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a Univ. of Florida, Institute for Food and Agric. Sciences, Soil and Water Science Dep., 2169 McCarty Hall, P.O. Box 110290, Gainesville, FL 32611-0290
b Univ. of Hawai'i at Manoa, College of Tropical Agriculture and Human Resources, Dep. of Natural Resources and Environmental Management, 1910 East-West Rd., Honolulu, HI 96822
c Everglades Division, South Florida Water Management District, West Palm Beach, FL 33416-4680
* Corresponding author (SGrunwald{at}ifas.ufl.edu)
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Abbreviations: Db, bulk density • IDW, inverse distance weighting • TAl, total aluminum • TC, total carbon • TCa, total calcium • TFe, total iron • TMg, total magnesium • TN, total nitrogen • TP, total P • TPi, total inorganic P
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Historically, the Everglades was characterized by its large spatial extent, its heterogeneous mosaic of Cladium jamaicense Crantz (sawgrass) marsh, wet sloughs and tree islands, and its hydrologic regime of slow-moving sheet flow with long periods of inundation (Parker, 1974; McCally, 1999). These conditions allowed for the development of a peat-based subtropical wetland that was oligotrophic (Davis and Ogden, 1994) and P limited (Koch and Reddy, 1992). Hydrologic modification, drainage, wetland conversion, landscape fragmentation, and nutrient enrichment have significantly affected the ecology of the Everglades (Davis and Ogden, 1994). Starting in the 1880s, the construction of a network of nearly 2400 km of canals and dikes served to drain and compartmentalize the Everglades landscape into multiplediscontinuous hydrologic units, including the Everglades Agricultural Area (EAA), the Water Conservation Areas (WCA-1, 2A, 2B, 3A, and 3B), and the Everglades National Park (Noe et al., 2001) (Fig. 1 ).
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Soils are an integrator of long-term environmental change (DeBusk et al., 1994; Craft and Richardson, 1997; Bruland et al., 2003; King et al., 2004). In the Everglades, soil chemistry has been shown to explain more variation in algal community attributes than water chemistry (Pan et al., 2000). Furthermore, nutrient inputs to Everglades wetlands are primarily stored in the peat, so the vegetation represents only a short-term nutrient sink (Craft and Richardson, 1993a; Newman et al., 1997). Thus, the spatial distribution of soil nutrients can be used as a means of assessing long-term nutrient impacts to this system (Newman et al., 1997). Correspondingly, soils are an ideal ecosystem component for assessing the baseline status of WCA-3 before initiation of landscape-scale restoration activities. Because this area will be a central focus of future Comprehensive Everglades Restoration Plan activities (U.S. Department of the Interior, 2005; U.S. Army Corps of Engineers, 2005), it is essential that the pre-restoration edaphic conditions of this ecosystem are quantified so that changes in soil properties caused by future management and restoration of WCA-3 can be assessed. Mapping the spatial distribution of soil properties across WCA-3 allows for the targeting and prioritization of areas for restoration and management and will facilitate the assessment of the future effects of restoration activities. The objectives of this study were to quantify spatial distributions of soil properties across three zones of Water Conservation Area 3 (3AN, 3AS, and 3B) (Fig. 1) and to compare and contrast these distributions among the floc, 0- to 10-cm, and 10- to 20-cm layers.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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The surface hydrology of WCA-3 is controlled by a system of levees and water control structures located along the perimeter and inside the area. In 1962, WCA-3 was divided into two hydrologic units, WCA-3A and WCA-3B, by the construction of two interior levees (L-67A and L-67C) to reduce water losses due to levee seepage (Reddy et al., 1998). Interstate 75 (Alligator Alley) bisects WCA-3A in an east-west direction, further dividing WCA-3A into two zones, 3AN (the area to the north of Interstate 75) and 3AS (the area south of I-75) (Fig. 1). About 60% of the hydrologic inputs to WCA-3 are from precipitation, whereas 17% enter the area from the S-11A, S-11B, and S-11C structures (South Florida Water Management District, 1992) (Fig. 1). Water exits WCA-3 and flows into the Everglades National Park through the four S12 structures located along the Tamiami Trail. The natural hydroperiod of WCA-3 has been modified because this area is used as a shallow storage reservoir to permit regulated diversion for recharge of well fields in the eastern developed area (Walters et al., 1992). These hydrologic modifications have generally caused excessive drainage in 3AN and overflooding in 3AS (David, 1996). The hydraulic residence time of water in WCA-3 is 0.73 yr, which is about two to three times longer than residence times in WCA-1 or WCA-2 (South Florida Water Management District, 1992).
