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JSEM: A Framework for Identifying and Evaluating Indicators

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Abstract

There are two issues in indicator development that have not been adequately addressed: (1) how to select an optimal combination of potentially redundant indicators that together best represent an endpoint, given cost constraints; (2) how to identify and evaluate indicators when the endpoint is unmeasured. This paper presents an approach to identifying and evaluating combinations of indicators when the mathematical relationships between the indicators and an endpoint may not be quantified, a limitation common to many ecological assessments. The approach uses the framework of Structural Equation Modeling (SEM), which combines path analysis withmeasurement models, to formalize available informationabout potential indicators and to evaluate their potential adequacy for representing an endpoint. Unlike traditional applications of SEM which require data on all variables, our approach – judgement-based SEM (JSEM) – can utilize expert judgement regarding the strengths and shapes of indicator-endpoint relationships. JSEM is applied in two stages. First, a conceptual model that relates variables in a network of direct and indirect linkages is developed, and is used to identify indicators relevant to an endpoint. Second, an index of indicator strength – i.e., the strength of the relationship between the endpoint and a set of indicators – is calculated from estimates of correlation between the modeled variables, and is used to compare alternative sets of indicators. The second stage is most appropriate for large, long-term assessments. Although JSEM is not a statistical technique, basing JSEM on SEM provides a structure for validating the conceptual model and for refining the index of indicator strength as data become available. Our main objective is to contribute to a rigorous and consistent selection of indicators even when knowledgeabout the ability of indicators to represent an endpoint is limited to expert judgement.

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References

  • Abbruzzese, B. and Leibowitz, S. G.: 1997, ‘A Synoptic Approach for Assessing Cumulative Impacts to Wetlands’, Environmental Management 21(3), 457–475.

    Google Scholar 

  • Adamus, P. R., Stockwell, L. T., Clairain Jr., E. J., Morrow, M. E., Rozas, L. P. and Smith, R. D.: 1991, Wetland Evaluation Technique (WET), Vol. I: Literature Review and Evaluation Rationale, Wetlands Research Program Technical Report WRP-DE-2, U.S. Army Engineer Waterways Experiment Station, Vicksburg, MS.

    Google Scholar 

  • Barber, C. M. (ed.): 1994, Environmental Monitoring and Assessment Program: Indicator Development Strategy, EPA/620/R-94/022, Office of Research and Development, U.S. Environmental Protection Agency, Washington, DC, 74 pp.

    Google Scholar 

  • Bartell, S. M., Gardner, R. H. and O'Neill, R. V.: 1992, Ecological Risk Estimation, Lewis Publishers, Ann Arbor, MI, 252 pp.

    Google Scholar 

  • Bollen, K. A.: 1989, Structural Equations with Latent Variables, Wiley-Interscience, John Wiley & Sons, Inc., New York, NY.

    Google Scholar 

  • Brinson, M.: 1995, ‘The HGM Approach Explained’, National Wetlands Newsletter, November– December, pp. 7–13.

  • Cairns Jr., J., McCormick, P. V. and Niederlehner, B. R.: 1993, ‘A Proposed Framework for Developing Indicators of Ecosystem Health’, Hydrobiologia 263, 1–44.

    Google Scholar 

  • Chapman, P. M., Dexter, R. N. and Long, E. R.: 1987, ‘Synoptic Measures of Sediment Contamination, Toxicity and Infaunal Community Composition (the Sediment Quality Triad) in San Francisco Bay’, Marine Ecology – Progress Series 37, 75–96.

    Google Scholar 

  • Conroy, M. J. and Noon, B. R.: 1996, ‘Mapping of Species Richness for Conservation of Biological Diversity: Conceptual and Methodological Issues’, Ecological Applications 6(3), 763–773.

    Google Scholar 

  • Dennis, B.: 1996, ‘Discussion: Should Ecologists Become Bayesians?’, Ecological Applications 6(4), 1095–1103.

    Google Scholar 

  • Flather, C. H., Wilson, K. R., Dean, D. J. and McComb, W. C.: 1997, ‘Identifying Gaps in Conservation Networks: Of Indicators and Uncertainty in Geographic-Based Analyses’, Ecological Applications 7(2), 531–542.

    Google Scholar 

  • Gentile, J. H. and Slimak, M. W.: 1992, ‘Endpoints and Indicators in Ecological Risk Assessments’, in: McKenzie, D. H., Hyatt, D. E. and McDonald, V. J. (eds), Ecological Indicators, Vol. 2. Elsevier Applied Science, NY, pp. 1385–1397.

    Google Scholar 

  • Hirvonen, H.: 1992, ‘The Development of Regional Scale Ecological Indicators: A Canadian Approach’, in: McKenzie, D. H., Hyatt, D. E. and McDonald, V. J. (eds), Ecological Indicators, Vol. 2. Elsevier Applied Science, NY, pp. 901–915.

    Google Scholar 

  • Hruby, T., Cesanek, W. E. and Miller, K. E.: 1995, ‘Estimating RelativeWetland Values for Regional Planning’, Wetlands 15(2), 93–107.

    Google Scholar 

  • Hunsaker, C.T., Graham, R. L., Suter II, G. W., O'Neill, R. V., Barnthouse, L. W. and Gardner, R.H.: 1990, ‘Assessing Ecological Risk on a Regional Scale’, Environmental Management 14(3),325–332.

