Modeling of the spatial variability of biogeochemical soil properties in ...

e c o l o g i c a l m o d e l l i n g 2 0 1 ( 2 0 0 7 ) 521–535

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Modeling of the spatial variability of biogeochemical soil properties in a freshwater ecosystem夽 S. Grunwald a,∗ , K.R. Reddy a , J.P. Prenger a , M.M. Fisher b a

Soil and Water Science Department, University of Florida, 2169 McCarty Hall, PO Box 110290, Gainesville, FL 32611-0290, United States b St. Johns Water Management District, Palatka, FL, United States

a r t i c l e

i n f o

a b s t r a c t

Article history:

Ecosystem services are dependent on the geospatial composition, structure, and function of

Received 29 June 2005

an ecosystem. Our goal was to gain a better understanding of the variability of biogeochem-

Received in revised form

ical soil properties along gradients of impacted and unimpacted zones within a subtropical

9 October 2006

wetland in Florida. Our objectives were to (i) characterize the spatial variability and distri-

Accepted 26 October 2006

bution of soil total phosphorus (TP), (ii) identify the magnitude and scale at which multiple

Published on line 4 January 2007

biogeochemical soil properties account for variability within the ecosystem, and (iii) map the distribution of this variability. We collected soil samples (0–10 cm) at 266 sites within

Keywords:

the Blue Cypress Marsh Conservation Area (4900 ha) in Florida that were analyzed for 18

Spatial patterns

different biogeochemical properties. Conditional sequential Gaussian simulation and prin-

Spatial variability

cipal component analysis was used to identify three major groups of behavior: (i) labile, fast

Wetland

response properties with fine-scale spatial autocorrelation; (ii) stable, slow response prop-

Soil phosphorus

erties with regional spatial autocorrelation; (iii) properties showing intermediate response.

Stochastic simulation

The uncertainty of the spatial variability measures was described using small and large

Principal component analysis

realizations as well as standard deviation maps. The first principal component (PC) [group (i)] contributed with 33.91%, the second PC [group (ii)] with 15.93%, and the third PC [group (iii)] with 11.32% to the total variance. Properties that explain much of the underlying variability in a wetland are expected to be more sensitive to change than others that show more homogeneous patterns. More research is needed to reveal geospatial interrelationships of biogeochemical properties and their underlying spatial structure in aquatic ecosystems. © 2006 Elsevier B.V. All rights reserved.

1.

Introduction

Anthropogenic activities has led to rapid alterations in the composition, structure and function of ecosystems (Vitousek et al., 1997) so that in many cases their capacity to provide necessary services has been either overwhelmed or eroded (Palmer et al., 2004). Ecosystem services are the set of ecosystem functions that are useful to humans. Many of these are

夽

critical to our survival (e.g. climate regulation, filtering of pollutants) while others enhance it (e.g. aesthetics) (Kremen, 2005). Management of ecosystem services is complex and requires assessing the key environmental factors and their spatio-temporal scale over which they operate among others (Kremen, 2005). Wetland ecosystems are often endpoints of transport flow paths accumulating matter and nutrients from upland areas. While many site-specific wetland studies have

This research was supported by the Florida Agricultural Experiment Station and approved for publication as Journal Series No. R-09967 (Institute of Food and Agricultural Sciences, University of Florida). ∗ Corresponding author. Tel.: +1 352 392 1951x204; fax: +1 352 392 3902. E-mail address: [email protected]fl.edu (S. Grunwald). 0304-3800/$ – see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolmodel.2006.10.026