Annual P loading to the WCAs from surface water inflows during the period from 1979–1988 ranged from 100 to 350 metric tons (Mg), with an average of 270 Mg (South Florida Water Management District, 1992). More recent data indicated that P loading of the WCAs totaled 
136 Mg in 2003 and 112 Mg in 2004 (South Florida Water Management District, 2005a). In 2004, the geometric mean concentration of TP in surface water inflows to WCA-3A was 26.3 µg L–1, which was three times higher than the geometric mean for TP in the interior regions of WCA-3A of 7.6 µg L–1.
Water Conservation Area 3 is also unique in that it is the heart of the "ridge and slough" portion of the Everglades landscape (Childers et al., 2003). Historically, strands of Cladium ridges and tree islands alternate with deeper sloughs in an orientation parallel to the direction of historical water flow (Davis, 1943; Loveless, 1959). Although these communities still exist in WCA-3, alterations to the hydrology of the system and increased nutrient loading have resulted in the loss of wet prairies, aquatic sloughs, and tree islands (Sklar et al., 2001).
The majority of the soils in WCA-3 are Histosols, including Everglades peats and Loxahatchee peats (Gleason et al., 1974). Everglades peats develop on topographically higher areas and are comprised of decomposing Cladium tissue with parts of other plants. These soils are typically brown to black with minimal mineral content. Loxahatchee peats develop in topographically low areas and are composed of the remains of the roots and rhizomes of Nymphea spp. (white water lily). These soils have been classified as the Terra Ceia series (Euic, hyperthermic Typic Haplosaprists) (Soil Conservation Service, 1978). Mixed marl peats are present in the western margin of 3AS that are derived from the underlying limestone (Brown et al., 1991).
Field Sampling
A stratified-random sampling design was used to collect soil samples from the three zones of WCA-3. Strata were derived using historic ecological datalayers, such as the Normalized Difference Vegetation Index, as a proxy for vegetative communities and soil and hydrologic data. The sampling design was optimized to account for the short-, medium-, and long-range variability of attributes. The shortest distance between sampling sites was 2 m. There were three pairs of sites separated by less than 50 m, five pairs of sites separated by less than 250 m, and 36 pairs of sites separated by less than 500 m. Predetermined sampling sites were located with a global positioning system mounted to a helicopter (Garmin International, Inc., Olathe, KS). Sampling was constrained to the marsh areas and excluded tree islands. Samples were collected from 388 sites between 30 July 2003 and 16 Sept. 2003 (Fig. 1). In addition, 37 triplicate samples were collected.
An intact soil core was collected at each site by driving a 10-cm (i.d.), thin-walled, stainless steel coring tube to a depth of 20-cm beneath the soil surface. The cores were sectioned in the field into floc, 0- to 10-cm, and 10- to 20-cm layers. In the interior sections of the WCAs, the floc layer often consisted of unconsolidated living and dead periphyton material, whereas in the areas near canals and water control structures dominated by cattail, floc consists of decaying macrophyte tissue and periphyton. Twenty supplemental sites were sampled by helicopter from 11–13 Aug. 2004 using the protocol described previously.
Laboratory Analyses
The soil samples were analyzed at the Wetland Biogeochemistry Laboratory for bulk density (Db), total P (TP), total inorganic P (TPi), total carbon (TC), total nitrogen (TN), total calcium (TCa), total magnesium (TMg), total iron (TFe), and total aluminum (TAl). A subsample of wet soil was dried at 70°C for 72 h to determine dry weight and water content. The Db was determined by calculating the dry weight of the sample and dividing it by the volume of the corer. Total P was measured with a dry ashing procedure (Anderson, 1976) followed by determination with an automated colorimetric procedure (Method 365.1; U.S. Environmental Protection Agency, 1993a). Total inorganic P was measured by extracting a dried, ground soil with 1.0 M HCL, followed by vacuum filtration (Reddy et al., 1998). The TPi extracts were analyzed with the same procedure as used for the TP extracts. Total C and N were measured using a Carlo-Erba NA-1500 CNS Analyzer (Haak-Buchler Instruments, Saddlebrook, NJ). The TP ashing solutions were also analyzed for TCa, TMg, TFe, and TAl by inductively coupled argon plasma spectrometry (Method 200.7; U.S. Environmental Protection Agency, 1993b). All analyses for this study followed National Environmental Laboratory Accreditation Conference quality control and quality assurance protocols.
