Threshold-Based Resource Management: A ... - Springer Link

DOI: 10.1007/s002670010143

Threshold-Based Resource Management: A Framework for Comprehensive Ecosystem Management EMERY ROE* Center for Sustainable Resource Development University of California, Berkeley c/o 4721 El Centro Ave. Oakland, California 94602, USA MICHEL VAN EETEN School of Systems Engineering, Policy Analysis & Management Delft University of Technology PO Box 5015 2600 GA Delft, The Netherlands ABSTRACT / The problems posed by adaptive management for improved ecosystem health are reviewed. Other kinds of science-informed ecosystem management are needed for those regions of conflict between rapid human population growth, increased resource extraction, and the rising demand for better environmental amenities, where large-scale experiments are not feasible. One new framework is thresholdbased resource management. Threshold-based resource

Adaptive Management Environmental policy-makers, scientists, engineers, planners, and administrators make increasingly urgent decisions about the management of species, natural resources, ecosystems, and landscapes on the basis of uncertain, complex, and incomplete scientific, engineering, and management information. One response has been the rise of ecosystem management initiatives informed by, if not actually based upon, adaptive management. Adaptive management is commonly understood as learning how to better manage the ecosystem (or aspects of it) through an incremental and interactive process of experimentation, reexperimentation, continuous hypothesis testing, with feedback and trialand-error knowledge generation, all guiding management redesign and implementation. To that end, KEY WORDS: Resource management; Ecosystem health; Management options; Adaptive management; Ecosystem management *Author to whom correspondence should be addressed.

Environmental Management Vol. 27, No. 2, pp. 195–214

management guides management choices among four major science and engineering approaches to achieve healthier ecosystems: self-sustaining ecosystem management, adaptive management, case-by-case resource management, and highreliability management. As resource conflicts increase over a landscape (i.e., as the ecosystems in the landscape move through different thresholds), management options change for the environmental decision-maker in terms of what can and cannot be attained by way of ecosystem health. The major policy and management implication of the framework is that the exclusive use or recommendation of any one management regime, be it self-sustaining, adaptive, case-by-case, or highreliability management, across all categories of ecosystems within a heterogeneous landscape that is variably populated and extractively used is not only inappropriate, it is fatal to the goals of improved ecosystem health. The article concludes with detailed proposals for environmental decision-makers to undertake “bandwidth management” in ways that blend the best of adaptive management and high-reliability management for improved ecosystem health while at the same time maintaining highly reliable flows of ecosystem services, such as water.

adaptive management has been defined as the “process of implementing policy decisions as scientifically driven management experiments that test predictions and assumptions in management plans, and using the resulting information to improve the plans” (USDA 1993). Adaptive management is currently the preeminently recommended form of science-based natural resource management to such an extent that ecosystem management and adaptive management are frequently seen to be equivalent, as when the former is defined as “the use of an ecological approach in land management to sustain diverse, healthy, and productive ecosystems . . . . [It] is applied at various scales to blend long-term societal and environmental values in a dynamic manner that may be adapted as more knowledge is gained through research and experience” (USDA 1994). A great deal of work on adaptive management (conceptual and practical) has taken place, with calls to extend the application of adaptive management in the United States and beyond (e.g., Holling 1978, Walters 1986, 1997, Walters and Holling 1990, Lee 1993, Gun©

2001 Springer-Verlag New York Inc.

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derson and others 1995, Haney and Power 1996, McLain and Lee 1996). Indeed, a strong case is made below for applying adaptive management to selected ecosystems within the proposed threshold-based resource management approach. This section summarizes the problems with adaptive management as presently formulated and practiced. Problems typically arise out of or center around ecosystem-related issues of population growth, human extraction, availability of adequate conceptual models for the ecosystems and experiments based on them, competing organizational demands in tension with demands for improved ecosystem health, and the increasingly stringent requirement that the ecosystem reliably provide services (e.g., water supplies) that have multiple purposes (e.g., for agricultural, urban, and environmental users). The interrelated specifics of the problems—no one of which is debilitating or without proposed remedy— have hampered adaptive management and include most importantly: 1.

The dearth or rudimentary nature of biological and ecological conceptual models upon which to base experiments, particularly for aquatic ecosystems, in ways that improve ecosystem health through restoring ecosystem processes and functions that better mimic the presettlement template of a self-sustaining ecosystem. Where there are models, a great deal of time is needed to estimate their parameters; where models do not exist, a great deal of study is needed to develop them. In both cases, the empirical work is often not generalizable beyond the locality researched (e.g., Walters 1997); 2. The design of experiments in field settings and on a large scale, in the absence of control groups and replicable systems and in the presence of a world where the no-treatment option is very often not feasible and where experiments must start with best management practices, many of which have not been experimentally derived (e.g., Carpenter 1996); 3. The high costs of experiments and the ecological risks of unintended impacts, especially when the lag time between the experiment and the discovery of associated error is long and the magnitude of that error is large, such that the errors when discovered are for all practical purposes irreversible. This is especially important because many proponents of adaptive management insist that real learning can only be achieved through large-scale, not small-scale, experimentation. While adaptive management is ideal when the potential error is

small or better yet nonexistent and the lag time short or better yet immediate, ecosystem complexity is sufficient to ensure that the ideal cannot be guaranteed (e.g., Gunderson 1999); 4. The identification of adaptive management with a structured process of “learning-by-doing” experimentation in relatively unforgiving or even hostile political, organizational, and social environments that expect any such intervention to be of little or no risk, right the first time around, and with few, if any, chances to do that experiment all over again (e.g., Gunderson and others 1995); and 5. The variable consensus, if any, over (i) how to define adaptive management; (ii) the extent and intensity of organizational and political commitment to adaptive management, however defined; and (iii) how such defined adaptive management should actually to be implemented across initiatives to improve ecosystem health (Walters 1997). To reiterate, each problem on its own need not be insurmountable and recommendations for addressing each continue to be developed. Extreme difficulties arise, however, in those increasing instances when the problems occur and interact together, as they most notably do in human-dominated ecosystems where population densities and intensified resource utilization place demanding reliability requirements on ecosystem services and functions [Matson and others 1997, Vitousek and others 1997 (in the Science special section on human-dominated ecosystems); see also Strange and others 1999]. In these regions, rapid population increase and resource extraction threaten to degrade the ecosystem at the same time people want higher quality environmental amenities, including clean water and air, to be provided and managed reliably and continuously (e.g., Holling 1995, Roe 2000). Unfortunately, high-reliability resource requirements have frequently worked against ecosystem health, as when massive waterworks (e.g., dams and irrigation canals) destroyed presettlement habitats in the name of ensuring reliable water and power supplies to cities and agriculture. Accordingly, it is where reliability requirements on ecosystem services and natural resource utilization are high that sustained or improved ecosystem health becomes all the more important, notwithstanding the limitations of adaptive management strategies in just such settings. The threshold-based resource management framework provides an institutionally sensitive framework for environmental decision-makers to identify and develop science- and engineering-based options that better address and accommodate ecosystem health and reliabil-

Threshold-Based Resource Management

ity requirements. The framework guides management choices among four major science and engineering approaches to achieve healthier ecosystems—adaptive management as well as the self-sustaining, high-reliability, and case-by-case resource management approaches. It does so by demonstrating how different resources management regimes are contingent upon ecosystem characteristics, thereby doing justice to each management regime without slighting the importance or necessity of the other approaches. From the perspective of our framework, the problems with adaptive management have arisen not so much from its approach as from its application to inappropriate ecosystems. We turn now to a description of the framework, followed by an application to a contemporary ecosystem management initiative, the San Francisco-Bay Delta (CALFED) Program.

