Adaptive Management for Resilience in Human and
2.3 Adaptive Processes Applied to Overgrazing
Adaptive management can successfully be applied at scales as small as a single farm. AEA has been used as a framework for effective collaboration between sci-entists, farmers, and citizens in exploring new agricultural practices that mimic ecological functions.
In the early 1990s six dairy farmers in southeastern Minnesota began experi-menting with new ways to feed their cattle out of concern for higher commodity prices and the effects of overgrazing.1 They dropped conventional cropping to explore rotational grazing, an approach that relies on the farmer to move grazers in response to changes in indicators of ecosystem health. A farm is subdivided into sections (paddocks), and cattle are moved from paddock to paddock for short periods of intense use followed by long periods of recovery. This idea has many roots, one of which recently began in Africa from observations that wildlife grazing caused less erosion than cattle. This idea grew to practical experiments to mimic with cattle the way wildlife would intensely utilize an area and then move on, giving the area a long rest before returning. These experiments eventually coalesced into an ecological management approach called Holistic Resource Management (HRM) which a local NGO, the Land Stewardship Project, had introduced to the region in a series of workshops. However, some Minnesota farmers had developed forms of rotational grazing on their own in decades past, so exploring this idea represents either a leap back in time (before the jump in agriculture intensity of the 1970s) or in space (to modern wildlife ecology emerging from Africa).
The Land Stewardship Project worked to create a partnership with the farm-ers, local citizens, government environmental agents, and scientists and students at the Minnesota Institute of Sustainable Agriculture. This alliance used an adaptive
1King, T., and DeVore, B., Bringing the land back to life, The Sierra Club, http://www.sc.org/sier ra/199901/goodfarms.html
36 Jan Sendzimir framework to develop hypotheses about what were indicators of the crucial eco-logical, economic, and social processes on the farm. They then worked jointly to monitor experiments with different grazing patterns (frequencies in time and dis-tributions in space), modifying experiments and indicators as their understanding changed.
The results summarized in the list below show a broad range of benefits eco-logically, economically, and socially. The key lesson for scientists is that even promising new theory and practice may take 10 years or more of experimenting to become practical in a particular ecosystem or society. But this coalition suc-cessfully applied ideas about African wildlife ecology on another continent. They showed that cattle could be an ecological and economic benefit if the cattle were managed to mimic the disturbance pattern (in space and time) that the system had evolved with, probably with buffalo. And the new ideas gained public support as the experience of participating citizens spread informally through society. In sum-mary, the experiment advanced scientific theory and practice at the same time that it strengthened the rural social network and the economies of the farms.
The accomplishments of this Adaptive Management experiment in southeast Minnesota are shown in Table 2.2.
Such experiments are instructive in how to develop programs that are practical in how one defines and probes to achieve what is “natural.” Definitions of what is natural can confound science and management when they are arbitrary and have little relationship to the operation of ecological processes. For example, “natu-ral” is often defined in the United States as the state of ecosystems prior to con-tact with Europeans. However, Botkin (1990) notes that ecosystems have changed dramatically in species composition and spatial patterns for many millenia before humans arrived in North America. There is no one ecosystem state that is the “orig-inal” or “natural” one; nature is a moving target. Similarly, Vera (1999) has shown through pollen analysis of lake bottom sediments that climax vegetation in Central and Western Europe in prehistoric times was not closed forest but more open and savanna-like due to herbivore browsing. Therefore, current management of parks as closed forest ecosystems may be based on an artificial, human misconception of what is “natural.” Restoring the importance of ecological processes (such as graz-ing) rather than species lists (biodiversity) to the definition of “natural” would help in correcting this misconception. And it requires sustained, flexible cooperation between scientists and non-scientists to experiment and discover the dimensions of ecological processes that make it resilient, and therefore, sustain its “naturalness.”
The same can be said for economic, and social processes. So the advantage offered by Adaptive Management is a rigorous scientific framework for experimenting with processes (ecological, economic, and social) that sustain the resilience of systems (both human and natural). Experiments are currently underway in Poland to see
Adaptive Management for Resilience in Human and Natural Systems 37 Table 2.2. Accomplishments of the Adaptive Management experiment on south-east Minnesota farms.
• The farms are successful at a time when 30 dairy farms a day fail.
