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Foundations of Ecological Resilience
By Lance H. Gunderson, Craig Reece Allen, C. S. Holling ISLAND PRESS
Copyright © 2010 Island Press
All rights reserved.
ISBN: 978-1-61091-133-7
CHAPTER 1
PART ONE Concepts and Theory
Commentary on Part One Articles
CRAIG R. ALLEN, LANCE H. GUNDERSON, AND C. S. HOLLING
WHILE SOME IN COMMON PARLANCE use the word theory to mean something that is speculative or opinion, we use it in a scientific sense to indicate "an explanation based on observation and reasoning," which is consistent with its meaning for over four hundred years (Harper 2001). As such, resilience and panarchy are an integral part of ecological theory because of their application in understanding and explaining commonalities in patterns of change in complex systems. Those systems can be ecosystems or human or social systems, or combinations thereof (Gunderson and Holling 2002, Walker et al. 2004). This section presents six papers that collectively describe much of the theory of ecological resilience. They include the first article to use the word resilience to describe a new way of conceptualizing ecosystem dynamics.
There are two views of human interactions with, and management of, the world. In one, efforts are focused on maintaining a degree of constancy by reducing natural variability. In the other, focus is on maintaining the "consistency of relationships" among various parts of the ecological system in question. The former comes from traditions of physics, engineering, and other similar quantitative sciences, while the latter focuses on qualitative properties of systems. The first focuses on equilibrium states; the latter, on the persistence of function and structure. Both approaches are useful, under the right circumstances.
Systems have a capacity to absorb disturbances, but this capacity has limits and bounds, and when these limits are exceeded the system may rapidly transform. Holling was the first to recognize the significance of thresholds in ecological systems, and the importance of avoiding them. Holling (1973) outlines how the response of systems can exhibit threshold behavior. Changes in either driving or state variables may cause collapses. Often, the system provides no warning, and collapse follows an unexpected, but often inevitable, event. More recently, increasing variability in some variables has been suggested as an indicator of impending collapse of a system as it approaches the limits of its resilience (Carpenter and Brock 2006, Wardwell and Allen 2009).
Knowledge of the form of critical population processes (particularly predation) suggests that ecological systems have more than one stable state (multiple stable states, multiple equilibria, and meta-stable states are all words used to describe such behavior). Variables move between some of those states, and that variability both results in and is caused by diversity in space, time, and species. Abrupt jumps in variables are the rule, not the exception.
Resilience is described here as the property that allows the fundamental functions of an ecosystem to persist in the face of extremes of disturbance. It can be measured by the size of the viable stability domains. Stability, in contrast to resilience, is used in a narrow sense of elasticity. It is the property that resists departure from equilibrium and that maximizes the speed of return to the equilibrium following small disturbances. Resilience focuses on the role of positive feedbacks, of behavior far from steady states and with internally generated variability. Stability, in the narrow sense above, deals with negative feedback, of behavior near steady states, and with constancy. Different views and definitions are still being used to distinguish between resilience and stability. The view expressed above is generally used by those ecologists who develop theory empirically, who often use simulation models, and who conduct their science integrated with policy and ecological management. They are typically trained within a biological tradition.
Those who define resilience differently use a measure of elasticity or return time, a definition that is the opposite of that above. It is a definition that implicitly assumes there is only one equilibrium state. Scientists holding this view tend to be more deductive in their formation of theory or are influenced by an engineering and applied mathematical tradition. They tend to apply theory to practice rather than to develop theory empirically as part of practice.
One empirically based critique of multi-stable states has been influential (Sousa and Connell 1985). But this critique is inadequate because it relies on only existing published time series data and ignores any kind of analysis of causation. It is a phenomenological investigation, not causal. As a result, behavior is seen as being determined or explainable by only one variable, the time horizon for the variable is too short, and multiscale interactions between variables of different speeds are ignored. This reflects a common limitation of many population studies.