The Framework of Threshold-Based Resource Management Assume a hypothetical landscape and its ecosystems located somewhere in the world. Ecological definitions of “landscape,” “ecosystem,” and “ecosystem health” differ and none are precise, but it is sufficient here to define a landscape as a heterogeneous land area with multiple ecosystems, where an ecosystem in that landscape is a local biological community and its diverse patterns of interaction with the landscape and the wider environment. A healthy ecosystem is one where that diversity and productivity in ecosystem functions and services are sustained together. Our landscape’s ecosystems are varied, although what these ecosystems are (freshwater, marine, mountain) is not as important as that they differ precisely along those dimensions found to be crucial for setting the limits of adaptive management: In our landscape, human population numbers in the ecosystems range from none to many per unit of land; resource extraction from the landscape’s ecosystems ranges from virtually nonexistent to high (as measured in, e.g., land values, barter terms of trade, pollution discharges); the degree to which the ecosystem is expected to reliably provide resources that have uses ranges from ecosystems having multiple resources with few, if any, human (consumptive) uses (e.g., the ecosystem may provide only a viewscape) to those ecosystems where single resources provide for many different uses (e.g., water is used for recreational, agricultural, environmental, and urban purposes); availability of causally adequate models to explain and predict relationships important for management purposes ranges from relatively few, if any,

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to many such models; and, last but not least, the mix of ecosystem and organizational health considerations varies across the ecosystems, ranging from those ecosystems where ecosystem health predominates in management to those where organizational health (i.e., high resource reliability requirements) predominate. These five dimensions define a continuum of ecosystems, their management regimes, and the thresholds between one management regime and another. The dimensions are interrelated rather than independent of each other. Simplified, the framework is summarized in Figure 1. As shown, the dimensions and the continuum of management regimes they map out set the boundaries within which management can improve ecosystem health, ranging from self-sustaining management which is focused exclusively on sustained ecosystem health to high-reliability management with its partial focus on the reliable management of a set of improved ecosystem services or resources, e.g., only clean water or listed species under the Endangered Species Act (ESA). Figure 1 represents the lateral ranking of ecosystems at one point in time, the present. An ecosystem is either in a state of control and use or in transition between old and new states. For heuristic purposes, ecosystems in our landscape have been grouped into four possible states or categories of control and use: those relatively undominated by people and their needs, others that are colonized to some extent by humans, still others that have already moved from colonization through to increasingly competing extractive uses and human domination, and finally ecosystems where human domination and regular extractive use for high-reliability purposes are their preeminent features. Of course, the real world has its mixed cases, e.g., some ecosystems are relatively unpopulated but nonetheless heavily extracted due to increasing demand for the ecosystem resources by rising population demand outside the ecosystem in question. As we will see, these mixed cases are also zones of conflict, where case-by-case resource management is appropriate. Returning to our hypothetical landscape, some examples of the four ecosystem categories in the order they are presented in Figure 1 are: wilderness or remote areas; large government parks; areas where rising population growth, natural resource utilization, and demand for environmental amenities are in conflict; and the full-blown urban ecosystems of cities and towns. Yet it would be misleading to think in terms of wilderness areas, parks, zones of conflict or cities, if we were to conclude that the thresholds that divide ecosystem categories are largely legal and administrative in nature.

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Figure 1. Threshold-based resource management framework.

The reason why the examples are legally and administratively different is due to increased controls and uses to which humans put the ecosystems in question. It is these latter factors that provide the context for the thresholds and the implications these thresholds have for managing the ecosystems. What are the thresholds and their management implications? Obviously, they vary empirically from landscape to landscape, and in a world of reliable (i.e., certain, causally comprehensible, and complete) knowledge, each landscape’s thresholds would be defined just as empirically in terms of the stressors, processes, and populations for any given state and transition. Our hypothetical landscape, however, faces the same core resource management dilemma as most other landscapes. Data simply do not exist in the detail required to make increasingly urgent ecosystem management decisions. Thus, our landscape’s environmental decision-maker must start with the prevailing theories and broad analytic approaches for resource management appropriate to each category for the landscape’s ecosystems. Under such circumstances, the thresholds necessarily become a function of these different theories. Once the theories have been described, we will be in a better position to see how the thresholds at issue in this article are really a function of the models of learning underlying the management theories in question. For

the time being, we peg these transitions in terms of as yet imprecisely defined human colonization, domination, and widening control and use of ecosystems and ecosystem services for high-reliability purposes. The following discussion is necessarily schematic, with a fuller presentation of the framework provided in van Eeten and Roe (in preparation). Nothing in what follows implies that the four are the only theories for resource management available (for others, see Roe 1998, Miller 1999). What is presented are the prevailing theoretical perspectives recommended in current professional literature for the major ecosystem categories identified above. Each theory can be clearly defined in terms of the five dimensions of the ecosystem continuum and each posits a management approach that (it contends) increases the chances of successful ecosystem management for the ecosystem category in question. The different theories and management regimes are described in some detail. Afterwards, it will be possible to identify how the dimensions of resource/use, health, and modeling (in Figure 1), along with those of human population densities and extraction, set the management regimes apart from each other, constitute the criteria for choosing these theories over others, and explain the nature of the thresholds between management regimes. For ease of exposition, we start at the ends of the continuum.


Human-Dominated Ecosystems for Highly Reliable Resource Control: High-Reliability Management and Theory High-reliability theory is a relatively recent development of organization theorists, who have been interested in how complex organizations and institutions maintain their activities in situations where failure, error, and accidents are highly probable (Rochlin 1996). The primary question is (Demchak 1996, La Porte 1996), how do some institutions, with complex systems and in predictably unstable environments, still manage to continually meet peakload production in a reliable and safe fashion. The high-reliability organizations (HROs) studied have included air traffic control systems, nuclear power plants, electricity companies, hospital intensive care units, and naval air carriers. Many heavily populated ecosystems, it bears repeating, require high-reliability management as well, if only in ensuring a steady stream of ecosystem services, including but not limited to water quality and supply. Rochlin (1993) summarized the principal features of high reliability management in his “Defining ‘High Reliability’ Organizations in Practice: A Taxonomic Prologue.” The work of other high-reliability theorists, particularly La Porte (1996), is used to supplement and extend the list into nine primary features: High technical competence. High-reliability institutions are characterized by the management of technologies that are increasingly complex and that require specialized knowledge and management skills in order to safely meet the organization’s peakload production requirements. What this means in practice is that the organizations concerned are continuously training their personnel, with constant attention devoted to recruitment, training, and performance incentives for realizing the high technical competence required. To do so means not only that there must be an extensive database in the organization on the technical processes and state of the system being managed, but that this “database” includes experience with differing operating scales and different phases of operation, the proposition being that the more experience there is with various operating scales and with starting and stopping risky phases of those operations, the greater the chances are that the organization can act in a reliable fashion, other things being equal. High performance and oversight. Technical competence in an HRO must be matched by continual high performance. The potential public consequences of operational error are so great that the organization’s continued success, let alone survival, depends on reli-

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ably maintaining high performance levels through constant oversight. Constant search for improvement. A feature related to high technical competence and continual monitoring is the continued drive to improve operations in highreliability institutions. Personnel are constantly searching to better their operations, even when (precisely because) they are performing at very high levels. Highly complex activities. Not unexpectedly, the actual operations and activities performed are themselves highly complex in that they are inherently numerous, differentiated, and interdependent. What this means in practice is that high reliability organizations often find it difficult to separate the physical and the technical, the internal from external, and the social from the organizational. In such organizations, its technology, social setting, and units are largely, although not completely, inseparable. High pressures, incentives and shared expectations for reliability. The activities and operations must meet social and political demands that necessitate high performance, with safety requirements met in the process. One way to do so is to ensure that those who do the management work and live close to the system they manage—they fly on the airplanes they build or guide, they live downwind of the chemical plants they run or on the floodplains they manage, and their homes depend on the electricity they generate. Hazard-driven flexibility to ensure safety. The operations and services provided by the high-reliability organization are inherently hazardous, where the hazards are numerous and varied, full of consequences and time urgency, and demand constant, flexible, technology-driven management to provide an acceptable level of safety to the managers, other personnel and the public. Culture of reliability. Since an HRO must maintain high levels of operational reliability, and do so safely, if it is to be permitted to continue to carry out its operations and service provision, a culture of reliability comes to characterize these organizations. This means in practice that these organizations often exhibit clear discipline dedicated to assuring failure-free, failureavoiding performance. Reliability is not fungible. Because of the extremely high consequences that come about because of error or failure, high-reliability organizations cannot easily make marginal trade-offs between increasing their services and the reliability with which those services are provided. There is a point at which such organizations are simply not able to trade reliability for other desired attributes, including money. Money and the like are not