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provided valuable insight into biogeochemical cycling (Reddy et al., 1998; White and Reddy, 2000; Fisher and Reddy, 2001; Craft and Chiang, 2002) there is a need for spatially explicit investigations rooted in inductive science. To gain understanding of the variability of biogeochemical properties within a wetland, the spatial scale of variation and interrelationships between properties it is essential to adopt a geospatial, holistic modeling framework. In Florida, human activities such as drainage, agriculture, and recreation (e.g. airboats) have impacted numerous wetlands which altered soil biogeochemical patterns, biodiversity, ecological structure, and stability of naturally oligotrophic wetland systems. Numerous studies documented the enrichment of phosphorus (P) in subtropical wetlands in Florida. Reddy et al. (1999) measured a maximum of 1608 mg kg−1 total soil phosphorus (TP) in a conservation wetland in south Florida. Grunwald et al. (2004) reported a mean topsoil TP of 660 mg kg−1 and maximum of 2100 mg kg−1 in 1990 and mean of 860 mg kg−1 and maximum of 3676 mg kg−1 in Water Conservation Area 2A (WCA 2A), Everglades, Florida. According to Newman et al. (1998), the average TP stored in the surface soil (0–10 cm depth) of nutrient-enriched soils in Holey Land and Rotenberger wetlands in south Florida were 7 and 13 g P m−2 , respectively. In contrast, pristine soils of the Everglades National Park showed TP storage of only 4 g P m−2 (Newman et al., 1998). In naturally, oligotrophic wetland ecosystems, very small changes in water and soil nutrient concentrations may result in dramatic shifts in vegetation and species composition. Soil TP has been identified as one of the most important variables to represent long-term impact of a wetland ecosystem (Reddy et al., 1998). Though land cover represents the most rapidly changing variable in wetlands, with plant turnover times on the order of 5–10 years, the resilience of freshwater marshes is mainly related to the soil nutrient content. According to Gunderson (2000) soil P concentrations are the slowest of the ecosystem variables to respond with turnover times estimated on the order of centuries. Water column nutrients are in direct contact with the microbial communities associated with periphyton mats and plant detritus in the water column, and changes in composition and activities of these communities and materials may provide an indication of recent (10–15 years in impacted and >50–100 years in unimpacted wetlands. Since the spatial and temporal variability of water, plant and detritus P is high, we decided to focus on the topsoil layer (0–10 cm depth) in this study to characterize the long-term impact of nutrient enrichment in a subtropical Florida wetland. To characterize spatial variability, patterns, and uncertainty of soil biogeochemical properties and infer on the environmental status of a wetland, spatially explicit model-

ing is required. Spatially explicit models rooted in regionalized variable theory distinguish between: (i) deterministic variation which can be expressed with a trend model; (ii) a stochastic, locally varying but spatially dependent residual from (i); (iii) a residual or noise term (Goovaerts, 1997). Geostatistical techniques such as kriging and cokriging have been given special attention because they incorporate the spatial autocorrelation concept explicitly in the modeling process and facilitate to assess the prediction quality (uncertainty) (Goovaerts, 1997; Webster and Oliver, 2001). Spatial autocorrelation is defined as the statistical concepts expressing the degree to which the value of an attribute at spatially adjacent points co-varies with the distance separating the points. Kriging estimators are exact interpolators that provide best local estimates of the variable by minimizing the estimation variance. They are low-pass filtering techniques that tend to smooth out local spatial variation and effectively ignore global statistics (Deutsch and Journel, 1998). Hence, kriging can effectively blur variability and spatial patterns that are potentially important when assessing environmental impact (e.g. nutrient enrichment). Spatial stochastic simulations provide an alternative concept focusing on the uncertainty of predictions. The set of multiple realizations generated by stochastic simulation algorithms are useful to assess the uncertainty of predictions, and the propagation of errors through GIS-based models or water quality simulation models (Heuvelink, 1998). Unlike kriging, stochastic simulation does not aim at minimizing a local error variance but focuses on the reproduction of statistics such as the sample histogram and the semivariogram model in addition to honoring data values. The output results, i.e., a set of alternative realizations, provide a visual and quantitative measure of the spatial uncertainty (Goovaerts, 1997; Deutsch and Journel, 1998). Thus, spatial stochastic simulation is increasingly preferred to kriging for applications where the spatial variation of the measured field must be preserved (Srivastava, 1996). A detailed discussion of spatial stochastic simulation methods is given by Goovaerts (1997), Deutsch ` and Delfiner (1999), and Lantuejoul ´ and Journel (1998), Chiles (2002). Our goal was to gain a better understanding of the variability of biogeochemical soil properties along gradients of impacted and unimpacted zones within a subtropical wetland in Florida. Our objectives were to (i) characterize the spatial variability and distribution of soil TP, (ii) identify the magnitude and scale at which multiple biogeochemical soil properties account for variability within the ecosystem, and (iii) map the distribution of this variability.

2.

Methodology

2.1.

