BIOGEOCHEMICAL INDICATORS OF WATERSHED INTEGRITY AND WETLAND EUTROPHICATION

Transcription

1 BIOGEOCHEMICAL INDICATORS OF WATERSHED INTEGRITY AND WETLAND EUTROPHICATION Final Report January 2004 Project Period U.S. Environmental Protection Agency Grant Number: R 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 and E.F. Lowe and L.W. Keenan St. Johns River Water Management District Palatka, FL i

2 TABLE OF CONTENTS Page Executive Summary Introduction Technical Objectives Description of Study Site Project Milestones (FY2001) Task 1: Determination Of Spatial Variability And Inter-Relationships Of Biogeochemical Processes And Efficient Indicators Sample Collection Soil Biogeochemical Analyses Spatial Analysis of Physico-chemical Properties Spatial Analysis of Microbial Biomass and 12 Labile Nutrient Pools Spatial Analysis of Microbial Activities Task 2: Temporal Variability Of Biogeochemical Processes And Indicators Sample Collection Soil Biogeochemical Analyses Temporal Patterns of Physico-chemical Properties Temporal Patterns of Microbial Biomass and 28 Labile Nutrient Pools 4.3 Task 3: Diversity And Composition Of Prokaryotic Groups Related To C, N, And P Cycling In Wetlands Task 4: Spatial Distribution Of Biogeochemical Indicators In Water, Litter And Soil Sample Collection Soil Biogeochemical Analyses Spatial Analysis of Physico-chemical Properties Results 37 i

3 4.5 Task 5: Validation Of Predictive Equations Using Independent Measurements Evaluation of Empirical Relationships Using Multivariate Statistics Validation of Relationships Conclusions Task 1: Determination Of Spatial Variability And Inter-Relationships Of Biogeochemical Processes And Efficient Indicators Spatial Analysis of Physico-chemical Properties Spatial Analysis of Microbial Biomass and Labile 48 Nutrient Pools Spatial Analysis Of Microbial Activities Task 2: Temporal Variability Of Biogeochemical Processes 51 And Indicators Temporal Patterns Of Physico-Chemical Properties Temporal Patterns Of Microbial Biomass And Labile 51 Nutrient Pools 5.3 Task 3: Diversity And Composition Of Prokaryotic Groups 52 Related To C, N, And P Cycling In Wetlands. 5.4 Task 4: Spatial Distribution Of Biogeochemical Indicators 52 In Water, Litter And Soil 5.5 Task 5: Validation Of Predictive Equations Using Independent 52 Measurements Evaluation Of Empirical Relationships Using 52 Multivariate Statistics Evaluation Of Empirical Relationships With 52 An Independent Dataset 5.6 Overall Conclusions Publications And Presentations Publications (Submitted) Publications (In Preparation) Theses And Dissertations Presentations References 54 ii

4 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

5 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 NH 4 + 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 NH 4 + 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 CH 4 production rate, was significantly higher in detritus at SW site than NW site. The CH 4 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 CH 4 than NE and SW soil, while there was no significant difference between NE and SW soil CH 4 production. When the CH 4 production data was compared by vegetation types, the CH 4 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 CO 2 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 CO 2 production rate was compared by vegetation types, the detritus at Typha area had significantly higher CO 2 production rate than Cladium area. As a result, the Typha dominant area produced higher CO 2 and CH 4 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 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

6 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 CO 2 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 NH 4 +, 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 NH 4 + 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 NH 4 + 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 NH 4 + 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

7 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

8 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 P- dynamics 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 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, 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

9 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

10 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

11 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 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 cm 2 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, 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 Soil Biogeochemical Analyses Biogeochemical processes related to C, N, and P cycling were measured in plant litter and soil samples (0-10 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

12 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 Site Vegetation Impacted Northeast NETypha Typha Northeast NECladium Cladium Southwest SWTypha/Woody Typha/Myrica/Salix/Cephalanthus Southwest SWTypha/Cladium/Woody Typha/Cladium/Salix/Cephalanthus Reference Northwest NWCladium Cladium Northwest NWPanicum Panicum 9

