligned, and a phylogenetic tree constructed. Remarkable differences were obtained in the archaeal
RFLP patterns with 2 different restriction enzymes in the impacted (NE) and the non-impacted (NW) sites.
Commonalities were also seen with soils from impacted sites spiked with propionate and butyrate, but clear
differences were obtained in the non-impacted sites enrich with the same substrate (propionate and butyrate). A high
diversity was observed in the bacterial community and there were few similarities in the impacted and the nonimpacted sites.
TASK 4: SPATIAL DISTRIBUTION OF BIOGEOCHEMICAL INDICATORS IN WATER, LITTER AND SOIL.
Based on the results obtained from the preliminary geostatistical analyses in Task 1, a large-scale network
was developed to characterize the spatial distribution of biogeochemical indicators in water, litter and soil
throughout the study site. Duplicate soil cores and detritus samples were obtained from 267 sample locations and
biogeochemical, physicochemical, and microbial parameters were characterized as described.
We used conditional sequential Gaussian simulation (CSGS), a stochastic simulation method, to generate
realizations of soil properties to describe the spatial patterns of properties and the range of possible outcomes of
realizations. An advantage of the Gaussian approach is that all conditional distributions are normal and determined
exactly by the mean and estimation variance. One hundred realizations at a pixel resolution of 100 meters were
generated using CSGS. To assess the accuracy of TP realizations we randomly split the dataset into model (67% of
observations) and an independent validation dataset (33% of observations) resulting in 183 model observations and
84 validation observations. The root mean square error (RMSE) was used to evaluate realizations.
Principle Components Analysis was used to transform a number of possibly correlated biogeochemical
variables into a smaller number of principal components. The semivariogram of TP was modeled with two basic
structures. Specific patterns such as lower TP values in the northern part and higher values in the southern part of the
study area prevail on all maps. However, local spatial patterns are slightly different on all realization TP maps.
These local deviations show the range of possible outcomes or the uncertainty of predictions based on 267
observations of TP. A crescent shape area in the northern part showed TP as low as 340 mg kg-1 which resembled
natural TP conditions. The smallest and largest realizations can be interpreted as “best” or “worse” case scenarios of
TP predictions rendering the uncertainty of TP predictions. High TP predictions are more uncertain compared to low
TP predictions. Realizations of TLON and TC were also generated. A simplified representation of spatial patterns
based on PCs facilitates to concentrate the information on the spatial structure rather than mapping all
biogeochemical properties. The variation of soil property realizations (TP, TC, and TLON) within classes was
considerable. We can conclude that relationships between soil biogeochemical properties change across the
BCMCA caused by ecosystem processes (e.g. mineralization, decomposition of vegetation, enzymatic activities, and
other). Mapping of the mean, minimum and maximum soil property values within classes facilitated to better
understand the variation of spatial patterns.
TASK 5: VALIDATION OF PREDICTIVE EQUATIONS USING INDEPENDENT MEASUREMENTS.
The objective of this portion of the project was to evaluate the ability of multivariate statistical procedures
such as cluster analysis (CA), principal component analysis, (PCA), canonical discriminant analysis (CDA), and
discriminant function analysis (DFA) to quantify the relationships among biogeochemical indicators,
biogeochemical processes, vegetation types and ecological impact. Results of Cluster Analysis indicate
biogeochemical characteristics appear to be more consistent within the SW and NW regions, while a variety of soil
4
biogeochemical characteristics exist in the NE region. Principle Component Analysis of this data set suggests that
four or five PCs are sufficient (rather than the 28 original variables) to describe the variability in the BCMCA data.
Canonical Discriminant Analysis provides good discrimination between the unimpacted region, NW, and the
impacted regions, NE and SW. The impacted regions exhibit high values of moisture content, loss on ignition, and
total organic phosphorus while the unimpacted area shows low concentrations of these values. At least 3 canonical
variables determined based on vegetation are necessary to discriminate between the 6 vegetation groups sufficiently.
The results indicate that the vegetation distribution shifts from native vegetation types (Cladium and Panicum) in the
unimpacted region, NW, to invasive vegetation types (Hyacinth, Mixed, and Typha) in the impacted regions, NE
and SW. When analyzed by vegetation community, the impacted regions exhibit high values of moisture content,
total organic phosphorus, and bicarbonate extractable total P and low values of loss on ignition, total phosphorus,
and microbial phosphorus. Discriminant Function Analysis can be expected to accurately classify new observations
into vegetation types at least 75% of the time, into regions at least 85% of the time, and into clusters at least 90% of
the time.
Twelve wetlands were selected in Florida and Georgia to test some of the predictive relationships
developed. At each site a minimum of three stations were sampled as previously described and soil biogeochemical
parameters were determined. Wetlands differed significantly by eco-region based on basic chemical
characterization. Differences in soil chemistry appeared to drive microbial nutrient cycling, exemplified by APA
levels. In order to isolate and identify factors influencing predictive relationships between biogeochemical factors,
modeling was limited to wetland communities of similar vegetation communities. Relationships between the abiotic
environment (independent variables) and biological responses (dependent variables) were analyzed in order to
validate the results across ecosystems. A clustering method using an independent dataset was developed to place the
soil chemical measures in groups that reflect the sampling location. We applied a combination of stepwise
discrimination and canonical discrimination (stepwise canonical discrimination) to determine which particular
combinations of chemical characteristics are influential in generating (abiotic) and predicting (biotic) the
multidimensional groups. Cluster analysis on soil abiotic parameters resulted in two consistent clusters representing
nutrient impacted and non-impacted sites and some overlap between intermediate and non-impacted sites. In terms
of abiotic characteristics, the impacted area is differentiated by high TC, P-forms and low TN from intermediate
sites and the non-impacted area; the intermediate and the non-impacted areas are distinguished by shifts in the Pdynamics and a substantial depletion in NH4-N at intermediate relative to un-impacted. The resulting set of biotic
linear combinations of measures was therefore selected to be applied as indicators of nutrient enrichment to
BCMCA. Results indicated a distinct separation of the unimpacted areas from the impacted areas. The interpretation
of the results reflects the diversity of vegetation communities in BCMCA in that the primary differentiation is Pavailability; a secondary factor is the predominant vegetation types. The effect of Typha sp. on the soil
biogeochemistry was highlighted by the increase in overall N and P turnover rates. Using this analytical approach,
microbial biomass does not play a significant role in describing the variability in BCMCA.
GENERAL CONCLUSIONS
1.
The impacted areas (NE and SW) had significantly higher TP than the unimpacted area (NW) for both
detritus and soil layers.
2. Microbial activities varied with both nutrient level and vegetation community.
3. Changes in hydrology and duration of unsaturated conditions appear to have significant confounding
influence on temporal patterns of microbial and nutrient dynamics.
4. Nutrient loading in the impacted regions enriched for particular bacterial groups as evidenced by the
common RFLP patterns.
5. The multivariate statistical analyses presented indicate that soil biogeochemical measurements are
indicative of vegetation types, and can be used to evaluate ecological integrity in the Blue Cypress Marsh
Conservation Area.
6. The effect of Typha sp. on the soil biogeochemistry was highlighted by the increase in overall N and P
turnover rates.
7. The primary differentiation among impacted and non-impacted areas is P-availability.
8. Results reflect the diversity of vegetation communities in BCMCA, making the predominant vegetation
type an important secondary factor distinguishing impacted and non-impacted.
9. The application of the biotic variables selected for in an independent dataset to BCMCA data resulted in a
distinct separation of the unimpacted areas from the impacted areas.
10. Microbial biomass does not play a significant role in describing the variability in BCMCA.
5
11. It is possible to extrapolate predictive variables among independent wetlands within the region.
12. Our study provided a shapshot in time giving insight into the current ecological status of BCMCA.
Elevated TP caused by previous adjacent agricultural activities is still present in the top soil. The transport
of P out of the wetland is dependent on biotic variables including vegetation communities, and may take
several more decades.
6
1.0 INTRODUCTION
The purpose of this research project is to develop sensitive, reliable, rapid, and inexpensive indicators of
ecological integrity for use in large-scale ecosystem management and restoration. We refer to these as efficient
indicators, and have chosen to focus on wetlands for their development. In many areas, wetlands will more
efficiently indicate the ecological integrity of the entire watershed than will any other portions of the landscape, for
the following reasons: (1) Wetlands are critical areas of the landscape. Many species depend upon wetlands for
successful completion of their life cycle and most species require, or benefit from, nearby aquatic habitat. If the
integrity of a wetland decreases, the effects on the biota eventually will be far-reaching. (2) Wetlands, as low lying
areas in the landscape, receive inputs from all adjacent uplands. If the integrity of an upland area is compromised, it
is likely that it will soon be reflected in the integrity of the associated wetlands. Conversely, if the integrity of the
wetland is compromised it may not be immediately reflected in the condition of the uplands, since materials transfer
is largely towards the wetlands. Thus, a loss of integrity in wetlands can provide an early indicator of impending
harm to the uplands or can nearly contemporaneously track upland degradation. (3) The response of a wetland to
inputs from the uplands is patterned. Unlike lakes, which will show a generalized response to inputs due to mixing,
or streams, which rapidly transport materials to other areas, wetlands absorb inflowing pollutants and nutrients
proximal to the points of inflow. This means that the pattern of response in a wetland can indicate the site within the
landscape which is experiencing degradation or improvement.
Biogeochemical processes are good candidates for efficient indicators of ecological integrity because they
are potentially very sensitive. They are also likely to be highly reliable in the sense that ecological changes at such a
fundamental level will affect all species utilizing the ecosystem. Changes at higher levels, such as a decline in
populations of a suite of higher organisms may be due to factors that affect only a small portion of the biota,
whereas changes in biogeochemical processes portend comprehensive alteration of the biota.
For this work, we have focused on developing indicators of wetland eutrophication, a phenomenon that is
presently threatening regionally significant wetlands in the southeastern United States. In order to develop efficient
indicators of eutrophication we addressed several questions. 1) What biogeochemical processes are most sensitive to
eutrophication? 2) What chemical substrates, intermediates, or end products of these processes most accurately
reflect their rates and thus are potential indicator variables? 3) Do the distributions and central tendencies of
biogeochemical indicators allow differentiation between natural spatial variability and change due to anthropogenic
impact? 4) Can the indicator/process information developed for one area be extrapolated in space and time within
the wetland and to other wetlands within the region?
2.0 TECHNICAL OBJECTIVES
The central hypothesis of the proposed research is that rates of biogeochemical cycling of carbon, nitrogen
and phosphorus (C, N, and P) in wetlands can be used to indicate the ecological integrity of wetlands, and that the
concentrations of certain forms of these elements can accurately predict the rates of ecologically important
processes.
The objectives of the proposed research were to:
•
Identify the key biogeochemical processes impacted by nutrient loading and measure the rates of these
processes along the nutrient gradient.
•
Develop relationships between a process and its related easily measurable indicator.
•
Determine the spatial and temporal distribution of easily measurable indicators for a test wetland ecosystem.
•
Determine the spatial variations in biogeochemical processes, and develop spatial maps for various processes to
determine the extent of impact and risk assessment.
Validate the predictability of empirical relationships by making independent measurements of biogeochemical
processes in different wetland ecosystems.
•
We have tested the hypotheses presented above in the Blue Cypress Marsh Conservation Area (BCMCA)
located within Upper St Johns River Basin, Florida. Some areas of the BCMCA have been impacted over the years
as a result of nutrient loading from adjacent uplands, resulting in distinct nutrient and vegetation gradients. The
BCMCA provides the benefit of established gradients of high-nutrient (impacted) to low-nutrient systems
(unimpacted), to test the hypotheses of this proposal. Specific Tasks and Accomplishments are listed below.
7
3.0 DESCRIPTION OF STUDY SITE:
The study area is Blue Cypress Marsh Conservation Area, an approximately 8,000 ha freshwater marsh in
the headwaters of the St Johns River, in east-central Florida (Fig. 1). The project area is predominately a mosaic of
sawgrass (Cladium jamaicense) stands and maidencane (Panicum sp.) flats, although significant areas of shrub
swamp, flag (Thalia geniculata) marshes, cattail (Typha sp.) marshes, and deep-water slough communities also
exist. Cattail and willow (Salix sp.) are prevalent in nutrient impacted areas. Approximately 75 % of the headwater
floodplain has been drained for cattle, citrus, and row crop usage (Brenner et al., 2001). The project area has inflows
from Fort Drum Marsh Conservation Area to the south, and two sub-basin watershed tributaries from the west.
Lands in the western watersheds have been purchased as part of the Upper St. Johns River Basin Project and
removed from agricultural production in the late 1980’s. All of the eastern inflows were primarily agricultural and
have been diverted away from the marsh since 1992 as part of the water management project. While nutrient runoff
into the marsh has largely been eliminated, vegetation changes from native sawgrass stands and maidencane flats to
cattail and willow communities are still evident in areas of impact. Brenner et al. (2001) showed that sediment
accretion rates at select sites in the Northeast impact area had been altered. This area has three advantages for use as
a study area: 1) it exhibits gradients from severely impacted at some margins to apparently pristine in the interior; 2)
it has multiple input points for pollutants with information on the flow, water quality and upstream land uses for
each source; 3) large amounts of ancillary ecological data exists for the area.
4.0 SUMMARY OF ACCOMPLISHMENTS (FY00-03)
4.1 TASK 1: DETERMINATION OF SPATIAL VARIABILITY AND INTER-RELATIONSHIPS OF
BIOGEOCHEMICAL PROCESSES AND EFFICIENT INDICATORS.
4.1.1 Sample Collection
Three zones of the BCMCA were used as study sites: the Northeast (NE) and Southwest (SW) areas as
impacted sites (nutrient enriched) and the Northwest (NW) area as an unimpacted site. Both the NE and SW
sampling areas have 41 sampling stations each, and the NW area has 39 sampling stations. These sampling stations
were selected according to a nested sampling plan, with samples taken at 1.5, 15, 75, and 750 meters apart. This
nested sampling was designed to provide the most efficient sample spacing for determination of spatial variability
(Burrough, 1991). Figure 1 shows approximate location of sampling stations in these 3 sampling areas. Two types
of samples were obtained at each station: soil and detritus. Duplicate soil cores were obtained by using a 10 cm I.D.
aluminum core tube, and they were composited in the laboratory. Detritus samples were collected by handgathering dead plant litter from 625 cm2 on soil surface. Soil and detritus were placed in water tight plastic bags and
transported on ice to the laboratory and stored at 4 o C until analysis. There was no appreciable detritus at some
stations. As a result, a total of 239 soil and detritus samples were collected for the spatial analysis during September
11 through 15, 2000.
For the spatial analysis, vegetation types in the sampling areas were classified into following 6 groups:
Typha, Typha/woody mix, Cladium, Cladium/woody mix, Panicum, and others (Table 1). Woody mix includes the
following vegetation types: Salix sp., Myrica sp., Cephalanthus sp., and Royal fern. In addition, others include
following additional vegetation types to above types: Ludwigia sp., Hyacinth sp., Polyganum sp., Nymphaea sp.,
Sagittaria sp., and Osmundia sp., and royal fern.
4.1.2 Soil Biogeochemical Analyses
Biogeochemical processes related to C, N, and P cycling were measured in plant litter and soil samples (010 cm), and all samples were characterized for basic physicochemical properties (pH, bulk density, dissolved
organic and total C, N, and P, inorganic N and P, C:N and C:P ratios , oxalate extractable Fe and Al, HCl extractable
Ca, Mg, Fe, Al). Microbial indicators (prokaryotic functional groups, soil enzyme activities, microbial biomass C,
N, and P, microbial respiration, organic N and P mineralization, nitrification, dentrification, sulfate reduction, and
methanogenesis) were analyzed on a subset of samples and correlated with the basic physicochemical properties.
8
Blue
Cypress
Lake
Figure 1. Sampling sites within Blue Cypress Marsh Conservation Area for spatial (87 sites) and temporal (6 sites)
studies. Stippled area indicates aerial extent of January 2001 fire. Triangles represent spatial study sampling sites;
stars indicate the six sites chosen for temporal study. Gray arrows indicate waterflow; white arrows indicate historic
inflows of elevated nutrient runoff.
Table 1. Dominant vegetation (>90% coverage) in the six temporal study sites. All soils were histosols.
Area
Northeast
Northeast
Southwest
Southwest
Site
Vegetation
Impacted
Typha
Cladium
NETypha
NECladium
Typha/Myrica/Salix/Cephalanthus
SWTypha/Woody
SWTypha/Cladium/Woody Typha/Cladium/Salix/Cephalanthus
Reference
Northwest NWCladium
Cladium
Northwest NWPanicum
Panicum
9
Soil pH was determined by pH meter on 20 g wet soil after equilibrating with 10 mL of deionized water.
Total C and N content was determined on dried, ground soil and detritus samples by dry combustion (Nelson and
Sommers, 1996) using a Carlo-Erba NA-1500 CNS Analyzer (Haak-Buchler Instruments, Saddlebrook, NJ). Total P
was determined by combusting approximately 0.2-0.5 g oven-dried, finely ground soil at 550o C for 4 hrs, digestion
of the ash with 6M HCl and continuous heating on a hot plate, followed by filtration through #41 Whatman filter
(Anderson, 1976) and analysis of P by automated ascorbic acid method (Method 365.4, USEPA, 1983). NaHCO3
extractable Pi was determined by extraction of the wet soil equivalent of 0.5 g dry weight with 25 mL 0.5 M
NaHCO3 (pH 8.5) shaken for 16 hr at low speed and filtration through 0.45 µm membrane filter (Hedley and
Stewart, 1982; Brookes et al., 1982). Exchangeable ammonium (NH4-N) was determined using the method of
Mulvaney (1996) by extraction of the wet soil equivalent of 0.5 g (dry weight) soil with 25 mL 2 M KCl followed
by filtration through Whatman No. 41 filter paper. HCl extractable cations were determined by extraction of 0.5 g
dry soil in 25 mL 1 N HCl with shaking for 3 hrs, filtration through 0.45µm membrane filter, and analysis by ICP
(EPA method 200.7).