Study area

The study area is comprised of about 4900 ha of freshwater marsh within the Blue Cypress Marsh Conservation Area (BCMCA), located in the headwater region of the St. Johns River in east-central Florida (Fig. 1). Water management aimed to control floods and drain excessive areas of the upper St. Johns River marshlands for agricultural production and private development. Approximately 65% of the headwater


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Fig. 1 – Geographic location of the Blue Cypress Marsh Conservation Area and soil sampling locations.

floodplain has been drained for cattle, citrus, and row crop usage with concomitant declines in populations of game fish, wading birds, waterfowl, and water quality in the remaining floodplain. Over the past several decades, a significant loss of historical floodplain marsh in the upper St. Johns River Basin resulted in major flooding and water quality problems. Since the late 1990s there have been major efforts by the St. Johns River Water Management District to restore the natural hydrologic cycles and reduce nutrient inputs to wetlands in the upper St. Johns River Basin. The BCMCA has inflows from Fort Drum Marsh Conservation Area to the south, and two sub-basin watershed tributaries from the west. The nutrient impact from agricultural activities ceased in the mid 1990s and the wetland is currently in a state of natural succession. The BCMCA includes areas impacted by nutrient enrichment as well as unimpacted zones. Native vegetation in the study area is predominately a mosaic of sawgrass (Cladium jamaicense) and maidencane flats (Panicum hemitomom); but it also contains significant areas of scrub-shrub vegetation (e.g. the coastal plain willow Salix caroliniana), cattail marshes (Typha ssp.), and deepwater slough communities (e.g. Nymphaea spp.). Drying of the marsh permitted the expansion of woody vegetation (e.g. S. caroliniana) into areas previously occupied by herbaceous, wetland marsh plants. Since the early 1970s S. caroliniana has expanded from the southern to the northern part of the study area (Kinser et al., 1997). Soils in the study area are Histosols.

2.2.

Dataset

We collected soil samples (0–10 cm depth) at 266 sites within the BCMCA in March–April 2002 using soil cores with diameter

of 10 cm (Fig. 1). In the southern part of the study area some sites were not accessible due to dense scrub-shrub vegetation. A complete list of measured biogeochemical soil properties is given in Table 1. Soil samples were extracted using the chemical fractionation scheme described by Ivanoff et al. (1998). Operationally defined labile inorganic and organic P forms were extracted with NaHCO3 (pH 8.5). Alkaline extracts were analyzed for soluble reactive P and TP. Labile organic P was determined as the difference between TP and inorganic P in the extracts. Microbial biomass P was determined using the chloroform fumigation techniques. Microbial biomass P was not corrected for extraction efficiency, as earlier studies have shown that the use of an efficiency factor overestimates the organic P pool in organic soils (Chua, 2000). Total P was determined using finely ground soil combusted for 4 h at 550 ◦ C and 6 M HCl digestion (Andersen, 1976). Digested samples were analyzed for orthophosphate by an automatic ascorbic acid method (Method 365.4, USEPA, 1993). Phosphorus content in all solutions including those extracted and digested was analyzed using a colorimetric analysis (Method 365.1; USEPA, 1993). A subsample of wet soil was dried at 70 ◦ C for 72 h to determine dry weight and water content. Loss on ignition (LOI) was calculated after the completion of the TP analysis to estimate the organic matter content within the soil samples. The bulk density (BD) was determined by calculating the dry weight of the sample and dividing it by the volume of the corer. Total carbon (TC) and nitrogen (TN) were measured using a Carlo-Erba NA-1500 CNS Analyzer (Haak-Buchler Instruments, Saddlebrook, NJ, USA). Total labile organic carbon (TLOC) was derived using 0.5 M K2 SO4 extractable TOC of the fumigated wet sample and total labile organic nitrogen (TLON) was derived using 0.5 M K2 SO4 extractable TKN of the fumigated wet sample.

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Table 1 – Description of biogeochemical soil properties Code

Description

APA Ash BicTP BD BG PI PO CN CTC NH4 N TLON TLOC MBP Moist PEP TC TN TP

Alkaline phosphatase activity Ash content Total labile phosphorus (calculated value: PI + PO + MBP) Bulk density Beta-glucosidase activity Labile inorganic phosphorus (bicarbonate extractable inorganic phosphorus) Labile organic phosphorus (bicarbonate extractable organic phosphorus) Carbon:nitrogen ratio CTC formazan Extractable ammonium-nitrogen Total labile organic nitrogen Total labile organic carbon Microbial biomass phosphorus Soil moisture in wt.% Peptidase activity Total carbon Total nitrogen Total phosphorus

2.3.

