Confronting socially generated uncertainty in adaptive management

As more and more organizations with responsibility for natural resource management adopt adaptive management as the rubric in which they wish to operate, it becomes increasingly important to consider the sources of uncertainty inherent in their endeavors. Without recognizing that uncertainty originates both in the natural world and in human undertakings, efforts to manage adaptively at the least will prove frustrating and at the worst will prove damaging to the very natural resources that are the management targets. There will be more surprises and those surprises potentially may prove at the very least unwanted and at the worst devastating. We illustrate how acknowledging uncertainty associated with the natural world is necessary but not sufficient to avoid surprise using case studies of efforts to manage three wildlife species; Hector's Dolphins, American Alligators and Pallid Sturgeon. Three characteristics of indeterminism are salient to all of them; non-stationarity, irreducibility and an inability to define objective probabilities. As an antidote, we recommend employing a holistic treatment of indeterminism, that includes recognizing that uncertainty originates in ecological systems and in how people perceive, interact and decide about the natural world of which they are integral players.     

Hydropower,adaptive management,and Biodiversity

Adaptive management is a policy framework within which an iterative process of decision making is followed based on the observed responses to and effectiveness of previous decisions. The use of adaptive management allows science-based research and monitoring of natural resource and ecological community responses, in conjunction with societal values and goals, to guide decisions concerning man's activities. The adaptive management process has been proposed for application to hydropower operations at Glen Canyon Dam on the Colorado River, a situation that requires complex balancing of natural resources requirements and competing human uses. This example is representative of the general increase in public interest in the operation of hydropower facilities and possible effects on downstream natural resources and of the growing conflicts between uses and users of river-based resources. This paper describes the adaptive management process, using the Glen Canyon Dam example, and discusses ways to make the process work effectively in managing downstream natural resources and biodiversity.     

Adaptive management for a turbulent future

The challenges that face humanity today differ from the past because as the scale of human influence has increased, our biggest challenges have become global in nature, and formerly local problems that could be addressed by shifting populations or switching resources, now aggregate (i.e., "scale up") limiting potential management options. Adaptive management is an approach to natural resource management that emphasizes learning through management based on the philosophy that knowledge is incomplete and much of what we think we know is actually wrong. Adaptive management has explicit structure, including careful elucidation of goals, identification of alternative management objectives and hypotheses of causation, and procedures for the collection of data followed by evaluation and reiteration. It is evident that adaptive management has matured, but it has also reached a crossroads. Practitioners and scientists have developed adaptive management and structured decision making techniques, and mathematicians have developed methods to reduce the uncertainties encountered in resource management, yet there continues to be misapplication of the method and misunderstanding of its purpose. Ironically, the confusion over the term "adaptive management" may stem from the flexibility inherent in the approach, which has resulted in multiple interpretations of "adaptive management" that fall along a continuum of complexity and a priori design. Adaptive management is not a panacea for the navigation of 'wicked problems' as it does not produce easy answers, and is only appropriate in a subset of natural resource management problems where both uncertainty and controllability are high. Nonetheless, the conceptual underpinnings of adaptive management are simple; there will always be inherent uncertainty and unpredictability in the dynamics and behavior of complex social-ecological systems, but management decisions must still be made, and whenever possible, we should incorporate learning into management.     

Pathology and failure in the design and implementation of adaptive management

The conceptual underpinnings for adaptive management are simple; there will always be inherent uncertainty and unpredictability in the dynamics and behavior of complex ecological systems as a result non-linear interactions among components and emergence, yet management decisions must still be made. The strength of adaptive management is in the recognition and confrontation of such uncertainty. Rather than ignore uncertainty, or use it to preclude management actions, adaptive management can foster resilience and flexibility to cope with an uncertain future, and develop safe to fail management approaches that acknowledge inevitable changes and surprises. Since its initial introduction, adaptive management has been hailed as a solution to endless trial and error approaches to complex natural resource management challenges. However, its implementation has failed more often than not. It does not produce easy answers, and it is appropriate in only a subset of natural resource management problems. Clearly adaptive management has great potential when applied appropriately. Just as clearly adaptive management has seemingly failed to live up to its high expectations. Why? We outline nine pathologies and challenges that can lead to failure in adaptive management programs. We focus on general sources of failures in adaptive management, so that others can avoid these pitfalls in the future. Adaptive management can be a powerful and beneficial tool when applied correctly to appropriate management problems; the challenge is to keep the concept of adaptive management from being hijacked for inappropriate use.     

