Environmental Management

, Volume 14, Issue 2, pp 249–258 | Cite as

An assessment methodology for successional systems. I. Null models and the regulatory framework

  • Craig Loehle
  • John Gladden
  • Eric Smith


Standard procedures for evaluating environmental impact involve comparison between before and after conditions or scenarios or between treatment and control site pairs. In many cases, however, endogenous directional change (natural succession) is expected to occur at a significant rate over the period of concern, particularly for manmade systems such as impoundments. Static evaluations do not provide an adequate approach to such problems. A new evaluation frame is proposed. Nominal system behavior over time is characterized by a stochastic envelope around a nominal trajectory. We show that both the state variance and the sampling variance can change over time. In this context, environmental regulations can be framed as constraints, targets, or conformance to ideal trajectories. Statistical tests for determining noncompliance are explored relative to process variance, sample error, and sample size. Criteria are elucidated for choosing properties to monitor, sample size, and sampling interval.

Key words

Stochastic models Monitoring Statistical tests Experimental design 


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Literature Cited

  1. Box, G. E. P., and G. C. Tiao. 1975. Intervention analysis with applications to economic and environmental problems.Journal of the American Statistical Association 70:70–79.CrossRefGoogle Scholar
  2. Carpenter, S. R. 1989. Replication and treatment strength in whole-lake experiments.Ecology 70:453–463.CrossRefGoogle Scholar
  3. Carpenter, S. R., and J. F. Kitchell. 1987. The temporal scale of variance in limnetic primary production.American Naturalist 129:417–433.CrossRefGoogle Scholar
  4. Christensen, N. L., and R. K. Peet. 1984. Convergence during secondary forest succession.Journal of Ecology 72:25–36.Google Scholar
  5. Clarke, R. 1986. The handbook of ecological monitoring. Clarendon Press, Oxford.Google Scholar
  6. Culhane, P. J. 1987. The precision and accuracy of US environmental impact statements.Environmental Monitoring and Assessment 8:217–238.CrossRefGoogle Scholar
  7. Dennis, B., and G. P. Patil. 1988. Applications in ecology. Pages 303–330in E. L. Crow and K. Shimizu (eds.), Lognormal distributions: Theory and applications. Marcel Dekker, New York.Google Scholar
  8. Edwards, D., and B. C. Coull. 1987. Autoregressive trend analysis: An example using long-term ecological data.Oikos 50:95–102.Google Scholar
  9. Gilbert, R. O. 1987. Statistical methods for environmental pollution monitoring. Van Nostrand Reinhold, New York.Google Scholar
  10. Green, R. H. 1979. Sampling design and statistical methods for environmental biologists. Wiley, New York.Google Scholar
  11. Green, R. H. 1984. Statistical and nonstatistical considerations for environmental monitoring studies.Environmental Monitoring and Assessment 4:293–301.CrossRefGoogle Scholar
  12. Halpern, C. B. 1988. Early successional pathways and the resistance and resilience of forest communities.Ecology 69:1703–1715.CrossRefGoogle Scholar
  13. Hill, A. R. 1975. Ecosystem stability in relation to stresses caused by human activities.Canadian Geographer 19:206–220.CrossRefGoogle Scholar
  14. Holling, C. S. 1978. Adaptive environmental assessment and management. Wiley, New York.Google Scholar
  15. Inouye, R. S., and D. Tilman. 1988. Convergence and divergence of old-field plant communities along experimental nitrogen gradients.Ecology 69:995–1004.CrossRefGoogle Scholar
  16. Johnson, R. A., and D. W. Wichern. 1982. Applied multivariate statistical analysis. Prentice Hall, New York.Google Scholar
  17. Kratz, T. K., T. M. Frost, and J. J. Magnuson. 1987. Inferences from spatial and temporal variability in ecosystems: Long-term zooplankton data from lakes.American Naturalist 129:830–846.CrossRefGoogle Scholar