Units

Step 2. The sample cumulative frequency of the datum z(u˛ ) with rank k is then computed as

Analyses

We combined spatial stochastic simulation and principal component analysis (PCA) to identify soil biogeochemical properties which account for much of the overall variation within the wetland and characterize their spatial variability, patterns and uncertainty. We used conditional sequential Gaussian simulation (CSGS), a spatial stochastic simulation method, to generate realizations of soil properties to describe their spatial patterns and uncertainties. Conditional sequential Gaussian simulation generates conditional cumulative distribution functions (ccdf) for each site (or pixel, i.e., a small rectangular model unit) to capture 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 is characterized by the smallest and largest realizations. We used CSGS for the generation of partial realizations using normal random functions. A detailed description of ` and Delfiner (1999) and Chiles ` and CSGS can be found in Chiles Allard (2006). The sequential Gaussian procedure entailed to:

(1) Employ a normal score transformation of the sample data. The original z-data (observations) are transformed into y-values with a standard normal histogram. Such a transform is referred to as a normal score transform, and the y-values y(u˛ ) = (z(u˛ )) with u˛ representing the location are called normal scores. The theory of normal score transformation is given by Goovaerts (1997, pp. 267–268). The normal score transform proceeds in three steps: Step 1. The n original data z(u˛ ) are first ranked in ascending order: [z(u˛ )]

(1)

≤ · · · ≤ [z(u˛ )]

␮g MUF g−1 h−1 % mg kg−1 g cm−3 ␮g MUF g−1 h−1 mg kg−1 mg kg−1 – mg kg−1 mg kg−1 mg kg−1 mg kg−1 mg kg−1 % ␮g MUF g−1 h−1 g kg−1 mg kg−1 mg kg−1

(k)

≤ · · · ≤ [z(u˛ )]

(n)

where the superscript k is the rank of datum z(u˛ ) among all n data. Two or more data values may be identical, e.g., zero-valued data corresponding to concentration below detection limit.

p∗k =

k 0.5 − n n

if all data receive the same weight 1/n; that is, if the sample histogram is deemed representative of the study area: p∗k =

k

wi − 0.5wk ∈ [0, 1]

i=1

if declustering weights wi = 1/n are applied to data. Step 3. The normal score transform of the z-datum with rank k is matched to the p∗k -quantile of the standard normal cdf: y(u˛ ) = G−1 [F∗ (z(u˛ ))] = G−1 (p∗k )

(2) (3)

(4) (5) (6)

The probability of being smaller than the minimum z-datum and the probability of being larger than the maximum z-datum are non-zero: p∗k = 0 and (1 − p∗k ) = 0. Thus, the transform never yields infinite normal scores. Other details of the normal score transform are given in Deutsch and Journel (1998). Construct a semivariogram of the normal score data to identify the spatial structure of the data. Use ordinary kriging, randomly over the simulation space and at each simulation node, using neighboring sample data, the semivariogram model, and a specified number of previously simulated nodes. Draw randomly from the conditional Gaussian distribution and assign that value to the simulation node. Perform the above simulation until all nodes are given a simulated value. Repeat 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. One hundred realizations at a pixel resolution of 100 m were generated using CSGS. We used a technique suggested by Journel (1983) to construct expectedvalue 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 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 the quality of realizations. Multivariate analysis methods have been frequently applied in environmental applications to characterize and map interrelationships that occur in large multi-dimensional datasets. Principal component analysis is a method for reducing the number of variables in a dataset with minimal information loss (Wackernagel, 2003). In PCA, all the variables in a dataset can be examined simultaneously, as the data matrix consisting of n variables can be considered to exist in n-dimensional space. A set of interrelated variables is mathematically transformed into a new coordinate system in which the axes (eigenvectors) are liner combinations of the original variables. The newly transformed variables account for the same information or variance as the original variables. We used PCA to transform a number of 18 correlated biogeochemical variables into a smaller number of uncorrelated variables called principal components (PCs). The PCs are ordered according to the amount of the variability in the data they account for. The first PC accounts for as much of the variability in the data as possible, and each succeeding component accounts 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). The PCA was performed on the correlation matrix as suggested by Wackernagel (2003). A principal component can be also considered as a regionalized variable (RV) and the scales of variation for each RV can

