Relationship between structural and functional vegetation characteristics in three wetlands created in the Piedmont region of Virginia

Background/Question/Methods

The objective of this study was to evaluate the predictive relationship between structural floristic measures and functionality in created palustrine wetlands in the Northern Virginia piedmont.  Structural monitoring serves as a defacto surrogate for functional monitoring even though the underlying functionality is not normally explicitly quantified or mandated since it is intrusive, costly, and time consuming.  The functional progress of created wetlands has implications for initial design, continuing compensation efforts, and the duration of the monitoring program.   Three mitigation wetlands, ranging in age from 3 to 11 years, located in Prince William and Loudoun Counties, Virginia, were selected for this study.  Eighteen (18) 10x10 meter plots were used for sampling during the 2009 growing season, and four random, matched functional and structural samples were taken from each plot.   Structural floristic collection included plant identification and percent cover to determine species richness, biodiversity, and floristic quality indexes, in addition to percent cover for seeded, volunteer, and non-native species.   Functional collection consisted of above ground biomass and soil organic matter/soil organic carbon and nitrogen.     

Results/Conclusions

Mean values for structural and functional variables for total percent cover ranged from 99-112% [SE±6%], richness was low at 4.2-5.2 [±0.6], FQAI was indicative of a successional community at 6-7 [±0.6], Shannon biodiversity index (H’) was 0.4-0.6 [±0.07], Prevalence Indexes (PI) aligned with facultative wet to obligate wetland indicator status at 1.4-1.9 [±0.2], above ground biomass was 760-1650 g- m² [±110 g-m²], and June 2009 percent moisture was 34-40% [±4%].  Pearson bi-variate correlation between AGB and the structural floristic variables yielded significant correlation at p<0.05 or p <0.001 for FQAI, richness, PI, and H’, in addition to age.   Analysis of Covariance (ANCOVA) using PI and H’ as the explanatory variables, age as the categorical variable, and AGB as the response variable resulted in significant effect on AGB from H’ and PI (p<<0.05) holding age constant, with an adjusted R² =0.368.    These preliminary results indicate that a viable model using structural floristic variables can predict the functional development of created wetlands.