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Wetlands

, Volume 27, Issue 3, pp 416–431 | Cite as

Assessment of wetland condition: An example from the Upper Juniata watershed in Pennsylvania, USA

  • Denice H. WardropEmail author
  • Mary E. Kentula
  • Donald L. Stevens
  • Susan F. Jensen
  • Robert P. Brooks

Abstract

The requirement of Section 305(b) of the Clean Water Act (CWA) that all waters of the U.S. be assessed every two years has been historically ignored for wetlands, even though they are included in the definition of “waters of the U.S.” This paper presents the use of a landscape and rapid assessment to describe the wetland resource and assess wetland condition in the Upper Juniata watershed in central Pennsylvania, USA. A Floristic Quality Assessment Index (FQAI) is used to calibrate and refine the landscape and rapid assessments. The landscape assessment defined ecological condition of sites in terms of the degree of departure from reference standard condition (i.e., wetlands in predominantly forested settings). Criteria for condition categories were based on the literature or best professional judgment and resulted in more than half of the area of the resource being rated in high or the highest condition, while about 12% was rated in low condition. The rapid assessment adjusts the landscape assessment by accounting for the presence of Stressors and the ameliorating effects of a buffer. This resulted in a 38% decrease in the proportion of wetland area in the highest and high condition categories and almost quadrupled the area in low condition. Classification and Regression Tree (CART) analysis was used to evaluate 1) whether the results of the landscape and rapid assessments correspond to those from the more quantitative data in FQAI and 2) whether the condition categories established for the landscape and rapid assessments agree with those established using FQAI. CART results indicate that our initial delineation of condition categories for the landscape and rapid assessments should be more stringent. However, it appears that the rapid assessment does a better job of gauging the factors important to wetland condition, as measured by FQAI, than the landscape assessment. This work can serve as a template for wetland monitoring and assessment and reporting as required by the U.S. Clean Water Act. Overall, such monitoring provides information that can be used to target areas for attention or protection, prioritize sites for restoration, design restoration projects, and choose best management practices.