Choice of Units
Everglades soil data have been reported on a mass (Volk et al., 1975; Craft and Richardson, 1993b; Reddy et al., 1998; Childers et al., 2003; King et al., 2004) and on a volumetric basis (Newman et al., 1997). In this study we chose to express our data on a mass basis for a number of reasons. First, units of mass allowed us to compare our results with previous studies of soils in WCA-3 (Volk et al., 1975; Reddy et al., 1994, 1998). Second, volumetric conversions involve the multiplication of precise concentration data by more uncertain Db values (Bruland and Richardson, 2004). For example, although quality assurance and quality control (QA/QC) protocols have been developed to assess the accuracy and precision of soil chemical data, there is no QA/QC protocol for Db data. Collecting a peat core inherently disturbs the soil profile and causes errors in the calculation of Db that are difficult to quantify. Third, measured Db values were fairly homogenous across 3AN, 3AS, and 3B, indicating that soil property maps with volumetric units would have resulted in similar spatial patterns when compared with maps produced with mass units.
Geostatistical Analyses
Because our spatial sampling design included sites that were located in close proximity to each other, the majority of the cores collected could not be considered independent. Therefore, although we calculated descriptive statistics (means, SD, and ranges) for each soil property measured in each zone of WCA-3, we did not compare these mean values with t tests or ANOVA because of the presence of spatial autocorrelation. Instead, variography and ordinary kriging were used to interpolate soil properties in 3AN and 3AS where the number of observations exceeded 100 (Webster and Oliver, 2001). The geostatistical analyses were conducted separately for each hydrologic unit (3AN, 3AS, and 3B) because variograms that include data pairs crossing hydrologic and/or physical boundaries are meaningless. Soil properties were log transformed to better conform to the assumption of normality, a prerequisite for semivariance analysis and kriging. Empirical semivariance values were fitted with omnidirectional spherical and exponential semivariogram models (Webster, 1985) with the program ISATIS (Geovariances America Inc., Houston, TX). The parameters from these models were used for kriging in ISATIS. Log values for the individual soil properties were backtransformed to produce the final maps. For 3B, which had a much smaller sample size (n < 60) and a sparser sampling density, we used completely regularized spline and inverse distance weighting (IDW) functions for interpolations in ArcGIS (Environmental Systems Research Institute, Redlands, CA). The spatial resolution of interpolated maps was 100 x 100 m. Cross-validations were performed for the kriged, splined, and IDW maps by sequentially removing each sample and calculating a value for that site based on the remaining data. These predicted values were compared with the measured values by calculating the fit between predicted and measured soil properties, using the mean error of the predictions, and the G value of the respective interpolated map (Schloeder et al., 2001). The G value represents how much better or worse an interpolated map captures the spatial variability in comparison with a map that interpolates the mean value across the entire study area. A positive G value indicates that the interpolated map is an improvement on the sample mean map, whereas a negative G value indicates that the sample mean map has better correspondence to the measured soil property than the interpolated map.
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RESULTS AND DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Total P in the 0- to 10-cm layer ranged from 29 mg kg–1 in 3AS to 1169 mg kg–1, also in 3AS. By comparison, 30 yr earlier, Volk et al. (1975) reported a TP range of 354–752 mg kg–1 for WCA-3, suggesting a considerable increase in TP over this time period. Our mean values for TP in 3AN were comparable with values reported for TP (457 mg kg–1) in the Reddy et al. (1998) study, but our mean values for 3AS and 3B were considerably lower (Table 1). Another study of the southern section of 3AS reported a considerably higher mean TP value of 592 mg kg–1 (Arfstrom et al., 2000). A more recent transect study in 3AS reported a mean TP value of 362 mg kg–1 (Childers et al., 2003), which was comparable to the mean value reported for 3AS in this study. Each of the studies listed above used similar, if not identical, ashing/acid hydrolysis procedures followed by analysis for soluble reactive phosphorus with an autoanalyzer. The major methodologic differences in these studies involved the field sampling. For example, the Reddy et al. (1998) and Arfstrom et al. (2000) studies included floc in the upper 0- to 10-cm soil layer, and this may explain why their numbers for TP were higher than the numbers reported in this study. Total P in the 10- to 20-cm layer showed the least variability, ranging from 18 mg kg–1 in 3AS to 781 mg kg–1 in 3AN. Mean TP values in the 10- to 20-cm layer were relatively similar across the three zones (Table 1).
