Theory into Practice: Implementing Ecosystem Management Objectives in the USDA Forest Service

In the United States and around the world, scientists and practitioners have debated the definition and merits of ecosystem management as a new approach to natural resource management. While these debates continue, a growing number of organizations formally have adopted ecosystem management. However, adoption does not necessarily lead to successful implementation, and theories are not always put into practice. In this article, we examine how a leading natural resource agency, the United States Department of Agriculture Forest Service, has translated ecosystem management theory into concrete policy objectives and how successfully these objectives are perceived to be implemented throughout the national forest system. Through document analysis, interviews, and survey responses from 345 Forest Service managers (district rangers, forest supervisors, and regional foresters), we find that the agency has incorporated numerous ecosystem management components into its objectives. Agency managers perceive that the greatest attainment of such objectives is related to collaborative stewardship and integration of scientific information, areas in which the organization has considerable prior experience. The objectives perceived to be least attained are adaptive management and integration of social and economic information, areas requiring substantial new resources and a knowledge base not traditionally emphasized by natural resource managers. Overall, success in implementing ecosystem management objectives is linked to committed forest managers.     

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 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.     

Luring anglers to enhance fisheries

Current fisheries management is, unfortunately, reactive rather than proactive to changes in fishery characteristics. Furthermore, anglers do not act independently on waterbodies, and thus, fisheries are complex socio-ecological systems. Proactive management of these complex systems necessitates an approach--adaptive fisheries management--that allows learning to occur simultaneously with management. A promising area for implementation of adaptive fisheries management is the study of luring anglers to or from specific waterbodies to meet management goals. Purposeful manipulation of anglers, and its associated field of study, is nonexistent in past management. Evaluation of different management practices (i.e., hypotheses) through an iterative adaptive management process should include both a biological and sociological survey to address changes in fish populations and changes in angler satisfaction related to changes in management. We believe adaptive management is ideal for development and assessment of management strategies targeted at angler participation. Moreover these concepts and understandings should be applicable to other natural resource users such as hunters and hikers.     

Adaptive Management and Watersheds: A Social Science Perspective1

Abstract:  Adaptive management is often proposed as the most effective way to manage complex watersheds. However, our experience suggests that social and institutional factors constrain the search for, and integration of, the genuine learning that defines adaptive management. Drawing on our work as social scientists, and on a guided panel discussion at a recent AWRA conference, we suggest that watershed‐scale adaptive management must be recognized as a radical departure from established ways of managing natural resources if it is to achieve its promise. Successful implementation will require new ways of thinking about management, new organizational structures and new implementation processes and tools. Adaptive management encourages scrutiny of prevailing social and organizational norms and this is unlikely to occur without a change in the culture of natural resource management and research. Planners and managers require educational, administrative, and political support as they seek to understand and implement adaptive management. Learning and reflection must be valued and rewarded, and fora established where learning through adaptive management can be shared and explored. The creation of new institutions, including educational curricula, organizational policies and practices, and professional norms and beliefs, will require support from within bureaucracies and from politicians. For adaptive management to be effective researchers and managers alike must work together at the watershed‐scale to bridge the gaps between theory and practice, and between social and technical understandings of watersheds and the people who occupy and use them.     

Learning and adaptation in the management of waterfowl harvests

A formal framework for the adaptive management of waterfowl harvests was adopted by the U.S. Fish and Wildlife Service in 1995. The process admits competing models of waterfowl population dynamics and harvest impacts, and relies on model averaging to compute optimal strategies for regulating harvest. Model weights, reflecting the relative ability of the alternative models to predict changes in population size, are used in the model averaging and are updated each year based on a comparison of model predictions and observations of population size. Since its inception the adaptive harvest program has focused principally on mallards (Anas platyrhynchos), which constitute a large portion of the U.S. waterfowl harvest. Four competing models, derived from a combination of two survival and two reproductive hypotheses, were originally assigned equal weights. In the last year of available information (2007), model weights favored the weakly density-dependent reproductive hypothesis over the strongly density-dependent one, and the additive mortality hypothesis over the compensatory one. The change in model weights led to a more conservative harvesting policy than what was in effect in the early years of the program. Adaptive harvest management has been successful in many ways, but nonetheless has exposed the difficulties in defining management objectives, in predicting and regulating harvests, and in coping with the tradeoffs inherent in managing multiple waterfowl stocks exposed to a common harvest. The key challenge now facing managers is whether adaptive harvest management as an institution can be sufficiently adaptive, and whether the knowledge and experience gained from the process can be reflected in higher-level policy decisions.     