Adaptive management of natural resources--framework and issues

Adaptive management, an approach for simultaneously managing and learning about natural resources, has been around for several decades. Interest in adaptive decision making has grown steadily over that time, and by now many in natural resources conservation claim that adaptive management is the approach they use in meeting their resource management responsibilities. Yet there remains considerable ambiguity about what adaptive management actually is, and how it is to be implemented by practitioners. The objective of this paper is to present a framework and conditions for adaptive decision making, and discuss some important challenges in its application. Adaptive management is described as a two-phase process of deliberative and iterative phases, which are implemented sequentially over the timeframe of an application. Key elements, processes, and issues in adaptive decision making are highlighted in terms of this framework. Special emphasis is given to the question of geographic scale, the difficulties presented by non-stationarity, and organizational challenges in implementing adaptive management.     

Improving our legacy: incorporation of adaptive management into state wildlife action plans

The loss of biodiversity is a mounting concern, but despite numerous attempts there are few large scale conservation efforts that have proven successful in reversing current declines. Given the challenge of biodiversity conservation, there is a need to develop strategic conservation plans that address species declines even with the inherent uncertainty in managing multiple species in complex environments. In 2002, the State Wildlife Grant program was initiated to fulfill this need, and while not explicitly outlined by Congress follows the fundamental premise of adaptive management, 'Learning by doing'. When action is necessary, but basic biological information and an understanding of appropriate management strategies are lacking, adaptive management enables managers to be proactive in spite of uncertainty. However, regardless of the strengths of adaptive management, the development of an effective adaptive management framework is challenging. In a review of 53 State Wildlife Action Plans, I found a keen awareness by planners that adaptive management was an effective method for addressing biodiversity conservation, but the development and incorporation of explicit adaptive management approaches within each plan remained elusive. Only ~25% of the plans included a framework for how adaptive management would be implemented at the project level within their state. There was, however, considerable support across plans for further development and implementation of adaptive management. By furthering the incorporation of adaptive management principles in conservation plans and explicitly outlining the decision making process, states will be poised to meet the pending challenges to biodiversity conservation.     

Fuzzy adaptive management of social and ecological carrying capacities for protected areas

Commonly used methods of evaluating the degree of consistency of protected area ecosystems with social and ecological carrying capacities are likely to result in decision errors. This occurs because such methods do not account for imprecision and uncertainty in inferring the degree of ecosystem consistency from an observed ecosystem indicator. This paper proposes a fuzzy adaptive management approach to determine whether a protected area ecosystem is consistent with ecological and social carrying capacities and, if not, to identify management actions that are most likely to achieve consistency when there is uncertainty about the current degree of consistency and how alternative management actions are likely to influence that consistency. The proposed approach is illustrated using a hypothetical example that uses an ecosystem indicator that reflects combinations of different levels of user satisfaction and conservation of threatened and endangered species. Application of the proposed fuzzy adaptive management approach requires a protected area manager to: (1) identify alternative management actions for achieving ecosystem consistency with social and ecological carrying capacities in each of several management zones in a protected area; (2) randomly assign alternative management actions to management zones; (3) define fuzzy sets for the ecosystem indicator and degree of ecosystem consistency, and fuzzy relations between the ecosystem indicator and the degree of ecosystem consistency; (4) monitor the indicator in each management zone; (5) define fuzzy sets based on the observed indicator in each management zone; and (6) combine the fuzzy sets defined on the observed indicator and the fuzzy relations between the indicator and the degree of ecosystem consistency to reach conclusions about the most likely degree of consistency for alternative management actions in each management zone. The fuzzy adaptive management approach proposed here is advantageous when the benefits of avoiding the decision errors inherent with crisp and stochastic decision rules outweigh the added cost of implementing the approach.     

