The Ecosystem Approach

Complexity, Uncertainty, and Managing for Sustainability

David Waltner-Toews, James Kay, Nina-Marie Lister

• Publisher: Columbia University Press
• Year: 2008
• ISBN:
  9780231507202
• Topic: Biological Sciences
• Subtopic: Ecology
• Index: Biological Sciences

Is sustainable development a workable solution for today's environmental problems? Is it scientifically defensible? Best known for applying ecological theory to the engineering problems of everyday life, the late scholar James J. Kay was a leader in the study of social and ecological complexity and the thermodynamics of ecosystems. Drawing from his immensely important work, as well as the research of his students and colleagues, The Ecosystem Approach is a guide to the aspects of complex systems theories relevant to social-ecological management.

Advancing a methodology that is rooted in good theory and practice, this book features case studies conducted in the Arctic and Africa, in Canada and Kathmandu, and in the Peruvian Amazon, Chesapeake Bay, and Chennai, India. Applying a systems approach to concrete environmental issues, this volume is geared toward scientists, engineers, and sustainable development scholars and practitioners who are attuned to the ideas of the Resilience Alliance-an international group of scientists who take a more holistic view of ecology and environmental problem-solving. Chapters cover the origins and rebirth of the ecosystem approach in ecology; the bridging of science and values; the challenge of governance in complex systems; systemic and participatory approaches to management; and the place for cultural diversity in the quest for global sustainability.

PART I

Some Theoretical Bases for a New Ecosystem Approach

In this section (chapters 1–9), we cover the main theoretical and practical challenges of an ecosystem approach to managing for sustainability and some important possible responses, particularly as reflected in the ideas of the late James Kay and a few close colleagues. The intent of this section is not to provide an in-depth review of complex systems thinking but rather to identify those features that are deemed most important for the implementation of a reasonable scholarly and management response to the complexity of the world. Chapter 1 provides a basis for the more applied chapters that follow and that serve as a kind of argument, or conversation, with the theories as posited in Part I.

1

An Introduction to Systems Thinking

James J. Kay

The Nature of the Beast

Environmental issues and sustainability have thwarted our society’s scientific approach to dealing with the world. One need only contemplate global climate change to experience the frustration and confusion. In this book, we are using the term complexity as a concept that covers problematic situations that have eluded traditional scientific solutions. Complex situations involve uncertainty and surprise. They give the impression that there is no right way of looking at them and no right answer to the problems they raise. The problem is really the singularity of our concept of the “right answer.” Complexity defies linear logic as it brings with it self-organization and feedback loops, wherein the effect is its own cause. Circular relationships between cause and effect require nonlinear logic, explanations in terms of morphogenetic causal loops where form is determined by and determines its own plans. In essence, complexity is characterized by situations where several different coherent future scenarios are possible, each of which may be desirable, all of which have an inherent irreducible uncertainty as to the likelihood for their actually coming about.

The differences between the above scenarios require a number of different perspectives at different scales of investigation. Understanding complex situations thus invokes alternative perspectives, which can be perplexing. Yet there is no avoiding our environmental concerns, and so we must take up the challenge of complexity. While not a panacea, systems thinking seems to offer some insights and approaches for dealing with complexity. As such, it holds the promise of helping us chart the course to sustainability.

Systems thinking is about patterns of relationships and how these translate into emergent behaviors. This section explores the notion of systems and its application to ecosystem thinking. Systems thinking provides us with a window on the world that informs our understanding of nature and our relationship to it. It provides us with a way of framing our investigations and a language for discussing our understanding. Translating systems thinking into action is what systems approaches are about. In this section the focus will be on systems thinking as it applies to biophysical systems.

Making Sense of Nonlinearity: Self-Organization

One of the puzzling observations about issues of sustainability is that everything seems to happen at once. Teasing apart causal links using conventional scientific techniques doesn’t appear to help us answer the important questions. Systems thinking can help us by providing a language and conceptual tools for talking about the richness that comes with complexity.