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interchangeable with reliability; they cannot substitute for it; high reliability is, in brief, not fungible. Limitations on trial-and-error learning. Given the above, it is not surprising that high-reliability organizations are very reluctant to allow their primary operations to proceed in a trial-and-error fashion for fear that the first error would be the last trial. While such institutions do have search and discovery processes, often elaborate ones, they will not undertake learning and experimentation that expose them to greater hazards than they already face. They undertake learning only within the bounds that they control operationally. Trialand-error learning does occur, but this is done outside the primary operations, through advanced modeling, simulations, and in other ways that avoid testing the boundary between system continuance and collapse. If we had to physically locate the above features, we would be looking for the control room with line operators and engineers who form the core of a HRO, such as is found in the large water supply systems of our San Francisco Bay Delta case study. In such control rooms, you find in one place technical competence, complex activities, high performance at peak levels, search for improvements, pressures for safety, and the best example of the culture of reliability at work. According to the framework, resource management within an ecosystem context based on these features of high reliability can be found in situations of high population densities or regular, widespread extractive uses. HRO characteristics have been called for in the management of urban ecosystems (Roe 2000), and found in pastoralist ecosystems (Roe and others 1998). Both require not just a healthy ecosystem, but also healthy institutions and organizations adept at dealing with complex technologies and systems that extend beyond the ecosystem to the landscape level and beyond. As one high-reliability theorist, Paul Schulman (1996, p. 74), puts it, “reliability often becomes synonymous with a proxy variable— organizational health.” Ecosystems with Little or No Human Domination and Extractive Uses: Self-Sustaining Management and Complex Adaptive Systems Theory At the other end of the continuum from intensely managed ecosystems are those that are comparatively untouched by human hands. Good people will disagree about what “relatively” means in this day and age of global climate change, full-planet satellite coverage, and worldwide anthropogenic sequelae. The notion of “managing” wilderness or remote areas (e.g., ice caps, hyperarid deserts, mountain peaks) so that they remain the way they are is itself an indication that the disagreement has merit. Still, the management regime for the

comparatively unmolested ecosystems in our hypothetical landscape is qualitatively different than that for the human-dominated and directly exploited ecosystems of the landscape. At this end of the continuum, environmental decision-makers are talking about self-sustaining ecosystems that manage themselves and remain self-sustaining. For many people, these ecosystems must remain self-sustaining so as to minimize, if not permanently forestall, human mismanagement of them. Here the ideal is self-sustaining management. The relevant analytic framework for this ecosystem category is drawn from the study of complex adaptive ecosystems not dominated by humans. Such ecosystems are complex adaptive systems because their numerous, varied, and interdependent components—primarily ecological—interact in ways that enable the system to self-organize and improve its chances for survival within the ecosystem or landscape. Only a fraction of the literature on complex adaptive ecosystems treats humans as a force for self-sustaining activities, and for the most part, self-regulation and self-correcting ecosystems work best, in the view of the pertinent literature, when human beings are not there or use the area lightly. Complex adaptive systems theory has been developed to understand principles underlying the dynamics of systems ranging from ecosystems to economies. These diverse systems are thought to resemble each other in that they are difficult to understand and manage from the outside, and their evolving nature provides a “moving target” that is extremely difficult to model. Currently, work is being done to develop fairly simple models of individual behavior, say, of ants, which, when combined with simple rules, give rise to surprisingly complex, coordinated behavior. Fullblown, robust models of complex adaptive systems, however, do not exist. The difficulty in modeling comes from the fact that complex adaptive systems, including ecosystems, share three characteristics: evolution, aggregate behavior, and anticipation, all of which generate the system’s capacity to self-organize its behavior and thereby ensure its self-sustaining management (eg., see Holland). “Evolution” refers to the ability of parts of the system (here, ecosystem) to adapt and learn. The systems of interest exhibit complex adaptive processes with many parts and widely varying individual criteria for effectiveness. “Aggregate behavior” is the ability of a system to exhibit behavior that is not simply derived from the action of its parts. In an ecosystem, this means the overall food web or the cycles of flow of materials and energy. “Anticipation” is the ability of the parts to develop rules that anticipate the consequences of certain


responses. This attribute makes the emergent behavior of complex adaptive systems both intricate and difficult to understand. In this way, a complex adaptive system builds and uses its own “internal models,” a characteristic that defies traditional modeling methods. Anticipation, along with evolution and aggregate behavior, enable new behavioral rules and macro/meso relationships to emerge, often spontaneously, from discrete individual behavior and microinteractions. Such selforganizing properties are the essence of complex adaptive systems. It should be stressed that self-sustaining management is recommended for what goes on “inside the fence” that, metaphorically or literally, protects wilderness or remote areas from outside human intervention. Obviously, the human management requirements of putting into place and maintaining that protection may be high. In fact, one may well need high-reliability management “outside the fence” in order to ensure that what goes on inside remains self-sustaining on its own terms. Ecosystems Colonized by People for Increasing Their Extractive Use: Adaptive Management and Theory Like reality, our hypothetical landscape has scarcely one ecosystem that is self-sustaining, either totally or even nearly so. The majority of comparatively unpopulated grasslands, lakes, rivers, and forests in this landscape have been directly influenced by past human intervention—the forests are second-growth, rivers have water levels regulated by structures elsewhere, lakes contain introduced species, and the mix of flora and fauna in the grasslands has changed because of previous livestock utilization. In these ecosystems, the numbers and impact of people have been considerable in comparison to whatever “untouched” or “unutilized” areas exist in the landscape, although far fewer numbers of people and impacts have been sustained than in the fully human-dominated ecosystems requiring highreliability management. Adaptive management has its greatest salience and applicability in these human-colonized but not intensely dominated ecosystems. We outlined the features of adaptive management earlier. According to Carl Walters (Walters 1986, p. 9), one early promoter of adaptive management, the business of designing adaptive management strategies involves four basic issues: 1.

bounding of management problems in terms of explicit and hidden objectives, practical constraints on action, and the breadth of factors considered in analysis;

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2.

representation of existing understanding of managed systems in terms of more explicit models of dynamic behavior, that spell out assumptions and predictions clearly enough so that errors can be detected and used for further learning; 3. representation of uncertainty and its propagation through time in relation to management actions, using statistical measures and imaginative identification of alternative hypotheses (models) that are consistent with experience but might point toward opportunities for improved productivity; and 4. design of balanced policies that provide for continuing resource production while simultaneously probing for better understanding and untested opportunities. The relevant question for the adaptive manager is: “What does the fact that it is impossible to foresee all (or even most) of the impacts imply for the structure of the basic development plan and assessment research?” (Holling 1978, p. 3). From this perspective, accommodation and response to uncertainty must be part of management as much, if not more so, as uncertainty identification and reduction. The central role of uncertainty in adaptive management follows directly from the fact that the chief feature of the ecosystems to be adaptively managed is their unpredictability, about which the environmental decision-maker must learn more before trying to manage the ecosystem (or specific ecosystem services and functions) in ways that better mimic its presettlement template. For Walters, adaptive environmental assessment is “a way of getting people involved in modeling as a learning process, rather than as something you hire a specialist to do” (Walters 1986, p. 46). This responsiveness to uncertainty in management takes several forms (Walters 1986): ●

trial-and-error management, where early management choices are essentially haphazard, while later choices are made from a subset that gives better results; ● passive adaptive management, where historical data available at each time are used to construct a single best estimate or model for response, and the decision choice is based on assuming this model is correct; and ● active adaptive management, where data available at each time are used to structure a range of alternative response models, and a policy choice is made that reflects some computed balance between shortterm performance and long-term value of knowing which alternative model (if any) is correct.

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Active adaptive management has received the strongest endorsement as the way to produce improved management through controlled experiments. Note “experiments” is plural: The chances of improved management are substantially enhanced when the same hypotheses are tested over multiple, but similar units, as “the balance of learning and risks often does not favor experimental disturbances in single, unique, managed systems” (Walters 1986). Also note that it does an injustice to the notion of active adaptive management to equate it to trial-and-error learning, as such an equivalence little conveys the interactive coupling of learning and management through the process of experimentation, reexperimentation, and continuous hypothesis generation and testing that guides actual decision-making. For these reasons, active adaptive management is best suited to ecosystems where the human footprint is evident, but not deep, namely, the humanly colonized but not dominated ecosystems. It is here where a series of experiments can take place over multiple localities in ways that can minimize human disruption and restore ecological functions and processes, in whole or significant part, to what existed prior to human settlement. As we will see in the next section, passive adaptive management, where experiments and restoration may play less of a role than monitoring and ecological adjustments, is suitable to more densely utilized systems. Active adaptive management is, nonetheless, increasingly recommended for urban and agricultural ecosystems, whose defining features are comparatively high population densities and/or extractive uses. Some of the best known ecosystem management initiatives (see Gunderson and others 1995), particularly for the Chesapeake Bay, Everglades (Kissimmee River), Great Lakes, Rhine River, Louisiana (Mississippi River-Delta), Columbia River, and, most recently, the San Francisco Bay-Delta system (the focus of this article’s case study), incorporate a mix of ecosystems, including agricultural, urban, and less populated ones. While many initiatives say they adopt “adaptive management,” much of their management learning does not take place through formal experimentation, reexperimentation, and hypothesis-testing. Moreover, real-world adaptive management, while respecting the presettlement template, may be less whole-ecosystem focused than centered around restoration, preservation, or stewardship of selected ecosystem services and functions, regardless of experimentation. In fact, the lack of pervasive experimentation is the chief distinguishing feature of these initiatives (e.g., Johnson and others 1999). In reality, they are zones of conflict between increasing human populations, resource utilization, and demands for environ-