13 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 g oven-dried, finely ground soil at 550 o 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). NaHCO 3 extractable P i was determined by extraction of the wet soil equivalent of 0.5 g dry weight with 25 ml 0.5 M NaHCO 3 (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 (k EC ) was applied to the final total organic carbon values to determine microbial biomass carbon (Horwath and Paul, 1994). For this analysis, the k EC 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 CO 2 and CH 4 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 NaHCO 3 (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) 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

14 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 ph Ca Fe Al NaHCO 3 - Pi NH 4 -N Total P Total N Total C g kg -1 mg kg -1 g kg -1 Detritus NE 16.7 (5.3) a 1.0 (0.8) ab 44.8 (60.0) ab (121.7) a 565 (392) a 921 (309) a 16.8 (8.6) a 419 (140) a impacted SW 20.0 (5.9) b 1.3 (0.8) a a (76.7) b 199 (438) b 766 (287) b 13.3 (4.1) b 474 (14) b reference NW 9.0 (3.0) c 0.8 (0.5) b b (44.5) b 198 (210) b 530 (133) c 11.6 (3.9) b 449 (77) ab Soil (0-10 cm) NE 5.8 (0.4) 15.6 (2.3) a 1.0 (0.4) a (99.9) a 34.5 (14.5) a 188 (99) a 847 (213) a 29.9 (5.5) a 467 (13) a impacted SW 6.2 (0.6) 23.8 (3.8) b 1.5 (0.5) b (166.0) b 32.4 (9.6) a 147 (64) b 860 (109) a 24.2 (1.9) b 465 (7) a reference NW 5.5 (0.1) 11.4 (3.2) c 1.2 (0.8) ab (41.0) a 28.4 (12.3) a 132 (45) b 641 (82) b 27.7 (2.4) c 465 (6) a Values in parenthesis are one standard deviation. Different letters indicate significant differences (α< 0.05) among detritus or soil values. 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 TP C:N C:P g kg mg kg mass ratio mass ratio Detritus Cladium (19) 12.1 (4.3) 555 (206) 42.8 (14.2) 949 (332) Cladium/woody Mix (13) 13.6 (3.9) 657 (151) 39.1 (15.1) 769 (196) Panicum (19) 11.6 (2.8) 550 (103) 40.5 (7.6) 843 (156) Typha (12) 19.4 (7.5) 1030 (255) 28.5 (14.3) 490 (172) Typha/woody Mix (14) 18.2 (4.5) 1058 (310) 27.3 (7.1) 481 (153) Others (17) 13.5 (5.8) 667 (232) 40.0 (14.7) 806 (355) Soil (0-10 cm) Cladium (8) 26.5 (2.7) 671 (102) 17.7 (1.7) 706 (99) Cladium/woody Mix (13) 23.9 (2.5) 724 (134) 19.5 (1.9) 662 (135) Panicum (6) 29.7 (1.7) 684 (88) 15.7 (0.9) 693 (105) Typha (7) 27.8 (4.1) 888 (172) 17.3 (2.0) 576 (253) Typha/woody Mix (8) 26.3 (3.2) 910 (92) 18.0 (2.1) 517 (64) Others (10) 28.9 (6.1) (219) 16.7 (3.1) 610 (231)

15 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 Spatial Analysis of Microbial Biomass and Labile Nutrient Pools 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 mg N kg -1 and 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

16 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 (k EC = 0.37) was applied. Site n MBC LOC TLOC g C kg Detritus Northeast (7.00) 5.23 (2.95) (12.27) NW (3.91) 2.87 (0.91) (5.36) Southwest (6.22) 4.34 (2.48) (9.37) Soil (0-10 cm) Northeast (4.10) 2.38 (0.63) (4.85) NW (4.28) 0.93 (0.22) (4.51) Southwest (2.32) 1.81 (0.43) (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 (k EC = 0.37) was applied. Vegetation type n MBC LOC TLOC g C kg Detritus Cladium (3.10) 3.18 (1.59) (4.99) Cladium/woody Mix (5.71) 2.47 (0.93) (7.34) Panicum (2.32) 3.46 (0.94) (2.39) Typha (5.22) 5.11 (1.76) (6.19) Typha/woody Mix (5.47) 5.20 (2.68) (10.64) Others (7.01) 5.27 (3.39) (12.67) Soil (0-10 cm) Cladium (2.78) 1.16 (0.49) (2.15) Cladium/woody Mix (2.85) 1.39 (0.59) (3.60) Panicum (6.20) 1.06 (0.17) (6.29) Typha (2.91) 2.39 (0.55) (3.57) Typha/woody Mix (2.45) 2.22 (0.55) (3.60) Others (4.72) 2.04 (0.67) (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. + MBN PMN NH 4 DEA mg N kg -1 Soil Northeast 522 ± ± ± 99 BCT 527 ± ± ± 44.7 Southwest 550 ± ± ± 64 Detritus Northeast 1007 ± ± ± 392 BCT 958 ± ± ± 210 Southwest 800 ± ± ± mg N kg -1 d -1 mg N 2 0-N kg -1 hr ± ± ±