Holling (1973) documents stability domains using empirical evidence from numerous studies. Stability is defined as the return of a system to an equilibrium state following disturbance, and resilience is defined as a measure of a system's persistence and its ability to absorb change and disturbance but still maintain the same relationships among population or state variables. A system can be highly unstable but very resilient. In fact, a key insight of this paper is that instability may create highly resilient systems (e.g., grassland persistence is reliant on frequently occurring fires). Managing for stability, as humans so often do, has the unexpected outcome of reducing a system's resilience (Holling and Meffe 1996, Allen and Holling 2008). An equilibrium-focused view is attractive to humans, who often focus on optimizing single elements of systems, but it fails to capture the behavior of complex systems.
By the early 1990s, many authors in the ecological literature (O'Neill et al. 1986, Pimm 1984, Tilman and Downing 1994) had applied the word resilience as the speed or time of return of an ecological system to an equilibrium following a disturbance. This was part of a multifaceted definition outlined by Holling in 1973, but it is a narrow definition and ignores the presence of alternative states. In response, Holling (1996) explicitly contrasts and compares two primary definitions of resilience, which he describes as engineering resilience and ecological resilience. Ecological systems differ from engineering ones in that change is not continuous but, rather, discontinuous; ecological change is characterized by surprising events (such as hurricanes, fires, or pest outbreaks) that open windows of opportunities for establishing new combinations of species and ecological processes (Allen and Holling 2008). Ecological attributes are also distributed discontinuously in space, across scales. Ecosystems don't have single equilibria; rather, they have multiple equilibrium and are often far from equilibrium and are on dynamic trajectories. Like the location of electrons about an atom, an ecological system has changed by the time it can be measured, and optimal approaches to ecosystem management are prone to failure. Additionally, management actions and policies focused on constant yields and the reduction of variability reduce the resilience of a system. Because these systems are "moving targets," management needs to be flexible, adaptive, and experimental and must recognize the multiple critical scales characterizing a given system.
Engineering resilience focuses on equilibrium states and is measured simply as the return time following disturbance. This definition, often used by population biologists, is analogous to the intrinsic rate of increase of a species (r). Engineering resilience focuses on stability, in the sense of elasticity. It is the ability of a system to resist departure from an equilibrium following disturbances and to return to the same equilibrium when sufficiently perturbed.
Ecological resilience focuses on conditions far from equilibrium, when abrupt shifts between multiple stable states are possible. Here, the measurement of resilience is the magnitude or amount of disturbance that can be absorbed without undergoing the shift to an alternative stable state characterized by changes in controlling variables and processes and their dominant scales. More recently, stable states have been characterized by their process regimes, and the term regime shift has been used (Scheffer et al. 2001). Ecological resilience can be measured by the size of the stability domains.
The two differing definitions of resilience lead to grossly different strategies for managing systems and responding to surprise. Managing for stability is suggested by engineering resilience; however, this often has demonstrable negative consequences in the long run. Productivity or yield is often increased over short time periods due to management efficiency and optimization but suffers in the long run as ecological surprises exceed the diminished resilience of the system. This happens because managing for reduced variability in one or a small number of variables alters competitive interactions and the buildup of capital (such as fuel for fires) and leads to the loss of important processes and functions. The reduction in variability means that key structuring variables and processes are lost or greatly diminished (e.g., pest outbreaks or fires).
The changes that occur when the resilience of a system is exceeded can lead to an undesirable, but highly resilient, system state. Reversing the system can be very difficult because undesirable systems can be extremely resilient and the regime shifts may exhibithysteresis. Concomitant with the reduction in resilience when management attempts to reduce variability is an increasingly rigid management bureaucracy and ever more dependent—and vulnerable—human societies. An increasing reliance by humans on systems where management has reduced variability often means that ecological shifts have ever greater and negative implications for human economies and societies.
Holling (1996) also begins to formulate a model of the relationship among ecological diversity, resilience, and scale (formally conceptualized in Peterson et al. 1998). He notes that resilient systems have multiple controls that are most efficient on different scales, and that the distribution of diversity within and across scales is what matters.