Threshold-Based Resource Management: A Framework for Comprehensive Ecosystem Management

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 threshold-based resource management. Threshold-based resource 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 high-reliability 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 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 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.     

‘Learning by doing’: adaptive planning as a strategy to address uncertainty in planning

Adaptive management, an established method in natural resource and ecosystem management, has not been widely applied to landscape planning due to the lack of an operational method that addresses the role of uncertainty and standardized monitoring protocols and methods. A review of adaptive management literature and practices reveals several key concepts and principles for adaptive planning: (1) management actions are best understood and practiced as experiments; (2) several plans/experiments can be implemented simultaneously; (3) monitoring of management actions are key; and (4) adaptive management can be understood as ‘learning by doing’. The paper identifies various uncertainties in landscape planning as the major obstacles for the adoption of an adaptive approach. To address the uncertainty in landscape planning, an adaptive planning method is proposed where monitoring plays an integral role to reduce uncertainty. The proposed method is then applied to a conceptual test in water resource planning addressing abiotic-biotic-cultural resources. To operationalize adaptive planning, it is argued that professionals, stakeholders and researchers need to function in a genuinely transdisciplinary mode where all contribute to, and benefit from, decision making and the continuous generation of new knowledge.     

Practicing natural resource management with a policy orientation

All natural resource managers want to contribute to successful conservation programs. Having and applying an explicit policy orientation is indispensable. The policy sciences are described and a case is made that, if natural resource managers utilize this set of conceptual and applied tools in their natural resource work, their effectiveness could be enhanced. The policy sciences offer a contextual, problem-oriented, and multimethod approach to meeting complex problems. Two kinds of knowledge are needed to solve problems—substantive knowledge about the resource and process knowledge about the decision and policy processes used to derive courses of management action. The interplay of science, analysis, and politics are examined. The wildlife management community is used to illustrate many points, including the important role implementation plays in the overall policy process.     

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.     

Sailing the Shoals of Adaptive Management: The Case of Salmon in the Pacific Northwest

/ Emerging ecosystem science builds on adaptive management as an approach to dealing with salmon problems in the Pacific Northwest. Adaptive management brings scientific and democratic processes together. However, managers, the public, resource users, and scientists differ in their views on the causes of salmon decline. Managers emphasize habitat loss and over-harvest as the primary causes; commercial fishers point to habitat loss, management practices, and predators; and the public gives greatest weight to water pollution and ocean drift nets. Scientific studies of salmon often produce results that seem contradictory or unclear to the public. For adaptive management to be effective, scientists' and the public need to better understand one another's perspectives.KEY WORDS: Perception; Fishery management; Salmon; Pacific Northwest; Science     

Multimodel inference and adaptive management

Ecology is an inherently complex science coping with correlated variables, nonlinear interactions and multiple scales of pattern and process, making it difficult for experiments to result in clear, strong inference. Natural resource managers, policy makers, and stakeholders rely on science to provide timely and accurate management recommendations. However, the time necessary to untangle the complexities of interactions within ecosystems is often far greater than the time available to make management decisions. One method of coping with this problem is multimodel inference. Multimodel inference assesses uncertainty by calculating likelihoods among multiple competing hypotheses, but multimodel inference results are often equivocal. Despite this, there may be pressure for ecologists to provide management recommendations regardless of the strength of their study's inference. We reviewed papers in the Journal of Wildlife Management (JWM) and the journal Conservation Biology (CB) to quantify the prevalence of multimodel inference approaches, the resulting inference (weak versus strong), and how authors dealt with the uncertainty. Thirty-eight percent and 14%, respectively, of articles in the JWM and CB used multimodel inference approaches. Strong inference was rarely observed, with only 7% of JWM and 20% of CB articles resulting in strong inference. We found the majority of weak inference papers in both journals (59%) gave specific management recommendations. Model selection uncertainty was ignored in most recommendations for management. We suggest that adaptive management is an ideal method to resolve uncertainty when research results in weak inference.     