525

be identified by estimating semivariograms (Pardo-Iguzquiza and Dowd, 2002). Semivariograms are characterized by three components: (i) the nugget at distance h = 0, represents a composite of the portion of fine-scale spatial variability that has not been sampled, uncertainty introduced by the field and experimental approaches and random variability; (ii) the partial sill variance represents the portion of the total semivariance that comprises spatial autocorrelation; (iii) the range establishes the outer limit at which points in space still interact spatially (Webster and Oliver, 2001). A model was fitted to the experimental semivariogram of each PC and nugget, sill variance, and range values were derived. Conditional sequential Gaussian simulation was used to generate realizations of PCs. This technique is similar to PCA kriging described by Goovaerts (1997) and demonstrated by Panahi et al. (2004). However, we modified the approach to mix a PCA with a stochastic simulation method. The emerging spatial patterns of PCs were subdivided into short-, intermediate, and long-range components. Realizations of PCs were averaged and smallest and largest realizations described the uncertainty of output.

3.

Results and discussion

The semivariogram of observed TP was modeled with two basic structures—a nugget of 0.1535 and a spherical model with a partial sill of 1.327 and range of 7240 m (Fig. 2). The nugget to sill ratio was 10.4% indicating that TP showed strong spatial dependence. According to Wang et al. (2001) if the ratio is less than 25% the variable has strong dependence; between 25 and 75% the variable has moderate dependence; greater than 75% the variable only shows weak spatial dependence. The long-range of 7240 m indicated that regional spatial autocorrelation prevailed. This might be explained by slow movement of TP in soils and slow P-cycling at the soil–water interface that is less sensitive to change the total element content in soils. Other studies found similar regional spatial

Fig. 2 – (a) Experimental (red) and fitted (green) semivariogram models of normalized observed TP; (b) experimental (red) and fitted (green) semivariogram models of normalized TP values derived from mean realizations.

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Fig. 3 – Histograms of TP observations and realizations.

autocorrelation for soil TP extending over 11,300 m in Water Conservation Area 2A in the Everglades (DeBusk et al., 2001), 7270 m in Water Conservation Area 3A South and 13,750 m in Water Conservation Area 3A North in the Everglades (Bruland et al., 2006), and 7200 m in a subtropical wetland in eastcentral Florida (Grunwald et al., 2006). Conditional sequential Gaussian simulation was used to generate 100 TP realizations. Histograms of mean TP realizations matched closely the histogram of TP observations with values ranging from 350 to 1014 mg kg−1 (Fig. 3). In a similar subtropical wetland, TP values exceeding 650 mg kg−1 causing vegetation shifts from C. jamaicense Crantz to Typha ssp. were considered elevated (Wu et al., 1997). The quality

of realizations was assessed by comparing the semivariogram of TP mean realizations to the semivariogram of observed TP values that matched closely (Fig. 2). Overall the TP realizations reproduced the spatial variability identified from TP observations indicating the robustness of our model. Ten out of the one hundred generated TP realizations are shown in Fig. 4 (not all 100 maps are shown). 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

Fig. 4 – Ten out of one hundred realizations of TP generated using CSGS.


on 266 TP observations summarized in form of a TP standard deviation map (Fig. 4). The standard deviation for TP was Null at the observation sites because a conditional simulation routine was used, 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. The smallest, mean of 100 realizations, and largest TP maps are shown in Fig. 5. The smallest and largest realizations can be interpreted as “best” or “worse” case scenarios of TP predictions rendering the uncertainty of TP predictions. A crescent shape area in the northern part showed TP as low as 340 mg kg−1 which resembled natural TP conditions. Maximum TP values of 1014 mg kg−1 were generated. The cause for high TP values can be attributed to previous P input into the wetland from adjacent agricultural land uses. The nutrientenriched soils in the southern part might have been boosted the expansion of S. caroliniana vegetation which prefers wet mesic soils. Total P observations showed a mean of 619, standard error of mean (S.E.) of 7.88, median of 601, standard deviation (S.D.) of 129, minimum of 350, and maximum of 1014 mg kg−1 (Table 2). Total phosphorus realizations closely resembled those statistical parameters with a mean of 632, S.E. of 1.51, median of 624, S.D. of 107, minimum of 353, and maximum of 1014 mg kg−1 . Validation analysis of TP resulted in a root mean square error of 96 mg kg−1 . The soil properties TP, TN and TC characterized total elemental contents in soils due to P, N and C-cycling. Bulk density characterized the physical status in the topsoil, here, mainly