mental amenities, where actual resource management and learning have to be tailored to the specific ecosystems and/or landscape being managed. Ecosystems as Zones of Conflict Among People, Resources, and the Environment: Case-By-Case Resource Management and Approach Our landscape also includes ecosystems where human pressures in the form of rapid population growth, increased natural resource utilization for extractive purposes, and the rising pressures for more environmental amenities are in conflict. (Again, the accelerated demand for ecosystem resources may be due to population increase outside the ecosystem in question.) Conflict is always possible in these ecosystems because the ecosystem’s inherent unpredictability and the wider demands for high reliability in resource goods and services extracted from that ecosystem are often inconsistent and opposed, especially in the absence of mediating mechanisms that reconcile (more formally, operationally couple) the unpredictability and reliability. It is important to be clear about the principal mediating mechanism, as its absence or attenuation is the primary way in which the conflict between rising population, resource use, and demand for a better environment is articulated and realized in these ecosystems. The mechanism, what we call the (coupling– decoupling–recoupling) CDR dynamic, works this way. If the environmental decision-maker takes the conflict seriously, as he/she must, then he/she sees issues of population, resources, and the environment (such as water quality, water supply, protection of endangered species, ecosystem restoration, hydropower generation, flood control) as necessarily interlinked. Indeed, what drives many ecosystem management initiatives is just this necessity to address such matters simultaneously. It follows that policies and programs dealing with the interlocked issues jeopardize their effectiveness and further threaten ecosystem health if they are themselves not coupled in important respects. Yet, even though population, resources, and the environment are tightly interconnected in zones of conflict, the issues become unmanageable when different and already complex policies are correspondingly treated just as tightly coupled. What started out as the valid recognition that issues are so interrelated that they have to be optimized together, ends up rendering policy difficult if not impossible to achieve in these terms alone. When faced with such a turbulent task environment, the pressure is, as any number of organization theorists have underscored, to decouple the issues of specific interest from that environment and buffer them in the form of their having their own


programs, agencies, or distinct professions (see Hukkinen 1999, Roe 1994, Chambers 1988). What is coupled at the policy level, becomes decoupled at the program level for implementation or management. Thus, the environmental decision-maker sees all manner of population, resource, and environmental programs professing their connectedness but operating in the real world on their own by professionals often trained in separate disciplines. However, the decoupling, while achieving shortterm reductions in turbulence and increases in program stability and effectiveness, ends up undermining the very optimization process that drove the initial systemwide coupling. Decoupling serves to highlight how connected the issues really are and how important it is to deal with them in a directly linked way. Where the initial coupling generated pressure to decouple, the decoupling, in turn, reinforces the pressure to recouple—the third element of the CDR dynamic. One salutary feature of decoupling is to make transparent just what can and needs to be coupled in practice. Often, we argue, a more effective recoupling of goals and issues becomes possible only after a programmatic decoupling (e.g., Roe 1994). In other words, decoupling sensitizes environmental decision-makers to where operational recoupling of population, resource and environmental issues is working or could work. The interconnectedness of population, resources, and the environment has to be reflected in policy, but that policy really is not policy unless the couplings can be operationalized “corner to corner,” in particular, we believe, by incorporating line operators—the people who actually manage the various activities in the field—at every stage of the policy’s development, implement, redesign, and on-going management. A crucial characteristic of this operationalization is that it enables a dynamic optimization process among different goals and issues, thereby making program and agency boundaries permeable. “Dynamic” means that the variables of the optimization problem can be manipulated at the same time to explore trade-offs and capitalize on opportunities for flexibility, as in our case study below. The decoupled situation, in contrast, supports a static optimization process at best, where program or goals set fixed boundary conditions within which line operators optimize the effectiveness of the program for which they are responsible, e.g., water supply operators working within ESA constraints. How this operationalization (i.e., real time management) actually occurs is necessarily done case by case, since the dynamics of coupling, decoupling, and recoupling are inevitably site-specific and contingent on the host of factors specific to the situation at hand over our

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already heterogeneous landscape. We define case-bycase resource management as those occasions when successful recoupling occurs at the operational level of policy goals that, in whole or in part, promote improved ecosystem health in zones of conflict. In such instances, the longer-term optimization process that drove the initial coupling can be carried on, but frequently in dramatically different (i.e., more case-specific) ways than initially conceived at the policy level. Thus, a latent function of successful recoupling can be to leave the preexisting programmatic decoupling in place, if simply by virtue of underscoring that it always takes operations (or the field or implementation) rather than just the program (or headquarters or planning) to put broad policy goals into practice. Several features characterize case-by-case resource management as a CDR dynamic for operational recoupling of ambitious goals and objectives coupled at the policy level and decoupled at the program level (for more, see Roe 1998): 1.

Case-by-case resource management is evolutionary: Managers start with the expectation that the ecosystem is out there waiting to be identified, realize once in the field that there are problems in delineating ecosystem features, later acknowledge that such problems arise in part because what is out there depends crucially on how what it is they are looking for is defined in the first place, and then end up better understanding that what works best by way of recouplings in any particular situation is a function of customizing the ideal and the practical to meet the specific objectives agreed upon in the resource management case by case. In this way, it is not be surprising that case-by-case resource management draws from the very different approaches and modeling of complex adaptive systems theory, adaptive management, and high-reliability theory at different stages of this evolutionary process. 2. Case-by-case resource management means analyzing each case of management on its own merits. At least five different criteria exist to evaluate management: (1) in terms of whether the management achieves its stated objectives; (2) against some ideal, which the management’s objectives may or may not match; (3) against the implementation record of like management; (4) in terms of the counterfactual, i.e., what would have happened had the management not been in effect; and (5) in terms of whether savings could be realized if the management were undertaken more cost-effectively. Judging each case on its merits is deciding

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the mix of criteria and weights to be assigned to each criterion for the case in question. 3. Not only do the above multiple criteria exist to assess management strategies, but the actual implementation of these strategies also results in evaluating what are the appropriate mix and weights for the criteria. We really do not know how to evaluate effective site-specific operational recouplings of ambitiously linked policy objectives, until we see just what those recouplings really are, on the ground. When this happens, the case is also being analyzed on its own merits by virtue of being locally syncretic rather than globally synoptic. (For more on case-by-case analysis and multiple evaluative criteria, see Roe 1994, 1996, 1998). These three features mean that there are many more models at work in case-by-case management compared to the fewer individual-based models of complex adaptive systems theory and the more formal conceptual models being developed and tested through adaptive management. Case-by-case management draws from these approaches as well as from the informal, tacit knowledge/bounded rationality models of environmental decision-makers themselves. (Indeed, the five evaluative criteria constitute their own “models.”) This is why, ironically, some passive adaptive management is really case-by-case management, where such management involves a broader range of modeling than does active (i.e., experiment-based) adaptive management. What these three features also mean is that case-bycase resource management in zones of conflict is rarely a total failure or a total success when it comes to operationally recouplings of interconnected policy objectives. The more evaluative criteria for site-specific management and/or the longer the evolution period of the management, the greater the chance that management will be analyzed and assessed in favorable terms on some, but not all, the criteria. This way, the performance record of case-by-case resource management will almost always be mixed—never totally negative but never entirely positive—which is precisely the reason why it is always difficult to generalize or replicate from case-by-case resource management. Threshold-Based Resource Management over the Four Ecosystem Categories Table 1 summarizes the chief features of self-sustaining, (active) adaptive, case-by-case, and high-reliability management regimes in the threshold-based resource management framework (see Roe and others 1999). It should be clear from Table 1 that the exclusive use or recommendation of any one management regime, be it