• Biodiversity has soared to 100 bird species on farms with no pesticides.
• The farmers have re-established their own social institutions - local networks of inquiry, knowledge, and encouragement among themselves and in partnership with local citizens, government employees, and academics.
• Knowledge is being passed on as the next generation apprentices on these farms and as other farmers and citizens use the adaptive methods developed here, now available on video as The Monitoring Toolbox.
• Farmer insights pushed ecological and agricultural science such that more respectful working relations between farmers and scientists bode well for more productive future collaborations.
• Farmers were enabled to take their risky insights all the way to proven agricultural production systems once they had the backing and trust of a partnership of NGOs, government, and academia. But these were the innovative farmers who need ideas less than they need the security of funding and trust to try their insights. This project does not address the needs of less innovative farmers.
• Knowledge and respect for farming and science are percolating through rural communities as people discuss their participation in monitoring over the dinner table and in the living rooms.
• The study exploded the myth of farming as a crippling disturbance. Stream erosion was severe in the total absence of disturbance (no cows) or if cows were allowed to visit the stream anytime. The farmers tinkered until they found the correct rate of disturbance (cow visits to the stream) and then erosion was minimized.
• Good ideas rarely work off the shelf. Farmer insights took a decade of
experimentation before the better practices became clear. This highlights the value of long term support for long range collaborations between farmers, NGOs, and scientists.
what level of herbivore grazing is not a disturbance but a boost to the biodiversity and resilience of floodplain ecosystems in the Narew valley.
2.4 Conclusions
The policy-based experimentation advocated by adaptive management is essential to reduce the ecological, social, and economic costs of learning. Adaptive manage-ment focuses upon developing alternative hypotheses, identifying gaps in knowl-edge, and assessing what knowledge would most effectively distinguish alternative
38 Jan Sendzimir hypotheses and, therefore, could be most useful in setting and updating research and action priorities. As Peterson et al. (1997) state:
Rather than simply testing and rejecting individual hypotheses, sci-entists and decision makers must consider diverse sets of alternative hypotheses. Alternatives need to be continually revised, modified, and discarded, based upon how they fare in tests against empirical data (Hilborn and Mangel, 1997). Maintaining the status quo must be ex-plicitly examined as one alternative among many, with its attendant consequences, benefits, and costs. More often than not, policy deci-sions have multiple dimendeci-sions that are difficult, if not impossible, to convert into a single metric. In these cases, techniques such as multi-attribute utility analysis, wherein tradeoffs between alternatives are evaluated using multiple metrics, may be necessary. In either case, such methods of analysis are best viewed not as authoritative objective procedures, but as modeling processes that provide a means of mak-ing underlymak-ing valuations open to scrutiny, discussion, and sensitivity analysis.
In order to exercise reasonable caution we should recognize that the greater our uncertainty, and therefore the less our capacity to precisely define risk, the more considered and “reversible” our management actions should be. Data accumulation and analysis may narrow our sense of uncertainty, but our capacity to predict risk is persistently undercut by the scale of our actions in creating new uncertainties.
Adaptive processes provide one of the most prudent frameworks for assessing and addressing the multiple scales at which flooding risk and damage emerge.
The laboratory for the theory and practice about floods has to be wider even than society; it has to span the range from local village experience to global sources of weather processes. The hard lessons of the last 40 years mandate that we learn to address all these scales, flexibly and repetitively, so that the most important ques-tion is always at hand.
References
Allen, T.F.H., and Starr, T.B., 1982, Hierarchy: Perspectives for Ecological Complexity, University of Chicago Press, Chicago, IL, USA.
Bancroft, G.T., 1989, Status and conservation of wading birds in the Everglades, American Birds, 43:1258–1265.
Botkin, D., 1990, Discordant Harmonies – A New Ecology for the Twenty-First Century, Oxford University Press, Oxford, UK.
Broecker, W.S., 1996, Thermohaline circulation, the Achilles heel of our climate system:
Will man-made CO2 upset the current balance?, Science, 278:1582–1588.
Adaptive Management for Resilience in Human and Natural Systems 39 Casti, J., 1982, Catastrophes, control and the inevitability of spruce budworm outbreaks,
Ecological Modelling, 14:293–300.