Underlying systems thinking is the premise that systems behave as a whole and that such behavior cannot be explained solely in terms that simply aggregate the individual elements. This premise is, of course, the antithesis of prevalent reductionist thinking. Take, for example, evapotranspiration in a wetland. If one measures the evapotranspiration for the plants that make up the wetland when they are isolated in pots and add this to the evaporation for open pans of water (the classical experiment), one gets a higher value than the evapotranspiration of the plants and the open water when they are together in a wetland. One perspective on this is that when the plants transpire, they increase the humidity of the local atmosphere, thus decreasing the evaporation from the open water. Then again, does increased open water decrease plant evapotranspiration? The nonlinear causality in the loops typical of such systems makes distinguishing causal order impossible. Furthermore, this emergent property of wetlands cannot be deduced from more intensely studying their individual elements in isolation. Yet the dominant reductionist approaches are so entrenched that I have personally dealt with senior scholars who cannot accept that the evapotranspiration of a wetland is not simply the sum of the evapotranspiration of its component parts. There is a certain myopia in the dominant reductionist approaches, and it hinders our ability to deal with situations where emergence (i.e., the whole is more than the sum of the parts) is an important feature. Systems thinking is well suited to understanding such situations that require considerations of the whole as an emergent with its own properties.

An important emergent property of the whole is self-organization. We shall discuss this in more detail in a later chapter. However, it is important for us to introduce the notion here because self-organization is the phenomenon that gives us a sense that a system has an “identity” of its own. A simple example is a school of fish or a herd of wildebeest. The school as a whole seems to move of its own accord. Understanding or modeling this movement comes from understanding the relationship that is maintained between individual fish and wildebeest rather than from independent behavior of the individual itself. Self-organization is about how coherent patterns of relationships are internally structured and develop over time. How these relationships develop over time leads to a number of surprising and counterintuitive phenomena.

One of the manifestations of self-organization, which gives us a sense of a “whole” is the way in which systems deal with disturbance and, indeed, often incorporate disturbance as an important element of their dynamics. DeAngelis (1986) gives an example of this from southeastern Australia. The dominant trees are sclerophyllous eucalyptus, but the undergrowth consists of lush mesophytic vegetation. Normally, these circumstances would give rise to a temperate rain forest. However, these systems are subject to frequent fire, which would not occur if the mesophytic vegetation dominated. Fire increases soil leaching and sclerophylls are better adapted to poorer soils than mesophylls. Thus the dominance by sclerophyllous forest depends on fire and the occurrence of fire depends on the dominance by sclerophyllous forest. So fire has been incorporated as an integral element to the existence of the sclerophyllous dominant forest.

In a sense, the self-organization is a happenstance outcome of fire and the vegetation meeting. The components of the vegetation are significantly already evolved before the components of the new stable configuration ever came together. While the organisms in the forest are coded by DNA, the self-organization supersedes all that. There may be some microevolution that causes the components to line up in detail as they stabilize the emergent vegetation type. However, as with all self-organization, it comes down to flux and process; there is no plan or script for how the situation plays out.

This example also illustrates the importance of feedbacks and morphogenetic causal loops in understanding self-organization. In this case the feedback loop is that fires increase soil leaching, which increases the sclerophylls at the expense of the mesophylls, thus increasing the amount of forest fire. This would quickly get out of control, except that the mesophytic undergrowth limits the amount of fire, and so the whole system is in balance. It is such a balanced network of nonlinear causality that is referred to as a morphogenetic causal loop. The morphogenetic causal loop of sclerophyllous dominance, fire, and soil infertility obstructs the development of temperate rain forests and preserves the status quo.

The nonlinear causality of such systems gets us into trouble as environmental managers. An example is forest fire in temperate forests of North America. Forests are adapted to fire and are organized in such a way that normal fires cause only small areas of damage. The fire releases nutrients and makes openings for seedlings, promoting reproduction. Normal forest fires rejuvenate forests, keep the fuel level down, and prevent larger, more damaging fires and pest outbreaks. Suppressing forest fires prevents the rejuvenation process, allows fuel to accumulate and sets the stage for conflagrations, like the one that occurred in Yellowstone in 1988. Even in hindsight, researchers remain ambivalent about the Yellowstone fire, as there was in place a management regime that encouraged fire suppression. Later research indicated that there are huge fires every 400 years or so, of which the 1988 fire may be argued as an example. Suppressing forest fires usually makes forests less healthy! Indeed, anyone who depends on linear causal models as the basis for their management decisions will find the world a perplexing place.