self-sustaining, adaptive, case-by-case, or high-reliability management, across all categories of ecosystems within a heterogeneous landscape that is variably populated and extractively used is not only inappropriate, it is lethal to the goals of effective resource management. The light impact management recommended by complex adaptive systems theory for virtually unpopulated ecosystems stands in sharp contradistinction to the decidedly heavy impact management recommended by high-reliability theory for the landscape’s highly populated and/or extracted ecosystems. Managing urban ecosystems and managing wilderness reserves may both require high-reliability management, but there is little, if anything, self-sustaining about urban ecosystems as ecosystems. As for case-by-case resource management, its recommendations by definition can not be generalized to other ecosystems and landscapes. Given the very different management implications of each approach, the crucial problem for the environmental decision-maker becomes the need to recognize the thresholds that govern the shifts between the different categories of ecosystems across the multidimensional continuum underlying threshold-based resource management. To see how this works, return Figure 1. Again, ecosystems vary from left to right in terms of increasing human densities and extractive use. Clearly, they vary along the three other dimensions as well. From left to right, you also have multiple resources being managed for few, if any, human uses under self-sustaining ecosystem management, to a single resource being managed for multiple human uses, as found in high reliability management. There is also movement from a growing concern with ecosystem health on the left to an equal concern with organizational health on the right (more properly, ecosystem health with organizational implications to organizational health with ecosystem implications). Lastly, the availability of adequate models to capture cause-and-effect relationships varies by ecosystem categories, with few adequate conceptual models available for self-sustaining ecosystems to many models and simulations required for effective high reliability management. The five different dimensions are what distinguish the threshold-based resource management framework from other approaches. Indeed, only the four management regimes—self-sustaining, adaptive, case-by-case, and high reliability— capture the kinds of changes that are underway along all five dimensions. How, then, to interpret the thresholds across this fivefold gradient? Obviously, empirical triggers are involved, observed, and learned about (e.g., we learn when different kinds of densities lead to different kinds


Table 1.

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Distinguishing characteristics of management regimes in threshold-based resource management Self-sustaining management

(Active) adaptive management

Case-by-case management

High-reliability management

Ecosystem properties

Relatively untouched by human hands

Some human disruption of essentially natural ecosystem

High human densities and extraction define the ecosystem

Beliefs about ecosystem being managed

Sustainability is assured through natural complex processes, such that ecosystem will manage itself optimally

Types of models employed

Ecosystem builds and uses its own internal models and exhibits behavior that defies extensive or easy formal modeling

Ecosystem can be incrementally managed overtime to mimic its presettlement (historic) functions and processes Formal and competing theoretical models and hypotheses are tested as a “learning process”

Zones of conflict with natural systems overlaid by human artifacts Each ecosystem is unique and its own case, where managers improve ecosystem performance but without full success Variety of formal and informal models are used and weighted differently reflecting merits of each case

Modes of learning

Learning is in the natural systems as part of its evolution, not in human beings

Learning through management experiments, preferably large-scale and multi-site

Learning through real-time management and evaluation of treating each case in its own right

Attribution of success

System is successful so long as it is selfsustaining

Attribution of failure

Failure is human intervention that disrupts or otherwise alters an ecosystem that is already selfsustaining

Science-based learning leads to improved understanding, and this leads to better management In the long run, there is no “failure,” as long learning for better management takes place in light of experimental results

Best metaphor for manager

Curator

Ecologist

Success measured against multiple criteria, such that it is always partial and uneven Some failure is inevitable, because of multiple criteria for evaluating management, i.e., a success can not be generalized beyond the case being managed Policy Analyst

Resource system is predictable within known tolerances and can be managed safely for peakload performance Extensive thought experiments and/or computer modeling & simulations are used before scaling up to real-time field management Simulation and other low-impact “experimentation;” continued search for improvement in management efficacy Success is meeting peakload demands safely, all the time

Failure is when a HRO does not meet peakload requirements safely on its own terms, i.e., when no “act of God” or exogenous factor caused the failure Engineer

Adapted from Roe and others (1999).

of land use changes). As Table 1 indicates, the thresholds are a function of the different models of learning that are assumed in the theories and analytic approaches that underpin the management regimes for the landscape’s ecosystem categories. The way environmental decision-makers obtain and learn information for management purposes varies considerably by the mode of management recommended for any given ecosystem category. The limits of management are very much set by the limits of learning, where learning includes learning the limits to ecosystem management. Adaptive management and high-reliability manage-

ment are separated by their very different orientations to large-scale trial-and-learning and experimentation: the former is more willing to accept it for precisely the same reasons the latter would prefer to shun it, namely, this experimentation means taking substantial risks, and each management regime is oriented differently to such risk. The models of generalized learning through adaptive management or high-reliability management, in turn, are very different from the markedly more site-specific, often opportunistic learning that takes place in case-by-case resource management. If this holds for case-by-case resource management, it holds

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doubly for self-sustaining management, where learning takes place, but as the learning of biological systems and genes evolving over geologic rather than human time. In this way, what triggers change in threshold-based resource management is what the environmental decision-makers have learned to be the limits of what they can satisfactorily manage while operating under the model of learning that governs the management they are then undertaking. Work by Louise Comfort (1999) suggests that these learning-based thresholds are highly sensitive to the initial conditions of the landscape and ecosystem, are defined by their vulnerability to outside forces (such as fire, earthquake, or other emergency), are irreversible in important respects for management (i.e., once you go down the road of one kind of management, it is hard to turn back), drive the mutual adjustments of environmental decision-makers that become self-reinforcing over time but which eventually become rigidities that force decision-makers to selforganize around different ways of learning and management regimes, and are the source of both unpredictable impacts as well as predictable patterns of behavior that seek to reproduce themselves within the ecosystem and extend themselves, inappropriately, to other ecosystems. In brief, thresholds are the transitions when people in the landscape and/or ecosystem discover a common threat to their current ways of learning and organize alternative ways or states of learning and management in response along the approaches just outlined (for a range ecology perspective on stateand-transition models, see Westoby and others 1989).

Threshold-Based Resource Management in Practice How does threshold-based resource management actually work? We conclude with an application to what is to be the world’s most expensive and complicated ecosystem management initiative, the CALFED Bay-Delta Program (a longer version of the initial case study with fuller references can be found in Roe and others 1999). The program is a cooperative effort among US federal and California state agencies to address environmental and water management problems associated with the San Francisco Bay-Delta River system. This system covers a watershed that drains more than 37% of the state and a delta of some 738,000 acres. In the last century or so, the bay-delta has been dramatically modified by human use. Over 700 miles of waterways are protected by more than 1100 miles of levees. Only 8000 acres of tidal marsh are left in the delta, down from an estimated 345,000 acres. Some delta islands have subsided

to such a degree that they are now 20 feet below sea level. It has been reported that two thirds of the state’s rain falls in northern California, while two thirds of the people reside southern California, an asymmetry used to justify the massive water conveyance system through the delta of the California State Water Project and the federal government’s Central Valley Project. The size of the bay-delta and the scale and variety of human activities that depend on it make it critically important to a wide range of stakeholders in the region. Environmentalists are concerned with conserving the largest estuary on the Pacific side of the Americas, home to 130 fish species alone (and over 300 species of birds, mammals, amphibians, reptiles, and fish) and millions of local and migratory birds. Anglers and commercial fishers are concerned about the sustained use of one of the most productive natural salmon fisheries on the American West Coast. California’s $24 billion agricultural industry seeks to ensure a steady supply of irrigation water to millions of acres of the world’s most productive farmland (in-delta production by itself accounts for $500 million). What works in the CALFED program by way of environmental restoration and ecosystem management has enormous implications for similar initiatives elsewhere. The CALFED program is an especially suitable case study for the threshold-based resource management framework. First, the bay-delta includes a wide range of ecosystems, both aquatic and terrestrial. Second, the centerpiece of CALFED is its program component for ecosystem restoration and management. Third, the program’s goals explicitly incorporate increasing the reliability of water supply, quality, and the delta levee system. Fourth, adaptive management is a core program concept that applies to all program components, including not just that for ecosystem management but also those for increasing the reliability of water supply, quality, and levees. Fifth, a significant portion of the CALFED documentation on adaptive management embraces the template of self-sustaining ecosystem processes and functions as appropriate for guiding restoration work. Last but not least, the program is an excellent example of case-by-case resource management across a heterogeneous landscape where population, resources and the environment are in conflict. The CALFED Bay-Delta Program officially started in May 1995 to address the complex management issues surrounding the use of the bay-delta resource, end decades of strife among stakeholder groups, and foster cooperation among the 15 state and federal agencies with management or regulatory responsibilities in the region (phase II report, p. 11). After identifying specific areas of conflict, CALFED arrived at four program