Collins, H., and Pinch, T., 1993, The Golem: What You Should Know About Science, Cam-bridge University Press, CamCam-bridge, UK.
De Leo, G.A., and Levin, S., 1997, The multifaceted aspects of ecosystem in-tegrity, Conservation Ecology [online], 1(1):3, available from the Internet, http://www.consecol.org/vol1/iss1/art3.
Gunderson, L., 1998, A Primer On Adaptive Environmental Management, Arthur R. Mar-shall Laboratory, Department of Zoology, University of Florida, Gainesville, FL, USA.
Hilborn, R., and Mangel, M., 1997, The Ecological Detective: Confronting Models with Data, Monographs in Population Biology, Princeton University Press, Princeton, NJ, USA.
Hilborn, R., 1987, Living with uncertainty in resource management, North American Jour-nal of Fisheries Management, 7:1-5.
Holling, C.S., Peterson, G., Marples, P., Sendzimir, J.P., Redford, K., Gunderson, L., and Lambert, D., 1996, Self-organization in ecosystems: lumpy geometries, periodic-ities and morphologies, in Global Change and Terrestrial Ecosystems, Cambridge University Press, Melbourne, Australia.
Holling, C.S., Schindler, D.W., Walker, B.W., and Roughgarden, J., 1995, Biodiversity in the functioning of ecosystems: an ecological synthesis, in C. Perrings, K-G. Maler, C. Folke, C.S. Holling, and B-O. Jansson, eds, Biodiversity Loss: Economic and Ecological Issues, Cambridge University Press, Cambridge, UK.
Holling, C.S., 1992, Cross-scale morphology, geometry and dynamics of ecosystems, Eco-logical Monographs, 62(4):447–502.
Holling, C.S., 1986, The resilience of terrestrial ecosystems; local surprise and global change, in W.C. Clark and R.E. Munn, eds, Sustainable Development of the Bio-sphere, Cambridge University Press, Cambridge, UK.
Holling, C.S., 1973, Resilience and stability of ecological systems, Annual Review of Ecol-ogy and Systematics, 4:1–23.
Interagency Floodplain Management Review Committee, 1994, Sharing the Challenge:
Floodplain Management into the 21st Century, Report of the Committee to the Administration Floodplain Management Task Force, Washington, DC, USA, June 1994.
Levin, S.A., 1992, Orchestrating environmental research assessment, Ecological Applica-tions, 2(2):103–106.
Light, S., Gunderson, L., and Holling, C.S., 1995, The Everglades: Evolution of man-agement in a turbulent ecosystem, in L. Gunderson, C.S. Holling, and S. Light, eds, Barriers and Bridges to the Renewal of Ecosystems and Institutions, Columbia University Press, New York, NY, USA.
Middelkoop, H., and de Boo, eds, 1999, Twice a River; Rhine and Meuse in the Nether-lands, RIZA Report, Ministry of Transport, Public Works and Water Management, pp. 127, ISBN 90 369 52 239.
40 Jan Sendzimir O’Neill, R.V., DeAngelis, D.L., Waide, J.B., and Allen, T.F.H., 1986, A Hierarchical
Con-cept of Ecosystems, Princeton University Press, Princeton, NJ, USA.
Peterson, G., Allen, C., and Holling, C.S., 1998, Ecological resilience, biodiversity and scale, Ecosystems, 1:6–18.
Peterson, G., De Leo, G.A., Hellmann, J.J., Janssen, M.A., Kinzig, A., Malcolm, J.R., O’Brien, K.L., Pope, S.E., Rothman, D.S., Shevliakova, E., and Tinch, R.R.T., 1997, Uncertainty, climate change, and adaptive management, Conservation Ecology [on-line], 1(2):4, available from the Internet, http://www.consecol.org/vol1/iss2/art4 Sendzimir, J., Light, S., and Szymanowska, K., 1999, Adaptively understanding and
man-aging for floods, Environments, 27:115–136.
Vera, F.W.M., 1999, Metaforen voor de wildernis. Eik, hazelaar, rund en paard (trans.
Metaphors for the wilderness; Oak, hazel, cattle and horse), Dutch Ministry of Agri-culture, Nature Management and Fisheries, ISBN: 90-5485-746-3.
Walters, C.J., 1986, Adaptive Management of Renewable Resources, McMillan, New York, NY, USA.
