Passive Adaptive Management Research Articles

Conceptual framework for adaptive management of coupled human and natural systems with respect to climate change uncertainty

ABSTRACTManagers of coupled human and natural systems (CHANS) face the challenging task of managing those systems when there is uncertainty about future changes in drivers of the outcomes of management actions. To assist in meeting that challenge, a fuzzy, multiple-objective, passive adaptive management (AM) framework is developed that can be used to determine preferred management actions for multiple planning periods when there is uncertainty about one or more system drivers. The proposed framework requires: (1) selecting management actions and objectives; (2) establishing standards for objectives; (3) choosing budgets, drivers, and driver scenarios; (4) estimating management objectives; (5) identifying acceptable management actions; (6) determining preferred management actions; and (7) evaluating whether or not passive AM is advantageous. The framework is demonstrated for managing a hypothetical highway corridor in a national park based on four objectives pertaining to the corridor: (1) minimising congestion at the visitor centre parking lot; (2) maximising plant biodiversity along hiking trails; (3) minimising soil erosion on hiking trails; and (4) minimising congestion on hiking trails when there is uncertainty about future climate change. The proposed framework is easier for managers to understand and apply than other fuzzy decision-making frameworks.

Judging adaptive management practices of U.S. agencies

All U.S. federal agencies administering environmental laws purport to practice adaptive management (AM), but little is known about how they actually implement this conservation tool. A gap between the theory and practice of AM is revealed in judicial decisions reviewing agency adaptive management plans. We analyzed all U.S. federal court opinions published through 1 January 2015 to identify the agency AM practices courts found most deficient. The shortcomings included lack of clear objectives and processes, monitoring thresholds, and defined actions triggered by thresholds. This trio of agency shortcuts around critical, iterative steps characterizes what we call AM-lite. Passive AM differs from active AM in its relative lack of management interventions through experimental strategies. In contrast, AM-lite is a distinctive form of passive AM that fails to provide for the iterative steps necessary to learn from management. Courts have developed a sophisticated understanding of AM and often offer instructive rather than merely critical opinions. The role of the judiciary is limited by agency discretion under U.S. administrative law. But courts have overturned some agency AM-lite practices and insisted on more rigorous analyses to ensure that the promised benefits of structured learning and fine-tuned management have a reasonable likelihood of occurring. Nonetheless, there remains a mismatch in U.S. administrative law between the flexibility demanded by adaptive management and the legal objectives of transparency, public participation, and finality.

An Integrated Conceptual Framework for Adapting Forest Management Practices to Alternative Futures

This paper proposes an integrated, conceptual framework that forest managers can use to simulate the multiple objectives/indicators of sustainability for different spatial patterns of forest management practices under alternative futures, rank feasible (affordable) treatment patterns for forested areas, and determine if and when it is advantageous to adapt or change the spatial pattern over time for each alternative future. The latter is defined in terms of three drivers: economic growth; land use policy; and climate change. Four forest management objectives are used to demonstrate the framework, minimizing wildfire risk and water pollution and maximizing expected net return from timber sales and the extent of potential wildlife habitat. The fuzzy technique for preference by similarity to the ideal solution is used to rank the feasible spatial patterns for each subperiod in a planning horizon and alternative future. The resulting rankings for subperiods are used in a passive adaptive management procedure to determine if and when it is advantageous to adapt the spatial pattern over subperiods. One of the objectives proposed for the conceptual framework is simulated for the period 2010–2059, namely, wildfire risk, as measured by expected residential losses from wildfire in the wildland-urban interface for Flathead County, Montana.

Increasing resilience of natural protected areas to future climate change: A fuzzy adaptive management approach

Climate change and variability is a major threat to natural protected areas that is likely to change the mix of ecosystem goods and services provided by such areas in the future. Managers of natural protected areas may be able to increase the resilience of natural protected areas to climate change and variability by implementing compensatory management actions and evaluating their efficacy using active adaptive management. A fuzzy multiple attribute-adaptive management method is proposed that determines the most preferred compensatory management actions for increasing the resilience of natural protected areas to future climate change and variability. Use of the method requires managers to: (1) identify compensatory management actions that can alleviate potential negative impacts of future climate change and variability on natural protected areas; (2) choose the attributes of compensatory management actions used to assess their efficacy; (3) conduct statistically valid experiments in each time period that generate sample values of the attributes; (4) use the fuzzy Technique for Order Preference by Similarity of Ideal Solution to rank compensatory management actions in each time period within a planning horizon; and (5) use the rankings to determine the optimal adaptive management strategy for a planning horizon. The method accounts for uncertainty about the nature of future climate change and variability and the extent to which compensatory management actions reduce potential adverse impacts of climate change and variability on natural protected areas and uses statistically valid experiments to evaluate the efficacy of compensatory management actions. Accordingly, the proposed method is superior to other methods such as safe minimum standard, precautionary principle, minimax regret criterion, scenario planning, and passive adaptive management.