It seems that TP concentrations in the floc and 0- to 10-cm layer are increasing in 3AN when compared with 3AS and 3B. Water flow in WCA-3 is from 3AN to 3AS, and retention of P in surface soils of 3AN may help to reduce the impacts to 3AS. A similar phenomenon has been observed in parts of WCA-2A near water control structures that have received inputs from canal water draining from the EAA (DeBusk et al., 1994; Qualls and Richardson, 1995; Reddy et al., 1998). However, on average, P enrichment in WCA-3AN does not seem to be as intensive as the P enrichment in WCA-2A.
Total N in the floc ranged from 9 g kg–1 in 3B to 46 g kg–1 in 3B. Total C ranged from 176 g kg–1 in 3AN to 505 g kg–1 in 3B. Mean floc TN and TC were highest in 3AS (Table 1). Total N in the 0- to 10-cm layer ranged from 2 g kg–1 in 3AS to 44 g kg–1 in 3B, whereas TC ranged from 20 g kg–1 in 3AS to 508 g kg–1 in 3AN. Mean TN and TC were fairly comparable across the three zones. Mean TN and TC values for the 0- to 10-cm layer in our study for 3AS and 3B were comparable with values from the Reddy et al. (1998) study (412 and 29 g kg–1), whereas our mean values for TN and TC in 3AN were considerably lower than those of the previous study. Total N in the 10- to 20-cm layer ranged from 1 g kg–1 in 3AS and 3AN to 44 g kg–1 in 3AS. Total C ranged from 4 g kg–1 in 3AS to 544 g kg–1 in 3AN. Mean TC was highest in 3B, whereas mean TN was comparable in 3AS and 3B and lower in 3AN.
Mean floc TCa in 3B was more than double that of 3AN and 3AS, and mean floc TMg in 3AN and 3B was nearly double that of 3AS (Table 1). In the 0- to 10-cm layer, mean TCa in 3B was nearly double that of 3AN and greater than double that of 3AS. The Reddy et al. (1994) study reported a mean for TCa of 42.3 g kg–1, which was higher than the means reported in this study for 3AN and 3AS but lower than the mean reported for 3B. Mean TMg was highest in 3AN and 3B. The Reddy et al. (1994) study reported a mean for TMg of 1.63 g kg–1, which was higher than the mean values reported in this study for 3AS but lower than the values reported for 3AN and 3B (Table 1). Mean TCa in the 10- to 20-cm layer of 3B was about double that of 3AN and 3AS. The extremely high values reported for TCa in this study, in comparison with the Reddy et al. (1994) study, may be due to the fact that this study extracted cations and metals with 6 M HCl, whereas the Reddy et al. (1994) study used 1 M HCl. Alternatively, the surface soils may have been mixed with the subsurface limestone material during sampling, causing enrichment in TCa and TMg levels. Mean TMg in 3AN and 3B were more than 1.5 times that of 3AS. Total Fe and Al in the floc were fairly consistent across the three zones. Although mean values of TFe in the 0- to 10-cm layer were comparable across the three zones, TAl in 3AN was considerably higher in 3AN than in 3AS and 3B. In the 10- to 20-cm layer, TFe was comparable across the three zones, whereas TAl was highest in 3AN and lowest in 3B.