Self-organizing systems have in their repertoire of behaviors a way of dealing with disturbance through their buffering capacity. In essence, one can substantially change the environmental context for such a system up to a point (a threshold or tipping point) with little apparent effect on the system. However, a slight change beyond the threshold and the system will suddenly change, that is, it reorganizes itself in a very dramatic and often unpredictable way. The effect of acid rain on lakes is an example of this phenomenon. The acidity of the precipitation running into lakes did not suddenly change; rather, it changed incrementally over decades. The pH of the lake water, however, did not change substantially, relatively speaking, over the same period (Stigliani 1988). The lakes maintained their organizational state (low pH) through a series of feedback loops that largely buffered the lake (in a chemical sense) from the environmental change. Eventually, the runoff from precipitation into the lake reached a level of acidity that exceeded the compensatory capacity of these loops. Once this happened, the effectiveness of the system decreased, which, in turn, decreased the capacity of the loops to compensate, which decreased the effectiveness of the system, and then quickly the organization unraveled and the system flipped to a different organizational state, in this case a “dead” acidified lake. The pH of the lakes dropped in a very short time period, less than one summer season. In some instances the change occurred in weeks.

Again, our linear thinking can get us in trouble when we make decisions regarding such systems. When Steve Carpenter began to work with human management of lacustrine systems, he was at first surprised by the flips of behavior he saw because there are not that many examples of them in basic science applied to lake systems. However, after a series of examples in managed systems, Carpenter is now surprised if he does not find such discrete jumps in the state of the system. Carpenter et al. (1999) report this as a common phenomenon. For quite a while our interaction with the system appears not to have any (deleterious) effect. As we increase what we are doing to the system, nothing appears to happen. Then suddenly, with little warning, a small change in our behavior causes the system to change dramatically, and too late we realize that we were impacting the system. The ability of systems to buffer themselves from external influences and to incorporate external disturbance as an integral part of their patterns of organization is part of what gives us our sense of them as a whole, a whole that is adapted to the situation that it is in.

The acid rain–lake interaction is also an example of important self-organizing phenomenon. Complex systems self-organize through feedback loops, and their openness predisposes them to dramatic reorganizations at critical points of instability (Nicolis and Prigogine 1977) (e.g., the dramatic “death” of an acidified lake, which is a flip to a plankton-dominated ecology). These instabilities and the resulting jumps or abrupt changes in the system are caused by self-amplified internal fluctuations mediated especially through positive feedback loops. These give rise to the spontaneous emergence of new structures and forms of behavior. Amplification is thus a source of new organization and complexity in the system. At the points at which these new structures emerge, the system may branch off into one of a number of quite different organizational states, often referred to as attractors. The existence of multiple stable states, multiple possibilities necessarily implies in-determinacy, as which path is taken depends on the system’s history and various external conditions that can never be completely predicted (Nicolis and Prigogine 1989), thus the unpredictable nature of complex systems.

It is one thing to recognize such complexity in multiple case studies, but how do we use this information to help us make decisions about sustainability? In the 1930s, von Bertalanffy (1968) noted that open self-organizing systems exhibited common attributes regardless of the disciplinary domain of study. He called this property of systems “isomorphism” (Blauberg et al. 1977: chap. 2). The existence of isomorphisms allows us to make generalizations about open self-organizing systems, that is, to build a general theory about their behavior and characteristics. This is one of the premises and the impetus behind the development of von Bertalanffy’s general systems theory as well as more recent advances in systems thinking. By furnishing us with a typology and description of the patterns of relationships that can occur, both within the system and between the system and its environment, and the types of behaviors that can emerge, systems thinking ...