Use of multi-criteria analysis (MCA) for supporting community forest management

Sustainable forest management usually involves the use of criteria and indicators (C&I) allowing the monitoring, reporting and assessment of management activities at national, regional and forest management unit levels. Experiences of such concepts are scarce in Nepal, particularly with regard to the evaluation of management activities within a Multi-Criteria Analysis (MCA) framework. In this contribution we describe how a MCA approach can be used to efficiently exploit information, knowledge, and preferences of stakeholders to address community forest management problems. Beside rating and ranking techniques, the Analytic Hierarchy Process (AHP) is used to examine the importance of six criteria and forty-four indicators in a sustainable forest management context with a broad range of stakeholder groups. An evaluation of four management strategies in the Shree Gyneshwar community forest user group allows to demonstrate the relevance of changing priorities for different criteria. A passive adaptive management strategy focusing on a multiple use of natural resources and the introduction of production-oriented measures were identified as the most preferable option. The results of this study show that the multi-criteria analysis approach, and in particular the AHP, can assist decision-makers in efficiently evaluating management problems and generating ideas for the long-term strategic planning process of community forest management, even under complex socio-economic and ecological conditions. In that context, compromise solutions enjoy a higher possibility of being successful, taking into account the different views of stakeholder groups.

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.

On Valuing Information in Adaptive‐Management Models

Active adaptive management looks at the benefit of using strategies that may be suboptimal in the near term but may provide additional information that will facilitate better management in the future. In many adaptive-management problems that have been studied, the optimal active and passive policies (accounting for learning when designing policies and designing policy on the basis of current best information, respectively) are very similar. This seems paradoxical; when faced with uncertainty about the best course of action, managers should spend very little effort on actively designing programs to learn about the system they are managing. We considered two possible reasons why active and passive adaptive solutions are often similar. First, the benefits of learning are often confined to the particular case study in the modeled scenario, whereas in reality information gained from local studies is often applied more broadly. Second, management objectives that incorporate the variance of an estimate may place greater emphasis on learning than more commonly used objectives that aim to maximize an expected value. We explored these issues in a case study of Merri Creek, Melbourne, Australia, in which the aim was to choose between two options for revegetation. We explicitly incorporated monitoring costs in the model. The value of the terminal rewards and the choice of objective both influenced the difference between active and passive adaptive solutions. Explicitly considering the cost of monitoring provided a different perspective on how the terminal reward and management objective affected learning. The states for which it was optimal to monitor did not always coincide with the states in which active and passive adaptive management differed. Our results emphasize that spending resources on monitoring is only optimal when the expected benefits of the options being considered are similar and when the pay-off for learning about their benefits is large.

Monitoring the impact of climate change on biodiversity: The challenge of megadiverse Mediterranean climate ecosystems

AbstractThe Mediterranean climate regions of Western Australia and South Africa are recognized as global hot spots of diversity. Both are threatened by climate changes that are projected to have significant impacts on the quantity and variability of rainfall and affect key ecosystem drivers (e.g. fire regimes). This poses significant challenges to monitoring programs designed to detect these impacts. Effective monitoring of the impact of climate change on biodiversity (rather than individual species) requires a cross‐disciplinary, coordinated, focused and integrated approach. Ideally, this should involve a multidisciplinary team of specialists working to a common plan on the same set of plots. The contributions of ‘citizen scientists’ are potentially useful if well managed. Biodiversity per se (across all kingdoms of life, and including the levels of the gene, population and community) should be monitored, especially key species interactions and processes. Forestcheckis an example of such a program which has been applied in forests in south‐west Western Australia since 2001. In concert with measuring the direct impact of climate change on biodiversity and the indirect impact of factors that affect biodiversity (such as disease, invasive species, fire regime and habitat removal), there is a need for a proactive focus on creating, maintaining and monitoring resilience to climate change impacts in ecosystems. It is also necessary to monitor the effectiveness of management actions such as vegetation thinning, changes in fire regimes, species translocations and revegetation of farmland to link isolated protected areas in agricultural landscapes, remnant native vegetation in rangelands and extensive protected areas. A pluralist approach is recommended. This should include natural experiments, matched photographs where available, passive adaptive management, active adaptive management and traditional reductionist scientific investigation. The resultant synthesis of information from this range of sources is likely to be a predictive, robust and credible record of historical change.

Optimal adaptive management for the translocation of a threatened species

Active adaptive management (AAM) is an approach to wildlife management that acknowledges our imperfect understanding of natural systems and allows for some resolution of our uncertainty. Such learning may be characterized by risky strategies in the short term. Experimentation is only considered acceptable if it is expected to be repaid by increased returns in the long term, generated by an improved understanding of the system. By setting AAM problems within a decision theory framework, we can find this optimal balance between achieving our objectives in the short term and learning for the long term. We apply this approach to managing the translocation of the bridled nailtail wallaby (Onychogalea fraenata), an endangered species from Queensland, Australia. Our task is to allocate captive-bred animals, between two sites or populations to maximize abundance at the end of the translocation project. One population, at the original site of occupancy, has a known growth rate. A population potentially could be established at a second site of suitable habitat, but we can only learn the growth rate of this new population by monitoring translocated animals. We use a mathematical programming technique called stochastic dynamic programming, which determines optimal management decisions for every possible management trajectory. We find optimal strategies under active and passive adaptive management, which enables us to examine the balance between learning and managing directly. Learning is more often optimal when we have less prior information about the uncertain population growth rate at the new site, when the growth rate at the original site is low, and when there is substantial time remaining in the translocation project. Few studies outside the area of optimal harvesting have framed AAM within a decision theory context. This is the first application to threatened species translocation.