Key Words

ecological condition wetland assessment wetland monitoring 

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Literature Cited

  1. Adamus, P. R. and K. Brandt. 1990. Impacts on quality of Inland Wetlands of the United States: a survey of indicators, techniques, and applications of community level biomonitoring data. U.S. Environmental Protection Agency, Environmental Research Laboratory, Corvallis, OR, USA. EPA/600/3-90/073.Google Scholar
  2. Anderson, J. R., E. E. Hardy, J. T. Roach, and R. E. Witmer. 1976. A land-use and land-cover classification for use with remote sensor data. U.S. Geological Survey, Reston, VA, USA. Professional Paper 964.Google Scholar
  3. Andreas, B. K. and R. W. Lichvar. 1995. Floristic index for establishing assessment standards: a case study for northern Ohio. U.S. Army Corps of Engineer Waterways Experiment Station, Vicksburg, MS, USA. Technical Report WRP-DE-8.Google Scholar
  4. Beatty, G. H., H. Henderson, C. Keener, and R. Gruver. 2002. Vascular plants of Centre County, Pennsylvania and their coefficients of conservatism: plants of Upper Penns Creek Watershed, documented and probable. Pennsylvania Native Plant Society and Pennsylvania Department of Environmental Protection, State College, PA, USA.Google Scholar
  5. Bedford, B. L. 1996. The need to define hydrologic equivalence at the landscape scale for freshwater wetland mitigation. Ecological Applications 6: 57–68.CrossRefGoogle Scholar
  6. Brinson, M. M. 1993. A Hydrogeomorphic Classification for Wetlands. U.S. Army Corps of Engineers, Waterways Experiment Station, Vicksburg, MS, USA. Technical Report WRP-DE-4.Google Scholar
  7. Brinson, M. M. and R. Rheinhardt. 1996. The role of reference wetlands in functional assessment and mitigation. Ecological Applications 6: 69–76.CrossRefGoogle Scholar
  8. Brooks, R. P., D. H. Wardrop, and J. A. Bishop. 2004. Assessing wetland condition on a watershed basis in the Mid-Atlantic Region using synoptic land-cover maps. Environmental Monitoring and Assessment 94: 9–22.CrossRefPubMedGoogle Scholar
  9. Brooks, R. T., D. H. Wardrop, and J. K. Perot. 1999. Development and Application of Assessment Protocols for Determining the Ecological Condition of Wetlands in the Juniata River Watershed. U.S. Environmental Protection Agency, National Health and Environmental Effects Laboratory, Western Ecology Division, Corvallis, OR, USA. EPA/ 600/R-98/181.Google Scholar
  10. De Ath, G. and K. E. Fabricius. 2000. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 81: 3178–92.Google Scholar
  11. Environmental Resources Research Institute. 1998. Networked streams of Pennsylvania: digital data. Environmental Resources Research Institute, The Pennsylvania State University, Pennsylvania Department of Environmental Protection, Harrisburg, PA, USA.Google Scholar
  12. Feldsman, M. 2002. Classification trees as an alternative to linear discriminant analysis. American Journal of Physical Anthropology 119: 257–75.CrossRefGoogle Scholar
  13. Fennessy, M. S., A. D. Jacobs, and M. E. Kentula. 2004. Review of Rapid Methods for Assessing Wetland Condition. U.S. Environmental Protection Agency, Washington, DC, USA. EPA/600/R-04/009.Google Scholar
  14. Francis, C. M., M. J. W. Austen, J. M. Bowles, and W. B. Draper. 2000. Assessing floristic quality in southern Ontario woodlands. Natural Areas Journal 20: 66–77.Google Scholar
  15. Gwin, S. E., M. E. Kentula, and P. W. Shaffer. 1999. Evaluating the effects of wetland regulation through hydrogeomorphic classification and landscape profiles. Wetlands 19: 477–89.CrossRefGoogle Scholar
  16. Horvitz, D. G. and D. J. Thompson. 1952. A generalizations of sampling without replacement from a finite universe. Journal of the American Statistical Association 47: 663–85.CrossRefGoogle Scholar
  17. Hychka, K. C., D. H. Wardrop, and R. P. Brooks. 2007. Enhancing a landscape assessment: a case study in the Upper Juniata Watershed, Pennsylvania, USA. Wetlands 27: 446–61.CrossRefGoogle Scholar
  18. Johnson, J. B. 2005. Hydrogeomorphic Wetland Profiling: An Approach to Landscape and Cumulative Effects Analysis. U.S. Environmental Protection Agency, Washington, DC, USA. EPA/620/R-05/001.Google Scholar
  19. Karr, J. R. and E. W. Chu. 1999. Restoring Life in Running Waters: Better Biological Monitoring. Island Press, Washington, DC, USA.Google Scholar
  20. Kentula, M. E., S. E. Gwin, and S. M. Pierson. 2004. Tracking changes in wetlands with urbanization: sixteen years of experience in Portland, Oregon, USA. Wetlands 24: 734–43.CrossRefGoogle Scholar
  21. Larsen, D. P., K. W. Thornton, N. S. Urquhart, and S. G. Paulsen. 1994. The role of sample surveys for monitoring the condition of the Nation’s lakes. Environmental Monitoring and Assessment 32: 101–34.CrossRefGoogle Scholar
  22. Lopez, R. D. and M. S. Fennessy. 2002. Testing the floristic quality assessment index as an indicator of wetland condition. Ecological Applications 12: 487–97.CrossRefGoogle Scholar
  23. Mack, J. J. 2001. Ohio Rapid Assessment Method for Wetlands, version 5.0: User’s Manual and Scoring Forms. Ohio Environmental Protection Agency, Division of Surface Water, 401/ Wetland Ecology Unit, Columbus, OH, USA. Technical Report WET/2001-1.Google Scholar
  24. Magee, T. K., T. L. Ernst, M. E. Kentula, and K. A. Dwire. 1999. Floristic comparison of freshwater wetlands in an urbanizing environment. Wetlands 19: 517–34.CrossRefGoogle Scholar
  25. McIlnay, D. P. 2002. Juniata, River of Sorrows. Seven Oaks Press, Hollidaysburg, PA, USA.Google Scholar
  26. Miller, S. J. and D. H. Wardrop. 2006. Adapting the Floristic Quality Assessment Index to Reflect Anthropogenic Disturbance in Central Pennsylvania Wetlands. Ecological Indicators 6: 313–26.CrossRefGoogle Scholar
  27. Mushet, D. M., N. H. Euliss, Jr., and T. L. Shaffer. 2002. Floristic quality assessment of one natural and three restored wetland complexes in North Dakota, USA. Wetlands 22: 126–38.CrossRefGoogle Scholar