goals and objectives to be simultaneously pursued in order to create a universal win–win resource policy: ecosystem quality, water supply reliability, water quality, and levee system integrity. Each goal seeks to resolve resource conflicts in large part through improved reliability for water supply, water quality, levee protection, and in the provision of sustained or improved ecosystem services and restoration. Consequently, four common programs in CALFED are ecosystem restoration, levee system integrity, water quality, and water use efficiency. The first and most developed common program is now known as the Ecosystem Restoration Program (ERP). ERP is to provide significant improvements in habitat for the environment, restoration of some critical water flows, and reduced conflict with other delta system resources. On paper, it represents one of the most ambitious and comprehensive ecosystem restoration programs in the United States and contains over 700 program elements to be implemented over the 30-year life of CALFED. Spanning the array of program goals and common programs is adaptive management. Adaptive management is defined by CALFED as “a fundamental Program concept” that is to be adopted and practiced by all program components and throughout the program. The Strategic Plan for Ecosystem Restoration, published separately as a guiding document for the ERP, closely follows the framework for adaptive management developed in the academic literature discussed earlier. Quoting from work by Walters and Holling, the strategic plan lays out a six-step adaptive management methodology involving modeling, designing management interventions on the basis of these models, implementing and monitoring the interventions, and then redesigning the interventions in light of that improved understanding. (For more information on the CALFED program, including the reports and figures mentioned in the preceding paragraphs, see the program’s website, http://calfed.ca.gov). We were able to interview or obtain information from some 20 people directly associated with the CALFED program, including those in the Ecosystem Restoration Program, the Levee Protection Program, and the Water Quality Program. Interviews were held with officials in the California Department of Water Resources (DWR), including those connected with the Operations Control Office (OCO), flood protection, flood operations, and the Interagency Ecological Program (IEP). Other interviews were undertaken with representatives from the CALFED Monitoring and Research Program (CMARP), US Environmental Protection Agency (US EPA), and the Metropolitan Water District. Our choice of interviewees and questions was guided by

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CALFED’s approach to meeting its high-profile dual requirements for adaptive management and, at the same time, for high reliability in water supply, levee protection, and water quality. Problems with Adaptive Management and the Challenge of the CDR Dynamic The CALFED planning region is so heterogeneous a landscape that it embraces ecosystems and areas for which the program’s approach to adaptive management is clearly appropriate. Unfortunately, the adaptive management approach has been extended to all ecosystems, many of which fall in those zones of conflict between population, resources, and the environment more suitable for case-by-case resource management. Consequently, the problems associated with the landscape-wide application of adaptive management are precisely those identified at the start of this article. The problems are especially pronounced because much less is understood about the biology and ecology of aquatic ecosystems, including the bay-delta. Here, “experiments” are, for good reasons, nonreplicable, largescale, based on the best information available, with potentially irreversible effects, undertaken in a world that is never one way only, and where you rarely have a chance to do things again. For all intents and purposes, such experiments are one-off interventions. One may learn from the interventions, but they better fit case-bycase management than adaptive management. Again, this is not a criticism of adaptive management, but rather of its application to ecosystems for which it is not suitable or useful. This conclusion, however, begs the question of what would be useful by way of case-by-case resource management in those ecosystem zones of conflict. The answer returns us to the CDR dynamic underlying such management, for it is that dynamic that must recouple on the ground the objectives of improved ecosystem health and improved water reliability that are coupled at the policy level but decoupled at the program and agency levels in CALFED. In CDR terms, the other three management approaches on their own work against simultaneously realizing improved ecosystem health and water reliability. The commitment of adaptive management to mimicking, as far as possible, the presettlement (self-sustaining) template represents, in effect, a decoupling of ecosystem management from human managers precisely at a time when human management of resourcerelated conflict is expected and demanded because of the coupled policy goals of ecosystem health and high water reliability. Where adaptive management is too focused on decoupling, high-reliability management is

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overly centered on recouplings. High-reliability management assumes that all the operational couplings and recouplings that matter are entirely in the technologies and organizations of interest, a state of affairs that has worked against real-time ecosystem health considerations. As for self-sustaining management, where it actually exists, it is by definition without human conflict (at least “inside the fence”) and therefore in no need of an intervening CDR dynamic to couple, decouple, or recouple anything. Yet—and this is the crucial point— high-reliability management remains important for improving ecosystem health, at best because the CALFED objectives include the reliable provision of improved ecosystem services (as from restored wetlands), at worst because of the mandated reliability imposed through the speciesby-species approach of the ESA, and most practically because highly reliable water supplies, quality, and protection are an explicit goal of the program. Thus, the challenge of the CALFED program is to take the best of adaptive management and high reliability management and recouple them into its own case-by-case approach to managing major CALFED ecosystems. This can be achieved through what has been termed bandwidth management (Paul Schulman, personal communication). Bandwidth Management In the bay-delta context, bandwidth management would seek to operationally recouple ecosystem health and the high reliability concerns over water supply, water quality, and levee system integrity. The idea, although simple, is difficult to execute: decouple setting ecosystem bandwidths from managing within the bandwidths. Then recouple the bandwidths and the management within them through organizational interventions that directly link ecosystem health and water supply, water quality, and levee reliability in mutually supportive ways. Adaptive management by ecologists would determine the bandwidths, that is, the limits that define ecosystem health or the boundaries within which ecosystem health is to be achieved (e.g., there should be connectivity of wetlands in a wetlands ecosystem, the saltwater intrusion zone should be at x in an estuarine ecosystem, and so on). High-reliability management by line operators and engineers, in turn, would expect these bandwidths to be feasible for adjusting within, i.e., ecologists set the bandwidths with the executing engineers and line operators in mind. [For brevity’s sake, the terms ecologist, engineer, and line operators refer to much wider groups, e.g., ecologists can include, e.g., other natural scientists such as conservation biologists, climatologists, toxicologists,

and hydrologists; for differences between line operators and engineers, see von Meier (1999)]. In this way, adaptive management and high-reliability management are coupled operationally as a process of identifying and setting the bandwidths in which real-time adjustments are to be made for coupled ecosystem health and high water reliability. We illustrate how this can be done below. More generally, the principal feature of bandwidth management is nothing less than the principal feature of the CDR dynamic in all such case-by-case management: The limits within which we operate for joint ecosystem management and high water reliability are precisely those that optimize for both over the longer term and in a way that enables the effective operationalization of broad policy objectives to address ecosystem health and water reliability together. We have to be careful, however, about equating bandwidth management with the simplistic notion that the ecologists and biologists set the bandwidths and engineers and line operators manage within the bandwidths. We need to go beyond this simplification in important ways in order for bandwidth management to be effective. As noted earlier, operational recoupling means dynamic optimization processes. In CALFED, as elsewhere, current optimization processes are mostly of a static nature, because of the decoupling at the program level. Ecologists and biologists define standards (e.g., minimal flow requirements or import/export ratios) that are treated by the line operators of the water system as fixed boundaries for their optimization process. Operational recoupling would require that, instead of fixed boundaries, ecosystem health considerations are defined on a case-by-case basis in trade-off or synergy with the water-reliability requirements. One way to achieve this, as we argue below, is to bring ecologists, as line operators, into the process of managing within the boundaries imposed by ecosystem health considerations. Of course, bandwidth management has no guarantee of success: The bandwidths may be impossible to set; if set, they can be breached, either because they were too narrowly defined or because of stochastic factors; line operators can remain within the bandwidths, but nevertheless produce negative impacts on ecosystem health or the bandwidths may be continually redefined in light of new ecological and operational information with no clear positive or negative impacts, on ecosystem health. How then would bandwidth management actually work in the CALFED context? Below are two proposals for realizing bandwidth management as initially recommended in Roe and others (1999). One proposal, to