527

responding to fire and disturbances. Other soil properties, such as BicTP, PI, PO, NH4 N indicated the reaction kinetics with extraction media responding to the manner in which form (labile or stable) the constituent (N, P or C) is present in the soil or sediment (Newman and Robinson, 1999). The standard deviations of labile soil components was relatively high with 63 (BicTP), 17 (PI), and 12 mg kg−1 (PO) confirming their dynamic, labile behavior. Microbial biomass P was suggested by Reddy et al. (1999) as a key component sensitive to nutrient inflow and energy flow. MBP observations ranged from 2.2 to 395 mg kg−1 with a mean of 151 mg kg−1 with a concentration of high value in the western part of the study area, possibly due to previous nutrient inflows from adjacent agricultural land uses. Other properties expected to respond sensitive to nutrient loading included extracellular enzyme activities represented by APA, BG, and PEP (Reddy et al., 1999). Alkaline phosphatase showed a mean of 342 and maximum of 825 ␮g MUF g−1 h−1 that were lower than APA found in a comparable subtropical wetland in south Florida in WCA 2A with 1277 (nutrient-enriched) and 3016 (“natural” zone) (Reddy et al., 1999). Beta-glucosidase activity was observed with a mean of 133 and maximum of 297 ␮g MUF g−1 h−1 that contrasts BG found in WCA 2A with 2055 (nutrient-enriched) and 562 (“natural” zone) (Reddy et al., 1999). Correlations between soil properties are summarized in Table 3. Overall, significant correlations ranged from 0.805 (TLOC − TLON) to −0.860 (TN − CN). Most other correlations were much lower. For example, TP showed significant correlations with BG (0.468), BicTP (0.417), and APA (−0.385). These are

Fig. 5 – Smallest, mean, and largest realization of TP.

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consistent with finding by Reddy et al. (1999) in WCA 2A that found that APA decreased with P enrichment due to higher levels of inorganic P at impacted sites that apparently inhibited APA production, whereas BG increased with P enrichment. High BG activity reflected the accumulation of organic substrates derived from P-enriched plant detritus at the impacted sites. To transform the correlated biogeochemical variables into a smaller number of uncorrelated variables we performed a PCA. Results of the analysis are summarized in Figs. 6 and 7 and Table 4. The eigenvalues describe the amount of the total variance associated with each factor. The first PC contributed with 33.91%, the second PC with 15.93%, and the third PC with 11.32% to the total variance. The first three PCs accounted for 61.2% of the overall variation within the marsh system. According to Table 4 the soil properties TLON (0.3444), TLOC (0.3309), and BicTP (0.3253) mainly contributed to PC1 and may indicate that this wetland systems variability is dominated by fast responding labile components. Bulk density showed a high negative loading of −0.3088 in PC1 indicating the opposing behavior of BD to all labile soil properties. Bulk densities values across the BCMCA were relatively low with a maximum of 0.36 g cm−3 indicative of wetland soils high in organic matter. Bulk density variations in this wetland were possibly caused by anthropogenic disturbances and fires that occurred regularly with varying extend across the marsh as documented on fire maps compiled by the St. Johns River Water Management District. Beta-glucosidase activity showed moderate loading in PC1 (0.2626) and PC2 (0.2751). The variables TP (0.3891) and PO (0.3682) showed high positive loading and TN (−0.3876) and APA (−0.3658) high negative loadings contributing to PC2. This suggests that variation was caused by anthropogenic nutrient gradients caused by P and nitrogen inputs from adjacent agricultural activities and subsequent transformations of P. High PO was found in areas that were elevated in TP. In general, synthesis of APA is induced in algae and bacteria in response to low orthophosphate concentrations (Brezonik and Pollman, 1999). Newman and

