LANDSCAPE INDICATORS OF WETLAND CONDITION IN THE NANTICOKE RIVER WATERSHED, MARYLAND AND DELAWARE, USA

We developed relationships for estimating wetland condition from remotely sensed data and digital maps. Assessment methods relying on maps rather than field sampling (level 1 assessment) are often expert systems summarizing the best professional judgments of wetland scientists. We instead developed level 1 assessment relationships by statistically analyzing results from field sampling. The field campaign applied the hydrogeomorphic (HGM) functional assessment approach to sample 143 freshwater flat and riverine wetlands in the Nanticoke River watershed, Maryland and Delaware, USA. Functional condition index (FCI) scores for five wetland functions were calculated from the field observations. We used geographic information system (GIS) analysis of digital maps to derive candidate landscape indicators for the sampled points. We tested which indicators correlated strongly with the field condition scores, and then we used stepwise multiple regression and regression tree analysis to identify the most effective combinations of landscape metrics for predicting the condition measurements. The best multiple regressions combined information from land-cover, road, and stream maps, especially a stream map resolving natural stream reaches from channelized or ditched reaches. For riverine wetlands, we obtained statistically significant regressions explaining 63%–85% of the variance of measured FCI scores for all five HGM functions (hydrology, biogeochemistry, habitat, plant community, and landscape). Comparable models for flat wetlands were also statistically significant but explained less (48%–54%) of the variance. Regression tree analysis produced more parsimonious models than did stepwise multiple regression. A tree model explained the same amount of variability as the multiple regression model for two flat and two riverine functions, but the tree model explained less variability for two flat and three riverine functions. Our level 1 relationships can be applied to estimate condition scores for unsampled wetlands and to provide confidence limits for those estimates. The uncertainty in predicting a condition scores for individual assessment points is high for most HGM functions, but the models can still help prioritize field visits to select sites for management action. Confidence limits are narrower for predicting mean scores across many wetlands, so the relationships are more powerful for predicting average wetland condition across an assessment area, such as a waters