Enzyme activities were assayed as described in Prenger et al. (in review). The procedure for SOD was
modified and developed from the standard method of BOD test (Greenberg et al, 1992). Carbon in the microbial
biomass was analyzed from detritus and soil samples by using the chloroform fumigation-extraction (CFE)
technique (Vance et al., 1987; Horwath and Paul, 1994) with some modifications. Extractable C in fumigated
samples is referred as total labile organic carbon (TLOC), while C extracted from unfumigated samples is referred
as labile organic carbon (LOC) (Fig. 2-4). The TLOC includes both living microbial biomass and non-living labile
C. The LOC includes only non-living labile C. Microbial biomass carbon (MBC) was determined by calculating
the difference in the TLOC and LOC. In order to obtain a direct estimate of microbial carbon from the extractable
carbon, a correction factor (kEC) was applied to the final total organic carbon values to determine microbial biomass
carbon (Horwath and Paul, 1994). For this analysis, the kEC value of 0.37 was used, based on the analysis of
extensive calibration for organic soils by Sparling et al (1992). Microbial biomass carbon was expressed as weight
of carbon per kilogram of dried soil (mg C kg soil-1). The rates of CO2 and CH4 production from soil and detritus
were determined under anaerobic conditions as described by Seo (2002). MBN was measured using the fumigationextraction method (Brookes et al, 1985, Horwath and Paul, 1994). The PMN rate is best described as an anaerobic
waterlogged incubation at 40ºC (Keeney, 1982; Sarathchandra et al., 1989; Tiedje, 1982; Williams and Sparling,
1988). Arginine ammonification rates have been used to estimate the activity of microorganisms present in the soil
(Alef and Kleiner, 1986). Denitrification enzyme activity was assayed using the method of White and Reddy (1999).
Determination of microbial biomass P (MBP) used an alkaline extraction solution of 0.5M NaHCO3 (pH=8.5)
filtered through 0.45-µm membrane filters after a 16-hr continuously shaking to remove the labile organic soil
fraction (Hedley and Stewart, 1982).
4.1.3 Spatial Analysis of Physico-chemical Properties
The pH of soil was measured in the laboratory (Table 2), and was significantly different (α < 0.05) for all 3
sites. SW soil had significantly higher (α < 0.05) pH followed by NE and NW. This indicates that the soil at
impacted areas were slightly more basic than the soil at unimpacted area. However, the average pH at 3 sampling
sites ranged from 5.5 to 6.2, indicating a very narrow range.
To determine the effect of nutrient input on microbial activities, total phosphorus (TP) was measured as an
indicator of the nutrient level in 3 sampling areas (Table 2). For detritus, TP was significantly different (α < 0.05)
for all 3 sites. NE had a highest TP level followed by SW and NW. For soil, TP at NE and SW was significantly
higher (α < 0.05) than TP at NW. However, there was no significant difference between NE and SW for soil TP.
Thus, the impacted areas (NE and SW) had higher TP than the unimpacted area (NW) for both detritus and soil
layers. This indicates that the nutrient level still remains significantly different between impacted and unimpacted
areas.
TP values were grouped and analyzed by vegetation types regardless of sites. TP level was significantly
different among some vegetation types (Table 3). Average TP at Typha and Typha/woody mix areas was
significantly higher (α < 0.05) than Cladium, Cladium/woody mix, and Panicum for detritus and soil. Higher TP
levels in Typha marsh area than Cladium area was also observed in soils at the Everglades by DeBusk et al. (1994).
In general, TP was significantly higher (α < 0.05) in impacted areas than unimpacted area regardless of vegetation
types. For example, TP of Cladium area at NW was significantly lower (α < 0.05) than Typha at NE and SW and
Typha/woody mix at NE and SW. However, TP of Cladium at NE and Cladium/woody mix at SW was not
significantly different than Typha at NE and SW and Typha/woody mix at NE and SW.
10
Table 2. Summary of soil and detritus chemical analyses for each of the three areas in the spatial study. Values are the means of approximately 40
measurements from each area (samples from 29 sites plus field replicates).
HCl ext.
Area
Total P
Total N
921 (309) a
766 (287) b
530 (133) c
16.8 (8.6) a
13.3 (4.1) b
11.6 (3.9) b
419 (140) a
474 (14) b
449 (77) ab
Soil (0-10 cm)
NE 5.8 (0.4)
15.6 (2.3) a 1.0 (0.4) a
276.7 (99.9) a
34.5 (14.5) a
188 (99) a
847 (213) a
29.9 (5.5) a
impacted
b
b
b
a
b
a
1.5 (0.5)
452.3 (166.0)
32.4 (9.6)
147 (64)
860 (109)
24.2 (1.9) b
SW 6.2 (0.6)
23.8 (3.8)
c
ab
a
a
b
b
reference NW 5.5 (0.1)
11.4 (3.2)
1.2 (0.8)
219.2 (41.0)
28.4 (12.3)
132 (45)
641 (82)
27.7 (2.4) c
Values in parenthesis are one standard deviation. Different letters indicate significant differences (α< 0.05) among detritus or soil values.
467 (13) a
465 (7) a
465 (6) a
impacted
reference
pH
NE
SW
NW
Ca
g kg-1
16.7 (5.3) a
20.0 (5.9) b
9.0 (3.0) c
Fe
1.0 (0.8) ab
1.3 (0.8) a
0.8 (0.5) b
Al
NaHCO3 - Pi
NH4-N
mg kg-1
Detritus
44.8 (60.0) ab
174.6 (121.7) a
565 (392) a
a
b
82.3 + 107.4
109.4 (76.7)
199 (438) b
b
b
14.8 + 14.1
101.3 (44.5)
198 (210) b
g kg-1
Total C
Table 3. Chemical analysis of detritus and soil (0-10 cm) by vegetation types collected September 11-15, 2000 from 3 areas within the BCMCA. Values
represent means with standard deviation in parentheses. TC = total carbon; TN = total nitrogen; TP = total phosphoru
Vegetation type
n
TC
TN
-1
Detritus
Cladium
Cladium/woody Mix
Panicum
Typha
Typha/woody Mix
Others
Soil (0-10 cm)
Cladium
Cladium/woody Mix
Panicum
Typha
Typha/woody Mix
Others
TP
C:N
C:P
-1
----------g kg ----------
---mg kg ---
mass ratio
mass ratio
25
16
14
16
18
27
464 (19)
478 (13)
449 (19)
467 (12)
467 (14)
469 (17)
12.1
13.6
11.6
19.4
18.2
13.5
(4.3)
(3.9)
(2.8)
(7.5)
(4.5)
(5.8)
555
657
550
1030
1058
667
(206)
(151)
(103)
(255)
(310)
(232)
42.8 (14.2)
39.1 (15.1)
40.5 (7.6)
28.5 (14.3)
27.3 (7.1)
40.0 (14.7)
949
769
843
490
481
806
(332)
(196)
(156)
(172)
(153)
(355)
26
16
14
16
18
31
464 (8)
463 (13)
465 (6)
472 (7)
465 (8)
465 (10)
26.5
23.9
29.7
27.8
26.3
28.9
(2.7)
(2.5)
(1.7)
(4.1)
(3.2)
(6.1)
671
724
684
888
910
834
(102)
(134)
(88)
(172)
(92)
(219)
17.7
19.5
15.7
17.3
18.0
16.7
706
662
693
576
517
610
(99)
(135)
(105)
(253)
(64)
(231)
11
(1.7)
(1.9)
(0.9)
(2.0)
(2.1)
(3.1)
The total carbon (TC) of detritus at SW was significantly higher (α < 0.05) than NE although there was no
significant difference (α < 0.05) between NW and SW and NE. For soil TC, there was no significant difference
among all 3 sites. Although the major vegetation types were different between impacted and unimpacted sites, it
was interesting that TC of soil was not significantly different by sites. On the other hand, the total nitrogen (TN) of
detritus at NE was significantly higher (α < 0.05) than NW and SW (Table 2). There was no significant difference
(α < 0.05) between TN of NW and SW. However, the TN of soil at SW was significantly different than the other
two sites (α < 0.05).
The ratio of total C to total nitrogen (C:N) can indicate the relative biodegradability (or quality) of
substrates, and the lower TC/TN ratio can indicate rapid degradation of organic substrates (Berg, 2000). In detritus
layer, the C:N ratio was significantly higher (α < 0.05) at NW and SW sites than NE site, and there was no
significant difference between NW and SW. On the other hand, the C:N ratio of the soil layer was significantly
higher (α < 0.05) at SW than NE and NW. There was no significant difference between the soil C:N ratio of NE
and NW. When the C:N ratio was compared only by vegetation types regardless of sites, the C:N ratios for detritus
at Cladium and Panicum areas were significantly higher (α < 0.05) than Typha and Typha/woody mix (Table 3).
However, there was no significant difference in the soil C:N ratio between Typha, Typha/woody mix and Cladium.
These results indicate that, in detrital layer, Typha and Typha/woody mix areas might have had more labile organic
matter than Cladium and Panicum areas. The detritus layer generally has plant materials that are less decomposed
than in the soil layer, thus possibly maintaining its original C/N ratio. The organic matter in soil layer was well
decomposed with loss of C and immobilization of N, probably masked the differences among vegetation types. In
both detritus and soil, the C:P ratio of NW was significantly higher (α < 0.05) than NE and SW. This indicates that
the increase in TP concentration resulted in shifting the C:P ratio down in impacted areas. In addition, the C:P ratio
in Cladium, Cladium/woody mix, and Panicum areas were significantly higher than Typha and Typha/woody mix
for both detritus and soil layers (Table 3). These results indicate higher phosphorus content in impacted areas,
especially in Typha sp. areas.
4.1.4 Spatial Analysis of Microbial Biomass and Labile Nutrient Pools
4.1.4.1 Microbial Biomass C, N, and P.
The microbial biomass plays important roles in an ecosystem, such as organic matter decomposition and
nutrient cycling (Wardle, 1992). If the ecosystem receives an environmental impact, microbial communities
generally respond by changing size and decreasing in diversity (Drake et al., 1996). As a result, change in microbial
communities could eventually affect the carbon cycle. Since the BCMCA had a history of receiving nutrient input at
NE and SW sites, it is likely that this difference will be reflected in microbial biomass between impacted and
unimpacted areas. It should be noted that the MBC includes both aerobic and anaerobic microbes. The microbial
biomass carbon (MBC) in soil at NW was significantly higher (α < 0.05) than both NE and SW soil (Table 4). In
addition, the NE soil had a significantly higher (α < 0.05) MBC than SW soil. In detritus layer, however, there was
no significant difference in the MBC content among all 3 sites. When the MBC in soil was compared by vegetation
type, there was no significant difference within the following vegetation groups: Cladium, Cladium/woody mix,
Typha, and Typha/woody mix (Table 5). However, the MBC in soil at Panicum area was significantly higher (α<
0.05) than most of above four vegetation types: Cladium/woody mix, Typha, and Typha/woody mix.
Microbial biomass N averaged 533 + 281 mg N kg-1 and 921 + 687 mg N kg-1 for the soil and detritus
layers respectively, combining all three regions (Table 6). This decrease in microbial biomass N with depth may be
because of the lower availability of C to support microbial communities (Williams and Sparling, 1988;
Franzluebbers et al., 1995). For both the soil and detrital layers, MBN was not found to be significantly different (α
< 0.05) between the three regions, even when taking into account vegetative communities. Therefore, increased
availability of P within the impacted regions may only have increased heterotrophic activity, not the size of MBN. It
has been shown that long-term P loading (i.e., months to years) has brought about an increase in microbial activity
as well as an increase in size of the microbial pool (White and Reddy, 2000).
The microbial biomass within a wetland mediates the transformation of P between the inorganic and
organic fractions through mineralization (Mitchell and Baldwin, 1998). Determination of the direct microbial P
release was estimated through MBP. The microbial biomass provides an estimate of the size of the microbial
community within the soil. There was a significant difference in the size of the microbial biomass pool along the
three impacted regions. Microbial biomass P in the NW soil (0-10 cm) samples was 181 ± 44.6 mg kg-1 dry weight,
12
Table 4. Chemical analysis of detritus and soil (0-10 cm) collected September 11-15, 2000 from 3 areas within the
BCMCA. Values represent means with standard deviation in parentheses. MBC = microbial biomass carbon; LOC
= labile organic carbon; TLOC = total labile organic carbon. MBC and TLOC were measured by chloroform
fumigation method, and correction factor (kEC = 0.37) was applied.
Site
n
MBC
LOC
TLOC
-1
---------------------------g C kg --------------------------Detritus
Northeast
NW
Southwest
36
29
41
12.90 (7.00)
9.98 (3.91)
12.74 (6.22)
5.23 (2.95)
2.87 (0.91)
4.34 (2.48)
27.02 (12.27)
17.74 (5.36)
24.48 (9.37)
Soil (0-10 cm)
Northeast 43
NW
39
Southwest 41
9.97 (4.10)
13.05 (4.28)
7.47 (2.32)
2.38 (0.63)
0.93 (0.22)
1.81 (0.43)
16.41 (4.85)
15.57 (4.51)
12.35 (2.71)
Table 5. Chemical analysis of detritus and soil (0-10 cm) by vegetation types collected September 11-15, 2000 from
3 areas within the BCMCA. Values represent means with standard deviation in parentheses. MBC = microbial
biomass carbon; LOC = labile organic carbon; TLOC = total labile organic carbon. MBC and TLOC were measured
by chloroform fumigation method, and correction factor (kEC = 0.37) was applied.
Vegetation type
n
MBC
LOC
TLOC
-1
-------------------------g C kg ------------------------Detritus
Cladium
Cladium/woody Mix
Panicum
Typha
Typha/woody Mix
Others
22
16
8
16
18
26
8.59
9.85
13.66
17.83
13.90
10.97
(3.10)
(5.71)
(2.32)
(5.22)
(5.47)
(7.01)
3.18
2.47
3.46
5.11
5.20
5.27
(1.59)
(0.93)
(0.94)
(1.76)
(2.68)
(3.39)
17.19
16.52
22.99
31.65
27.96
25.20
(4.99)
(7.34)
(2.39)
(6.19)
(10.64)
(12.67)
Soil (0-10 cm)
Cladium
Cladium/woody Mix
Panicum
Typha
Typha/woody Mix
Others
26
16
14
16
19
32
10.96
8.66
15.25
9.56
8.74
9.01
(2.78)
(2.85)
(6.20)
(2.91)
(2.45)
(4.72)
1.16
1.39
1.06
2.39
2.22
2.04
(0.49)
(0.59)
(0.17)
(0.55)
(0.55)
(0.67)
14.09
12.40
18.13
16.02
14.75
14.52
(2.15)
(3.60)
(6.29)
(3.57)
(3.60)
(5.39)
Table 6. Selected physiochemical properties of detritus and soil samples collected from the spatial sampling
(9/12/00). Data are mean values (n = 40) with one standard deviation.
Soil
Northeast
BCT
Southwest
Detritus Northeast
BCT
Southwest
NH4+
MBN
mg N kg-1
PMN
522 ± 375
527 ± 230
550 ± 212
1007 ± 731
958 ± 831
800 ± 498
31.5 ± 21.7
60.5 ± 12.7
47 ± 12.7
160 ± 77.3
80.9 ± 32.1
125 ± 113
-1
mg N kg d
13
-1
188 ± 99
132 ± 44.7
147 ± 64
565 ± 392
198 ± 210
199 ± 433
DEA
mg N20-N kg-1 hr-1
0.0017 ± 0.00132
0.0121 ± 0.00759
0.00329 ± 0.00226
which was significantly higher than the concentrations found in the NE, 151 ± 92.8 mg kg-1 dry weight and SW, 119
± 23.0 mg kg-1 dry weight (Table 7). The elevated MBP fraction in the NW regions indicates that the elevated P
concentrations in the impacted zones had a detrimental effect on the microbial community, diminishing the
populations. However, these findings are contradictory to the general theory that the size of microbial populations
and therefore the quantity of MBP in the soil, increases in response to the addition of P (Drake et al., 1996; DeBusk
and Reddy, 1998; Reddy et al., 1998; Reddy et al., 1999, Quall and Richardson, 2000; Noe et al., 2001). The
increased MBP pool within the NW region may be a result of vegetation. Dense root masses associated with the
plant species, Panicum spp. and Cladium spp., within the NW region could provide a large aerobic area for
microbial colonization activity (Tobe et al., 1998). Ohtonen and Väre (1998) noted that the amount of fine roots
could likely influence the microbial activity within the soil.
There was no significant difference in the size of the microbial biomass P associated with the detrital layer
between the three regions. A lack of significant difference in the detrital layer could indicate that the amount of
microbes present in the soil is not affected by the quantity of P in the detrital layer. Examination of MBP by depth
showed there was a significant difference between the detrital layer and the 0-10 cm soil layer and that there was a
regional interaction as well. The size of the MBP in the non-impacted region was significantly greater at the 0-10
cm soil layer than in the detritus. However, comparison of the two layers in the impacted regions, NE and SW
showed the MBP significantly decreased with depth, and this finding correlates with the results discussed in Reddy
et al., (1998). The size of the microbial pool in the NW soil layer could also be a result of the dense root masses
associated with the shrub species present. Rooted macrophytes supply oxygen to the root zone, which facilitate
colonization and maintains the population of aerobic microorganisms.
4.1.4.2 Spatial Analysis of Labile Nutrient Pools
The labile organic carbon (LOC) and the total labile organic carbon (TLOC) are generally readily available
carbon, therefore showing differences with nutrient availability. The LOC of both detritus and soil at NE site was
significantly higher (α < 0.05) than NW and SW (Table 4). The LOC of soil at SW was significantly higher (α <
0.05) than that of NW, however there was no significant difference between the LOC of detritus at SW and NW.
When the LOC was compared by vegetation types, the LOC at Typha and Typha/woody mix areas was significantly
higher (α < 0.05) than Cladium and Cladium/woody mix areas (Table 5). This was true for both detritus and soil
layers. As a result, Typha sp. could be more capable of providing higher available carbon than Cladium sp. The
LOC of detritus at Panicum area was not significantly different from any other vegetation types. However, the LOC
of soil at Panicum area was significantly lower than Typha and Typha/woody mix.
The TLOC includes both LOC and MBC. The TLOC in detritus at NE and SW was significantly higher (α
< 0.05) than NW, and there was no difference between the TLOC of NE and SW. Moreover, the TLOC in soil at
NE and NW was significantly higher (α < 0.05) than that of SW, while the TLOC of NE and NW soils was not
significantly different from each other. When the TLOC was compared by vegetation types, the TLOC of detritus in
Typha and Typha/woody mix areas was significantly higher than that of Cladium and Cladium/woody mix (Table 5).
The TLOC of detritus in Panicum area was significantly higher (α < 0.05) than only Cladium and Cladium/woody
mix areas. The TLOC of soil showed more complex and different trend than that of detritus. The TLOC of soil at
both Cladium and Typha areas were not significantly different than any other vegetation types. However, the
Panicum area had significantly higher (α < 0.05) TLOC in soil than Cladium/woody mix and Typha/woody mix. As
a result, the trends by sites and vegetation types were not necessarily similar to each other although LOC, MBC, and
TLOC were related to each other. The unimpacted area had higher MBC but lower LOC than impacted areas.