ECOSYSTEM MANAGEMENT AND THE CONSERVATION OF AQUATIC BIODWERSITY AND ECOLOGICAL INTEGRITY1

ABSTRACT: Ecologically effective ecosystem management will require the development of a robust logic, rationale, and framework for addressing the inherent limitations of scientific understanding. It must incorporate a strategy for avoiding irreversible or large-scale environmental mistakes that arise from social and political forces that tend to promote fragmented, uncritical, short-sighted, inflexible, and overly optimistic assessments of resource status, management capabilities, and the consequences of decisions and policies. Aquatic resources are vulnerable to the effects of human activities catchment-wide, and many of the landscape changes humans routinely induce cause irreversible damage (e.g., some species introductions, extinctions of ecotypes and species) or give rise to cumulative, long-term, large-scale biological and cultural consequences (e.g., accelerated erosion and sedimentation, deforestation, toxic contamination of sediments). In aquatic ecosystems, biotic impoverishment and environmental disruption caused by past management and natural events profoundly constrain the ability of future management to maintain biodiversity and restore historical ecosystem functions and values. To provide for rational, adaptive progress in ecosystem management and to reduce the risk of irreversible and unanticipated consequences, managers and scientists must identify catchments and aquatic networks where ecological integrity has been least damaged by prior management, and jointly develop means to ensure their protection as reservoirs of natural biodiversity, keystones for regional restoration, management models, monitoring benchmarks, and resources for ecological research.     

Developing a collaborative model for environmental planning and management

Methods for involving the public in natural resource management are changing as agencies adjust to an increasingly turbulent social and political environment. There is growing interest among managers and scholars in collaborative approaches to public involvement. Collaboration is conceptually defined and elaborated using examples from the natural resource management field. This paper then examines how collaboration theory from the organizational behavior field can help environmental managers to better understand those factors that facilitate and inhibit collaborative solutions to resource problems. A process-oriented model is presented that proposes that collaboration emerges out of an environmental context and then proceeds sequentially through a problem-setting, direction-setting, and structuring phase. Factors constraining collaboration are also specified, including organizational culture and power differentials. Designs for managing collaboration are identified, which include appreciative planning, joint agreements, dialogues, and negotiated settlements. Environmental managers need new skills to manage collaboration within a dynamic social and political environment. Further research is needed to test the propositions outlined here.     

Multiple-resource modeling as a tool for conservation: Its applicability in Mexico

The current Mexican environmental law provides the legal basis for comprehensive land-use planning. Under the law, development of natural ecosystems must combine goals, policies, and practices towards the sustainable use of natural resources and the protection of biological diversity. Thus, ecosystem manipulation must be able to counter fragmentation of natural ecosystems and isolation of natural reserves, while providing for human needs. Assessment of the potential of natural ecosystems and management impacts are required. Multiple-resource simulation is an assessment and land-use planning tool that permits managers and decision makers to comply with the law, providing a flexible, user-oriented system that can meet the needs of managers, conservationists, and researchers. A multiple-resource model and an example of how it can be applied to meet planning needs is presented for discussion.     

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.     

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 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.     

Adaptive Management: From More Talk to Real Action

The challenges currently facing resource managers are large-scale and complex, and demand new approaches to balance development and conservation goals. One approach that shows considerable promise for addressing these challenges is adaptive management, which by now is broadly seen as a natural, intuitive, and potentially effective way to address decision-making in the face of uncertainties. Yet the concept of adaptive management continues to evolve, and its record of success remains limited. In this article, we present an operational framework for adaptive decision-making, and describe the challenges and opportunities in applying it to real-world problems. We discuss the key elements required for adaptive decision-making, and their integration into an iterative process that highlights and distinguishes technical and social learning. We illustrate the elements and processes of the framework with some successful on-the-ground examples of natural resource management. Finally, we address some of the difficulties in applying learning-based management, and finish with a discussion of future directions and strategic challenges.     

Gradient modeling: A new approach to fire modeling and wilderness resource management

Managers of wilderness resources must maintain, preserve, and sometimes restore pristine ecosystems while providing for public use and enjoyment of these areas. These managers require a resource information system that can store, retrieve and integrate basic data, synthesize components to solve particular problems, and provide simulations and predictions of natural processes and management actions. Traditional information systems based on land classification and type-mapping do not provide these capabilities.Gradient modeling, a new approach to resource management and forest fire simulation, has been developed to meet these needs in Glacier National Park. The method links four major components: (1) a terrestrial site inventory coded from aerial photographs that offers 10-m resolution; (2) gradient models of vegetation and fuel that derive quantitative stand compositional data from the parameters stored in the coded inventory; (3) a fuel moisture and microclimate model that extrapolates basestation weather data to remote sites using the parameters stored in the inventory; and (4) fire behavior and fire ecology models that integrate the data from the inventory and models to calculate real-time fire behavior and ecological succession following a fire.