Adaptive management in the U.S. National Wildlife Refuge System: science-management partnerships for conservation delivery

Adaptive management is an approach to recurrent decision making in which uncertainty about the decision is reduced over time through comparison of outcomes predicted by competing models against observed values of those outcomes. The National Wildlife Refuge System (NWRS) of the U.S. Fish and Wildlife Service is a large land management program charged with making natural resource management decisions, which often are made under considerable uncertainty, severe operational constraints, and conditions that limit ability to precisely carry out actions as intended. The NWRS presents outstanding opportunities for the application of adaptive management, but also difficult challenges. We describe two cooperative programs between the Fish and Wildlife Service and the U.S. Geological Survey to implement adaptive management at scales ranging from small, single refuge applications to large, multi-refuge, multi-region projects. Our experience to date suggests three important attributes common to successful implementation: a vigorous multi-partner collaboration, practical and informative decision framework components, and a sustained commitment to the process. Administrators in both agencies should consider these attributes when developing programs to promote the use and acceptance of adaptive management in the NWRS.     

Evaluating the efficacy of adaptive management approaches: Is there a formula for success?

Within the field of natural-resources management, the application of adaptive management is appropriate for complex problems high in uncertainty. Adaptive management is becoming an increasingly popular management-decision tool within the scientific community and has developed into two primary schools of thought: the Resilience-Experimentalist School (with high emphasis on stakeholder involvement, resilience, and highly complex models) and the Decision-Theoretic School (which results in relatively simple models through emphasizing stakeholder involvement for identifying management objectives). Because of these differences, adaptive management plans implemented under each of these schools may yield varying levels of success. We evaluated peer-reviewed literature focused on incorporation of adaptive management to identify components of successful adaptive management plans. Our evaluation included adaptive management elements such as stakeholder involvement, definitions of management objectives and actions, use and complexity of predictive models, and the sequence in which these elements were applied. We also defined a scale of degrees of success to make comparisons between the two adaptive management schools of thought. Our results include the relationship between the adaptive management process documented in the reviewed literature and our defined continuum of successful outcomes. Our data suggest an increase in the number of published articles with substantive discussion of adaptive management from 2000 to 2009 at a mean rate of annual change of 0.92 (r2 = 0.56). Additionally, our examination of data for temporal patterns related to each school resulted in an increase in acknowledgement of the Decision-Theoretic School of thought at a mean annual rate of change of 0.02 (r2 = 0.6679) and a stable acknowledgement for the Resilience-Experimentalist School of thought (r2 = 0.0042; slope = 0.0013). Identifying the elements of successful adaptive management will be advantageous to natural-resources managers considering adaptive management as a decision tool.     

More is not always better: coping with ambiguity in natural resources management

Coping with ambiguities in natural resources management has become unavoidable. Ambiguity is a distinct type of uncertainty that results from the simultaneous presence of multiple valid, and sometimes conflicting, ways of framing a problem. As such, it reflects discrepancies in meanings and interpretations. Under the presence of ambiguity it is not clear what problem is to be solved, who should be involved in the decision processes or what is an appropriate course of action. Despite the extensive literature about methodologies and tools to deal with uncertainty, not much has been said about how to handle ambiguities. In this paper, we discuss the notions of framing and ambiguity, and we identify five broad strategies to handle it: rational problem solving, persuasion, dialogical learning, negotiation and opposition. We compare these approaches in terms of their assumptions, mechanisms and outcomes and illustrate each approach with a number of concrete methods.     