Reddy (1992) found that APA in sediments is associated mainly with sediment particles rather than the interstitial water. APA activity was highest in the top few cm of soils, and sediment resuspension led to short-term increases in APA activity in the water column in dependence of redox status that affected APA activity, with significant lower activity occurring under anoxic conditions. Bentzen et al. (1992) pointed out that the utilization of dissolved organic P is dependent upon the production of extracellular enzymes such as APA, which cleave orthophosphate from organophosphorus compounds. Reddy et al. (1999) pointed out that production of APA is proportional to P demand and is repressed when dissolved inorganic P availability is high. The third PC was dominated by the contribution of TC (0.6011) and Ash (−0.5589). The variability of both soil properties is likely caused by microtopography of sloughs and ridges throughout the marsh that is related to vegetative communities adapted to either ridge or slough environments (e.g. slough adapted Nymphaea spp.). Other soil properties contributed relative low loadings to the PCs (e.g. NH4 N, PEP, PI, CTC) indicating that their variability was much more muted and insignificantly contributing to the overall soil variability in this wetland. Those properties are expected to show less sensitivity to internal and external forcing functions (e.g. airboats, fire, and biogeochemical cycling) resulting in relatively homogenous spatial patterns throughout the wetland. We created semivariograms of the PCs to analyze their spatial structure and scale of variability (Fig. 6). The semivariograms were distinctly different with a relatively short-range of 1228 for PC1, a long-range of 6393 for PC2, and an intermediate-range of 4498 m for PC3 corresponding to small, coarse (regional) and intermediate spatial scales, respectively. Soil properties such as BD, BicTP, TLOC, TNON, moist and MBP that showed high loadings in PC1 are likely to vary over short distances in this wetland. Other properties such as TP, TN, PO, CN and APA with high loadings in PC2 are slower responding variables with predominant regional spatial variation. Other studies confirmed that regional spatial patterns

Table 2 – Statistics of biogeochemical soil properties Variable APA Ash BicTP BD BG PI PO CN CTC NH4 N TLON TLOC MBP Moist PEP TC TN TP

n 266 266 256 266 266 257 251 265 266 266 266 266 254 266 201 265 265 266

Minimum 13.6 1.5 51.6 0.01 1.2 13.8 0 5.5 605.2 15.0 128.1 1091.0 2.2 86.3 0.1 310.7 17.7 349.6

Maximum 825.0 38.0 447.5 0.06 296.7 187.2 75.1 23.0 3026.9 364.7 443.4 4998.1 395.6 98.0 52.3 483.5 82.4 1013.6

Mean

Median

S.D.

342.0 7.5 213.6 0.04 133.4 42.1 21.1 17.2 1984.4 96.3 276.5 3182.4 151.2 91.3 11.4 455.8 27.0 619.9

326.7 7.1 207.2 0.36 132.4 38.5 20.5 17.2 1971.1 93.3 269.6 3125.5 145.2 91.5 8.1 457.7 26.7 601.6

153.5 2.7 63.1 0.01 38.4 16.9 11.6 2.2 352.4 39.1 58.2 703.8 51.7 1.6 10.1 16.7 4.8 128.1

Table 3 – Correlation matrix

APA Ash BD BG BicTP CN CTC NH4 N TLON TLOC MBP Moist PEP PI PO TC TN TP

Ash

BD

1

−0.218 0.277 1

1

BG

−0.665 1

BicTP

CN

CTC

NH4 N

TLON

TLOC

MBP

Moist

0.184

−0.407

0.250

0.238

0.351

0.456

0.319

0.202

−0.413 0.454 1

−0.512 0.431 −0.166 1

−0.200 1

0.320 −0.228 0.181 1

−00.592 0.478 0.674 −0.415 0.319 0.575 1

−0.504 0.391 0.526 −0.549 0.398 0.424 0.805 1

−0.417 0.328 0.938 −0.240 0.304 0.624 0.542 1

−0.331 0.676 0.459 −0.189 0.748 0.259 0.512 0.495 0.365 1

PEP

PI

PO

TC

−0.300 −0.377 0.482 0.294

−0.413 0.431 0.578

−0.248 0.359 0.377

TN

TP

0.342

−0.385

−0.777 −0.222

−0.264 0.468 0.417 −0.860

0.263 0.238 0.236 0.380 1

0.255 0.477 0.323 0.317 0.387 0.197 1

0.191 0.263 0.431 0.222

0.171

0.271 0.246 0.260 1

−0.172

0.352 0.207 0.250 0.249 0.535 0.522

1 1


APA

1

Only significant correlations at the 0.01 level (two-tailed) are shown.

