Please join the Great Lakes Center for a seminar presented by Dr. Martin Stapanian, from the U.S. Geological Survey's Lake Erie Biological Station. The seminar is titled “Soil and vegetation indices for wetland quality: A predictive modeling approach,” and will be held on Thursday, April 10th from 12:15 to 1:30 p.m. in Classroom Building, Room B332.
Dr. Stapanian used novel statistical approaches to address three basic questions. First, what ecological variables are most effective at tracking constructed wetlands toward a “natural” state? Which plant species best predict overall wetland vegetation quality? And what ecological variables best predict an established index of wetland plant biotic integrity (Ohio VIBI)?
His results indicated that % total organic carbon and % dry weight in wetland soils can be used as candidate indicators for long-term monitoring to see whether their values in constructed wetlands approach those in natural wetlands over a long period. Three plant species--skunk-cabbage (Symplocarpus foetidus), cinnamon fern (Osmunda cinnamomea), and swamp rose (Rosa palustris)--best predicted wetland vegetation quality. However, poor wetland plant quality was best predicted by the absence of these species.
For emergent and forest wetlands, the most important predictor of Ohio VIBI score was a metric that assessed habitat alteration and development in the wetland. Of secondary importance as a predictor was a metric that assessed microtopography, interspersion, and quality of vegetation communities in the wetland. Metrics and indices assessing disturbance and land use of the buffer area were generally poor predictors of Ohio VIBI scores. Dr. Stapanian's results and statistical procedures have excellent potential for further applications in ecology and environmental management.