Reflections on the use of Bayesian belief networks for adaptive management

A broad range of tools are available for integrated water resource management (IWRM). In the EU research project NeWater, a hypothesis exists that IWRM cannot be realised unless current management regimes undergo a transition toward adaptive management (AM). This includes a structured process of learning, dealing with complexity, uncertainty etc. We assume that it is no longer enough for managers and tool researchers to understand the complexity and uncertainty of the outer natural system-the environment. It is just as important, to understand what goes on in the complex and uncertain participatory processes between the water managers, different stakeholders, authorities and researchers when a specific tool and process is used for environmental management. The paper revisits a case study carried out 2001-2004 where the tool Bayesian networks (BNs) was tested for groundwater management with full stakeholder involvement. With the participation of two researchers (the authors) and two water managers previously involved in the case study, a qualitative interview was prepared and carried out in June 2006. The aim of this ex-post evaluation was to capture and explore the water managers' experience with Bayesian belief networks when used for integrated and adaptive water management and provide a narrative approach for tool enhancement.     

Adaptive management for sound ecosystem management

Sound ecosystem management meshes socioeconomic attitudes and values with sustainable natural resource practices. Adaptive management is a model for guiding natural resource managers in this process. Ecosystems and the societies that use them are continually evolving. Therefore, managers must be flexible and adaptable in the face of uncertainty and lack of knowledge. To couple good science to management, it is important to develop goals, models, and hypotheses that allow us to systematically learn as we manage. Goals and models guide the development and implementation of management practices. The need to evaluate models and test hypotheses mandates monitoring, which feeds into a continuous cycle of goal and model reformulation. This paper reviews the process of adaptive management and describes how it is being applied to oak/pine savanna restoration at Necedah National Wildlife Refuge as an illustration. Our aim is to help managers design their own adaptive management models for successful ecosystem management.     

Detection and decision-making in environmental effects monitoring

The majority of environmental effects monitoring (EEM) frameworks that have been proposed compare selected indicator variables as a means of assessing whether significant changes in stressed ecosystems have occurred. Most are deterministic in nature and do not appropriately account for the natural variability and dynamics within the systems being comapred. This suggests that the comparative procedures should be statistically based and immediately raises the issue of whether the selected comparative procedures are to be used as decision-making tools or conclusive procedures. Conclusive procedures require a significant body of evidence before rejecting the postulated null hypothesis. The costs and time involved in environmental data collection accordingly bias action toward the maintenance of a status quo approach to environmental management. if, however, EEM is treated as a decision-making procedure, risk functions that include consideration of type I and II statistical error may be developed and combined with costs to select a minimum expected loss strategy for environemental management. Examples of the interpretative difficulties and conclusion reversal phenomena caused when EEM is used as a conclusive procedure are presented. In addition, risk functions appropriate for environmental management within an EEM context are constructed and applied. Only when such tools are fully developed and applied can EEM expect to have significant impacts on minimizing environmental degradation.     

Phytosociological inference and resource management

The application of vegetation information to resource management problems is reviewed in four areas: (1) the stratification of vegetation data by classification and gradient analysis, (2) the storage and retrieval of these data, (3) the sources of vegetation information, and (4) the accuracy and resolution requirements posed by different management applications. Conclusions indicate that the successful application of inference methods to resource management problems requires an appropriate integration of these four components.     

Adaptive management: Promises and pitfalls

Proponents of the scientific adaptive management approach argue that it increases knowledge acquisition rates, enhances information flow among policy actors, and provides opportunities for creating shared understandings. However, evidence from efforts to implement the approach in New Brunswick, British Columbia, Canada, and the Columbia River Basin indicates that these promises have not been met. The data show that scientific adaptive management relies excessively on the use of linear systems models, discounts nonscientific forms of knowledge, and pays inadequate attention to policy processes that promote the development of shared understandings among diverse stakeholders. To be effective, new adaptive management efforts will need to incorporate knowledge from multiple sources, make use of multiple systems models, and support new forms of cooperation among stakeholders.     