529

530


Fig. 6 – Experimental (red) and fitted (green) semivariogram models of principal components.

with low nugget variance of TP, PO and TN are prominent in subtropical wetlands in Florida (DeBusk et al., 2001; Grunwald et al., 2006; Corstanje et al., 2006; Bruland et al., 2006). Principal component 3 combined intermediate response variables

such as TC and ash that are likely to show spatial autocorrelation that extends over distances of about 4500 m. The nugget to sill ratio indicated strong spatial dependence with 4.7% for PCA1 and 17.8% for PC2. In contrast, the nugget to sill ratio was

Fig. 7 – Smallest (top), mean (center) and largest (bottom) realizations of the first three principal components.

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Table 4 – Eigenvalues and eigenvectors of the first five principal components Variables

PC1

PC2

PC3

PC4

PC5

APA Ash BD BG BicTP CN CTC MBP Moist NH4 N PEP PI PO TC TLOC TLON TN TP

0.1468 −0.1030 −0.3088 0.2626 0.3253 −0.1903 0.2277 0.2889 0.3057 0.1932 0.1729 0.2313 0.1074 0.0726 0.3309 0.3444 0.2051 0.1583

−0.3658 0.0238 −0.1549 0.2751 0.0239 0.3768 0.0233 −0.1387 0.1549 −0.1248 0.1629 0.2410 0.3682 −0.0267 −0.1814 −0.0931 −0.3876 0.3891

0.0331 −0.5589 −0.2371 0.0526 −0.1214 0.2178 0.1731 −0.0739 0.1488 −0.0822 0.0535 −0.1574 −0.0816 0.6011 −0.0725 −0.1911 0.0272 −0.2509

0.2452 0.3300 −0.1169 0.2542 −0.2782 −0.0212 0.5143 −0.2386 0.3161 −0.1499 0.1759 −0.1517 −0.1967 −0.2941 −0.0088 −0.1229 −0.0651 −0.1800

0.3751 0.0221 −0.0320 0.0158 0.2841 0.4162 −0.1280 0.3625 −0.1418 −0.0500 0.1768 0.1722 −0.3385 −0.0929 0.0093 −0.0173 −0.4448 −0.2256

Eigenvalue Ratio % Cumlative %

6.1038 33.91 33.91

2.8683 15.93 49.84

2.0373 11.32 61.16

1.6157 8.98 70.14

1.1195 6.33 76.36

much higher with 52.2% for PC3. This suggests that fine-scale variability and measurement error was more prevalent to contribute to the overall variability for PC3 when compared to PC1 and PC2. For stable, slow response variables grouped into PC2 and labile, fast response variables the nugget was very small. 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. The results are shown in Fig. 7. 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. The smallest and largest realization maps of PC1, PC2 and PC3 show both, the range of minimum and maximum realizations of principal components as well as their spatial distribution. This novel combination of geospatial methods enables to synergize knowledge on the variability of biogeochemical soil properties within a given geographic domain. Since CSGS facilitates to describe spatially distributed uncertainty of simulated biogeochemical values within an ecosystem it provides clues on the total (overall) variability. Spatial patterns of PC2 matched closely the spatial patterns generated for TP (compare Fig. 5). In Fig. 8 realizations of TLON are shown which matched closely the spatial patterns of PC1, whereas spatial patterns of TC matched closely the spatial patterns of PC3 (Fig. 9). Other property prediction maps (maps not shown) resembled the spatial patterns that were derived based on the mixed PCA-CSGS analyses. Table 5 summarizes the semivariogram properties of all observed biogeochemical properties. Properties MBP, NH4 N, PEP, TLOC, BicTP, CTC, TLON and BD showed high loadings in PC1 and showed relatively short spatial autocorrelation with ranges