The total labile organic P (TLOP) followed the same regional and depth trends as the MBP previously
discussed. The TLOP parameter includes the quantity of MBP determined for the sample; MBP accounts for > 75%
of the total labile organic P fraction and therefore is able to mask possible labile organic P not resulting from
microbial activities going on in the soil. Determination of the labile organic P (LOP) showed there was no
significant difference in the regional soil (0-10 cm) samples. This confirms that the elevated TLOP concentrations
in the NW soil were a result of microbial activity. Analysis of the TLOP regional detritus layer displayed a
significant difference between all three regions with the greatest amount present in the SW (43.4 ± 21.7 mg kg-1 dry
weight) followed by the NE (29.3 ± 19.8 mg kg-1 dry weight) and the NW (14.5 ± 11.2 mg kg-1 dry weight). The
labile Po represents the dead organic forms including the detrital tissue of plant roots and microorganisms (Reddy et
al., 1998). The data indicate there were large quantities of dead detrital material accumulated in the SW region in
14
Table 7: Regional organic P characteristics for soil 0-10 cm layer and detritus. Data shown are the mean values
and one standard deviation (n=20). Letters designate significant difference at a=0.05. (n.d = no data available).
Region
MBP
(mg kg-1)
TLOP
LOP
(mg kg-1)
(mg kg-1)
Soil (0-10 cm)
NW
181 ± 44.6 a
218 ± 48.4 a
NE
151 ± 92.8 b
SW
119 ± 23.0 c
BD
(g cm-3)
LOI
(%)
36.8 ± 10.0
0.067 ± 0.014
93.4 ± 1.24
190 ± 90.2 a
39.2 ± 20.3
0.066 ± 0.019
92.7 ± 1.76
163 ± 28.0 b
43.3 ± 13.4
0.072 ± 0.011
93.4 ± 1.62
Detritus
NW
147 ± 44.1 a
161 ± 46.2
14.5 ± 11.2 a
n.d
96.4 ± 1.04
NE
188 ± 113 b
217 ± 125
29.3 ± 19.8 b
n.d
95.5 ± 1.39
SW
142 ± 93.5 a
185 ± 111
43.4 ± 21.7 c
n.d
94.3 ± 186
15
relation to the other two sites, however the loss of ignition, an indication of the quantity of organic matter or carbon
present in the system, determined for the detrital layer showed no significant difference between the regions.
Potentially mineralizable N measures the release rate of NH4+ from the decomposition of large into small
and eventually dissolved organic N compounds. The rate-limiting step(s) for this process involves the microbial
enzyme activity associated with the break down of the larger organic N compounds (Bonde et al., 2001). Anaerobic
incubation conditions for PMN permit the use of higher temperatures allowing faster N release under shorter
incubation times and represent typical flooded wetland soil conditions. Differences, however, may arise between
conditions within the lab and the field. Exposure of the soil surface to the atmosphere during drawdown events will
increase the release of inorganic N due to contact with O2 than would be observed under anaerobic conditions; thus
spatial variability based on hydrologic conditions are expected. Potentially mineralizable N averaged 121 + 74 mg
N kg-1 d-1 and 46 + 16 mg N kg-1 d-1 for the detritus and soil layers, respectively combining all three regions (Table
3-1). Others have also observed a decrease in PMN with depth in anaerobic (White and Reddy, 2000) and aerobic
soils (Franzluebbers et al., 1995; Hossain et al., 1995; Humphrey and Pluth, 1996; Lavermaan et al., 2000). These
PMN rates are similar to rates found in impacted regions of the Everglades (WCA-2A), which had rates of 34 mg N
kg-1 d-1 and 126 mg N kg-1 d-1 for 0- to 10-cm soil depth and detritus, respectively (White and Reddy, 2000).
Extractable NH4+ concentrations decreased with depth as well, averaging 322 + 411 mg N kg-1 and 156 + 76 mg N
kg-1 for all three regions within the detritus and soil layers, respectively (Table 6). The decrease of PMN and
extractable NH4+ with depth through the soil profile may be because of the distribution of the MBN.
With respect to the detrital layer, PMN rates did not exhibit a trend with distance from the two surface
water inflow points in the impacted regions along the total P gradient, as well as along the flow path of water from
the interior of the marsh north towards Blue Cypress Lake within the unimpacted NW region. This suggests a
decrease in nutrient loading since the discontinuing of surface water inflows into the system (White and Reddy,
2000). In the detrital layer, PMN and extractable NH4+ concentrations were found to be significantly higher (α <
0.05) in the Northeast region than the NW and the Southwest regions. Within the soil, PMN rates were significantly
different (α < 0.05) between all three regions, with the NW region having the highest activity. However, extractable
NH4+ concentrations within the soil were significantly higher (α < 0.05) for the Northeast and Southwest regions
than the NW region. The distribution of PMN rates and extractable NH4+ concentrations between regions should be
related to MBN, as N mineralization is mediated by soil microbial activity; however, MBN was not significantly
different between the three regions. Total P concentrations found within the detritus layer followed the same spatial
pattern as PMN and extractable NH4+ (α < 0.05). However, contrary to PMN rates within the soil, total P was found
to be higher (α < 0.05) within both the Southwest and Northeast regions than the unimpacted NW region (Table 2).
Within the Northeast, Typha sp. communities had higher PMN rates and extractable NH4+ concentrations (α < 0.05)
for the detrital layer than the Southwest (Cladium/Woody Mix) and NW (Panicum and Cladium) communities (Fig.
2). For the soil, NW (Panicum and Cladium) and Southwest (Typha sp.) communities had greater PMN rates (α <
0.05) than other vegetative communities, especially Typha sp. communities within the Northeast (Fig. 3).
Extractable NH4+ concentrations were not significantly different (α < 0.05) between vegetative communities within
the soil.
The PMN activity associated with vegetation type therefore coincided with the distribution of PMN
between impacted and unimpacted regions. Within the impacted regions, detrital PMN activity was the highest
within the Northeast region, corresponding to the high activity found in Typha sp. communities in the Northeast
region. Changes in C/N values for detritus between regions due to differences in vegetative communities may also
have played a role in establishing PMN rates (Chen and Stark, 2000; Nadelhoffer et al., 1991; Stelzer and Bowman,
1998). Higher PMN activity was found to be associated with lower C/N values occurring primarily in the Northeast
region.
4.1.5 Spatial Analysis of Microbial Activities
4.1.5.1 Carbon Metabolism
The activity of carbon acquisition enzyme β-glucosidase associated with Typha detritus was elevated
relative to that in detritus of other vegetation types, indicating more labile, easily degraded material. Soil oxygen
demand (SOD) was measured to estimate the aerobic microbial activity in both soil and detritus. One of the major
factors affecting this activity was the availability of substrate since the oxygen supply was not limited and the
incubation time length was only 24 hours. The SOD of detritus at NW was significantly lower (α < 0.05) than that
16
PMN (mg N kg-1 d-1)
250
200
a
b
150
100
b
b
50
0
Ext. NH4+ (mg N kg-1)
P
800
C
b
T
T/WM C/WM
Vegetation
O
a
600
400
b
b
200
0
P
C
T
T/WM C/WM
Vegetation
O
Figure 2. Potentially mineralizable N and extractable NH4+ concentrations for detritus with respect to vegetative
communities, P = NW Panicum, C = NE, NW Cladium, T = NE, SW Typha sp., T/WM = NE, SW Typha
sp./Woody Mix, C/WM = NE, SW Cladium/Woody Mix, and O = NE, SW Others. Data are mean values (+ 1 SD)
from the respective sampling locations (n=40).
PMN (mg N kg -1 d-1)
120
100
b
a
a
80
b
b
60
b
40
20
0
P
C
T
T/WM
C/WM
O
Vegetation
Figure 3. Potentially mineralizable N rates for the soil layer with respect to vegetative communities, P = NW
Panicum, C = NE, NW Cladium, T = NE, SW Typha sp., T/WM = NE, SW Typha sp./Woody Mix, C/WM = NE,
SW Cladium/Woody Mix, and O = NE, SW Others. Data are mean values (+ 1 SD) from the respective sampling
locations (n=40). Contrasting letters above the bars represent differences in significant values (α < 0.05).
17
of NE and SW, and there was no difference in SOD of detritus between NE and SW. In addition, the SOD of soil at
NE and NW was significantly higher (α < 0.05) than that of SW, while the SOD of soil between
NE and NW were not significantly different from each other. These patterns were exactly the same as the TLOC
patterns. These results show that the amount of readily available substrates such as TLOC clearly affected the
activity of aerobic microorganisms. When the SOD values were compared by vegetation types, the SOD of detritus
at Typha and Typha/woody mix areas was significantly higher (α < 0.05) than Cladium and Cladium/woody mix
areas (Table 8). There was no significant difference in SOD between Panicum and other vegetation types.
Moreover, the SOD of soil at Cladium/woody mix area was significantly lower (α < 0.05) than Cladium, Typha,
Typha/woody mix, and Panicum.
Under anaerobic condition with the complete absence of electron acceptors, methnogenesis is the major
metabolic pathway (Westermann, 1993). The potential methane (CH4) production was measured to determine the
activity of methanogenic bacteria. The CH4 production rate in detritus at SW site was significantly higher (α <
0.05) than NW site. The CH4 production rate from NE detritus was not significantly different than either that of NW
or SW. On the other hand, the NW soil produced significantly lower (α < 0.05) amount of CH4 than NE and SW
soil, while there was no significant difference between NE and SW soil CH4 production. Although the MBC in soil
at impacted area was lower than unimpacted areas, the potential CH4 production in impacted soil was actually higher
than unimpacted soil. D’Angelo and Reddy (1999) also observed higher production of CH4 from hypereutrophic
marsh soil. When the CH4 production data was compared by vegetation types, the CH4 production rates in detritus
from Typha and Typha/woody mix areas were significantly higher (α < 0.05) than the detritus from Cladium and
Cladium/woody mix (Table 8). However, the CH4 production rate in detritus from Panicum area was not
significantly different from any other vegetation types. On the other hand, the soil from Panicum area produced
significantly higher (α < 0.05) CH4 than only Cladium and Cladium/woody mix. Moreover, the CH4 production rate
from Typha area was significantly higher (α < 0.05) than Cladium area, and Typha/woody mix area produced
significantly higher (α < 0.05) CH4 than Cladium and Cladium/woody mix areas. In both soil and detritus, the
methanogenic activity was higher at Typha sp. dominated area than Cladium sp. dominated area. This pattern could
be caused by difference in plant composition between Typha sp. and Cladium sp., eventually affecting the substrate
availability for methanogenic bacteria. In fact, there was no significant difference (α < 0.05) in the potential CH4
production rates between the following 3 groups: the Cladium at NW, Cladium at NE, and Cladium/woody mix at
SW.
Anaerobic microbial respiration (CO2) reflects the activity of anaerobic microorganisms under surrounding
substrate and nutrient concentrations. It is known that the addition of low levels of P to a P-limited system can
enhance microbial activity (Amador and Jones, 1995). The potential anaerobic CO2 production rate in both detritus
and soil from the SW site was significantly higher (α < 0.05) than that of NE and NW. There was no significant
difference between the CO2 production rates of NE and NW detritus and soil. Therefore, an increase in CO2
production rate was observed in both detritus and soil layers of the impacted site (only SW) as compared to the
reference site (NW). Similar trends were also observed by DeBusk and Reddy (1998) and Wright and Reddy
(2001). In addition, Morris and Bradley (1999) reported that soil respiration measured under field conditions
increased due to nutrient loading in oligotrophic wetlands. They also confirmed that sediment respiration in
laboratory incubations also showed positive effects of nutrients. The increase in microbial respiration at impacted
site may be caused by an increase in primary productivity (Morris and Bradley, 1999). When the anaerobic CO2
production rate was compared by vegetation types, the detritus at Typha area had significantly higher (α < 0.05) CO2
production rate than Cladium area (Table 8). In addition, for both soil and detritus, the CO2 production rate at
Typha/woody mix area was significantly higher (α < 0.05) than that of Cladium and Cladium/woody mix. Although
the CO2 production rate of detritus at Panicum area was not significantly different from other vegetation types, the
soil from Panicum dominated area exhibited significantly higher (α < 0.05) CO2 production rate than Cladium
dominated area. Moreover, for soil and detritus, there was no significant difference in the CO2 production rates of
Cladium area at NE, Cladium area at NW, and Cladium/woody mix area at SW. As a result, the Typha dominant
area produced higher CO2 and CH4 than Cladium dominant area.
When evaluating microbial mediated processes in soil or detritus, ratios between values of different
microbiological parameters could be more reliable than microbial parameters by themselves (Dilly and Munch,
1998). For example, the ratio of microbial biomass C to total C (MBC/TC) can indicate the fraction of microbial
biomass in TC as a whole. Mean MBC/TC ratio for soil increased significantly (α < 0.05) in the order: SW < NE <
NW. There was no significant difference in the MBC/TC ratio for detritus between 3 sites. Although the microbial
activities such as anaerobic microbial respiration and methanogenesis were higher in impacted areas, microbes per
unit TC were lower in soil layer at impacted areas. These results suggest the buildup of microbial biomass,
18
Table 8. Chemical analysis of detritus and soil (0-10 cm) by vegetation types collected September 11-15, 2000
from 3 areas within the BCMCA. Values represent means with standard deviation in parentheses. SOD = soil
oxygen demand. Number of samples is same across horizontally unless noted ( *, n = 25; **, n = 10).
Vegetation type
n
SOD
-1
CH4
-1
--mg DO g hr -Detritus
Cladium
Cladium/woody Mix
Panicum
Typha
Typha/woody Mix
Others
23
88.3* (56.8)
16
89.1 (50.8)
8 116.2** (23.2)
16
200.0 (103.8)
18
190.1 (119.8)
24
136.4 (137.0)
Soil (0-10 cm)
Cladium
Cladium/woody Mix
Panicum
Typha
Typha/woody Mix
Others
26
16
14
16
19
32
50.2
40.1
63.2
56.9
54.9
47.8
(10.3)
(13.0)
(16.6)
(14.6)
(11.4)
(13.8)
19
CO2
-1
-1
---------------mg C g d --------------270.0
264.0
399.0
481.0
413.0
436.0
(92.0)
(156.0)
(103.0)
(200.0)
(169.0)
(221.0)
306.0
370.0
415.0
574.0
736.0
612.0
(126.0)
(240.0)
(108.0)
(201.0)
(386.0)
(352.0)
35.8
39.6
66.1
51.1
66.5
48.4
(12.9)
(16.7)
(21.5)
(19.6)
(36.5)
(27.4)
85.7
90.5
107.1
98.8
121.9
103.8
(10.4)
(19.5)
(19.2)
(19.1)
(33.5)
(27.7)
indicating possible nutrient limitation and immobilization of available nutrients. When the MBC/TC ratio was
compared by vegetation types, the MBC/TC ratio for detritus was significantly higher (α < 0.05) in Typha, soil at
only Panicum area had significantly higher (α < 0.05) MBC/TC ratio than Typha, Typha/woody mix, and
Cladium/woody mix. Since Panicum sp. was present only in NW site, the NW had probably highest MBC/TC ratio
of soil than impacted sites as a result.
Ratio of MBC to total labile organic carbon (MBC/TLOC) was determined to obtain more information
regarding microbial population per readily available carbon. Mean MBC/TLOC of detritus from NW site was
significantly higher (α < 0.05) than that of NE. However, mean MBC/TLOC ratio of detritus from SW was not
significantly different from both that of NE and NW. In addition, the NW soil had a significantly higher (α < 0.05)
MBC/TLOC ratio than NE and SW soils, while there was no significant difference between the NE and SW
MBC/TLOC ratios of soil. Similar to the result of MBC/TC ratio, there was more microbial population per
immediately available carbon expressed as TLOC at unimpacted site. When the MBC/TLOC ratio was compared by
vegetation types, there was no significant difference in that ratio of detritus among vegetation types. However, the
MBC/TLOC ratio for soil was significantly higher (α < 0.05) in Typha and Typha/woody mix areas than Cladium,
Cladium/woody mix, and Panicum areas. Moreover, the MBC/TLOC ratio for soil at NW Cladium dominant area
was significantly higher than that of NE Cladium and SW Cladium/woody mix areas. This indicates that this ratio
can be affected by both nutrient level and vegetation types.
The ratio of the soil oxygen demand per microbial biomass carbon (SOD/MBC) provides information
regarding efficiency of aerobic microbes in soil or detritus. This ratio is also referred to as metabolic quotient (qCO2
or CO2/MBC), one of the most widely studied quotients related to environmental changes (Anderson and Domsch,
1985; Anderson and Gray, 1991). For detritus, there was no significant difference in the mean SOD/MBC ratio
between 3 sites. However, the mean SOD/MBC ratio of soil from the impacted sites (both NE and SW) was
significantly higher (α < 0.05) than unimpacted site (NW). There was no significant difference between that mean
ratio of NE and SW soils. These results clearly show that the aerobic microbial activity per microbial biomass at
impacted sites was greater than unimpacted site, although the mean MBC at impacted sites were lower than
unimpacted site. When the SOD/MBC ratio was compared by vegetation types, there was no significant difference
in that ratio for detritus between vegetation types. However, the SOD/MBC ratio was significantly higher (α <
0.05) in the soil from Typha/woody mix area than Cladium area.
The ratio of potential methanogenesis per microbial biomass C (CH4/MBC) was also determined to find out
whether methanogenic activity per microbial biomass was higher at impacted sites, same as SOD/MBC. There was
no significant difference in the CH4/MBC ratio for detritus between 3 sites. However, mean CH4/MBC ratio for soil
increased significantly (α < 0.05) in the order: SW> NE > NW. This pattern, same as the pattern for the SOD/MBC
ratio, indicates that the ratio related to microbial activity tends to be higher at impacted soil than unimpacted soil. In
addition, although there was no significant difference in the CH4/MBC ratio for detritus between vegetation groups,
the mean CH4/MBC ratio was significantly higher (α < 0.05) for soil from Typha area than Cladium area. In
addition, the soil from Typha/woody mix area exhibited significantly higher CH4/MBC ratio than from Cladium and
Panicum areas. Moreover, the CH4/MBC ratio for soil from NE Cladium and SW Cladium/woody areas was
significantly higher (α < 0.05) than the soil from NW Cladium area. As a result, this ratio seems to be affected by
both nutrient level and vegetation types.
