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Environmental Science And Engineering For The 21st Century: The Role of the National Science Foundation [NSB 00-22, February 2000]
    
CONTENTS



Title Page

National Science Board

Foreword

Acknowledg-
ments


Executive
Summary


1     Introduction

2    The Larger Context

3    Scope of
NSF's Current
Environmental
Activities


4    Input Received About Unmet Needs and Opportunities

5    Findings and
Recom-
mendations


6    Conclusion

References



Appendix A

Appendix B

Appendix C

Appendix D

Appendix E

Appendix F

Appendix G



Final Page



  Box 1
  Box 2
  Box 3
  Box 4
  Box 5
  Box 6
  Box 7
  BOX 8
  Box 9
  Box 10
  Box 11
  Box 12
  Box 13




BOX 8.
COMPLEXITY THEORY AND ECOSYSTEMS

Ecologist Gene Likens recently said that a major intellectual limitation for environmental studies is the false assumption that there will be simple, all-inclusive answers (Pace and Groffman 1998). He went on to say that we must confront the complexity of ecosystems and incorporate that complexity into our scientific endeavors.

Ecological systems are highly nonlinear, characterized by abrupt thresholds in dynamics and possibly chaotic behavior. It is unreasonable to expect consistently accurate predictions for these systems—even with additional resources for generating scientific information combined with the prodigious computing power now available. On the other hand, conceptual and analytical progress is accelerating, and we can increasingly expect serviceable forecasts of the range of likely behaviors and the probabilities of various outcomes. The key in this regard lies in viewing systems as complex and not as the simple sum of their parts.

Ecosystem theory encompasses a wide range of approaches to understanding complex systems: Empirical work, including experimental manipulation of natural and model systems, as well as mathematical methods drawn from other disciplines such as cybernetics, control theory, information theory, network theory, thermodynamics, self-organization, and emergence and hierarchy theory (Muller 1992, 1997). A fundamental issue is to integrate systems behavior across levels of resolution in space and time to address the generation and maintenance of biological complexity across multiple spatio-temporal levels of resolution.

Scientists have learned that even simple rules can generate very complex behaviors and that systems can be very sensitive to initial conditions. This means that making precise long-term or large-scale predictions may be much more difficult than we initially thought—if not impossible in some cases. Complex systems are probably not understandable in the same way as simple systems, although sometimes complex rules can generate simple behavior, arguing the need to extract the "knowable" from the "unknowable" (Levin 1999). Also, small variations may lead to large changes that are not always predictable. So-called "exceptional" events turn out to be not all that rare. This new understanding is leading to fundamentally new approaches that will provide essential insight and guidance to members of the public and policy-makers. Improved understanding of the behavior of complex biological systems will greatly facilitate ecological forecasting and environmental decision-making.

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