Coastal wet longleaf pine flats experience long periods of standing water (Harms et al.

1998). This flooding causes changes in the biogeochemical cycling of nutrients. These forested

wetlands also contain highly acidic soils that require modifications to standard soil biochemical

analysis techniques normally used in moderate pH (6.0) wetlands. The following modifications

were necessary in order to produce good laboratory results.

 The microbial biomass carbon was extracted from soils using a lower 0.05 M K2SO4

extractant instead of the standard molarity (0.5 M) for improved efficiency in these low pH soils

(Haney et al. 2001). The samples were centrifuged before filtering to reduce the high amount of

woody material found present in the soil samples. A relatively new ergosterol extraction method

by physical disruption was utilized to simplify the process for analyzing fungi in a large number

of soil samples (Gong et al. 2001). A lower conversion factor for fungal biomass was used to

account for the flooded conditions on soil fungal growth (Montgomery et al. 2000).

Data Analysis

 A three stage balanced nested design was used to integrate the indicators measured at

different scales and among sites. Hypothesis testing for differences between means was

accomplished by using two-sample t-test with an alpha of .05 and a two-tailed confidence

interval. Since the monitoring of the restoration site with nine distinct reference locations

produced a dataset where the assumptions for analysis of variance (ANOVA) were not ensured,

non-parametric tests were used to detect any significant differences among the reference sites

and among the distinct age class segments (SAS, 2002).

 Correlations between soil moisture, soil chemical and microbial abundances were

determined using Spearman's rank (r) correlations (Dumortier et al. 2002; SAS, 2002; Spyreas

and Mathews, 2006). Trends between variables were obtained from linear regression using the

general linear model (PROC GLM) (Yang et al. 2006; SAS, 2002). The chronosequencial trends