17 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 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

18 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) BD (g cm -3 ) LOI (%) NW 181 ± 44.6 a 218 ± 48.4 a 36.8 ± ± ± 1.24 NE 151 ± 92.8 b 190 ± 90.2 a 39.2 ± ± ± 1.76 SW 119 ± 23.0 c 163 ± 28.0 b 43.3 ± ± ± 1.62 Detritus NW 147 ± 44.1 a 161 ± ± 11.2 a n.d 96.4 ± 1.04 NE 188 ± 113 b 217 ± ± 19.8 b n.d 95.5 ± 1.39 SW 142 ± 93.5 a 185 ± ± 21.7 c n.d 94.3 ±

19 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 NH 4 + 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 O 2 than would be observed under anaerobic conditions; thus spatial variability based on hydrologic conditions are expected. Potentially mineralizable N averaged mg N kg -1 d -1 and 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 NH 4 + concentrations decreased with depth as well, averaging mg N kg -1 and mg N kg -1 for all three regions within the detritus and soil layers, respectively (Table 6). The decrease of PMN and extractable NH 4 + 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 NH 4 + 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 NH 4 + 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 NH 4 + 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 NH 4 + (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 NH 4 + 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 NH 4 + 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 Spatial Analysis of Microbial Activities 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

20 250 PMN (mg N kg -1 d -1 ) b b a b 0 P C T T/WM C/WM O Vegetation + Ext. NH 4 (mg N kg -1 ) b b a b 0 P C T T/WM C/WM O Vegetation Figure 2. Potentially mineralizable N and extractable NH 4 + 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 ) a a b b b b 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

21 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 (CH 4 ) production was measured to determine the activity of methanogenic bacteria. The CH 4 production rate in detritus at SW site was significantly higher (α < 0.05) than NW site. The CH 4 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 CH 4 than NE and SW soil, while there was no significant difference between NE and SW soil CH 4 production. Although the MBC in soil at impacted area was lower than unimpacted areas, the potential CH 4 production in impacted soil was actually higher than unimpacted soil. D Angelo and Reddy (1999) also observed higher production of CH 4 from hypereutrophic marsh soil. When the CH 4 production data was compared by vegetation types, the CH 4 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 CH 4 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) CH 4 than only Cladium and Cladium/woody mix. Moreover, the CH 4 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) CH 4 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 CH 4 production rates between the following 3 groups: the Cladium at NW, Cladium at NE, and Cladium/woody mix at SW. Anaerobic microbial respiration (CO 2 ) 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 CO 2 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 CO 2 production rates of NE and NW detritus and soil. Therefore, an increase in CO 2 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 CO 2 production rate was compared by vegetation types, the detritus at Typha area had significantly higher (α < 0.05) CO 2 production rate than Cladium area (Table 8). In addition, for both soil and detritus, the CO 2 production rate at Typha/woody mix area was significantly higher (α < 0.05) than that of Cladium and Cladium/woody mix. Although the CO 2 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) CO 2 production rate than Cladium dominated area. Moreover, for soil and detritus, there was no significant difference in the CO 2 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 CO 2 and CH 4 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