Walters, C., 1997, Challenges in adaptive management of riparian and coastal ecosystems, Conservation Ecology [online], 1(2):1, available from the Internet, http://www.consecol.org/vol1/iss2/art1.
Walters, C.J., and Collie, J.S., 1988, Is research on environmental effects on recruitment worthwhile?, Canadian Journal of Fisheries and Aquatic Sciences, 45:1848–1854.
Walters, C., and Holling, C.S., 1990, Large-scale management and learning by doing, Ecol-ogy, 71(6):2060–2068.
Chapter 3
Modeling Techniques for Complex Environmental Problems
Marek Makowski
Abstract
Mathematical models can be useful in decision-making processes whenever the amount of data and relations are too complex to be analyzed based solely on ex-perience and/or intuition. Models, when properly developed and maintained, and equipped with proper tools for their analysis can integrate relevant knowledge avail-able from various disciplines and sources. Most environmental decision problems are complex. However, some of them pose additional challenges owing to the large amount of data, the complex relations between variables, the characteristics of the resulting mathematical programming problems, and the requirements for compre-hensive problem analyses. Such challenges call for applications of advanced tech-niques for model generation and analysis. Several of these techtech-niques are outlined in this chapter and illustrated by the RAINS model, a large non-linear model, which has been used in international negotiations about the reduction of air pollution.
Keywords: Modeling paradigms, decision support systems, air quality, object-oriented programming, robustness, multicriteria model analysis, non-linear opti-mization, model management.
41
42 Marek Makowski
3.1 Introduction
Most decision problems are no longer well-structured problems that are easy to solve by intuition or experience supported by relatively simple calculations. Even the same kind of problem that was once easy to define and solve, has now become much more complex because of the globalization of the economy, and a much greater awareness of its linkages with various environmental, social, and politi-cal issues. Modern decision makers (DMs) typipoliti-cally want to integrate knowledge quickly and reliably from these various areas of science and practice. Unfortu-nately, the culture, language, and tools developed to represent knowledge in the key areas (e.g., economy, engineering, finance, environment management, social and political sciences) are very different. Everyone who has ever worked on a team with researchers and practitioners having backgrounds in different areas knows this.
Given the great heterogeneity of knowledge representations in various disciplines, and the fast-growing amount of knowledge in most areas, we need to find a way to integrate knowledge for decision support efficiently.
Rational decision making is becoming more and more difficult, despite the quick development of methodology for decision support and an even quicker de-velopment of computing hardware, networks, and software. Two commonly known observations support this statement:
• first, the complexity of problems for which decisions are made grows even faster;
• second, knowledge and experiences related to rational decision making develop rapidly but heterogeneously, therefore integration of various methodologies and tools is practically impossible.
A critical element of model-based decision support is a mathematical model, which represents data and relations that are too complex to be adequately analyzed based solely on experience and/or intuition of a DM or his/her advisors. Models, when properly developed and maintained, can represent not only a part of knowl-edge of a DM but also integrate relevant knowlknowl-edge available from various disci-plines and sources. Moreover, models, if properly analyzed, can help the DM to extend his/her knowledge and intuition. However, models can also mislead users by providing wrong or inadequate information. Such misinformation can result not only from flaws or mistakes in model specification and/or implementation, the data used, or unreliable elements of software, but also by misunderstandings between model users and developers about underlying assumptions, limitations of applied methods of model analysis, and differences in interpretation of results, to name a few. Therefore, the quality of the entire modeling cycle determines to a large extent the quality of the decision-making process for any complex decision problem.
Modeling Techniques for Complex Environmental Problems 43 A recent comprehensive overview of model-based decision support methodolo-gies, tools, and environmental applications is provided in Wierzbicki et al. (2000).
The monograph1also contains a detailed discussion on the modern decision making process, and on guidelines for model development and analysis, focusing mainly on multicriteria model analysis (MCMA).
This chapter concentrates on an overview of modeling paradigms and tech-niques applicable to complex models and illustrates them by the RAINS model.
The structure of the chapter is as follows. The RAINS model is outlined in Sec-tion 3.2, which is followed by a discussion of modeling problems and applied tech-niques in Section 3.3. Section 3.4 presents an overview of MCMA methods.