  28. Myers, W., J. Bishop, R. Brooks, T. O’Connell, D. Argent, G. Storm, J. Stauffer, and R. Carline. 2000. Pennsylvania Gap Analysis Project: Leading Landscapes for Collaborative Conservation School of Forest Resources, Cooperative Fish and Wildlife Research Unit, and Environmental Resources Research Institute, The Pennsylvania State University, University Park, PA, USA.Google Scholar
  29. O’Connell, T. J., L. E. Jackson, and R. P. Brooks. 2000. Bird guilds as indicators of ecological condition in the Central Appalachians. Ecological Applications 10: 1706–21.CrossRefGoogle Scholar
  30. Rheinhardt, R. D., M. M. Brinson, and P. M. Farley. 1997. Applying wetland reference data to functional assessment, mitigation, and restoration. Wetlands 17: 195–215.Google Scholar
  31. Rheinhardt, R. D., M. C. Rheinhardt, M. M. Brinson, and K. E. Faser, Jr. 1999. Application of reference data for assessing and restoring headwater ecosystems. Restoration Ecology 7: 241–51.CrossRefGoogle Scholar
  32. Särndal, C., B. Swensen, and J. Wretman. 1992. Model Assisted Survey Sampling. Springer-Verlag, New York, NY, USA.Google Scholar
  33. Schein, R. D. and E. W. Miller. 1995. Forest resources. p. 74–83. In E. W. Miller (ed.) The Geography of Pennsylvania. The Pennsylvania State University Press, University Park, PA, USA.Google Scholar
  34. Schuft, M. J., T. J. Moser, P. J. Wigington, Jr., D. L. Stevens, Jr., L. S. McAllister, S. S. Chapman, and T. L. Ernst. 1999. Development of landscape metrics for characterizing riparianstream networks. Photogrammetric Engineering & Remote Sensing 65: 1157–67.Google Scholar
  35. Smith, R., A. Ammann, C. Bartlodus, and M. M. Brinson. 1995. An Approach for Assessing Wetland Functions Using Hydrogeomorphic Classification, Reference Wetlands, and Functional Indices. U.S. Army Corps of Engineers, Waterways Experiment Station, Vicksburg, MS, USA. Technical Report WRP-DE-9. 431Google Scholar
  36. Smith, T. M. F. 1991. Post-stratification. The Statistician 40: 315–23.CrossRefGoogle Scholar
  37. Stevens, D. L., Jr. and S. F. Jensen. 2007. Sampling Design, Implementation, and Analysis for Wetland Assessment. Wetlands 27: 515–23.CrossRefGoogle Scholar
  38. Stevens, D. L., Jr. and A. R. Olsen. 1999. Spatially restricted surveys over time for aquatic resources. Journal of Agricultural, Biological, and Environmental Statistics 4: 415–28.CrossRefGoogle Scholar
  39. Stevens, D. L., Jr. and A. R. Olsen. 2000. Spatially restricted random sampling designs for design-based and model-based estimation. p. 609–16. In Accuracy 2000: Proceedings of the 4th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences. Delft University Press, The Netherlands.Google Scholar
  40. Stevens, D. L., Jr. and A. R. Olsen. 2003. Variance estimation for spatially balanced samples of environmental resources. Environmetrics 14: 593–610.CrossRefGoogle Scholar
  41. Stevens, D. L., Jr. and A. R. Olsen. 2004. Spatially-balanced sampling of natural resources. Journal of American Statistical Association 99: 262–78.CrossRefGoogle Scholar
  42. Swink, F. and G. Wilhelm. 1994. Plants of the Chicago region. Indiana Academy of Science, Indianapolis, IN, USA.Google Scholar
  43. Taft, J. B., G. S. Wilhelm, D. M. Ladd, and L. A. Masters. 1997. Floristic quality assessment for vegetation in Illinois: a method for assessing vegetation integrity. Erigenia 15: 3–95.Google Scholar
  44. Tiner, R. W. 2004. Remotely-sensed indicators for the monitoring the general condition of “natural habitat” in watersheds: an application of Delaware’s Nanticoke River watershed. Ecological Indicators 4: 227–43.CrossRefGoogle Scholar
  45. Tiner, R. W. 2005. Assessing cumulative loss of wetland functions in the Nanticoke River watershed using enhanced National Wetlands Inventory data. Wetlands 25: 405–19.CrossRefGoogle Scholar
  46. Wardrop, D. H. and R. P. Brooks. 1998. The occurrence and impact of sedimentation in central Pennsylvania wetlands. Environmental Monitoring and Assessment 51: 119–30.CrossRefGoogle Scholar
  47. Wardrop, D. H., M. E. Kentula, S. F. Jensen, D. L. Stevens, Jr, and R. P. Brooks. 2007. Assessment of wetlands in the Upper Juniata watershed in Pennsylvania, USA, using the hydrogeomorphic approach. Wetlands 27: 432–45.CrossRefGoogle Scholar
  48. Wayland, R. H., III. Director, Office of Wetlands, Oceans, and Watersheds, U.S. Environmental Protection Agency (USEPA). 2001. Memorandum of November 19, 2001, to USEPA Regional Water Management Directors, USEPA Regional Science and Technology Directors, State, Territory, and Authorized Tribe Water Quality Program Directors. Subject: 2002 Integrated Water Quality Monitoring and Assessment Report Guidance.Google Scholar
  49. Weller, D. E., D. F. Whigham, and A. D. Jacobs. 2007. Assessing wetland condition using landscape indicators: an example from the Nanticoke watershed in Delaware and Maryland, USA. Wetlands 27: 498–514.CrossRefGoogle Scholar
  50. Whittier, T. R., S. G. Paulsen, D. P. Larsen, S. A. Peterson, A. T. Herlihy, and P. R. Kaufmann. 2002. Indicators of ecological stress and their extent in the population of Northeastern lakes: a regional-scale assessment. BioScience 52: 235–47.CrossRefGoogle Scholar

Copyright information

© Society of Wetland Scientists 2007

Authors and Affiliations

  • Denice H. Wardrop
    • 1
    Email author
  • Mary E. Kentula
    • 2
  • Donald L. Stevens
    • 3
  • Susan F. Jensen
    • 3
  • Robert P. Brooks
    • 1
  1. 1.Penn State Cooperative Wetlands CenterUniversity ParkUSA
  2. 2.National Health and Environmental Effects Research Laboratory Western Ecology DivisionU.S. Environmental Protection AgencyCorvallisUSA
  3. 3.Department of StatisticsOregon State UniversityCorvallisUSA

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