22 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 CH 4 CO 2 --mg DO g -1 hr mg C g -1 d Detritus Cladium * (56.8) (92.0) (126.0) Cladium/woody Mix (50.8) (156.0) (240.0) Panicum ** (23.2) (103.0) (108.0) Typha (103.8) (200.0) (201.0) Typha/woody Mix (119.8) (169.0) (386.0) Others (137.0) (221.0) (352.0) Soil (0-10 cm) Cladium (10.3) 35.8 (12.9) 85.7 (10.4) Cladium/woody Mix (13.0) 39.6 (16.7) 90.5 (19.5) Panicum (16.6) 66.1 (21.5) (19.2) Typha (14.6) 51.1 (19.6) 98.8 (19.1) Typha/woody Mix (11.4) 66.5 (36.5) (33.5) Others (13.8) 48.4 (27.4) (27.7) 19

23 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 (qco 2 or CO 2 /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 (CH 4 /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 CH 4 /MBC ratio for detritus between 3 sites. However, mean CH 4 /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 CH 4 /MBC ratio for detritus between vegetation groups, the mean CH 4 /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 CH 4 /MBC ratio than from Cladium and Panicum areas. Moreover, the CH 4 /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 (qco 2 ) (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 (CO 2 /MBC) was used to determine microbial metabolic efficiency. For detritus, the mean CO 2 /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 CO 2 /MBC ratio increased significantly (α < 0.05) in the order: SW> NE > NW. Higher qco 2 for disturbed soil was also observed by Ohtonen (1994). Similar pattern was also observed for CH 4 /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 CO 2 /MBC ratio for detritus between vegetation groups, the mean CO 2 /MBC ratio was significantly higher (α < 0.05) for soil from Typha/woody mix area than Cladium and Panicum areas. Moreover, exactly same as CH 4 /MBC ratio, the CO 2 /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 CO 2 /MBC ratio also seems to be affected by both nutrient level and vegetation types. 20

24 Using the ratio parameter data for SOD/MBC, CH 4 /MBC, and CO 2 /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 CH 4 /MBC and CO 2 /MBC. The averages for anaerobic microbial turnover through CH 4 production for detritus and soil were 31 ± 4 days and 201 ± 84 days, respectively. The averages for anaerobic microbial turnover through CO 2 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 O 2 is higher than for alternate electron acceptors (NO 3 -, Mn 4+, Fe 3+, SO 4 2-, CO 2 ); 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 CH 4 and CO 2 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, CH 4, CO 2, TC, TN, TP, C:N, and C:P (Table 9). Especially for detritus, heterotrophic microbial activity such as SOD, CH 4, and CO 2 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 CO 2 and CH 4 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 CH 4 and CO 2 was not very high for both detritus and soil since the anaerobic microbial respiration and methanogenesis were highly interrelated with each other Nitrogen Metabolism Within the soil layer, DEA averaged mg N 2 0-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 N 2 0-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 NH 4 + 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 NO 3 - 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 NO 3 - 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

25 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 CO 2 CH 4 Detritus MBC LOC TLOC TC NS NS NS TN NS NS NS TP NS NS C:N NS NS NS C:P NS NS Soil (0-10 cm) MBC NS NS LOC NS TLOC NS NS TC NS NS NS TN NS NS TP NS C:N NS NS C:P NS DEA (mg N 2 O-N kg -1 h -1 ) a a b b b 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

26 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 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 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 cm 2 ) 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 Soil Biogeochemical Analyses above. Biogeochemical, physicochemical, and microbial parameters were characterized as described in 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

27 APA µmole MUF g -1 hr -1 APA µmole MUF g -1 hr Soil (0-10 cm) b a c NW NE SW Area 5 Soil (0-10 cm) 2 b ab a ab a ab C C-W O P T 0 T-W Vegetation Detritus a a a NW NE SW Area Detritus a a a a a a C C-W O P T T-W Vegetation 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