One of the widely studied ratio parameters related to microbial activity is the metabolic quotient (qCO2)
(Anderson and Domsch, 1985; Anderson and Gray, 1991). It is the ratio of respiration per microbial biomass, and
theoretically it should be higher in a young or disturbed ecosystem than in a mature or more stable one (Odum,
1969). Although only anaerobic respiration rate was measured for this research, the ratio between potential
anaerobic respiration rate and microbial biomass C (CO2/MBC) was used to determine microbial metabolic
efficiency. For detritus, the mean CO2/MBC ratio of SW site was significantly higher (α < 0.05) than that of NE
and NW sites. There was no significant difference between that ratio of NE and NW sites. For soil, the mean
CO2/MBC ratio increased significantly (α < 0.05) in the order: SW> NE > NW. Higher qCO2 for disturbed soil was
also observed by Ohtonen (1994). Similar pattern was also observed for CH4/MBC ratio, confirming that disturbed
(impacted) soil tends to have higher ratio which related to microbial activity than unimpacted soil. There was no
significant difference in the CO2/MBC ratio for detritus between vegetation groups, the mean CO2/MBC ratio was
significantly higher (α < 0.05) for soil from Typha/woody mix area than Cladium and Panicum areas. Moreover,
exactly same as CH4/MBC ratio, the CO2/MBC ratio for soil from NE Cladium and SW Cladium/woody areas was
significantly higher (α < 0.05) than the soil from NW Cladium area. There was no significant difference between
this ratio for soil from NE Cladium and SW Cladium/woody areas. As a result, the CO2/MBC ratio also seems to be
affected by both nutrient level and vegetation types.
20
Using the ratio parameter data for SOD/MBC, CH4/MBC, and CO2/MBC, turnover time was also
calculated. Aerobic turnover of microbial degradation was calculated from SOD/MBC ratio, and the averages for
detritus and soil were 3.7 ± 0.8 days and 7.6 ± 1.7 days, respectively. In addition, anaerobic turnover of microbial
degradation was calculated from CH4/MBC and CO2/MBC. The averages for anaerobic microbial turnover through
CH4 production for detritus and soil were 31 ± 4 days and 201 ± 84 days, respectively. The averages for anaerobic
microbial turnover through CO2 production for detritus and soil were 26 ± 9 days and 96 ± 38 days, respectively.
The aerobic microbial turnover was much faster than anaerobic turnover. This is because the catabolic energy yields
for bacteria utilizing O2 is higher than for alternate electron acceptors (NO3-, Mn4+, Fe3+, SO42-, CO2); therefore,
microbial growth rates are generally higher in aerobic environments (Westermann, 1993; Reddy and D’Angelo,
1994). As a result, the turnover time of degradation was shorter in aerobic condition. In addition, microbial
turnover in detritus was much faster than that of soil. This is due to higher substrate quality in the less-decomposed
detritus compared to soil (DeBusk and Reddy, 1998).
Selected microbial parameters were additionally analyzed for any significant difference between soil and
detritus. Most parameters except TC, TN, TP, and MBC/TLOC showed that mean value for detritus was
significantly higher (α < 0.05) than that of soil. A similar trend, a decrease of CH4 and CO2 production with depth,
was observed in Everglades soils (Bachoon and Jones, 1992; DeBusk and Reddy, 1998; Wright and Reddy, 2001).
There are two possible reasons for higher microbial activity in detritus than soil. First, wetland soils have an
oxidized layer at the soil-water interface, and the aerobic condition in this layer may increase microbial biomass in
surface detritus in comparison to the underlying 0-10 cm soil (Reddy and D’Angelo, 1994). As a result, the
enhanced microbial biomass might have increased heterotrophic microbial activity in detritus compared to soil.
Secondly, the higher microbial activity in detritus could be caused by the higher substrate quality in lessdecomposed detritus than underlying soil (DeBusk and Reddy, 1998).
For spatial analysis, Kendall’s Tau correlation coefficient was determined for the following selected
microbial parameters from detritus and 0-10 cm soil: SOD, MBC, LOC, TLOC, CH4, CO2, TC, TN, TP, C:N, and
C:P (Table 9). Especially for detritus, heterotrophic microbial activity such as SOD, CH4, and CO2 was significantly
(α < 0.1) correlated with many microbial parameters such as MBC, LOC, and TLOC. It has been reported in several
studies that heterotrophic microbial activity is increased by P additions or in soils with high P concentrations
(Bridgham and Richardson, 1992; Amador and Jones, 1993; Amador and Jones, 1995; DeBusk and Reddy, 1998).
Both LOC and TLOC represent the most utilizable portion of TC; therefore, they were expected to be correlated
with CO2 and CH4 production rates. On the other hand, for both detritus and soil, TC was not significantly
correlated with heterotrophic microbial activity, indicating that the bulk of TC was not utilizable to microorganisms.
The correlation between CH4 and CO2 was not very high for both detritus and soil since the anaerobic microbial
respiration and methanogenesis were highly interrelated with each other.
4.1.5.1 Nitrogen Metabolism
Within the soil layer, DEA averaged 0.0057 + 0.0037 mg N20-N kg dry soil-1 h-1 combining all three
regions (Table 6). These DEA values are significantly lower than rates found in impacted regions of the Everglades
(WCA-2A), which had rates of 2.69 mg N20-N kg dry soil-1 h-1 for the surface soil (0-10 cm) (White and Reddy,
1999). In the soil layer, DEA was found to be significantly higher (α < 0.05) within the unimpacted NW region than
the Southwest and Northeast regions. Greater PMN rates also occurring in the NW region may influence DEA
within the soil because of the availability of extractable NH4+ for nitrification.
With regards to the soil, no simple trend with distance was revealed with respect to DEA rates from the two
surface water inflow points in the impacted sites. For the Northeast and Southwest regions, DEA rates were not
shown to decrease with distance from either inflow point (distance = 0) along the total P gradient. This high spatial
variability of DEA was also apparent in the NW or unimpacted region along the flow path of water from the interior
of the marsh north towards Blue Cypress Lake. High spatial variability associated with DEA has also been noted by
other studies (Christensen et al., 1990a; Duncan and Groffman, 1994; Forlorunso and Rolston, 1984; Myrold, 1988;
Parkin, 1987; Reddy and D’Angelo, 1994). This demonstrates that NO3- concentrations, associated with nutrient
loading, are not present at high levels at the impacted regions, as inflows have recently been curtailed.
Denitrification rates for the BCM are likely related to nitrification processes in the water column driving NO3- to
diffuse into the anaerobic sediment for denitrifiers.
Concerning the descriptive spatial analysis for vegetative communities associated with each region for the
soil, NW (Panicum and Cladium) vegetation had higher DEA rates (α < 0.05) than other vegetative communities
within the Northeast and Southwest regions (Fig. 4). Denitrification enzyme activity associated with vegetation
therefore similarly follows the distribution of DEA between impacted and unimpacted regions. Higher
21
Table 9. Kendall’s Tau correlation coefficient for selected microbial parameters from detritus (n = 116) and 0-10
cm soil (n = 123) samples. Value indicates significance at α < 0.1 and NS indicates non significance. SOD = soil
oxygen demand; MBC = microbial biomass carbon; LOC = labile organic carbon; TLOC = total labile organic
carbon; TC = total carbon; TN = total nitrogen; TP = total phosphorus.
Parameter
SOD
CO2
CH4
Detritus
MBC
LOC
TLOC
TC
TN
TP
C:N
C:P
0.475
0.454
0.507
NS
NS
0.219
NS
-0.239
0.393
0.207
0.378
NS
NS
NS
NS
NS
0.418
0.373
0.447
NS
NS
NS
NS
NS
Soil (0-10 cm)
MBC
LOC
TLOC
TC
TN
TP
C:N
C:P
0.290
NS
0.336
NS
0.269
NS
-0.250
NS
NS
0.341
NS
NS
NS
0.370
NS
-0.385
NS
0.331
NS
NS
NS
0.405
NS
-0.410
DEA
(mg N2O-N kg-1 h-1)
0.025
a
0.02
0.015
a
b
0.01
b
b
0.005
b
0
P
C
T
T/WM C/WM
O
Vegetation
Figure 4. Denitrification enzyme activity for the soil with respect to vegetative communities, P = NW Panicum, C =
NE, NW Cladium, T = NE, SW Typha sp., T/WM = NE, SW Typha sp./Woody Mix, C/WM = NE, SW
Cladium/Woody Mix, and O = NE, SW Others. Data are mean values (+ 1 SD) from the respective sampling
locations (n=40). Contrasting letters above the bars represent differences in significant values (α < 0.05).
22
DEA activity was found within the NW region corresponding to the high DEA activity for NW Panicum and
Cladium communities. Changes in soil quality, including C/N values, between regions due to shifts in vegetation
may have partially influenced the spatial variability of DEA.
4.1.5.1 Phosphorus Metabolism
In general, acid phosphatase activity (APA) showed no definitive trends with regard to nutrient impact
when compared among the three areas. Mean APA was significantly lower (α=0.05) in soils from the SW, but was
highest in the NE (Fig. 5). Mean APA in detritus was highest in NW but the difference was not significant. Variation
in enzyme activities between different vegetation communities confounded direct comparisons between areas. APA
was significantly higher in soils of Typha areas than those of Cladium/woody mix areas (Figs. 5). APA was not
significantly different in detritus of any vegetation type. Increased availability of P likely facilitates greater
microbial uptake, thus favoring lower APA levels.
The quantity of TLOP by vegetation type and soil depth followed the same trends as the MBP that was
discussed earlier. Microbial biomass P constitutes the majority of the TLOP within the 0-10 cm soil layer (~80 %)
and therefore both follow a similar trend of the microbial community reacting to adverse environmental conditions.
The proportion of MBP is higher in the detrital layer, accounting for about 85% of the total labile organic P fraction.
Significant quantities of LOP were available within the Typha and Typha/woody mix communities with slightly
lower concentrations found in the Cladium, Cladium/woody mix, and Others. The labile organic P data displayed a
significantly decreased concentration within Panicum, in relation to TLOP. The labile organic P accounts for the
quantity of P that is not associated with microbial activity. Comparison of the LOP and TLOP data showed the
majority of available organic P within the non-impacted Panicum soils was tied up in the microbial biomass and
very little was readily available in the soil. Comparison of MBP to TP suggested the microbial pool in the NW
region accounted for a larger portion of the total P at the 0-10 cm depth than in the NE and SW regions with ratios
of 28.1, 17.8 and 13.8, respectively. The same regional trends were seen in the detritus, with MBP accounting for a
greater proportion of TP. These findings indicate that the microbial community in the non-impacted region was
more P efficient than the ones in the P impacted regions.
4.2 TASK 2: TEMPORAL VARIABILITY OF BIOGEOCHEMICAL PROCESSES AND INDICATORS.
4.2.1 Sample Collection
Based on results from the spatial sampling, two sites were established in each of the reference and impacted
areas for bi-monthly sampling over 12 months in order to examine temporal variability. Three 2x2 m plots were
established at each of the six sites. A composite of four soil and four (400 cm2) detritus samples (collected as
described for spatial sampling) were collected at randomly selected locations in each plot. Soil and detritus were
placed in water tight plastic bags and transported on ice to the laboratory and stored at 4 o C until analysis. Soil
redox potential was measured bi-monthly at three plots at each site in order to determine aerobic status. Redox was
measured at four depths (5, 10, 20, and 30 cm) using Pt electrodes connected to portable pH meter along with a
calomel reference electrode. The sites were chosen to account for the dominant vegetation communities (Table 1)
and nutrient impact. The NE Cladium site had slightly elevated total phosphorus values (based on the spatial
sampling), but retained the native vegetation community.
4.2.2 Soil Biogeochemical Analyses
Biogeochemical, physicochemical, and microbial parameters were characterized as described in 4.1.2
above.
4.2.3 Temporal Patterns of Physico-chemical Properties
In January 2001, a large portion of the marsh burned, including two reference and the two impacted sites in
the southwest (Fig. 1). Monitoring began in March 2001, after vegetation re-growth had begun. Because the
BCMCA is located within subtropical region, soil temperature did not drastically fluctuate throughout the year, nor
between sites. The average soil temperature of 3 sites ranged from 26.3 ± 1.2 ºC in August 2001 to 16.1 ± 3.8 ºC in
23
APA
µmole MUF g-1hr-1
2.5
Soil (0-10 cm)
b
2
a
1.5
APA
µmole MUF g-1hr-1
4
3
2
c
1
0.5
0
NW
2.5
Soil (0-10 cm)
2
a
ab
a
1.5
1
0.5
0
5
C
NE
Area
1
0
SW
b
ab
C-W O P
T
Vegetation
Detritus
a
a
NW
5
Detritus
a
4
ab
3
T-W
2
1
0
a
a
C
NE
Area
SW
a
a
a
a
C-W O P
T
Vegetation
T-W
Figure 5. Comparison of means for acid phosphatase activities (APA) in soil and detritus samples from the three
sampling areas and six major vegetation communities in the spatial study. C, Cladium; C-W, Cladium-Woody Mix;
O, Others; P, Panicum; T, Typha; and T-W, Typha-Woody Mix. Values are means of 29 sites from each area, and 18
(C), 10 (C-W), 23 (O), 10 (P), 14 (T), and 12 (T-W) sites from each vegetation class. Different letters indicate
significant differences (α < 0.05). Dashed line indicates mean for all samples. The line in the body of each box
represents the median; top and bottom of the box represent the 75th and 25th quantiles; the lines above and below
each box represent the 10th and 90th quantiles.
24
January 2002. The hydrologic condition, on the other hand, drastically changed during the year of temporal study.
The state of Florida had experienced a 3-year drought during this study period. Monthly precipitation data supplied
by the St. Johns River Water Management District (SJRWMD) showed the small amount of precipitation BCMCA
received from November 2000, to the beginning of May 2001 (Fig. 6). The low rainfall caused a complete
drawdown of the marsh to occur from March to August 2001 of the seasonal study. Anaerobic conditions (Eh <
300mV) were not established until July 2001 in the NE and SW regions and September 2001 in the NW region.
Soils were designated dry if aerobic conditions existed and the corrected Eh value was >300 mV. It should be noted
that the platinum electrodes were placed in the field at the onset of the temporal study and were left there for a
length of one year without removal or calibration, sampled bimonthly.
Physio-chemical parameters including pH, bulk density, loss of ignition (LOI), total N (TN), and total C
(TC) in the 0-10 cm soil layer across all three regions showed no site specific or regional differences. The majority
of the soil (>90%) within BCMCA was comprised of organic matter that was indicated by a high loss on ignition,
with very little mineral material. Comparison of the 0-10 cm soil and detrital layers showed no significant
difference (α = 0.05) in organic matter content. The spatial analysis of BCMCA revealed there were two areas of
high TP concentration (µ=854 mg kg-1) coinciding with the identification of regions (NE and SW) known to have
received nutrient loading from surface water inflows and one area of lower concentration (µ=643 mg kg-1) in the
NW region.
The temporal data showed the TP content in the NE and SW regions was greater than the reference
concentrations taken in the NW region, irrespective of plant species. Total P data, fell into two distinct groups, low
P concentrations (µ=535 mg kg-1) and high P concentrations (µ=846 mg kg-1) (Table 10). The formation of P
concentration gradients was related to both the hydrologic flow paths and the different water inflow rates (Fennessy
and Mitsch, 2001). The TP concentrations primarily followed the identifications of impacted (NE and SW) and nonimpacted (NW) regions, except for the Cladium sp. site in the NE region. This site exhibited low TP concentrations
perhaps as a result of the hummocky terrain and uneven hydrologic distribution. The Cladium sp. site in the NE
region is south of the inflow point and not in the direct flow path of the surface water that formerly entered through
this point.
Distinct variation in the detrital TP concentrations between the six sites was also seen (Table 10). Three
separate TP ranges for detritus did not coincide with the designations of impacted and non-impacted regions nor
were they related to the dominant vegetation species. Reason for the detrital variability between sites is unclear at
this time, but the occurrences of two fire events and an extended drought could have been contributing factors to
increased variability. The TP concentrations in the detrital layer were generally contained less TP except for the
NW Panicum and SW Other vegetation sites.
Comparison of the C: N: P ratios indicates P-limitation in the system at three of the sites, the two NW sites
and the Cladium sp. NE site (Reddy et al., 1993). The sites separate into two distinct groups with lower N:P ratios at
the three sites nearest the surface water inflow points. The N: P ratios by site averaged 55:1 in the NW(Cladium sp),
60:1 in the NW(Panicum sp), 49:1 in the NE(Cladium sp), 26:1 in the NE(Typha sp), 27:1 in the SW(Typha
sp/woody mix), and 29:1 for the SW (Other) at the 0-10 cm soils. A higher N:P ratio was found in the detritus layer,
which contained N:P ratios of 34:1 in the NW(Cladium sp), 34:1 in the NW(Panicum sp), 27:1 in the NE(Cladium
sp), 22:1 in the NE(Typha sp), 20:1 in the SW(Typha sp/woody mix), and 20:1 for the SW (Other). The significant
difference in the N: P ratios within the NE region between the two sites (Typha sp. and Cladium sp.) showed the
heterogeneity of the wetland and the impact of hydrology and landscape changes, from slough to hummocky terrain,
can have on P distribution.
The majority of the P in these soils (histosols) was found in organic forms. The total organic P (TPo)
concentration accounted for approximately 85 % of the TP at the 0-10 cm soil layer. Distribution of the total
inorganic P (TPi) and TPo fractions showed that TPo constituted the majority of the TP present in all regions of the
marsh (Fig. 7). Analysis of the TPo and TPi concentrations showed a separation of the sites into two distinct groups
with the NE Typha and SW sites having greater concentrations of both TPo and TPi at both the detritus and soil
levels when compared with NE Cladium sp and NW sites. The separation of the two groups appears to coincide
with TP concentrations. The relative increases in organic and inorganic bound P were proportional to an overall
increase in TP. The concentration of TPo and TPi showed little variation between the depths, but the percentages of
P were different with detritus containing a higher proportion of inorganic P. Lower TPi concentrations with depth
could be a result of rapid adsorption to soil particles through the formation of phosphates-metal complexes at the
surface.
25
35
30
20
Drought
Conditions
15
10
5
N
ov
2001
Se
pt
ly
Ju
ay
M
ar
M
2000
Ja
n
N
ov
0
Se
pt
Average Rainfall (cm)
25
Figure 6. Average monthly rainfall data from the BCMCA from September 2000 to December 2001, with
designation of the drought period.
Table 10. Summary of soil and detritus chemical analyses for each site in the temporal study. Values are the means
of 18 measurements (three composite samples taken bimonthly for one year).