create a separate ERP agency, is directed to ensuring ERP can establish the bandwidths in which a mutually reinforcing ecosystem management and high water reliability management are sustained. The other proposal, to establish a branch, “ecosystem operations,” within the state’s major HRO for water supplies, the Department of Water Resource’s Operations Control Office, is directed at ensuring that high water reliability management takes place within an ecosystem context set by the bandwidths. While specific, detailed and prescriptive, the proposals should interest the general reader who is concerned with how, in concrete terms, elements of two very different management regimes— adaptive management and high-reliability management— can be organizationally accommodated within the threshold-based resource management framework to produce what we call ecoreliability. ERP as an agency. A number of reasons have been given for creating a new structure around CALFED’s Ecosystem Restoration Program, i.e., environmentalists and others have long desired a separate unit that practiced science-based management, subjected itself to peer review of interventions, operated under a dedicated budget for research, and had an inviolate budget for monitoring. We support the creation of a separate agency around ERP, but for reasons that follow from the logic of the CDR dynamic. ERP should be decoupled from the CALFED program structure, that is, created as a separate agency in its own right, but only in ways that make the subsequent operational recoupling of ERP with other CALFED agencies and program elements more effective than the current ERP structure or proposals for restructuring. Separating ERP from the CALFED program structure means decoupling long-term ecosystem restoration and management from the environmental activities of other agencies. A number of state and federal agencies (namely those in CALFED) already do “environmental restoration” or ancillary activities, e.g., the California Department of Fish and Game (DFG) has a mandate to protect and conserve the state’s natural resources, and DWR has a mandate to ensure that their levee improvement projects also result in “net habitat improvement.” At first it may seem that such mandates put these agencies in a excellent position to operationally recouple ecosystem health and water reliability. In practice, we find the reverse is true. The need to decouple ERP from these agencies arises from the current asymmetry where agencies support the importance of a longterm, ecosystem perspective for restoration, but their management is dominated by all manner of shorterterm, site-specific, and urgent considerations. For ex-

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ample, the need to comply with stringent and complex ESA and DFG requirements during levee improvement projects leaves little room to develop and implement a longer-term ecosystem perspective beyond the levee site. Because of lack of staffing, the political mandate, or the propensity to pay full attention to environmental restoration within an ecosystem context, these and related agencies are not in a position to effectively operationally recouple ecosystem health to water and levee reliability. CALFED is not unique in this respect. In many cases, environmental management is dominated by dealing with short-term complexities and goals, effectively undermining the much needed longer-term perspective (Hukkinen 1999). Only when ERP is decoupled from the CALFED agencies and set up as a separate structure, instead of maintaining its current status as an interagency program, would the state have an organization that could contribute to setting the bandwidths on the basis of a longer-term ecosystem approach. In short, decoupling is needed to enable successful recoupling. Setting up an independent, autonomous state agency having the sole institutional mandate of managing ecosystem restoration and recovery involves issues of authority, responsibility, legal mandates, funding, and staffing that cannot be easily resolved and that we cannot address here. In addition to possible duplication of mandates with existing agencies, commitments would be needed beforehand that those interagency differences over ecosystem restoration and recovery that cannot be negotiated between ERP and the other agencies on their own would have to be settled formally, if not in the courts, then by a statutory regulatory unit having authority (the State Water Resources Control Board comes to mind when dealing with aquatic ecosystems within California). Despite these real drawbacks, we believe the potential benefits of an independent statewide agency are clear. As a way of enabling bandwidth management and operational recoupling, an ERP agency would greatly reduce the stress placed on the state’s water-related agencies. The aforementioned tensions inherent in the internal goal conflict in such organizations and networks would accordingly be reduced. Second, the new statewide agency would bear the brunt of the political debate surrounding the suggestion and ranking of environmental restoration solutions and proposals, as well as the responsibility for developing alternatives in cooperation with the other agencies. Knowing who is responsible for environmental restoration and how much is dedicated to the state’s environmental budget introduces stability (indeed reliability) into decision-

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making and helps distinguish political conflicts over ensuring a better environment from those over ensuring reliability of water supply and quality. Third, the credibility of the agencies in charge of ensuring water supply and quality reliability would be increased if they were to be dedicated solely to ensuring that reliability. Keeping the trade-offs between ecosystem health and water reliability in the public arena avoids accusations of conflict of interest or private deals by the agencies currently in charge of doing parts of both. The long-run survival of a separate ERP agency would, however, depend on how well ecologists make themselves indispensable to the other CALFED program components and their line operations. Instead of ERP going to the other components and buying their time and staff to undertake joint environmental restoration efforts, one ERP goal should be for these components to come to ERP and initiate the joint interventions. An unfortunate feature of extending ERP’s active adaptive management framework to the other CALFED programs is that ERP has much more to contribute to these components than a six-step adaptive management process that ends up being six ways to fail. ERP’s policy objective should be to give, e.g., levee protection and water quality programs, an ecological dimension that would not only enhance ecosystem values but at the same time meet reliability concerns for these components. To put it concretely, the objective would be to improve the benefit– cost ratios of these programs by ensuring they have a value-added environmental component rather than simply trying to improve the benefit– cost ratio of ERP interventions alone. An indispensable ERP would be one that was operationally coupled with the other programs, agencies, and their line operators in major ways. ERP, for example, could contract the design and construction of levees and would itself be a contractor for DWR’s habitat restoration efforts on levees. In this way, ERP would be taking the reliability concerns of DWR and the Levee Protection Program seriously enough to become experts themselves in levee design, construction, and maintenance, e.g., by employing its own engineers/ consultants and by canvassing other levee designs adopted by different countries such as the Netherlands to maintain comparable levels of reliability but within a ecosystem context (e.g., Van Eeten 1997, 1999). Ecosystem operations branch. Formalizing a separate organizational structure around ERP is not enough, for its focus would be on determining the bandwidths, not on determining how to manage within them. ERP would worry about how to set up joint management interventions with line operators in order to learn what

it takes to assure ecosystem health in ways that are not harmed by also assuring the high water reliability requirements of the system. What is missing is the learning to manage within these assurances once they have been identified. There is only one state agency that has the latter responsibility: DWR’s Operations Control Office (OCO). No other unit is better positioned than the OCO to ensure such assurances are met in a consistently and highly reliable fashion. Its real-time operation of the massive State Water Project, for which it is responsible, depends directly on the health of the delta and above-delta ecosystems. No other organization is better positioned than the OCO to link ecosystem management and water reliability as they both relate to the SWP. As said earlier, if we had to physically locate the principal features of high reliability, what we would be looking for is the control room where in one place are found the technical competence, complex activities, high performance at peak levels, search for improvements, pressures for safety, and the best example of the culture of reliability at work. That control room is DWR’s Operations Control Office. Our second proposal is that OCO (and through it, DWR) increase its ability to undertake ecosystem management in connection with the State Water Project (SWP). As with the preceding proposal, this one involves issues of authority, responsibility, mandates, funding, and staffing within DWR that can only be touched upon here. Specifically, DWR should consider establishing a fourth branch, provisionally called “ecosystem operations,” in the OCO to work along side the Project Operations Planning Branch, Project Operation Center, and the Project Operations Support Branch. The OCO already has the goal of managing the SWP’s operations with environmental sensitivity, and more needs to be done to connect the line operators responsible for water reliability to ecosystem management requirements. The proposed Ecosystems Operations Branch (ECO/OPS, for short) would provide that operational recoupling for the SWP by translating a generalized commitment to environmental sensitivity into concrete ecosystem management interventions within a high reliability context. There are a number of scenarios as to how ECO/ OPS would work. We suggest only one. The proposed branch would consist of a small team of engineers, other line operators, and ecologists recruited from within DWR and from the agencies on the branch’s interagency oversight committee (more below). As with all bandwidth management, it is not possible to prejudge just what the ECO/OPS interventions would be in advance of knowing what the bandwidths are for the


ecosystem in question. In all cases, the management would involve real-time operations and monitoring of managed ecosystems for higher reliability of water supply and quality. ERP, as well as other agencies, would be doing similar interventions (e.g., wetlands construction), but for very different reasons. ECO/OPS would be explicitly and always concerned with the coupling of water reliability and ecosystem management in real time, while ERP would be addressing larger issues of how to restore ecosystem health while assuring high water reliability mandates. Line operators and engineers are already working to find the flexibility in a tight SWP for ESA-driven water requirements. What is needed is a two-way process: determining how line operators and engineers can increase their flexibility by using improved ecosystem management as a way to generate more water reliably over time for the SWP, and also how flexibility in water supply can be used to improve ecosystem health. CALFED recognizes this flexibility is present in the system and has conducted gaming exercises with line operators of different agencies (including both engineers and biologists) to explore and find ways to increase it in OCO’s management of the system. The current environmental standards and constraints within which OCO operates—i.e., optimizes water supply and quality— do not support the twofold use of flexibility. The reverse is true. Both ecosystem health and water reliability suffer from the inflexibility of these standards (concerning fish numbers, flow requirements, import/export ratios, salinity levels, etc.). To put it differently, these standards are not defined and applied in real time on a case-by-case basis, as is required for effective operational recoupling. Of course, where the standard does not seem to fit the situation, there is always the search for a better standard, but given the complexity of the system, it seems impossible to build the use of this flexibility into the standards directly. Currently, it can only be done in real time, i.e., by line operators. ECO/OPS staff would work with the OCO line operators and engineers to find the flexibility, both within the SWP and in collaboration with the CVP. The OCO becomes all the more important as a locus of trying to balance reliability and ecosystem needs, since the office is already balancing the related trade-offs between water reliability and power generation in a deregulated electricity sector. Indeed, the proposed branch should be part of the OCO precisely because it is in the OCO where the complex real-time optimization process is being managed for water supply, (some) water quality, energy generation, consumption, and, last but not least, fish management. Many