28 January 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

29 Average Rainfall (cm) Sept 2000 Drought Conditions Nov Jan Mar May July 2001 Sept Nov 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 NaHCO 3 - Pi NH 4 -N Total P Total N Total C mg kg -1 g kg -1 Impacted Reference Impacted Reference Detritus NETypha (52.6) a (126.7) a 770 (102) a 18.0 (3.6) a 456 (12) a NECladium 96.9 (43.5) ab (50.4) ab 435 (58) b 12.1 (1.6) b 455 (10) a SWTypha/Woody (65.5) a (72.7) a 922 (139) c 17.4 (4.1) a 428 (19) bd SWTypha/Cladium/Woody 91.8 (51.0) ab 93.3 (42.7) ab 687 (195) a 13.5 (2.5) b 416 (45) cd NWCladium 37.1 (16.0) c 46.1 (47.6) b 393 (109) b 13.7 (3.0) b 444 (14) ab NWPanicum 45.8 (16.2) bc 88.8 (127.6) ab 451 (247) b 14.2 (3.1) b 435 (14) bd Soil (0-10 cm) NETypha 48.0 (9.9) ab 78.7 (38.9) a 834 (90) a 26.4 (1.3 a 448 (9) a NECladium 42.4 (14.4) a 68.7 (42.9) a 565 (109) b 25.8 (1.0) a 451 (10) ac SWTypha/Woody 56.7 (8.2) b 68.6 (21.5) a 878 (83) a 24.2 (1.2) d 435 (16) d SWTypha/Cladium/Woody 50.3 (9.2) ab 54.5 (19.5) a 826 (104) a 24.3 (0.9) d 442 (7) ad NWCladium 44.2 (18.5) ab 60.1 (18.0) a 507 (54) b 27.9 (1.3) b 458 (5) bc NWPanicum 45.9 (18.5) ab 64.3 (20.7) a 533 (64) b 31.6 (1.4) c 461 (8) b Values in parenthesis are one standard deviation. Different letters indicate significant differences (α< 0.05) among detritus or soil values. 26

30 SW Typha sp / woody mix SW Other NE Typha sp Detrital TP o Detrital TP i Soil TP o Soil TP i NE Cladium sp BCT Cladium sp BCT Panicum sp 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

31 4.2.4 Temporal Patterns of Microbial Biomass and Labile Nutrient Pools 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 CH 4 production ratio generally increased toward the latter phase of temporal study for all three sites. A similar pattern was also observed in anaerobic CO 2 production rate for detritus in all three sites: a general increase toward the end of temporal study. On the other hand, anaerobic CO 2 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, CH 4 /MBC for detritus was higher in July at SW area and toward the latter phase of study at NE area. For soil, CH 4 /MBC was higher toward the end of study for all three sites. Finally, CO 2 /MBC ratio for detritus and soil showed different temporal pattern for all three sites. The CO 2 /MBC ratio at NW was higher in September for both detritus and soil. However, there was an increase in CO 2 /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 CH 4 /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 CO 2 /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 CO 2 /MBC ratio for soil. These parameters could be the best indicators for ecological disturbance when they need to be monitored throughout a year 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

32 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 < 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. α values α values Time & Time & Station Station Parameter n Time Interaction Station n Time Interaction Station Detritus Soil (0-10 cm) SOD NS < <.0001 NS MBC <.0001 NS <.0001 LOC 15 < <.0001 NS TLOC 15 < <.0001 NS CH NS CO NS <.0001 NS MBC/TC 16 <.0001 NS <.0001 MBC/TLOC NS NS NS SOD/MBC NS < CH 4 /MBC NS NS CO 2 /MBC NS TP <.0001 NS <.0001 TC 16 < <.0001 TN 16 < <.0001 C:N 16 NS NS <.0001 C:P 16 < <

33 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 NH 4 +, 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 NH 4 + concentrations within all three regions for both the soil and detritus layers due to the increase of extractable NH 4 + 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 (NH 4 + to NO 3 - ) is influenced by dissolved O 2 concentrations, therefore allowing the build up of NH 4 + 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 O 2 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 NH 4 + 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 NH 4 + 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 NH 4 + 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 NH 4 + 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 NH 4 + 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 NH 4 + in both the Southwest and Northeast regions (Davis, 1991). For the soil layer, the only significant difference (α < 0.05) with regards to extractable NH 4 + 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 NH 4 + 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