Site
NaHCO3 - Pi
NH4-N
Total P
Total N
mg kg-1
Impacted
Reference
Impacted
Reference
NETypha
NECladium
SWTypha/Woody
SWTypha/Cladium/Woody
NWCladium
NWPanicum
Detritus
133.0 (52.6) a
96.9 (43.5) ab
134.3 (65.5) a
91.8 (51.0) ab
37.1 (16.0) c
45.8 (16.2) bc
NETypha
NECladium
SWTypha/Woody
SWTypha/Cladium/Woody
NWCladium
NWPanicum
Soil (0-10 cm)
48.0 (9.9) ab
42.4 (14.4) a
56.7 (8.2) b
50.3 (9.2) ab
44.2 (18.5) ab
45.9 (18.5) ab
Total C
g kg-1
159.7 (126.7) a
126.9 (50.4) ab
139.6 (72.7) a
93.3 (42.7) ab
46.1 (47.6) b
88.8 (127.6) ab
770 (102) a
435 (58) b
922 (139) c
687 (195) a
393 (109) b
451 (247) b
18.0 (3.6) a
12.1 (1.6) b
17.4 (4.1) a
13.5 (2.5) b
13.7 (3.0) b
14.2 (3.1) b
456 (12) a
455 (10) a
428 (19) bd
416 (45) cd
444 (14) ab
435 (14) bd
78.7 (38.9) a
68.7 (42.9) a
68.6 (21.5) a
54.5 (19.5) a
60.1 (18.0) a
64.3 (20.7) a
834 (90) a
565 (109) b
878 (83) a
826 (104) a
507 (54) b
533 (64) b
26.4 (1.3 a
25.8 (1.0) a
24.2 (1.2) d
24.3 (0.9) d
27.9 (1.3) b
31.6 (1.4) c
448 (9) a
451 (10) ac
435 (16) d
442 (7) ad
458 (5) bc
461 (8) b
Values in parenthesis are one standard deviation. Different letters indicate significant differences (α< 0.05) among
detritus or soil values.
26
SW Typha sp
/ woody mix
Detrital TPo
SW Other
Detrital TPi
Soil TPo
NE Typha sp
Soil TPi
NE Cladium sp
BCT Cladium sp
BCT Panicum sp
0
200
400
600
800
1000
mg P/ kg
Figure 7: Distribution of the total inorganic P (TPi) and total organic P (TPo) fractions in
the detrital and 0-10 cm soil layers at all six temporal sites in BCM. Data shown are the
mean values and standard error (n=18).
27
4.2.4 Temporal Patterns of Microbial Biomass and Labile Nutrient Pools
4.2.4.1 Microbial Biomass Carbon and Carbon Metabolism
MBC for detritus showed different temporal patterns among three sites. Although there was no drastic
temporal pattern for MBC of NW detritus, MBC was higher at NE site in June and at SW site in January. For soil,
MBC was higher in the early phase of temporal study (in March and June) for all three sites. A similar pattern was
also observed in LOC for detritus and soil. For both detritus and soil, LOC was higher in the early phase of the
study for all three sites. TLOC was also higher around March and June for detritus and soil, and it decreased toward
the latter phase of the study. On the other hand, SOD showed a unique temporal pattern for detritus and soil. An
increase in SOD was observed in July for soil at NW and SW areas compared to other months. A similar trend was
also observed in SOD for detritus at SW area. For detritus and soil, potential CH4 production ratio generally
increased toward the latter phase of temporal study for all three sites. A similar pattern was also observed in
anaerobic CO2 production rate for detritus in all three sites: a general increase toward the end of temporal study. On
the other hand, anaerobic CO2 production rate was higher in the early phase of the study for soil in all three sites.
The ratio of MBC/TC for detritus was higher in June at NE area and in January at SW area, although there
was no clear temporal pattern for NW area. For soil, MBC/TC was generally higher in the early phase of the study
for all three sites. On the other hand, MBC/TLOC was higher in July for detritus and soil at NW area and only for
soil at SW area. MBC/TLOC at NE area did not show a clear seasonal pattern for detritus and soil. In addition,
there was an increase in SOD/MBC for detritus in September at NW site and in July at SW site. For soil,
SOD/MBC was higher in July at NE and SW areas and in September at NW area. Moreover, CH4/MBC for detritus
was higher in July at SW area and toward the latter phase of study at NE area. For soil, CH4/MBC was higher
toward the end of study for all three sites. Finally, CO2/MBC ratio for detritus and soil showed different temporal
pattern for all three sites. The CO2/MBC ratio at NW was higher in September for both detritus and soil. However,
there was an increase in CO2/MBC ratio in July for detritus and in June for soil at SW area.
In addition to the temporal effect (Time), the effects of site and vegetation together (Station) and
combination of temporal and station (Time & Station) were analyzed for selected microbial parameters (Table 11).
Each site had two stations with different dominant vegetation types, and there were a total of 6 stations: NE Typha
sp., NE Cladium sp., NW Cladium sp., NW Panicum sp., SW Typha/woody mix, and SW others. Woody mix
indicates Myrica sp. Others include Typha, Cladium, and Salix sp.
Results showed that most of the parameters including ratio parameters were significantly (α < 0.05)
affected by seasonality. Only the C:N ratio and CH4/MBC ratio parameters for soil were not significantly affected
by temporal changes. In addition, most of the parameters showed significant difference (α < 0.05) between stations,
except the MBC/TLOC and CO2/MCB ratios for detritus. Although many of the microbial parameters were
significantly affected (α < 0.05) by time itself and station itself, many of them did not show significant difference
when time and station were combined. For example, microbial parameters measured at impacted sites may not be
consistently higher than unimpacted site throughout a year. There were also many microbial parameters that showed
significant difference (α < 0.05) to seasonality, station, and seasonality and station combined. One of them is the
CO2/MBC ratio for soil. These parameters could be the best indicators for ecological disturbance when they need to
be monitored throughout a year.
4.2.4.2 Microbial Biomass Nitrogen and Nitrogen Metabolism
Within both the soil and detritus layers, MBN was affected by changes in seasonality (α < 0.05). A
significant difference (α < 0.05) in seasonal patterns of MBN between each region was also observed for both the
soil and detritus layers. This significant difference between the three regions for the soil and detritus layers may
have been due to the differences in vegetation and/or type of microbial communities present. Different rates of
nutrient release and/or uptake may exist between the various vegetative communities over time. This can create
different fluxes in nutrient levels available for microbial functions. Furthermore, this result may possibly support
previous studies where variations in nutrient release from biomass have been attributed to changes in temperature
(Sarathchandra et al., 1988; Verburg et al., 1999; Zak et al., 1999). The gradual decrease in MBN from June to
January may be explained by the reduction in energy captured by the microorganisms using alternate electron
28
Table 11. Probability values from Manova repeated measures analysis (Pillai’s Trace Criterion) for the temporal
analysis of selected microbial parameters of detritus and 0-10 cm soil. Bold type indicates significant values at <
0.05. SOD = soil oxygen demand; MBC = microbial biomass carbon; LOC = labile organic carbon; TLOC = total
labile organic carbon; TC = total carbon; TN = total nitrogen; TP = total phosphorus.
Parameter
SOD
MBC
LOC
TLOC
CH4
CO2
MBC/TC
MBC/TLOC
SOD/MBC
CH4/MBC
CO2/MBC
TP
TC
TN
C:N
C:P
n
Time
Detritus
12
0.0123
15
0.0021
15
<.0001
15
<.0001
13
0.0069
13
0.0100
14
11
12
13
15
0.0299
0.0019
0.0128
0.0162
0.0359
α values
Time &
Station
Interaction Station
NS
0.0142
0.0049
0.0489
0.0119
NS
<.0001
0.0146
0.0036
0.0012
0.0049
0.0217
NS
NS
NS
0.036
0.0463
NS
0.0023
0.0131
0.0228
0.0194
29
n
Time
Soil (0-10 cm)
18
<.0001
18
<.0001
18
<.0001
18
<.0001
10
0.0388
18
<.0001
16
<.0001
18
0.0213
18
<.0001
10
NS
18
0.0071
18
<.0001
16
<.0001
16
<.0001
16
NS
16
<.0001
α values
Time &
Station
Interaction Station
NS
NS
NS
NS
NS
NS
NS
NS
0.0422
NS
0.0182
NS
0.0184
0.0184
NS
0.0083
0.0035
<.0001
0.0066
0.0004
0.0412
0.0019
<.0001
0.0001
0.0003
0.0172
0.0005
<.0001
<.0001
<.0001
<.0001
<.0001
acceptors as the soils became more reduced due to inundation. As the microbial biomass activity decreases, the
amount of enzyme activity may decrease leading to a reduction in organic N mineralization (McLatchey and Reddy,
1998).
Microbial biomass N was significantly higher (α < 0.05) in the Southwest and NW regions than the
Northeast region for the soil and detritus layers. Concerning the descriptive temporal analysis of vegetative
communities associated with each region, NW (Panicum) vegetation had higher MBN within the detritus layer than
the Northeast (Cladium and Typha sp.) and NW (Cladium) communities. For the soil, higher MBN was associated
with NW (Panicum) vegetation than the Northeast (Cladium and Typha sp.) communities. With respect to the soil
layer, MBN was the higher within the NW than Northeast region, corresponding to the high MBN found regarding
Panicum communities in the NW region.
Significantly lower MBN was present for detritus associated with Cladium communities from both the NW
and Northeast regions. The low nutrient status of Cladium may limit the microbial community due to the forms of
available C and the small quantity of detritus produced (Davis, 1991). The quantity of microbial biomass may be
influenced by the forms of C available due to the type of the substrate (Anderson and Domsch, 1985; Schnurer et al.,
1985).
For extractable NH4+, a seasonal effect was observed as well as a significant difference (α < 0.05) in
seasonal patterns between the three regions within both the soil and detrital layers. Temperature was observed to
partly influence seasonal patterns of extractable NH4+ concentrations within all three regions for both the soil and
detritus layers due to the increase of extractable NH4+ concentrations with increasing temperatures. An increase of
N mineralization can lead to an accumulation of ammonium under anaerobic conditions because of the lower
metabolic efficiencies of anaerobic microbial populations (Gale and Gilmour, 1988). Nitrification (NH4+ to NO3-) is
influenced by dissolved O2 concentrations, therefore allowing the build up of NH4+ in the system during the warmer
months of July and September after the establishment of anaerobic soil conditions (Reddy, 1982; Singh et al., 2000).
Dissolved O2 has also been found to decrease in summer when sediment biological oxygen demand (BOD) was the
highest (Howard-Williams and Downes, 1993).
For the detritus layer, a significant difference (α < 0.05) in PMN rates was found over time with the
Northeast and Southwest regions having higher mineralization rates than the NW region. Extractable NH4+
concentrations were found to be significantly higher (α < 0.05) in the Northeast and Southwest regions within the
detritus layer, corresponding to PMN rates. PMN rates and extractable NH4+ concentrations were not significantly
different (α < 0.05) within the soil layer between the impacted and unimpacted regions; therefore, not influenced by
total P concentrations.
For the detritus layer, total P was found to influence PMN rates and extractable NH4+concentrations.
Similar correlations have also been found between N mineralization and total P by other studies (White and Reddy,
2000, 2001). Higher total P values within the Northeast and Southwest regions coincide with higher PMN rates and
extractable NH4+ for detritus. Others have also found greater release of inorganic N from detritus than the soil
(White and Reddy, 2001). Phosphorus additions have been shown to increase microbial activity (measured by N
mineralization rates) for a variety of ecosystems (Biederbeck et al., 1984; Hossain et al., 1995; Munevar and
Wollum, 1977; Prescott et al., 1992) while other studies have shown no responses to P additions (Ross et al., 1995;
Tate et al., 1991).
Concerning the descriptive temporal analysis of vegetative communities associated with each region, PMN
rates were significantly lower (α < 0.05) for NW (Cladium) vegetation than other vegetative communities within the
detritus layer. This corresponds to the low PMN rates in the NW region reflecting a higher C/N ratio or lower
decomposition rate of the Cladium substrate. No significant difference (α < 0.05) between vegetative communities
was found for PMN within the soil.
Both Northeast (Typha sp. and Cladium) and Southwest (Typha sp/Woody mix) vegetative communities
had higher extractable NH4+ concentrations (α < 0.05) for the detritus layer than NW (Panicum). The rapid
decomposition rate of Typha sp. likely contributed to the high amount of extractable NH4+ in both the Southwest and
Northeast regions (Davis, 1991). For the soil layer, the only significant difference (α < 0.05) with regards to
extractable NH4+ was between Typha sp. and Cladium communities both from within the Northeast region.
Seasonal patterns were found to be significantly different (α < 0.05) between all three regions for MBN and
extractable NH4+ for both the soil and detritus layers. With regards to PMN and DEA rates, seasonal patterns were
only significantly different (α < 0.05) between all three regions for the detritus layer. Arginine ammonification rates
had significantly different (α < 0.05) seasonal patterns between all three regions for the soil layer only. Vegetative
communities as well as the distribution of various microbial populations likely influenced these differences in
seasonal patterns between regions. The forms of available C, C/N ratios of the substrate, and the amount of detrital
material produced can affect microbial activity and subsequently impact N processes.
30
4.2.4.3 Microbial Biomass Phosphorus and Phosphorus Metabolism
Organic P fractions were determined at the 0-10 cm soil and detritus layers (Table 12) to characterize the
microbial biomass P (MBP), labile organic P (LOP), fulvic-acid P (FAP), humic-acid P (HAP), and residual organic
P pools. Microbial biomass P and LOP are considered relatively labile forms of organic P and are short-term
storage for P in wetland soils. The fractionation scheme results in a series of operationally defined P fractions
(Newman and Robinson, 1999). Based on the extraction method, inferences were made on the relative availability
of the P pools.
The relative proportion of MBP in the soil was significantly greater (α=0.05) in the NW region when
compared to the NE and SW regions (Fig. 8). These findings correlated with the data from the spatial study that
noted the dominant species in the NW (Panicum sp.) showed significantly greater concentrations of MBP despite the
lower overall TP concentrations in the soil. The elevated concentrations of MBP in the non-impacted area could be
related to vegetation; Panicum sp. has thick, fine root masses. This root morphology lends itself to greater
colonization by bacteria and fungi and can support both a greater microbial pool and fungal interaction. Microbial
biomass P has a tendency to increase in overall percentage in low nutrient areas, holding the available nutrients in a
tightly closed cycle (Reddy et al., 1998).
Organic P fractionation results showed that the relative proportion of MBP was higher in the NW than at
any other site. An increase in MBP proportion coincided with a reduction of the FAP and HAP fractions.
Comparison of the trend to the actual concentrations determined for the samples did not necessarily support the idea
of a 1:1 reduction in FAP and HAP concentrations to an increase in MBP. Microbial biomass P was significantly
greater in the detrital layer when compared to the 0-10 cm soil layer (Table 12). The distribution of MBP in the
detritus followed the same general trend seen in the soil layer, with FAP and HAP concentrations diminishing with
an increase MBP (Fig. 9). Fulvic acid and humic acid P fractions are considered moderately labile Po pool within
wetland soils (Paludan and Jensen, 1995; Kastelan-Macan and Petrovic, 1996; Reddy et al., 1998).
The distribution of detrital MBP did not seem to fall into two distinct groups but rather separated into a
group of high concentration (NE Typha sp. and SW other; µ=110 mg kg-1), followed by a gradual gradation in
concentration from the NW Cladium (µ=80.6 mg kg-1), NE Cladium (µ=76.9 mg kg-1), SW Typha/ woody mix
(µ=68.8 mg kg-1), and Panicum (µ=57.4mg kg-1). There were no significant differences (α = 0.05) between any of
the sites but the MBP proportions do suggest a trend among similar vegetation communities. The amount of labile
organic P (LOP) showed similar trends in both the soil and detrital layers as MBP. The residual Po fraction was
significantly higher in the soil layer than the detritus, a result of the humification process as this pool consists of the
most refractory forms of Po associated with highly stable organic matter (Ivanoff et al., 1998). The distribution of
the forms of P followed regional TP trends at the 0-10 cm soil layer. However, the detrital layer did not follow the
same trend. Detrital TP distribution was more clearly linked to vegetative communities than to the soil TP
concentrations.
The organic P fraction of the soil across all six sampling periods showed fluctuations in the MBP, LOP,
FAP, HAP and residual Po in the soil over time. Within the soil layer, MBP had highest concentrations in the
detritus and soil of the non-impacted region (NW), likely as a result of the root morphology associated with
Panicum sp. The response of FAP and HAP concentrations decreased in areas with higher MBP concentrations.
The fulvic fraction is in the more labile soil fraction, possibly acting as an important substrate for the microbial
groups. The counter oscillation between these fractions may suggest substrate/ population dynamics (Stenberg,
1999). The utilization of FAP as a relatively simple C source for decomposition by microbes would therefore
reduce the overall FAP concentration in the soil, while facilitating the growth of MBP.
Microbial biomass changed by seasonality with maxima occurring in the summer (June and July) and
winter (December and January) months (Bardgett et al., 1999). Larger microbial communities present during the
summer months could be a result of the drought that occurred, reestablishing aerobic soil conditions. The MBP
concentrations showed that microbial communities were present throughout the entire year.
The one-year temporal study showed that differences in vegetation type, soil conditions and hydrology had
an impact on the TP of the soil. The majority (> 85 %) of the soil and detritus TP was in the organic form. Total
inorganic P, which is more readily available for microbial uptake and utilization by vegetation, did not show
significant seasonal or site variations. The fraction of organic P most significantly affected by seasonal and site
effects was LOP with reduced concentrations during the summer months of June and July correlating with the
drought period. Therefore, labile organic P may be considered a sensitive indicator to nutrient and environmental
changes in a system, even after termination of nutrient impact. The P trends documented in the marsh differ
31
Table 12: Averaged organic P fractionation parameters including microbial biomass P (MBP), labile organic P
(LOP), fulvic acid P (FAP), humic acid P (HAP), and residual P at the 0-10 cm soil and detritus layers for the entire
length of the temporal study. Data shown are the mean values and standard error in brackets (n=18).
Site
NaHCO3
Pi
(mg kg-1)
(mgkg-1)
NE
(Cladium sp.)
96.9
(11.6)
32.1
(3.24)
102
(16.4)
NE
(Typha sp.)
133
(13.6)
58.6
(4.05)
NW
(Cladium sp).
37.4
(4.11)
NW
(Panicum sp.)
HCl Pi
TLOP
MBP
Fulvic
Po
(mg kg-1)
Humic
Po
(mg kg-1)
Residual
Po
(mg kg-1)
76.9
(13.3)
53.1
(6.67)
54.7
(7.93)
85.1
(10.6)
156
(16.9)
122
(22.2)
92.5
(8.36)
92.1
(12.3)
145
(13.2)
30.6
(3.11)
88.8
(12.9)
80.6
(12.9)
40.5
(5.53)
55.0
(11.8)
82.8
(8.88)
57.1
(10.1)
56.8
(10.1)
83.9
(19.3)
57.4
(22.8)
28.4
(7.39)
30.8
(14.0)
46.8
(19.5)
SW
(Typha sp
/woody mix)
96.8
(16.2)
84.8
(10.3)
94.3
(18.9)
68.8
(14.4)
73.1
(14.5)
70.6
(11.5)
108
(77.5)
SW
(Other)
131
(14.9)
108
(7.78)
134
(11.3)
97.9
(13.5)
112
(11.3)
103
(10.7)
89.7
(16.7)
(mg kg-1)
(mg kg-1)
Soil (0-10 cm)
Detritus
NE
(Cladium sp.)