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of the ESA-driven concerns are already being handled operationally with some success at the line operator level in DWR, as long as standards are explicit, planned, and subject to real-time monitoring and management. ECO/OPS would incorporate these already ongoing activities in its core mandate. The improved operational coupling of ecosystem health and water reliability requires more than improved optimization. In line with the CDR dynamic, also needed is the injection of a longer-term perspective and feasible policies for both health and reliability that can actually be operationalized. Just bringing ecologists into the line operations via ECO/OPS will extend the planning and management time horizons, but more is required to ensure effective coupling. To that end, an interagency committee is recommended to provide oversight of the branch’s activities. Several models for this oversight committee already exist in the CALFED context. In bandwidth management terms, the interagency committee would be responsible for advising the branch on the reliable bandwidths in which it should operate, while the branch would continue to report within the DWR chain of command on the real-time monitoring and adjustments by branch operators that take place within these bandwidths. Where the challenge faced by a separate ERP agency is to ensure its policies and goals are always operationalized in the field, the challenge faced by the Ecosystem Operations Branch comes from the other side, namely, to ensure that its activities operate within a policy structure committed to coupling ecosystem health and water reliability in real, multiple ways that matter for policy. The proposed interagency committee should therefore consist of representatives from US Bureau of Reclamation (precisely because operational activities of the SWP and the CVP are so tightly coupled), DFG, US Fish and Wildlife Service, US EPA, and DWR’s Interagency Ecological Program, among others. The oversight committee could as well be chaired by ERP in order to maximize operational coupling between ERP and the branch. In fact, the branch could contract ERP to design some of its ecosystem management improvements just as ERP could contract with the branch to undertake real-time ecosystem interventions along the lines and rationale already described. These operational links would be the way in which bandwidth management is made coherent, where activities of ERP in setting the bandwidth and activities of the branch in working within those bandwidths are coupled in concrete ways.

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Table 2.


Management regime differences between ERP and ECO/OPS functionalities

Management regime Adaptive

High reliability

Self-sustaining

ERP

ECO/OPS

Ideally provides experiments, practically interventions, that define what are limits (bandwidths) within which environmental restorations can be reliably undertaken (1) confirms that environmental restorations do improve resource reliability (e.g., determining the conditions under which wetlands are a viable alternative to tertiary treatment plants) and (2) more important, implements the actual restoration so that effective high reliability management can continue in the long run Effectively constitutes, through the presettlement template, adaptive management’s great experiment

Conclusion: Eco-Reliability as Bandwidth Management Within the Threshold-Based Resource Management Framework Now, most important, generalize beyond the CALFED context. Think of ERP and ECO/OPS as two kinds of functionality necessary for bandwidth management in zones of conflict between people, resources, and the environment. That is, in your landscape (not CALFED’s), the ECO/OPS functionality could be constituted, e.g., as a team in the HRO responsible for resource management, while the ERP functionality may take the form of a branch or division in the government’s main environmental ministry. Indeed, the permutations of ERP and ECO/OPS are myriad, although their respective functionalities remain the mutually interactive setting of bandwidths and operating within them. It should be clear by this point that the ERP and ECO/OPS functionalities not only allow the environmental decision-maker to meld elements of adaptive management and high reliability management, but in so doing they also accommodate features of self-sustaining management (at least in the form of the presettlement template as it pertains to adaptive management). Indeed, the three management regimes—self-sustaining, adaptive, and high reliability— uniquely come together and take on a case-specific relevance through bandwidth management, as in Table 2. From a bandwidth management perspective, the ERP functionality makes no sense if it does not incorporate features of the adaptive, high-reliability, and self-sustaining management regimes. ERP requires the regimes because each has something to say about coupling ecosystem health and resource reliability together

Ideally helps to lift mandated species-byspecies requirements and thereby improves resource reliability Provides real-time monitoring and managing of ecosystem resources (e.g., fisheries and habitat) within predetermined bandwidths

Effectively serves as a reminder to high reliability management that important elements of the bandwidth remain unknown or unknowable

in meaningful ways. Those responsible for the ERP functionality rely on: adaptive management to provide the experiments (practically, the interventions) that determine the limits (bandwidths) within which environmental restorations can be reliably undertaken; high-reliability management to confirm that environmental restorations actually do improve resource reliability (e.g., determining the conditions under which wetlands are a viable alternative to tertiary treatment plants) and to implement the actual restoration so reliably that high-reliability management can continue over the long-run in ways that do not harm ecosystem health; and self-sustaining management, whose presettlement template for ecosystem functions and processes to be mimicked is perhaps adaptive management’s truly great experiment. Similar considerations hold for the ECO/OPS functionality, but from the other direction. This functionality seeks to ensure that adjustments can be made to reliably manage or protect the ecosystem, if not as a system then for its selected ecosystem services or functions. Those responsible for the ECO/OPS functionality rely on: adaptive management because its success would to help lift the mandated species-by-species requirements and thereby improve resource reliability; high-reliability management because it provides realtime monitoring and managing of ecosystem resources (e.g., fisheries and habitat) within predetermined bandwidths; and self-sustaining management because the presettlement template serves as a constant reminder to high-reliability management that important elements of the bandwidth remain unknown or unknowable. If the ERP and ECO/OPS functionalities are able to


meld the three management regimes in these ways along the lines of what we have been calling bandwidth management—in other landscapes as well as CALFED’s—then they will have truly coupled in practice what policy has hitherto been unable to couple beyond theory. They will have rendered what was once oxymoronic— ecoreliability—into something that is, albeit difficult, nonetheless achievable in real-time. A functional equivalent to ERP that can manage to learn how to undertake and implement environmental restoration in ways that enable high-reliability management to continue thereafter is a functionality that reverses the history of high-reliability management as one of undermining ecosystem health rather than sustaining or actually improving it. An ECO/OPS functionality that can learn how to manage reliably within bandwidths sensitive to, if not actually derived from, ecosystem health is a functionality that significantly qualifies the notion of ecosystem management as a contradiction in terms, namely, thoroughly unpredictable ecosystems impossible to manage reliably. If you would have an ERP and ECO/OPS acting and coupled as proposed, you have achieved eco-reliability—and along with it a different kind of professionalism—in profoundly new ways [Chambers (1988) is the starting point for the new professionalism.] This perhaps would be the most important contribution of the CALFED program and other ecosystem management initiatives. It certainly would be the most important contribution of the threshold-based resource management approach.

Acknowledgments We thank Peter Gratzinger for his help in writing the article’s sections on the background to the CALFED program, self-sustaining and adaptive management regimes, and Table 1. We are grateful to the Rockefeller Foundation for funding the original study and Shiv Someshwar for his support of our efforts. We also thank without implicating: Steve Edwards, Todd La Porte, Robert Frosch, Louise Fortmann, Donald Moore, Craig Thomas, Louise Comfort, Paul Schulman, Rob Cooke, Curtis Creel, Dick Daniel, Tom Hagler, Bruce Herbold, Margaret Johnston, Peter Kiel, Wim Kimmerer, Gwen Knittweis, Dennis O’Bryant, Ron Ott, Jatinder Punia, Pete Rhoads, Curt Schmutte, Sally and Jim Shanks, Jim Spence, John Winther, Rick Woodard, Randy Brown, Victor Pacheco, Leo Winternitz, Russ Brown, Erwin Van Nieuwenhuyse, Larry Smith, and two Environmental Management reviewers. We alone are responsible for any errors that remain. This article is an earlier version of an argument more fully presented, with additional case material, in Van Eeten and Roe (in preparation).

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