Holling (1986) is part of a groundbreaking volume that was one of the first works to build and synthesize understanding around themes of sustainability and global environmental change (Clark and Munn 1986). This was years before global climate change was a widespread research topic or undertaken by large international research bodies. As part of this work, Holling (1986) applied the concept of resilience to help understand how a wide range of ecosystems would respond to broad-scale environmental (climatic) change. Ecological systems exhibit a diverse array of responses to global changes—a characteristic that is inherent in their resilience. Nonadaptive systems with little flexibility in response to perturbation and disturbance would be in a constant state of flux and disarray. This paper provides an early recognition of discontinuous and nonlinear response in ecological systems and represents an early attempt to link ecological and social systems across scales. Holling (1986) recognized that positive feedbacks are responsible for maintaining the systems on which humanity relies—for example, feedbacks between the atmosphere and vegetation.
In many ways, this paper was an early warning of the very real possibility that the resilience of global systems could be exceeded, resulting in very sudden and effectively irreversible regime shifts. Because many of the anticipated changes are global, rather than local, in nature, adaptation to changes caused by the human footprint will need to occur not only within individuals but within institutions and social systems as well. Twenty years later, approaches linking social-economic-ecological systems are commonplace and viewed as the frontier in global change and resilience research (Walker and Salt 2006).
A theme of the Holling (1986) paper is recognizing the inevitability of "surprises"—unexpected outcomes with causes and responses very different from those anticipated, or results or behaviors that are induced by human actions but which are very different than expected. Such thoughts were given a wide public airing when Donald Rumsfeld, secretary of defense under U.S. president George W. Bush, stated the following at a Department of Defense news briefing on February 12, 2002: "Because as we know, there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns—the ones we don't know we don't know."
This quote is often offered as an example of incompetence and incoherence, but it is actually an explicit acknowledgment of the inevitability of surprise, even when every envisioned contingency has been planned for. That nearly all of the global public did not understand it suggests that humanity still cherishes the idea of predictability and linear response. However, an increasing emphasis on adaptive management in the United States and elsewhere—for example, the shift of the U.S. Department of Interior to an adaptive management paradigm (Williams et al. 2007) with an emphasis on identifying and reducing uncertainty—offers some hope for the future of resource management. The viewpoint of linear causation and response, and constancy as a management goal, is still common and suggests to policy makers and the public alike that mistakes can be made but that the affected system, be it local or global, will eventually return to equilibrium. The two viewpoints—that of discontinuous change and surprise versus that of continuous change and predictability—lead to very different approaches to policy and institutions. For looming global changes, such as those potentially wrought by climate change, the latter suggests that there is plenty of time for social and ecological adaptation to change, while the former suggests sudden and perhaps catastrophic change exceeding the limits of human adaptability.
Importantly, Holling (1986) describes how a resilient system might be self- maintaining. First, self-organizing processes within and across scales provide positive feedbacks. Second, instabilities and variability experienced by the system, if not of a magnitude sufficient to exceed the systems' resilience, help to strengthen structures and encourage adaptation and may generate novelty (Allen and Holling 2008).
Scaling, and the characteristic time and space domains in systems, is described as discontinuous and critical to understanding the organization, and thus the evolution and resilience, of systems. This description and related understanding builds directly on hierarchy theory (Allen and Starr 1982) and itself has evolved to the theory of complex adaptive systems (Arthur et al. 1997, Levin 1998) and complexity theory. This insight, though not unique, was critical in developing resilience theory. It suggests that systems are characterized by having discrete structures and functions and processes at each time and space domain present (Holling 1992); that scale-specific interactions and positive feedbacks maintain scale-specific structures and functions; that changes between scales are discontinuous; that the number of scales present is finite and limited; and that when the resilience of a system is exceeded, reorganization rapidly occurs and the scale- specific structure and function present in the new system may be vastly different from the old.
(Continues...)
Excerpted from Foundations of Ecological Resilience by Lance H. Gunderson, Craig Reece Allen, C. S. Holling. Copyright © 2010 Island Press. Excerpted by permission of ISLAND PRESS.
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