Document Type


Publication Date



We used an assessment approach combining the USEPA EMAP probabilistic sampling design with existing Ohio wetland assessment tools, including the Ohio rapid assessment method (ORAM), the modified Penn State Stressor Checklist, the Vegetation IBI and the Amphibian IBI, along with a landscape analysis (the Landscape Development Intensity Index) to evaluate the ecological condition of wetlands in the 1,300 km2 Cuyahoga River watershed. Sample sites were selected using the Generalized Random Tesselation Stratified (GRTS) survey design, which provides a geospatially balanced, stratified random sample. The Ohio Wetland Inventory was used as the sample frame for the population of wetlands in the watershed. We evaluated 366 mapped wetland sites and assessed 243 wetlands to determine condition and report on their response to surrounding land-use. Of the 366 sites, we determined that 243 points (66.4 %) were wetlands while the remainder (16.4 %) were characterized as non-wetlands (n = 60) or duplicate points (n = 18). In 12.3 % of the cases (n = 45), field crews were denied site access by property owners. For the wetlands sampled, ORAM scores were normally distributed with a minimum of 16.0, a maximum of 94.0, and a mean of 55.6 (± 14.5 SD). Across the entire watershed, 9.1% of wetlands were in poor condition, 13.2% in fair condition, 51.0% in good condition, and 26.7% in very good condition. There was dramatic decline in the numbers of Category 3 wetlands from the upper parts of the watershed in Geauga county (49.3% of all wetlands sampled), to the middle parts of the watershed in Portage (18.5% and Summit (19.6%) counties, and the near disappearance of Category 3 wetlands in Cuyahoga county (8.3%). Using the Landscape Development Index (LDI), we evaluated the scale at which the effects of land-use are strongest over six buffer widths: 100, 250, 500, 1000, 2000, and 4000 m. ORAM scores were negatively correlated with increasing intensity of land use (high LDI scores) for depressional, riverine, and slope wetlands for each buffer width to a distance of 1000 m, with the strongest correlations for the 100 and 250 m buffer distances. For impoundments, land-use in the first three buffer distances through 500 m did not relate to ORAM score. Overall, land use intensity in the watershed can be characterized as in "low" to "moderately-low". Wetlands in Geauga county had significantly lower LDI scores across most buffer distances than wetlands in Cuyahoga, Summit, and Portage counties, particularly for the 1000 m, 2000 m, and 4000 m buffers. The predictive power of the Level 1 LDI assessment at the individual site level for all wetlands was low (R2 = 12-17%; p < 0.05) for 100 m to 1000 m buffer classes, and no significant correlations were found at the 2000 m or 4000 m distances. Classification and regression tree analysis indicates that wetland size is also a strong predictor of wetland condition, probably as a function of landscape fragmentation. The utility of the Level 3 data collected in this study was limited by insufficient sample size, restricting our ability to calibrate and validate the Level 1 and 2 protocols with Level 3 data. In particular the Level 3 vegetation data was absent for Category 1, poor condition wetlands. However, the VIBI distribution still had sufficient breadth in disturbance to be highly correlated with the Level 2 assessment tools. The limitation of small sample size was even more of a problem for amphibian data and prevented its use in validation. A secondary objective of this project was to explore key biogeochemical properties of the wetlands being assessed through soil analysis and the development of a soil spectral library. Soil samples were collected at 202 of the wetlands assessed. Soil data showed no consistent trends with condition category. We found depressions contained significantly higher nutrient concentrations (total nitrogen, total phosphorus and total carbon) than riverine sites, and attribute the difference to the accumulation of organic matter in the longer, more stable hydroperiod characteristic of depressional settings. This project demonstrates that the State of Ohio has developed the prerequisite tools required to successfully implement a statewide wetland-monitoring program using statistically- based water quality assessment approaches.


Ohio EPA Technical Report



Included in

Biology Commons