  18. Loehle, C. 1988. Forest decline: Endogenous dynamics, tree defenses, and the elimination of spurious correlation.Vegetatio 77:65–78.CrossRefGoogle Scholar
  19. Loehle, C. 1990. Managing and monitoring ecosystems in the face of heterogeneityin J. Kolasa and S. T. A. Pickett (eds.), Ecological heterogeneity. Springer, New York.Google Scholar
  20. Loehle, C., and E. P. Smith. 1990. An assessment methodology for successional systems. II. Statistical tests and specific examples.Environmental Management 14:259–268.Google Scholar
  21. May, R. M., and G. F. Oster. 1976. Bifurcations and dynamic complexity in simple ecological models.American Naturalist 110:573–599.CrossRefGoogle Scholar
  22. Noble, I. R., and R. O. Slatyer. 1980. The use of vital attributes to predict successional changes in plant communities subject to recurrent disturbances.Vegetatio 43:5–21.CrossRefGoogle Scholar
  23. O'Neill, R. V., R. H. Gardner, and D. E. Weller. 1982. Chaotic models as representations of ecological systems.American Naturalist 120:259–263.CrossRefGoogle Scholar
  24. Rao, H. G., W. R. Terry, and A. Kumar. 1988. Modeling non-homogeneous time series: An application to sulfur dioxide data.Environmental Monitoring and Assessment 10;123–131.CrossRefGoogle Scholar
  25. Robinson, J. V., and M. A. Edgemon. 1988. An experimental evaluation of invasion history on community structure.Ecology 69:1410–1417.CrossRefGoogle Scholar
  26. Rydin, H., and S. O. Borgegård. 1988. Plant species richness on islands over a century of primary succession: Lake Hjalmaren.Ecology 69:916–927.CrossRefGoogle Scholar
  27. Rykiel, E. J., Jr. 1985. Toward a definition of ecological disturbance.Australian Journal of Ecology 10:361–365.Google Scholar
  28. Smith, W. 1984. Design of efficient environmental surveys over time. Pages 90–97in S. M. Gertz and M. D. London (eds.), Statistics in the environmental sciences. ASTM STP 845, Philadelphia.Google Scholar
  29. Stewart-Oaten, A., W. W. Murdoch, and K. R. Parker. 1986. Environmental impact assessment: “Pseudoreplication” in time?Ecology 67:929–940.CrossRefGoogle Scholar
  30. Swartzmanm, G. L. 1987. Long-term research in ecological models for environmental management. Pages 68–88in S. Draggan and others (eds.), Environmental monitoring, assessment, and management: The agenda for long-term research and development. Praeger, New York.Google Scholar
  31. Ter Keurs, W. J., and E. Meelis. 1986. Monitoring the biotic aspects of our environment as a policy instrument.Environmental Monitoring and Assessment 7:161–168.CrossRefGoogle Scholar
  32. Thomas, J. M., J. A. Mahaffey, K. L. Gore, and D. G. Watson. 1978. Statistical methods used to assess biological impact at nuclear power plants.Journal of Environmental Management 7:269–290.Google Scholar
  33. Tilman, D. 1987. Secondary succession and the pattern of plant dominance along experimental nitrogen gradients.Ecological Monographs 57:189–214.CrossRefGoogle Scholar
  34. Van Latesteijn, H. C., and R. H. D. Lambeck. 1986. The analysis of monitoring data with the aid of time-series analyses.Environmental Monitoring and Assessment 7:287–297.CrossRefGoogle Scholar
  35. Walters, C. 1986. Adaptive management of renewable resources. Macmillan, New York.Google Scholar
  36. Walters, C. J., E. Krause, W. E. Neill, and T. G. Northcote. 1987. Equilibrium models for seasonal dynamics of plankton biomass in four oligotrophic lakes.Canadian Journal of Fisheries and Aquatic Science 44:1002–1017.Google Scholar
  37. Walters, C. J., J. S. Collie, and T. Webb. 1988. Experimental designs for estimating transient responses to management disturbances.Canadian Journal of Fisheries and Aquatic Science 45:530–538.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag New York Inc. 1990

Authors and Affiliations

  • Craig Loehle
    • 1
  • John Gladden
    • 1
  • Eric Smith
    • 2
  1. 1.Environmental Sciences DivisionSavannah River Laboratory Savannah River SiteAikenUSA
  2. 2.Department of StatisticsVirginia Polytechnic Institute and State UniversityBlacksburgUSA

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