41.5
(3.47)
30.9
(1.30)
78.3
(9.25)
60.3
(8.00)
63.4
(9.51)
103
(12.5)
103
(6.60)
NE
(Typha sp.)
48.0
(2.40)
34.3
(1.76)
83.4
(9.97)
57.1
(8.27)
89.0
(13.4)
135
(17.0)
215
(9.67)
NW
(Cladium sp).
44.2
(4.49)
32.9
(2.25)
94.0
(12.8)
87.2
(10.3)
44.0
(5.14)
85.7
(8.26)
111
(4.14)
NW
(Panicum sp.)
45.9
(4.36)
35.4
(3.01)
111
(14.4)
105
(12.4)
52.5
(5.30)
81.2
(9.79)
105
(4.73)
SW
(Other)
50.3
(1.94)
51.7
(2.84)
87.1
(12.1)
63.8
(12.4)
84.0
(11.3)
143.9
(19.1)
139
(14.2)
SW
(Typha sp
/woody mix)
56.7
(2.18)
58.1
(3.24)
101
(12.0)
72.4
(12.7)
98.6
(13.1)
143.3
(19.5)
172
(8.85)
32
SW (Typha/Woody mix)
SW (Other)
TLOP
MBP
Site
NE (Typha sp.)
Fulvic Acid Po
NE (Cladium sp.)
Humic Acid Po
Residual Po
BCT (Panicum sp.)
BCT (Cladium sp.)
0%
20%
40%
60%
80%
100%
120%
Relative Distribution of Po
Figure 8: Relative distribution of organic P including labile organic P (LOP), microbial biomass P (MBP), fulvic
acid P (FAP), humic acid P (HAP), residual P averaged over the one-year temporal study for the soil layer (0-10
cm). Data are averaged over time and shown with standard error (n=18).
SW (Typha/Woody mix)
SW (Other)
TLOP
MBP
Site
NE (Typha sp.)
Fulvic Acid Po
NE (Cladium sp.)
Humic Acid Po
Residual Po
BCT (Panicum sp.)
BCT (Cladium sp.)
0%
20%
40%
60%
80%
100%
120%
Relative Distribution of Po
Figure 9: Relative distribution of organic P including labile organic P (LOP), microbial biomass P (MBP), fulvic
acid P (FAP), humic acid P (HAP), residual P averaged over the one-year temporal study for the detritus. Data are
averaged over time and shown with standard error (n=18).
33
depending on the sample interval. The 0-10 cm soil P forms followed the regional TP patterns with higher
concentrations in the NE and SW and lower concentrations in the NW. The detrital layer was influenced by the
dominant type of vegetation present more than differences in soil TP. Overall the TP concentrations appear to be
diminishing overtime, indicating the system is redistributing P and recovering. Variations in soil characteristics as a
result of fluctuations in water levels appeared to have a greater effect on the P pools than the two surface fires that
occurred in BCM prior to sampling. Changes in hydrology appear to have a sustained impact over several sampling
periods, dependent on duration of unsaturated conditions. Fire can be a sudden and intense impact to the system,
however, it appeared to have a minimum impact on the soil P over the year focused on by the first sampling period
after the event.
4.3 TASK 3: DIVERSITY AND COMPOSITION OF PROKARYOTIC GROUPS RELATED TO C, N, AND P
CYCLING IN WETLANDS.
The objectives of this task were to: 1) investigate the diversity and composition of prokaryotic groups
related to carbon cycling in Blue Cypress Marsh; 2) to understand the diversity of indicator groups with respect to
nutrient loading in the BCM impacted versus non-impacted sites; and 3) to examine the structure-function
relationships of syntrophic-methanogenic groups with respect to the nutrient loading in impacted versus nonimpacted sites.
Attempts were made to investigate the diversity and composition of key assemblages of prokaryotes in
BCM soil samples which are responsible for crucial steps in carbon cycling, with an emphasis on the syntrophic
bacteria and methanogens. Sub-samples of July 2001 samples from NE Typha, NW Cladium, and SW
Typha/Cladium/Woody mix were analyzed. Our approach was to use Polymerase Chain Reaction (PCR) based
cloning and sequencing or analytical techniques such as Terminal Restriction Fragment Length Polymorphisms (TRFLP). PCR reactions were optimized for these specific groups of anaerobic microbes at different temperatures
(affecting the denaturation of DNA and binding of nucleaotides to the template), cycles (affecting the amplification
of the template) and with different primers (specific for hydrogenotrophs or acetoclastic methanogens).
Shifts in composition of assemblages within the methanogens were investigated by microcosm studies. The
approach was to enrich for syntrophic associations that might be present in the BCM samples by spiking the samples
with the substrates encouraging syntrophy to occur and monitoring the accumulation of the final product i.e.,
methane. Substrates included propionate and butyrate. Under conditions where sulfate reduction is the dominant
process, acetate is most likely directly consumed by sulfate-reducing bacteria like Desulfobacter spp. and
Desulfotomaculum acetoxidans. For propionate, however, a number of possible pathways have been described. First,
incomplete-oxidizing sulfate reducers, like Desulfobulbus spp., may convert propionate to acetate and CO2 The
intermediate acetate is subsequently consumed by other sulfate reducers. Second, syntrophic propionate-oxidizing
bacteria may be involved, leading again to acetate as an intermediate in addition to hydrogen gas. However, these
syntrophic bacteria are probably outcompeted by propionate using sulfate reducers under sulfate-rich conditions.
Finally, several sulfate-reducing bacteria have been described that can oxidize propionate completely to CO2.
In the absence of sulfate, propionate conversion is thermodynamically possible only at a low partial
hydrogen pressure and with a low formate concentration. These conditions are met in syntrophic consortia, where
the syntrophs convert propionate and/or butyrate into acetate, CO2, hydrogen and/or formate that are subsequently
used by the methanogens. As stated earlier, in the presence of sulfate, sulfate reducing bacteria like Desulfobulbus
spp., can convert propionate into acetate and hydrogen sulfide. However, recent studies revealed that syntrophic
propionate-oxidizing bacteria such as Syntrophobacter spp., are themselves capable of oxidizing propionate by
sulfate reduction.
Serum bottle incubations were set up with soils from two sites: one in the NE Typha area and one in the NW
Cladium area. Methane concentrations were monitored weekly in all microcosms over a period of 23 weeks.
Addition of sulfate inhibited methanogenesis in the NE Typha soils when butyrate was the substrate whereas for
propionate, methanogenesis increased two fold (Fig. 10). This reflects the need of syntrophic bacteria for sulfate to
oxidize propionate. Since propionate and butyrate oxidizing syntrophs are usually different, they have different
biochemsitry to utilize these fatty acids. It might also be possible that incomplete sulfate reducing bacteria are
encouraged by the exogenous addition of sulfate and they utilize fatty acids resulting in acetate, from which the
acetoclastic methanogens produce methane from this substarte instead of syntrophy. These results indicate a
34
1200
343
Methane (µ mole/g soil)
1000
343+P
343+B
800
343+P+S
600
343+B+S
400
347
347+P
200
347+B
0
0
5
10
15
20
25
347+P+S
347+B+S
Weeks
Figure 10. Methane production in soils from impacted (343; from NE Typha) and non-impacted (347; from NW
Cladium) areas. Substrate additions were: propionate (P); butyrate (B); and sulfate (S).
35
competetion between the sulfate reducing bacteria (SRBs) and the syntrophs for carbon donors (propionate and or
butyrate). However, in the non-impacted NW Cladium site, exogenous addition of sulfate completely inhibited
methanogenesis, indicating the greater involvement of SRBs there.
DNA extraction, PCR amplification and cloning of bacterial and archaeal 16S rDNAs in enrichment
cultures were performed, followed by T-RFLP, rarefaction analyses, sequencing and phylogenetic studies. These
rDNA clones were grouped into operational taxonomic units based on their RFLP patterns. Representative clones
having different and similar RFLP patterns were then sequenced and their sequences compared and aligned, and a
phylogenetic tree constructed.
Remarkable differences were obtained in the archaeal RFLP patterns with 2 different restriction enzymes in
the impacted (NE) and the non-impacted (NW) sites. Commonalities were also seen with soils from impacted sites
spiked with propionate and butyrate, but clear differences were obtained in the non-impacted sites enrich with the
same substrate (propionate and butyrate). A high diversity was observed in the bacterial community and there were
few similarities in the impacted and the non-impacted sites.
Most of the clones aligned with Methanosarcina barkerii, which is a reported versatile acetoclastic,
hydrogenotrophic as well as methylotrophic methanogen and thrives at high acetate concentration.
Sequences from the non-impacted sample spiked with propionate and butyrate were comprised mainly of
Methanosaeta concilii, which is an obligate acetoclastic methanogen and thrives at low acetate concentrations.
Therefore it may be generalized that in the non-impacted regions, the methanogenic community is mainly comprised
of sarcinas, but since acetate levels might be falling very low at times, Methanosaeta can survive as well and is
essentially dependent on the acetate flux.
In the impacted samples spiked with propionate and butyrate, novel sequences aligned very closely to an
uncultured clone in the database, WCHD3-34, which is 97% similar to Methanospirillum sp. Methanospirillum is a
known hydrogenotroph, suggesting that, when the system is challenged with butyrate, there is a shift observed
towards syntrophic conversion of butyrate giving rise to acetate and hydrogen. Hydrogen in turn would then be
consumed by the hydrogenotroph forming high levels of methane and since acetoclastic methanogens are lacking
here, acetate should accumulate. Interstingly when we analysed acetate concenctrations in these samples, the level of
acetate was found to be 4 -5 times higher than in other samples where it must have been consumed by the
acetoclastic methanogens sarcina and saeta.
4.4 TASK 4: SPATIAL DISTRIBUTION OF BIOGEOCHEMICAL INDICATORS IN WATER, LITTER AND
SOIL.
4.4.1 Sample Collection
Based on the results obtained from the preliminary geostatistical analyses in Task 1, a large-scale
monitoring network was developed in Task 4 to characterize the spatial distribution of biogeochemical indicators in
water, litter and soil throughout the study site. This spatial sampling was accomplished in March 2002.
Approximately 300 sample locations were identified at 400m intervals on a regular grid throughout the marsh, and
detritus and soil samples were obtained from 267 of these sites. Duplicate soil cores were obtained by using a 10 cm
I.D. aluminum core tube, and they were composited in the laboratory. Detritus samples were collected by handgathering dead plant litter from 625 cm2 on soil surface. Soil and detritus were placed in water tight plastic bags and
transported on ice to the laboratory and stored at 4 o C until analysis.
4.4.2 Soil Biogeochemical Analyses
Biogeochemical, physicochemical, and microbial parameters were characterized as described in 4.1.2
above.
4.4.3 Spatial Analysis of Physico-chemical Properties
Our conceptual model combines stochastic simulation and Principal Component Analysis (PCA) to identify
soil biogeochemical properties which account for much of the overall variation and their spatial variability, patterns
and uncertainty of simulations. We used conditional sequential Gaussian simulation (CSGS), a stochastic simulation
method, to generate realizations of soil properties to describe the spatial patterns of properties and the range of
36
possible outcomes of realizations. Conditional sequential Gaussian simulation generates conditional cumulative
distribution functions (ccdf) for each pixel which captures the uncertainty of predictions. The mean of realizations of
a specific soil property represent the dominant signal, the variation of predictions is expressed by the standard
deviation, and the range of possible outcomes can be characterized by the smallest and largest realizations. We
transformed the correlated biogeochemical soil properties into uncorrelated units (principal components) and
generated realizations of PC to identify the spatial structures present in the dataset. Each generated PC pixel can be
characterized by a ccdf of biogeochemical properties. Since we do not assume any threshold values representing
natural ecosystem conditions but rather focus on possible outcomes of properties our approach is rooted in stochastic
ecosystem resilience.
We used CSGS for the generation of partial realizations using normal random functions. A detailed
description of CSGS can be found in Grunwald et al. (200_) and Chilès and Delfiner (1999). The sequential
Gaussian procedure entails:
(1) Developing normal scores of the original sample data
(2) Construction of a semivariogram of the normal score data
(3) Kriging (simple or ordinary kriging), randomly over the simulation space, at each simulation node, using
neighboring sample data and a specified number of previously simulated nodes
(4) Drawing randomly from the conditional distribution and assigning that value to the simulation node
(5) Performing the above simulation until all nodes were given a simulated value
(6) Repeating the simulation procedure for a specified number of realizations.
An advantage of the Gaussian approach is that all conditional distributions are normal and determined
exactly by the mean and estimation variance. Conditional sequential Gaussian simulation generates conditional
cumulative distribution functions (ccdf) for each pixel. One hundred realizations at a pixel resolution of 100 meters
were generated using CSGS. We used a technique suggested by Journel (1983) to construct expected-value estimate
maps (E-type maps) summarizing the mean, standard deviation, and other statistical parameters for each pixel
location. To assess the accuracy of TP realizations we randomly splitted the dataset into model (67% of
observations) and an independent validation dataset (33% of observations) resulting in 183 model observations and
84 validation observations. The root mean square error (RMSE) was used to evaluate realizations.
We used PCA to transform a number of possibly correlated biogeochemical variables into a smaller number
of uncorrelated variables called principal components reducing character space. The first PC accounts for as much of
the variability in the data as possible, and each succeeding component account for as much of the remaining
variability as possible. Principal components are obtained by projecting the multivariate datavectors on the space
spanned by the eigenvectors (Goovaerts, 1997; Wackernagel, 2003). We identified the significance of
biogeochemical properties to explain the variability in the dataset. Conditional sequential Gaussian simulation was
used to generate realizations of PCs. The emerging spatial patterns of PCs were subdivided into short-, intermediate,
and long-range components. We used a two-step self-organizing cluster analysis with the Akaike Information
Criterion as the clustering criterion to group the first three PCs accounting for most of the variability in the dataset.
We distinguished 6 different classes. For each class we assessed the variation of soil properties within the classes by
calculating the mean, minimum, maximum and standard variation for each property. To characterize the uncertainty
of realizations (potential outcomes) we used the smallest, largest, and mean of 100 realizations.
4.4.4 Results
The semivariogram of TP was modeled with two basic structures – a nugget of 0.1535 and a spherical
model with sill 1.327 and range 7,240 meters. Conditional sequential Gaussian simulation generated 100 realizations
of which 10 are shown in Fig. 11. Specific patterns such as lower TP values in the northern part and higher values in
the southern part of the study area prevail on all maps. However, local spatial patterns are slightly different on all
realization TP maps. This is due to the random number generator used to generate multiple realizations. These local
deviations show the range of possible outcomes or the uncertainty of predictions based on 267 observations of TP.
The smallest, mean of 100 realizations and largest TP maps are shown in Fig. 12. A crescent shape area in the
northern part showed TP as low as 340 mg kg-1 which resembled natural TP conditions. Maximum total phosphorus
of 1014 mg kg-1 was generated. The cause for high TP values can be attributed to previous P input into the wetland
from adjacent agricultural used land. The nutrient enriched soils in the southern part might have been boosted the
expansion of Salix caroliniana vegetation which prefers wet mesic soils. The smallest and largest realizations can be
interpreted as “best” or “worse” case scenarios of TP predictions rendering the uncertainty of TP predictions. The
37
standard deviation for TP was Null at the observation sites, smallest in the crescent shaped area of low TP
predictions, and highest in the southern part of the study area. This indicates that high TP predictions are more
uncertain compared to low TP predictions. Total phosphorus realizations were able to reproduce the semivariogram
derived from TP observations. The semivariogram derived from TP realizations were fitted with a nugget of 0.3202,
a sill of 1,202 and range of 7,344 meters. Overall the TP realizations reproduced the spatial variability identified in
the semivariogram using TP observations. The histogram of observed TP values matched closely the histogram of
TP realizations (Fig. 13). Total phosphorus observations showed a mean of 619, standard error of mean (SE) of 7.88,
median of 601, standard deviation (std.dev.) of 129, minimum of 350 and maximum of 1,014 mg kg-1. Total
phosphorus realizations closely resembled those statistical parameters with a mean of 632, SE of 1.51, median of
624, std.dev., of 107, minimum of 353 and maximum of 1,014 mg kg-1. Validation analysis of TP resulted in a root
mean square error of 96 mg kg-1. Statistics of soil properties are summarized in Table 13.
Correlations between soil properties are summarized in Table 14. Overall correlations were small between
most variables. However, many biogeochemical properties were correlated with each other. The highest significant
correlation of 0.805 was between TLON and TLOC. Most correlations were lower; e.g. total phosphorus showed
significant correlations with BG (0.468), BicTP (0.417), and APA (-0.385). To transform the correlated
biogeochemical variables into a smaller number of uncorrelated variables we performed a PCA. Results of the
analysis are summarized in Table 15. The eigenvalues describe the amount of the total variance associated with each
factor. The first PC contributed with 33.91 %, the second PC with 15.93 %, and the third PC with 11.32 % to the
total variance in the dataset. The circle of correlations shows the proximity of the variables inside a unit circle and is
useful to evaluate affinities and the antagonisms between the variables. The first few principal components account
for most of the variance to display possible interrelations between variables. A vector can be drawn from the origin
in the plane (0,0) to each plotted point The orientation of that vector with respect to the two axes reflect the
correlation between the variable and the two principal components plotted in the circle of correlation. The length of
the vector measures the percentage of the variance explained by the two components. If the two components
completely account for the variance of i, the point would lie on a circle of unit radius. The closer the point is to the
center, the smaller the proportion of variance accounted for by factor F1 and F2. Variables close to the center show
low correlations. According to Table 15 the dynamic soil properties TLON (0.3444), TLOC (0.3309), and BicTP
(0.3253) mainly contributed to PC1 and the variables TP (0.3891), TN (-0.3876), and APA (-0.3658) mainly
contributed to PC2. The third PC was dominated by the contribution of TC (0.6011) and Ash (-0.5589). The first
three PC accounted for 61.16% of the overall variation in the dataset. We created semivariograms of the PCs to
analyze the spatial variability. The semivariograms were distinctly different with a relatively short range of 1,228 for
PC1, a long range of 6,393 for PC2, and an intermediate range of 4,498 for PC3. These findings indicate that the
dataset includes three different sets of spatial autocorrelation representing variability at short, intermediate and long
distances.