Point Maps of the Spatial Distribution of Soil Properties in the Floc Layer
Floc was present at 34 out of the 147 sites sampled in 3AN (23%), at 75 out of 187 sites in 3AS (40%), and at 39 out of 54 sites in 3B (73%). Because of the small sample size, floc mapping results are shown in Fig. 2
as point maps instead of kriged continuous maps. The distribution of floc is affected by a number of factors, including the vegetative community, hydroperiod, and nutrient loading. Overdrained hydrologic conditions (Walters et al., 1992; David, 1996; Newman et al., 1996), the prevalence of Typha and mixed Typha/Cladium marsh vegetative communities that shade the water surface (Grimshaw et al., 1997), and higher nutrient loading in 3AN compared with the extended hydroperiods (Childers et al., 2003), prevalence of slough communities, and lower nutrient loading in 3AS and 3B may explain the differences in the floc distributions observed across WCA-3. The total absence of floc from a large section of the western portion of 3AN may be explained by a number of factors, including a fire in 1999 that burned 40% of 3A (Stober et al., 2001), high nutrient inputs from the Miami Canal, or the overdrained hydrologic conditions (Walters et al., 1992; David, 1996; Newman et al., 1996). For example, in 3AN from 1972 to 1984, frequencies of inundation ranged from 32 to 61% of the year, and mean water depths ranged from 10 to 18 cm, compared with frequencies of inundation in 3AS that were greater than 96% of the yearand mean water depths that were greater than 60 cm (David, 1996). Floc depth was also highly variable across the three zones, ranging from 0 to 21 cm (Fig. 2b). The deepest floc was generally located in the southeastern section of 3AS, the part of WCA-3 that has been least affected by nutrient loading and also has the longest hydroperiod.
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Interpolated Maps for the 0- to 10-cm Layer
Mapping the distribution of soils across the 0- to 10-cm layer of WCA-3 is important because this layer, assuming an average peat accumulation rate for WCA-3 of 2.5 mm yr–1 (Craft and Richardson, 1993b), represents the accumulation of sediment and nutrients from the last 40 yr. We chose to include interpolated maps for four selected soil properties (Db, TP, TN, and TC). These properties are important to the ecologic functioning of WCA-3, and maps of each were sufficiently unique to avoid redundancies. Maps for the 0- to 10-cm and 10- to 20-cm layers were presented on the same scale to facilitate comparisons between the two layers.
Spherical semivariogram models were fitted to the experimental semivariance data (Fig. 3 ). For all of the mapped soil properties, nugget values were greater than 0, indicating that there were fine-scale discontinuities in soil properties of WCA-3. This could be caused by a number of factors, including (i) differences in microtopography (sampling from a ridge and then collecting another adjacent core from a slough); (ii) other small scale patterns of variability that were not captured by our sampling design, such as variations in hydrologic flow paths; (iii) errors in the accuracy of sample locations recorded with the global positioning system unit; and (iv) laboratory measurement error. Nugget values for the 0- to 10-cm layer were generally high, indicating uncertainty in the prediction of fine-scale variability of the measured soil properties. Sill values, representing overall sample variability, were greater in 3AN than in 3AS for each of the four mapped soil properties (Table 2). This indicated that the soil properties from the 0- to 10-cm layer were more heterogeneous in 3AN than in 3AS. The increased heterogeneity of soil properties in 3AN may be due to a number of factors, including (i) changes in hydrology resulting from the construction and operation of the canal and levee system (Walters et al., 1992; David, 1996), (ii) inputs of surface water to 3AN with elevated P concentrations, and (iii) recent fires in parts of 3A that have altered the physical and chemical soil properties of localized areas (Volk et al., 1975; Stober et al., 2001). It has also been noted that sections of 3AS that have been subjected to wetter conditions and more stabilized water tables have shown a loss of vegetative diversity (Department of the Interior, 2005). This homogenization of the plant community may also contribute to homogenization of soil properties in 3AS.
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Cross-validation indicated that correlations between predicted and measured soil properties ranged from a low of 0.14 for TP in 3B to a high of 0.78 for TC in 3AN (Table 2). Correlations were generally highest for 3AN and lowest for 3B. Mean errors were negative for most soil properties in the three zones, indicating that on average, we slightly underpredicted Db, TP, TN, and TC. G values for 10 of the 12 properties mapped for the 0- to 10-cm layer were positive, indicating that our interpolated maps represented a significant improvement from an interpolation of the sample mean across the different zones of WCA-3 (Schloeder et al., 2001). G values were generally highest for 3AN and lowest in 3B.