Evaluating land use plans under uncertainty

A method is proposed that planners and stakeholders can use to select an optimal land use plan that balances the socioeconomic benefits and environmental costs of regional growth. It entails seven steps: (1) specifying alternative growth futures; (2) formulating land use policies to alleviate potential adverse environmental impacts of alternative futures; (3) evaluating socioeconomic and environmental impacts (outcomes) with land use policies and alternative futures; (4) using a multiple attribute utility function to score outcomes and rank land use plans based on those scores; (5) determining an optimal plan for stakeholders based on some decision criterion; (6) employing a conflict resolution method to resolve stakeholder disagreements about optimal plans; and (7) using passive adaptive management to determine whether an implemented plan remains optimal over time. The proposed method is demonstrated for a hypothetical case study that incorporates: (1) low, medium, and high future growth rates for major industries in a region; (2) unrestrictive, moderately restrictive, and highly restrictive land use policies that limit the location and density of residential and non-residential development; (3) economic output and habitat fragmentation attributes of outcomes; (4) a linear additive, multiple attribute utility function; (5) pro-development, neutral, and pro-environment stakeholder preferences for attributes; and (6) optimal plan selection based on the maximin principle. Results for the hypothetical case study indicate that the ranking of the three plans and the optimal plan differ across the three stakeholder groups, and the optimal collective plan varies depending on which conflict resolution method is used and whether the assessment is ex ante or ex post.

Nipped in the Bud: Why Regional Scale Adaptive Management Is Not Blooming

Adaptive management is an approach to managing natural resources that emphasizes learning from the implementation of policies and strategies. Adaptive management appears to offer a solution to the management gridlock caused by increasing complexity and uncertainty. The concept of adaptive management has been embraced by natural resource managers worldwide, but there are relatively few published examples of adaptive management in use. In this article, we explore two watershed management projects in southeastern Australia to better understand the potential of adaptive management in regional scale programs through qualitative, case study-based investigation. The program logic of one case implies the use of passive adaptive management, whereas the second case claims to be based on active adaptive management. Data were created using participant observation, semistructured interviews with individuals and groups, and document review. Using thematic content and metaphor analysis to explore the case data, we found that each case was successful as an implementation project. However, the use of both passive and active adaptive management was constrained by deeply entrenched social norms and institutional frameworks. We identified seven "imperatives" that guided the behavior of project stakeholders, and that have consequences for the use of adaptive management. Reference to recent evaluations of the Adaptive Management Areas of the Pacific Northwest of the United States suggests that some of these imperatives and their consequences have broad applicability. The implications of our findings are discussed, and suggestions for improving the outcomes of regional scale adaptive management are provided.

ADAPTIVE MANAGEMENT OF LARGE RIVERS WITH SPECIAL REFERENCE TO THE MISSOURI RIVER1

ABSTRACT: The technocratic approach for managing the Missouri River and other large rivers is not effective in resolving conflicts among competing uses of water and dealing with uncertainty about how river ecosystems respond to alternative management actions. Adaptive management offers an alternative way to address these and other issues. It has the potential to alleviate management gridlock and provide lasting solutions to management of the Missouri River and other large river ecosystems. In passive adaptive management, simulation models and expert judgment are combined to select a preferred management action. While passive adaptive management is relatively simple and inexpensive to use, it does not necessarily provide reliable information for making management decisions. Active adaptive management uses statistically designed experiments to test assumptions or hypotheses about ecosystem responses to management actions. It is best carried out by a collaborative working group. Active adaptive management has several advantages, but the inability to satisfy certain prerequisites for successful application makes it more difficult to implement in large river ecosystems. A second‐best approach is proposed here to select, implement, monitor, and evaluate a preferred management action and retain that action provided ecological conditions improve and socioeconomic indicators do not fall below established acceptability limits.

Uncertainty, learning, and the optimal management of wildlife

Wildlife management is limited by uncontrolled and often unrecognized environmental variation, by limited capabilities to observe and control animal populations, and by a lack of understanding about the biological processes driving population dynamics. In this paper I describe a comprehensive framework for management that includes multiple models and likelihood values to account for structural uncertainty, along with stochastic factors to account for environmental variation, random sampling, and partial controllability. Adaptive optimization is developed in terms of the optimal control of incompletely understood populations, with the expected value of perfect information measuring the potential for improving control through learning. The framework for optimal adaptive control is generalized by including partial observability and non-adaptive, sample-based updating of model likelihoods. Passive adaptive management is derived as a special case of constrained adaptive optimization, representing a potentially efficient suboptimal alternative that nonetheless accounts for structural uncertainty.