We used conditional sequential Gaussian simulation to generate 100 realizations of each principal
component. The realizations reveal the spatial patterns emerging from mapping PCs with contrasting ranges, nugget
and sill variances. Each of the PCs showed distinct spatial patterns with highest eigenvalues in the east-west
direction for PC1 and highest eigenvalues in the north-south direction for PC3. Spatial patterns of PC2 matched
closely the spatial patterns generated for TP (see Figs. 11 and 12). Realizations of TLON are shown which match
closely the spatial patterns of PC1, whereas spatial patterns of TC match closely the spatial patterns of PC3. One
important aspect of combining CSGS with PC analysis is to reduce character space focusing on spatial variability,
patterns, and uncertainty of uncorrelated principal components rather than correlated biogeochemical soil properties.
This means that some of the biogeochemical properties map the same (similar) phenomena and do not carry
additional information about biogeochemical processes forming spatial patterns. For example, a map generated
using CSGS for TLOC and TNON showed the same overall spatial patterns. A simplified representation of spatial
patterns based on PCs facilitates to concentrate the information on the spatial structure rather than mapping all
biogeochemical properties.
The variation of soil property realizations (TP, TC, and TLON) within classes was considerable. Class 6
showed very high TP (min: 558.3; mean: 747.4; max. 1,013.6 mg kg-1), high TC (min: 444.6; mean: 459.1; max.:
476.8 g kg-1) and low TLON (min: 172.4; mean: 252.5; max: 391.1 mg kg-1) values. In contrast, class 4 showed very
low TP (min: 352.5; mean: 525.3; max. 790.4 mg kg-1) and intermediate TC (min: 420.3; mean: 453.1; max: 467.5 g
kg-1) and TLON (min: 193.9; mean: 276.5; max.: 408.0 mg kg-1) values. Class 2 showed high TP (min: 442.1; mean:
721.9; max.: 957.6 mg kg-1) and relatively high TLON values (min: 200.2; mean: 304.8; max: 432.8 mg kg-1)
whereas class 1 showed high TP (min: 456.1; mean: 727.2; max: 873.8 mg kg-1) but relatively low TC values (min:
312.3; mean: 448.3; max: 465.1 g kg-1). We can conclude that relationships between soil biogeochemical properties
change across the BCMCA caused by ecosystem processes (e.g. mineralization, decomposition of vegetation,
38
Figure 11. Ten out of 100 realizations of TP generated using CSGS.
Figure 12. Smallest, mean, and largest realization of TP.
39
20
700
600
500
400
10
300
200
100
0
360.0 440.0 520.0 600.0 680.0 760.0 840.0 920.0 1000.0
0
360.0 440.0 520.0 600.0 680.0 760.0 840.0 920.0 1000.0
Figure 13. Histograms of TP observations and realizations (TP mg kg-1).
Variable
n Minimum
Maximum
APA
267
13.58
825.00
Ash
267
1.50
38.00
BicTP
256
51.62
447.48
BD
267
0.01
0.06
BG
267
1.20
296.70
PI
257
13.82
187.20
PO
251
0
75.14
CN
265
5.48
23.01
CTC
266
605.24
3,026.86
NH4N
266
14.99
364.70
TLON
267
128.12
443.36
TLOC
267
1,091.03
4,998.13
MBP
254
2.23
395.62
% Moisture
267
86.30
98.02
PEP
201
0.14
52.26
TC
265
310.67
483.46
TN
265
17.66
82.42
TP
267
349.61
1,013.7
Table 13. Statistics of biogeochemical soil properties.
Mean
342.04
7.51
213.63
0.04
133.41
42.13
21.09
17.21
1,984.36
96.25
276.49
3,182.44
151.18
91.33
11.38
455.79
27.03
619.9
40
Median
326.70
7.11
207.23
0.36
132.41
38.49
20.46
17.23
1,971.12
93.31
269.62
3,125.50
145.18
91.45
8.08
457.68
26.66
601.60
Std.dev.
153.46
2.72
63.06
0.01
38.39
16.91
11.58
2.24
352.38
39.09
58.24
703.79
51.75
1.55
10.14
16.71
4.83
128.13
Table 14. Correlation matrix.
APA
Ash BD
BG
BicTP
CN
CTC NH4N
1
-.218
.184
-.407
.250
0.238
APA
1
.277
Ash
1
-.665
.413
-.512
BD
1
.454
.431
BG
1
-.166
.320
BicTP
1
-.200
-.228
CN
1
0.181
CTC
1
NH4N
TLON
TLOC
MBP
Moist
PEP
PI
PO
TC
TN
TP
Only significant correlations at the 0.01 level (2-tailed) are shown
TLON
.351
TLOC
.456
MBP
.319
Moist
.202
PEP
PI
PO
-.300
-0.592
.478
.674
-.415
.319
.575
1
-.504
.391
.526
-.549
.398
.424
.805
1
-.417
.328
.938
-.240
-.331
.676
.459
-.189
.748
.259
.512
.495
.365
1
-.377
.482
.294
-.413
.431
.578
-.248
.359
.377
.304
.624
.542
1
TC
TN
.342
-.777
-.222
TP
-0.385
-.264
.468
.417
-.860
.263
.238
.236
.380
1
.255
.477
.323
.317
.387
.197
1
.191
.263
.431
.222
.171
.271
.246
.260
1
-.172
.352
.207
.250
.249
.535
.522
1
1
1
41
Table 15. Eigenvalues and eigenvectors of the first five principal components.
Variables
APA
Ash
BD
BG
BicTP
CN
CTC
MBP
Moist
NH4N
PEP
PI
PO
TC
TLOC
TNON
TN
TP
Eigenvalue
Ratio %
Cumlative %
PC1
0.1468
-0.1030
-0.3088
0.2626
0.3253
-0.1903
0.2277
0.2889
0.3057
0.1932
0.1729
0.2313
0.1074
0.0726
0.3309
0.3444
0.2051
0.1583
6.1038
33.91
33.91
PC2
-0.3658
0.0238
-0.1549
0.2751
0.0239
0.3768
0.0233
-0.1387
0.1549
-0.1248
0.1629
0.2410
0.3682
-0.0267
-0.1814
-0.0931
-0.3876
0.3891
2.8683
15.93
49.84
42
PC3
0.0331
-0.5589
-0.2371
0.0526
-0.1214
0.2178
0.1731
-0.0739
0.1488
-0.0822
0.0535
-0.1574
-0.0816
0.6011
-0.0725
-0.1911
0.0272
-0.2509
2.0373
11.32
61.16
PC4
0.2452
0.3300
-0.1169
0.2542
-0.2782
-0.0212
0.5143
-0.2386
0.3161
-0.1499
0.1759
-0.1517
-0.1967
-0.2941
-0.0088
-0.1229
-0.0651
-0.1800
1.6157
8.98
70.14
PC5
0.3751
0.0221
-0.0320
0.0158
0.2841
0.4162
-0.1280
0.3625
-0.1418
-0.0500
0.1768
0.1722
-0.3385
-0.0929
0.0093
-0.0173
-0.4448
-0.2256
1.1195
6.33
76.36
enzymatic activities, and other). Total phosphorus showed a relatively wide range of realizations from as low as
349.6 up to 1,013.6 mg kg-1. The range of TC was smaller with 310.6 to 483.5 g kg-1. Mapping of the mean,
minimum and maximum soil property values within classes facilitated to better understand the variation of spatial
patterns.
4.5 TASK 5: VALIDATION OF PREDICTIVE EQUATIONS USING INDEPENDENT MEASUREMENTS.
4.5.1 Evaluation of Empirical Relationships Using Multivariate Statistics
The objective of this portion of the project was to evaluate the ability of multivariate statistical procedures
such as cluster analysis (CA), principal component analysis, (PCA), canonical discriminant analysis (CDA), and
discriminant function analysis (DFA) to quantify the relationships among biogeochemical indicators,
biogeochemical processes, vegetation types and ecological impact.
4.5.1.1 Cluster Analysis
Cluster Analysis (CA) is used to separate a set of observations into different sub-groups such that observations in the
same sub-group are much more similar to each other than to those in other groups. Ward’s Minimum Variance
Approach (Khattree and Naik 2000) for CA was performed to separate the observation locations into distinct groups
based on differences in the 28 soil biogeochemical parameters measured. The results indicated that the 121
observations could be classified into 6 distinct clusters. Tables 16 and 17 summarize the distribution of observations
from each region and vegetation type within the clusters. Both tables show that cluster 1 is dominated by
observations from mixed vegetation locations in the SW region, while Cluster 2 is dominated by Cladium and
Panicum locations in the NW region. Cluster 3 consists of observations taken from Cladium, Hyacinth, and Mixed
vegetation locations in the NE region. Cluster 4 is comprised of observations from Mixed vegetation locations taken
from all three regions. Clusters 5 and 6 consist of observations taken primarily from Sloughs in the NE region.
These results indicate biogeochemical characteristics appear to be more consistent within the SW and NW regions,
while a variety of soil biogeochemical characteristics exist in the NE region.
4.5.1.2 Principle Component Analysis
Principle Component Analysis (PCA) is a dimension reduction technique used to help visualize patterns in
multivariate data sets. The methodology determines the principal components, which are weighted linear
combinations of the original variables, which cause the greatest variation among the observations. Using a few PCs
instead of the many original variables, the major factors causing variability in the data can be assessed. For the
BCMCA soil biogeochemical data set, 5 PCs explained 69% of the variation among the observations. As seen in
Fig. 14, a plot of PC2 versus PC4 provides good discrimination among the 6 clusters identified in the data set.
Figure 14 and Tables 16 and 17 confirm that the biogeochemical variability in the BCMCA data set can be
explained, to a large extent, by location (proximity to nutrient impact) and the vegetation type. The variables that
scored high in PC2 were: TN, TPo, and Ca. This indicates that the amount of organic matter and calcium present in
the soils accounts for a large portion of the variability between the observations. PC4 showed high loadings of
PMN, Fe, TC, and TP. This group of variables represents bio-available nitrogen, iron and organic matter in the soil.
PCA of this data set suggests that four or five PCs are sufficient (rather than the 28 original variables) to describe the
variability in the BCMCA data set.
4.5.1.3 Canonical Discriminant Analysis
Canonical Discriminant Analysis (CDA) is a dimension reduction technique similar to PCA, but this
technique is specialized to the context of discriminant analysis. In CDA canonical variables, which are also
weighted linear combinations of the original variables, are derived to maximize the variability between different,
pre-defined, sub-populations of the data set. In this project two different sets of canonical variables were derived: a
set of 2 canonical variables which discriminated the observations based on region, and a set of 5 canonical variables
which discriminated the observations based on vegetation type. Figure 15a shows a plot of the observations against
the canonical variables determined based on region. Can1 provides good discrimination between the unimpacted
43
Table 16. Sample Distribution Sorted by Location for Each Cluster
Table 17. Sample Distribution Sorted by Vegetation Type in Each Cluster
Figure 14. Plot of Clusters in Two Principle Components
44
region, NW, and the impacted regions, NE and SW, with the NW observations associated with positive values of
Can1 and the NE/SW observations associated with negative values of Can1. Furthermore, Can2 provides good
discrimination between the SW and NE regions, with SW observations associated with negative values of Can2 and
NE observations associated with positive values of Can2. The raw canonical scores indicate that Can1 is negatively
dominated by MC, LOI, and TPo and Can2 is dominated by LOI, TPo, TP, TC. The impacted regions exhibit high
values of moisture content, loss on ignition, and total organic phosphorus while the unimpacted area shows low
concentrations of these values.
Figs. 4b and 4c show that at least 3 canonical variables determined based on vegetation are necessary to
discriminate between the 6 vegetation groups sufficiently. Figures 15b’ and 15c’ display the same plots as Figures
15b and 15c, respectively but utilize different markers to identify each observation’s location. These figures show
that the vegetation distribution shifts from native vegetation types (Cladium and Panicum) in the unimpacted region,
NW, to invasive vegetation types (Hyacinth, Mixed, and Typha) in the impacted regions, NE and SW, as the value
of Can2 for vegetation becomes more negative. The raw canonical scores for the first two canonical variables
indicate that Can1 is dominated by LOI, bicarbonate extractable total P, MC, MBP and Can2 is dominated by LOI,
MC, TP, TPo. The impacted regions exhibit high values of moisture content, total organic phosphorus, and
bicarbonate extractable total P and low values of loss on ignition, total phosphorus, and microbial phosphorus.
4.5.1.4 Discriminant Function Analysis
Discriminant Function Analysis (DFA) is able to predict group membership, and hence allocate a new
observation to a membership class. DFA was performed on the BCMCA data to develop functions to discriminate
new observations into impact groups based on region, vegetation class, and cluster. A test of multivariate normality
based on multivariate skewness and kurtosis statistics was performed to assess compliance with the underlying
normality assumption required for DFA. The results indicated that, when stratified by region, vegetation type, and
cluster, the data were approximately normally distributed. To evaluate the validity of the discriminant functions
developed, two types of misclassification were evaluated, 1) the misclassification rate for the original data set used
to develop the functions, and 2) the misclassification rate for a cross-validation data set in which 10% of the data
was withheld while developing the functions. Table 18 summarizes both sets of misclassification rates, and
indicates that the discriminant functions can be expected to accurately classify new observations into vegetation
types at least 75% of the time, into regions at least 85% of the time, and into clusters at least 90% of the time.
4.5.2 Validation of Relationships
4.5.2.1 Selected Wetlands in Southeastern US
Twelve wetlands were selected (eight in Florida, four in Georgia) in coordination with ongoing research
developing numeric nutrient criteria for wetlands in the southeastern United States (Assessment and Validation of
Wetland Biogeochemical Indicators for Establishment of Numeric Nutrient Criteria). The sites (Fig. 16) were
chosen for diversity of vegetation, and had no known nutrient impacts. At each site a minimum of three stations
were sampled as previously described and soil biogeochemical parameters were determined. Wetlands differed
significantly by eco-region based on basic chemical characterization, mainly due to higher calcium levels in Florida
wetlands and higher iron content in Georgia. Differences in cation concentrations have a profound effect on
availability of P, as evidenced by significantly higher P levels in Florida (α<0.1). Differences in soil chemistry
appeared to drive microbial nutrient cycling, exemplified by APA levels (Fig. 17). Differences in nutrient dynamics
due to surrounding land use were confounded by vegetation and landscape position (riverine vs. isolated). In order
to isolate and identify factors influencing predictive relationships between biogeochemical factors, modeling was
limited to wetland communities of similar vegetation communities.
4.5.2.2 Evaluation of Empirical Relationships with an Independent Dataset
The objective of this study was to determine whether, in wetlands of similar organic content and vegetation
communities, integrator measures such as microbial responses are a better indicator of nutrient impact than primary
measures such as soil chemical composition and to identify the most sensitive indicator(s) at both levels of response.
The analysis of combinations of biological and chemical characteristics requires statistical techniques involving a
suite of multivariate methods. The intent of this particular multivariate data analyses is to elucidate relationships
between the abiotic environment (independent variables) and biological responses (dependent variables) and to
45
a)
b)
b’)
c)
c’)
Figure 15. Plots of Observation Groups in Two Canonical Variables
46
Table 18. Comparison of Misclassification Rates by different DFA approaches.
Observation
Misclassification
Cross-Validation
Misclassification
DFA
Rate
DFA
Rate
quadratic
0
quadratic
14.41
Vegetation
linear
11.86
linear
24.58
Cluster
linear
2.54
linear
9.32
Groups
Region
Figure 16. Location of wetlands sampled for Task 5. Colors indicate different level 2 ecoregions (8.3 and 8.5).
Figure 17. Actual vs. predicted acid phosphatase activity. APA was predicted using the following model:
APA=P + Fe + Al + P*Fe + P*Al + Fe*Al where APA = acid phosphatase activity; and P, Fe, and Al = Mehlich 1
extractable phosphorus, iron, and aluminum.
validate the results across ecosystems.
47
4.5.2.3 Model Development
To preclude pre-classification of the data, we developed a clustering method using an independent dataset
from another large subtropical wetland (the Everglades; see Corstanje 2003) with the expectation that the internal
structure of the soil chemical measures would result in groups that reflect the sampling location. The clustering
method selected was Wards, which is a minimum variance, hierarchical clustering method. Once established, we
applied a combination of stepwise discrimination and canonical discrimination (stepwise canonical discrimination)
to determine which particular combinations of chemical characteristics are influential in generating (abiotic) and
predicting (biotic) the multidimensional groups. Validation of the stepwise canonical discriminant analysis was done
through by the jackknife procedure. Cluster analysis on soil abiotic parameters from the Everglades study resulted in
two consistent clusters representing nutrient impacted and non-impacted sites and some overlap between
intermediate and non-impacted sites. In terms of abiotic characteristics, the impacted area is differentiated by high
TC, P-forms and low TN from intermediate sites and the non-impacted area; the intermediate and the non-impacted
areas are distinguished by shifts in the P-dynamics and a substantial depletion in NH4-N at intermediate relative to
un-impacted. Application of the biotic parameters results in a smaller group of discriminatory variables. The
resultant linear contrasts can then be used to describe the impacted system in general terms. The unimpacted area is
primarily characterized by APA, which represents the biotic response to the lack of P-availability. The impacted
area generally has higher levels of PMP contrasted to low levels of PMN whilst the intermediate area is
characterized by increases in microbial biomass (MBP). The Jackknife cross-validation indicated that the array of
biotic parameters is more consistently selected (robust) as indicators than those in the abiotic groups. The resulting
set of biotic linear combinations of measures was therefore selected to be applied as indicators of nutrient
enrichment to BCMCA.
4.5.2.4 Application Of Model Results: BCMCA
The application by stepwise discrimination of the biotic variables selected for in an independent dataset on
BCMCA data resulted in a distinct separation of the unimpacted areas from the impacted areas (Fig. 18). The
interpretation of the results reflects the diversity of vegetation communities in BCMCA in that the primary
differentiation is P-availability; a secondary factor is the predominant vegetation types. The effect of Typha sp. on
the soil biogeochemistry was highlighted by the increase in overall N and P turnover rates. Using this analytical
approach, MBP does not play a significant role in describing the variability in BCMCA.
5.0 CONCLUSIONS
5.1 TASK 1: DETERMINATION OF SPATIAL VARIABILITY AND INTER-RELATIONSHIPS OF
BIOGEOCHEMICAL PROCESSES AND EFFICIENT INDICATORS.
5.1.1 Spatial Analysis of Physico-chemical Properties
1.
2.
3.
4.
The impacted areas (NE and SW) had significantly higher TP than the unimpacted area (NW) for both
detritus and soil layers.
TP level was significantly different among some vegetation types.
Total nitrogen (TN) of detritus at NE was significantly higher than NW and SW. There was no significant
difference between detrital TN of NW and SW.