The interpolated map for Db in 3AN, 3AS, and 3B indicated that the highest values were located in the western section of 3AN (Fig. 3a). This pattern was also observed in previous studies of soil properties in WCA-3 (Reddy et al., 1994, 1998). The higher Db values in western 3AN may be explained by the excessive drainage and subsequent peat oxidation in 3AN (Walters et al., 1992; David, 1996) and the 1999 fire (Stober et al., 2001). The northeastern section of 3AN that is part of the Miccosukee and Seminole Indian Reservations is subject to vehicle and airboat traffic, which also causes compaction of surface soils and leads to higher Db values. Unlike the previous study (Reddy et al., 1994), interpolated maps from this study indicated that a Db hot spot (predicted Db values from 0.40 to 0.72 g cm–3) had developed in the southwestern corner of 3AN and the northwestern corner of 3AS near the point where the L-28 canal drains into 3AS. This area seems to be an impacted zone that has experienced considerable compaction or loss of surface soil relative to the rest of WCA-3. In contrast to 3AN, maps of Db in 3AS and 3B were quite homogeneous, with interpolated values that ranged from 0.01 to 0.16 g cm–3.
The interpolated TP maps showed a much different and more variable spatial pattern than that of Db (Fig. 3b). Total P was highest in 3AN and in areas of all three zones adjacent to the Miami Canal. An analysis of South Florida Water Management District water quality data from 1995 to 2004 (South Florida Water Management District, 2005b) indicated that mean TP concentrations at the S8 water control structure (Fig. 1) of the Miami Canal were higher (0.06 mg P L–1) than any of the other major surface water inputs to this system (S11A = 0.03 mg P L–1, S11B = 0.03 mg P L–1, S11C = 0.04 mg P L–1, and S140 = 0.04 mg P L–1). Hydrologic modifications to the Everglades landscape have created focal points in the landscape that receive water and nutrient inputs (Sklar et al., 2001). One of these focal points occurs in WCA-3, where the Miami Canal enters into 3AS. In this 234-ha area, TP values ranged from 640 to 720 mg kg–1. This is considerably higher than the values in the 400 to 580 mg kg–1 range that generally occur in the remainder of 3AS. Another such hotspot exists in the northwestern section of 3AS that has received inputs from the L-28 canal. This 0.1-ha area was also a hotspot for Db, indicating that it is experiencing compaction and nutrient loading. The Reddy et al. (1994) study also reported the existence of TP hotspots in these two locations. However, the maximum TP values in the hotspots from 1992 were in the 450 to 550 mg kg–1 range, rather than the 620 to 720 mg kg–1 range reported in this study.
Historical background TP concentrations in WCA-2A have been estimated to be less than 500 mg kg–1 (DeBusk et al., 2001). Adopting this estimate for WCA-3, according to our interpolated map, 25% (179 ha) of 3AN currently has elevated TP levels. By comparison,only 4.7% (60 ha) of 3AS and 6.0% (24 ha) of 3B showed elevated TP levels. DeBusk et al. (2001) calculated that for soil cores collected in 1998, 73%, or 31777 ha, of WCA-2A had elevated soil TP levels. This indicated that nutrient loading to WCA-3 has been much less than that to WCA-2A.
For the most part, the mapped distributions of TN and TC in WCA-3 showed similar patterns (Fig. 3c and 3d). Total N and TC concentrations were lowest in western 3AN and in northwestern 3AS. Historically, this area of WCA-3 may have had shallower and more mineral-based soils that the rest of WCA-3 (Jones, 1948; Stober et al., 2001). In addition, much as this area burned in the 1999 fire (Stober et al., 2001) and may also have burned various times in the 1970s (Volk et al., 1975). The overdrained hydrologic conditions in 3AN (Walters et al., 1992; David, 1996; Newman et al., 1996) may have also contributed to the decreased TN and TC concentrations that were observed across 3AN due to peat oxidation from decomposition rather than fire. In contrast, a recent study that sampled a 5-km transect in the overflooded area of 3AS reported that this area "had not burned in many years" (Doren et al., 1997). Interpolated TN values and especially TC values in these central and southern sections of 3AS were higher and more homogeneous. The lack of variation in TC in 3AS may also be caused by the loss of diversity in the vegetation of this zone due to the stabilization of water depths caused by the Tamiami Canal (David, 1996; U.S. Department of the Interior, 2005).
Interpolated Maps for the 10- to 20-cm Layer
The 10- to 20-cm layer represents the progressive long-term accumulation of nutrients from about 40–80 yr ago (Craft and Richardson, 1993b; Reddy et al., 1994). Similar to the 0- to 10-cm layer, nugget values were generally high, indicating uncertainty in the prediction of the fine-scale variability of the measured soil properties (Table 3). Sill values for the 10- to 20-cm layer were also greater in 3AN than in 3AS for each of the four mapped soil properties (Db, TP, TN, and TC) (Table 3), indicating that soil properties from the 10- to 20-cm layer showed the highest heterogeneity in 3AN. This heterogeneity may be a result of hydrologic modifications to the Everglades that began as early as the 1880s (Noe et al., 2001). The range values for the 10- to 20-cm layer were comparable with those of the 0- to 10-cm layer and were greater in 3AN than in 3AS for all mapped properties.