34 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

35 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 NaHCO 3 P i (mg kg -1 ) HCl P i (mgkg -1 ) TLOP (mg kg -1 ) MBP (mg kg -1 ) Fulvic P o (mg kg -1 ) Humic P o (mg kg -1 ) Residual P o (mg kg -1 ) Soil (0-10 cm) NE (Cladium sp.) 96.9 (11.6) 32.1 (3.24) 102 (16.4) 76.9 (13.3) 53.1 (6.67) 54.7 (7.93) 85.1 (10.6) NE (Typha sp.) 133 (13.6) 58.6 (4.05) 156 (16.9) 122 (22.2) 92.5 (8.36) 92.1 (12.3) 145 (13.2) NW (Cladium sp) (4.11) 30.6 (3.11) 88.8 (12.9) 80.6 (12.9) 40.5 (5.53) 55.0 (11.8) 82.8 (8.88) NW (Panicum sp.) 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) 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) (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) (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) (19.5) 172 (8.85) 32

36 SW (Typha/Woody mix) Site SW (Other) NE (Typha sp.) NE (Cladium sp.) BCT (Panicum sp.) TLOP MBP Fulvic Acid Po Humic Acid Po Residual Po 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) Site SW (Other) NE (Typha sp.) NE (Cladium sp.) BCT (Panicum sp.) TLOP MBP Fulvic Acid Po Humic Acid Po Residual Po 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

37 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 (T- RFLP). 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 CO 2 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 CO 2. 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, CO 2, 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

38 Methane (µmole/g soil) Weeks P 343+B 343+P+S 343+B+S P 347+B 347+P+S 347+B+S 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

39 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 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 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 cm 2 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 Soil Biogeochemical Analyses above. Biogeochemical, physicochemical, and microbial parameters were characterized as described in 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

40 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 Results The semivariogram of TP was modeled with two basic structures a nugget of and a spherical model with sill 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

41 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 , 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 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 %, the second PC with %, and the third PC with % 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 ( ), and APA ( ) mainly contributed to PC2. The third PC was dominated by the contribution of TC (0.6011) and Ash ( ). 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.: g kg -1 ) and low TLON (min: 172.4; mean: 252.5; max: mg kg -1 ) values. In contrast, class 4 showed very low TP (min: 352.5; mean: 525.3; max mg kg -1 ) and intermediate TC (min: 420.3; mean: 453.1; max: g kg -1 ) and TLON (min: 193.9; mean: 276.5; max.: mg kg -1 ) values. Class 2 showed high TP (min: 442.1; mean: 721.9; max.: mg kg -1 ) and relatively high TLON values (min: 200.2; mean: 304.8; max: mg kg -1 ) whereas class 1 showed high TP (min: 456.1; mean: 727.2; max: mg kg -1 ) but relatively low TC values (min: 312.3; mean: 448.3; max: 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

42 Figure 11. Ten out of 100 realizations of TP generated using CSGS. Figure 12. Smallest, mean, and largest realization of TP. 39

43 Figure 13. Histograms of TP observations and realizations (TP mg kg -1 ). Variable n Minimum Maximum Mean Median Std.dev. APA Ash BicTP BD BG PI PO CN CTC , , , NH4N TLON TLOC 267 1, , , , MBP % Moisture PEP TC TN TP , Table 13. Statistics of biogeochemical soil properties. 40

44 Table 14. Correlation matrix. APA Ash BD BG BicTP CN CTC NH4N TLON TLOC MBP Moist PEP PI PO TC TN TP APA Ash BD BG BicTP CN CTC NH4N TLON TLOC MBP Moist PEP PI PO TC 1 TN 1 TP 1 Only significant correlations at the 0.01 level (2-tailed) are shown 41

45 Table 15. Eigenvalues and eigenvectors of the first five principal components. Variables PC1 PC2 PC3 PC4 PC5 APA Ash BD BG BicTP CN CTC MBP Moist NH4N PEP PI PO TC TLOC TNON TN TP Eigenvalue Ratio % Cumlative %

46 enzymatic activities, and other). Total phosphorus showed a relatively wide range of realizations from as low as up to 1,013.6 mg kg -1. The range of TC was smaller with to 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 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 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 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 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

47 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

48 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 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 Validation of Relationships 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 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

49 a) b) b ) c) c ) Figure 15. Plots of Observation Groups in Two Canonical Variables 46

50 Table 18. Comparison of Misclassification Rates by different DFA approaches. Observation Misclassification Cross-Validation Misclassification Groups DFA Rate DFA Rate Region quadratic 0 quadratic Vegetation linear linear Cluster linear 2.54 linear 9.32 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