Higher TP concentration in impacted areas results in lower C:P ratio. In addition, C:P in Cladium,
Cladium/woody mix, and Panicum areas were significantly higher than Typha and Typha/woody mix for
both detritus and soil layers. These results indicate higher phosphorus content in impacted areas, especially
in Typha sp. areas, which may affect sediment deposition and quality.
5.1.2 Spatial Analysis of Microbial Biomass and Labile Nutrient Pools
1.
MBC of soil in Panicum area was significantly higher than Cladium/woody mix, Typha, and Typha/woody
mix areas.
48
Figure 16. Interpretation of the canonical discriminant analysis of microbiological characteristics, each quadrant
given a certain designation based on the canonical variates constructed from the most biotic parameters identified in
WCA-2a (MBP (mg kg-1), PMP (mg kg-1 d-1); PMN (mg kg-1 d-1) and APA (mg g-1 h-1)). The left panel (b)
identifies the predominant vegetation communities associated present in the different areas.
49
2.
3.
4.
5.
6.
The elevated MBP fraction in the NW regions indicates that the elevated P concentrations in the impacted
zones had a detrimental effect on the microbial community, resulting in diminished populations. However,
these findings are contradictory to results that suggest the size of microbial populations and the quantity of
MBP in the soil increase in response to the addition of P (Drake et al., 1996; DeBusk and Reddy, 1998;
Reddy et al., 1998; Reddy et al., 1999, Quall and Richardson, 2000; Noe et al., 2001).
The larger MBP pool within the NW region may be a result of vegetation. Dense root mass associated with
Panicum spp. and Cladium spp. in the NW region could provide a large aerobic area for microbial
colonization activity (Tobe et al., 1998).
The LOC of both detritus and soil at NE site was significantly higher (α < 0.05) than NW and SW (Table
4). The LOC of soil in SW was significantly higher (α < 0.05) than that in the NW, however there was no
significant difference between the LOC of detritus at SW and NW. When the LOC was compared by
vegetation type, LOC of detritus and soil at Typha and Typha/woody mix areas was significantly higher (α
< 0.05) than Cladium and Cladium/woody mix areas (Table 5). The LOC of detritus at Panicum area was
not significantly different from any other vegetation types. However, the LOC of soil at Panicum area was
significantly lower than Typha and Typha/woody mix. This indicates that Typha sp. could provide higher
available carbon than Cladium and Panicum sp.
Higher PMN levels were found to be associated with lower C/N values occurring primarily in the Northeast
region. This supports the conclusion that Typha sp. areas have higher quality and more easily degraded
detrital material.
Increased availability of P within the impacted regions may only have increased heterotrophic activity, not
the size of microbial biomass nutrient pools.
5.1.3 Spatial Analysis of Microbial Activities
1.
2.
3.
4.
5.
6.
7.
8.
TC for both detritus and soil was not significantly correlated with heterotrophic microbial activity,
indicating that the bulk of TC was not utilizable to microorganisms.
The SOD of detritus at NW was significantly lower (α< 0.05) than that of NE and SW, and there was no
difference in SOD of detritus between NE and SW. SOD of soil at NE and NW was significantly higher
(α< 0.05) than that of SW. This pattern was identical to that of TLOC. These results suggest that the
amount of readily available substrates affect the activity of aerobic microorganisms. The SOD of detritus in
Typha and Typha/woody mix areas was significantly higher (α< 0.05) than Cladium and Cladium/woody
mix areas (Table 8).
Potential CH4 production in impacted soil was higher than unimpacted soil. The Typha dominated areas
produced higher CO2 and CH4 than Cladium dominated area.
The MBC/TLOC, CH4/MBC, and CO2/MBC ratios for soil showed significant differences among NW
Cladium, NE Cladium, and SW Cladium/woody mix areas. This indicates that these ratios can be affected
by both nutrient level and vegetation types.
Higher DEA activity was found within the NW region corresponding to the high DEA activity for NW
Panicum and Cladium communities. Changes in soil quality, including C/N values, between regions due to
shifts in vegetation may influence the spatial variability of DEA.
APA was significantly higher in soils of Typha areas than those of Cladium/woody mix areas (Figs. 5).
APA was not significantly different in detritus of any vegetation type. Increased availability of P likely
facilitates greater microbial uptake, thus favoring lower APA levels.
The activity of carbon acquisition enzyme β-glucosidase associated with Typha detritus was elevated
relative to that in detritus of other vegetation types. This is consistent with more labile, easily degraded
material.
Comparison of MBP to TP suggested the microbial pool in the NW region accounted for a larger portion of
the total P at the 0-10 cm depth than in the NE and SW regions with ratios of 28.1, 17.8 and 13.8,
respectively. The same regional trends were seen in the detritus, with MBP accounting for a greater
proportion of TP. These findings indicate that the microbial community in the non-impacted region was
more P efficient than that in the P impacted regions.
50
5.2 TASK 2: TEMPORAL VARIABILITY OF BIOGEOCHEMICAL PROCESSES AND INDICATORS.
5.2.1 Temporal Patterns of Physico-chemical Properties
1.
The low rainfall caused a complete drawdown of the marsh to occur from March to August 2001 of the
seasonal study. Anaerobic conditions (Eh < 300mV) were not established until July 2001 in the NE and SW
regions and September 2001 in the NW region.
2.
As in the spatial study, TP content in the NE and SW regions was greater than the reference area in the NW
region, irrespective of plant species. Comparison of the C: N: P ratios indicates P-limitation in the system at
the two NW reference sites and the NE Cladium sp. site. The N: P ratio was much higher in the NE
Cladium sp (49:1) than in the NE Typha sp (26:1).
3.
The majority of the P in BCMCA soils (histosols) was found in organic forms. The total organic P (TPo)
concentration accounted for approximately 85 % of the TP at the 0-10 cm soil layer.
4.
Both Northeast (Typha sp. and Cladium) and Southwest (Typha sp/Woody mix) vegetative communities
had higher extractable NH4+ concentrations (α < 0.05) for the detritus layer than NW (Panicum). The rapid
decomposition rate of Typha sp. likely contributed to the high amount of extractable NH4+ in both the
Southwest and Northeast regions (Davis, 1991).
5.2.2 Temporal Patterns of Microbial Biomass and Labile Nutrient Pools
1.
2.
3.
4.
5.
6.
7.
8.
Variations in water levels appeared to have a greater effect on soil P pools than the surface fire that
occurred in BCM prior to sampling. Changes in hydrology appear to have a sustained impact over several
sampling periods, dependent on duration of unsaturated conditions. Fire can be a sudden and intense
impact to the system, but appeared to have a minimum impact on the soil P observed during the first
sampling period.
CO2/MBC ratio for soil demonstrated significant patterns due to seasonality, station, and seasonality and
station combined. Activities of the microbial biomass monitored throughout the year may be an indicator
for ecological disturbance.
Significantly lower MBN was present for detritus associated with Cladium communities from both the NW
and Northeast regions. The low nutrient status of Cladium may limit the microbial community due to the
forms of available C and the small quantity of detritus produced (Davis, 1991). The quantity of microbial
biomass may be influenced by the forms of C available due to the type of the substrate (Anderson and
Domsch, 1985; Schnurer et al., 1985).
For the detritus layer, a significant difference (α < 0.05) in PMN rates was found over time with the
Northeast and Southwest regions having higher mineralization rates than the NW region. Extractable NH4+
concentrations were found to be significantly higher (α < 0.05) in the Northeast and Southwest regions
within the detritus layer, in agreement with PMN rates.
Seasonal patterns were found to be significantly different (α < 0.05) between all three regions for MBN and
extractable NH4+ for both the soil and detritus layers.
Microbial biomass changed by seasonality with maxima occurring in the summer (June and July) and
winter (December and January) months. Larger microbial communities are likely a result of aerobic soil
conditions.
The labile organic P was significantly affected by seasonal and site effects with reduced concentrations
during the summer months (June and July) correlating with the drought period.
The detrital layer TP patterns were influenced by the dominant type of vegetation present more than
differences in soil TP. Overall the TP concentrations appear to be diminishing overtime, indicating the
system is redistributing and assimilating the exogenous P.
51
5.3 TASK 3: DIVERSITY AND COMPOSITION OF PROKARYOTIC GROUPS RELATED TO C, N, AND P
CYCLING IN WETLANDS.
1.
2.
3.
4.
5.
6.
Much higher butyrate induced methanogenesis was observed in impacted regions of the marsh, with basal
levels in non-impacted zones.
RFLP patterns indicated that certain bacterial groups were enriched in the impacted regions.
Non-impacted regions were dominated by Methanosarcina (versatile methanogen utilizing H2/CO2,
methanol/methylamines and acetate); Methanosaeta, (known acetoclastic methanogen) was also found.
Bacterial sequences from impacted regions clustered with the Syntrophomonas strain MGB, representing
novel syntrophic lineage(s).
Impacted regions contained novel archaeal sequences clustering with genus Methanofollis, a well known
obligate hydrogenotrophic methanogen.
Archaeal clone libraries constructed directly from soil DNA corroborated these results with clones obtained
from impacted regions clustering with hydrogenotrophs and acetotrophs while clones from non-impacted
zones clustered with acetoclastic methanogen sequences alone (data not shown).
5.4 TASK 4: SPATIAL DISTRIBUTION OF BIOGEOCHEMICAL INDICATORS IN WATER, LITTER AND
SOIL
1.
Results indicated that the spatial patterns and the relationships between soil biogeochemical properties are
variable across the wetland.
2.
Elevated TP caused by previous adjacent agricultural activities is still present in the top soil. The transport
of P out of the wetland might take several more decades. Our study provided a shapshot in time giving
insight into the current ecological status.
3.
Total phosphorus realizations showed high values up to a maximum of 1,013.6 mg kg-1 which impacts
nutrient cycling in this naturally P-limited wetland.
4.
The zone of elevated TP did not fully overlap with the zone of Salix caroliniana vegetation which
expanded far beyond the nutrient enriched zones.
5.
Our study was conducted in context of stochastic ecosystem resilience which does not aim to reach a
historic “natural” equilibrium but assumes different equilibria at different times.
5.5 TASK 5: VALIDATION OF PREDICTIVE EQUATIONS USING INDEPENDENT MEASUREMENTS.
5.5.1 Evaluation of Empirical Relationships Using Multivariate Statistics
The multivariate statistical analyses presented above indicate that soil biogeochemical measurements are
indicative of vegetation types, and can be used to evaluate ecological integrity in the Blue Cypress Marsh
Conservation Area.
5.5.2 Evaluation of Empirical Relationships with an Independent Dataset
1.
2.
3.
4.
5.
The application of the biotic variables selected for in an independent dataset on BCMCA data resulted in a
distinct separation of the unimpacted areas from the impacted areas.
The primary differentiation of impacted and non-impacted areas is due to P-availability.
A secondary factor is the predominant vegetation types, reflecting the effect of the diverse vegetation
communities in BCMCA (i.e. detrital quality).
The effect of Typha sp. on the soil biogeochemistry was highlighted by the increase in overall N and P
turnover rates.
Using this analytical approach, microbial biomass does not play a significant role in describing the
variability in BCMCA.
52
5.6 OVERALL CONCLUSIONS:
1.
The impacted areas (NE and SW) had significantly higher TP than the unimpacted area (NW) for both
detritus and soil layers.
2. Microbial activities varied with both nutrient level and vegetation community.
3. Changes in hydrology and duration of unsaturated conditions appear to have significant confounding
influence on temporal patterns of microbial and nutrient dynamics.
4. Nutrient loading in the impacted regions enriched for particular bacterial groups as evidenced by the
common RFLP patterns.
5. The multivariate statistical analyses presented indicate that soil biogeochemical measurements are
indicative of vegetation types, and can be used to evaluate ecological integrity in the Blue Cypress Marsh
Conservation Area.
6. The effect of Typha sp. on the soil biogeochemistry was highlighted by the increase in overall N and P
turnover rates.
7. The primary differentiation among impacted and non-impacted areas is P-availability.
8. Results reflect the diversity of vegetation communities in BCMCA, making the predominant vegetation
type an important secondary factor distinguishing impacted and non-impacted.
9. The application of the biotic variables selected for in an independent dataset to BCMCA data resulted in a
distinct separation of the unimpacted areas from the impacted areas.
10. Microbial biomass does not play a significant role in describing the variability in BCMCA.
11. It is possible to extrapolate predictive variables among independent wetlands within the region.
12. Our study provided a shapshot in time giving insight into the current ecological status of BCMCA.
Elevated TP caused by previous adjacent agricultural activities is still present in the top soil. The transport
of P out of the wetland is dependent on biotic variables including vegetation communities, and may take
several more decades.
6.0 PUBLICATIONS AND PRESENTATIONS
6.1 PUBLICATIONS (SUBMITTED)
Grunwald S., K.R. Reddy, J.P. Prenger, and M. M. Fisher. 2004. Spatial methods for assessing the distribution and
impact of soil phosphorus in a sub-tropical freshwater wetland. Geoderma Special Issue Pedometrics 2003. In
Review.
Prenger, J.P., and K.R. Reddy. 2004. Microbial enzyme activities in a freshwater marsh after cessation of nutrient
loading. Soil Sci. Soc. of Am. J. In Review
Corstanje, R. and K.R. Reddy. 2004. Seasonal variability in microbial communities and associated physiological
response measures in a subtropical wetland. Wetlands (submitted)
Bostic, E. and J. White. 2004. Phosphorus and vegetation effects on phosphorus retention in wetland soil after a
drawdown/reflood event. For submission to Soil Sci. Soc. of Am. J.
Corstanje, R. and K.R., Reddy. Microbial indicators of nutrient enrichment: a mesocosm study. Soil Science
Society of America Journal (submitted)
Corstanje, R. and K.R. Reddy. Typha latifolia and Cladium jamaicense litter decay in response to exogenous
nutrient enrichment. Soil Biology and Biochemistry (submitted)
Corstanje, R. and K.R. Reddy. 2004. Response of biogeochemical indicators to a drawdown and subsequent reflood. Journal of Environmental Quality (submitted)
53
6.2 PUBLICATIONS (IN PREPARATION)
Grunwald, S., B. Weinrich, K. R. Reddy, and J. Prenger. 2004. Spatial patterns and of labile and non-labile of pools
of soil phosphorus in a subtropical wetlands. (in preparation)
Corstanje, R. and K.R. Reddy. 2004. Seasonal variability in microbial communities and associated physiological
response measures in a subtropical wetland. Limnology and Oceanography
Corstanje, R. and K.R. Reddy. 2004. Response of biogeochemical indicators to a drawdown and subsequent reflood. Journal of Environmental Quality
Bostic, E.M., J.R. White, and K.R. Reddy. 2004. Spatial Characterization of Phosphorus Pools Following Nutrient
Enrichment of a Freshwater Marsh.
Prenger, J., M. Seo, and K.R. Reddy. 200_. Effect of vegetation type and nutrient levels on microbial activities in a
subtropical wetland.
White, J., K.J. Barch, and K.R. Reddy. 200_. Nitrogen Cycling In A Nutrient Impacted
Subtropical Wetland.
6.3 THESES AND DISSERTATIONS
Seo, M. 2002. Influence of nutrients, vegetation types and seasonality on microbial activities of carbon in a
subtropical wetland. M.S. Thesis. University of Florida.
Bostic, E.M. 2003. Phosphorus Cycling In A Nutrient-Impacted Freshwater Wetland: Spatial, Temporal, And
Hydrologic Effects. M.Sc. University Of Florida
Barch, K.J. 2002. Spatial And Temporal Patterns Of Biogeochemical Cycling Of Nitrogen In A
Nutrient Impacted Subtropical Wetland. M.Sc. University Of Florida
Corstanje, R. 2003. Experimental and Multivariate Analysis of Biogeochemical Indicators of Change in Wetland
Ecosystems. Ph.D. Dissertation. University of Florida.
6.4 PRESENTATIONS
Bostic, E. M. Reddy, K.R., White, J.R and R. Corstanje. Assessing Seasonal Phosphorus Cycling in a Freshwater
Marsh, USA: 10 Years after Nutrient Enrichment. 8th Symposium on Biogeochemistry of Wetlands, Gent, 2003
Bostic, E. M. Reddy, K.R., and J.R. White. Investigation of phosphorus forms and transformations in soil as
influenced by hydrologic changes in a subtropical freshwater wetland. ASA-CSSA-SSSA Annual Meeting,
Indianapolis IN. November 2002.
Bostic, E. M. and J.R. White. Influence of simulated drought conditions on wetland soil phosphorus dynamics: Blue
Cypress Marsh. 3rd Annual Departmental Research forum, Soil and Water Science Dept. University of Florida,
September 2002.
Bostic, E. M. Reddy, K.R., and J.R. White. Effects of non-point source runoff on phosphorus enrichment in wetland
soils. 2002 Graduate Student Forum, University of Florida.
Bostic, E. M. Reddy, K.R., and J.R. White. Biogeochemical cycling of phosphorus in a nutrient impacted
subtropical Florida wetland. ASA-CSSA-SSSA Annual Meeting, Charlotte, NC. November 2001.
Seo, M, Corstanje, R., Prenger, J.P., and Reddy, K.R. Spatial Distribution of Selected Microbial Parameters in the
Blue Cypress Marsh, Florida Society of Wetland Scientist 22th Annual Meeting, Lake Placid, New York, 2002.
54
Barch, K. Reddy, K.R., and J.R. White. Biogeochemical cycling of nitrogen in a nutrient impacted subtropical
Florida wetland. ASA-CSSA-SSSA Annual Meeting, Charlotte, NC. November 2001.
Corstanje, R. and Reddy, K.R.., Temporal variability in selected wetland microbial indicators of ecological
perturbation. IFAS Graduate Student Forum, 2002.
Corstanje, R. Application of genetic tools to assess nutrient impacts in a wetland system. Soil and Water Science
Graduate Student Research Forum, 2000.
Corstanje, R. Impacted Induced Changes in Select Microbial Groups. Soil and Water Science Graduate Student
Research Forum, 2000.
Corstanje, R., Ogram, A. and Reddy, K.R, Impact Induced Changes in Selected Wetland Microbial Groups. Society
of Wetland Scientist 20th Annual Meeting, Quebec, Canada, 1999.
Prenger, J. P., L. Yang, W. Graham, and K. R. Reddy. Soil Microbial and Enzyme Activity Levels Related to
Historic Nutrient Enrichment in a Freshwater Marsh. Society of Wetland Scientist 23nd Annual Meeting, Lake
Placid, NY. 2002.
Prenger, J. P., K. R. Reddy, and M. M. Fisher. Spatial Variability of Soil Enzyme Activities in a
Freshwater Marsh Impacted by Agricultural Runoff. Society of Wetland Scientist 22nd Annual Meeting,
Chicago,Ill. 2001.
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