    Google Scholar 

  • Hyman, J. B. and Leibowitz, S. G.: 2000, ‘A General Framework for Prioritizing Land Units for Ecological Protection and Restoration’, Environmental Management 25(1), 23–35.

    Google Scholar 

  • Johnson, M. L., Huggins, D. G. and DeNoyelles Jr., F.: 1991, ‘Ecosystem Modeling with LISREL: A New Approach for Measuring Direct and Indirect Effects’, Ecological Applications 1(4), 383–398.

    Google Scholar 

  • Johnson, R. A and Wichern, D. W.: 1982, Applied Multivariate Statistical Analysis, Prentice-Hall, Inc. Englewood Cliffs, New Jersey, 594 pp.

    Google Scholar 

  • Kelly, J. R. and Harwell, H. A.: 1990, ‘Indicators of Ecosystem Recovery’, Environmental Management 14(5), 527–545.

    Google Scholar 

  • Larson, D. L. and Fivizzani, A. J.: 1994, ‘Hormonal Response to Acute Stress as a Biomarker for Chronic Stress in Larval Ambystoma tigrinum’, Proceedings of the North Dakota Academy of Science 48, 18.

    Google Scholar 

  • Leibowitz, S. G. and Hyman J. B.: 1999, ‘Use of Scale Invariance in Evaluating Judgement Indicators’, Environmental Monitoring and Assessment 58, 283–303.

    Google Scholar 

  • Li, C. C.: 1986, Path Analysis, The Boxwood Press, Pacific Grove, CA.

    Google Scholar 

  • Loehlin, J. C.: 1987,Latent Variable Models: An Introduction to Factor, Path, and Structural Analysis, Lawrence Erlbaum Associates, Hillsdale, New Jersey.

    Google Scholar 

  • McAllister, L. S., Peniston, B. E., Leibowitz, S. G., Abbruzzese, B. and Hyman, J. B.: 2000, ‘A Synoptic Assessment for Prioritizing Wetland Restoration Efforts to Optimize Flood Attenuation’, Wetlands (in press).

  • McClung, G. and Sayre, P. G.: 1994, ‘Risk Assessment for the Release of Recombinant Rhizobia at a Small-Scale Agricultural Field Site’, in A Review of Ecological Assessment Case Studies from a Risk Assessment Perspective, Volume II. EPA/630/R-94/003, Risk Assessment Forum, Office of Research and Development, U.S. Environmental Protection Agency, Washington, DC, pp. 2.1–2.35.

    Google Scholar 

  • McDonald, R. P.: 1985, Factor Analysis and Related Methods, Lawrence Erlbaum Associates, Hillsdale, New Jersey, 259 pp.

    Google Scholar 

  • Murtaugh, P. A.: 1996, ‘The Statistical Evaluation of Ecological Indicators’, Ecological Applications 6(1), 132–139.

    Google Scholar 

  • Neter, J., Wasserman W. and Kutner, M. H.: 1985, Applied Linear Statistical Models, 2nd ed., Irwin, Homewood, IL, 1127 pp.

    Google Scholar 

  • Rovine, M. J.: 1994, ‘Latent Variables Models and Missing Data Analysis’, in: von Eye, A. and Clogg, C. C. (eds), Latent Variables Analysis: Applications for Developmental Research, SAGE Publications, Thousand Oaks, CA, pp. 181–225.

    Google Scholar 

  • SAS Institute, Inc.: 1989, SAS/STAT Users Guide, Version 6, 4th ed., Cary, NC.

  • Scheines, R., Hoijtink, H. and Boomsma, A.: 1999, ‘Bayesian Estimation and Testing of Structural Equation Models’, Psychometrika 64, 37–52.

    Google Scholar 

  • Schumaker, N. H.: 1996, ‘Using Landscape Indices to Predict Habitat Connectivity’, Ecology 77(4),1210–1225.

    Google Scholar 

  • Suter II, G. W.: 1990, ‘Endpoints for Regional Ecological Risk Assessments’, Environmental Management 14(1), 9–23.

    Google Scholar 

  • USEPA(U.S. Environmental Protection Agency): 1991, EMAP – SurfaceWaters Monitoring and Research Strategy – Fiscal Year 1991, EPA/600/3-91/022. U.S. Environmental Protection Agency, Washington, DC, 184 pp.

  • USEPA (U.S. Environmental Protection Agency): 1992, Framework for Ecological Risk Assessment, EPA/630/R-92/001. Risk Assessment Forum, U.S. Environmental Protection Agency, Washington, DC, 33 pp.

    Google Scholar 

  • USEPA (U.S. Environmental Protection Agency): 1995, Beyond the Horizon: Using Foresight to Protect the Environmental Future, EPA-SAB-EC-95-007. Science Advisory Board, U.S. Environmental Protection Agency, Washington, DC, 41 pp.

    Google Scholar 

  • USFWS (U.S. Fish and Wildlife Service): 1981, Standards for the Development of Habitat Suitability Index Models. 103 ESM, USDI Fish and Wildlife Service, Division of Ecological Services, Washington, DC.

    Google Scholar 

Download references

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Correspondence to Jeffrey B. Hyman.

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Hyman, J.B., Leibowitz, S.G. JSEM: A Framework for Identifying and Evaluating Indicators. Environ Monit Assess 66, 207–232 (2001). https://doi.org/10.1023/A:1006397031160

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