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The interpolated map for Db in the 10- to 20-cm layer of 3AN, 3AS, and 3B was similar to that of the 0- to 10-cm layer. The highest Db values in the 10- to 20-cm layer were located in the western section of 3AN (Fig. 4a ). Bulk density in the 10- to 20-cm layer of 3AS and 3B was also very homogeneous across the central sections of 3AS and 3B.
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Spatial Sampling Implications
In this study, we collected soil cores from 388 sites within WCA-3. Our sampling density averaged 0.16 samples km–2. We had a slightly higher density in 3AN (0.20) and lower densities in 3AS (0.15) and 3B (0.14). As a result of the higher density in 3AN, our maps had better cross-validation fit statistics than maps of 3AS and 3B. Our stratified random sampling design was optimized to capture short-, medium-, and long-range variability in soil properties. Such a design allowed us to use geostatistical tools, such as variography and kriging, to map the distribution of soil properties across 3AN and 3AS. This is the preferred method for interpolation in this system because it produced better fit statistics and more reliable maps. Had we restricted our observations to a limited number of sites (e.g., along a transect or to a coarse-scale uniform grid), we would not have been able to capture the complex multi-scale edaphic signature of the landscape (Grunwald et al., 2006b). Future mapping efforts should also include even greater sampling of fine-scale variability (sites separated by short distances) because we observed high uncertainty at this scale.
Restoration Implications
The approach taken in this study to assess the pre-restoration status of soil properties in WCA-3 is of interest and use to others involved in landscape-scale wetland restoration projects in the Chesapeake Bay, Mississippi River Basin, and Mesopotamian wetlands of Iraq for several reasons. First, the spatially explicit sampling of multiple scales of variability can elucidate response patterns that are undetected by traditional or nonspatial sampling designs. It has been argued that ecological problems often require the upscaling of fine-scale measurements for the analysis of coarse-scale phenomena (Turner et al., 1989). A holistic view of the landscape is required to understand the spatial patterns and processes creating those patterns at multiple scales (Grunwald et al., 2006b). Second, results from this sampling will be important for evaluating the effects of restoration activities (such as canal removal) on ecosystem structure and function of WCA-3. Data from the 2003–2004 sampling can be compared with data collected from future samplings to determine if soil properties are responding to hydrologic modifications and to determine the temporal and spatial dynamics of these changes.
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CONCLUSIONS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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25% of 3AN had TP concentrations that were greater than 500 mg kg–1, indicating elevated nutrient conditions. By comparison, less than 5% of 3AS and 6% of 3B showed elevated TP levels. None of the 10- to 20-cm layer had interpolated TP concentrations greater than 500 mg kg–1. The most impacted areas of WCA-3 seemed to be in western and northern 3AN, which had high Db and TP, and low TN and TC. The least impacted areas of WCA-3 were the eastern and southern sections of 3AS, which had low and homogenous nutrient distributions. This indicated that despite hydrologic alterations and changes in nutrient loading to WCA-3, the soils in parts of 3AS remain relatively unimpacted. The spatially explicit sampling approach taken in this study in combination with the geostatistical mapping effort can serve as a model for the assessment of soil properties and guide restoration efforts in the Everglades and other landscape-scale wetland restoration projects. |
ACKNOWLEDGMENTS |
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Received for publication April 27, 2005.
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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M. J. Cohen, S. Lamsal, T. Z. Osborne, J. C. J. Bonzongo, S. Newman, and K. R. Reddy Soil Total Mercury Concentrations across the Greater Everglades Soil Sci. Soc. Am. J., March 1, 2009; 73(2): 675 - 685. [Abstract] [Full Text] [PDF]
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S. Grunwald and K. R. Reddy Spatial Behavior of Phosphorus and Nitrogen in a Subtropical Wetland Soil Sci. Soc. Am. J., June 18, 2008; 72(4): 1174 - 1183. [Abstract] [Full Text] [PDF]
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