Uncertainty and risk in wildland fire management: a review

Wildland fire management is subject to manifold sources of uncertainty. Beyond the unpredictability of wildfire behavior, uncertainty stems from inaccurate/missing data, limited resource value measures to guide prioritization across fires and resources at risk, and an incomplete scientific understanding of ecological response to fire, of fire behavior response to treatments, and of spatiotemporal dynamics involving disturbance regimes and climate change. This work attempts to systematically align sources of uncertainty with the most appropriate decision support methodologies, in order to facilitate cost-effective, risk-based wildfire planning efforts. We review the state of wildfire risk assessment and management, with a specific focus on uncertainties challenging implementation of integrated risk assessments that consider a suite of human and ecological values. Recent advances in wildfire simulation and geospatial mapping of highly valued resources have enabled robust risk-based analyses to inform planning across a variety of scales, although improvements are needed in fire behavior and ignition occurrence models. A key remaining challenge is a better characterization of non-market resources at risk, both in terms of their response to fire and how society values those resources. Our findings echo earlier literature identifying wildfire effects analysis and value uncertainty as the primary challenges to integrated wildfire risk assessment and wildfire management. We stress the importance of identifying and characterizing uncertainties in order to better quantify and manage them. Leveraging the most appropriate decision support tools can facilitate wildfire risk assessment and ideally improve decision-making.     

An inexact optimization approach for river water-quality management

A previously developed fuzzy waste load allocation model (FWLAM) for a river system is extended to address uncertainty involved in fixing the membership functions for the fuzzy goals of the pollution control agency (PCA) and the dischargers using the concept of grey systems. The model provides flexibility for the PCA and the dischargers to specify their goals independently, as the parameters for membership functions are considered as interval grey numbers instead of deterministic real numbers. An inexact or a grey fuzzy optimization model is developed in a multiobjective framework, to maximize the width of the interval valued fractional removal levels for providing latitude in decision-making and to minimize the width of the goal fulfillment level for reducing the system uncertainty. The concept of an acceptability index for order relation between two partially or fully overlapping intervals is used to get a deterministic equivalent of the grey fuzzy optimization model developed. The improvement of the optimal solutions over a previously developed grey fuzzy waste load allocation model (GFWLAM) is shown through an application to a hypothetical river system. The fuzzy multiobjective optimization and fuzzy goal programming techniques are used to solve the deterministic equivalent of the GFWLAM.     

Adaptive environmental management of tourism in the Province of Siena, Italy using the ecological footprint

Adaptive management as applied to tourism policy treats management policies as experiments that probe the responses of the system as human behavior changes. We present a conceptual systems model that incorporates the gap between observed and desired levels of the ecological footprint with respect to biocapacity. Addressing this gap (or 'overshoot') can inform strategies to increase or decrease visitation or its associated consumption in the coming years. The feedback mechanism in this conceptual model incorporates a gap between observed and desired ecological footprint levels of tourists and residents. The work is based on longer-term and ongoing study of tourism impacts and ecological footprint assessments from the SPIN-Eco Project. We present historical tourism and environmental data from the province of Siena, Italy and discuss the use of discrete, static environmental indicators as part of an iterative feedback process to manage tourism within biophysical limits. We discuss a necessary shift of emphasis from certain and static numbers to a process-based management model that can reflect slow changes to biophysical resources. As underscored by ecological footprint analysis, the energy and material use associated with tourism and local activity can erode natural capital foundations if that use exceeds the area's biological capacity to support it. The dynamic, and iterative process of using such indicators as management feedback allows us to view sustainability more accurately as a transition and journey, rather than a static destination to which management must arrive.     

Passive and active adaptive management: approaches and an example

Adaptive management is a framework for resource conservation that promotes iterative learning-based decision making. Yet there remains considerable confusion about what adaptive management entails, and how to actually make resource decisions adaptively. A key but somewhat ambiguous distinction in adaptive management is between active and passive forms of adaptive decision making. The objective of this paper is to illustrate some approaches to active and passive adaptive management with a simple example involving the drawdown of water impoundments on a wildlife refuge. The approaches are illustrated for the drawdown example, and contrasted in terms of objectives, costs, and potential learning rates. Some key challenges to the actual practice of AM are discussed, and tradeoffs between implementation costs and long-term benefits are highlighted.